CN-121997224-A - Dam safety monitoring automation data rough difference eliminating method

Abstract

The invention discloses a dam safety monitoring automation data coarse error removing method, and belongs to the technical field of dam safety monitoring automation. The method comprises the steps of collecting dam safety monitoring automation data, obtaining observation values, storing the observation values in a database, removing rough differences of the dam safety monitoring automation data by adopting a 3-time error method to obtain X data, carrying out curve fitting of the X data by adopting a Fourier fourth-order number to obtain a fitted observation value set, taking the fitted observation value set as a true value of the automation detection data, replacing the average number of the observation values in the 3-time error method to obtain the difference value between the observation values and the fitted observation values, calculating the error, judging the observation value, of which the difference value exceeds a threshold value, as the rough differences, and removing the rough differences again to obtain the dam safety monitoring automation data without the rough differences, thereby improving the data rough difference removing efficiency and accuracy and avoiding the rough differences from being removed in place.

Inventors

• LI MAOLIN
• CAI JUNYI
• ZHOU XIAOYAN
• RAN LUGUANG
• ZHANG WEIHAO
• SHI QINGHONG
• WANG CHONGXUN

Assignees

• 三峡金沙江川云水电开发有限公司

Dates

Publication Date

20260508

Application Date

20260115

Claims (8)

1. 1. The dam safety monitoring automatic data coarse reject method is characterized by comprising the following steps of: Step 100, acquiring dam safety monitoring automation data, acquiring a real-time observation value and storing the real-time observation value into a database; Step 200, eliminating rough differences of dam safety monitoring automatic data by adopting a 3-time error method to obtain X data; Step S300, performing curve fitting of X data by adopting Fourier fourth-order numbers to obtain a fitting observation value set; And S400, taking the fitted observation value set as a true value of the automatic detection data, replacing the average number of the observation values in the error method in 3 times, obtaining the difference value between the observation value and the fitted observation value, calculating the error, setting a threshold value, judging the observation value, of which the difference value between the observation value and the fitted observation value exceeds the threshold value, as the rough difference, and rejecting again to obtain the dam safety monitoring automatic data without the rough difference.
2. 2. The method for automatically removing rough data from dam safety monitoring according to claim 1, wherein the step S200 comprises the steps of: Step S201, calculating average number of observed values : Wherein, the In order to observe the value of the value, The observation time for monitoring; step S202, calculating the difference between the observed value and the average value : Step S203 error in calculation : Step S204, the difference value is calculated Error of middle and middle And comparing, and removing the coarse difference to obtain X data without the coarse difference.
3. 3. The method for automatic data gross error removal for dam safety monitoring according to claim 2, wherein the step S300 comprises the steps of: step S301 Fourier series The formula is as follows: Wherein, the In order to be able to carry out a cycle, ; Step S302, calculating the period of the Fourier transform data Performing discrete fast Fourier transform on the X data sequence, then taking the first 1/2 part of the transformed data to calculate the modular long square of the complex number, and searching the maximum value of the modular long square data as the fitting period of the corresponding data set : Step S303, performing curve fitting by adopting Fourier fourth order numbers: substituting the formula parameters in step S301 into step S303; Step S304, substituting the X data sequence into the formula of step S303 to obtain a fitting observation value set , 。
4. 4. A method for automatic data gross error removal for dam safety monitoring according to claim 3, wherein step S400 comprises the steps of: Step S401, fitting the observation value set As an automated monitoring data true value, the average of the observations in step S200 is replaced Obtaining the difference between the observed value and the fitting observed value : Wherein, the Is an observed value; Step S402 error in calculation : Step S403, the difference value Error of middle and middle And comparing, and removing the rough difference again to obtain dam safety monitoring automatic data without the rough difference.
5. 5. A method for automatic data gross error removal for dam safety monitoring according to claim 3, wherein the parameters in step S303 The calculation is performed by The formula substituted in step S301 is calculated.
6. 6. A method of dam safety monitoring automated data robust elimination according to claim 3, wherein the integration in step S300 takes the form of discrete integration.
7. 7. The method for removing the gross errors of the dam safety monitoring automatic data according to claim 1 is characterized in that the gross errors of the dam safety monitoring automatic data are removed for the first time through a 3-time middle error method to obtain X data, and then the gross errors are removed for the second time through a middle error obtained through a Fourier fourth order number fitting 3-time middle error method to obtain the dam safety monitoring automatic data without the gross errors.
8. 8. The method for removing the gross error of the dam safety monitoring automation data according to claim 4, wherein the dam safety monitoring automation data without the gross error obtained in the step S403 is detected, whether the gross error is contained is judged, if the gross error is not contained, the data is used as final data, and if the gross error is contained, the steps S200 to S400 are repeated to remove the gross error.

Description

Dam safety monitoring automation data rough difference eliminating method Technical Field The invention belongs to the technical field of dam safety monitoring automation, and particularly relates to a dam safety monitoring automation data gross error eliminating method. Background Safety monitoring is an important means for knowing the operation state of the dam in operation, and dam safety monitoring data is a direct data source for judging the health condition of the dam, so that the validity of the dam safety monitoring data must be ensured. With the continuous development of dam safety monitoring automation, an automatic monitoring system gradually replaces a traditional manual observation system with the advantages of high observation frequency, real-time performance, high precision, accuracy, economy, practicability, stability, reliability and the like. However, the automatic monitoring system data are also affected by factors such as human beings, external environments, instruments and the like, so that part of the monitoring data are obviously inconsistent with the overall trend, and the data which deviate from reasonable measurement values obviously are called gross errors. Common monitoring data gross error judging methods comprise a triple error, a manual judging method, an envelope curve method, a statistical judging method and the like, but the methods often need to manually set thresholds and build complex models, and the problems of low efficiency or low precision exist when large-scale monitoring data are faced. The error in the triple is based on the probability theory of normal distribution of data to judge whether the data is coarse or not, but for dam safety monitoring data, the data is changed by the outside air temperature, water level and the like to present annual cycle change, the error method in the triple can not well remove jump data in the normal fluctuation range of the data, so the actual application condition is greatly limited, only partial coarse or the like which is obviously deviated from the normal data can be removed, the efficiency of the manual interpretation method is lower, the coarse or the poor detection requirement of massive automatic monitoring data is hardly met depending on the professional level of operators, the envelope method and the statistical discrimination method need to establish complex models, and different parameter settings and model adjustment are needed for different data sequences, so the method has certain limitation in the actual application. Disclosure of Invention Aiming at the problems, the invention provides the dam safety monitoring automatic data gross error removing method, which is expected to improve the efficiency and accuracy limitation existing in the safety monitoring of the dam data in the existing method and improve the accuracy and efficiency of the dam safety monitoring automatic data gross error removing. The technical scheme adopted by the invention is as follows, the dam safety monitoring automatic data gross error eliminating method comprises the following steps: Step 100, acquiring dam safety monitoring automation data, acquiring a real-time observation value and storing the real-time observation value into a database; Step 200, eliminating rough differences of dam safety monitoring automatic data by adopting a 3-time error method to obtain X data; Step S300, performing curve fitting of X data by adopting Fourier fourth-order numbers to obtain a fitting observation value set; And S400, taking the fitted observation value set as a true value of the automatic detection data, replacing the average number of the observation values in the error method in 3 times, obtaining the difference value between the observation value and the fitted observation value, calculating the error, setting a threshold value, judging the observation value, of which the difference value between the observation value and the fitted observation value exceeds the threshold value, as the rough difference, and rejecting again to obtain the dam safety monitoring automatic data without the rough difference. Further, step S200 includes the steps of: Step S201, calculating average number of observed values : Wherein, the In order to observe the value of the value,The observation time for monitoring; step S202, calculating the difference between the observed value and the average value : Step S203 error in calculation: Step S204, the difference value is calculatedError of middle and middleAnd comparing, and removing the coarse difference to obtain X data without the coarse difference. Further, step S300 includes the steps of: step S301 Fourier series The formula is as follows: Wherein, the In order to be able to carry out a cycle,; Step S302, calculating the period of the Fourier transform dataPerforming discrete fast Fourier transform on the X data sequence, then taking the first 1/2 part of the transformed data to calculate the modular long square of t