Coordinated monitoring method and device for dynamic deformation of engineering structure

Abstract

This invention provides a collaborative monitoring method and device for dynamic deformation of engineering structures, comprising: synchronously acquiring position and velocity observation data output by GNSS and dynamic specific force and angular velocity data output by MIMUs; performing dual-channel filtering on the data to extract quasi-static slowly varying components and vibration components, and constructing dual physical geometric constraints; establishing an extended Kalman filter to expand the three-dimensional spatial lever error vector reflecting the relative position deviation between the GNSS and MIMU measurement centers into the state vector, and using the dual constraints as virtual observation inputs during the measurement update stage to estimate and compensate for lever error online; using the compensated MIMUs state transition matrix to forward calculate the theoretical observation threshold interval for the next epoch, comparing it with the position and velocity observation data to obtain a news sequence, triggering a gross error isolation mechanism based on the news sequence, and outputting the smoothed positioning coordinates of the monitoring points. This achieves online adaptive calibration of lever error and isolation and elimination of sudden GNSS gross errors under dynamic deformation conditions of engineering structures.

Description

Technical Field

[0001] This invention relates to the field of engineering structure safety monitoring technology, and in particular to a collaborative monitoring method and device for dynamic deformation of engineering structures. Background Technology

[0002] In the field of health monitoring of large-scale engineering structures such as super high-rise buildings and long-span bridges, real-time acquisition of high-precision dynamic deformation information of the structure is of great significance for assessing structural safety and durability. Since a single sensor cannot simultaneously meet the monitoring requirements of high precision, high frequency, and strong anti-interference, a combined monitoring terminal is usually introduced by combining Micro-Electro-Mechanical Systems Inertial Navigation Systems (MIMUs) and Global Navigation Satellite Systems (GNSS). By utilizing the complementary characteristics of GNSS providing absolute position reference and MIMUs providing high-frequency dynamic response, the coordinated perception of structural vibration and slow deformation can be achieved.

[0003] In the aforementioned heterogeneous sensor hardware integration, the relative spatial physical position vector between the GNSS antenna phase center and the MIMU measurement center is referred to as the lever-arm. Accurate calibration of the lever-arm parameters is a prerequisite for ensuring correct fusion of GNSS and MIMU data. Currently, the existing technologies most similar to this one mainly fall into two categories: first, performing a one-time static lever-arm measurement using surveying tools such as a total station before equipment installation, and using it as a constant parameter in subsequent integrated navigation calculations; second, assigning constant empirical Gaussian white noise to the lever-arm parameters in integrated navigation Kalman filtering, and estimating them online through filtering iterations.

[0004] However, the aforementioned prior art has the following drawbacks: First, under the influence of strong wind loads and solar temperature differences, super high-rise buildings will not only experience high-frequency vibrations, but also low-frequency torsion and tilting (i.e., quasi-static slow deformation). This structural flexibility deformation will cause dynamic deformation of the physical levers between hardware devices. The traditional assumption of constant levers completely fails under this condition, resulting in a serious degradation of the combined filtering accuracy, which cannot meet the accuracy requirements of millimeter-level deformation monitoring.

[0005] Second, in complex high-altitude environments, GNSS signals are easily blocked by obstacles such as birds and temporary tower cranes, resulting in sudden gross errors in observation. Existing loose / tight coupling filters lack effective redundancy verification mechanisms when facing such sudden gross errors, and are very likely to incorrectly absorb them into the state vector, causing the entire integrated navigation system to diverge and collapse rapidly, seriously affecting the continuity and reliability of monitoring data.

[0006] Therefore, there is an urgent need for a GNSS / MIMUs collaborative monitoring scheme that can adapt to the dynamic inaccuracy of lever arm errors caused by structural flexible deformation and has strong resistance to gross errors, so as to improve the accuracy and robustness of health monitoring of large-scale engineering structures. Summary of the Invention

[0007] This invention provides a collaborative monitoring method and device for dynamic deformation of engineering structures, which solves the defects of existing technologies that cannot adapt to the dynamic deviation of lever arm parameters caused by the flexible deformation of structures, and the weak ability of filters to resist sudden observation gross errors. It realizes online adaptive calibration of lever arm errors and isolation and elimination of sudden GNSS gross errors under the dynamic deformation conditions of engineering structures, so as to output continuous and smooth high-precision monitoring point positioning coordinates.

[0008] This invention provides a collaborative monitoring method for dynamic deformation of engineering structures, comprising: Simultaneously acquire absolute position and velocity observation data output by the antenna array of the global satellite navigation system at the monitoring points of the engineering structure, as well as dynamic specific force and angular velocity measurement data output by the microelectromechanical inertial navigation system module at a frequency not lower than the first sampling frequency; The dynamic force and angular velocity measurement data are subjected to low-pass and high-pass filtering to extract quasi-static slowly varying components and vibration components, and dual physical geometric constraints are constructed based on the quasi-static slowly varying components and vibration components. An extended Kalman filter for data fusion is established. The three-dimensional spatial lever error vector, which reflects the relative positional deviation between the phase center of the antenna array of the global satellite navigation system and the measurement center of the microelectromechanical inertial navigation system module, is expanded into the state vector of the extended Kalman filter. During the measurement update phase of the extended Kalman filter, the dual physical geometric constraints are input as virtual observations into the extended Kalman filter. The three-dimensional spatial lever error is estimated and compensated online through filtering iteration to obtain the compensated state information. Using the state transition matrix of the microelectromechanical inertial navigation system corresponding to the compensated state information, the theoretical observation threshold interval for the next epoch is calculated forward, and the theoretical observation threshold interval is compared with the absolute position and velocity observation data to obtain the innovation sequence; Based on the information sequence, it is determined whether the gross error isolation mechanism is triggered, so as to output the smooth positioning coordinates of the monitoring point as the monitoring result of the dynamic deformation of the engineering structure.

[0009] In one possible implementation, the method further includes: A hardware clock synchronization trigger circuit outputs a synchronization trigger signal to the antenna array of the global satellite navigation system and the microelectromechanical inertial navigation system module at the monitoring point; In response to the synchronization trigger signal, absolute position and velocity observation data and dynamic force and angular velocity measurement data are acquired, and the absolute position and velocity observation data and the dynamic force and angular velocity measurement data are controlled to be aligned in time.

[0010] In one possible implementation, the method further includes: The dynamic specific force and angular velocity measurement data are filtered using a low-pass filter channel to obtain the quasi-static slowly varying component, wherein the cutoff frequency of the low-pass filter channel is determined based on the fundamental frequency of the engineering structure. The dynamic specific force and angular velocity measurement data are filtered using a high-pass filter channel to obtain the vibration component, wherein the cutoff frequency of the high-pass filter channel is higher than the fundamental frequency of the engineering structure. Based on the quasi-static slowly varying component and the vibration component, a dual physical-geometric constraint is constructed to describe the relative motion between the phase center of the antenna array of the global satellite navigation system and the measurement center of the microelectromechanical inertial navigation system module.

[0011] In one possible implementation, the method further includes: The dual physical geometric constraints include: quasi-static geometric constraint equations and vibrational geometric constraint equations; Based on the quasi-static slowly varying components, a quasi-static geometric constraint equation is constructed. The quasi-static geometric constraint equation is used to describe the low-frequency relative positional change relationship between the phase center of the antenna array and the measurement center. Vibration geometric constraint equations are constructed based on the vibration components. These equations are used to describe the dynamic relative attitude change relationship between the phase center of the antenna array and the measurement center.

[0012] In one possible implementation, the method further includes: The state vector of the Kalman filter also includes position, velocity, attitude, and inertial navigation zero bias; The three-dimensional spatial lever error vector includes the installation deviation component between the phase center of the antenna array and the measurement center, as well as the dynamic deformation component caused by the flexible deformation of the structure.

[0013] In one possible implementation, the method further includes: The quasi-static geometric constraint equation is used as the first virtual observation equation, and the vibration geometric constraint equation is used as the second virtual observation equation, which are then input into the extended Kalman filter. By combining the first virtual observation equation and the second virtual observation equation, the state vector of the extended Kalman filter is updated under constraints.

[0014] In one possible implementation, the method further includes: Using the state transition matrix, the predicted position and velocity values ​​for the next epoch are extrapolated based on the state vector of the current epoch. Based on the measurement accuracy and extrapolation time length of the microelectromechanical inertial navigation system module, the safety threshold boundary of the theoretical observation threshold interval for the next epoch is calculated.

[0015] In one possible implementation, the method further includes: The innovation sequence is statistically tested based on a preset hypothesis testing model. When the innovation sequence exceeds the safety threshold boundary of the theoretical observation threshold interval of the next epoch, the gross error isolation mechanism is triggered. The hypothesis testing models include innovation testing models based on chi-square distribution or residual threshold testing models based on the 3σ criterion.

[0016] In one possible implementation, the method further includes: Dynamically reduce the measurement weights of the global satellite navigation system, or mark the absolute position and velocity observations of the current epoch as gross errors and reject them; During the period when the absolute position and velocity observations are marked as gross errors, the positioning coordinate output is calculated based on the microelectromechanical inertial navigation system module to maintain the position until the information sequence falls back to within the safe threshold boundary of the theoretical observation threshold range of the next epoch.

[0017] The present invention also provides a collaborative monitoring device for dynamic deformation of engineering structures, comprising the following modules: The acquisition module is used to synchronously acquire the absolute position and velocity observation data output by the antenna array of the global satellite navigation system at the monitoring point of the engineering structure, as well as the dynamic specific force and angular velocity measurement data output by the microelectromechanical inertial navigation system module at a frequency not lower than the first sampling frequency; The filtering module is used to perform low-pass and high-pass filtering on the dynamic force and angular velocity measurement data, extract the quasi-static slowly varying component and the vibration component, and construct dual physical geometric constraints based on the quasi-static slowly varying component and the vibration component. The compensation module is used to establish an extended Kalman filter for data fusion. It expands the three-dimensional spatial lever error vector, which reflects the relative positional deviation between the phase center of the antenna array of the global satellite navigation system and the measurement center of the microelectromechanical inertial navigation system module, into the state vector of the extended Kalman filter. During the measurement update phase of the extended Kalman filter, the dual physical geometric constraints are input as virtual observations into the extended Kalman filter. The three-dimensional spatial lever error is estimated and compensated online through filtering iteration to obtain the compensated state information. The calculation module is used to forward calculate the theoretical observation threshold interval of the next epoch using the state transition matrix of the microelectromechanical inertial navigation system corresponding to the compensated state information, and compare the theoretical observation threshold interval with the absolute position and velocity observation data to obtain the information sequence. The determination module is used to determine whether the gross error isolation mechanism is triggered based on the information sequence, so as to output the smooth positioning coordinates of the monitoring point as the monitoring result of the dynamic deformation of the engineering structure.

[0018] The present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the collaborative monitoring method for dynamic deformation of engineering structures as described above.

[0019] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the collaborative monitoring method for dynamic deformation of engineering structures as described above.

[0020] The present invention also provides a computer program product, including a computer program that, when executed by a processor, implements the collaborative monitoring method for dynamic deformation of engineering structures as described above.

[0021] The present invention provides a collaborative monitoring method and apparatus for dynamic deformation of engineering structures. This method synchronously acquires absolute position and velocity observation data output from the antenna array of a global navigation satellite system at monitoring points on the engineering structure, as well as dynamic specific force and angular velocity measurement data output from a microelectromechanical inertial navigation system (MEMS) module at a sampling frequency not lower than a first sampling frequency. The dynamic specific force and angular velocity measurement data are subjected to low-pass and high-pass filtering to extract quasi-static slowly varying components and vibration components. Dual physical geometric constraints are constructed based on the quasi-static slowly varying components and the vibration components. An extended Kalman filter is established for data fusion to convert the three-dimensional spatial data reflecting the relative positional deviation between the phase center of the global navigation satellite system's antenna array and the measurement center of the MEMS module into a single data stream. The lever arm error vector is expanded into the state vector of the extended Kalman filter. During the measurement update phase of the extended Kalman filter, the dual physical geometric constraints are used as virtual observations and input into the extended Kalman filter. The three-dimensional lever arm error is estimated and compensated online through filtering iteration to obtain the compensated state information. The theoretical observation threshold interval for the next epoch is calculated forward using the state transition matrix of the microelectromechanical inertial navigation system corresponding to the compensated state information. The theoretical observation threshold interval is compared with the absolute position and velocity observation data to obtain a news sequence. Based on the news sequence, it is determined whether the gross error isolation mechanism is triggered to output the smooth positioning coordinates of the monitoring point as the monitoring result of the dynamic deformation of the engineering structure. Compared with the shortcomings of the prior art, which cannot adapt to the dynamic deviation of lever arm parameters caused by the flexible deformation of the structure and the weak resistance of the filter to sudden observation gross errors, this scheme realizes online adaptive calibration of lever arm error and isolation and elimination of sudden GNSS gross errors under the dynamic deformation condition of the engineering structure, so as to output continuous and smooth high-precision monitoring point positioning coordinates. Attached Figure Description

[0022] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0023] Figure 1 This is one of the flowcharts of the collaborative monitoring method for dynamic deformation of engineering structures provided by the present invention.

[0024] Figure 2 This is the second flowchart of the collaborative monitoring method for dynamic deformation of engineering structures provided by the present invention.

[0025] Figure 3 This is a construction schematic diagram of the collaborative monitoring system for dynamic deformation of engineering structures provided by the present invention.

[0026] Figure 4 This is a schematic diagram of the hardware platform architecture of the collaborative monitoring system for dynamic deformation of engineering structures provided by the present invention.

[0027] Figure 5 This is a structural framework diagram of the collaborative monitoring cloud platform system for dynamic deformation of engineering structures provided by the present invention.

[0028] Figure 6 This is a schematic diagram of the collaborative monitoring device for dynamic deformation of engineering structures provided by the present invention.

[0029] Figure 7 This is a schematic diagram of the structure of the electronic device provided by the present invention. Detailed Implementation

[0030] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0031] To facilitate understanding of the embodiments of the present invention, further explanations and descriptions will be provided below with reference to the accompanying drawings and specific embodiments. These embodiments do not constitute a limitation on the embodiments of the present invention.

[0032] Figure 1 This is one of the flowcharts illustrating the collaborative monitoring method for dynamic deformation of engineering structures provided by this invention, such as... Figure 1 As shown, the method includes the following: S11. Simultaneously acquire absolute position and velocity observation data output by the antenna array of the global satellite navigation system at the monitoring points of the engineering structure, as well as dynamic specific force and angular velocity measurement data output by the microelectromechanical inertial navigation system module at a frequency not lower than the first sampling frequency.

[0033] In this embodiment of the invention, the monitoring terminal integrates a Global Navigation Satellite System (GNSS) antenna array positioning module and a Microelectromechanical Inertial Navigation System (MIMU) high-frequency acquisition module. Both are mounted on the same monitoring point of the engineering structure (such as the top of a high-rise building or the main beam of a long-span bridge) via rigid or semi-rigid mechanical structures. The GNSS positioning module's antenna array receives satellite navigation signals and outputs the absolute position and velocity observation data of the antenna phase center at the monitoring point through carrier phase differential or Doppler velocimetry. The MIMU high-frequency acquisition module outputs dynamic specific force and angular velocity measurement data at the monitoring point. The dynamic specific force is the non-gravitational external force measurement value output by the accelerometer, including the dynamic acceleration component caused by structural vibration and gravity compensation terms. The angular velocity measurement data reflects the instantaneous rotational angular velocity of the monitoring point around three axes. Its sampling frequency (i.e., the first sampling frequency) is typically set to be no less than 100Hz and higher than the sampling frequency of the GNSS antenna array to capture the high-frequency dynamic response of the engineering structure under excitations such as wind vibration.

[0034] To ensure a unified time reference for data from heterogeneous sensors, the monitoring terminal incorporates a hardware clock synchronization trigger circuit. This circuit outputs synchronization trigger signals to the GNSS antenna array and MIMUs module, ensuring strict time alignment of the absolute position and velocity observation data, as well as the dynamic force and angular velocity measurement data, achieving sub-millisecond time synchronization and laying the foundation for subsequent data fusion.

[0035] S12. Perform low-pass and high-pass filtering on the dynamic force and angular velocity measurement data to extract the quasi-static slowly varying component and vibration component, and construct dual physical geometric constraints based on the quasi-static slowly varying component and the vibration component.

[0036] After receiving the synchronized dynamic force data and angular velocity measurement data, the processor of the monitoring terminal sets up a low-pass filter channel and a high-pass filter channel for dual-channel separation based on the macroscopic dynamic response characteristics of the engineering structure.

[0037] The dynamic specific force and angular velocity measurement data are filtered using a low-pass filter channel to obtain quasi-static slowly varying components. The cutoff frequency of this low-pass filter channel is determined based on the fundamental frequency of the engineering structure (for example, for super high-rise buildings, the fundamental frequency is usually in the range of 0.1Hz to 1Hz), and is used to extract quasi-static slowly varying components with frequencies lower than the fundamental frequency. These components mainly originate from uneven structural expansion caused by solar radiation temperature differences, slow torsion and tilting caused by long-term eccentric loading, etc., and manifest as low-frequency, slow deformation of the overall structure.

[0038] The dynamic specific force and angular velocity measurement data were filtered using a high-pass filter channel to obtain the vibration component. The cutoff frequency of this high-pass filter channel is higher than the fundamental frequency of the engineering structure, and it is used to extract vibration components with frequencies above the fundamental frequency. This component mainly originates from high-frequency structural vibrations caused by gusts, pulsating wind pressure, etc., and manifests as rapid attitude changes and acceleration responses at the monitoring points.

[0039] Based on the above two types of components, a dual physical-geometric constraint is constructed to describe the relative motion between the phase center of the GNSS antenna array and the measurement center of the MIMUs. This dual physical-geometric constraint includes: Quasi-static geometric constraint equations: Constructed based on quasi-static slowly varying components, these equations describe the low-frequency relative positional changes between the GNSS antenna phase center and the MIMU measurement center under low-frequency slowly varying effects. This equation quantifies the lever arm displacement caused by overall structural torsion and tilt as a virtual observation constraint.

[0040] Vibration geometric constraint equations: Constructed based on vibration components, these equations describe the dynamic relative attitude change between the GNSS antenna phase center and the MIMU measurement center under high-frequency vibration. This equation quantifies the lever arm attitude change caused by high-frequency structural vibration into virtual observation constraints.

[0041] Through the aforementioned dual physical and geometric constraints, the unique structural dynamics characteristics of civil engineering structures are transformed into physical prior information that can be recognized by the integrated navigation filter.

[0042] S13. Establish an extended Kalman filter for data fusion. Expand the state vector of the extended Kalman filter with the three-dimensional spatial lever error vector, which reflects the relative positional deviation between the phase center of the antenna array of the global satellite navigation system and the measurement center of the microelectromechanical inertial navigation system module. In the measurement update stage of the extended Kalman filter, the dual physical geometric constraints are input as virtual observations into the extended Kalman filter. The three-dimensional spatial lever error is estimated and compensated online through filtering iteration to obtain the compensated state information.

[0043] In this embodiment of the invention, the processor of the monitoring terminal establishes an extended Kalman filter for data fusion. In addition to the conventional position, velocity, attitude, and inertial navigation zero bias, the state vector of this filter explicitly expands upon the three-dimensional spatial lever error vector. This three-dimensional spatial lever error vector reflects the relative positional deviation between the phase center of the GNSS antenna array and the measurement center of the MIMUs, and its geometric definition is: lever vector ,in, These represent the installation offsets of the GNSS antenna phase center relative to the MIMU measurement center in the X, Y, and Z directions of the carrier coordinate system, respectively. The lever vector characterizes the spatial geometric relationship between the two sensors, comprising two parts: first, the installation deviation component, i.e., the fixed spatial offset generated during equipment manufacturing and on-site installation; and second, the dynamic deformation component, i.e., the time-varying spatial offset caused by structural flexible deformation (such as structural torsion, tilting, and vibration caused by strong wind loads and solar temperature differences). In traditional methods, this lever parameter is usually assumed to be a constant value. However, in the monitoring of flexible structures such as super high-rise buildings, this assumption completely fails, leading to a severe degradation in the accuracy of the combined filtering.

[0044] In the measurement update phase of the extended Kalman filter, the dual physical geometric constraints constructed in step S12 are used as virtual observation inputs. Specifically, the quasi-static geometric constraint equation is used as the first virtual observation equation, and the vibration geometric constraint equation is used as the second virtual observation equation. These are jointly input into the measurement update phase, and the state vector of the extended Kalman filter is updated by combining the first and second virtual observation equations. During the iteration process, the filter utilizes the prior information provided by the dual physical geometric constraints to online optimally estimate the dynamic deformation component in the three-dimensional lever arm error vector and compensate for the error in real time, thereby obtaining high-precision state information after compensation (including precise position, velocity, attitude, and corrected lever arm parameters).

[0045] S14. Using the state transition matrix of the microelectromechanical inertial navigation system corresponding to the compensated state information, the theoretical observation threshold interval of the next epoch is calculated forward, and the theoretical observation threshold interval is compared with the absolute position and velocity observation data to obtain the information sequence.

[0046] After obtaining the compensated state information, the monitoring terminal uses the MIMUs state transition matrix corresponding to this state information to extrapolate the predicted position and velocity values ​​for the next epoch based on the state vector (including position, velocity, and attitude) of the current epoch. This forward extrapolation fully utilizes the temporal continuity of MIMUs high-frequency data, providing high-density state predictions within the GNSS sampling interval.

[0047] Based on the measurement accuracy of the MIMUs module (such as accelerometer bias, gyroscope drift, and other error indicators) and the extrapolation time length (i.e., the time interval from the current epoch to the next epoch), the safe threshold boundary of the theoretical observation threshold interval for the next epoch is calculated. This safe threshold boundary defines the reasonable fluctuation range of GNSS observations in a statistical sense.

[0048] The theoretical observation threshold range is compared with the GNSS absolute position and velocity observation data obtained in step S11. The difference between the theoretical predictions of MIMUs and the actual observations of GNSS is calculated to obtain the innovation sequence (i.e., the residual sequence). This innovation sequence reflects the degree of deviation of the GNSS observations from the predicted trajectory of MIMUs and is the core indicator for determining the quality of GNSS observations.

[0049] S15. Based on the information sequence, determine whether the gross error isolation mechanism is triggered, and output the smoothed positioning coordinates of the monitoring point as the monitoring result of the dynamic deformation of the engineering structure.

[0050] The monitoring terminal incorporates a hypothesis testing model to statistically test the innovation sequence. This hypothesis testing model includes, but is not limited to, a innovation testing model based on the chi-square distribution, or a residual threshold testing model based on the 3σ criterion. When the innovation sequence exceeds the safety threshold boundary calculated in step S14, it is determined that there is a sudden gross error in the GNSS observation value of the current epoch, triggering the gross error isolation mechanism.

[0051] The specific implementation methods of the gross error isolation mechanism include: dynamically reducing the weight of GNSS measurements in the extended Kalman filter, or directly marking the absolute position and velocity observations of the current epoch as gross errors and rejecting them. During the period when the absolute position and velocity observations are marked as gross errors, the monitoring terminal relies entirely on the short-term high-precision calculations of the MIMUs module to maintain the positioning coordinate output. Since the MIMUs have high integration accuracy in a short time, and step S13 has accurately compensated for the lever error, the monitoring terminal can maintain the continuous and smooth output of the monitoring point's positioning coordinates during periods when the GNSS signal is blocked (such as by birds flying overhead or interference from temporary tower cranes), avoiding gross errors being incorrectly absorbed into the filter state vector and causing system divergence.

[0052] When the GNSS signal returns to normal and the innovation sequence falls back below the safety threshold, the monitoring terminal restores the normal fusion weights of the GNSS observations and continues to perform combined filtering. Finally, the monitoring terminal continuously outputs the smoothed positioning coordinate time series of the monitoring points as the monitoring results of the dynamic deformation of the engineering structure, for use in subsequent structural modal analysis, fatigue early warning, and safety assessment.

[0053] The collaborative monitoring method for dynamic deformation of engineering structures provided by this invention synchronously acquires absolute position and velocity observation data output by the antenna array of a global navigation satellite system at monitoring points on the engineering structure, as well as dynamic specific force and angular velocity measurement data output by a microelectromechanical inertial navigation system module at a frequency not lower than a first sampling frequency; performs low-pass and high-pass filtering on the dynamic specific force and angular velocity measurement data to extract quasi-static slowly varying components and vibration components, and constructs dual physical geometric constraints based on the quasi-static slowly varying components and the vibration components; establishes an extended Kalman filter for data fusion, and converts the three-dimensional spatial bar reflecting the relative positional deviation between the phase center of the antenna array of the global navigation satellite system and the measurement center of the microelectromechanical inertial navigation system module into a three-dimensional spatial bar. The arm error vector is expanded into the state vector of the extended Kalman filter. During the measurement update phase of the extended Kalman filter, the dual physical geometric constraints are used as virtual observations and input into the extended Kalman filter. The arm error in three-dimensional space is estimated and compensated online through filtering iteration to obtain the compensated state information. The theoretical observation threshold interval for the next epoch is calculated forward using the state transition matrix of the microelectromechanical inertial navigation system corresponding to the compensated state information. The theoretical observation threshold interval is compared with the absolute position and velocity observation data to obtain a news sequence. Based on the news sequence, it is determined whether the gross error isolation mechanism is triggered to output the smooth positioning coordinates of the monitoring point as the monitoring result of the dynamic deformation of the engineering structure. Compared with the shortcomings of the prior art, which cannot adapt to the dynamic deviation of the arm parameters caused by the flexible deformation of the structure and the weak resistance of the filter to sudden observation gross errors, this method realizes online adaptive calibration of the arm error and isolation and elimination of sudden GNSS gross errors under the dynamic deformation condition of the engineering structure, so as to output continuous and smooth high-precision monitoring point positioning coordinates.

[0054] Figure 2 This is a construction schematic diagram of the collaborative monitoring system for dynamic deformation of engineering structures provided in this application. (For example...) Figure 2 As shown, monitoring terminals are deployed at key monitoring points (such as the top of the building, the top of the bridge tower, or the mid-span of the main beam) on large engineering structures such as super high-rise buildings or long-span bridges. The monitoring terminals are fixed to the structural surface via rigid supports or semi-rigid bases to ensure the stability of the initial installation geometry between the GNSS antenna array and the MIMU modules. The GNSS antenna array faces the open sky to receive satellite signals; the MIMU modules are in close contact with the structural surface to sense the specific force and angular velocity response of the structure. Under strong wind loads and solar thermal temperature differences, the engineering structure experiences low-frequency torsion, tilting, and high-frequency vibrations, causing dynamic deformation of the physical lever arm between the GNSS antenna phase center and the MIMU measurement center. In this embodiment, the monitoring terminal uses a built-in collaborative monitoring algorithm to sense and compensate for this dynamic deformation in real time, overcoming the limitations of traditional static calibration or constant white noise assumptions.

[0055] Figure 3 This is a schematic diagram of the hardware platform architecture for collaborative monitoring of dynamic deformation of engineering structures provided in this application. (For example...) Figure 3 As shown, the monitoring terminal's hardware platform includes: a GNSS positioning module, a MIMUs high-frequency acquisition module, a hardware clock synchronization trigger circuit, a processor, a data storage unit, and a communication module. The GNSS positioning module, equipped with a GNSS antenna array and RF front-end, is responsible for acquiring and processing satellite signals; the MIMUs high-frequency acquisition module is responsible for acquiring high-frequency dynamic force and angular velocity measurement data. The hardware clock synchronization trigger circuit is connected to both the GNSS positioning module and the MIMUs high-frequency acquisition module, outputting a high-precision synchronization trigger signal to ensure that the two types of data are aligned in time. The processor, as the core computing unit, runs the aforementioned collaborative monitoring algorithm, performing feature extraction, EKF filtering, and gross error removal. The data storage unit is used to cache raw observation data and calculation results, and the communication module is used to upload the monitoring results to a remote monitoring center or cloud platform.

[0056] Figure 4 This is a structural framework diagram of the collaborative monitoring cloud platform system for dynamic deformation of engineering structures provided in this application. (See diagram for example.) Figure 4 As shown, the system adopts an edge-cloud collaborative architecture. The edge side consists of multiple monitoring terminals deployed at the engineering structure site. Each terminal independently completes local data acquisition, lever compensation, and gross error removal, outputting high-precision monitoring point location coordinates. The edge side transmits monitoring data to the cloud server in real time via 4G / 5G wireless communication networks or a dedicated fiber optic network. The cloud server is equipped with a database, a real-time calculation engine, a historical data analysis module, and a visualization platform. The database stores long-term deformation time series from multiple monitoring points; the real-time calculation engine performs quality checks and secondary fusion on the data uploaded from the edge; the historical data analysis module supports structural modal parameter identification and long-term trend analysis; and the visualization platform provides structural deformation curves, over-limit alarms, and health status reports to management personnel via a web or mobile terminal.

[0057] Figure 5 This is a schematic diagram of data interaction on the collaborative monitoring cloud platform provided in this application. For example... Figure 5 As shown, the bidirectional data flow between the edge monitoring terminal and the cloud server is further illustrated. The edge monitoring terminal not only uploads compressed positioning coordinate data and raw observation data to the cloud, but also receives configuration parameters (such as initial filter values, structure fundamental frequency update values, and safety threshold adjustment commands) from the cloud. Based on the multi-terminal data fusion results, the cloud server dynamically optimizes the algorithm parameters for each monitoring point and performs firmware updates and strategy adjustments to the edge terminals via remote commands, achieving collaborative optimization and unified management of a large-scale engineering structure monitoring network.

[0058] The following describes the collaborative monitoring device for dynamic deformation of engineering structures provided by the present invention. The collaborative monitoring device for dynamic deformation of engineering structures described below and the collaborative monitoring method for dynamic deformation of engineering structures described above can be referred to in correspondence.

[0059] Figure 6 This is a schematic diagram of the collaborative monitoring device for dynamic deformation of engineering structures provided by the present invention, specifically including: The acquisition module 601 is used to simultaneously acquire absolute position and velocity observation data output by the antenna array of the global satellite navigation system at the monitoring points of the engineering structure, as well as dynamic specific force and angular velocity measurement data output by the microelectromechanical inertial navigation system module at a frequency not lower than the first sampling frequency. For detailed explanations, please refer to the relevant descriptions in the above method embodiments; they will not be repeated here.

[0060] The filtering module 602 is used to perform low-pass and high-pass filtering on the dynamic force and angular velocity measurement data to extract the quasi-static slowly varying component and the vibration component, and to construct dual physical geometric constraints based on the quasi-static slowly varying component and the vibration component. For detailed explanations, please refer to the relevant descriptions in the above method embodiments; they will not be repeated here.

[0061] The compensation module 603 is used to establish an extended Kalman filter for data fusion. It expands the state vector of the extended Kalman filter with a three-dimensional spatial lever error vector reflecting the relative positional deviation between the phase center of the antenna array of the global satellite navigation system and the measurement center of the microelectromechanical inertial navigation system module. During the measurement update phase of the extended Kalman filter, the dual physical geometric constraints are input as virtual observations into the extended Kalman filter. The three-dimensional spatial lever error is estimated and compensated online through filtering iterations to obtain the compensated state information. For detailed explanations, please refer to the relevant descriptions in the above method embodiments; they will not be repeated here.

[0062] The calculation module 604 is used to forward calculate the theoretical observation threshold interval for the next epoch using the state transition matrix of the microelectromechanical inertial navigation system corresponding to the compensated state information, and compares the theoretical observation threshold interval with the absolute position and velocity observation data to obtain the innovation sequence. For detailed explanations, please refer to the relevant descriptions in the above method embodiments; they will not be repeated here.

[0063] The determination module 605 is used to determine whether the gross error isolation mechanism is triggered based on the news sequence, and outputs the smoothed positioning coordinates of the monitoring point as the monitoring result of the dynamic deformation of the engineering structure. For detailed explanation, please refer to the relevant descriptions in the above method embodiments, which will not be repeated here.

[0064] Figure 7 An example is a schematic diagram of the physical structure of an electronic device, such as... Figure 7As shown, the electronic device may include: a processor 710, a communication interface 720, a memory 730, and a communication bus 740, wherein the processor 710, the communication interface 720, and the memory 730 communicate with each other through the communication bus 740. The processor 710 can call logical instructions in the memory 730 to execute a collaborative monitoring method for dynamic deformation of an engineering structure. This method includes: synchronously acquiring absolute position and velocity observation data output by the antenna array of a global navigation satellite system at monitoring points on the engineering structure, and dynamic specific force and angular velocity measurement data output by a microelectromechanical inertial navigation system module at a frequency not lower than a first sampling frequency; performing low-pass and high-pass filtering on the dynamic specific force and angular velocity measurement data to extract quasi-static slowly varying components and vibration components, and constructing dual physical geometric constraints based on the quasi-static slowly varying components and the vibration components; establishing an extended Kalman filter for data fusion to reflect the phase center of the antenna array of the global navigation satellite system and the measurement center of the microelectromechanical inertial navigation system module. The three-dimensional spatial lever arm error vector of relative position deviation is expanded into the state vector of the extended Kalman filter. During the measurement update phase of the extended Kalman filter, the dual physical geometric constraints are input as virtual observations into the extended Kalman filter. The three-dimensional spatial lever arm error is estimated and compensated online through filtering iteration to obtain the compensated state information. The theoretical observation threshold interval for the next epoch is calculated forward using the state transition matrix of the microelectromechanical inertial navigation system corresponding to the compensated state information. The theoretical observation threshold interval is compared with the absolute position and velocity observation data to obtain the innovation sequence. Based on the innovation sequence, it is determined whether the gross error isolation mechanism is triggered, and the smoothed positioning coordinates of the monitoring point are output as the monitoring result of the dynamic deformation of the engineering structure.

[0065] Furthermore, the logical instructions in the aforementioned memory 730 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, essentially, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0066] On the other hand, the present invention also provides a computer program product, which includes a computer program that can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the collaborative monitoring method for dynamic deformation of engineering structures provided by the above methods. This method includes: synchronously acquiring absolute position and velocity observation data output by the antenna array of a global satellite navigation system at the monitoring point of the engineering structure, and dynamic specific force and angular velocity measurement data output by a microelectromechanical inertial navigation system module at a frequency not lower than a first sampling frequency; performing low-pass filtering and high-pass filtering on the dynamic specific force and angular velocity measurement data to extract quasi-static slowly varying components and vibration components, and constructing dual physical geometric constraints based on the quasi-static slowly varying components and the vibration components; establishing an extended Kalman filter for data fusion to reflect the global... The three-dimensional spatial lever error vector of the relative position deviation between the phase center of the antenna array of the satellite navigation system and the measurement center of the microelectromechanical inertial navigation system module is expanded into the state vector of the extended Kalman filter. During the measurement update phase of the extended Kalman filter, the dual physical geometric constraints are input as virtual observations into the extended Kalman filter. The three-dimensional spatial lever error is estimated and compensated online through filtering iteration to obtain the compensated state information. The theoretical observation threshold interval for the next epoch is calculated forward using the state transition matrix of the microelectromechanical inertial navigation system corresponding to the compensated state information. The theoretical observation threshold interval is compared with the absolute position and velocity observation data to obtain the innovation sequence. Based on the innovation sequence, it is determined whether the gross error isolation mechanism is triggered, so as to output the smoothed positioning coordinates of the monitoring point as the monitoring result of the dynamic deformation of the engineering structure.

[0067] In another aspect, the present invention also provides a non-transitory computer-readable storage medium storing a computer program thereon. When executed by a processor, the computer program implements a collaborative monitoring method for dynamic deformation of an engineering structure provided by the methods described above. This method includes: synchronously acquiring absolute position and velocity observation data output by the antenna array of a global navigation satellite system at monitoring points on the engineering structure, and dynamic specific force and angular velocity measurement data output by a microelectromechanical inertial navigation system module at a frequency not lower than a first sampling frequency; performing low-pass and high-pass filtering on the dynamic specific force and angular velocity measurement data to extract quasi-static slowly varying components and vibration components, and constructing dual physical geometric constraints based on the quasi-static slowly varying components and the vibration components; establishing an extended Kalman filter for data fusion to reflect the phase of the antenna array of the global navigation satellite system. The three-dimensional spatial lever error vector of the relative position deviation between the center of the microelectromechanical inertial navigation system (MEMS) module and the measurement center of the MEMS module is expanded into the state vector of the extended Kalman filter. During the measurement update phase of the extended Kalman filter, the dual physical geometric constraints are input as virtual observations into the extended Kalman filter. The three-dimensional spatial lever error is estimated and compensated online through filtering iteration to obtain the compensated state information. The theoretical observation threshold interval for the next epoch is calculated forward using the state transition matrix of the MEMS corresponding to the compensated state information. The theoretical observation threshold interval is compared with the absolute position and velocity observation data to obtain the innovation sequence. Based on the innovation sequence, it is determined whether the gross error isolation mechanism is triggered, and the smoothed positioning coordinates of the monitoring point are output as the monitoring result of the dynamic deformation of the engineering structure.

[0068] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0069] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0070] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for synergistic monitoring of dynamic deformation of an engineered structure, characterized by, include: Simultaneously acquire absolute position and velocity observation data output by the antenna array of the global satellite navigation system at the monitoring points of the engineering structure, as well as dynamic specific force and angular velocity measurement data output by the microelectromechanical inertial navigation system module at a frequency not lower than the first sampling frequency; The dynamic force and angular velocity measurement data are subjected to low-pass and high-pass filtering to extract quasi-static slowly varying components and vibration components, and dual physical geometric constraints are constructed based on the quasi-static slowly varying components and vibration components. An extended Kalman filter for data fusion is established. The three-dimensional spatial lever error vector, which reflects the relative positional deviation between the phase center of the antenna array of the global satellite navigation system and the measurement center of the microelectromechanical inertial navigation system module, is expanded into the state vector of the extended Kalman filter. During the measurement update phase of the extended Kalman filter, the dual physical geometric constraints are input as virtual observations into the extended Kalman filter. The three-dimensional spatial lever error is estimated and compensated online through filtering iteration to obtain the compensated state information. Using the state transition matrix of the microelectromechanical inertial navigation system corresponding to the compensated state information, the theoretical observation threshold interval for the next epoch is calculated forward, and the theoretical observation threshold interval is compared with the absolute position and velocity observation data to obtain the innovation sequence; Based on the information sequence, it is determined whether the gross error isolation mechanism is triggered, so as to output the smooth positioning coordinates of the monitoring point as the monitoring result of the dynamic deformation of the engineering structure.

2. The method of claim 1, wherein, The synchronous acquisition of absolute position and velocity observation data output by the antenna array of the global satellite navigation system at the monitoring point of the engineering structure, and dynamic specific force and angular velocity measurement data output by the microelectromechanical inertial navigation system module at a frequency not lower than the first sampling frequency, includes: A hardware clock synchronization trigger circuit outputs a synchronization trigger signal to the antenna array of the global satellite navigation system and the microelectromechanical inertial navigation system module at the monitoring point; In response to the synchronization trigger signal, absolute position and velocity observation data and dynamic force and angular velocity measurement data are acquired, and the absolute position and velocity observation data and the dynamic force and angular velocity measurement data are controlled to be aligned in time.

3. The method of claim 1, wherein, The dynamic force and angular velocity measurement data are subjected to low-pass and high-pass filtering to extract quasi-static slowly varying components and vibration components. Based on these components, a dual physical geometric constraint is constructed, including: The dynamic specific force and angular velocity measurement data are filtered using a low-pass filter channel to obtain the quasi-static slowly varying component, wherein the cutoff frequency of the low-pass filter channel is determined based on the fundamental frequency of the engineering structure. The dynamic specific force and angular velocity measurement data are filtered using a high-pass filter channel to obtain the vibration component, wherein the cutoff frequency of the high-pass filter channel is higher than the fundamental frequency of the engineering structure. Based on the quasi-static slowly varying component and the vibration component, a dual physical-geometric constraint is constructed to describe the relative motion between the phase center of the antenna array of the global satellite navigation system and the measurement center of the microelectromechanical inertial navigation system module.

4. The method according to claim 1 or 3, characterized in that, The dual physical geometric constraints include: quasi-static geometric constraint equations and vibrational geometric constraint equations; the method further includes: Based on the quasi-static slowly varying components, a quasi-static geometric constraint equation is constructed. The quasi-static geometric constraint equation is used to describe the low-frequency relative positional change relationship between the phase center of the antenna array and the measurement center. Vibration geometric constraint equations are constructed based on the vibration components. These equations are used to describe the dynamic relative attitude change relationship between the phase center of the antenna array and the measurement center.

5. The method according to claim 1, characterized in that, The state vector of the Kalman filter also includes position, velocity, attitude, and inertial navigation zero bias; The three-dimensional spatial lever error vector includes the installation deviation component between the phase center of the antenna array and the measurement center, as well as the dynamic deformation component caused by the flexible deformation of the structure.

6. The method according to claim 4, characterized in that, In the measurement update phase of the extended Kalman filter, the dual physical geometric constraints are input as virtual observations into the extended Kalman filter, including: The quasi-static geometric constraint equation is used as the first virtual observation equation, and the vibration geometric constraint equation is used as the second virtual observation equation, which are then input into the extended Kalman filter. By combining the first virtual observation equation and the second virtual observation equation, the state vector of the extended Kalman filter is updated under constraints.

7. The method according to claim 1, characterized in that, The step of using the state transition matrix of the microelectromechanical inertial navigation system corresponding to the compensated state information to forward calculate the theoretical observation threshold interval for the next epoch includes: Using the state transition matrix, the predicted position and velocity values ​​for the next epoch are extrapolated based on the state vector of the current epoch. Based on the measurement accuracy and extrapolation time length of the microelectromechanical inertial navigation system module, the safety threshold boundary of the theoretical observation threshold interval for the next epoch is calculated.

8. The method according to claim 7, characterized in that, The step of determining whether the gross error isolation mechanism is triggered based on the news sequence includes: The innovation sequence is statistically tested based on a preset hypothesis testing model. When the innovation sequence exceeds the safety threshold boundary of the theoretical observation threshold interval of the next epoch, the gross error isolation mechanism is triggered. The hypothesis testing models include innovation testing models based on chi-square distribution or residual threshold testing models based on the 3σ criterion.

9. The method according to claim 8, characterized in that, The gross error isolation mechanism includes: Dynamically reduce the measurement weights of the global satellite navigation system, or mark the absolute position and velocity observations of the current epoch as gross errors and reject them; During the period when the absolute position and velocity observations are marked as gross errors, the positioning coordinate output is calculated based on the microelectromechanical inertial navigation system module to maintain the position until the information sequence falls back to within the safe threshold boundary of the theoretical observation threshold range of the next epoch.

10. A collaborative monitoring device for dynamic deformation of engineering structures, characterized in that, include: The acquisition module is used to synchronously acquire the absolute position and velocity observation data output by the antenna array of the global satellite navigation system at the monitoring point of the engineering structure, as well as the dynamic specific force and angular velocity measurement data output by the microelectromechanical inertial navigation system module at a frequency not lower than the first sampling frequency; The filtering module is used to perform low-pass and high-pass filtering on the dynamic force and angular velocity measurement data, extract the quasi-static slowly varying component and the vibration component, and construct dual physical geometric constraints based on the quasi-static slowly varying component and the vibration component. The compensation module is used to establish an extended Kalman filter for data fusion. It expands the three-dimensional spatial lever error vector, which reflects the relative positional deviation between the phase center of the antenna array of the global satellite navigation system and the measurement center of the microelectromechanical inertial navigation system module, into the state vector of the extended Kalman filter. During the measurement update phase of the extended Kalman filter, the dual physical geometric constraints are input as virtual observations into the extended Kalman filter. The three-dimensional spatial lever error is estimated and compensated online through filtering iteration to obtain the compensated state information. The calculation module is used to forward calculate the theoretical observation threshold interval of the next epoch using the state transition matrix of the microelectromechanical inertial navigation system corresponding to the compensated state information, and compare the theoretical observation threshold interval with the absolute position and velocity observation data to obtain the information sequence. The determination module is used to determine whether the gross error isolation mechanism is triggered based on the information sequence, so as to output the smooth positioning coordinates of the monitoring point as the monitoring result of the dynamic deformation of the engineering structure.

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