Method and system for monitoring center of gravity in horizontal rotation process of high-pier long-span bridge

Abstract

The invention relates to the technical field of bridge rotation process monitoring, in particular to a gravity center monitoring method and system in the horizontal rotation process of a high-pier long-span bridge. The method comprises the following steps: firstly, obtaining theoretical data in a bridge rotation process based on finite element simulation, and obtaining actual data in the bridge rotation process; further, at the current moment, based on the difference between actual data and theoretical data, the simulation deviation conditions of different pore channels are analyzed, and according to the similar characteristics of the actual stress data of different pore channels relative to the change of the theoretical stress data, the authenticity degree of the actual stress data is analyzed; further fusing the stress true coefficient and the stress change correlation coefficient to obtain a gain compensation parameter; and finally, performing Kalman filtering on the actual stress data based on the gain compensation parameter, effectively distinguishing environmental interference and noise, enhancing data reliability, monitoring the center of gravity of the bridge, and improving the accuracy of center of gravity monitoring in the bridge turning process.

Description

Technical Field

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

[0002] During the horizontal rotation of the bridge, the monitoring of the bridge rotation center of gravity is crucial. By real-time monitoring of the position of the bridge rotation center of gravity, the imbalance problem in the rotation process can be discovered and adjusted in time to ensure the smooth progress of the rotation. The rotation process is easily disturbed by external environmental factors, such as the lateral force generated on the bridge during the rotation process, which causes the bridge to deflect and vibrate; this vibration will cause the stress inside the bridge structure to change, resulting in inaccurate stress data, so the stress data needs to be denoised.

[0003] The Kalman filter algorithm only performs noise interference analysis based on the time series data of a single stress, by comparing the corresponding stress data during the ideal rotation of the bridge with the actual stress data; however, it is unable to determine whether the difference in the monitored bridge stress data is affected by normal environmental factors or the normal noise of the rotation, which will cause the bridge to tilt to a certain extent and produce slight stress changes that will be regarded as noise data, and cannot truly reflect the changes in the center of gravity. It also ignores the impact of environmental factors on the entire bridge, resulting in inaccurate filtered stress data, and thus inaccurate monitoring of the center of gravity. Summary of the invention

[0004] In order to solve the technical problem that the existing Kalman filter method is inaccurate in processing stress data 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 a high-pier and long-span bridge. The technical solution adopted is as follows: A method for monitoring the center of gravity during a horizontal rotation process of a high-pier and long-span bridge, the method comprising: 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.

[0005] Furthermore, the method for obtaining the true stress 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.

[0006] Furthermore, the correlation coefficient is a Pearson correlation coefficient.

[0007] Furthermore, 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.

[0008] Furthermore, 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.

[0009] Furthermore, 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.

[0010] Furthermore, 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.

[0011] Furthermore, 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.

[0012] Furthermore, the preset time length is 10 seconds.

[0013] The present invention also proposes a system for monitoring the center of gravity during 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, it implements any one of the steps of a method for monitoring the center of gravity during the horizontal rotation process of a high-pier and long-span bridge.

[0014] The present invention has the following beneficial effects: The present invention first obtains theoretical data in the process of bridge rotation based on finite element simulation and obtains actual data in the process of bridge rotation, so as to provide a data basis for the subsequent analysis process; further, at the current moment, based on the difference between actual data and theoretical data, the simulation deviation of different channels is analyzed to distinguish the deviation caused by normal deviation and noise, and the stress true coefficient of each channel is obtained from the simulation deviation angle of different channels, so as to characterize the credibility of the channel stress data in representing the channel stress; further, according to the similar characteristics of the change of actual stress data of different channels relative to the theoretical stress data, from the similar angle of relative stress change, the interference of normal difference in stress change amplitude is eliminated, and the stress change correlation coefficient of each channel is obtained, which also reflects the authenticity of the actual stress data; further, the stress true coefficient and the stress change correlation coefficient are integrated to obtain the gain compensation parameter, so as to provide a basis for adjusting the Kalman filter and obtaining more accurate filtered stress data; finally, the actual stress data is Kalman filtered based on the gain compensation parameter to monitor the center of gravity of the bridge. The present invention compares finite element simulation with actual data, analyzes the channel stress deviation, calculates the true stress coefficient and the stress change correlation coefficient, generates gain compensation parameters through fusion, optimizes Kalman filtering, effectively distinguishes environmental interference and noise, and improves the accuracy of center of gravity monitoring during bridge rotation. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0016] Figure 1 A flow chart of a method for monitoring the center of gravity during the horizontal rotation of a high-pier and long-span bridge provided by one embodiment of the present invention; Figure 2 A schematic diagram of the distribution of bridge reinforcement holes provided by an embodiment of the present invention; Figure 3 A flowchart of a method for obtaining a true stress coefficient provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0017] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following is a detailed description of the method and system for monitoring the center of gravity of a high-pier and long-span bridge during horizontal rotation according to the present invention, in combination with the accompanying drawings and preferred embodiments, and its specific implementation, structure, features and effects are described as follows. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0018] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0019] The specific scheme of the method and system for monitoring the center of gravity during the horizontal rotation of a high-pier and long-span bridge provided by the present invention is described in detail below with reference to the accompanying drawings.

[0020] See also Figure 1 , which shows a flow chart of a method for monitoring the center of gravity during the horizontal rotation of a high-pier and long-span bridge provided by an embodiment of the present invention, specifically comprising: Step S1: obtaining theoretical data of the bridge rotation process based on finite element simulation and obtaining actual data of the bridge rotation process; the actual data includes actual stress data; the theoretical data includes theoretical stress data.

[0021] The structural data of the bridge are collected and input 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, the optimal rotation angular velocity and rotation direction during the horizontal rotation of the bridge are obtained according to the current environmental status of the bridge and the structural safety of the bridge itself.

[0022] The horizontal rotation process of the bridge is simulated according to the optimal rotation angular velocity and direction to obtain theoretical data in the simulation; and in the actual horizontal rotation process, the horizontal rotation of the bridge is also carried out according to the optimal rotation angular velocity and direction to obtain actual data. Since the center of gravity needs to be monitored based on stress data in the end, the actual data includes actual stress data, and the theoretical data includes theoretical stress data, providing a data basis for the subsequent analysis process.

[0023] It should be noted that the channel refers to the longitudinal prestressed tendon channel of the bridge. The actual stress data and theoretical stress data are both stress data of the longitudinal prestressed tendon channel of the bridge. Please refer to Figure 2 , which shows a schematic diagram of the distribution of bridge reinforcement channels provided by an embodiment of the present invention; Figure 2The marking in the figure distinguishes the longitudinal prestressed tendon channels and the transverse prestressed tendon channels of the bridge; the finite element analysis method and the stress data collection method are technical means well known to those skilled in the art and will not be described in detail here.

[0024] Step S2: At the current moment, based on the difference between actual data and theoretical data, analyze the simulation deviation of different channels to obtain the true stress coefficient of each channel; according to the similar characteristics of the changes in the actual stress data of different channels relative to the theoretical stress data, obtain the stress change correlation coefficient of each channel.

[0025] 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, there will be differences between the actual data and the theoretical data. This difference is a normal deviation between the model simulation and the actual data. At the same time, data noise will also cause the actual data to deviate from the theoretical data. Therefore, firstly, 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 distinguish the normal deviation from the deviation caused by noise, and the true stress coefficient of each channel is obtained to characterize the credibility of the channel stress data to truly represent the channel stress, so as to prepare for the subsequent adjustment of the Kalman filter and the center of gravity monitoring.

[0026] Preferably, in one embodiment of the present invention, please refer to Figure 3 , which shows a flow chart of a method for obtaining a stress true coefficient provided by an embodiment of the present invention, specifically comprising: Step S201: when the bridge is stationary, obtain a data difference sequence between actual stress data and theoretical stress data of each hole; obtain a correlation coefficient between the data difference sequences of any two holes as a first correlation coefficient.

[0027] When the bridge is stationary, that is, before the bridge rotates horizontally, the difference between the actual stress data and the theoretical stress data of the channel represents the normal simulation deviation of the corresponding channel when it is stationary. The data difference sequence between the actual stress data and the theoretical stress data of each channel is obtained, which reflects the normal interference of environmental factors on the actual stress data. The first correlation coefficient is further obtained to obtain the normal difference between the actual stress data of the two channels.

[0028] The analysis process for each channel is the same. Here, any channel is selected as the target channel, and the remaining channels are used as comparison channels to obtain the true stress coefficient of the target channel. No further explanation will be given.

[0029] As an example, , F represents the actual stress data of the target channel when the bridge is stationary, represents the theoretical stress data of the target channel 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 value with a preset time length of 10 seconds is obtained to form a data difference sequence ; The calculation formula of the first correlation coefficient includes: ; Among them, j represents the serial number of the comparison channel; represents the first correlation coefficient between the target channel and the jth comparison channel; A data difference sequence representing the target pore; represents the data difference sequence of the jth comparison channel; It means calculating the Pearson correlation coefficient of two series.

[0030] 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 the Pearson correlation coefficient is used to express the correlation between the normal deviations of stress data between different channels, which is used to reflect the simulated deviations of different channels before the bridge rotates, in preparation for the subsequent acquisition of the true stress coefficient.

[0031] It should be noted that, in one embodiment of the present invention, the data acquisition frequency is 500 Hz.

[0032] Step S202: 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 is the same length as the theoretical stress data sequence, and the time series 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 channels is obtained as the second correlation coefficient.

[0033] After analyzing the static state of the bridge, the simulation deviation during the rotation of the bridge is further analyzed. 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; the rotation angle is the angle between the along-the-bridge direction before rotation (before rotation) and the along-the-bridge direction at the current moment, and the rotation angle and stress data are collected synchronously.

[0034] As an example, the preset history neighborhood is 10 seconds. Taking the target channel as an example, the actual rotation angle at the current moment is obtained. and the actual rotation angle 10 seconds ago , the actual rotation angle range is , the intercept angle range is The corresponding theoretical stress data is obtained to obtain the theoretical stress data sequence , , also expressed as , ; The theoretical rotation angle is The time point, The theoretical rotation angle is The time point of time.

[0035] Considering the influence of environmental factors, which may cause normal deviation in the data, the theoretical stress data sequence is corrected based on the data difference sequence to obtain the corrected theoretical data sequence. : , , It means taking the mean value, which represents the stress data under the influence of external factors.

[0036] Considering that there may be slight deviations between the actual rotation and the simulated rotation, such as the actual angular velocity being too slow or too fast in the local time domain, There is a deviation between the time domain length of the preset historical neighborhood and the time domain length of the theoretical stress data after considering environmental factors and the simulated deviation of the actual stress data, the actual stress data is intercepted 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 latest historical actual stress data in the time domain is used as the actual stress data sequence. , , , t represents the current time.

[0037] Since the data acquisition frequency is 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. : , which represents the simulated deviation between the theoretical stress data and the actual stress data after considering environmental factors; Considering that the simulation deviations of different channels during the rotation process are highly consistent, it means that the stress deviation at this time belongs to the normal influence of the external environment 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 sequence of any two channels is obtained as the second correlation coefficient. The calculation formula of the second correlation coefficient includes: ; Among them, j represents the serial number of the comparison channel; represents the second correlation coefficient between the target channel and the jth comparison channel; Represents the stress error sequence of the target pore at the current moment; represents the stress error sequence of the jth comparison channel at the current moment; It means calculating the Pearson correlation coefficient of two series.

[0038] The calculation formula of the second correlation coefficient includes: Since the actual angular velocity of rotation is the angular velocity of the entire bridge, all channels are affected by the angular velocity in the same way, so and are the same length; The larger it is, the stronger the consistency of the change in the simulated deviation of the stress of the target channel and the comparison channel during the rotation process, which means that the simulated deviation of the target channel is more likely to be a normal deviation, rather than noise caused by local vibration of the bridge due to horizontal rotation. It shows the simulated deviation of different channels from the dynamic perspective of the bridge.

[0039] Step S203: Correct the second correlation coefficient based on the first correlation coefficient to obtain a third correlation coefficient.

[0040] Taking into account the different positions of different channels, and the simulation deviations of stresses of channels at different positions are different, the second correlation coefficient is corrected by the first correlation coefficient in the static state: As an example: 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, and the calculation formula of the third correlation coefficient includes: ; in represents the first correlation coefficient between the target channel and the jth comparison channel; represents the second correlation coefficient between the target channel and the jth comparison channel; Represents the third correlation coefficient between the target channel and the jth comparison channel.

[0041] In the calculation formula of the third correlation coefficient, the third correlation coefficient is obtained by difference, which can eliminate the inherent correlation between channels in the static state (normal deviation caused by environmental factors), highlight the abnormal fluctuations caused by dynamic factors (such as vibration and imbalance) during the rotation process, and provide more basis for the subsequent analysis of the authenticity of the actual stress data of the target channel.

[0042] Step S204: at the current moment, according to the central tendency of all first correlation coefficients corresponding to each duct and the amplitude fluctuation of all third correlation coefficients, the true stress coefficient of each duct at the current moment is obtained.

[0043] The central tendency of the first correlation coefficient is positively correlated with the true coefficient of stress; the amplitude fluctuation of the third correlation coefficient is negatively correlated with the true coefficient of stress.

[0044] The simulation deviation caused by the influence of external environmental factors on the entire bridge during the horizontal rotation process belongs to the real change of stress data. For example, due to the external wind force, the entire bridge beam is tilted to a certain extent, and the prestressed tendons in the channel on the inclined side are stretched, and the stress increases; the force on the channel 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 simulation data of the channel. At this time, the prestressed tendons of each channel will produce stress changes in order to resist this lateral deformation, but the degree of change varies due to the different characteristics of the channel itself, but the overall error trend is similar, reflecting the normal stress conditions of the bridge rotation, which is real and valid data; Based on this, the calculation formula of the stress true coefficient includes: ; Wherein, b represents the true stress coefficient of the target pore at the current moment; represents the set of second correlation coefficients between the target channel and all comparison channels at the current moment, Indicates taking the average value; Indicates the preset zero-dividing positive parameter. In this example, C0=0.01; It represents the set of absolute values ​​of the third correlation coefficients between the target channel and all comparison channels at the current moment. represents the standard deviation, Indicates taking the absolute value; represents the linear normalization function.

[0045] In the calculation formula of the stress true coefficient, the average value is used to calculate Represents the central tendency of all first correlation coefficients corresponding to the target channel, The larger the value is, the greater the correlation coefficient between the simulation deviation of the target channel during the rotation and the simulation deviation of all comparison channels is, reflecting that the consistency of the simulation deviation is stronger, 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 expressed by the standard deviation. The smaller it is, the stronger the consistency of stress changes between the target channel and the comparison channel is, the more likely it is that the external environment and other factors have a normal impact on the bridge as a whole, the more authentic the actual stress data is, and the larger the stress true coefficient is.

[0046] The stress true coefficient integrates the simulation deviations of the bridge in static and dynamic conditions, reflecting the true situation of the actual stress data and providing a basis for the subsequent adjustment of the Kalman filter.

[0047] Taking into account that during the horizontal rotation of the bridge, the stress data of the channels at different positions have normal changes, and the stress changes are affected by the force differences of the normal structure of the bridge. As a result, although the stress data of different channels have consistency in change, there are differences in the magnitude of stress changes, which in turn causes the simulation deviations of different channels to be affected by the bridge structure. Therefore, it is also necessary to analyze the relative changes in stress based on the similar characteristics of the changes in the actual stress data of different channels relative to the theoretical stress data, eliminate the interference of normal differences in the amplitude of stress changes, and obtain the stress change correlation coefficient of each channel. This characterizes the correlation strength between the stress change of a channel and the stress change of other channels, and also reflects the authenticity of the actual stress data.

[0048] Preferably, in one embodiment of the present invention, the starting points of the simulated rotation process of the finite element simulation and the actual rotation process in the time domain are aligned to facilitate comparison of stress changes in the same horizontal rotation stage; At the current moment, the change values ​​of the actual stress data at both ends of the second preset historical neighborhood of each hole are obtained as the actual stress change value; the change values ​​of the theoretical stress data at both ends of the second preset historical neighborhood of each hole are obtained as the theoretical stress change value; As an example, the time domain length of the second preset historical neighborhood is 10 seconds, that is, the current moment is taken as the end point and 10 seconds ago is taken as the starting point, and the second preset historical neighborhood is constructed, and 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 obtained accordingly, and 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; 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; the change of the actual stress data relative to the theoretical stress data is expressed in the form of a ratio, and the theoretical stress change value is in the denominator. Since the bridge rotation process is a complex dynamic process, the stress will change accordingly with time, and the theoretical stress change value is usually not zero; if it is zero, the denominator is set to a very small positive parameter such as 0.01.

[0049] Further, based on the similar characteristics of the relative stress change rate of each channel and all other channels, the stress change correlation coefficient of each channel at the current moment is obtained.

[0050] As an example, at the current moment, the average of the absolute values ​​of the differences between the relative stress change rate of each pore and the relative stress change rates of all other pores is negatively correlated and mapped, and the negative correlation mapping result is used as the stress change correlation coefficient of the corresponding pore. The calculation formula of the stress change correlation coefficient includes: ; Where c represents the stress change correlation coefficient of the target pore at the current moment; represents an exponential function with the natural constant e as the base; J represents the number of comparison channels; j represents the sequence number of the comparison channel; Indicates the relative change rate of stress in the target pore at the current moment; The relative change rate of stress of the jth comparison channel at the current moment; Indicates taking the absolute value.

[0051] In the calculation formula of the stress change correlation coefficient, the difference characteristics of the relative change rate between the target channel and the comparison channel are expressed by the absolute value of the difference, and the overall difference characteristics are expressed by the average method. The function is negatively correlated to reflect the similar characteristics of the relative change rate of stress between the target channel and all the comparison channels, indicating the similar characteristics of the change of the actual stress data of the target channel and all the comparison channels relative to the theoretical stress data. The larger it is, the stronger the correlation of the relative change rate of stress is, the larger the stress change correlation coefficient is, and the stronger the authenticity of the stress change in the target channel is; where x is the independent variable.

[0052] Step S3: At the current moment, the stress true coefficient and the stress change correlation coefficient are integrated to obtain the gain compensation parameter; the stress true coefficient and the stress change correlation coefficient are both positively correlated with the gain compensation parameter; based on the gain compensation parameter, the actual stress data is Kalman filtered to monitor the center of gravity of the bridge.

[0053] The stress true coefficient evaluates the authenticity of the actual stress data from the perspective of simulation deviation of different channels, while the stress change correlation coefficient eliminates the interference of normal differences in stress change amplitude from the similar perspective of relative stress change, and also reflects the authenticity of the actual stress data. Therefore, the stress true coefficient and the stress change correlation coefficient are integrated to obtain the gain compensation parameter, which provides a basis for adjusting the Kalman filter and obtaining more accurate filtered stress data, thereby accurately monitoring the center of the bridge.

[0054] Since the larger the stress true coefficient and the stress change correlation coefficient are, the more realistic the actual stress data are, the stress true coefficient and the stress change correlation coefficient are both positively correlated with the gain compensation parameter. As a preferred embodiment, the stress true coefficient and the stress change correlation coefficient are integrated by multiplication, and the calculation formula of the gain compensation parameter includes: ; in, Indicates the gain compensation parameter of the target channel at the current moment; Indicates the true stress coefficient of the target pore at the current moment; It represents the stress change correlation coefficient of the target pore at the current moment.

[0055] In another embodiment of the present invention, considering that the stronger the trend of the true coefficient of stress in a local time period is, the higher the stability or consistency of the true coefficient of stress in the pore in the time dimension is, the less affected by random noise interference is, and the higher the data reliability is, the gain compensation parameter can be obtained by combining the stability of the true coefficient of stress in a local time period, integrating the true coefficient of stress and the stress change correlation coefficient.

[0056] As an example, the stress true coefficient of the target channel at each moment in the second preset historical neighborhood at the current moment is obtained, and these stress true coefficients are fitted with a straight line to obtain the correction determination coefficient r of the fitting line, which represents the fitting effect of the fitting line. The larger the r, 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 of the gain compensation parameter is: .

[0057] After obtaining the gain compensation parameters, Kalman filtering can be performed on the actual stress data based on the gain compensation parameters to accurately reduce the noise of the actual stress data, so as to accurately monitor the center of gravity of the bridge and ensure construction safety.

[0058] Preferably, in one embodiment of the present invention, considering that the larger the original Kalman gain of the Kalman filter algorithm is, the smaller the uncertainty of the corresponding actual stress data is, the filtering result tends to the actual stress data, and the larger the gain compensation parameter is, the stronger the authenticity of the actual stress data is, and the less the degree of noise influence is, the filtering result should tend to the actual stress data. Based on this, the original Kalman gain and the gain compensation parameter at the current moment are integrated to obtain the corrected Kalman gain; the original Kalman gain and the gain compensation parameter are both positively correlated with the corrected Kalman gain; As an example, after the product of the original Kalman gain of the target hole at the current moment and the gain compensation parameter is linearly normalized, 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 actual stress data monitored, and the larger the Kalman gain used in filtering; It should be noted that the Kalman filter algorithm and the method for obtaining the original Kalman gain are both existing technologies and will not be described in detail.

[0059] Finally, the center of gravity of the bridge is monitored based on the filtered stress data after filtering the actual stress data based on the corrected Kalman gain.

[0060] As an example, 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, including: Strain-stress conversion: Stress data is usually measured by strain sensors. The strain data is first converted 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, the stress is converted into the deformation of the structure (such as displacement, rotation angle, etc.). This is an existing technology and will not be described in detail.

[0061] Deformation and center of gravity correlation: Changes in the center of gravity position of the 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 deformation and 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 a difference in displacement at both ends of the bridge. By monitoring this displacement difference and combining the geometric dimensions and mechanical properties of the structure, the displacement and direction of the center of gravity can be calculated. Combined with the initial center position before the rotation, the center of gravity of the bridge at the current moment can be obtained. This is already an existing technology and will not be repeated here.

[0062] Real-time monitoring and updating: During the rotation process, the strain data is continuously monitored, and the above-mentioned conversion and calculation are performed in real time to continuously update the position information of the center of gravity. This method can more intuitively reflect the impact of center of gravity changes on structural deformation and is suitable for bridge structures that are more sensitive to center of gravity changes.

[0063] An embodiment of the present invention also provides a system for monitoring the center of gravity of a high-pier and large-span bridge during a horizontal rotation process. The system includes a memory, a processor, and a computer program, wherein the memory is used to store a 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 method for monitoring the center of gravity of a high-pier and large-span bridge during a horizontal rotation process described in steps S1-S3.

[0064] In summary, in order to solve the technical problem that the existing Kalman filtering method is inaccurate in processing stress data and affects the accuracy of center of gravity monitoring, the present invention proposes a method and system for monitoring the center of gravity during the horizontal rotation process of a high-pier and large-span bridge. The present invention first obtains theoretical data during the bridge rotation process based on finite element simulation and obtains actual data during the bridge rotation process; further, at the current moment, based on the difference between the actual data and the theoretical data, the simulation deviation of different channels is analyzed, and according to the similar characteristics of the changes in the actual stress data of different channels relative to the theoretical stress data, the authenticity of the actual stress data is analyzed; further, the stress true coefficient and the stress change correlation coefficient are integrated to obtain the gain compensation parameter; finally, the actual stress data is Kalman filtered 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.

[0065] It should be noted that the sequence of the above embodiments of the present invention is only for description and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the accompanying 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.

[0066] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on 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.

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 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.

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

4. The method for monitoring the center of gravity during the horizontal rotation of a high-pier and long-span bridge according to claim 2 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.

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 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.

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 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.

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 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.

8. The method for monitoring the center of gravity during the horizontal rotation of a high-pier and long-span bridge according to claim 7 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.

9. The method for monitoring the center of gravity during the horizontal rotation of a high-pier and long-span bridge according to claim 2 is characterized in that: The preset time length is 10 seconds.

10. A system for monitoring the center of gravity during the horizontal rotation of a high-pier and long-span bridge, 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 9.

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