Swivel bridge posture intelligent monitoring method, equipment and medium

Abstract

The invention relates to the technical field of high-precision positioning, in particular to a swivel bridge attitude intelligent monitoring method and device and a medium, and the method specifically comprises the steps: decomposing a GNSS signal of a swivel bridge to obtain component signals, and analyzing the features of the component signals to construct an amplitude abnormal fluctuation coefficient of each component signal; an error correction coefficient of each component signal is constructed based on a trend characteristic difference between the multipath error signal and the original signal and a phase difference generated by the multipath error signal due to own characteristics in combination with an amplitude abnormal fluctuation coefficient; according to the method, the GNSS signal is reconstructed based on the error correction coefficient, the swivel speed of the swivel bridge is analyzed based on the reconstructed signal, and the real-time attitude of the swivel bridge is judged according to the swivel speed of the bridge, so that the problem that the swivel bridge has positioning errors due to the fact that communication signals of the GNSS satellite positioning technology are interfered by the outside world is avoided; and the accuracy of the rotating speed monitoring result of the swivel bridge is improved.

Description

Technical Field

[0001] The present application relates to the field of high-precision positioning technology, and in particular to a method, device and medium for intelligently monitoring the posture of a rotating bridge. Background Art

[0002] As an important transportation infrastructure, bridges carry a huge amount of traffic in modern society. With the rapid development of my country's economy, the urbanization process has gradually accelerated, and a new round of infrastructure construction has been launched. More and more bridges are built across existing railways, steep canyons or deep water areas. In such environments, bridges are mostly built using rotation construction methods. The use of rotation construction does not affect the normal operation of existing lines, and can also cross canyons and rivers that are difficult to cross using traditional methods. However, bridge rotation is a dynamic process, and the real-time and digital monitoring of bridges during the rotation process has put forward higher technical requirements. If the angular velocity and linear speed of the rotation are too fast during the rotation process, the bridge will be in an unbalanced state during the rotation process, which may cause accidents in serious cases. Therefore, it is necessary to monitor its rotation posture in real time and grasp its rotation speed in real time.

[0003] With the rapid development of sensor technology and satellite positioning technology, 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 needs of all-round real-time monitoring, and has the advantages of being less affected by weather and being able to locate at night. However, in actual applications, the communication signals of GNSS satellite positioning technology are greatly interfered by the outside world. During the propagation process, GNSS signals may be reflected on the surface or other objects before reaching the receiver, causing multipath effects, which in turn leads to an increase in positioning errors, making it impossible to accurately monitor the rotation speed of the bridge during rotation, and unable to meet the monitoring needs of the rotating bridge attitude at this stage. Summary of the invention

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

[0005] In a first aspect, an embodiment of the present application provides a method for intelligently monitoring the posture of a rotating bridge, the method comprising the following steps:

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

[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 of 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 in combination with the fluctuation degree of the peak at each maximum value in each component signal and the data distribution of all maximum values;

[0008] 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, the error correction coefficient of each component signal is constructed;

[0009] Acquire a reconstructed GNSS signal of the local signal sequence based on the error correction coefficient;

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

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

[0012] The local signal sequence is used as the input of a sequence segmentation algorithm, and the output is the component signal 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 moment before any moment in each component signal, the sequence composed of data within a preset time before the previous moment in the component signal is used as the input of the prediction algorithm, and the output is the predicted value of the data at any moment in the component signal, and the absolute value of the difference between the true value of the data at any moment and the predicted value is recorded as the prediction error of the data at 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 abnormal amplitude fluctuation coefficient of each component signal is as follows:

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

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

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

[0019] The kth component signal of the local signal sequence at the current moment and the reference signal sequence at the current moment are used as the input of the differential positioning algorithm, and the output is the phase difference between the kth component signal and the reference signal sequence, which is recorded as The error correction coefficient of the kth component signal of the local signal sequence at the current moment is recorded as SCF a,k , SCF a,k The expression is:

[0020]

[0021] In the formula, ANF a,k Represents the amplitude abnormal fluctuation coefficient of the kth component signal of the local signal sequence at the current moment; R a,k Represents the Pearson correlation coefficient between the kth 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 to be greater than 1.

[0022] In one embodiment, the expression for reconstructing the 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,krepresents the normalized value of the inverse of the error correction coefficient of the kth component signal of the local signal sequence, X a,k represents the kth component signal of the local signal sequence.

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

[0025] The reconstructed GNSS signals from all satellites at the current moment are used as input to 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 bridge rotation speed is as follows:

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

[0028] In a second aspect, the embodiment of the present application further provides an intelligent monitoring device for the posture of a rotating bridge, including:

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

[0030] Signal detection module: for any GNSS signal of a satellite received by the monitoring station, the GNSS signal within a preset time before the current moment is recorded as the local signal sequence of 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; the amplitude abnormal fluctuation coefficient of each component signal is constructed in combination with the fluctuation degree of the peak at each maximum value in each component signal and the data distribution of all maximum values;

[0031] Signal correction module: 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, the error correction coefficient of each component signal is constructed;

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

[0033] 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 rotation angular velocity of the bridge based on the position coordinates, and adjust the rotation speed of the bridge.

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

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

[0036] The present application acquires GNSS signal data of a rotating bridge, decomposes the GNSS signal to obtain component signals of the GNSS signal, analyzes the characteristics of the component signals, and constructs an amplitude abnormal fluctuation coefficient of each component signal based on the randomness and uncertainty of the multipath interference signal, taking into account the predictability of each component signal and the abnormal fluctuation phenomenon caused by the component signal being affected by the multipath interference signal, which 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 caused by the multipath error signal due to its own characteristics and combined with the amplitude abnormal fluctuation coefficient, an error correction coefficient of each component signal is constructed, taking into account the signal strength caused by multiple reflections of the multipath error signal. The attenuation phenomenon of degree and the interference of mirror reflection on the original signal accurately reflect the interference degree of each component signal caused by the multipath error signal; the collected GNSS signal of the rotating bridge is reconstructed based on the error correction coefficient, and the current position of the rotating bridge is solved based on the reconstructed GNSS signal. Finally, the rotation speed of the rotating bridge is calculated based on the position change of the rotating bridge. According to whether the rotation speed of the bridge exceeds the set threshold, the real-time posture of the rotating bridge is judged, thereby realizing continuous, high-precision, fully automatic and visual intelligent monitoring of the bridge rotation posture, avoiding the problem of positioning error of the rotating bridge due to external interference of the communication signal of the GNSS satellite positioning technology, and improving the accuracy of the rotation speed monitoring result of the rotating bridge. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present application 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 application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0038] Figure 1 A flowchart of a method for intelligently monitoring the posture of a rotating bridge provided in one embodiment of the present application;

[0039] Figure 2 Schematic diagram of the process of obtaining the prediction error degree;

[0040] Figure 3 The figure is a structural diagram of an intelligent monitoring device for the posture of a rotating bridge. DETAILED DESCRIPTION

[0041] In order to further explain the technical means and effects adopted by the present application to achieve the predetermined invention purpose, the following, in combination with the accompanying drawings and preferred embodiments, describes in detail the specific implementation methods, structures, features and effects of a rotating bridge posture intelligent monitoring method, equipment and medium proposed in the present application. 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.

[0042] 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 application belongs.

[0043] The specific scheme of the intelligent monitoring method, equipment and medium for the posture of a rotating bridge provided by the present application is described in detail below with reference to the accompanying drawings.

[0044] See also Figure 1 , which shows a flowchart of a method for intelligently monitoring the posture of a rotating bridge provided by an embodiment of the present application, the method comprising the following steps:

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

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

[0047] First, use a tripod to fix the GNSS receiver in an open and unobstructed position at any end of the bridge to ensure that the GNSS receiver can receive signals from multiple satellites to obtain satellite positioning data. This location is recorded as a monitoring station, and the GNSS receiver of the monitoring station is initialized. The GNSS signals from each satellite collected by the monitoring station through the GNSS receiver are recorded as original signals.

[0048] Secondly, within the preset distance around the rotating bridge, select an open location and place a fixed GNSS receiver as a reference station. Since the GNSS receiver of the reference station is located in an open location, it is basically not affected by multipath errors, and the GNSS signal from each satellite collected by the reference station is recorded as a reference signal. Preferably, in the embodiment of the present application, the preset distance is set to 800 meters. As other embodiments of the present application, the implementer can set the value of the preset distance according to actual conditions.

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

[0050] Step S2: for any GNSS signal of a satellite received by the monitoring station, the GNSS signal within a preset time before the current moment is recorded as the local signal sequence of the current moment; based on the local signal sequence, the component signals of the local signal sequence are decomposed to obtain the component signals; 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; combined with the fluctuation degree of the peak where each maximum value in each component signal is located and the data distribution of all maximum values, the amplitude abnormal fluctuation coefficient of each component signal is constructed.

[0051] GNSS signals are easily affected by surface reflections during propagation, which changes the propagation direction, amplitude, frequency, and phase of GNSS signals. This causes the GNSS antenna receiver to receive reflected signals from other objects while receiving direct signals from navigation satellites, thereby generating multipath errors. During the monitoring of rotating bridges, due to environmental factors, the collected GNSS signals will inevitably have multipath errors, and because the bridge is in 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 posture of rotating bridges, it is necessary to remove the multipath errors in the GNSS signals to improve the monitoring accuracy of rotating bridges.

[0052] Ideally, the monitoring station only receives real signals from satellites. At this time, the collected GNSS signals are relatively stable. In addition, due to the regularity of satellite movement and the movement of the rotating bridge, the received GNSS signals also show 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 include not only real signals from satellites, but also multipath interference signals from interference signal sources. In addition, due to the different propagation paths of multipath interference signals and the large uncertainty of the interference signal strength 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) the local GNSS signal collected by the monitoring station is first decomposed as follows:

[0054] The current moment is recorded as moment a, and for the GNSS signal of any satellite received by the monitoring station, the original signal within T minutes before the current moment a is obtained, and the time series of the original signal within T minutes is recorded as the local signal sequence of the current moment a. Preferably, in the embodiment 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] The local signal sequence at the current time a is used as the input of the empirical wavelet transform algorithm, and the local signal sequence is decomposed, and the output is the component signals of the local signal sequence in several different frequency bands. In the decomposition process, the embodiment of the present application adopts an adaptive segmentation method to determine the segmentation boundary. There are many existing segmentation methods. The implementer can also use other segmentation methods to determine the segmentation boundary. This application does not make specific restrictions. Among them, the empirical wavelet transform algorithm is a well-known technology, and the specific process will not be repeated.

[0056] It should be noted that for the decomposition of the local signal sequence at the current moment a, this application only provides a 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. This application does not make specific restrictions.

[0057] Furthermore, the set of all component signals of the local signal sequence at the current time a is recorded as the component signal set X a ={X a,1 ,X a,2 …X a,b}, where X a,b represents the bth component signal of the local signal sequence at the current time a, and b represents the number of component signals of the local signal sequence. In the embodiment of the present application, b = 7. As other embodiments of the present application, the implementer can set the value of b according to the actual situation.

[0058] (2) Then, the states of different component signals are analyzed. Since the real signal collected by the monitoring station has certain trend change characteristics, the real signal collected by the monitoring station has certain predictability; however, for the multipath interference signal, due to its own uncertainty, the multipath interference signal shows irregular change characteristics, which makes the change state of the multipath interference signal difficult to predict. Therefore, taking the kth component signal of the local signal sequence at the current time a as an example, the predictability of the GNSS signal of any satellite received by the monitoring station is analyzed, specifically:

[0059] For the kth component signal, the data at time t in the component signal is taken as the true value, and the sequence composed of the data within 1 minute before time t-1 is taken as the predicted data sequence, and the predicted data sequence is taken as the input of the ARIMA prediction algorithm, and the output is the predicted value of the data at time t in the component signal, and the absolute value of the difference between the predicted value and its true value is recorded as the prediction error of the data at time t in the kth component signal. It should be noted that in order to ensure the rationality of data analysis, this application does not perform predictive analysis on the data within the first 1 minute in the component signal. Among them, the ARIMA prediction algorithm is a well-known technology, and the specific process will not be repeated. It should be noted that the length of the predicted time series can be set according to the actual situation, and this application does not make specific restrictions.

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

[0061] Furthermore, the prediction error of the data at each moment in the kth component signal is obtained by the above method, and the sum of the prediction errors of the data at all moments in the kth component signal is recorded as the prediction error degree of the kth component signal. The larger the prediction error degree, the more serious the fluctuation phenomenon of the kth component signal is, and the more likely it is to be a multipath error signal.

[0062] (3) Secondly, the kth component signal is used as the input of the automatic multi-scale peak search algorithm, and the output is each maximum value in the kth component signal. The automatic multi-scale peak search algorithm is a well-known technology, and the specific process is not repeated here.

[0063] It should be understood that for obtaining the maximum value in the kth 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 kth component signal. The present application does not make specific restrictions.

[0064] Furthermore, due to the randomness and uncertainty of the multipath error signal itself, the local analysis of the multipath error signal presents irregular phenomena, and in the multipath error signal, its maximum value usually mutates due to uncertainty and presents a peak state. Therefore, a statistical method is used to calculate the kurtosis of each maximum value. Specifically, in the kth 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 is not repeated here. The larger the value of kurtosis, the more serious 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, the implementer can set the neighborhood size of each maximum value according to actual conditions.

[0065] Furthermore, the frequency of the maximum value in the kth component signal reflects the abnormal state of the kth component signal. The shorter the time interval between two adjacent maximum values ​​and the higher the frequency of the maximum values, the more serious the abnormality of the kth component signal. Therefore, the time interval between any two adjacent maximum values ​​in the kth component signal is calculated, recorded as the first time interval, and the average value and standard deviation of all first time intervals in the kth component signal are calculated, recorded as and σ a,k . and σ a,k The smaller the value is, the denser the distribution of the maximum values ​​in the component signal is, the more serious the abnormal fluctuation of the component signal is, and the more multipath error signal components 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] In the formula, 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 maximum values ​​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 more multipath interference signals, firstly, due to the randomness and uncertainty of the multipath interference signals, the kth component signal is difficult to predict, thereby increasing the prediction error of the calculated kth component signal; 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, thereby increasing the kurtosis of the calculated kth component signal maximum value; 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, thereby reducing the mean and standard deviation of the time intervals between adjacent maximum values ​​in the calculated kth component signal, thereby increasing 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, record the GNSS signal within a preset time before the current moment as the reference signal sequence of 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, construct the error correction coefficient of each component signal.

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

[0072] Therefore, taking the kth component signal of the local signal sequence at the current time a as an example, the Pearson correlation coefficient between the component signal and the local signal sequence at the current time a is calculated. The value range of the Pearson correlation coefficient is [-1,1]. The Pearson correlation coefficient reflects the trend consistency between the kth component signal and the original signal. The larger the value, the more similar the trend characteristics between the kth component signal and the original signal are, and the more likely it is to contain a real signal; conversely, the smaller the value of the Pearson correlation coefficient, the greater the trend difference between the kth component signal and the original signal, and the more likely it is to contain a multipath error signal. Among them, the Pearson correlation coefficient is a well-known technology, and the specific process will not be repeated.

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

[0074] In addition, there may be a mirror reflection error signal from the glass in the multipath error signal of the GNSS original signal, and the mirror reflection error may interfere with the identification of the real component signal in the original signal. Since the GNSS signal has less signal strength loss when the mirror reflection occurs, the collected GNSS original signal may be interfered by the mirror reflection error signal and thus present certain mirror reflection error signal characteristics. As a result, when the Pearson correlation coefficient between the kth component signal and the original signal is large, the kth component signal may also be a mirror reflection error signal. Therefore, the present application further identifies the mirror reflection error signal in the multipath error signal.

[0075] Specifically, since the mirror reflection error is also a multipath error signal, and the multipath error signal is a reflection signal generated by the interference signal source during the propagation of the GNSS signal, that is, relative to the real signal collected by the monitoring station, the multipath error signal has an additional reflection path, which causes a certain phase difference between the real signal collected by the monitoring station at each moment and the multipath error signal. However, since the real signal of the monitoring station and the GNSS signal received by the reference station are both direct signals, the phase difference between the real 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 collected by the reference station from any one of the satellites, the reference signal within T minutes before the current time a is obtained, and the time series of the reference signal within T minutes is recorded as the reference signal sequence at the current time a, that is, the reference signal corresponding to the local signal sequence at the current time a is obtained.

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

[0078] It should be noted that for the calculation of the phase difference between the kth component signal and the reference signal sequence, the present 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 kth component signal and the reference signal sequence. The present application does not make specific restrictions.

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

[0080]

[0081] Where SCF a,k Represents the error correction coefficient of the kth component signal of the local signal sequence at the current moment, ANF a,k Represents the amplitude abnormal fluctuation coefficient of the kth component signal of the local signal sequence at the current moment; Represents the phase difference between the kth 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 kth component signal of the local signal sequence at the current moment and the local signal sequence at the current moment; β is an artificially preset positive number greater than 1, and its function is to prevent the denominator from being 0. Preferably, in the embodiment of the present 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 more 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 is weakened after multiple reflections, the original signal collected by the monitoring station mainly shows the trend characteristics of the real signal, and then when the kth component signal contains more multipath error signals, the trend characteristics of the kth component signal and the original signal are quite different, and then the value of the Pearson correlation coefficient between the calculated kth component signal and the original signal increases; secondly, when the multipath error signal is a mirror reflection signal, due to the existence of an additional reflection path of the multipath error signal, the phase difference between the multipath error signal and the reference signal sequence increases, and then the value of the error correction coefficient of the finally calculated kth component signal increases, 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 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 the component signal is normalized by the Softmax function, and the normalized result is used as the weight of the 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] Thus, the weight of each component signal is obtained. Based on the weight of each component signal, the original GNSS signal from any satellite collected by the monitoring station is reconstructed, and the expression is:

[0088] In the formula, represents the reconstructed GNSS signal of 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 kth component signal of the local signal sequence at the current moment, X a,k Represents the kth 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 signal of all satellite GNSS signals received by the monitoring station; determining the rotation angular velocity of the bridge based on the position coordinates, and adjusting the rotation speed of the bridge.

[0090] For the GNSS signal received by the monitoring station from each satellite, the above method can be used to obtain the reconstructed GNSS signal of the local signal sequence of the GNSS signal from each satellite at the current moment, and the reconstructed GNSS signal from all satellites at the current moment is used as the input of the triangulation positioning method, and the output is the position coordinates of the GNSS receiver of the monitoring station at the current moment.

[0091] Each moment is taken as the current moment, and the position coordinates of the GNSS receiver of the monitoring station at each moment are obtained by obtaining the position coordinates of the GNSS receiver of the monitoring station at the current moment, thereby obtaining the real-time position coordinates of the monitoring station, which corresponds to the real-time position of the bridge during the rotation process. The angular velocity of rotation during the rotation of the bridge is calculated according to the real-time position coordinates of the monitoring station. Among them, calculating the angular velocity of rotation by real-time position coordinates is a well-known technology, and the specific process will not be repeated. The angular velocity threshold of rotation is set. Preferably, in the embodiment of the present application, the angular velocity threshold of rotation is set to 0.02rad / min. As other embodiments of the present application, the implementer can set the angular velocity threshold of rotation according to the actual situation.

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

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

[0094] See also Figure 3 , Figure 3 is a schematic diagram of the structure of an intelligent monitoring device for the posture of a rotating bridge provided in an embodiment of the present application. In this embodiment, each unit included in the terminal is used to execute each step in an embodiment corresponding to an intelligent monitoring method for the posture of a rotating bridge. Figure 3 , monitoring equipment includes:

[0095] Signal acquisition module: A monitoring station is set up on the rotating bridge, and a reference station is set up at a preset position to collect GNSS signals of each satellite received by the monitoring station and the reference station respectively;

[0096] Signal detection module: for any GNSS signal of a satellite received by the monitoring station, the GNSS signal within a preset time before the current moment is recorded as the local signal sequence of 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; the amplitude abnormal fluctuation coefficient of each component signal is constructed in combination with the fluctuation degree of the peak at each maximum value in each component signal and the data distribution of all maximum values;

[0097] Signal correction module: 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, the error correction coefficient of each component signal is constructed;

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

[0099] 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 rotation angular velocity of the bridge based on the position coordinates, and adjust the rotation speed of the bridge.

[0100] Based on the same inventive concept as the above method, an embodiment of the present application also provides an intelligent monitoring medium for the posture 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-mentioned intelligent monitoring methods for the posture of a rotating bridge are implemented.

[0101] In summary, the embodiment of the present application provides an intelligent monitoring method for the posture of a rotating bridge. By acquiring the GNSS signal data of the rotating bridge, the GNSS signal is decomposed to obtain the component signals of the GNSS signal, and the characteristics of the component signals are analyzed. The amplitude abnormal fluctuation coefficient of each component signal is constructed based on the randomness and uncertainty of the multipath interference signal, and the predictability of each component signal and the abnormal fluctuation phenomenon caused by the component signal affected by the multipath interference signal are taken into account, which 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 caused 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, and the multipath error is taken into account. The signal strength attenuation phenomenon caused by multiple reflections of the signal and the interference of mirror reflection on the original signal accurately reflect the degree of interference of each component signal by the multipath error signal; the collected GNSS signal of the rotating bridge is reconstructed based on the error correction coefficient, and the current position of the rotating bridge is solved based on the reconstructed GNSS signal, and finally the rotation speed of the rotating bridge is calculated based on the position change of the rotating bridge. According to whether the rotation speed of the bridge exceeds the set threshold, the real-time posture of the rotating bridge is judged, thereby realizing continuous, high-precision, fully automatic and visual intelligent monitoring of the bridge rotation posture, avoiding the problem of positioning error of the rotating bridge due to external interference of the communication signal of the GNSS satellite positioning technology, and improving the accuracy of the rotating bridge rotation speed monitoring results.

[0102] It should be noted that the above-mentioned sequence of the embodiments of the present application is only for description and does not represent the advantages and disadvantages of the embodiments. The above-mentioned specific embodiments of the present application are described. In addition, 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.

[0103] The various embodiments in the present application 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.

[0104] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the principles of the present application should be included in the protection scope of the present application.

Claims

1. A method for intelligently monitoring the posture of a rotating bridge, characterized in that: The method comprises the following steps: A monitoring station is set up on the rotating bridge, and a reference station is set up at a preset position to collect GNSS signals of each satellite received by the monitoring station and the reference station respectively; 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 of 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 in combination with the fluctuation degree of the peak at each maximum value in each component signal and the data distribution of all maximum values; 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, the error correction coefficient of each component signal is constructed; Acquire a reconstructed GNSS signal of the local signal sequence based on the error correction coefficient; The position coordinates of the monitoring station at the current moment are obtained based on the reconstructed GNSS signal of all satellite GNSS signals received by the monitoring station; the rotation angular velocity of the bridge is determined based on the position coordinates, and the rotation speed of the bridge is adjusted.

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

3. The intelligent monitoring method for the posture of a rotating 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 moment before any moment in each component signal, the sequence composed of data within a preset time before the previous moment in the component signal is used as the input of the prediction algorithm, and the output is the predicted value of the data at any moment in the component signal, and the absolute value of the difference between the true value of the data at any moment and the predicted value is recorded as the prediction error of the data at 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.

4. The intelligent monitoring method for the posture of a rotating bridge according to claim 1, characterized in that: The process of obtaining the amplitude abnormal fluctuation coefficient of each component signal is as follows: Each component signal is used as the input of the peak search algorithm, and the output is the maximum value in each component signal; in the kth component signal of the local signal sequence of the monitoring station at the current moment, the kurtosis of the data in the neighborhood of the nth maximum value is calculated, denoted as ku a,k,n ; Calculate the time interval between any two adjacent maximum values ​​in the kth component signal, recorded as the first time interval, and calculate the average value and standard deviation of all first time intervals in the kth component signal, recorded as and σ a,k ; The amplitude abnormal fluctuation coefficient of the kth component signal in the local signal sequence of the monitoring station at the current moment is recorded as ANF a,k , ANF a,k The expression is: In the formula, E a,k It represents the prediction error degree of the kth component signal in the local signal sequence of the monitoring station at the current moment; N represents the number of maximum values ​​in the kth component signal in the local signal sequence of the monitoring station at the current moment.

5. The intelligent monitoring method for the posture of a rotating bridge according to claim 1, characterized in that: The process of obtaining the error correction coefficient of each component signal is as follows: The kth component signal of the local signal sequence at the current moment and the reference signal sequence at the current moment are used as the input of the differential positioning algorithm, and the output is the phase difference between the kth component signal and the reference signal sequence, which is recorded as The error correction coefficient of the kth component signal of the local signal sequence at the current moment is recorded as SCF a,k , SCF a,k The expression is: In the formula, ANF a,k Represents the amplitude abnormal fluctuation coefficient of the kth component signal of the local signal sequence at the current moment; R a,k Represents the Pearson correlation coefficient between the kth 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 to be greater than 1.

6. The intelligent monitoring method for the posture of a rotating bridge according to claim 1, characterized in that: The expression of reconstructing the GNSS signal is: 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,k represents the normalized value of the inverse of the error correction coefficient of the kth component signal of the local signal sequence, X a,k represents the kth component signal of the local signal sequence.

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

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

9. An intelligent monitoring device for the posture of a rotating bridge, implementing the method as claimed in claim 1, characterized in that: The device comprises: Signal acquisition module: A monitoring station is set up on the rotating bridge, and a reference station is set up at a preset position to collect GNSS signals of each satellite received by the monitoring station and the reference station respectively; Signal detection module: for any GNSS signal of a satellite received by the monitoring station, the GNSS signal within a preset time before the current moment is recorded as the local signal sequence of 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; the amplitude abnormal fluctuation coefficient of each component signal is constructed in combination with the fluctuation degree of the peak at each maximum value in each component signal and the data distribution of all maximum values; Signal correction module: 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, the error correction coefficient of each component signal is constructed; Signal reconstruction module: obtaining a 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 rotation angular velocity of the bridge based on the position coordinates, and adjust the rotation speed of the bridge.

10. An intelligent monitoring medium for the posture of a rotating 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, the steps of the method according to any one of claims 1 to 8 are implemented.

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