A method and system for monitoring the center of gravity during the horizontal rotation process of a high pier and long-span bridge

Abstract

The present invention relates to the technical field of bridge rotation process monitoring, and specifically relates to a method and system for monitoring the center of gravity during the horizontal rotation process of high-pier and long-span bridges. The present invention first obtains theoretical data during the bridge rotation process based on finite element simulation and acquires actual data of the bridge rotation process; further, at the current moment, based on the difference between the actual data and the theoretical data, analyzes the simulation deviation conditions of different ducts, and according to the similar characteristics of the change of the actual stress data of different ducts relative to the theoretical stress data, analyzes the authenticity degree of the actual stress data; further fuses the stress true coefficient and the stress change correlation coefficient to obtain a gain compensation parameter; finally, performs Kalman filtering on the actual stress data based on the gain compensation parameter, effectively differentiates environmental interference and noise, enhances data reliability, monitors the bridge center of gravity, and improves the accuracy of center of gravity monitoring during the bridge rotation process.

Description

Technical Field

[0001] The present invention relates to the technical field of monitoring during the horizontal rotation process of bridges, and particularly to a method and system for monitoring the center of gravity during the horizontal rotation process of high-pier and long-span bridges. Background Art

[0002] During the horizontal rotation process of a bridge, the monitoring of the center of gravity of the bridge rotation is crucial. By real-time monitoring the position of the center of gravity of the bridge rotation, the imbalance problems during the rotation process can be detected and adjusted in a timely manner to ensure the smooth progress of the rotation. The rotation process is easily interfered by external environmental factors. For example, the lateral force generated on the bridge during the rotation process causes the bridge to deflect and vibrate. This vibration will cause changes in the stress inside the bridge structure, resulting in inaccurate stress data obtained. Therefore, it is necessary to denoise the stress data.

[0003] The Kalman filtering algorithm only analyzes the noise interference based on the time-series data of a single stress, by comparing the corresponding stress data in the ideal rotation process of the bridge with the actual stress data. However, it cannot determine whether the difference in the monitored bridge stress data is due to the influence of normal environmental factors or the normal noise of the rotation. This will cause a certain degree of inclination of the bridge, and the subtle stress changes generated will be regarded as noise data, which cannot truly reflect the change of the center of gravity, and ignores the influence of environmental factors on the entire bridge, resulting in inaccurate filtered stress data, and further leading to inaccurate monitoring of the center of gravity. Summary of the Invention

[0004] In order to solve the technical problem that the existing Kalman filtering method processes stress data inaccurately and affects the accuracy of center of gravity monitoring, the purpose of the present invention is to provide a method and system for monitoring the center of gravity during the horizontal rotation process of high-pier and long-span bridges. The specific technical solutions adopted are as follows:

[0005] A method for monitoring the center of gravity during the horizontal rotation process of a high-pier and long-span bridge, the method comprising:

[0006] Obtaining theoretical data during the bridge rotation process based on finite element simulation and obtaining actual data during the bridge rotation process; the actual data includes actual stress data; the theoretical data includes theoretical stress data;

[0007] At the current moment, based on the difference between the actual data and the theoretical data, analyzing the simulation deviation conditions of different ducts, obtaining the stress true coefficient of each duct; according to the similar characteristics of the change of the actual stress data of different ducts relative to the theoretical stress data, obtaining the stress change correlation coefficient of each duct;

[0008] At the current moment, fuse the true stress coefficient and the stress change correlation coefficient to obtain a gain compensation parameter; both the true stress coefficient and the stress change correlation coefficient are positively correlated with the gain compensation parameter; perform Kalman filtering on the actual stress data based on the gain compensation parameter to monitor the center of gravity of the bridge.

[0009] Further, the method for obtaining the true stress coefficient includes:

[0010] The actual data further includes the actual rotation angle corresponding to the actual stress data; the theoretical data further includes the theoretical rotation angle corresponding to the theoretical stress data;

[0011] When the bridge is stationary, obtain the data difference sequence between the actual stress data and the theoretical stress data of each duct; obtain the correlation coefficient between the data difference sequences of any two ducts as the first correlation coefficient;

[0012] During the bridge rotation process, intercept the theoretical stress data corresponding to the theoretical rotation angle within the change range of the actual rotation angle in the preset historical neighborhood at the current moment to obtain a theoretical stress data sequence; correct the theoretical stress data sequence based on the data difference sequence to obtain a corrected theoretical data sequence; intercept the actual stress data to obtain an actual stress data sequence, and the actual stress data sequence has the same length as the theoretical stress data sequence and the maximum time sequence; obtain a stress error sequence according to the difference between the actual stress data sequence and the theoretical stress data sequence; obtain the correlation coefficient between the stress error sequences of any two ducts as the second correlation coefficient;

[0013] Correct the second correlation coefficient based on the first correlation coefficient to obtain a third correlation coefficient;

[0014] At the current moment, according to the central tendency of all the first correlation coefficients corresponding to each duct and the amplitude fluctuation of all the third correlation coefficients, obtain the true stress coefficient of each duct at the current moment; the central tendency of the first correlation coefficient is positively correlated with the true stress coefficient; the amplitude fluctuation of the third correlation coefficient is negatively correlated with the true stress coefficient;

[0015] Both the data difference sequence and the time domain length of the preset historical neighborhood are of a preset time length.

[0016] Further, the correlation coefficient is the Pearson correlation coefficient.

[0017] Further, the method for obtaining the third correlation coefficient includes:

[0018] Take the difference between the second correlation coefficient and the corresponding first correlation coefficient as the third correlation coefficient; the second correlation coefficient is the minuend.

[0019] Further, the method for obtaining the stress change correlation coefficient includes:

[0020] Align the starting points in the time domain of the simulated rotation process and the actual rotation process of the finite element simulation, and at the current moment, obtain the change value of the actual stress data at both ends of the second preset historical neighborhood of each duct as the actual stress change value; obtain the change value of the theoretical stress data at both ends of the second preset historical neighborhood of each duct as the theoretical stress change value;

[0021] Take the ratio of the actual stress change value and the theoretical stress change value of each duct at the current moment as the stress relative change rate; according to the similarity characteristics of the stress relative change rates of each duct and all other ducts, obtain the stress change correlation coefficient of each duct at the current moment.

[0022] Further, the method for obtaining the stress change correlation coefficient of each duct at the current moment according to the similarity characteristics of the stress relative change rates of each duct and all other ducts includes:

[0023] At the current moment, after performing negative correlation mapping on the mean value of the absolute value of the difference between the stress relative change rate of each duct and the stress relative change rates of all other ducts, take the negative correlation mapping result as the stress change correlation coefficient of the corresponding duct.

[0024] Further, the method for monitoring the bridge center of gravity based on the Kalman filtering of the actual stress data using the gain compensation parameter includes:

[0025] Fuse the original Kalman gain at the current moment and the gain compensation parameter to obtain the corrected Kalman gain; both the original Kalman gain and the gain compensation parameter are positively correlated with the corrected Kalman gain;

[0026] Monitor the bridge center of gravity based on the filtered stress data obtained by filtering the actual stress data using the corrected Kalman gain.

[0027] Further, the method for monitoring the bridge center of gravity based on the filtered stress data obtained by filtering the actual stress data using the corrected Kalman gain includes:

[0028] Convert the filtered stress data into the deformation amount of the structure; obtain the bridge center of gravity by using the pre-constructed deformation-center of gravity position model with the deformation amount.

[0029] Further, the preset time length is 10 seconds.

[0030] The present invention also provides a gravity center monitoring system for the horizontal rotation process of a high pier and long-span bridge. The system includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of any one of the gravity center monitoring methods for the horizontal rotation process of a high pier and long-span bridge are implemented.

[0031] The present invention has the following beneficial effects:

[0032] The present invention first obtains theoretical data during the bridge rotation process based on finite element simulation and acquires actual data of the bridge rotation process, providing a data basis for subsequent analysis. Further, at the current moment, based on the difference between the actual data and the theoretical data, the simulation deviation conditions of different ducts are analyzed to distinguish normal deviations and deviations caused by noise, and the true stress coefficient of each duct is obtained from the perspective of the simulation deviation of different ducts, representing the credibility degree of the duct stress data being able to truly represent the duct stress. Further, according to the similar characteristics of the change of the actual stress data of different ducts relative to the theoretical stress data, from the perspective of the similarity of the relative stress change, the interference of the normal difference in the stress change amplitude is excluded, and the stress change correlation coefficient of each duct is obtained, which also reflects the authenticity degree of the actual stress data. Further, the true stress coefficient and the stress change correlation coefficient are fused to obtain a gain compensation parameter, providing a basis for adjusting the Kalman filter to obtain more accurate filtered stress data. Finally, the Kalman filter is performed on the actual stress data based on the gain compensation parameter to monitor the bridge gravity center. By comparing finite element simulation with actual data, analyzing duct stress deviation, calculating the true stress coefficient and the stress change correlation coefficient, fusing to generate a gain compensation parameter, and optimizing the Kalman filter, the present invention effectively distinguishes environmental interference and noise and improves the accuracy of gravity center monitoring during the bridge rotation process. Description of the Drawings

[0033] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required to be used in the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0034] Figure 1 It is a flowchart of a gravity center monitoring method for the horizontal rotation process of a high pier and long-span bridge provided by an embodiment of the present invention;

[0035] Figure 2 It is a schematic diagram of the distribution of bridge tendon ducts provided by an embodiment of the present invention;

[0036] Figure 3 The flowchart of a method for obtaining the true stress coefficient provided by an embodiment of the present invention. Detailed implementation manners

[0037] In order to further elaborate on the technical means and effects adopted by the present invention to achieve the intended invention purpose, the following combines the accompanying drawings and preferred embodiments to detail the specific implementation manners, structures, features and effects of a method and system for monitoring the center of gravity during the horizontal rotation process of a high-pier and long-span bridge according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, the specific features, structures or characteristics in one or more embodiments can be combined in any suitable form.

[0038] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which the present invention belongs.

[0039] The following specifically describes the specific scheme of a method and system for monitoring the center of gravity during the horizontal rotation process of a high-pier and long-span bridge provided by the present invention with reference to the accompanying drawings.

[0040] Please refer to Figure 1 , which shows the flowchart of a method for monitoring the center of gravity during the horizontal rotation process of a high-pier and long-span bridge provided by an embodiment of the present invention, specifically including:

[0041] Step S1: Obtain the theoretical data during the bridge rotation process based on finite element simulation and obtain the actual data during the bridge rotation process; the actual data includes actual stress data; the theoretical data includes theoretical stress data.

[0042] Collect data such as the structure of the bridge and input it into finite element analysis software, such as Abaqus, ANSYS, etc., to simulate the high-pier and long-span bridge to be analyzed. Based on the finite element model, according to the current environmental state of the bridge and the structural safety of the bridge itself, obtain the optimal rotation angular velocity and rotation direction during the horizontal rotation process of the bridge.

[0043] Simulate the horizontal rotation process of the bridge according to the optimal rotation angular velocity and direction to obtain the theoretical data in the simulation; and during the actual horizontal rotation process of the bridge, also rotate the bridge horizontally according to the optimal rotation angular velocity and direction to obtain the actual data. Since the center of gravity monitoring ultimately needs to be based on the stress data, the actual data includes actual stress data, and the theoretical data includes theoretical stress data, providing a data basis for the subsequent analysis process.

[0044] It should be noted that the duct refers to the longitudinal prestressed tendon duct of the bridge, and both the actual stress data and the theoretical stress data are the stress data of the longitudinal prestressed tendon duct of the bridge. Please refer toFigure 2 , which shows a schematic diagram of the distribution of bridge tendon ducts provided by an embodiment of the present invention; Figure 2 The markings in the figure distinguish the longitudinal prestressed tendon ducts and the transverse prestressed tendon ducts of the bridge; the finite element analysis method and the method of collecting stress data are both well-known technical means to those skilled in the art, and will not be elaborated here.

[0045] Step S2: At the current moment, based on the difference between the actual data and the theoretical data, analyze the simulation deviation of different ducts, and obtain the stress true coefficient of each duct; according to the similar characteristics of the change of the actual stress data of different ducts relative to the theoretical stress data, obtain the stress change correlation coefficient of each duct.

[0046] Considering that the actual bridge stress data will be affected by factors such as temperature, humidity, bridge vibration friction, and structural changes of bridge materials, resulting in differences between the actual data and the theoretical data. This difference is a normal deviation between the model simulation and the actual situation; at the same time, data noise will also cause deviations of the actual data relative to the theoretical data. Therefore, first at the current moment, based on the difference between the actual data and the theoretical data, analyze the simulation deviation of different ducts to distinguish the normal deviation and the deviation caused by noise, and obtain the stress true coefficient of each duct, which characterizes the credibility of the duct stress data to truly represent the duct stress, and prepares for subsequent adjustment of the Kalman filter for centroid monitoring.

[0047] Preferably, in an embodiment of the present invention, please refer to Figure 3 , which shows a flowchart of a method for obtaining the stress true coefficient provided by an embodiment of the present invention, specifically including:

[0048] Step S201: When the bridge is stationary, obtain the data difference sequence between the actual stress data and the theoretical stress data of each duct; obtain the correlation coefficient of the data difference sequences of any two ducts as the first correlation coefficient.

[0049] When the bridge is stationary, that is, before the horizontal rotation of the bridge, the difference between the actual stress data and the theoretical stress data of the duct represents the normal simulation deviation of the corresponding duct when it is stationary. Obtaining the data difference sequence between the actual stress data and the theoretical stress data of each duct reflects the normal interference of environmental factors on the actual stress data; further obtaining the first correlation coefficient to obtain the normal difference between the actual stress data of two ducts.

[0050] The analysis process for each duct is the same. Here, any duct is selected as the target duct, and the remaining ducts are used as comparison ducts to obtain the stress true coefficient of the target duct, and will not be repeated.

[0051] As an example, , F represents the actual stress data of the target duct when the bridge is stationary, represents the theoretical stress data of the target duct when the bridge is stationary, a represents the data difference value between the actual stress data and the theoretical stress data, and the data difference values with a preset time length of 10 seconds are obtained to form a data difference sequence ;

[0052] The calculation formula of the first correlation coefficient includes:

[0053] ;

[0054] where, j represents the serial number of the comparison duct; represents the first correlation coefficient between the target duct and the j-th comparison duct; represents the data difference sequence of the target duct; represents the data difference sequence of the j-th comparison duct; represents calculating the Pearson correlation coefficient of the two sequences.

[0055] In the calculation formula of the first correlation coefficient, the data difference sequence is used to represent the normal deviation characteristics of the bridge when it is stationary, and then the Pearson correlation coefficient is used to represent the correlation of the normal deviation of the stress data between different ducts, so as to reflect the simulation deviation of different ducts before the bridge rotation, and prepare for obtaining the true stress coefficient subsequently.

[0056] It should be noted that in an embodiment of the present invention, the data acquisition frequency is 500Hz.

[0057] Step S202: During the bridge rotation process, within the change range of the actual rotation angle in the preset historical neighborhood at the current moment, intercept the theoretical stress data corresponding to the theoretical rotation angle to obtain a theoretical stress data sequence; based on the data difference sequence, correct the theoretical stress data sequence to obtain a corrected theoretical data sequence; intercept the actual stress data to obtain an actual stress data sequence, and the length of the actual stress data sequence is the same as that of the theoretical stress data sequence and the time sequence is the largest; obtain a stress error sequence according to the difference between the actual stress data sequence and the theoretical stress data sequence; obtain the correlation coefficient of the stress error sequences of any two ducts as the second correlation coefficient.

[0058] After analyzing the static state of the bridge, further analyze the simulation deviation during the bridge rotation process. Since the external environment at different rotation angles has different effects on the bridge, for example, the same wind speed in the same direction at different rotation angles has different effects on the entire bridge, the actual data also includes the actual rotation angle corresponding to the actual stress data; the theoretical data also includes the theoretical rotation angle corresponding to the theoretical stress data, providing data in the angle dimension; where the rotation angle is the angle between the longitudinal direction of the bridge before rotation (before the rotation) and the longitudinal direction of the bridge at the current moment, and the rotation angle and stress data are collected synchronously.

[0059] As an example, the preset historical neighborhood is 10 seconds. Taking the target duct as an example, obtain the actual rotation angle at the current moment and the actual rotation angle 10 seconds ago , and record the change range of the actual rotation angle as , and intercept the theoretical stress data corresponding to the angle range of to obtain the theoretical stress data sequence , , which is also expressed as , ; represents the time point when the theoretical rotation angle is , represents the time point when the theoretical rotation angle is .

[0060] Considering the influence of factors such as the environment, it will cause normal deviation in the data. Therefore, based on the data difference sequence, the theoretical stress data sequence is corrected to obtain the corrected theoretical data sequence : , , represents taking the average value, indicating the stress data considering the influence of external factors.

[0061] Considering that there may be slight deviations between the actual rotation and the simulated rotation, such as the actual angular velocity being slower or faster in a local time domain, resulting in the time domain length of , , There is a deviation from the time domain length of the preset historical neighborhood. To facilitate the comparison of the simulation deviation between the theoretical stress data and the actual stress data considering factors such as the environment, intercept the actual stress data to obtain the actual stress data sequence. The length of the actual stress data sequence is the same as that of the theoretical stress data sequence, and the time series is the largest, that is, the intercepted time domain length is the same as that of the theoretical stress data sequence, and the historical actual stress data with the latest time domain is used as the actual stress data sequence

[0062] Since the data acquisition frequencies are the same, the data sequences with the same time domain length are of equal length; the stress error sequence is obtained based on the difference between the actual stress data sequence and the theoretical stress data sequence : , representing the simulation deviation between the theoretical stress data and the actual stress data after considering factors such as the environment;

[0063] Considering that there is a high consistency in the simulation deviations of different ducts during the rotation process, it indicates that the stress deviation at this time belongs to the normal influence of external environmental wind speed, wind direction, etc. on the entire bridge, and belongs to the real change of stress. Therefore, the correlation coefficient of the stress error sequences of any two ducts is obtained as the second correlation coefficient. The calculation formula of the second correlation coefficient includes: ;

[0064] Among them, j represents the serial number of the comparison duct; represents the second correlation coefficient between the target duct and the j-th comparison duct; represents the stress error sequence of the target duct at the current moment; represents the stress error sequence of the j-th comparison duct at the current moment; represents calculating the Pearson correlation coefficient of the two sequences.

[0065] In the calculation formula of the second correlation coefficient, since the actual rotation angular velocity is the angular velocity of the entire bridge, all ducts are affected by the angular velocity in the same way. Therefore and are of the same length; The larger it is, the stronger the consistency of the change in the simulation deviation of the stress between the target duct and the comparison duct during the rotation process, indicating that the simulation deviation of the target duct is more likely to be a normal deviation, rather than noise caused by local vibration of the bridge due to horizontal rotation, representing the simulation deviation situation of different ducts from the perspective of bridge dynamics.

[0066] Step S203: Modify the second correlation coefficient based on the first correlation coefficient to obtain the third correlation coefficient.

[0067] Considering that the positions of different ducts are different, and there are differences in the simulation deviations of ducts with different positions in stress, the second correlation coefficient is modified by the first correlation coefficient in the static state:

[0068] As an example: taking the difference between the second correlation coefficient and the corresponding first correlation coefficient as the third correlation coefficient; the second correlation coefficient is the minuend, and the calculation formula of the third correlation coefficient includes: ;

[0069] Among them represents the first correlation coefficient between the target duct and the j-th comparison duct; Denote the second correlation coefficient between the target duct and the j-th comparison duct; Denote the third correlation coefficient between the target duct and the j-th comparison duct.

[0070] In the calculation formula of the third correlation coefficient, the third correlation coefficient is obtained by the way of difference, which can eliminate the inherent correlation between ducts in the static state (normal deviation caused by environmental factors), highlight the abnormal fluctuations caused by dynamic factors (such as vibration, imbalance) during the rotation process, and provide more basis for subsequent analysis of the authenticity of the actual stress data of the target duct.

[0071] Step S204: At the current moment, according to the central tendency of all the first correlation coefficients corresponding to each duct and the amplitude fluctuation of all the third correlation coefficients, obtain the stress true coefficient of each duct at the current moment.

[0072] The central tendency of the first correlation coefficient is positively correlated with the stress true coefficient; the amplitude fluctuation of the third correlation coefficient is negatively correlated with the stress true coefficient.

[0073] During the horizontal rotation process, the simulation deviation caused by the influence of external environmental factors on the whole bridge belongs to the real change of the stress data. For example, due to the external environmental wind force, the whole bridge girder shows a certain degree of inclination. The prestressed tendons in the ducts on the inclined side are stretched and the stress increases; the stress on the ducts in the opposite direction is relatively reduced, and the stress of the prestressed tendons decreases accordingly, resulting in a deviation between the actual data and the simulated data of the ducts. At this time, the prestressed tendons of each duct will generate stress changes to resist this lateral deformation. Only the degree of change is different due to the different characteristics of the ducts themselves, but the overall error trend is similar, reflecting the normal stress situation during the bridge rotation, and it is real and effective data;

[0074] Based on this, the calculation formula of the stress true coefficient includes:

[0075] ;

[0076] where b represents the stress true coefficient of the target duct at the current moment; represents the set of the second correlation coefficients between the target duct and all comparison ducts at the current moment, represents taking the average value; represents a preset positive parameter for division by zero. In this example, C0 = 0.01; represents the set of the absolute values of the third correlation coefficients between the target duct and all comparison ducts at the current moment, represents taking the standard deviation, represents taking the absolute value; represents a linear normalization function.

[0077] In the calculation formula of the stress true coefficient, in the way of average value, represents the central tendency of all the first correlation coefficients corresponding to the target duct, The larger it is, the greater the correlation coefficient between the simulation deviation of the target duct during the rotation process and the simulation deviation of all comparison ducts, reflecting the stronger the consistency of the simulation deviation, the more likely it is the normal influence of external environmental factors, the stronger the authenticity of the actual stress data, and the larger the stress true coefficient; The amplitude fluctuation of the third correlation coefficient is represented by the standard deviation, The smaller it is, the stronger the consistency of the stress changes between the target duct and the comparison ducts, the more likely it is the normal influence of external environment and other factors on the whole bridge, the stronger the authenticity of the actual stress data, and the larger the stress true coefficient.

[0078] The stress true coefficient integrates the simulation deviations in the static and dynamic states of the bridge, reflects the real situation of the actual stress data, and provides a basis for subsequent adjustment of the Kalman filter.

[0079] Considering that during the horizontal rotation process of the bridge, the stress data of the ducts at different positions have normal changes, and the stress changes are affected by the differences in the normal structural forces of the bridge, resulting in differences in the magnitudes of the stress changes of different ducts although there is consistency in the stress changes, and further resulting in the simulation deviation of different ducts being affected by the bridge structure. Therefore, it is also necessary to obtain the stress change correlation coefficient of each duct according to the similar characteristics of the changes of the actual stress data of different ducts relative to the theoretical stress data, by analyzing the relative changes of the stress, excluding the interference of the normal differences in the stress change amplitudes, to characterize the correlation strength between the stress changes of the duct and the stress changes of other ducts, and also reflect the authenticity of the actual stress data.

[0080] Preferably, in an embodiment of the present invention, the starting points of the time domains of the simulated rotation process and the actual rotation process in the finite element simulation are aligned to facilitate the comparison of the stress changes in the same horizontal rotation stage;

[0081] At the current moment, obtain the change value of the actual stress data at both ends of the second preset historical neighborhood of each duct as the actual stress change value; obtain the change value of the theoretical stress data at both ends of the second preset historical neighborhood of each duct as the theoretical stress change value;

[0082] As an example, the time domain length of the second preset historical neighborhood is 10 seconds. That is, taking the current moment as the end point and 10 seconds before as the starting point, the second preset historical neighborhood is constructed. The actual stress data at the end point is uniformly subtracted from the actual stress data at the starting point as the actual stress change value, and the change value of the data is represented by the difference; since the time domain starting points of the simulated rotation process and the actual rotation process are aligned in advance, the theoretical stress data at the end point and the theoretical stress data at the starting point can be correspondingly obtained. The theoretical stress data at the end point is subtracted from the theoretical stress data at the starting point as the theoretical stress change value;

[0083] The ratio of the actual stress change value to the theoretical stress change value of each duct at the current moment is used as the relative stress change rate; among them, the change of the actual stress data relative to the theoretical stress data is represented in the form of a ratio, and the theoretical stress change value is in the denominator position. Since the bridge rotation process is a complex dynamic process, the stress will change accordingly over time, and the theoretical stress change value is usually not zero; if it is zero, a very small positive parameter such as 0.01 is set as the denominator.

[0084] Furthermore, according to the similarity characteristics of the relative stress change rates of each duct and all other ducts, the stress change correlation coefficient of each duct at the current moment is obtained.

[0085] As an example, at the current moment, after performing a negative correlation mapping on the mean value of the absolute value of the difference between the relative stress change rate of each duct and the relative stress change rates of all other ducts, the negative correlation mapping result is used as the stress change correlation coefficient of the corresponding duct. The calculation formula of the stress change correlation coefficient includes:

[0086] ;

[0087] Among them, c represents the stress change correlation coefficient of the target duct at the current moment; represents the exponential function with the natural constant e as the base; J represents the number of comparison ducts; j represents the serial number of the comparison duct; represents the relative stress change rate of the target duct at the current moment; The relative stress change rate of the jth comparison duct at the current moment; represents taking the absolute value.

[0088] In the calculation formula of the stress change correlation coefficient, the difference characteristics of the relative change rates between the target duct and the comparison ducts are represented by the absolute value of the difference, the overall difference characteristics are represented by taking the average, and then the function is used for negative correlation mapping, so as to reflect the similarity characteristics of the relative stress change rates of the target duct and all comparison ducts, and represent the similarity characteristics of the changes of the actual stress data of the target duct and all comparison ducts relative to the theoretical stress data. The larger it is, the stronger the correlation of the relative stress change rate, the larger the stress change correlation coefficient, and the stronger the authenticity of the stress change in the target duct; where x is the independent variable.

[0089] Step S3: At the current moment, fuse the stress authenticity coefficient and the stress change correlation coefficient to obtain a gain compensation parameter; both the stress authenticity coefficient and the stress change correlation coefficient are positively correlated with the gain compensation parameter; perform Kalman filtering on the actual stress data based on the gain compensation parameter to monitor the bridge center of gravity.

[0090] The stress authenticity coefficient evaluates the authenticity degree of the actual stress data from the perspective of the simulation deviation of different ducts, while the stress change correlation coefficient excludes the interference of normal differences in the stress change amplitude from the perspective of the similarity of the relative stress change, and also reflects the authenticity degree of the actual stress data. Therefore, fusing the stress authenticity coefficient and the stress change correlation coefficient to obtain a gain compensation parameter provides a basis for adjusting the Kalman filter to obtain more accurate filtered stress data and thus accurately monitoring the bridge center.

[0091] Since the larger the stress authenticity coefficient and the stress change correlation coefficient are, the more real the actual stress data is, both the stress authenticity coefficient and the stress change correlation coefficient are positively correlated with the gain compensation parameter. As a preferred embodiment, the stress authenticity coefficient and the stress change correlation coefficient are fused by multiplication, and the calculation formula of the gain compensation parameter includes:

[0092] ;

[0093] Where represents the gain compensation parameter of the target duct at the current moment; represents the stress authenticity coefficient of the target duct at the current moment; represents the stress change correlation coefficient of the target duct at the current moment.

[0094] In another embodiment of the present invention, considering that the stronger the trend of the stress authenticity coefficient in a local time period, the higher the stability or consistency of the stress authenticity coefficient of the duct in the time dimension, the less affected by random noise interference, and the higher the data reliability. Therefore, the stability of the stress authenticity coefficient in the local time period can also be combined to fuse the stress authenticity coefficient and the stress change correlation coefficient to obtain a gain compensation parameter.

[0095] As an example, obtain the stress true coefficients at each moment within the second preset historical neighborhood of the target pore at the current moment, perform linear fitting on these stress true coefficients, and obtain the corrected determination coefficient r of the fitting line, which represents the fitting effect of the fitting line. The larger r is, the more it indicates that the change of the stress true coefficient conforms to the linear law rather than random fluctuations, and the higher the authenticity of the stress data. At this time, the calculation formula for the gain compensation parameter is: 。

[0096] After obtaining the gain compensation parameter, the actual stress data can be subjected to Kalman filtering based on the gain compensation parameter to accurately reduce the noise of the actual stress data, so as to accurately monitor the bridge's center of gravity and ensure construction safety.

[0097] Preferably, in an embodiment of the present invention, considering that the larger the original Kalman gain of the Kalman filtering algorithm, the smaller the uncertainty of the corresponding actual stress data, and the filtering result tends to the actual stress data. The larger the gain compensation parameter, the stronger the authenticity of the actual stress data and the smaller the degree of being affected by noise, and the filtering result should tend to the actual stress data. Based on this, the corrected Kalman gain is obtained by fusing the original Kalman gain and the gain compensation parameter at the current moment; both the original Kalman gain and the gain compensation parameter are positively correlated with the corrected Kalman gain;

[0098] As an example, after linearly normalizing the product of the original Kalman gain and the gain compensation parameter of the target hole at the current moment, the normalized result is used as the corrected Kalman gain of the target hole at the current moment; the larger the corrected Kalman gain, the higher the accuracy of the monitored actual stress data, and the larger the Kalman gain used in filtering;

[0099] It should be noted that the Kalman filtering algorithm and the method for obtaining the original Kalman gain are both existing technologies and will not be elaborated here.

[0100] Finally, the filtered stress data obtained by filtering the actual stress data based on the corrected Kalman gain is used to monitor the bridge's center of gravity.

[0101] As an example, convert the filtered stress data into the deformation amount of the structure; use the pre-constructed deformation-center of gravity position model for the deformation amount to obtain the center of gravity of the bridge, which specifically includes:

[0102] Strain-stress conversion: Stress data is usually measured by strain sensors. First, convert the strain data into stress data according to the elastic modulus of the material. Then, according to the geometric relationship of the structure and the deformation formula in material mechanics, such as Hooke's law, convert the stress into the deformation amount of the structure (such as displacement, rotation angle, etc.), which is an existing technology and will not be elaborated here.

[0103] Association between Deformation and Center of Gravity: Changes in the position of the center of gravity of a bridge will cause changes in the deformation distribution of the structure. By analyzing the relationship between the deformation amounts at different positions, a mathematical model of the deformation and the center of gravity position can be established to obtain a pre-constructed deformation-center of gravity position model. For example, when the center of gravity shifts, there will be differences in the displacements at both ends of the bridge. By monitoring this displacement difference and combining the geometric dimensions and mechanical properties of the structure, the amount and direction of the center of gravity shift can be deduced. Combining with the initial center position before the rotation, the center of gravity of the bridge at the current moment can be obtained, which is already in the prior art and will not be elaborated here.

[0104] Real-time Monitoring and Update: During the rotation process, continuously monitor the strain data and perform the above-mentioned conversion and calculation in real time to continuously update the position information of the center of gravity. This method can more intuitively reflect the influence of the center of gravity change on the structural deformation and is applicable to bridge structures that are more sensitive to the center of gravity change.

[0105] An embodiment of the present invention also provides a center of gravity monitoring system for the horizontal rotation process of a high-pier and long-span bridge. The system includes a memory, a processor, and a computer program. The memory is used to store the corresponding computer program, and the processor is used to run the corresponding computer program. When the computer program runs in the processor, it can implement a center of gravity monitoring method for the horizontal rotation process of a high-pier and long-span bridge described in steps S1-S3.

[0106] In summary, aiming at the technical problem that the existing Kalman filtering method has inaccurate processing of stress data and affects the accuracy of center of gravity monitoring, the present invention proposes a center of gravity monitoring method and system for the horizontal rotation process of a high-pier and long-span bridge. The present invention first obtains the theoretical data during the bridge rotation process based on finite element simulation and obtains the actual data of the bridge rotation process; further, at the current moment, based on the difference between the actual data and the theoretical data, analyze the simulation deviation conditions of different ducts, and according to the similar characteristics of the change of the actual stress data of different ducts relative to the theoretical stress data, analyze the authenticity degree of the actual stress data; further fuse the stress real coefficient and the stress change correlation coefficient to obtain a gain compensation parameter; finally, perform Kalman filtering on the actual stress data based on the gain compensation parameter to effectively distinguish environmental interference and noise, enhance data reliability, monitor the center of gravity of the bridge, and improve the accuracy of center of gravity monitoring during the bridge rotation process.

[0107] It should be noted that: the above sequence of embodiments of the present invention is only for description and does not represent the superiority or inferiority of the embodiments. The processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0108] Each embodiment in this specification is described in a progressive manner. For the same or similar parts among the embodiments, reference can be made to each other, and the key point of each embodiment is to illustrate the differences from other embodiments.

Claims

1. A method for monitoring the center of gravity during the horizontal rotation of a high-pier and long-span bridge, characterized in that: The method comprises: Obtain theoretical data of the bridge rotation process based on finite element simulation and obtain actual data of the bridge rotation process; the actual data includes actual stress data; the theoretical data includes theoretical stress data; At the current moment, based on the difference between the actual data and the theoretical data, the simulation deviation of different channels is analyzed to obtain the true stress coefficient of each channel; according to the similar characteristics of the changes of the actual stress data of different channels relative to the theoretical stress data, the stress change correlation coefficient of each channel is obtained; At the current moment, the stress true coefficient and the stress change correlation coefficient are integrated to obtain a gain compensation parameter; both the stress true coefficient and the stress change correlation coefficient are positively correlated with the gain compensation parameter; based on the gain compensation parameter, the actual stress data is subjected to Kalman filtering to monitor the center of gravity of the bridge; The method for obtaining the stress true coefficient includes: The actual data also includes the actual rotation angle corresponding to the actual stress data; the theoretical data also includes the theoretical rotation angle corresponding to the theoretical stress data; When the bridge is stationary, obtaining a data difference sequence between the actual stress data and the theoretical stress data of each hole; obtaining a correlation coefficient between the data difference sequences of any two holes as a first correlation coefficient; During the bridge rotation process, the theoretical stress data corresponding to the theoretical rotation angle is intercepted based on the variation range of the actual rotation angle within the preset historical neighborhood at the current moment to obtain a theoretical stress data sequence; the theoretical stress data sequence is corrected based on the data difference sequence to obtain a corrected theoretical data sequence; the actual stress data is intercepted to obtain an actual stress data sequence, the actual stress data sequence has the same length as the theoretical stress data sequence, and the time sequence is the largest; the stress error sequence is obtained according to the difference between the actual stress data sequence and the theoretical stress data sequence; the correlation coefficient of the stress error sequences of any two of the holes is obtained as the second correlation coefficient; Correcting the second correlation coefficient based on the first correlation coefficient to obtain a third correlation coefficient; At the current moment, according to the central tendency of all the first correlation coefficients corresponding to each of the pores and the amplitude fluctuation of all the third correlation coefficients, the stress true coefficient of each of the pores at the current moment is obtained; the central tendency of the first correlation coefficient is positively correlated with the stress true coefficient; the amplitude fluctuation of the third correlation coefficient is negatively correlated with the stress true coefficient; The time domain lengths of the data difference sequence and the preset historical neighborhood are both preset time lengths; The method for obtaining the stress change correlation coefficient includes: Align the starting points of the time domain of the simulated rotation process of the finite element simulation and the actual rotation process, and at the current moment, obtain the change values ​​of the actual stress data at both ends of the second preset historical neighborhood of each of the holes as the actual stress change values; obtain the change values ​​of the theoretical stress data at both ends of the second preset historical neighborhood of each of the holes as the theoretical stress change values; The ratio of the actual stress change value and the theoretical stress change value of each channel at the current moment is taken as the relative stress change rate; based on the similar characteristics of the relative stress change rates of each channel and all other channels, the stress change correlation coefficient of each channel at the current moment is obtained.

2. The method for monitoring the center of gravity during the horizontal rotation of a high-pier and long-span bridge according to claim 1 is characterized in that: The correlation coefficient is the Pearson correlation coefficient.

3. The method for monitoring the center of gravity during the horizontal rotation of a high-pier and long-span bridge according to claim 1 is characterized in that: The method for obtaining the third correlation coefficient includes: The difference between the second correlation coefficient and the corresponding first correlation coefficient is used as the third correlation coefficient; the second correlation coefficient is the minuend.

4. The method for monitoring the center of gravity during the horizontal rotation of a high-pier and long-span bridge according to claim 1 is characterized in that: The method for obtaining the stress change correlation coefficient of each pore at the current moment according to the similar characteristics of the stress relative change rate of each pore and all other pores includes: At the current moment, after negative correlation mapping is performed on the average of the absolute values ​​of the differences between the relative stress change rates of each pore and the relative stress change rates of all other pores, the negative correlation mapping result is used as the stress change correlation coefficient corresponding to the pore.

5. The method for monitoring the center of gravity during the horizontal rotation of a high-pier and long-span bridge according to claim 1 is characterized in that: The method of performing Kalman filtering on the actual stress data based on the gain compensation parameter to monitor the center of gravity of the bridge includes: The original Kalman gain at the current moment and the gain compensation parameter are integrated to obtain a corrected Kalman gain; the original Kalman gain and the gain compensation parameter are both positively correlated with the corrected Kalman gain; The center of gravity of the bridge is monitored based on the filtered stress data obtained by filtering the actual stress data with the corrected Kalman gain.

6. The method for monitoring the center of gravity during the horizontal rotation of a high-pier and long-span bridge according to claim 5 is characterized in that: The method for monitoring the center of gravity of a bridge based on filtered stress data obtained by filtering the actual stress data with the corrected Kalman gain includes: The filtered stress data is converted into the deformation of the structure; the deformation is used in a pre-built deformation-center of gravity position model to obtain the center of gravity of the bridge.

7. The method for monitoring the center of gravity during the horizontal rotation of a high-pier and long-span bridge according to claim 1 is characterized in that: The preset time length is 10 seconds.

8. A system for monitoring the center of gravity of a high-pier and long-span bridge during horizontal rotation, the system comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of a method for monitoring the center of gravity during the horizontal rotation process of a high-pier and long-span bridge are implemented as described in any one of claims 1 to 7.

Images