A multi-sensor fusion bridge synchronous jacking error adaptive compensation method

Abstract

The application discloses a bridge synchronous jacking error adaptive compensation method based on multi-sensor fusion, and comprises the following steps: displacement sensing elements, pressure sensing elements and double-shaft inclination sensing elements are respectively arranged on all jacking jacks of a bridge, key sections of a beam body and four corners of the bridge to build a full-coverage monitoring network; high-speed synchronous acquisition equipment is used to synchronously collect original data collected by the three types of sensing elements; noise reduction processing is performed on the collected original sensing data, and abnormal distorted data is removed to obtain effective monitoring data available for operation; and the bridge synchronous jacking error adaptive compensation method based on multi-sensor fusion has the beneficial effects that: two types of error change rate parameters of displacement and pressure are introduced, a prediction control module is matched to perform pre-compensation, and disturbances generated by jacking jack oil return, temporary support compression and repeated alternate jacking can be offset in advance, so that the accumulated deviation of multiple jacking can be greatly reduced.

Description

Technical Field

[0001] This invention belongs to the field of bridge engineering construction control technology, specifically relating to a multi-sensor fusion-based adaptive compensation method for bridge synchronous jacking error. Background Technology

[0002] The reinforcement of old bridges and the replacement of bearings require the overall lifting of the beam. During construction, the lifting height, stress, and beam tilt of all jacks must be synchronized. If the synchronization deviation is too large, the bridge will generate additional stress, resulting in cracks, bearing damage, and in severe cases, the beam may overturn. Therefore, precise control of the difference and real-time compensation of errors during the lifting process are crucial.

[0003] Currently, most construction sites use PLC hydraulic synchronous jacking systems, which rely on displacement and pressure sensors combined with traditional PID algorithms for displacement closed-loop control. This system is slow to respond, has poor anti-interference capabilities, and lacks adaptive adjustment capabilities.

[0004] In the prior art, the patent publication number CN121254592A discloses a method for overall jacking of an old bridge based on multi-sensor fusion and adaptive control. This method constructs a multi-sensor monitoring system including displacement sensors, pressure sensors, stress sensors, and tilt sensors. It uses an improved Kalman filter to complete data fusion and relies on a fuzzy rule base to achieve adaptive control. Although this method achieves multi-sensor data fusion and basic adaptive control, it still has obvious shortcomings: Directly using raw sensor data to calculate control quantities does not involve statistical analysis of the rate of error change, making it impossible to predict deviations in advance. Corrections can only be made after errors occur, resulting in control lag. Relying solely on a single fuzzy adaptive control system without a predictive compensation module, it cannot proactively offset disturbances caused by jack oil return, support compression, and multiple alternating jacking operations. As a result, errors will accumulate continuously, making it unsuitable for the construction of high piers, large elevation differences, and multi-span continuous beams. Without the logic for cross-calibrating displacement and tilt angle data, it is impossible to distinguish between sensor failure, mechanical jamming, and asynchronous lifting, which can easily lead to false alarms or missed faults. Forcibly adjusting the jacking height when there is uneven local stress can easily cause local overload and structural damage to the bridge. Summary of the Invention

[0005] The purpose of this invention is to provide a multi-sensor fusion-based adaptive compensation method for bridge synchronous jacking errors, in order to solve the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a multi-sensor fusion-based adaptive compensation method for bridge synchronous jacking error, comprising the following steps: Displacement sensors, pressure sensors, and dual-axis tilt sensors were installed on all lifting jacks of the bridge, key sections of the beam, and at the four corners of the bridge to build a full-coverage monitoring network; high-speed synchronous acquisition equipment was used to collect the raw data collected by the three types of sensors simultaneously. The collected raw sensing data is denoised to remove abnormal and distorted data, thus obtaining effective monitoring data that can be used for calculation. A multi-dimensional error state vector is built based on the processed effective monitoring data to quantify the deviations in the jacking operation; The completed error state vector is sent to the combined automatic correction controller. With the help of the fuzzy logic operation module and combined with the jack pressure deviation limit, the proportional, integral and derivative parameters of the PID controller are adjusted in real time to complete the first layer of automatic error correction. With the help of the predictive control module, the optimal control value is calculated by combining multiple safety constraints of bridge jacking construction, and a pre-correction signal is generated and superimposed on the output of the PID controller to complete the second layer of global deviation prediction and correction. The data collected by various sensors are cross-checked and verified, and a graded safety protection mechanism is implemented. By combining the data verification results with real-time deviation values, fault handling, construction early warning, or shutdown protection are automatically completed to ensure the continuous and safe operation of the jacking construction.

[0007] As a preferred technical solution of the present invention, the Kalman filter algorithm is used to process the raw sensing data. Through state prediction and observation update loop calculation, data noise caused by construction vibration, ambient temperature and electromagnetic interference is eliminated, abrupt abnormal data is identified and removed, and stable and accurate displacement, pressure and tilt effective data are output.

[0008] As a preferred technical solution of the present invention, the formula for constructing the multidimensional fusion error state vector is as follows: E(t) = e d (t),e p (t),e θ (t),△e d (t) / △t,△e P (t) / △t; where, e d (t): Displacement synchronization error of each lifting point relative to the target displacement; e p (t): Pressure deviation between the actual load and the theoretically calculated load of each jack; e θ (t): The inclination error between the actual inclination angle and the design inclination angle of the beam; △e d (t) / △t: Rate of change of displacement error; △e P(t) / △t: Rate of change of pressure error.

[0009] As a preferred embodiment of the present invention, the fuzzy logic operation module adopts a dual-input setting, with the core input parameter being the displacement synchronization error e. d (t), rate of change of displacement error Δe d (t) / △t, while also including the pressure deviation e p (t) serves as a hard constraint condition; the fuzzy judgment rules derived from engineering practice and simulation experiments are pre-entered into the module, and the proportional coefficient Kp, integral coefficient Ki, and derivative coefficient Kd of the PID controller are adjusted in real time according to the input parameters and pressure constraints.

[0010] As a preferred technical solution of the present invention, when the predictive control module calculates the optimal control value, it sets three hard safety standards: the synchronous deviation of the displacement of each jacking point cannot exceed 1mm, the real-time pressure of a single jack does not exceed 1.2 times the theoretical load, and the overall tilt angle of the bridge is less than 0.1°.

[0011] As a preferred technical solution of the present invention, the predictive control module relies on the current multi-dimensional error state vector to predict the subsequent deviations caused by the jack oil return, temporary support compression, and alternating jacking operations in advance, calculates the optimal lifting increment of each jacking point within the cycle, and superimposes the increment as a pre-correction signal to the output of the PID controller to achieve early correction of jacking deviation.

[0012] As a preferred technical solution of the present invention, the multi-sensor cross-checking method is as follows: the theoretical tilt angle of the bridge is calculated by using the height difference measured by the displacement sensors at the four corners of the bridge, and then compared with the actual tilt angle measured by the tilt sensors; if the difference between the two exceeds the preset critical value, it is determined that the sensor has failed, the system automatically cuts off the data channel of the faulty sensor, and continues to carry out jacking control by relying on the data of the remaining intact sensors.

[0013] Compared with the prior art, the beneficial effects of the present invention are: By introducing two types of error change rate parameters, displacement and pressure, and using a predictive control module for pre-compensation, disturbances caused by jack oil return, temporary support compression, and repeated alternating jacking can be offset in advance, significantly reducing the deviation accumulated from multiple jackings. This can meet the needs of jacking scenarios with high construction difficulty, such as high piers, large elevation differences, and multi-span continuous beams. Using the jack pressure deviation as a hard constraint for fuzzy adjustment, when the force at a point exceeds the standard, the adjustment force at that point will be automatically reduced, and the surrounding jacks will be mobilized to share the load, so as not to force the lifting and cause local overload. Eliminating stress-sensing components that are susceptible to interference from the construction site environment reduces the overall cost of the monitoring equipment; through cross-comparison and verification of displacement and tilt angle data, it can accurately distinguish between three types of problems: sensor damage, mechanical jamming, and asynchronous jacking, thereby reducing the occurrence of accidental shutdowns and false alarms during field construction. The system can adjust all PID control parameters online in real time, eliminating the need for manual parameter adjustments by staff before construction. It can automatically adapt to engineering scenarios with different bridge stiffness, different jacking heights, and different load sizes, reducing the professional requirements of operators and minimizing manual workload. Attached Figure Description

[0014] Figure 1 This is a flowchart of the bridge synchronous jacking error adaptive compensation method based on multi-sensor fusion according to the present invention. Detailed Implementation

[0015] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0016] Please see Figure 1 This invention provides a multi-sensor fusion-based adaptive compensation method for bridge synchronous jacking errors, comprising the following steps: Displacement sensors, pressure sensors, and dual-axis tilt sensors are installed on all lifting jacks, key sections of the beam, and at the four corners of the bridge to establish a comprehensive monitoring network. High-speed synchronous acquisition equipment is used to collect the raw data from these three types of sensors simultaneously. The specific operation of sensor deployment and data acquisition is as follows: each jack is equipped with a high-precision wire-type displacement sensor or a laser displacement sensor to collect the actual lifting height at each lifting point; pressure sensors are installed inside the hydraulic circuits of the jacks to collect the jack's bearing pressure in real time; dual-axis tilt sensors are installed at the mid-span, supports, and four corners of the beam to collect longitudinal and lateral tilt data of the bridge; a synchronous acquisition card with a acquisition speed of no less than 100Hz is selected to uniformly summarize all sensor data and transmit it to the back-end control equipment.

[0017] The collected raw sensing data is denoised to remove abnormal and distorted data, resulting in effective monitoring data that can be used for calculation. Specifically, the raw sensing data is processed using a Kalman filter algorithm. Through state prediction and observation update loop calculation, data noise caused by construction vibration, ambient temperature, and electromagnetic interference is eliminated, and abrupt abnormal data is identified and removed, outputting stable and accurate effective displacement, pressure, and tilt data.

[0018] A multi-dimensional error state vector is built based on the processed effective monitoring data to quantify the deviations in the jacking operation; The completed error state vector is sent to the combined automatic correction controller. With the help of the fuzzy logic operation module and combined with the jack pressure deviation limit, the proportional, integral and derivative parameters of the PID controller are adjusted in real time to complete the first layer of automatic error correction. With the help of the predictive control module, the optimal control value is calculated by combining multiple safety constraints of bridge jacking construction, and a pre-correction signal is generated and superimposed on the output of the PID controller to complete the second layer of global deviation prediction and correction. The data collected by various sensors are cross-checked and verified, and a graded safety protection mechanism is implemented. By combining the data verification results with real-time deviation values, fault handling, construction early warning, or shutdown protection are automatically completed to ensure the continuous and safe operation of the jacking construction.

[0019] In this embodiment, the formula for the constructed multidimensional fusion error state vector is: E(t) = e d (t),e p (t),e θ (t),△e d (t) / △t,△e P (t) / △t; where, e d (t): Displacement synchronization error of each lifting point relative to the target displacement; e p (t): Pressure deviation between the actual load and the theoretically calculated load of each jack; e θ (t): The inclination error between the actual inclination angle and the design inclination angle of the beam; △e d (t) / △t: Rate of change of displacement error; △e P (t) / △t: Rate of change of pressure error.

[0020] In this embodiment, the fuzzy logic operation module adopts a dual-input setting, with the core input parameter being the displacement synchronization error e. d (t), rate of change of displacement error Δe d (t) / △t, while also including the pressure deviation e p (t) serves as a hard constraint condition; fuzzy judgment rules derived from engineering practice and simulation experiments are pre-entered into the module, and the proportional coefficient Kp, integral coefficient Ki, and derivative coefficient Kd of the PID controller are adjusted in real time according to the input parameters and pressure constraints; the real-time adjustment rule for PID controller parameters is: when the displacement synchronization error e d (t) or tilt angle error e θWhen (t) increases, the proportional coefficient Kp and the differential coefficient Kd are increased simultaneously to quickly correct the lifting height deviation; once the pressure deviation e p (t) If the value exceeds the preset critical value, the proportional coefficient Kp corresponding to that jacking point will be reduced. At the same time, the pressure and displacement coordinated control mode will be activated to fine-tune the hydraulic flow of adjacent jacks to distribute local pressure and prevent damage to the bridge. In this embodiment, when the predictive control module calculates the optimal control value, it sets three hard safety standards: the synchronous deviation of the displacement of each jacking point cannot exceed 1mm, the real-time pressure of a single jack does not exceed 1.2 times the theoretical load, and the overall tilt angle of the bridge is less than 0.1°.

[0021] In this embodiment, the predictive control module relies on the current multidimensional error state vector to predict the subsequent deviations caused by the jack oil return, temporary support compression, and alternating jacking operations in advance. It calculates the optimal lifting increment for each jacking point within the cycle and uses this increment as a pre-correction signal to be superimposed on the output of the PID controller to achieve early correction of jacking deviations.

[0022] Multi-sensor cross-checking method: The theoretical tilt angle of the bridge is calculated by using the height difference measured by the displacement sensors at the four corners of the bridge, and then compared with the actual tilt angle measured by the tilt sensors. If the difference between the two exceeds the preset threshold, it is determined that the sensor is faulty. The system automatically cuts off the data channel of the faulty sensor and continues to carry out jacking control by relying on the data of the remaining intact sensors.

[0023] Although embodiments of the invention have been shown and described (see the detailed description above), it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A multi-sensor fusion-based adaptive compensation method for bridge synchronous jacking error, characterized in that: Includes the following steps: Displacement sensors, pressure sensors, and dual-axis tilt sensors were installed on all lifting jacks of the bridge, key sections of the beam, and at the four corners of the bridge to build a full-coverage monitoring network; high-speed synchronous acquisition equipment was used to collect the raw data collected by the three types of sensors simultaneously. The collected raw sensing data is denoised to remove abnormal and distorted data, thus obtaining effective monitoring data that can be used for calculation. A multi-dimensional error state vector is built based on the processed effective monitoring data to quantify the deviations in the jacking operation; The completed error state vector is sent to the combined automatic correction controller. With the help of the fuzzy logic operation module and combined with the jack pressure deviation limit, the proportional, integral and derivative parameters of the PID controller are adjusted in real time to complete the first layer of automatic error correction. With the help of the predictive control module, the optimal control value is calculated by combining multiple safety constraints of bridge jacking construction, and a pre-correction signal is generated and superimposed on the output of the PID controller to complete the second layer of global deviation prediction and correction. The data collected by various sensors are cross-checked and verified, and a graded safety protection mechanism is implemented. By combining the data verification results with real-time deviation values, fault handling, construction early warning, or shutdown protection are automatically completed to ensure the continuous and safe operation of the jacking construction.

2. The adaptive compensation method for bridge synchronous jacking error based on multi-sensor fusion according to claim 1, characterized in that: The raw induction data is processed using a Kalman filter algorithm. Through state prediction and observation update loop calculations, data noise caused by construction vibration, ambient temperature and electromagnetic interference is eliminated, and abrupt abnormal data is identified and removed, outputting stable and accurate effective displacement, pressure and tilt data.

3. The adaptive compensation method for bridge synchronous jacking error based on multi-sensor fusion according to claim 1, characterized in that: The formula for constructing the multidimensional fusion error state vector is: E(t)=e d (t),e p (t),e θ (t),△e d (t) / △t, △e P (t) / △t; wherein, e d (t): Displacement synchronization error of each lifting point relative to the target displacement; e p (t): Pressure deviation between the actual load and the theoretically calculated load of each jack; e θ (t): The inclination error between the actual inclination angle and the design inclination angle of the beam; △e d (t) / △t: Rate of change of displacement error; △e P (t) / △t: Rate of change of pressure error.

4. The adaptive compensation method for bridge synchronous jacking error based on multi-sensor fusion according to claim 1, characterized in that: The fuzzy logic operation module adopts a dual-input setting, with the core input parameter being the displacement synchronization error e. d (t), rate of change of displacement error Δe d (t) / △t, while also including the pressure deviation e p (t) serves as a hard constraint condition; The module pre-inputs fuzzy judgment rules derived from engineering practice and simulation experiments, and adjusts the proportional coefficient Kp, integral coefficient Ki, and derivative coefficient Kd of the PID controller in real time based on input parameters and pressure constraints.

5. The adaptive compensation method for bridge synchronous jacking error based on multi-sensor fusion according to claim 1, characterized in that: When the predictive control module calculates the optimal control values, it sets three hard safety standards: the synchronous deviation of the displacement of each jacking point cannot exceed 1mm, the real-time pressure of a single jack cannot exceed 1.2 times the theoretical load, and the overall tilt angle of the bridge is less than 0.1°.

6. The adaptive compensation method for bridge synchronous jacking error based on multi-sensor fusion according to claim 1, characterized in that: Based on the current multidimensional error state vector, the predictive control module anticipates the subsequent deviations caused by jack oil return, temporary support compression, and alternating jacking operations. It calculates the optimal lifting increment for each jacking point within the cycle and uses this increment as a pre-correction signal to be superimposed on the output of the PID controller, thereby achieving early correction of jacking deviations.

7. The adaptive compensation method for bridge synchronous jacking error based on multi-sensor fusion according to claim 1, characterized in that: Multi-sensor cross-checking method: The theoretical tilt angle of the bridge is calculated by using the height difference measured by the displacement sensors at the four corners of the bridge, and then compared with the actual tilt angle measured by the tilt sensors. If the difference between the two exceeds the preset threshold, it is determined that the sensor is faulty. The system automatically cuts off the data channel of the faulty sensor and continues to carry out jacking control by relying on the data of the remaining intact sensors.

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