Self-adaptive anti-interference automatic monitoring data processing method and system

Abstract

The invention provides a self-adaptive anti-interference automatic monitoring data processing method and system, and relates to the technical field of measurement and control, and the method comprises the steps: obtaining an observation value sequence of each observation target which comprises a reference point and a monitoring point; calculating a point location median error according to the observation value sequence of the reference point, and judging whether the observation value sequence of each observation target is obtained again or not according to the point location median error; calculating a coordinate adjustment value sequence of each monitoring point according to the coordinate adjustment value of the station center calculated according to the observation value sequence of the reference point and the observation value sequence of the monitoring point; and updating the coordinate adjustment value sequence of each monitoring point based on the Pauta criterion. According to the invention, the measurement data of the reference point and the monitoring point obtained in real time are detected, when the measurement data are found to be unqualified, the measurement data are re-obtained in time, and the measurement data of the monitoring point is updated based on Erada accurate measurement, so that the reliability and effectiveness of automatic monitoring data are greatly improved.

Description

Technical Field

[0001] The present invention relates to the field of measurement and control technology, and in particular to an adaptive anti-interference automatic monitoring data processing method and system. Background Art

[0002] During the implementation of a high-speed railway underpass project, in order to monitor the disturbance of the existing line caused by the construction project in real time, an automated monitoring system was built, and measurement targets were placed in the monitoring target section. Cloud control and cloud computing were used to control the measurement robot to collect data, so as to reflect the disturbance and deformation of the existing line in real time, and the data accuracy was required to be better than 1mm.

[0003] At present, the scope of automated monitoring is becoming more and more diverse, and there are more and more automated monitoring methods. However, with the changes in the implementation progress of monitoring projects, the accuracy of automated monitoring is severely tested. Affected by factors such as construction interference, external environmental factors and vibration disturbance, the automated monitoring data fluctuates greatly. The deformation data obtained contains the combined influence of factors such as noise and gross errors, which conceals the true deformation of the monitored target and makes the reaction pattern unclear. Existing data processing methods are mostly post-processing methods, which cannot meet the data accuracy requirements for real-time acquisition of automated monitoring data. Summary of the invention

[0004] The purpose of the present invention is to provide an adaptive anti-interference automatic monitoring data processing method and system to improve the above problems. In order to achieve the above purpose, the technical solution adopted by the present invention is as follows:

[0005] In a first aspect, the present application provides an adaptive anti-interference automated monitoring data processing method, comprising:

[0006] Obtaining an observation value sequence of each observation target, wherein the observation value sequence is a sequence of observation values ​​obtained after the measurement instrument measures the observation target for a first preset number of times in a measurement order, wherein the measurement target includes a monitoring point and a reference point;

[0007] Calculate the point position error according to the observation value sequence of the reference point, and if the point position error is greater than a preset first threshold, re-acquire the observation value sequence of each observation target;

[0008] If the point position error is less than a preset first threshold, the coordinate adjustment value of the station center is calculated according to the observation value sequence of the reference point, and the coordinate adjustment value sequence of each monitoring point is calculated based on the coordinate adjustment value of the station center and the observation value sequence of the monitoring point to obtain the coordinate adjustment value sequence of the monitoring point, and the coordinate adjustment value sequence is used to calculate the monitoring data of each monitoring point;

[0009] The coordinate adjustment value sequence of each monitoring point is updated based on the Laida criterion to obtain the updated coordinate adjustment value sequence.

[0010] In a second aspect, the present application also provides an adaptive anti-interference automatic monitoring data processing system, comprising:

[0011] An acquisition module, used to acquire an observation value sequence of each observation target, wherein the observation value sequence is a sequence of observation values ​​obtained after the measurement instrument measures the observation target for a first preset number of times in a measurement order, and the measurement target includes a monitoring point and a reference point;

[0012] A first processing module, configured to calculate a point position error according to the observation value sequence of the reference point, and if the point position error is greater than a preset first threshold, reacquire an observation value sequence of each observation target;

[0013] A second processing module is used for calculating the coordinate adjustment value of the station center according to the observation value sequence of the reference point if the point position error is less than a preset first threshold value, and calculating the coordinate adjustment value sequence of each monitoring point based on the coordinate adjustment value of the station center and the observation value sequence of the monitoring point to obtain the coordinate adjustment value sequence of the monitoring point, and the coordinate adjustment value sequence is used to calculate the monitoring data of each monitoring point;

[0014] The third processing module is used to update the coordinate adjustment value sequence of each monitoring point based on the Laida criterion to obtain the updated coordinate adjustment value sequence.

[0015] The beneficial effects of the present invention are:

[0016] The present invention detects the measurement data of the benchmark points and monitoring points acquired in real time, re-acquires the measurement data in time when it is found that the measurement data is unqualified, and updates the measurement data of the monitoring points based on the Irada accuracy, thereby greatly improving the reliability and effectiveness of the automated monitoring data.

[0017] Other features and advantages of the present invention will be set forth in the following description, and in part will become apparent from the description, or may be understood by practicing embodiments of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments are briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without creative work.

[0019] Figure 1A schematic flow chart of an adaptive anti-interference automated monitoring data processing method according to an embodiment of the present invention;

[0020] Figure 2 It is a schematic diagram of the structure of an adaptive anti-interference automatic monitoring data processing system described in an embodiment of the present invention.

[0021] Markings in the figure: 901, acquisition module; 902, first processing module; 903, second processing module; 904, third processing module; 905, fourth processing module; 906, fifth processing module; 9041, first processing unit; 9042, second processing unit; 9043, third processing unit; 9061, fourth processing unit; 9062, fifth processing unit; 9063, sixth processing unit; 90411, first processing submodule; 90412, second processing submodule; 90413, third processing submodule; 90414, fourth processing submodule; 90415, fifth processing submodule. DETAILED DESCRIPTION

[0022] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of the embodiments. The components of the embodiments of the present invention generally described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the claimed invention, but merely represents selected embodiments of the present invention. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0023] It should be noted that similar reference numerals and letters represent similar items in the following drawings, so once an item is defined in one drawing, it does not need to be further defined and explained in the subsequent drawings. At the same time, in the description of the present invention, the terms "first", "second", etc. are only used to distinguish the description and cannot be understood as indicating or implying relative importance.

[0024] Embodiment 1:

[0025] This embodiment provides an adaptive anti-interference automatic monitoring data processing method.

[0026] See also Figure 1 , the figure shows that the method includes step S1, step S2, step S3, and step S4.

[0027] S1. Obtaining an observation value sequence for each observation target, wherein the observation value sequence is a sequence of observation values ​​obtained after the measurement instrument measures the observation target for a first preset number of times in a measurement order, and the measurement target includes a monitoring point and a reference point;

[0028] Specifically, step S1 includes:

[0029] S11. Before the implementation of the automated monitoring project, it is necessary to organize and implement the monitoring plan based on the monitoring target characteristics, monitoring scope, monitoring accuracy requirements, etc., including roadbed, bridges, tunnels, etc., especially the layout of station, benchmark and monitoring points. Usually, the coordinates of the benchmark point are specified first, and then the initial coordinates of the instrument center are obtained by the intersection method, that is, the initial station center coordinates (X 0 ,Y 0 ,Z 0 );

[0030] S12. After obtaining the initial station center coordinates, the direction of the line connecting a certain reference point and the measuring robot is defined as the starting zero direction, that is, the 0 angle side, and the horizontal angles β of the remaining reference points and monitoring points compared to the starting zero direction are obtained by target aiming, laser ranging and coordinate calculation. i , vertical angle γ i , distance S i and the initial coordinates (x i ,y i ,z i ), i = 1, 2, 3…n, n is the total number of monitored targets, where the calculation formula for the initial coordinate value is:

[0031]

[0032] Among them, (X 0 ,Y 0 ,Z 0 ) is the initial station center coordinate, (x i ,y i ,z i ) is the initial coordinate value of the reference point or monitoring point, β i , γ i , S i They are the horizontal angle, vertical angle and distance of the reference point or monitoring point compared to the starting zero direction respectively;

[0033] S13. The present invention takes 24 hours as a monitoring cycle, and divides each cycle into multiple measurements according to a fixed collection interval. For example, if the fixed collection interval is 1 time / 2 hours, the number of measurements required in this cycle is m=24÷2=12. If the monitoring data obtained from a certain measurement is found to be unqualified according to the subsequent detection method, there is time to re-measure and collect data within the collection interval;

[0034] The measurement of all observation targets is considered as one measurement. During each measurement, the remote control measuring instrument calculates the horizontal angle β of each observation target point obtained in step S12. i The measurement is carried out in order from small to large, which is the directional observation method. This method is conducive to improving the measurement accuracy. After the measurement is completed, the direct observation values ​​of all the observation targets are obtained.

[0035] It should be noted that if it is not the first measurement, the initial coordinate value of the station center in this measurement is the station center coordinate obtained in the last measurement. In the tth measurement, the horizontal angle of the i-th observation target is Vertical Angle and distance In this embodiment, m is the number of measurements required in one cycle, and in this embodiment, m is set to 12;

[0036] After completing all the measurements in a cycle, all the observation values ​​obtained from the measurements of each observation target in this cycle are organized into an observation value sequence in chronological order to obtain the observation value sequence of each observation target in this cycle.

[0037] S2, calculating the point position error according to the observation value sequence of the reference point, and if the point position error is greater than a preset first threshold, re-obtaining the observation value sequence of each observation target;

[0038] Specifically, step S2 includes:

[0039] S21. After obtaining the observation value sequence after a measurement, the observation value of each reference point is used as the approximate value of the coordinates of each reference point. The initial coordinates of the station center are taken as the approximate coordinates of the station center. Then the coordinate adjustment value of the station center of this measurement is calculated by the rear intersection and indirect adjustment method;

[0040] The following uses the benchmark points A, B and C to illustrate the calculation of the coordinate adjustment value of the point position error and the station center based on the observation value sequence of the benchmark points;

[0041] First, calculate the approximate coordinate azimuth and approximate side length of the reference point observation side. The formula for calculating the approximate coordinate azimuth of the reference point observation side is:

[0042]

[0043] The formula for calculating the approximate length of the observed side of the reference point is:

[0044]

[0045] in, is the approximate coordinate azimuth of the observation side of reference point A, is the approximate length of the observed side at reference point A, and are the initial coordinate values ​​of the reference point A on the x-axis and y-axis respectively. Similarly, the approximate coordinate azimuths of the reference point B and the reference point C are obtained according to the above formula. and approximate side length

[0046] Approximate coordinate azimuth based on reference points A, B, and C and the approximate length of the observed side Calculate the correction number of the observation side length of each reference point and the correction number of the angle between two adjacent observation sides. The calculation formula is:

[0047]

[0048] Among them, s 1 、s 2 、s 3 are the initial values ​​of the lengths of the observed edges of the reference points A, B, and C, respectively, 1 and α 2 are the initial values ​​of the angles between the observation sides of reference points A and B, and reference points B and C, respectively. Respectively represent the correction values ​​of the observed sides of the reference points A, B, and C. Respectively represent the correction number of the angle between the observation sides of the reference points A and B, and the reference points B and C, are the approximate values ​​of the angles of the observed sides of the reference points A, B and C, respectively. are the approximate values ​​of the observed side lengths of the reference points A, B, and C, respectively. and They represent the corrections of the approximate coordinates of the station center on the x and y axes, respectively, and the coefficient ρ” is 206265;

[0049] According to the indirect adjustment method, the above formula is rewritten into matrix form:

[0050] V=B*δ+l

[0051] Among them, V is the first matrix composed of the correction numbers of the side length of the observed side, δ is the second matrix composed of the correction numbers of the approximate coordinates of the station center coordinates on each coordinate axis, l is the third matrix composed of the approximate value and initial value of the angle of the observed side according to the reference point, the approximate value and initial value of the side length, B is the fourth matrix composed of the trigonometric function of the approximate value of the angle of the observed side and the side length of the observed side. Specifically, the first matrix V, the second matrix δ, the third matrix l and the fourth matrix B are as follows:

[0052]

[0053]

[0054] Among them, s 1 、s 2 、s 3 are the initial values ​​of the lengths of the observed edges of the reference points A, B, and C, respectively, 1 and α 2 are the initial values ​​of the angles between the observation sides of reference points A and B, and reference points B and C, respectively. Respectively represent the correction values ​​of the observed sides of the reference points A, B, and C. Respectively represent the correction number of the angle between the observation sides of the reference points A and B, and the reference points B and C, are the approximate values ​​of the angles of the observed sides of the reference points A, B and C, respectively. are the approximate values ​​of the observed side lengths of the reference points A, B, and C, respectively. and They represent the corrections of the approximate coordinates of the station center on the x and y axes, respectively, and the coefficient ρ” is 206265;

[0055] According to the above steps, the approximate coordinate azimuth and approximate side length of all reference points obtained in each measurement within this cycle, the correction number of the observed side length distance, the correction number of the angle between two adjacent observed sides, and the approximate coordinates of the station center are obtained, so as to calculate the point position error of this cycle and the coordinate adjustment value of the station center;

[0056] S212, calculate the weight matrix P, and take the weight matrix P as the main diagonal element matrix:

[0057]

[0058] Among them, u 2 is the unit weight error, is the error in ranging, is the error in angle measurement.

[0059] Using the indirect adjustment method, the second matrix δ, the third matrix l, the fourth matrix B and the weight matrix P are combined into the adjustment equation, and the least squares principle is used to solve the algorithm equation to obtain the correction number δ of the approximate value of the station center coordinates, that is, the second matrix δ. The calculation formula is:

[0060] B T PBδ+B T Pl = 0;

[0061] δ=-(B T PB) -1 B T Pl;

[0062] Among them, δ is the second matrix, l is the third matrix, B is the fourth matrix, P is the weight matrix, and δ is the correction number of the approximate value of the station center coordinates. After obtaining the correction number of the approximate value of the station center coordinates, the subsequent coordinate adjustment value calculation of the station center coordinates and the calculation of the error in the point position can be performed;

[0063] The formula for calculating the coordinate adjustment value of the station center through rear intersection is:

[0064]

[0065] Among them, (x p ,y p ,z p ) is the coordinate adjustment value of the station center, is the initial coordinate value of the station center, are the corrections of the approximate coordinates of the station center on the x, y, and z axes, respectively;

[0066] Furthermore, the mean error of the measurement point in this cycle is obtained, and the calculation formula is:

[0067]

[0068] Among them, m p is the point position error, D x , D y They are matrices (B T PB) -1 The main diagonal elements of , B is the fourth matrix, and P is the weight matrix.

[0069] S3, if the point position error is less than a preset first threshold value, then the coordinate adjustment value of the station center is calculated according to the observation value sequence of the reference point, and the coordinate adjustment value sequence of each monitoring point is calculated based on the coordinate adjustment value of the station center and the observation value sequence of the monitoring point, to obtain the coordinate adjustment value sequence of the monitoring point, and the coordinate adjustment value sequence is used to calculate the monitoring data of each monitoring point;

[0070] Specifically, step S3 includes:

[0071] S31. In this embodiment, when the mean error of the point position is not less than 1 mm, it is considered that there is a large error in the measurement of this cycle, and the observation results obtained are unreliable. It is necessary to re-perform a cycle of data collection and accuracy verification, and at the same time issue a reminder to require the user to observe the on-site status in time;

[0072] S32, if the point position error is less than 1mm, the coordinate adjustment value sequence of each monitoring point is calculated, wherein, after obtaining the station center coordinate adjustment value, the coordinate adjustment value of each monitoring target is calculated respectively:

[0073]

[0074] Among them, β i , γ i , S i are respectively the horizontal angle, vertical angle, and distance of the i-th monitoring point relative to the starting zero direction corresponding to the number of measurements. (X i , Y i , Z i ) is the adjusted coordinate value of the i-th monitoring point in a certain measurement, (x p , y p , z p ) is the adjusted coordinate value of the station center corresponding to the measurement period. After obtaining the adjusted coordinate values of all measurements of a certain monitoring point within the period, an adjusted coordinate value sequence of this monitoring point within the period is formed.

[0075] S4. Since automated monitoring has characteristics such as high frequency, long period, and large amount of data, the distribution of measurement errors basically follows a normal distribution. During the measurement process, due to some abnormal reasons, such as instrument deviation, instability of reference points, external interference, etc., observation results that deviate significantly from the true value are generated, and such results need to be identified and processed in a timely manner. Among them, in this embodiment, based on the Pauta criterion, the adjusted coordinate value sequence of each of the monitoring points is updated to obtain the updated adjusted coordinate value sequence.

[0076] Specifically, step S4 includes:

[0077] Detect whether the adjusted coordinate value sequence corresponding to the first monitoring point contains gross errors according to the Pauta criterion. If there are gross errors in the first monitoring point, re-obtain the observation value sequence of the first monitoring point until the adjusted coordinate value sequence corresponding to the re-obtained observation value sequence of the first monitoring point does not contain gross errors or the number of re-obtained times reaches the second preset number of times, to obtain the updated adjusted coordinate value sequence;

[0078] When the number of re-obtained times reaches the second preset number of times, conduct a review on the second monitoring point for which the number of re-obtained times reaches the second preset number of times, and issue a warning or correct the gross error according to the result of the review. Among them, conduct on-site measurement on this monitoring point to obtain the on-site measurement adjusted coordinate value sequence of the second monitoring point. If the on-site measurement adjusted coordinate value sequence of the second monitoring point is consistent with the adjusted coordinate value sequence of the second monitoring point, issue a warning signal and retain the adjusted coordinate value sequence;

[0079] If the on-site measurement adjusted coordinate value sequence of the second monitoring point is inconsistent with the adjusted coordinate value sequence of the second monitoring point, use the mean shift algorithm model to correct the gross error of the adjusted coordinate value sequence of the second monitoring point to obtain the adjusted coordinate value sequence after gross error correction;

[0080] The specific steps of detecting whether the coordinate adjustment value sequence corresponding to the first monitoring point contains gross errors according to the Laida criterion include:

[0081] Decomposing the coordinate adjustment value sequence corresponding to the first monitoring point in the direction of the three-dimensional coordinate axis to obtain three coordinate adjustment value subsequences corresponding to the coordinate axis directions;

[0082] Calculating the mean and mean error of the first coordinate adjustment value subsequence to obtain the mean and mean error;

[0083] Calculate a first threshold value according to the mean value to obtain the first threshold value;

[0084] Calculate the difference between each coordinate value in the first coordinate adjustment value subsequence and the mean value to obtain a plurality of the differences;

[0085] If the difference in the first coordinate adjustment value subsequence is greater than the first threshold, then a gross error exists in the coordinate adjustment value sequence corresponding to the first coordinate adjustment value subsequence.

[0086] Specifically, in this embodiment, the coordinate adjustment values ​​in the x, y, and z directions obtained by each monitoring point in each cycle are sorted in time to form a coordinate adjustment value sequence. For example, the coordinate adjustment value sequence in the x direction is X=(X 1 ,X 2 ,…,X n ), where n is the cumulative number of observations, and each element in the sequence corresponds to the coordinate adjustment value of a monitoring point in the X-axis direction corresponding to a certain measurement;

[0087] The Laida criterion is used to detect gross errors in the coordinate adjustment sequence X, where the mean of the sequence X is taken as Δ, the point position error is σ, and the coordinate adjustment value X in the X direction corresponding to the t-th measurement of a certain monitoring point is t , then when |X t When -Δ|>3σ, it is considered that there is a gross error in the measurement result. When a gross error is detected, it is processed in the following ways:

[0088] First, when the initial detection is a gross error, the observation value sequence of the monitoring point within one period relative to the current station center coordinates is re-collected, and then the coordinate adjustment values ​​of the monitoring point in three directions are calculated based on the re-measured observation value sequence, and then the gross error detection is performed again;

[0089] If the number of re-measurements of the monitoring point N>3, prompt the on-site staff to conduct manual review in time;

[0090] When the manual review and automatic monitoring results agree on the stability of the monitoring point: that is, both the manual review and automatic monitoring results believe that the measuring point is currently in a stable or unstable state, an early warning should be issued immediately and the adjustment result should be saved;

[0091] Otherwise, the measurement result of this point is considered to be a gross error, and you can choose to eliminate the gross error, that is, directly delete the measurement result, or use the mean shift algorithm model to correct the gross error or use the manual re-measurement result;

[0092] Specifically, when the number of repeated measurements is greater than 3, the third result is taken as the coordinate result of the measured point this time. At this time, manual measurement is required on site. If the manual measurement finds that the point has indeed changed, the third measurement result is retained and entered into the network database for storing data, and then an early warning is issued to indicate that the point has changed. If there is no change in the manual re-measurement, it is considered that the result of this point contains gross errors, and gross errors need to be proposed or corrected.

[0093] S5, after updating the coordinate adjustment value sequence of each monitoring point based on the Laida criterion, further comprising:

[0094] S51, filtering the updated coordinate adjustment value sequence by an adaptive wavelet threshold denoising analysis method to obtain the filtered coordinate adjustment value sequence, wherein the measurement gross error is eliminated or corrected by step S4, but the data still contains noise, and filtering the data by an adaptive wavelet threshold denoising analysis method, taking the X direction as an example, obtaining the filtered data sequence X=(x 1 ,x 2 ,…,x n ), where n is the cumulative number of observations, and each element in the sequence corresponds to the filtered coordinate adjustment value of a monitoring point in the X-axis direction corresponding to a certain measurement;

[0095] S52, construct a gray system model, and perform data prediction on the filtered coordinate adjustment value sequence based on the gray system model to obtain a prediction result, which is used to predict the monitoring data of the monitoring point corresponding to the coordinate adjustment value sequence during the next measurement. In the process of deformation monitoring implementation, in addition to evaluating the current state of the monitoring point according to the above steps, it is also necessary to predict the next measurement result of the point, which is helpful to timely discover deformation anomalies and ensure the safety of the monitored object, so as to achieve the effect of early warning. The steps for data prediction are as follows, including:

[0096] S521, performing a level ratio test on the filtered coordinate adjustment value sequence, if the filtered coordinate adjustment value sequence does not satisfy the level ratio test, then performing a translation transformation on the filtered coordinate adjustment value sequence according to a preset translation transformation constant, until the coordinate adjustment value sequence after the translation transformation satisfies the level ratio test, to obtain the coordinate adjustment value sequence that satisfies the level ratio test;

[0097] Specifically, in order to construct a prediction model, firstly, the filtered data sequence X = (x 1 ,x 2 ,…,x n ), perform a level ratio test, the filtered data sequence X is a coordinate adjustment value sequence of any coordinate axis direction of a monitoring point, and this embodiment takes the X coordinate axis direction as an example. When the level ratio ε satisfies the level ratio test formula, a grey system (GM(1,1)) model can be established for data prediction, and the level ratio test formula is:

[0098]

[0099] Among them, x i is the coordinate adjustment value in the x direction after filtering corresponding to the ith measurement of the monitoring point, and n is the number of measurements performed in the cycle;

[0100] If the data sequence X=(x 1 ,x 2 ,…,x n ) does not satisfy the level ratio test formula, then the data sequence X=(x 1 ,x 2 ,…,x n ) is translated and transformed to obtain the new data sequence Y, the calculation formula is:

[0101] Y i =X i +C;

[0102] Among them, C is a constant term, X i and Y i are the data sequences before and after the translation transformation corresponding to a monitoring point in the i-th period, and the new data sequence Y obtained after the translation transformation i Re-test the level ratio until the new data sequence Y i If the level ratio test is passed, the constant term C is changed again to perform a translation transformation.

[0103] When the level ratio test passes, a prediction model is constructed using the new data sequence Y that passes the level ratio test:

[0104] Y k +aZ k =b,k=1,2,…,n;

[0105] Among them, Y k is the new data sequence corresponding to the kth period after the level ratio test based on the translation transformation, Z k is the Y corresponding to the kth cycle k The adjacent mean sequence after the sequence accumulation, a is the development coefficient, b is the gray action amount;

[0106] The development coefficient a and the grey action b can be obtained by the parameter vector It is expressed and estimated by the least squares method. The calculation formula is:

[0107]

[0108] The matrix Y consists of the data sequence X in each period. k The matrix B is composed of the new data sequence Y k The corresponding adjacent mean sequence Z k The matrix composed of.

[0109] S522, inputting the coordinate adjustment value sequence satisfying the grade ratio test into the grey system model for sequence accumulation and elimination accumulation, and obtaining the coordinate adjustment value sequence after elimination accumulation, wherein, the final prediction model is calculated according to step S521:

[0110]

[0111] in, is the cumulative sequence corresponding to a monitoring point in the Kth cycle, is the accumulated elimination sequence corresponding to a monitoring point in the Kth period, a is the development coefficient, and b is the grey action.

[0112] S523, according to the preset translation transformation constant, the coordinate adjustment value sequence after eliminating the accumulation is restored to the state before the translation transformation, and the prediction result is obtained, wherein, due to eliminating the sequence after the accumulation The constant term C in step S521 is included, so the data needs to be restored to the state before the translation transformation. After the translation transformation is restored, the predicted value of the coordinate adjustment value of each monitoring point in the X-axis direction during the next measurement can be obtained, thereby achieving the purpose of data prediction. The calculation formula is:

[0113]

[0114] Wherein, C is the constant term in step S521, is the corresponding sequence after elimination accumulation in the i-th cycle, is the sequence of the corresponding restored translation transformation after elimination and accumulation in the i-th cycle. After that, the prediction of the monitoring data of the next cycle is completed. When the collection of the monitoring data of the next cycle begins, the sequence Determine whether the data at each monitoring point is abnormal so as to issue a timely warning and repair the monitoring point.

[0115] In this embodiment, the automatic monitoring system controls the equipment collection frequency. Whenever there is new collection data, the system will automatically perform the data processing operations of step S1 to step S5, thereby achieving the effect of obtaining automatic monitoring data with high precision.

[0116] Embodiment 2:

[0117] like Figure 2 As shown, this embodiment provides an adaptive anti-interference automatic monitoring data processing system, the system includes an acquisition module 901, a first processing module 902, a second processing module 903 and a third processing module 904:

[0118] An acquisition module 901 is used to acquire an observation value sequence of each observation target, wherein the observation value sequence is a sequence of observation values ​​obtained after the measurement instrument measures the observation target for a first preset number of times in a measurement order, and the measurement target includes a monitoring point and a reference point;

[0119] A first processing module 902 is used to calculate the point position error according to the observation value sequence of the reference point, and if the point position error is greater than a preset first threshold, re-acquire the observation value sequence of each observation target;

[0120] The second processing module 903 is used for calculating the coordinate adjustment value of the station center according to the observation value sequence of the reference point if the point position error is less than a preset first threshold value, and calculating the coordinate adjustment value sequence of each monitoring point based on the coordinate adjustment value of the station center and the observation value sequence of the monitoring point to obtain the coordinate adjustment value sequence of the monitoring point, and the coordinate adjustment value sequence is used to calculate the monitoring data of each monitoring point;

[0121] The third processing module 904 is used to update the coordinate adjustment value sequence of each monitoring point based on the Laida criterion to obtain the updated coordinate adjustment value sequence.

[0122] The third processing module 904 includes a first processing unit 9041:

[0123] The first processing unit 9041 is used to detect whether the coordinate adjustment value sequence corresponding to the first monitoring point contains gross errors according to the Laida criterion. If the first monitoring point has gross errors, the observation value sequence of the first monitoring point is re-acquired until the coordinate adjustment value sequence corresponding to the re-acquired observation value sequence of the first monitoring point does not contain gross errors or the number of re-acquisitions reaches a second preset number, thereby obtaining the updated coordinate adjustment value sequence.

[0124] The third processing module 904 further includes a second processing unit 9042 and a third processing unit 9043:

[0125] The second processing unit 9042 is used to review the second monitoring point whose number of re-acquisitions reaches a second preset number, and issue a warning or make a gross error correction according to the result of the review, wherein the monitoring point is measured on site to obtain a field measurement coordinate adjustment value sequence of the second monitoring point, and if the field measurement coordinate adjustment value sequence of the second monitoring point is consistent with the coordinate adjustment value sequence of the second monitoring point, a warning signal is issued, and the coordinate adjustment value sequence is retained;

[0126] The third processing unit 9043 is used to use a mean shift algorithm model to perform gross error correction on the coordinate adjustment value sequence of the second monitoring point if the field measurement coordinate adjustment value sequence of the second monitoring point is inconsistent with the coordinate adjustment value sequence of the second monitoring point, so as to obtain the coordinate adjustment value sequence after gross error correction.

[0127] The first processing unit 9041 includes a first processing submodule 90411, a second processing submodule 90412, a third processing submodule 90413, a fourth processing submodule 90414 and a fifth processing submodule 90415:

[0128] The first processing submodule 90411 is used to decompose the coordinate adjustment value sequence corresponding to the first monitoring point in the direction of the three-dimensional coordinate axis to obtain three coordinate adjustment value subsequences corresponding to the coordinate axis directions;

[0129] The second processing submodule 90412 is used to calculate the mean and mean error of the first coordinate adjustment value subsequence to obtain the mean and mean error;

[0130] A third processing submodule 90413 is configured to calculate a first threshold value according to the mean value to obtain the first threshold value;

[0131] The fourth processing submodule 90414 is used to calculate the difference between each coordinate value in the first coordinate adjustment value subsequence and the mean value to obtain a plurality of the differences;

[0132] The fifth processing submodule 90415 is configured to determine that a gross error exists in the coordinate adjustment value sequence corresponding to the first coordinate adjustment value subsequence if the difference in the first coordinate adjustment value subsequence is greater than the first threshold.

[0133] An adaptive anti-interference automatic monitoring data processing system further includes a fourth processing module 905 and a fifth processing module 906:

[0134] The fourth processing module 905 is used to filter the updated coordinate adjustment value sequence by an adaptive wavelet threshold denoising analysis method to obtain the filtered coordinate adjustment value sequence;

[0135] The fifth processing module 906 is used to construct a grey system model, and perform data prediction on the filtered coordinate adjustment value sequence based on the grey system model to obtain a prediction result, which is used to predict the monitoring data of the monitoring point corresponding to the coordinate adjustment value sequence during the next measurement.

[0136] The fifth processing module 906 includes a fourth processing unit 9061, a fifth processing unit 9062 and a sixth processing unit 9063:

[0137] The fourth processing unit 9061 is used to perform a level ratio test on the filtered coordinate adjustment value sequence. If the filtered coordinate adjustment value sequence does not meet the level ratio test, the filtered coordinate adjustment value sequence is subjected to a translation transformation according to a preset translation transformation constant until the coordinate adjustment value sequence after the translation transformation meets the level ratio test, thereby obtaining the coordinate adjustment value sequence meeting the level ratio test.

[0138] The fifth processing unit 9062 is used for inputting the coordinate adjustment value sequence satisfying the grade ratio test into the grey system model for sequence accumulation and elimination accumulation, so as to obtain the coordinate adjustment value sequence after elimination accumulation;

[0139] The sixth processing unit 9063 is used to restore the coordinate adjustment value sequence after eliminating accumulation to the state before translation transformation according to the preset translation transformation constant to obtain the prediction result.

[0140] It should be noted that, regarding the system in the above embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment of the method, and will not be elaborated here.

[0141] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

[0142] The above description is only a specific implementation mode of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. An adaptive anti-interference automated monitoring data processing method, characterized in that: include: Obtaining an observation value sequence of each observation target, wherein the observation value sequence is a sequence of observation values ​​obtained after the measurement instrument measures the observation target for a first preset number of times in a measurement order, wherein the measurement target includes a monitoring point and a reference point; Calculate the point position error according to the observation value sequence of the reference point, and if the point position error is greater than a preset first threshold, re-acquire the observation value sequence of each observation target; If the point position error is less than a preset first threshold, the coordinate adjustment value of the station center is calculated according to the observation value sequence of the reference point, and the coordinate adjustment value sequence of each monitoring point is calculated based on the coordinate adjustment value of the station center and the observation value sequence of the monitoring point to obtain the coordinate adjustment value sequence of the monitoring point, and the coordinate adjustment value sequence is used to calculate the monitoring data of each monitoring point; The coordinate adjustment value sequence of each monitoring point is updated based on the Laida criterion to obtain the updated coordinate adjustment value sequence.

2. The method for processing automatic monitoring data with adaptive anti-interference according to claim 1 is characterized in that The updating of the coordinate adjustment value sequence of each monitoring point based on the Laida criterion includes: According to the Laida criterion, it is detected whether the coordinate adjustment value sequence corresponding to the first monitoring point contains gross errors. If the first monitoring point has gross errors, the observation value sequence of the first monitoring point is re-acquired until the coordinate adjustment value sequence corresponding to the re-acquired observation value sequence of the first monitoring point does not contain gross errors or the number of re-acquisitions reaches a second preset number, so as to obtain the updated coordinate adjustment value sequence.

3. The method for processing automatic monitoring data with adaptive anti-interference according to claim 2 is characterized in that After the number of re-acquisitions reaches a second preset number, the method further includes: A second monitoring point whose number of reacquisitions reaches a second preset number is reviewed, and a warning is issued or a gross error correction is performed according to the result of the review, wherein the monitoring point is measured on site to obtain a field measurement coordinate adjustment value sequence of the second monitoring point, and if the field measurement coordinate adjustment value sequence of the second monitoring point is consistent with the coordinate adjustment value sequence of the second monitoring point, a warning signal is issued, and the coordinate adjustment value sequence is retained; If the on-site measured coordinate adjustment value sequence of the second monitoring point is inconsistent with the coordinate adjustment value sequence of the second monitoring point, a mean shift algorithm model is used to perform gross error correction on the coordinate adjustment value sequence of the second monitoring point to obtain the coordinate adjustment value sequence after gross error correction.

4. The method for processing automatic monitoring data with adaptive anti-interference according to claim 2 is characterized in that , the step of detecting whether the coordinate adjustment value sequence corresponding to the first monitoring point contains a gross error according to the Laida criterion includes: Decomposing the coordinate adjustment value sequence corresponding to the first monitoring point in the direction of the three-dimensional coordinate axis to obtain three coordinate adjustment value subsequences corresponding to the coordinate axis directions; Calculating the mean and mean error of the first coordinate adjustment value subsequence to obtain the mean and mean error; Calculate a first threshold value according to the mean value to obtain the first threshold value; Calculate the difference between each coordinate value in the first coordinate adjustment value subsequence and the mean value to obtain a plurality of the differences; If the difference in the first coordinate adjustment value subsequence is greater than the first threshold, then a gross error exists in the coordinate adjustment value sequence corresponding to the first coordinate adjustment value subsequence.

5. The method for processing automatic monitoring data with adaptive anti-interference according to claim 1 is characterized in that After the coordinate adjustment value sequence of each monitoring point is updated based on the Laida criterion, the method further includes: Filtering the updated coordinate adjustment value sequence by an adaptive wavelet threshold denoising analysis method to obtain the filtered coordinate adjustment value sequence; A grey system model is constructed, and data prediction is performed on the filtered coordinate adjustment value sequence based on the grey system model to obtain a prediction result, which is used to predict the monitoring data of the monitoring point corresponding to the coordinate adjustment value sequence during the next measurement.

6. An adaptive anti-interference automatic monitoring data processing system, characterized in that: include: An acquisition module, used to acquire an observation value sequence of each observation target, wherein the observation value sequence is a sequence of observation values ​​obtained after the measurement instrument measures the observation target for a first preset number of times in a measurement order, and the measurement target includes a monitoring point and a reference point; A first processing module, configured to calculate a point position error according to the observation value sequence of the reference point, and if the point position error is greater than a preset first threshold, reacquire an observation value sequence of each observation target; A second processing module is used for calculating the coordinate adjustment value of the station center according to the observation value sequence of the reference point if the point position error is less than a preset first threshold value, and calculating the coordinate adjustment value sequence of each monitoring point based on the coordinate adjustment value of the station center and the observation value sequence of the monitoring point to obtain the coordinate adjustment value sequence of the monitoring point, and the coordinate adjustment value sequence is used to calculate the monitoring data of each monitoring point; The third processing module is used to update the coordinate adjustment value sequence of each monitoring point based on the Laida criterion to obtain the updated coordinate adjustment value sequence.

7. The adaptive anti-interference automatic monitoring data processing system according to claim 6, characterized in that: The third processing module comprises: The first processing unit is used to detect whether the coordinate adjustment value sequence corresponding to the first monitoring point contains gross errors according to the Laida criterion. If the first monitoring point has gross errors, the observation value sequence of the first monitoring point is re-acquired until the coordinate adjustment value sequence corresponding to the re-acquired observation value sequence of the first monitoring point does not contain gross errors or the number of re-acquisitions reaches a second preset number, thereby obtaining the updated coordinate adjustment value sequence.

8. The adaptive anti-interference automatic monitoring data processing system according to claim 7, characterized in that: The third processing module further includes: A second processing unit is used to review the second monitoring point whose number of re-acquisitions reaches a second preset number of times, and issue a warning or make a gross error correction according to the result of the review, wherein the monitoring point is measured on site to obtain a field measurement coordinate adjustment value sequence of the second monitoring point, and if the field measurement coordinate adjustment value sequence of the second monitoring point is consistent with the coordinate adjustment value sequence of the second monitoring point, a warning signal is issued, and the coordinate adjustment value sequence is retained; The third processing unit is used to use a mean shift algorithm model to perform gross error correction on the coordinate adjustment value sequence of the second monitoring point if the field measurement coordinate adjustment value sequence of the second monitoring point is inconsistent with the coordinate adjustment value sequence of the second monitoring point, so as to obtain the coordinate adjustment value sequence after gross error correction.

9. The adaptive anti-interference automatic monitoring data processing system according to claim 7, characterized in that: The first processing unit comprises: A first processing submodule is used to decompose the coordinate adjustment value sequence corresponding to the first monitoring point in the direction of the three-dimensional coordinate axis to obtain three coordinate adjustment value subsequences corresponding to the coordinate axis directions; The second processing submodule is used to calculate the mean and mean error of the first coordinate adjustment value subsequence to obtain the mean and mean error; A third processing submodule, configured to calculate a first threshold value according to the mean value to obtain the first threshold value; A fourth processing submodule is used to calculate the difference between each coordinate value in the first coordinate adjustment value subsequence and the mean value to obtain a plurality of the differences; The fifth processing submodule is configured to determine that a gross error exists in the coordinate adjustment value sequence corresponding to the first coordinate adjustment value subsequence if the difference in the first coordinate adjustment value subsequence is greater than the first threshold.

10. The adaptive anti-interference automatic monitoring data processing system according to claim 6, characterized in that: Also includes: A fourth processing module is used for filtering the updated coordinate adjustment value sequence by an adaptive wavelet threshold denoising analysis method to obtain the filtered coordinate adjustment value sequence; The fifth processing module is used to construct a grey system model, and perform data prediction on the filtered coordinate adjustment value sequence based on the grey system model to obtain a prediction result, which is used to predict the monitoring data of the monitoring point corresponding to the coordinate adjustment value sequence during the next measurement.

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