An intelligent monitoring method, device and medium for the attitude of a swivel bridge

Abstract

This application relates to the field of high-precision positioning technology, and specifically relates to an intelligent monitoring method, device and medium for the attitude of a rotating bridge, which specifically includes: decomposing the GNSS signal of the rotating bridge to obtain each component signal, analyzing the characteristics of the component signals to construct the amplitude abnormal fluctuation coefficient of each component signal; constructing the error correction coefficient of each component signal based on the trend feature difference between the multipath error signal and the original signal and the phase difference generated by the multipath error signal due to its own characteristics and combining the amplitude abnormal fluctuation coefficient; reconstructing the GNSS signal based on the error correction coefficient, analyzing the rotation speed of the rotating bridge based on the reconstructed signal, and judging the real-time attitude of the rotating bridge according to the rotation speed of the bridge, avoiding the problem of positioning error of the rotating bridge caused by the communication signal of the GNSS satellite positioning technology being interfered by the outside world, and improving the accuracy of the monitoring result of the rotation speed of the rotating bridge.

Description

Technical Field

[0001] This application relates to the field of high-precision positioning technology, and particularly relates to an intelligent monitoring method, device and medium for the attitude of a rotating bridge. Background Art

[0002] As an important transportation infrastructure, bridges carry a huge traffic flow in modern society. With the rapid development of China's economy and the gradual acceleration of the urbanization process, a new round of infrastructure construction climax has been set off, and there are more and more bridges spanning existing railways, steep canyons or deep water areas. In such environments, the rotating construction method is mostly used for bridge construction. Using the rotating construction method can not affect the normal operation of the existing line and can also span canyons and rivers that are difficult to cross using traditional methods. However, the rotation of the bridge is a dynamic process, which poses higher technical requirements for the real-time and digital monitoring of the bridge during the rotation process. If the angular velocity and linear velocity of the rotation are too fast during the rotation process, it will cause the bridge to be in an unbalanced state during the rotation process, and in severe cases, it will lead to accidents. Therefore, it is necessary to monitor the rotation attitude of the bridge in real time and master its rotation speed in real time.

[0003] With the rapid development of sensor technology and satellite positioning technology, the bridge attitude monitoring technology based on GNSS satellite positioning has been widely used. The bridge attitude monitoring method based on satellite positioning can meet the all-round real-time monitoring requirements and has the advantages of being less affected by weather and being able to perform positioning at night. However, in practical applications, the communication signal of the GNSS satellite positioning technology is greatly interfered by the outside world. The GNSS signal may be reflected on the ground or other objects during the propagation process and then reach the receiver, causing the multipath effect, which in turn leads to an increase in the positioning error, and thus it is impossible to accurately monitor the rotation speed during the rotation process of the bridge and cannot meet the monitoring requirements of the attitude of the rotating bridge at the present stage. Summary of the Invention

[0004] In order to solve the above technical problems, the purpose of this application is to provide an intelligent monitoring method, device and medium for the attitude of a rotating bridge, and the specific technical solutions adopted are as follows:

[0005] In the first aspect, an embodiment of this application provides an intelligent monitoring method for the attitude of a rotating bridge, and the method includes the following steps:

[0006] Set up a monitoring station on the rotating bridge and a reference station at a preset position, and respectively collect the GNSS signals of each satellite received by the monitoring station and the reference station;

[0007] For the GNSS signal of any satellite received by the monitoring station, the GNSS signal within a preset time before the current moment is recorded as the local signal sequence at the current moment; the local signal sequence is decomposed to obtain each component signal of the local signal sequence; the data at each moment in each component signal is predicted, and based on the difference between the predicted value and the true value of the data at each moment in each component signal, the prediction error degree of each component signal is constructed. Combining the undulation degree of the wave crest at each maximum value and the data distribution of all maximum values in each component signal, the amplitude abnormal fluctuation coefficient of each component signal is constructed.

[0008] For the GNSS signal of the any satellite received by the reference station, the GNSS signal within a preset time before the current moment is recorded as the reference signal sequence at the current moment; based on the similarity between each component signal and the local signal sequence, and the difference between each component signal and the reference signal sequence, combined with the amplitude abnormal fluctuation coefficient, the error correction coefficient of each component signal is constructed.

[0009] Based on the error correction coefficient, the reconstructed GNSS signal of the local signal sequence is obtained.

[0010] Based on the reconstructed GNSS signal of all satellite GNSS signals received by the monitoring station, the position coordinates of the monitoring station at the current moment are obtained; based on the position coordinates, the rotation angular velocity of the bridge is determined to adjust the rotation speed of the bridge.

[0011] In one embodiment, the process of obtaining each component signal of the local signal sequence is as follows:

[0012] Taking the local signal sequence as the input of the sequence segmentation algorithm, and the output is the component signals of the local signal sequence in each frequency band.

[0013] In one embodiment, the process of obtaining the prediction error degree of each component signal is as follows:

[0014] For the previous moment of any moment in each component signal, taking the sequence composed of the data within a preset time before the previous moment in the component signal as the input of the prediction algorithm, and the output is the predicted value of the data at the any moment in the component signal. The absolute value of the difference between the true value of the data at the any moment and the predicted value is recorded as the prediction error of the data at the any moment in the component signal; the sum of the prediction errors of the data at all moments in the component signal is recorded as the prediction error degree of the component signal.

[0015] In one embodiment, the process of obtaining the amplitude abnormal fluctuation coefficient of each component signal is as follows:

[0016] Taking each component signal as the input of the peak search algorithm, the output is each maximum value in each component signal; in the k-th component signal of the local signal sequence of the monitoring station at the current moment, calculate the kurtosis of the data in the neighborhood of the n-th maximum value, denoted as ku a,k,n ; Calculate the time interval between any two adjacent maximum values in the k-th component signal, denoted as the first time interval, and calculate the average value and standard deviation of all the first time intervals in the k-th component signal respectively, denoted as and σ a,k ; Denote the amplitude anomaly fluctuation coefficient of the k-th component signal in the local signal sequence of the monitoring station at the current moment as ANF a,k , ANF a,k The expression of is:

[0017] In the formula, E a,k represents the prediction error degree of the k-th component signal in the local signal sequence of the monitoring station at the current moment; N represents the number of maximum values in the k-th component signal in the local signal sequence of the monitoring station at the current moment.

[0018] In one of the embodiments, the process of obtaining the error correction coefficient of each component signal is as follows:

[0019] Taking the k-th component signal of the local signal sequence at the current moment and the reference signal sequence at the current moment as the input of the differential positioning algorithm, the output is the phase difference between the k-th component signal and the reference signal sequence, denoted as Denote the error correction coefficient of the k-th component signal of the local signal sequence at the current moment as SCF a,k , SCF a,k The expression of is:

[0020]

[0021] In the formula, ANF a,k represents the amplitude anomaly fluctuation coefficient of the k-th component signal of the local signal sequence at the current moment; R a,k represents the Pearson correlation coefficient between the k-th component signal of the local signal sequence at the current moment and the local signal sequence at the current moment; β is a positive number preset by humans and greater than 1.

[0022] In one of the embodiments, the expression of the reconstructed GNSS signal is:

[0023] In the formula, represents the reconstructed GNSS signal of the local signal sequence, b represents the number of component signals of the local signal sequence, α a,kThe normalized value of the reciprocal of the error correction coefficient of the k-th component signal representing the local signal sequence, X a,k Represents the k-th component signal of the local signal sequence.

[0024] In one embodiment, the process of obtaining the position coordinates of the monitoring station at the current moment is as follows:

[0025] Taking the reconstructed GNSS signals from all satellites at the current moment as the input of the positioning algorithm, and the output is the position coordinates of the GNSS receiver of the monitoring station at the current moment.

[0026] In one embodiment, the process of adjusting the rotation speed of the bridge during rotation is as follows:

[0027] Determining the real-time position coordinates of the monitoring station through the position coordinates of the GNSS receiver of the monitoring station at the current moment, calculating the angular velocity of the bridge rotation during the rotation process according to the real-time position coordinates of the monitoring station; if the angular velocity of rotation is greater than the preset angular velocity threshold of rotation, an alarm is issued and the rotation speed of the bridge is reduced; otherwise, no alarm is made and the original speed is maintained for the bridge rotation.

[0028] In a second aspect, the embodiments of the present application further provide a device for intelligently monitoring the attitude of a rotating bridge, including:

[0029] A signal acquisition module: A monitoring station is set on the rotating bridge, and a reference station is set at a preset position, and the GNSS signals of each satellite received by the monitoring station and the reference station are respectively collected;

[0030] A signal detection module: For the GNSS signal of any satellite received by the monitoring station, the GNSS signal within a preset time before the current moment is recorded as the local signal sequence at the current moment; the local signal sequence is decomposed to obtain each component signal of the local signal sequence; the data at each moment in each component signal is predicted, and the prediction error degree of each component signal is constructed based on the difference between the predicted value and the true value of the data at each moment in each component signal, and the amplitude abnormal fluctuation coefficient of each component signal is constructed by combining the undulation degree of the wave crest at each maximum value in each component signal and the data distribution of all maximum values;

[0031] A signal correction module: For the GNSS signal of the any satellite received by the reference station, the GNSS signal within a preset time before the current moment is recorded as the reference signal sequence at the current moment; based on the similarity between each component signal and the local signal sequence, and the difference between each component signal and the reference signal sequence, combined with the amplitude abnormal fluctuation coefficient, the error correction coefficient of each component signal is constructed;

[0032] Signal reconstruction module: Obtain the reconstructed GNSS signal of the local signal sequence based on the error correction coefficient;

[0033] Slewing speed monitoring module: Obtain the position coordinates of the monitoring station at the current moment based on the reconstructed GNSS signals of all satellite GNSS signals received by the monitoring station; Determine the slewing angular velocity of the bridge based on the position coordinates, and adjust the slewing speed of the bridge.

[0034] In a third aspect, an embodiment of the present application further provides a medium for intelligent monitoring of the attitude of a slewing bridge, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, the steps of the method described in the first aspect above are implemented.

[0035] The embodiments of the present application have at least the following beneficial effects:

[0036] By obtaining the GNSS signal data of the slewing bridge in the present application, the component signals of the GNSS signal are obtained by decomposing the GNSS signal, the characteristics of the component signals are analyzed, the amplitude anomaly fluctuation coefficients of each component signal are constructed based on the randomness and uncertainty of the multipath interference signal, considering the predictability of each component signal and the anomaly fluctuation phenomenon caused by the influence of the multipath interference signal on the component signal, which can better reflect the anomaly degree of each component signal; Based on the trend feature difference between the multipath error signal and the original signal and the phase difference generated by the multipath error signal due to its own characteristics and combined with the amplitude anomaly fluctuation coefficient, the error correction coefficient of each component signal is constructed, considering the signal strength attenuation phenomenon caused by multiple reflections of the multipath error signal and the interference of specular reflection on the original signal, accurately reflecting the interference degree of each component signal by the multipath error signal; Reconstruct the GNSS signal of the slewing bridge collected based on the error correction coefficient, calculate the current position of the slewing bridge based on the reconstructed GNSS signal, and finally calculate the slewing speed of the slewing bridge based on the position change of the slewing bridge. According to whether the slewing speed of the bridge exceeds the set threshold, judge the real-time attitude of the slewing bridge, thereby realizing continuous, high-precision, fully automatic and visual intelligent monitoring of the slewing attitude of the bridge, avoiding the problem of positioning errors of the slewing bridge due to the interference of the communication signal of the GNSS satellite positioning technology by the outside world, and improving the accuracy of the monitoring result of the slewing speed of the slewing bridge. Description of the Drawings

[0037] To more clearly illustrate the technical solutions and advantages in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0038] Figure 1 It is a step flowchart of an intelligent monitoring method for the attitude of a rotating bridge provided by an embodiment of the present application;

[0039] Figure 2 It is a schematic diagram of the acquisition process of the prediction error degree;

[0040] Figure 3 It is a schematic structural diagram of an intelligent monitoring device for the attitude of a rotating bridge. Specific Embodiments

[0041] In order to further elaborate on the technical means and effects adopted by the present application to achieve the intended invention purpose, the following, in combination with the drawings and preferred embodiments, details the specific embodiments, structures, features, and effects of an intelligent monitoring method, device, and medium for the attitude of a rotating bridge proposed according to the present application. 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.

[0042] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the technical field to which the present application belongs.

[0043] The following specifically describes the specific solutions of an intelligent monitoring method, device, and medium for the attitude of a rotating bridge provided by the present application in combination with the drawings.

[0044] Please refer to Figure 1 , which shows a step flowchart of an intelligent monitoring method for the attitude of a rotating bridge provided by an embodiment of the present application. The method includes the following steps:

[0045] Step S1, set up a monitoring station on the rotating bridge and a reference station near the rotating bridge, and respectively collect the GNSS signals of each satellite received by the monitoring station and the reference station.

[0046] During the construction monitoring of a rotating bridge, it is necessary to monitor the rotation speed of the rotating bridge. The primary condition for monitoring the rotation speed of the rotating bridge is to obtain the position information of the rotating bridge. Since GNSS signals are less affected by weather factors and support night construction monitoring, this application uses a GNSS receiver to obtain the position information of the rotating bridge. The process of obtaining the position information of the rotating bridge is as follows:

[0047] First, fix the GNSS receiver on a tripod and place it at an open and unobstructed position at either end of the bridge to ensure that the GNSS receiver can receive signals from multiple satellites to obtain satellite positioning data. Mark this position as the monitoring station, and initialize the GNSS receiver at the monitoring station. Denote the GNSS signals from each satellite collected by the GNSS receiver at the monitoring station as the original signals.

[0048] Second, within a preset distance around the rotating bridge, select an open position at random and place a fixed GNSS receiver as a reference station. Since the GNSS receiver at the reference station is located in an open position, it is basically not affected by multipath errors. Then, denote the GNSS signals from each satellite collected by the reference station as the reference signals. Preferably, in the embodiments of this application, the preset distance is set to 800 meters. As other embodiments of this application, the implementer can set the value of the preset distance according to the actual situation.

[0049] Preferably, for the collection of the original signals and the reference signals, in the embodiments of this application, the number of satellites that the GNSS receiver can receive signals from is set to 4, and the signal collection time interval is set to 1 second. As other embodiments of this application, the implementer can set the number of satellites that can receive signals and the signal collection time interval according to the actual situation.

[0050] Step S2, for the GNSS signal of any one satellite received at the monitoring station, denote the GNSS signals within a preset time before the current moment as the local signal sequence at the current moment; decompose the local signal sequence to obtain each component signal of the local signal sequence; construct the prediction error degree of each component signal based on the difference between the predicted value and the true value of the data at each moment in each component signal, and combine the undulation degree of the wave peaks where each maximum value is located and the data distribution of all maximum values in each component signal to construct the amplitude abnormal fluctuation coefficient of each component signal.

[0051] During the propagation process, GNSS signals are vulnerable to the influence of ground surface reflections, which can change the propagation direction, amplitude, frequency, and phase of GNSS signals. As a result, when a GNSS antenna receiver receives the direct signal transmitted by a navigation satellite, it will also receive the reflected signal reflected by the surface of other objects, thus generating multipath errors. During the monitoring process of a rotating bridge, due to environmental factors, the collected GNSS signals almost inevitably contain multipath errors. Moreover, since the bridge is in a rotating motion, the multipath errors in the GNSS signals collected by the monitoring station are also constantly changing. Therefore, when using GNSS satellite positioning technology to monitor the attitude of a rotating bridge, it is necessary to remove the multipath errors in the GNSS signals to improve the monitoring accuracy of the rotating bridge.

[0052] In an ideal situation, the monitoring station only receives the real signals from the satellites. At this time, the collected GNSS signals are relatively stable. And due to the regularities of the satellite motion and the motion of the rotating bridge, the received GNSS signals also exhibit certain trend change characteristics. However, in practical applications, there are inevitably some interference signal sources near the construction of the rotating bridge, such as buildings, trees, and construction facilities. As a result, the actual signals collected by the monitoring station not only contain the real signals from the satellites but also include the multipath interference signals from the interference signal sources. Moreover, due to the different propagation paths of the multipath interference signals and the large uncertainties in the interference signal intensities from different interference signal sources, it is difficult to predict the change trend of the interference signals received by the monitoring station.

[0053] Therefore, in order to obtain the real position information of the rotating bridge, (1) first, decompose the local GNSS signals collected by the monitoring station, specifically as follows:

[0054] Denote the current moment as moment a. For the GNSS signal of any satellite received by the monitoring station, obtain the original signal within T minutes before the current moment a, and record the time series of the original signal within these T minutes as the local signal sequence at the current moment a. Preferably, in the embodiments of the present application, the value of T is set to 5. As other embodiments of the present application, the implementer can set the value of T according to the actual situation.

[0055] Take the local signal sequence at the current moment a as the input of the empirical wavelet transform algorithm, decompose this local signal sequence, and the output is the component signals of this local signal sequence in several different frequency bands. Among them, in the decomposition process, the embodiments of the present application use an adaptive segmentation method to determine the segmentation boundary. There are many existing segmentation methods, and the implementer can also use other segmentation methods to determine the segmentation boundary. The present application does not make specific limitations. Among them, the empirical wavelet transform algorithm is a well-known technology, and the specific process will not be elaborated.

[0056] It should be noted that for the decomposition of the local signal sequence at the current moment a, the present application only provides one sequence decomposition method. There are many existing sequence decomposition methods, and implementers can also use other sequence decomposition algorithms to decompose the local signal sequence at the current moment a. The present application does not make specific restrictions.

[0057] Furthermore, denote the set composed of all component signals of the local signal sequence at the current moment a as the component signal set X a ={X a,1 , X a,2 … X a,b}, where X a,b represents the b-th component signal of the local signal sequence at the current moment a. At the same time, b represents the number of component signals of this local signal sequence. In the embodiment of the present application, b = 7. As other embodiments of the present application, implementers can set the value of b according to actual situations.

[0058] (2) Then, analyze the states of different component signals. Since the real signals collected by the monitoring station have certain trend change characteristics, the real signals collected by the monitoring station have a certain degree of predictability; however, for multipath interference signals, due to their own uncertainties, the multipath interference signals show irregular change characteristics, and thus the change states of the multipath interference signals are difficult to predict. Therefore, taking the k-th component signal of the local signal sequence at the current moment a as an example, analyze the predictability of the GNSS signal of any one satellite received by the monitoring station. Specifically:

[0059] For the k-th component signal, use the data at the t-th moment in this component signal as the real value, and use the sequence composed of the data within 1 minute before the (t - 1)-th moment as the prediction data sequence. Take this prediction data sequence as the input of the ARIMA prediction algorithm, and the output is the predicted value of the data at the t-th moment in this component signal. Denote the absolute value of the difference between this predicted value and its real value as the prediction error of the data at the t-th moment in the k-th component signal. It should be noted that in order to ensure the rationality of data analysis, the present application does not perform prediction analysis on the data within the first 1 minute in this component signal. Among them, the ARIMA prediction algorithm is a well-known technology, and the specific process will not be elaborated. It should be noted that the length of the predicted time series can be set by implementers according to actual situations, and the present application does not make specific restrictions.

[0060] It should be understood that for the prediction of the data at the t-th moment in this component signal, the present application only provides one prediction method. There are many existing prediction methods, and implementers can also use other prediction algorithms to predict the data at the t-th moment in this component signal. The present application does not make specific restrictions.

[0061] Further, the prediction error of each moment's data in the k-th component signal is obtained through the above method, and the sum of the prediction errors of all moments' data in the k-th component signal is denoted as the prediction error degree of the k-th component signal. The larger the prediction error degree, the more severe the fluctuation phenomenon of the k-th component signal, and the more likely it belongs to the multipath error signal.

[0062] (3) Secondly, the k-th component signal is used as the input of the automatic multi-scale peak search algorithm, and the output is each maximum value in the k-th component signal. Among them, the automatic multi-scale peak search algorithm is a well-known technology, and the specific process will not be elaborated here.

[0063] It should be understood that for the acquisition of the maximum value in the k-th component signal, the present application only provides a peak search method. There are many existing peak search methods, and implementers can also use other peak search algorithms to obtain the maximum value in the k-th component signal. The present application does not make specific restrictions.

[0064] Further, due to the randomness and uncertainty of the multipath error signal itself, the local analysis of the multipath error signal shows irregular phenomena, and the maximum value in the multipath error signal usually mutates due to uncertainty and presents a spike state. Therefore, a statistical method is used to calculate the kurtosis of each maximum value. Specifically, in the k-th component signal, a neighborhood of each maximum value is constructed with each maximum value as the center, and the kurtosis of the data in the neighborhood of each maximum value is calculated as the kurtosis of each maximum value. Among them, the calculation of kurtosis is a well-known technology, and the specific process will not be elaborated here. The larger the value of kurtosis, the more severe the mutation phenomenon of the maximum value. Preferably, in the embodiment of the present application, the size of the neighborhood of each maximum value is set to 1×7. As other embodiments of the present application, implementers can set the size of the neighborhood of each maximum value according to the actual situation.

[0065] Further, the frequency of the maximum value appearing in the k-th component signal reflects the abnormal state of the k-th component signal. The shorter the time interval between two adjacent maximum values and the higher the frequency of the maximum value appearing, the more severe the abnormal degree of the k-th component signal. Therefore, calculate the time interval between any two adjacent maximum values in the k-th component signal, denoted as the first time interval, and calculate the average value and standard deviation of all the first time intervals in the k-th component signal, denoted as and σ a,k . and σ a,k The smaller the value of, the denser the distribution of the maximum value in the component signal, the more severe the abnormal fluctuation of the component signal, and the more components of the multipath error signal it contains.

[0066] (4) Based on the above analysis, the amplitude abnormal fluctuation coefficient of the kth component signal in the local signal sequence of the monitoring station at the current time a is constructed, and the expression is:

[0067]

[0068] Where, ANF a,k E represents the amplitude abnormal fluctuation coefficient of the kth component signal in the local signal sequence of the monitoring station at the current moment; a,k Indicates the prediction error degree of the kth component signal in the local signal sequence of the monitoring station at the current moment; and σ a,k They represent the mean and standard deviation of all first time intervals in the kth component signal in the local signal sequence of the monitoring station at the current moment; N represents the number of maxima in the kth component signal in the local signal sequence of the monitoring station at the current moment; ku a,k,n It represents the kurtosis of the nth maximum value in the kth component signal in the local signal sequence of the monitoring station at the current moment.

[0069] Therefore, when the kth component signal of the GNSS signal of the monitoring station contains a large number of multipath interference signals, first of all, due to the randomness and uncertainty of the multipath interference signal, the kth component signal is difficult to predict, and thus the prediction error of the calculated kth component signal increases; at the same time, the randomness and uncertainty of the multipath error signal itself will also cause the kth component to produce a sudden peak, and thus increase the kurtosis of the maximum value of the calculated kth component signal; in addition, the more seriously the kth component signal is affected by the multipath interference signal, the greater the possibility of local mutation of the kth component signal and the higher the frequency, which reduces the mean and standard deviation of the time intervals between adjacent maximum values in the calculated kth component signal, and increases the value of the amplitude abnormal fluctuation coefficient of the kth component signal.

[0070] Step S3: For the GNSS signal of any satellite received by the reference station, the GNSS signal within a preset time before the current moment is recorded as the reference signal sequence at the current moment; based on the similarity between each component signal and the local signal sequence, and the difference between each component signal and the reference signal sequence, combined with the amplitude abnormal fluctuation coefficient, an error correction coefficient for each component signal is constructed.

[0071] In the acquired GNSS original signal, the real signal from the satellite has a higher signal strength because it is not blocked by obstacles. However, the multipath error signal needs to be reflected by obstacles before it can be received by the monitoring station. As a result, the signal strength of the multipath error signal is weakened after multiple reflections, and the collected GNSS signal mainly presents the trend characteristics of the real signal.

[0072] Therefore, taking the k-th component signal of the local signal sequence at the current moment a as an example, calculate the Pearson correlation coefficient between this component signal and the local signal sequence at the current moment a. The value range of the Pearson correlation coefficient is [-1, 1]. This Pearson correlation coefficient reflects the trend consistency between the k-th component signal and the original signal. The larger its value, the more similar the trend characteristics between the k-th component signal and the original signal, and the more likely it contains the true signal; on the contrary, the smaller the value of this Pearson correlation coefficient, the greater the trend difference between the k-th component signal and the original signal, and the more likely it contains the multipath error signal. Among them, the Pearson correlation coefficient is a well-known technology, and the specific process will not be elaborated here.

[0073] It should be noted that for the calculation of the similarity between the k-th component signal and the original signal, this application only provides a similarity calculation method. There are many existing similarity calculation methods, and implementers can also use other similarity algorithms to calculate the similarity between the k-th component signal and the original signal. This application does not make specific restrictions.

[0074] In addition, there may be a specular reflection error signal from glass in the multipath error signal of the GNSS original signal. The specular reflection error may interfere with the identification of the true component signal in the original signal. Since the GNSS signal has less signal strength loss during specular reflection, the collected GNSS original signal may be interfered by the specular reflection error signal and thus exhibit certain specular reflection error signal characteristics. Furthermore, when the Pearson correlation coefficient between the k-th component signal and the original signal is large, the k-th component signal may also be a specular reflection error signal. Therefore, this application further identifies the specular reflection error signal in the multipath error signal.

[0075] Specifically, since the specular reflection error is also a kind of multipath error signal, and the multipath error signal is a reflection signal generated by an interference signal source during the propagation of the GNSS signal, that is, relative to the true signal collected by the monitoring station, the multipath error signal has an additional reflection path, which causes a certain phase difference between the true signal collected by the monitoring station at each moment and the multipath error signal. However, since both the true signal of the monitoring station and the GNSS signal received by the reference station are direct signals, the phase difference between the true signal of the monitoring station and the reference signal is small, while the phase difference between the multipath error signal and the reference signal is large.

[0076] Therefore, for the GNSS signal from any one of the satellites collected by the reference station, obtain the reference signal within T minutes before the current moment a, and record the time series of this reference signal within T minutes as the reference signal sequence at the current moment a, that is, obtain the reference signal corresponding to the local signal sequence at the current moment a.

[0077] Further, taking the k-th component signal of the local signal sequence at the current moment a as an example, this component signal and the reference signal sequence at the current moment a are used as the input of the carrier phase difference technology, and the output is the phase difference between this component signal and the reference signal sequence. The larger this phase difference is, the more likely the k-th component signal is to be a multipath reflection error signal. Among them, the carrier phase difference technology is a well-known technology, and the specific process will not be elaborated here.

[0078] It should be noted that for the calculation of the phase difference between the k-th component signal and the reference signal sequence, this application only provides a differential positioning method. There are many existing differential positioning methods, and implementers can also use other differential positioning algorithms to calculate the phase difference between the k-th component signal and the reference signal sequence. This application does not make specific restrictions.

[0079] Further, based on the above analysis and combined with the amplitude anomaly fluctuation coefficient, an error correction coefficient is constructed, and the expression is:

[0080]

[0081] In the formula, SCF a,k represents the error correction coefficient of the k-th component signal of the local signal sequence at the current moment, and ANF a,k represents the amplitude anomaly fluctuation coefficient of the k-th component signal of the local signal sequence at the current moment; represents the phase difference between the k-th component signal of the local signal sequence at the current moment and the reference signal sequence; R a,k represents the Pearson correlation coefficient between the k-th component signal of the local signal sequence at the current moment and the local signal sequence at the current moment; β is a positive number preset by humans and greater than 1, and its function is to prevent the denominator from being 0. Preferably, in the embodiments of this application, the value of β is set to 1.1.

[0082] Therefore, when the kth component signal of the original signal collected by the monitoring station contains a large number of multipath interference signals, the value of the calculated amplitude abnormal fluctuation coefficient of the kth component signal increases due to the randomness and uncertainty of the multipath interference signals; secondly, since the signal strength of the multipath error signal weakens after multiple reflections, the original signal collected by the monitoring station mainly exhibits the trend characteristics of the real signal. Therefore, when the kth component signal contains a large number of multipath error signals, the trend characteristics of the kth component signal and the original signal are quite different, which increases the value of the calculated Pearson correlation coefficient between the kth component signal and the original signal; secondly, when the multipath error signal is a mirror reflection signal, due to the existence of additional reflection paths in the multipath error signal, the phase difference between the multipath error signal and the reference signal sequence increases, which increases the value of the error correction coefficient of the finally calculated kth component signal, indicating that the kth component signal is more affected by the multipath error signal.

[0083] The error correction coefficient takes into account the randomness and uncertainty of the multipath interference signal, the trend characteristic difference between the multipath error signal and the original signal, and the phase difference between the multipath error signal and the reference signal sequence. Therefore, the error correction coefficient can accurately reflect the degree of interference of each component signal by the multipath error signal. The error correction coefficient SCF of the kth component signal is k The larger the value is, the greater the impact of the multipath error signal is.

[0084] Step S4: obtaining a reconstructed GNSS signal of the local signal sequence based on the error correction coefficient.

[0085] Taking the kth component signal of the local signal sequence at the current moment as an example, the inverse of the error correction coefficient of this component signal is normalized using the Softmax function, and the normalized result is used as the weight of this component signal. The Softmax function is a well-known technology, and the specific process is not repeated here.

[0086] It should be noted that for the normalization of the inverse of the error correction coefficient, this application only provides one normalization method. There are many existing normalization methods, and implementers can also use other normalization algorithms to normalize the inverse of the error correction coefficient. This application does not make specific restrictions.

[0087] The weight of each component signal is thus obtained. Based on the weight of each component signal, the original GNSS signal collected by the monitoring station from any satellite is reconstructed, and the expression is:

[0088] Where, The reconstructed GNSS signal representing the local signal sequence at the current moment, b represents the number of component signals of the local signal sequence at the current moment, α a,k represents the weight of the k-th component signal of the local signal sequence at the current moment, X a,k represents the k-th component signal of the local signal sequence at the current moment.

[0089] Step S5, obtaining the position coordinates of the monitoring station at the current moment based on the reconstructed GNSS signals of all satellite GNSS signals received by the monitoring station; determining the rotational angular velocity of the bridge based on the position coordinates, and adjusting the rotational speed of the bridge.

[0090] For the GNSS signals from each satellite received by the monitoring station, the reconstructed GNSS signals of the local signal sequences of the GNSS signals from each satellite at the current moment can be obtained through the above method. Taking the reconstructed GNSS signals from all satellites at the current moment as the input of the triangulation method, the output is the position coordinates of the GNSS receiver of the monitoring station at the current moment.

[0091] Regarding each moment as the current moment, through the acquisition method of the position coordinates of the GNSS receiver of the monitoring station at the current moment, the position coordinates of the GNSS receiver of the monitoring station at each moment are obtained, so as to obtain the real-time position coordinates of the monitoring station, which correspond to the real-time position of the bridge during the rotation process. Calculate the rotational angular velocity of the bridge during the rotation process according to the real-time position coordinates of the monitoring station. Among them, calculating the rotational angular velocity through the real-time position coordinates is a well-known technology, and the specific process will not be elaborated here. Set a rotational angular velocity threshold. Preferably, in the embodiments of the present application, the rotational angular velocity threshold is set to 0.02 rad / min. As other embodiments of the present application, the implementer can set the rotational angular velocity threshold according to the actual situation.

[0092] If the rotational angular velocity of the bridge during the rotation process is greater than the rotational angular velocity threshold, an alarm is issued, and the rotational speed of the bridge is reduced to the rotational angular velocity threshold; otherwise, no alarm is made, and the bridge rotates at the original speed.

[0093] The schematic diagram of the acquisition process of the prediction error degree is as Figure 2 shown.

[0094] Please refer to Figure 3 , Figure 3 is the schematic structural diagram of a rotational bridge attitude intelligent monitoring device provided by the embodiments of the present application. In this embodiment, each unit included in the terminal is used to execute each step in the corresponding embodiment of a rotational bridge attitude intelligent monitoring method. Refer to Figure 3 , the monitoring device includes:

[0095] Signal acquisition module: Set up monitoring stations on the rotating bridge and reference stations at preset positions, and collect the GNSS signals of each satellite received by the monitoring stations and reference stations respectively;

[0096] Signal detection module: For the GNSS signal of any satellite received by the monitoring station, record the GNSS signal within a preset time before the current moment as the local signal sequence at the current moment; decompose the local signal sequence to obtain each component signal of the local signal sequence; predict the data at each moment in each component signal, construct the prediction error degree of each component signal based on the difference between the predicted value and the true value of the data at each moment in each component signal, and combine the undulation degree of the wave crest at each maximum value and the data distribution of all maximum values in each component signal to construct the amplitude abnormal fluctuation coefficient of each component signal;

[0097] Signal correction module: For the GNSS signal of the above-mentioned any satellite received by the reference station, record the GNSS signal within a preset time before the current moment as the reference signal sequence at the current moment; construct the error correction coefficient of each component signal based on the similarity between each component signal and the local signal sequence, the difference between each component signal and the reference signal sequence, and in combination with the amplitude abnormal fluctuation coefficient;

[0098] Signal reconstruction module: Obtain the reconstructed GNSS signal of the local signal sequence based on the error correction coefficient;

[0099] Rotating speed monitoring module: Obtain the position coordinates of the monitoring station at the current moment based on the reconstructed GNSS signal of all satellite GNSS signals received by the monitoring station; determine the rotating angular velocity of the bridge based on the position coordinates, and adjust the rotating speed of the bridge.

[0100] Based on the same inventive concept as the above method, the embodiment of the present application also provides an intelligent monitoring medium for the attitude of a rotating bridge, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, the steps of any one of the above methods for an intelligent monitoring method for the attitude of a rotating bridge are implemented.

[0101] In summary, the embodiment of the present application provides an intelligent monitoring method for the attitude of a rotating bridge. By acquiring the GNSS signal data of the rotating bridge, the component signals of the GNSS signal are obtained through decomposition of the GNSS signal, the characteristics of the component signals are analyzed, and the amplitude abnormal fluctuation coefficient of each component signal is constructed based on the randomness and uncertainty of the multipath interference signal. Considering the predictability of each component signal and the abnormal fluctuation phenomenon caused by the influence of the multipath interference signal on the component signal, it can better reflect the abnormal degree of each component signal; based on the trend feature difference between the multipath error signal and the original signal and the phase difference generated by the multipath error signal due to its own characteristics, and combined with the amplitude abnormal fluctuation coefficient, the error correction coefficient of each component signal is constructed. Considering the signal intensity attenuation phenomenon caused by multiple reflections of the multipath error signal and the interference of specular reflection on the original signal, it accurately reflects the interference degree of each component signal by the multipath error signal; the GNSS signal collected from the rotating bridge is reconstructed based on the error correction coefficient, and the current position of the rotating bridge is calculated based on the reconstructed GNSS signal. Finally, the rotating speed of the rotating bridge is calculated based on the position change of the rotating bridge. According to whether the rotating speed of the bridge exceeds the set threshold, the real-time attitude of the rotating bridge is judged, thereby realizing continuous, high-precision, fully automatic and visual intelligent monitoring of the bridge rotation attitude, avoiding the problem of positioning error of the rotating bridge caused by the interference of the communication signal of the GNSS satellite positioning technology by the outside world, and improving the accuracy of the monitoring result of the rotating speed of the rotating bridge.

[0102] It should be noted that the above sequence of the embodiments of the present application is only for description and does not represent the superiority or inferiority of the embodiments. And the above specific embodiments of the present application have been described. In addition, 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.

[0103] Each embodiment in the present application is described in a progressive manner. The same or similar parts among the embodiments can be referred to each other, and the key point of each embodiment is to illustrate the differences from other embodiments.

[0104] The above are only the preferred embodiments of the present application and are not intended to limit the present application. Any modifications, equivalent replacements, improvements, etc. made within the principle of the present application shall be included in the protection scope of the present application.

Claims

1. An intelligent monitoring method for the attitude of a swivel bridge, characterized in that, The method includes the following steps: Set up a monitoring station on the rotating bridge and a reference station at a preset position, and collect the GNSS signals of each satellite received by the monitoring station and the reference station respectively; For the GNSS signal of any one satellite received by the monitoring station, record the GNSS signals within a preset time before the current moment as the local signal sequence at the current moment; decompose the local signal sequence to obtain each component signal of the local signal sequence; predict the data at each moment in each component signal, construct the prediction error degree of each component signal based on the difference between the predicted value and the true value of the data at each moment in each component signal, and combine the undulation degree of the wave peaks at each maximum value and the data distribution of all maximum values in each component signal to construct the amplitude abnormal fluctuation coefficient of each component signal; For the GNSS signal of the above-mentioned any one satellite received by the reference station, record the GNSS signals within a preset time before the current moment as the reference signal sequence at the current moment; construct the error correction coefficient of each component signal based on the similarity between each component signal and the local signal sequence, the difference between each component signal and the reference signal sequence, and in combination with the amplitude abnormal fluctuation coefficient; Obtain the reconstructed GNSS signal of the local signal sequence based on the error correction coefficient; Obtain the position coordinates of the monitoring station at the current moment based on the reconstructed GNSS signals of all satellite GNSS signals received by the monitoring station; determine the rotating angular velocity of the bridge based on the position coordinates and adjust the rotating speed of the bridge; The process of obtaining the amplitude abnormal fluctuation coefficient of each component signal is as follows: Taking each component signal as the input of the peak search algorithm, the output is each maximum value in each component signal; in the k-th component signal of the local signal sequence of the monitoring station at the current moment, calculate the kurtosis of the data in the neighborhood of the n-th maximum value, denoted as ; calculate the time interval between any two adjacent maximum values in the k-th component signal, denoted as the first time interval, and calculate the average value and standard deviation of all the first time intervals in the k-th component signal respectively, denoted as and ; denote the amplitude abnormal fluctuation coefficient of the k-th component signal in the local signal sequence of the monitoring station at the current moment as , The expression of , where represents the prediction error degree of the k-th component signal in the local signal sequence of the monitoring station at the current moment; N represents the number of maximum values in the k-th component signal in the local signal sequence of the monitoring station at the current moment; The process of obtaining the error correction coefficient of each component signal is as follows: The k-th component signal of the local signal sequence at the current moment and the reference signal sequence at the current moment are used as the inputs of the differential positioning algorithm, and the output is the phase difference between the k-th component signal and the reference signal sequence, denoted as ; The error correction coefficient of the k-th component signal of the local signal sequence at the current moment is denoted as , The expression of is: In the formula, represents the amplitude anomaly fluctuation coefficient of the k-th component signal of the local signal sequence at the current moment; represents the Pearson correlation coefficient between the k-th component signal of the local signal sequence at the current moment and the local signal sequence at the current moment; is a positive number preset by humans and greater than 1.

2. The intelligent monitoring method for the posture of a swivel bridge according to claim 1, characterized in that, The process of obtaining each component signal of the local signal sequence is as follows: Take the local signal sequence as the input of the sequence segmentation algorithm, and the output is the component signals of the local signal sequence in each frequency band.

3. The intelligent monitoring method for the attitude of a swivel bridge according to claim 1, characterized in that, The process of obtaining the prediction error degree of each component signal is as follows: For the previous moment of any moment in each component signal, take the sequence composed of the data within a preset time before the previous moment in the component signal as the input of the prediction algorithm, and the output is the predicted value of the data at the any moment in the component signal. Denote the absolute value of the difference between the true value and the predicted value of the data at the any moment as the prediction error of the data at the any moment in the component signal; denote the sum of the prediction errors of the data at all moments in the component signal as the prediction error degree of the component signal.

4. The intelligent monitoring method for the posture of a swivel bridge according to claim 1, characterized in that, The expression of the reconstructed GNSS signal is: , where represents the reconstructed GNSS signal of the local signal sequence, represents the number of component signals of the local signal sequence, represents the normalized value of the reciprocal of the error correction coefficient of the k-th component signal of the local signal sequence, represents the k-th component signal of the local signal sequence.

5. The intelligent monitoring method for the posture of a swivel bridge according to claim 1, characterized in that The process of obtaining the position coordinates of the monitoring station at the current moment is as follows: Take the reconstructed GNSS signals from all satellites at the current moment as the input of the positioning algorithm, and the output is the position coordinates of the GNSS receiver of the monitoring station at the current moment.

6. The intelligent monitoring method for the attitude of a swivel bridge according to claim 1, characterized in that The process of adjusting the rotating speed of the bridge is as follows: Determine the real-time position coordinates of the monitoring station through the position coordinates of the GNSS receiver of the monitoring station at the current moment, and calculate the angular velocity of the bridge rotation during the bridge rotation process according to the real-time position coordinates of the monitoring station; if the angular velocity of rotation is greater than the preset angular velocity threshold of rotation, an alarm is issued and the rotation speed of the bridge is reduced; otherwise, no alarm is made and the original speed is maintained for the bridge rotation.

7. An intelligent monitoring device for the attitude of a swivel bridge, which implements the method described in claim 1, characterized in that The device includes: Signal acquisition module: Set up a monitoring station on the rotating bridge and a reference station at a preset position, and respectively acquire the GNSS signals of each satellite received by the monitoring station and the reference station; Signal detection module: For the GNSS signal of any satellite received by the monitoring station, record the GNSS signal within a preset time before the current moment as the local signal sequence at the current moment; decompose the local signal sequence to obtain each component signal of the local signal sequence; predict the data at each moment in each component signal, construct the prediction error degree of each component signal based on the difference between the predicted value and the true value of the data at each moment in each component signal, and combine the undulation degree of the wave crest at each maximum value in each component signal and the data distribution of all maximum values to construct the amplitude abnormal fluctuation coefficient of each component signal; Signal correction module: For the GNSS signal of the any satellite received by the reference station, record the GNSS signal within a preset time before the current moment as the reference signal sequence at the current moment; construct the error correction coefficient of each component signal based on the similarity between each component signal and the local signal sequence, the difference between each component signal and the reference signal sequence, and the amplitude abnormal fluctuation coefficient; Signal reconstruction module: Obtain the reconstructed GNSS signal of the local signal sequence based on the error correction coefficient; Rotation speed monitoring module: Obtain the position coordinates of the monitoring station at the current moment based on the reconstructed GNSS signal of all satellite GNSS signals received by the monitoring station; determine the angular velocity of the bridge rotation based on the position coordinates and adjust the rotation speed of the bridge.

8. An intelligent monitoring medium for the attitude of a swivel bridge, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1-6.

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