Method, system, device and medium for detecting rotational deformation of a building

Abstract

The application is suitable for the technical field of structural health monitoring, and provides a building rotation deformation detection method, system, device and medium. The method comprises the following steps: acquiring a state vector of a target structure, a state update equation, a measurement update relationship, high-frequency acceleration data and low-frequency speed data; updating the state vector at the k-1 time according to the state update equation to obtain a deformation prediction result of the target structure at the k time; updating the deformation prediction result according to the low-frequency speed data, the high-frequency acceleration data and the measurement update relationship at the k time to obtain an optimal estimation result of the state vector at the k time, and determining a rotation deformation result of the target structure; and repeating the above process to obtain a rotation deformation monitoring sequence result of the target structure. The above scheme can accurately detect the rotation deformation of a single-point structure in a large-span bridge at a low cost.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of structural health monitoring, and particularly relates to a building rotation deformation detection method, system, device and medium. BACKGROUND

[0002] As a key node of the traffic network, the structural safety of a large-span bridge is directly related to public safety and transportation efficiency. However, after being affected by multiple factors such as traffic load, temperature change, wind force and ground motion, the large-span bridge often produces complex deformation, and the rotation deformation is a core index for representing the dynamic response of the bridge structure, which plays an irreplaceable role in identifying early damage of the bridge and disaster warning.

[0003] However, current bridge deformation detection schemes mostly focus on the monitoring of displacement deformation. For the rotation deformation of the bridge, there are defects such as few monitoring means, low accuracy, high cost, difficulty in obtaining the dynamic rotation deformation of a single point of the bridge and inability to be deployed on an off-shore building such as a large-span bridge.

[0004] Therefore, how to accurately and low-cost detect the rotation deformation of a single point of a large-span bridge has become a technical problem to be solved at present. SUMMARY

[0005] The embodiments of the application provide a building rotation deformation detection method, system, device and medium, which can solve the problem of how to accurately and low-cost detect the rotation deformation of a single point of a large-span bridge.

[0006] In a first aspect, the embodiments of the application provide a building deformation monitoring method, a GNSS single-antenna velocity measurement component and an accelerometer are placed at a target structure, and the method comprises the following steps:

[0007] Obtaining a state vector of the target structure, a state update equation, a measurement update relationship, high-frequency acceleration data and low-frequency velocity data, the high-frequency acceleration data is obtained by the accelerometer, the low-frequency velocity data is obtained by the data collected by the GNSS single-antenna velocity measurement component, the state vector comprises a velocity component, a rotation angle component and an acceleration component of the target structure, the state update equation is used to represent the influence of the sampling frequency of the accelerometer on the state vector, the measurement update relationship is used to map the low-frequency velocity to the velocity component and map the high-frequency acceleration to the acceleration component and the rotation angle component;

[0008] According to the state update equation, the state vector at the k-1 time is updated to obtain a deformation prediction result of the target structure at the k time;

[0009] According to the low-frequency velocity data, the high-frequency acceleration data and the measurement update relationship at the k time, the deformation prediction result is updated to obtain a deformation optimization result at the k time;

[0010] According to the rotation angle component in the deformation optimization result of the target structure at the plurality of moments, a rotation deformation result of the target structure is determined.

[0011] In some embodiments, the measurement update relationship includes a first sub-update relationship and a second sub-update relationship, the first sub-update relationship is used to represent a mapping relationship between the low-frequency velocity and the velocity component, and the second sub-update relationship is used to represent a mapping relationship between the high-frequency acceleration and the acceleration component and the rotation angle component.

[0012] According to the low-frequency velocity data, the high-frequency acceleration data, and the measurement update relationship at the kth moment, the deformation prediction result is updated to obtain the deformation optimization result at the kth moment, including:

[0013] The first acquisition time of the low-frequency velocity data and the second acquisition time of the high-frequency acceleration data are obtained.

[0014] In the case that the first acquisition time is earlier than the second acquisition time, the deformation prediction result is updated according to the low-frequency velocity data and the first sub-update relationship to obtain the first deformation result at the kth moment.

[0015] The first deformation result is updated according to the high-frequency acceleration data and the second sub-update relationship to obtain the deformation optimization result.

[0016] In some embodiments, the method further includes:

[0017] In the case that the first acquisition time is later than or equal to the second acquisition time, the deformation prediction result at the kth moment is updated according to the high-frequency acceleration data and the second sub-update relationship to obtain the second deformation result.

[0018] The second deformation result is updated according to the low-frequency velocity data and the first sub-update relationship to obtain the deformation optimization result.

[0019] In some embodiments, the high-frequency acceleration data includes a first sub-acceleration on the x-axis, a second sub-acceleration on the y-axis, and a third sub-acceleration on the z-axis in the first coordinate system; the second sub-update relationship is as shown in the formula , wherein represents a measurement vector corresponding to the reference acceleration data, represents a state vector, represents a first measurement noise, represents a Gaussian white noise with a mean of 0 and a variance of , , g represents the acceleration of gravity, represents the first sub-acceleration, represents the second sub-acceleration, represents the third sub-acceleration, and k represents the kth moment.

[0020] In some embodiments, the first sub-update relationship is as shown in the following formula wherein, represents a measurement vector corresponding to the low-frequency velocity data, , , represents a Gaussian white noise with a mean of 0 and a variance of , represents a second measurement noise.

[0021] In some embodiments, the state update equation is as shown in the following formula wherein, represents a state vector at the k-1 time, represents a state vector at the k time, represents a process noise, represents a Gaussian white noise with a mean of 0 and a variance of , represents a state transition matrix, , represents an accelerometer sampling time interval.

[0022] In some embodiments, the state vector further includes a displacement component, and the method further includes:

[0023] According to the displacement component and the rotation angle component in the deformation optimization result at the plurality of times, a displacement velocity time sequence and a rotation deformation time sequence corresponding to the target structure are obtained, and the displacement velocity time sequence is used to represent the deformation velocity of the target structure.

[0024] The displacement velocity time sequence and the rotation deformation time sequence are output.

[0025] In a second aspect, the embodiments of the present application provide a building rotation deformation detection system, comprising:

[0026] A data sensor module comprises a GNSS single-antenna velocity measurement component and an accelerometer configured on a target structure, the GNSS single-antenna velocity measurement component is used to collect carrier measurement data, and the accelerometer is used to collect high-frequency acceleration data of the target structure.

[0027] The data processing module performs time-difference velocimetry on the carrier phase and pseudorange in the carrier measurement data to obtain low-frequency velocity data of the target structure; it acquires the state vector, state update equation, and measurement update relation of the target structure. The state vector includes the velocity component, rotation angular component, and acceleration component of the target structure. The state update equation represents the influence of the accelerometer sampling frequency on the state vector. The measurement update relation maps the low-frequency velocity to the velocity component and the high-frequency acceleration to the acceleration component and rotation angular component. Based on the state update equation, it updates the state vector at time k-1 to obtain the deformation prediction result of the target structure at time k. Based on the low-frequency velocity data, high-frequency acceleration data, and measurement update relation, it updates the deformation prediction result at time k to obtain the deformation optimization result at time k.

[0028] The data output module is used to determine the rotational deformation result of the target structure based on the rotational angular components of the target structure in multiple deformation optimization results.

[0029] Thirdly, embodiments of this application provide an electronic device including a processor, a memory, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it causes the electronic device to perform the method described in any of the embodiments of the first aspect.

[0030] Fourthly, embodiments of this application provide a computer-readable storage medium storing a computer program that, when executed by a processor, implements the method described in any of the embodiments of the first aspect.

[0031] Fifthly, embodiments of this application provide a computer program product, including a computer program, which, when run, causes the method described in any embodiment of the first aspect to be executed.

[0032] The beneficial effects of this application's embodiments compared to related technologies are as follows: By using measurement update relationships, high-frequency acceleration data collected by the accelerometer and low-frequency velocity data collected by the GNSS single-antenna velocimetry component can be mapped to the corresponding components of the state vector, achieving the fusion of high-frequency and low-frequency data. This allows the long-term stability of the GNSS single-antenna velocimetry component to complement the high-frequency dynamic characteristics of the accelerometer, thereby simultaneously covering both the low-frequency trend term and the high-frequency vibration term of the rotational deformation at a single detection point, resulting in accurate rotational deformation results. Furthermore, only data collected by a single antenna and accelerometer is needed to determine the rotational deformation, allowing for flexible deployment in scenarios such as long-span bridges or high-rise buildings, enhancing the independence and engineering applicability of rotational deformation detection. Compared to multi-antenna GNSS systems or gyroscopes, this saves equipment costs and deployment costs associated with multi-antenna systems, achieving the goal of low-cost and accurate detection of single-point rotational deformation in structures of long-span bridges. Attached Figure Description

[0033] To more clearly illustrate the technical solutions in the embodiments of this application, the drawings used in the description of the embodiments or related technologies will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0034] Figure 1 This is a schematic flowchart of a method for detecting the rotational deformation of a building provided in an embodiment of this application.

[0035] Figure 2 This is a schematic diagram of a coordinate system rotation provided in an embodiment of this application.

[0036] Figure 3 This is a flowchart illustrating a method for detecting building rotational deformation in an application scenario, as provided in an embodiment of this application.

[0037] Figure 4 This is a comparison chart of the angular rotation amount obtained by the building rotation deformation detection method in this application scenario and the reference angular rotation amount.

[0038] Figure 5 This is a comparison chart of the angular rotation amount obtained by the building rotation deformation detection method in this application scenario and the inclinometer observation amount.

[0039] Figure 6 This is a flowchart illustrating another method for detecting building rotational deformation provided in this application embodiment.

[0040] Figure 7 This is a schematic diagram of the structure of a building rotation deformation detection system provided in an embodiment of this application.

[0041] Figure 8 This is a schematic diagram of the structure of an electronic device terminal provided in an embodiment of this application. Detailed Implementation

[0042] Currently, deformation detection for long-span bridges often employs a system consisting of multiple continuously observing Global Navigation Satellite System (GNSS) monitoring stations and at least one reference station. This provides long-term, stable baseline observations (the distance vector between the phase centers of the GNSS antennas of the reference station and the monitoring station) to determine the real-time displacement deformation of the bridge structure. If rotational deformation of the bridge structure needs to be detected, the following five methods are typically used.

[0043] The first solution is to use an inclinometer to detect the rotation angle of the bridge structure. However, this solution often suffers from the crosstalk effect of linear vibration in the up-down, left-right, or front-back direction caused by vehicle passing, wind, or earthquake, leading to distortion of the measured rotation angle.

[0044] The second solution is to use a fiber-optic gyroscope that can detect data with high precision to measure the dynamic rotation of the bridge structure. However, the fiber-optic gyroscope obtains rotation rate observations, which often need to be integrated before use, leading to drift of the fiber-optic gyroscope data affected by system errors. Moreover, the fiber-optic gyroscope is not sensitive to low-frequency signals and cannot capture low-frequency components in the bridge rotation deformation, causing deviation in the overall measurement results. Although the fusion of GNSS, accelerometers, and fiber-optic gyroscopes can suppress the influence of system error drift to some extent, the high price of fiber-optic gyroscopes limits their widespread deployment in bridge monitoring, leading to insufficient monitoring point density and affecting comprehensive monitoring of the overall rotation deformation of the bridge.

[0045] The third solution is to use the observation values of multiple GNSS antennas to calculate the rotation deformation of the structure. However, this solution requires the deployment of multiple GNSS antennas and the accurate distance between them, as well as the assumption that the structural components between the antennas are rigid. However, actual bridge structures are not completely rigid under load and may produce local deformation, making the rotation calculation based on the rigid body model inaccurate. Moreover, multiple GNSS antennas can only capture the relative rotation between two points and cannot directly measure the rotation deformation of a single point, limiting the detection of single-point rotation deformation in the bridge structure.

[0046] The fourth solution is to use data collected by a distributed strain sensing array to estimate the rotation deformation of key nodes in the bridge structure. However, this solution has a low sampling rate and is difficult to capture rapid dynamic changes in the bridge structure. Moreover, the estimation of rotation deformation relies on mathematical models that convert strain distribution into rotation angle, and the bridge structure may produce non-uniform deformation, making the direct relationship between strain distribution and rotation deformation complex and reducing the measurement accuracy of rotation deformation.

[0047] The fifth solution is to use visual sensing technology based on multi-point observation to calculate the rotation deformation of the bridge structure. However, the measurement accuracy of this solution is easily affected by weather conditions and observation distance, making it difficult to provide accurate results stably.

[0048] In view of the above problems in detecting the rotational deformation of a bridge, the applicant finds that the dynamic response frequency of an accelerometer is relatively high, the accelerometer can capture high-frequency vibration, and the data collected by the accelerometer is usually affected by the rotation of the bridge structure, so that the data collected by the accelerometer contains the rotational deformation information of the bridge structure. However, when an accelerometer is used to detect the deformation of a bridge structure, only the displacement deformation is usually output, or the above second scheme is used, mainly relying on a gyroscope, and the rotational deformation information collected by the accelerometer is not fully utilized. Or relying on displacement observation of a plurality of GNSS antenna systems, the data collected by the accelerometer is filtered by high-pass filtering (that is, the data change caused by the rotation of the structure is regarded as signal pollution), which still fails to fully utilize the rotational deformation information collected by the accelerometer. Moreover, the system of a plurality of GNSS antennas usually needs to be based on the relative positioning of GNSS antennas, and has strict requirements on the deployment and distance of the plurality of GNSS antennas, which limits the independence and applicability of the system.

[0049] If the characteristics of long-term stability and low-frequency data provided by a GNSS can be combined with the characteristics of high-frequency vibration captured by an accelerometer, the rotational deformation of a bridge can be accurately detected from two dimensions of low frequency and high frequency. Based on this, the embodiment of the present application provides a building rotational deformation detection method, system, device and medium. A GNSS single-antenna velocity measurement component and an accelerometer are configured on a target structure, multiple GNSS antennas and a high-cost gyroscope are not needed, and the system can be widely deployed in large structures such as large-span bridges or high-rise buildings. When the data collected by the two sensors is obtained, the deformation result at the current time is predicted through a state update equation, and the low-frequency velocity data collected by the GNSS single-antenna velocity measurement component and the high-frequency acceleration data collected by the accelerometer are mapped to the corresponding components of the state vector of the target structure through a measurement update relationship, so as to realize the fusion of high-frequency data and low-frequency data. The predicted deformation result is updated from two dimensions of low frequency and high frequency, and an accurate single-point rotational deformation result can be obtained. The method provides a high-precision and high-frequency rotational deformation response measurement method for structural health monitoring (SHM), and has important engineering application value.

[0050] The building rotational deformation detection method in the embodiment of the present application will be described below through specific embodiments.

[0051] Figure 1 is a flowchart of a building rotational deformation detection method in the embodiment of the present application, as Figure 1 shown, the method comprises the following steps.

[0052] In S101, a state vector of a target structure, a state update equation, a measurement update relationship, high-frequency acceleration data, and low-frequency speed data are obtained.

[0053] The building deformation method provided in the embodiments of the present application can be applied to an electronic device, which can be a digital computing processor, a desktop computer, a server, a mobile phone, a tablet computer, a vehicle-mounted device, a notebook computer, an ultra-mobile personal computer (UMPC), a netbook, a personal digital assistant (PDA), and the like. The embodiments of the present application do not limit the specific type of the electronic device. For convenience, the embodiments of the present application take the digital computing processor as an example.

[0054] The GNSS single-antenna velocity measurement component and the accelerometer are arranged at the target structure.

[0055] The target structure can be a large-span bridge, a net rack structure, a net shell structure, a suspension structure, or a high-rise building, which is easily affected by wind load, temperature load, vehicle and pedestrian load, and / or earthquake and the like, and generates a rotational deformation. In the embodiments of the present application, the large-span bridge is taken as an example. The target structure can be a single detection point.

[0056] The high-frequency acceleration data is obtained by the accelerometer.

[0057] The low-frequency speed data is obtained by the GNSS single-antenna velocity measurement component.

[0058] The GNSS single-antenna velocity measurement component can be a component including a single GNSS antenna (also referred to as a GNSS single antenna).

[0059] The state vector includes a speed component, a rotation angle component, and an acceleration component of the target structure.

[0060] The state update equation is used to represent the influence of the sampling frequency of the accelerometer on the state vector.

[0061] The measurement update relationship is used to map the low-frequency speed to the speed component, and map the high-frequency acceleration to the acceleration component and the rotation angle component.

[0062] In order to facilitate the understanding of the embodiments of the present application, the reason why the rotational deformation result can be obtained based on the state vector of the structure, the state update equation, and the measurement update relationship is first described.

[0063] The GNSS single-antenna velocity measurement component and the accelerometer are two different sensors, and correspond to different coordinate systems. The data collected by the GNSS single-antenna velocity measurement component is based on the geocentric geodetic coordinate system and can be converted to the local (target structure, for example, the bridge) coordinate system, while the data collected by the accelerometer is based on the coordinate system of the sensor itself. If the accelerometer coordinate system is aligned with the local coordinate system, the data collected by the GNSS single-antenna velocity measurement component and the accelerometer in the same coordinate system can be obtained. When the target structure (for example, the bridge) is excited by external loads and rotates and deforms, the installed accelerometer will rotate synchronously, causing the rotation between the accelerometer coordinate system and the local coordinate system. The rotation between the two coordinate systems is exactly the rotational deformation of the target structure, and this rotational deformation is exactly contained in the high-frequency acceleration data collected by the accelerometer. Therefore, in theory, the rotational deformation of the target structure can be used to represent the change of the accelerometer coordinate system due to the rotation of the target structure. The low-frequency velocity data collected by the GNSS single-antenna velocity measurement component can be used to calculate the acceleration in the local coordinate system. By comparing the acceleration in the local coordinate system with the acceleration in the accelerometer coordinate system, the rotation angle between the accelerometer coordinate system and the local coordinate system can be obtained, which is the rotational deformation of the target structure. Therefore, if the accurate acceleration in the local coordinate system can be obtained, the single-point rotational deformation of the target structure can be obtained.

[0064] In the actual data collection process of the GNSS single-antenna velocity measurement component and the accelerometer, various noises often accompany, which requires an effective data processing strategy to improve the accuracy and reliability of the data collected by the sensor. As a recursive least mean square error (least variance) estimation method, Kalman filtering can estimate the required data in real time by establishing a state space model of the dynamic system in a noisy measurement environment. The core idea of this method includes two steps of circulation. The first step is prediction, which is to predict the state vector at the next time according to the motion model (state update equation) of the system. The components in the state vector are the predicted values after the system motion. The second step is update, which is to compare the actual observation value (low-frequency velocity data and high-frequency acceleration data) with the predicted value, and correct the predicted value by using the observation update equation (measurement update relationship) to obtain the posterior optimal estimate.

[0065] Therefore, by using the state update equation, the measurement update relationship and the state vector of Kalman filtering, more accurate deformation information of the target structure can be extracted from the low-frequency velocity data and high-frequency acceleration data containing noise, thereby providing a scientific basis for the safety evaluation and maintenance of the target structure.

[0066] The above and other Figure 2Explain the process of creating the state vector, state update equation, and measurement update relationship.

[0067] When initially configuring an accelerometer and GNSS single-antenna velocity measurement assembly, the first coordinate system corresponding to the accelerometer (i.e., the accelerometer coordinate system, Accelerometer Coordinate Frame) is... Figure 2 The O-xyz coordinate system (hereinafter referred to as ACF) and the second coordinate system (i.e., the local coordinate system, Bridge CoordinateFrame) corresponding to the GNSS single-antenna velocity measurement component are also mentioned. Figure 2 The three axes of the O-XYZ coordinate system (hereinafter referred to as BCF) are aligned. The high-frequency acceleration data acquired by the accelerometer includes time stamps corresponding to the GNSS single-antenna velocity measurement component.

[0068] The BCF (Body Frame Coordinate System) is fixed in space and is typically defined based on the geometry of the target structure. Specifically, in the second coordinate system (BCF), the X-axis is parallel to the central axis of the target structure, the Y-axis lies in a horizontal plane perpendicular to the X-axis, and the Z-axis is perpendicular to the plane containing the X and Y axes. Although the ACF (Accelerometer Coordinate System) is initially aligned with the BCF when the accelerometer and GNSS single-antenna velocity measurement assembly are configured, when the target structure deforms, the accelerometer and its associated structural elements will also shift and rotate. At this time, the ACF may rotate to O-x'y'z', and there is a rotation angle between the three coordinate axes of the ACF and the three coordinate axes of the BCF. The roll angle (…) ), pitch angle ( ) and yaw angle ( The x, y, and z axes of the ACF are defined as the angles of rotation about the X, Y, and Z axes, respectively. Thus, the roll angle (...) can be used... ), pitch angle ( ) and yaw angle ( Construct rotation matrix Connecting the transformed ACF and BCF, the transformation relationship between the two coordinate systems can be shown in Formula 1: Formula 1: As shown, where, , Indicates acceleration under ACF. This represents the acceleration along the x-axis (which becomes the x'-axis after rotation). This represents the acceleration along the y-axis (which becomes the y' axis after rotation). This represents the acceleration along the z-axis (which becomes the z'-axis after rotation). Indicates the acceleration under BCF. This represents acceleration along the X-axis. This represents acceleration along the Y-axis. a represents the acceleration on the Z axis, a represents the ACF, and b represents the BCF.

[0069] When the target structure is a bridge structure, the bridge deck is usually connected with the pier or anchoring device at both ends, and the tower structure usually has very high in-plane stiffness. The rotation angle of the ACF around the z axis (i.e., the yaw angle ) is very limited. It can be assumed that the yaw angle is zero (i.e., the rotation deformation of the target structure on the Z axis is 0), and the rotation matrix can be simplified as The high-frequency acceleration data of the accelerometer in the ACF can be converted to the BCF by using the rotation angle in the simplified rotation matrix. The specific conversion relationship can be shown in Equation 2. wherein represents the measurement noise of the accelerometer, represents the high-frequency acceleration collected by the accelerometer, the high-frequency acceleration data includes a first sub-acceleration on the x axis, a second sub-acceleration on the y axis, and a third sub-acceleration on the z axis in the ACF, represents the first sub-acceleration, represents the second sub-acceleration, represents the third sub-acceleration, represents the acceleration in the BCF, represents the acceleration on the X axis, represents the acceleration on the Y axis, represents the acceleration on the Z axis, g is the acceleration constant, and k represents the k-th moment, and k is a positive integer.

[0070] The above Equation 2 can be transformed into Equation 3. According to Equation 3, the rotation angle between the ACF and the BCF, i.e., the rotation angle of the target structure when the rotation deformation occurs can be calculated by the accelerations in the ACF and the BCF.

[0071] The above processes of Equations 1 to 3 parameterize the rotation deformation of the target structure, establish the conversion relationship between the accelerometer in its own coordinate system (ACF) and the fixed coordinate system (BCF) corresponding to the target structure, and can eliminate the influence of the target structure tilt (rotation deformation) on the data collected by the accelerometer. Based on this, the Kalman filtering framework can be introduced to accurately determine the rotation deformation result of the target structure.

[0072] The specific process of introducing the Kalman filtering framework can be as follows.

[0073] The electronic device can pre-create a state vector including a velocity component, a rotation angle component, and an acceleration component corresponding to the target structure. The state vector can be shown in Equation 4.

[0074] Equation 4: wherein, represents the displacement component of the target structure in three directions (X-axis, Y-axis and the direction indicated by Z-axis) in BCF, represents the velocity component of the target structure in three directions in BCF, at this time represents the acceleration component of the target structure in three directions in BCF, represents the rotation angle component of the target structure in three directions in BCF.

[0075] The recursive relationship of the rotation angle component in Equation 4 can be shown in Equation 5, Equation 5: , k-1 represents the k-1 time, represents the rotation angle noise, represents the pitch angle noise, represents the yaw angle noise. That is, Equation 5 indicates that the rotation angle component at the k time can be obtained by adding the rotation angle component at the k-1 time to the process noise term.

[0076] According to the relationship between the displacement, velocity and acceleration of the target structure, the state update equation of the state vector can be established, which can be shown in Equation 6.

[0077] Equation 6: wherein, represents the state vector at the k-1 time, represents the state vector at the k time, represents the state transition matrix, , represents the accelerometer sampling time interval, represents the process noise, represents the Gaussian white noise with mean 0 and variance , represents the process noise covariance matrix at the k-1 time.

[0078] The accelerometer sampling time interval is introduced in the state update equation, and the acceleration data is used as the control input of the state vector, and the state vector at the current time is predicted by combining the state transition matrix with the state vector at the last time. In this way, the high-frequency data of the accelerometer is effectively integrated into the state estimation, thereby improving the prediction accuracy of the rotational deformation.

[0079] According to the state update equation of Equation 6 combined with Kalman filtering, the measurement update relationship shown in Equation 7 can be obtained, that is, the observation update equation.

[0080] Equation 7: wherein, denotes a measurement vector, including low-frequency data and / or high-frequency acceleration data, denotes a Gaussian white noise with mean 0 and variance denotes a measurement noise covariance matrix, denotes an observation noise at the k-th moment, denotes a measurement design matrix. The electronic device can create a state vector including a velocity component, a rotation angle component, and an acceleration component of target detection in advance through the above formulas 1 to 7; create a state update equation according to the relationship between displacement, velocity, and acceleration when the target structure deforms; and determine a measurement update relationship according to the state update equation. It can be understood that the electronic device can directly read the aforementioned three if the electronic device has constructed the state vector, the state update equation, and the measurement update relationship of the target structure. Otherwise, the electronic device constructs the state vector, the state update equation, and the measurement update relationship of the target structure.

[0081] The electronic device can receive the collected high-frequency acceleration data sent by the accelerometer and the collected low-frequency velocity data sent by the GNSS single-antenna velocity component, or can obtain the raw data collected by the accelerometer and the GNSS single-antenna velocity component, respectively, and process the raw data collected by the two to obtain low-speed velocity data and high-frequency acceleration data.

[0082] In an implementation manner, the step of obtaining low-frequency velocity data by the electronic device can include obtaining carrier measurement data collected by the GNSS single-antenna velocity component; and performing time difference velocity measurement operation on carrier phase and pseudo-range in the carrier measurement data to obtain low-frequency velocity data.

[0083] Specifically, the electronic device can calculate the displacement velocity time series of the GNSS single-antenna velocity component in the earth-fixed coordinate system by using the time difference velocity measurement algorithm, and convert it to the second coordinate system to obtain low-frequency velocity data. The electronic device performs data preprocessing operation on the carrier measurement data, such as data gross error elimination and cycle slip detection, to obtain processed carrier measurement data.

[0084] The electronic device performs antenna phase center correction and troposphere delay correction on the processed carrier measurement data to obtain corrected carrier measurement data. This is mainly because the phase center of the GNSS single-antenna velocity component is not consistent with the geometric center of the antenna, and the phase center will change with the direction of the carrier signal. Therefore, the deviation and change of the antenna phase center need to be corrected according to the previously determined change model to reduce the influence on the subsequent results. The troposphere delay is the path delay of the carrier signal when passing through the troposphere due to atmospheric refraction, and the electronic device can use the troposphere model to correct the troposphere delay to reduce the error caused by the troposphere delay.

[0085] ​

[0086] The electronic device can establish a single-antenna time-difference velocity measurement model according to the corrected carrier measurement data, and specifically input the pseudo-range and carrier phase into the model to obtain low-frequency velocity data. The time-difference velocity measurement technology can directly obtain high-precision velocity data of a single antenna by using only broadcast ephemeris, without relying on external correction information such as GNSS reference stations or precise products (such as clock error and precise orbit product) as in the traditional multi-GNSS antenna system, and can improve the independence and reliability of building deformation detection, and is particularly suitable for offshore structures such as sea-crossing bridges, offshore wind turbines and other structure health monitoring.

[0087] S102, updating the state vector at the k-1 time according to the state update equation to obtain the deformation prediction result of the target structure at the k time.

[0088] The electronic device can obtain the state vector of the target structure at the k-1 time, and substitute it into formula 6 to obtain the state vector at the k time. The state vector at the k time is the deformation prediction result of the target structure at the k time, which also includes displacement component, rotation angle component, velocity component and acceleration component. The four components are respectively used to represent the prediction result of the displacement deformation of the target structure at the k time, the prediction result of the rotation deformation, the prediction result of the low-frequency velocity and the prediction result of the acceleration under the BCF at the k time.

[0089] S103, updating the deformation prediction result according to the low-frequency velocity data, the high-frequency acceleration data and the measurement update relationship at the k time to obtain the deformation optimization result at the k time.

[0090] The electronic device can substitute the deformation prediction result at the k time and the low-frequency velocity data (and / or high-frequency acceleration data) at the k time into formula 7 to determine the measurement vector at the k time , and recursively optimize the deformation prediction result at the k time according to the measurement vector at the k time and the Kalman filtering algorithm to obtain the deformation optimization result at the k time.

[0091] The specific process of recursively optimizing the deformation prediction result at the k time by using the Kalman filtering algorithm and the measurement vector at the k time is described below.

[0092] The electronic device can obtain the posteriori variance matrix of the target structure at the k-1 time and the process noise covariance matrix, and predict the a priori variance matrix at the k time through formula 8, where formula 8 is , represents the posteriori variance matrix at the k-1 time, represents the process noise covariance matrix at the k-1 time, the a priori variance matrix at the k time.

[0093] The electronic device substitutes the priori variance matrix at the k th moment into formula 9 to obtain the Kalman gain at the k th moment, where formula 9 is , The Kalman gain at the k th moment (also referred to as a gain matrix) is a weight factor for determining the importance of the low-frequency velocity data and the high-frequency acceleration data in updating the state vector.

[0094] The electronic device substitutes the Kalman gain at the k th moment, the measurement vector at the k th moment, and the deformation prediction result at the k th moment into formula 10 to obtain the deformation optimization result at the k th moment, where formula 10 is , The deformation prediction result at the k th moment is represented by The measurement vector at the k th moment is also referred to as an actual measurement value.

[0095] To facilitate obtaining the deformation optimization result at a subsequent moment, the electronic device also substitutes the priori variance matrix at the k th moment and the Kalman gain at the k th moment into formula 11 to obtain the posteriori variance matrix at the k th moment, where formula 11 is , The posteriori variance matrix at the k th moment is represented by The unit matrix is represented by

[0096] In S104, the electronic device determines the rotational deformation result of the target structure according to the rotational angle component in the deformation optimization result of the target structure at each moment.

[0097] The electronic device can record the deformation optimization result at each moment, and finally determine the rotational deformation result of the target structure according to whether the rotational angle component in the target structure in the plurality of deformation optimization results is abnormal.

[0098] It can be understood that the electronic device can also analyze the real-time rotational deformation result of the target structure according to the deformation optimization result at each moment.

[0099] In an implementation manner, in combination with formula 1, the state vector also includes a displacement component, and the electronic device can also obtain a displacement velocity time sequence and a rotational deformation time sequence corresponding to the target structure according to the displacement component and the rotational angle component in the deformation optimization result at each moment; and output the displacement velocity time sequence and the rotational deformation time sequence, where the displacement velocity time sequence is used to represent the deformation velocity of the target structure.

[0100] The electronic device can extract displacement and rotational angular components at multiple time points from the deformation optimization results of the target structure at multiple time points. Based on the differences between the displacement components at multiple time points, the displacement velocity (also known as deformation velocity) of the target structure at multiple time points is determined. The displacement velocity and rotational angular components are sorted in chronological order from earliest to latest, resulting in and outputting displacement velocity time series and rotational deformation time series. This not only facilitates subsequent determination of the target structure's displacement deformation results based on the displacement velocity time series, but also allows for tracking and prediction of the target structure's displacement and rotational deformation based on the two time series. Of course, the electronic device can also output carrier measurement data and high-frequency acceleration data for subsequent tracing of data collected by the GNSS single-antenna velocimetry component and accelerometer.

[0101] In one application scenario, such as Figure 3 As shown, the electronic device integrates a building rotation deformation detection system. This system includes a data sensor acquisition module, a data processing module, and a data output module, which will be described in subsequent embodiments. At a certain location on a long-span bridge (an example of the target structure), a GNSS receiver (an example of a GNSS single-antenna velocity measurement component) with a single antenna is installed. This receiver is used to acquire carrier measurement data in real time. A triaxial accelerometer is also installed and fixed to the GNSS single antenna for synchronous acquisition of acceleration observations in three directions (an example of high-frequency acceleration data).

[0102] The electronic device can acquire data from the GNSS receiver and accelerometer in real time via the data sensor acquisition module (i.e., synchronous real-time data acquisition) and store the acquired data. The electronic device can use the GNSS single-antenna velocity measurement unit in the data processing module to perform GNSS carrier phase time differential velocity measurement processing on the carrier measurement data, thereby acquiring the deformation (vibration) velocity of the GNSS single antenna in real time (an example of low-frequency velocity data). The electronic device can also use the structural rotation deformation fusion calculation unit in the data processing module to achieve the fusion calculation of the deformation (vibration) velocity and acceleration observations of the GNSS single antenna, obtaining real-time, high-precision, and wideband rotation deformation observations at a certain location on the bridge (an example of deformation optimization results at time k).

[0103] Specifically, the electronic device can parameterize the rotation angle caused by the rotational deformation at a certain position of the bridge through the structure rotation deformation fusion solving unit, realize the conversion of the accelerometer observation between the bridge coordinate system (BCF) and the acceleration coordinate system (ACF), and at the same time, use the Kalman filter to construct the recursive observation equation (the foregoing formulas) based on the velocity, acceleration and rotation angle, fuse the obtained low-frequency velocity data with the high-frequency acceleration data by using the GNSS time difference velocity calculation algorithm, compensate for the relative disadvantages of each sensor in observation noise and system error, obtain the rotational deformation information that a single sensor cannot easily or cannot obtain, and obtain the accurate and reliable rotational deformation result of the wideband bridge structure, to obtain the displacement velocity time sequence and the rotational deformation time sequence (i.e., the rotational deformation time sequence) at a certain position of the bridge.

[0104] Finally, the electronic device can output the time sequences obtained by the data processing module, and the original data collected by the GNSS receiver and the accelerometer, respectively, so as to subsequently track or predict the deformation. Finally, the high-frequency dynamic rotational deformation detection is realized in an efficient and low-cost manner, a solution for six-degree-of-freedom deformation detection of the bridge structure is provided, and the structural health monitoring capability is improved.

[0105] The beneficial effects of the building rotational deformation detection method in the embodiments of the present application will be further described below. Figure 4 and Figure 5 The beneficial effects of the building rotational deformation detection method in the embodiments of the present application will be further described below. Figure 4 For comparison of the angle rotation amount (rotation angle component at multiple time points) obtained by the building rotational deformation detection method in the embodiments of the present application and the reference angle rotation amount in the bridge coordinate system of the station. The test data obtained by simulating the structure resonance condition observation by using the vibration table, the GNSS receiver, the accelerometer and the fiber optic gyroscope in a certain region. Among them, the fusion result of the GNSS receiver, the accelerometer and the high-precision fiber optic gyroscope (ixblue Atlans-C) (the observed data) is taken as the reference true value. As shown in Figure 4 , the blue line represents the angle rotation amount of a certain structure around the X and Y axes of the (BCF coordinate system) at different time points obtained by the building rotational deformation detection method in the embodiments of the present application, the light blue line represents the reference true value of the rotation angle, and the red line represents the difference between the calculated value and the reference true value. It can be seen from Figure 4 that the angle rotation amount obtained by the building rotational deformation detection method in the embodiments of the present application is very close to the reference true value, and the accuracy can reach 0.007°.

[0106] Figure 5For comparison of the angle rotation quantity (rotation angle component at multiple time points) obtained by the building rotation deformation detection method in the embodiments of the present application in the station bridge coordinate system and the tiltmeter observation quantity. In the heavy vehicle load experiment of a certain cable-stayed bridge in a certain area, the test data recorded by the GNSS receiver, accelerometer and tiltmeter. As shown in Figure 5 , the light blue line represents the angle rotation quantity of a certain structure around the X and Y axes of the (BCF coordinate system) at different times obtained by the building rotation deformation detection method in the embodiments of the present application, and the purple line represents the synchronous observation quantity of the tiltmeter, which can be used as the true value of the rotation angle. As shown in Figure 5 , the comparison in the embodiments of the present application can be seen that the angle rotation quantity obtained by the building rotation deformation detection method is very close to the reference true value, and the accuracy can reach 0.002°, which further verifies the effectiveness of the building rotation deformation detection method in the embodiments of the present application in the actual engineering structure health monitoring.

[0107] In summary, in the embodiments of the present application, the high-frequency acceleration data of the target structure is collected by the accelerometer which is sensitive to rotation and can collect high-frequency data, so the high-frequency acceleration data can include the high-frequency rotation deformation information of the target structure. The low-frequency velocity data is obtained according to the data collected by the GNSS single-antenna velocity measurement component, and the GNSS single-antenna velocity measurement component has the advantage of providing long-term stable low-frequency data, so the low-frequency velocity data can accurately reflect the low-frequency velocity deformation of the target structure. The high-frequency acceleration data and the low-frequency velocity data can be mapped into the corresponding components of the state vector through the measurement update relationship, realizing the fusion of high-frequency data and low-frequency data, complementing the long-term stability of the GNSS single-antenna velocity measurement component and the high-frequency dynamic characteristics of the accelerometer, so as to cover the low-frequency trend item and the high-frequency vibration item of the rotation deformation of a single detection point at the same time, and obtain accurate rotation deformation results. And only the data collected by the single antenna and the accelerometer can realize the determination of the rotation deformation of a single point structure, which can be flexibly deployed in large-span bridge or high-rise building scenes, and enhances the independence and engineering applicability of the rotation deformation detection. In addition, the building rotation deformation detection method in the embodiments of the present application does not require a multi-antenna system or a gyroscope, which can save equipment cost and deployment cost of the multi-antenna system, realize the purpose of low-cost and accurate detection of the rotation deformation of a single point structure in a large-span bridge, and does not need to deploy a GNSS reference station, which is especially suitable for structure rotation deformation monitoring of offshore buildings (such as offshore wind turbines or oil platforms).

[0108] In one implementation manner, as shown in Figure 6 , the deformation prediction result is updated according to the low-frequency velocity data, the high-frequency acceleration data and the measurement update relationship at the kth time, to obtain the deformation optimization result at the kth time, including S201 to S205.

[0109] S201, acquire a first acquisition time of the low-frequency velocity data and a second acquisition time of the high-frequency acceleration data.

[0110] The measurement update relationship includes a first sub-update relationship and a second sub-update relationship.

[0111] The first sub-update relationship is used to represent a mapping relationship between the low-frequency velocity and the velocity component.

[0112] The second sub-update relationship is used to represent a mapping relationship between the high-frequency acceleration and the acceleration component and the rotation angle component.

[0113] The sampling frequency of the GNSS single-antenna velocity measurement component is different from that of the accelerometer. Generally speaking, the sampling frequency of the GNSS single-antenna velocity measurement component is lower than that of the accelerometer. This means that there is a time deviation between the low-frequency velocity data and the high-frequency acceleration data obtained by the electronic device. Based on this, a dual-rate observation update framework is designed, and the measurement update relationship shown in formula 7 is divided into a first sub-update relationship responsible for mapping the low-frequency velocity data and a second sub-update relationship responsible for mapping the high-frequency angular velocity data. When the low-frequency velocity data is available, the deformation prediction result at the kth moment is updated according to the first sub-update relationship and the low-frequency velocity data, and when the high-frequency acceleration data is available, the deformation prediction result at the kth moment is updated according to the second sub-update relationship and the high-frequency acceleration data.

[0114] The sampling time of the data is included in the low-frequency velocity data, and the sampling time of the data is also included in the acceleration data. When the electronic device obtains the low-frequency velocity data and the acceleration data, it can read the first sampling time and the second sampling time from them, respectively.

[0115] S202, in the case where the first acquisition time is earlier than the second acquisition time, updating the deformation prediction result at the kth moment according to the low-frequency velocity data and the first sub-update relationship to obtain a first deformation result.

[0116] The low-frequency velocity data includes a first sub-velocity on the X-axis, a second sub-velocity on the Y-axis, and a third sub-velocity on the Z-axis in the second coordinate system (BCF).

[0117] The first sub-update relationship is shown in formula 12, formula 12: , wherein, represents the second measurement noise, represents the measurement vector corresponding to the low-frequency velocity data at the kth moment, , represents the first sub-velocity, represents the second sub-velocity, represents the third sub-velocity, represents a Gaussian white noise with a mean of 0 and a variance of denotes a measurement noise covariance matrix corresponding to the low-frequency velocity data, , .

[0118] The first sub-updating relationship indicates that the low-frequency velocity data at the kth moment is equal to the sum of the predicted velocity component in the deformation prediction result at the kth moment and the measurement error.

[0119] When it is determined that the first acquisition time is earlier than the second acquisition time, that is, the low-frequency velocity data is obtained first, the low-frequency velocity data is available at this time, and the electronic device can update the deformation prediction result at the kth moment according to the low-frequency velocity. Specifically, the electronic device can substitute the low-frequency velocity data into formula 12 to obtain the measurement vector corresponding to the low-frequency velocity data at the kth moment , and recursively optimize the deformation prediction result at the kth moment according to the measurement vector corresponding to the low-frequency velocity data at the kth moment and the Kalman filtering algorithm, to obtain the first deformation result. The process of recursive optimization is similar to that in S103, and will not be repeated here.

[0120] S203, updating the first deformation result according to the high-frequency acceleration data and the second sub-updating relationship to obtain a deformation optimization result.

[0121] The high-frequency acceleration data includes a first sub-acceleration on the x-axis, a second sub-acceleration on the y-axis and a third sub-acceleration on the z-axis in the first coordinate system (ACF).

[0122] The second sub-updating relationship is shown in formula 13, formula 13: , wherein denotes a measurement vector corresponding to the reference acceleration data (that is, the acceleration in the second coordinate system), , denotes the deformation prediction result, denotes the first measurement noise, denotes a Gaussian white noise with a mean of 0 and a variance of , , g denotes the gravitational acceleration, denotes the first sub-acceleration, denotes the second sub-acceleration, denotes the third sub-acceleration, and k denotes the kth moment.

[0123] The second sub-updating relationship indicates that when the acceleration component predicted by the deformation prediction result at the kth moment is converted into the reference acceleration data in the measurement vector, the coordinate system conversion and the gravitational acceleration caused by the high-frequency acceleration data need to be considered.

[0124] The electronic device can substitute the deformation prediction result at the kth moment and the high-frequency acceleration data into formula 13 to obtain a measurement vector corresponding to the reference acceleration data, i.e., the specific value (i.e., the reference value) , and recursively optimize the first deformation result according to the measurement vector corresponding to the reference acceleration data at the kth moment and the Kalman filtering algorithm to obtain the deformation optimization result. The recursive optimization process is similar to the process in S103, and thus is not described herein.

[0125] In S204, or in the case where the first collection time is later than or equal to the second collection time, the deformation prediction result at the kth moment is updated according to the high-frequency acceleration data and the second sub-update relationship to obtain a second deformation result.

[0126] In S205, the second deformation result is updated according to the low-frequency speed data and the first sub-update relationship to obtain the deformation optimization result.

[0127] In the case where the first collection time is determined to be later than the second collection time, i.e., the high-frequency acceleration data is obtained first, the deformation prediction result at the kth moment can be updated according to the high-frequency acceleration data first, and then updated according to the low-frequency speed data. The specific process is similar to S202 to S203, and thus is not described herein.

[0128] It can be understood that in the case where the first collection time is equal to the second collection time, the low-frequency speed data or the high-frequency acceleration data can be randomly selected for updating first, and then the other is used for updating.

[0129] In the embodiments of the present application, the low-frequency speed data and the high-frequency acceleration data are allowed to update the current deformation prediction result according to the sampling time of different data, a flexible updating mode is provided, and the most suitable updating combination can be selected according to the actually available data. In some cases, the low-frequency speed data (or the high-frequency acceleration data) can not be available or accurate due to environmental factors or device limitations, and the other can still be used for updating to obtain the rotational deformation result, thereby improving the robustness of the building deformation detection. In combination with the updating of the two kinds of data, the error accumulated in the long-term integration of the GNSS single-antenna velocity component at low frequency and the accurate acceleration measurement of the accelerometer at short term can be combined, thereby reducing the error and improving the accuracy and reliability of the rotational angle component. In addition, different sensors are not required to use the same sampling frequency, and the adaptability is strong.

[0130] It should be understood that the size of the serial number of each step in the above embodiments does not mean the order of execution, and the execution order of each process should be determined according to its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0131] Figure 7 is a structural schematic diagram of a building rotational deformation detection system provided by the embodiments of the present application, as shown inFigure 7 As shown, the system comprises:

[0132] a data sensor module 710, comprising a GNSS single-antenna velocity measurement component 711 and an accelerometer 712 configured on the target structure, the GNSS single-antenna velocity measurement component 711 being configured to collect carrier measurement data, and the accelerometer 712 being configured to collect high-frequency acceleration data of the target structure;

[0133] a data processing module 720, configured to perform time difference measurement operation on carrier phases and pseudo ranges in the carrier measurement data to obtain low-frequency velocity data of the target structure; obtain a state vector of the target structure, a state update equation and a measurement update relationship, the state vector comprising a velocity component, a rotation angle component and an acceleration component of the target structure, the state update equation being configured to represent an influence of a sampling frequency of the accelerometer on the state vector, and the measurement update relationship being configured to map the low-frequency velocity to the velocity component and map the high-frequency acceleration to the acceleration component and the rotation angle component; update the state vector at a (k-1) th time according to the state update equation to obtain a deformation prediction result of the target structure at a k th time; and update the deformation prediction result according to the low-frequency velocity data, the high-frequency acceleration data and the measurement update relationship at the k th time to obtain a deformation optimization result at the k th time;

[0134] a data output module 730, configured to determine a rotational deformation result of the target structure according to the rotation angle component of the target structure in a plurality of deformation optimization results.

[0135] In some embodiments, the measurement update relationship comprises a first sub-update relationship and a second sub-update relationship, the first sub-update relationship being configured to represent a mapping relationship between the low-frequency velocity and the velocity component, and the second sub-update relationship being configured to represent a mapping relationship between the high-frequency acceleration and the acceleration component and the rotation angle component; the data processing module is further configured to obtain a first collection time of the low-frequency velocity data and a second collection time of the high-frequency acceleration data; and in a case where the first collection time is earlier than the second collection time, update the deformation prediction result according to the low-frequency velocity data and the first sub-update relationship to obtain a first deformation result at the k th time; and update the first deformation result according to the high-frequency acceleration data and the second sub-update relationship to obtain the deformation optimization result.

[0136] In some embodiments, the data processing module is further configured to, in a case where the first collection time is later than or equal to the second collection time, update the deformation prediction result at the k th time according to the high-frequency acceleration data and the second sub-update relationship to obtain a second deformation result; and update the second deformation result according to the low-frequency velocity data and the first sub-update relationship to obtain the deformation optimization result.

[0137] In some embodiments, the displacement component is further included in the state vector, and the data output module is further configured to obtain a displacement time sequence, a displacement velocity time sequence and a rotational deformation time sequence corresponding to the target structure according to the deformation optimization result of the target structure at the plurality of time points; and output the displacement velocity time sequence and the rotational deformation time sequence.

[0138] Figure 8 A structural schematic diagram of an electronic device is provided for an embodiment of the present application. As shown in the figure, Figure 8 the electronic device 6 of the embodiment includes at least one processor 60 (only one processor is shown in the figure), a memory 61, and a computer program 62 stored in the memory 61 and executable on the at least one processor 60, wherein the processor 60 implements the steps in any of the method embodiments described above when executing the computer program 62. Figure 8

[0139] The electronic device 6 can be a desktop computer, a notebook computer, a palm computer, a cloud server and other computing devices. The electronic device can include, but is not limited to, the processor 60 and the memory 61. Those skilled in the art can understand that, Figure 8 the electronic device 6 shown in the figure is only an example and does not limit the electronic device 6, which can include more or fewer components than those shown in the figure, or combine certain components or different components, for example, it can also include an input / output device, a network access device, etc.

[0140] The processor 60 can be a central processing unit (CPU), and the processor 60 can also be other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor.

[0141] ​The memory 61 can be an internal storage unit of the electronic device 6, such as a hard disk or a memory of the electronic device 6 in some embodiments. The memory 61 can also be an external storage device of the electronic device 6, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) card, a Flash Card, etc. equipped on the electronic device 6 in other embodiments. Further, the memory 61 can include both an internal storage unit and an external storage device of the electronic device 6. The memory 61 is used to store an operating system, an application program, a BootLoader, data, and other programs, such as program codes of the computer program, etc. The memory 61 can also be used to temporarily store data that has been output or is to be output.

[0142] Those skilled in the art can clearly understand that, for the convenience and brevity of description, only the division of the above functional units and modules is exemplified, and in actual application, the above functions can be completed by different functional units and modules according to needs, that is, the internal structure of the apparatus is divided into different functional units or modules to complete all or part of the above-described functions. Each functional unit and module in the embodiment can be integrated in one processing unit, or each unit can exist physically, or two or more units can be integrated in one unit, and the integrated unit can be realized in the form of hardware or in the form of a software functional unit. In addition, the specific names of each functional unit and module are only for convenient distinction, and do not limit the protection scope of the present application. The specific working process of the units and modules in the system can refer to the corresponding process in the foregoing method embodiments, which will not be described here.

[0143] The embodiment of the present application further provides a computer readable storage medium, which stores a computer program. The computer program is executed by a processor to implement the steps in each of the above method embodiments.

[0144] The embodiment of the present application provides a computer program product. When the computer program product is run on a mobile terminal, the mobile terminal is caused to implement the steps in each of the above method embodiments.

[0145] The integrated unit, if implemented in the form of a software function unit and sold or used as an independent product, can be stored in a computer readable storage medium. Based on such understanding, the present application can implement all or part of the processes in the above-mentioned embodiment methods through a computer program to instruct related hardware, and the computer program can be stored in a computer readable storage medium. When executed by a processor, the computer program can implement the steps of each method embodiment described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or some intermediate forms. The computer readable medium at least includes any entity or device capable of carrying the computer program code to the photographing device / terminal equipment, recording medium, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal, and software distribution medium. For example, U disk, mobile hard disk, magnetic disk or optical disk, etc. It should be understood that the size of the serial number of each step in the above embodiments does not mean the execution order, and the execution order of each process should be determined according to its function and inherent logic, and should not constitute any limitation on the implementation process of the embodiments of the present application. In the description, specific details such as specific system structures, technologies, etc. are proposed for illustration but not for limitation, so as to thoroughly understand the embodiments of the present application. However, it should be clear for those skilled in the art that the present application can also be implemented in other embodiments without these specific details. In other cases, detailed description of well-known systems, devices, circuits and methods is omitted to avoid unnecessary details that hinder the description of the present application.

[0146] It should be understood that when used in the specification and the appended claims of the present application, the term "comprising" indicates the presence of described features, integers, steps, operations, elements, and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof.

[0147] It should also be understood that the term "and / or" used in the specification and the appended claims of the present application means any combination of one or more of the associated listed items and all possible combinations, and includes these combinations.

[0148] In addition, in the description of the present application and the appended claims, the terms "include", "contain", "have" and their variants mean "include but not limited to", unless otherwise specifically emphasized.

[0149] In the above embodiments, the description of each embodiment has its own focus, and the parts not described or recorded in detail in a certain embodiment can be referred to the relevant description of other embodiments.

[0150] Those skilled in the art can understand that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be realized by electronic hardware or a combination of computer software and electronic hardware. Whether the functions are realized in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to realize the described functions for each specific application, but such implementation should not be considered beyond the scope of the present application.

[0151] In the embodiments provided in the present application, it should be understood that the disclosed apparatus, computer device and method can be implemented in other ways. For example, the apparatus and computer device embodiments described above are only schematic. The division of units is only a logical function division. There can be another division during actual implementation. For example, a plurality of units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the displayed or discussed mutual coupling or direct coupling or communication connection can be indirect coupling or communication connection through some interface, device or unit, and can be electrical, mechanical or in other forms.

[0152] The above embodiments are only used to illustrate the technical solutions of the present application, but not limit it; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: it can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement for part of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application, and should be included in the protection scope of the present application.

Claims

1. A method for detecting rotational deformation of a building, characterized in that, The method involves placing a GNSS single-antenna velocity measurement assembly and an accelerometer at the target structure, and includes: The state vector, state update equation, measurement update relationship, high-frequency acceleration data, and low-frequency velocity data of the target structure are obtained. The high-frequency acceleration data is acquired by the accelerometer, and the low-frequency velocity data is acquired by the GNSS single-antenna velocity measurement component. The state vector includes the velocity component, rotation angle component, and acceleration component of the target structure. The state update equation is used to represent the influence of the accelerometer's sampling frequency on the state vector. The measurement update relationship is used to map the low-frequency velocity to the velocity component and the high-frequency acceleration to the acceleration component and the rotation angle component. According to the state update equation, the state vector at time k-1 is updated to obtain the deformation prediction result of the target structure at time k. Based on the low-frequency velocity data, the high-frequency acceleration data, and the measurement update relationship at time k, the deformation prediction result is updated to obtain the deformation optimization result at time k. Based on the rotational angular components in the deformation optimization results of the target structure at multiple time points, the rotational deformation result of the target structure is determined; The measurement update relationship includes a first sub-update relationship and a second sub-update relationship. The first sub-update relationship is used to represent the mapping relationship between low-frequency velocity and the velocity component, and the second sub-update relationship is used to represent the mapping relationship between high-frequency acceleration and the acceleration component and the rotation angle component. The step of updating the deformation prediction result based on the low-frequency velocity data, the high-frequency acceleration data, and the measurement update relationship at time k, to obtain the deformation optimization result at time k, includes: The first acquisition time for acquiring the low-frequency velocity data and the second acquisition time for acquiring the high-frequency acceleration data; If the first acquisition time is earlier than the second acquisition time, the deformation prediction result is updated according to the low-frequency velocity data and the first sub-update relationship to obtain the first deformation result at the k-th time. Based on the high-frequency acceleration data and the second sub-update relationship, the first deformation result is updated to obtain the deformation optimization result; The high-frequency acceleration data includes a first sub-acceleration on the x-axis, a second sub-acceleration on the y-axis, and a third sub-acceleration on the z-axis in the first coordinate system; the update relationship of the second sub-acceleration is as follows: As shown, where, This represents the measurement vector corresponding to the reference acceleration data. Represents the state vector. Indicates the first measurement noise. This indicates that the mean is 0 and the variance is... Gaussian white noise, , g represents the acceleration due to gravity. Indicates the first sub-acceleration, This indicates the second sub-acceleration. The third sub-acceleration is represented by k, where k represents the k-th time. The first child update relationship is as shown in the formula. As shown, where, This represents the measurement vector corresponding to the low-frequency velocity data. , , This indicates that the mean is 0 and the variance is... Gaussian white noise, Indicates the second measurement noise; The state update equation is as follows: As shown, where, This represents the state vector at time k-1. This represents the state vector at time k. Indicates process noise. This indicates that the mean is 0 and the variance is... Gaussian white noise, Represents the state transition matrix. , This indicates the accelerometer sampling time interval.

2. The method as described in claim 1, characterized in that, The method further includes: If the first acquisition time is later than or equal to the second acquisition time, the deformation prediction result at the k-th time is updated according to the high-frequency acceleration data and the second sub-update relationship to obtain the second deformation result; Based on the low-frequency velocity data and the first sub-update relationship, the second deformation result is updated to obtain the deformation optimization result.

3. The method as described in claim 1, characterized in that, The state vector also includes a displacement component, and the method further includes: Based on the displacement and rotation angle components in the deformation optimization results at multiple time points, the displacement velocity time series and rotation deformation time series corresponding to the target structure are obtained, and the displacement velocity time series is used to represent the deformation velocity of the target structure. Output the displacement velocity time series and the rotational deformation time series.

4. The method as described in claim 1, characterized in that, The method is applied to a building rotation deformation detection system, which includes: The data sensor module includes a GNSS single-antenna velocity measurement component and an accelerometer configured on the target structure. The GNSS single-antenna velocity measurement component is used to collect carrier measurement data, and the accelerometer is used to collect high-frequency acceleration data of the target structure. The data processing module is used to perform time-difference velocimetry on the carrier phase and pseudorange in the carrier measurement data to obtain low-frequency velocity data of the target structure; acquire the state vector, state update equation, and measurement update relationship of the target structure, wherein the state vector includes velocity components, rotation angular components, and acceleration components of the target structure, the state update equation is used to represent the influence of the accelerometer sampling frequency on the state vector, and the measurement update relationship is used to map the low-frequency velocity to the velocity components and the high-frequency acceleration to the acceleration components and the rotation angular components; update the state vector at time k-1 according to the state update equation to obtain the deformation prediction result of the target structure at time k; update the deformation prediction result at time k according to the low-frequency velocity data, the high-frequency acceleration data, and the measurement update relationship to obtain the deformation optimization result at time k. The data output module is used to determine the rotational deformation result of the target structure based on the rotational angular component in the deformation optimization results of the target structure at multiple time points.

5. An electronic device, characterized in that, The device includes a processor, a memory, and a computer program stored in the memory and executable on the processor, characterized in that, when the processor executes the computer program, the electronic device performs the method as described in any one of claims 1-4.

6. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the method as described in any one of claims 1 to 4.

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