Swivel measurement and control system for swivel bridge

Abstract

The invention provides a swivel measurement and control system for a swivel bridge. The method comprises the following steps: establishing a multi-parameter monitoring model in a bridge swivel process; the multi-parameter monitoring model is a monitoring model which is based on N times of sampling and comprises p sensor parameters; establishing a control model of swivel measurement and control of the swivel bridge; for the control model, establishing an adaptive adjustment algorithm under a multi-parameter monitoring model; and performing swivel measurement and control on the swivel bridge through a self-adaptive adjustment algorithm. According to the invention, accurate monitoring and control of the bridge swivel process can be realized, the safety and accuracy of swivel operation are ensured, good adaptability and real-time performance are achieved, and the efficiency and quality of bridge swivel engineering can be effectively improved.

Description

Technical Field

[0001] The present invention relates to the field of bridge engineering, and more specifically, to a rotation measurement and control system for a rotating bridge. Background Art

[0002] Bridge rotation construction technology is a method widely used in bridge engineering, especially for bridge construction across complex terrain or obstacles. This technology prefabricates the bridge structure at a non-design axis position and then rotates it into place. Rotation construction can convert high-altitude operations into ground or near-ground operations, thereby reducing construction difficulty and risk. The rotation system usually includes components such as a lower turntable, an upper turntable, a ball joint, a slideway, and a traction system. The rotation process is usually achieved by continuously pulling the traction cable with a jack to form a rotating force couple.

[0003] During the construction of the rotating bridge, the monitoring method mainly relies on measuring robots for measurement and then manual calculations. However, this method has a low degree of automation, a long coordination cycle between construction steps, and a low degree of visualization. In addition, the difficulty of the rotating bridge construction lies in how to accurately position and smoothly rotate, which requires accurate recording of various data during the rotation process in order to optimize the construction process and supplement technical standards.

[0004] In the process of implementing the embodiments of the present invention, the inventors found that there are at least the following problems or defects in the prior art: low degree of automation, long coordination period between construction steps, low degree of visualization, and difficulty in precise positioning and smooth rotation during the rotation process. Summary of the invention

[0005] The present invention provides a rotating bridge rotation measurement and control system, comprising:

[0006] Establishing a multi-parameter monitoring model during the bridge rotation process; the multi-parameter monitoring model is a monitoring model including p sensor parameters based on N samplings;

[0007] Establish a control model for the rotation measurement and control of the rotating bridge;

[0008] For the control model, an adaptive adjustment algorithm is established under a multi-parameter monitoring model;

[0009] The rotation measurement and control of the rotating bridge is carried out through an adaptive adjustment algorithm.

[0010] Furthermore, the expression of the multi-parameter monitoring model is:

[0011]

[0012] Where X,Y∈R p , are two one-dimensional monitoring parameters; N is the number of sampling times; k=1,…N;

[0013] σi is the adjustment coefficient of the i-th sensor parameter, i=1,…p, p is the number of sensor parameters, e i (k) = x i (k)-y i (k) is the error of the i-th parameter at the k-th sampling, x i (k), y i (k) is the sampling value of the i-th parameter of the one-dimensional monitoring parameters X and Y at the k-th sampling.

[0014] Furthermore, the control model of the rotation measurement and control of the rotating bridge is established as follows:

[0015]

[0016] y=Cx+r

[0017] where x∈R n is the state vector of the rotation measurement and control of the rotating bridge, y∈R m is the observation vector of the rotating bridge rotation measurement and control, A is the state transfer matrix, B is the control input matrix, C is the observation matrix, q and r are interference terms assumed to be independent of each other; the interference measure of q is Q; the interference measure of r is R.

[0018] Furthermore, the control model for establishing the rotation measurement and control of the rotating bridge is described as:

[0019] x k+1 =Fx k +Gw k

[0020] z k =Hx k +v k

[0021] where I∈R n is the identity matrix, x k is x k A priori estimate of k yes:

[0022] w k ~N(0,W)

[0023] Interference term w k The interference measure is W;

[0024] Among them, P k is the prior error variance, B p and B r Obtained by Cholesky decomposition of Q and R;

[0025]

[0026] In P k Multiply both sides by B k Get P′ k ;

[0027]

[0028] Among them, P′ k is the posterior error variance; ξ k For white interference,

[0029] Furthermore, the objective function of the adaptive adjustment algorithm under the multi-parameter monitoring model is:

[0030]

[0031] where θ is the parameter vector, z k is the observation value, H is the observation matrix, and N is the number of observations.

[0032] Furthermore, under the adaptive adjustment algorithm, the optimal parameter estimator is:

[0033]

[0034] Among them, θ represents the parameter vector, z k represents the observed value at the kth sampling, H represents the observation matrix, and N represents the total number of sampling times.

[0035] Further, the parameter vector θ that satisfies the optimal solution;

[0036] in, P θ =(H T H) -1 ;

[0037] σ p =[σ1,σ2,...,σ n ] T is the process disturbance measure vector, σ r =[σ n+1 ,σ n+2 ,...,σ n+m ] T is the measured interference metric vector, e p is the process error, e r is the measurement error.

[0038] Furthermore, the process of performing rotation measurement and control of a rotating bridge by using an adaptive adjustment algorithm includes: initializing the rotation measurement and control;

[0039] The established control model of the rotating bridge rotation measurement and control is used to predict the state;

[0040] Use the predicted state to update the state at the initial moment;

[0041] Enter the adaptive adjustment algorithm; determine whether the adjustment constraints are met according to the updated state; if not, execute the adjustment process of the adaptive adjustment algorithm under the multi-parameter monitoring model, and continue to determine whether the adjustment constraints are met for the adjusted state update; if not, continue to execute the adaptive adjustment algorithm process; if satisfied, proceed to the next step;

[0042] Update the posterior error and output the measurement and control results.

[0043] Furthermore, the adjustment process of the adaptive adjustment algorithm under the multi-parameter monitoring model includes:

[0044]

[0045] in, is the state update value at time t, is the state update value at time t-1, is the predicted value of the state, K k is the estimated gain at time t, P k is the prior error variance P k The estimated value of W k is the estimated value of the variance matrix W, M p is the estimated value of Q, M r is the estimated value of R, e p is the process error, e r is the measurement error.

[0046] Furthermore, the constraints of the adaptive adjustment algorithm under the multi-parameter monitoring model are:

[0047]

[0048] in, is the state update value at time t, is the state update value at time t-1, δ is the constraint threshold, α and β are two small positive numbers, which are set at initialization.

[0049] The above-mentioned embodiments of the present invention have at least the following beneficial effects: the rotating bridge rotation measurement and control system of the present invention can significantly improve the accuracy and control efficiency of the bridge rotation process by integrating a multi-parameter monitoring model and an adaptive adjustment algorithm. The system can monitor and analyze key parameters in the bridge rotation process, such as displacement, angle, stress, etc., in real time to ensure the accuracy and safety of the rotation operation. In addition, the adaptive adjustment algorithm can dynamically adjust the control strategy according to real-time data, optimize the rotation process, reduce errors and potential risks, and thus improve the overall quality and reliability of the bridge rotation project.

[0050] In addition, the system is designed with ease of operation and maintenance in mind. By establishing clear control and monitoring models, the system can simplify the operation process and reduce the reliance on the professional skills of operators. At the same time, the modular design of the system facilitates maintenance and upgrading, can adapt to the needs of different bridge projects, and has good versatility and scalability. This flexibility and adaptability enables the system to play an important role in a variety of bridge rotation projects, improve construction efficiency, shorten construction period, and reduce project costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] The above and other objects, features and advantages of the exemplary embodiments of the present invention will become readily understood by reading the detailed description below with reference to the accompanying drawings. In the accompanying drawings, several embodiments of the present invention are shown in an exemplary and non-limiting manner, in which:

[0052] Figure 1 A schematic flow chart of a rotation measurement and control system for a rotating bridge provided in one embodiment of the present invention. DETAILED DESCRIPTION

[0053] The principles and spirit of the present invention will be described below with reference to several exemplary embodiments. It should be understood that these embodiments are provided only to enable those skilled in the art to better understand and implement the present invention, and are not intended to limit the scope of the present invention in any way. On the contrary, these embodiments are provided to make the present invention more thorough and complete, and to fully convey the scope of the present invention to those skilled in the art.

[0054] Those skilled in the art know that the embodiments of the present invention can be implemented as a system, device, apparatus, method or computer program product. Therefore, the present invention can be specifically implemented in the following forms, namely: complete hardware, complete software (including firmware, resident software, microcode, etc.), or a combination of hardware and software.

[0055] It should be noted that any number of elements in the drawings is for illustration rather than limitation, and any naming is only for distinction and does not have any limiting meaning.

[0056] Reference below Figure 1 , Figure 1 This is a flow chart of a rotating bridge rotation measurement and control system provided by an embodiment of the present invention. Figure 1 As shown, a rotating bridge rotation measurement and control system 100 includes:

[0057] Step 101, establishing a multi-parameter monitoring model during the bridge rotation process; the multi-parameter monitoring model is a monitoring model including p sensor parameters based on N samplings;

[0058] Step 102, establishing a control model for measuring and controlling the rotation of a rotating bridge;

[0059] Step 103, for the control model, establishing an adaptive adjustment algorithm under a multi-parameter monitoring model;

[0060] Step 104, performing rotation measurement and control of the rotating bridge by using an adaptive adjustment algorithm.

[0061] It should be noted that this system has established a multi-parameter monitoring model for the bridge rotation process. This model is based on N samplings and contains p sensor parameters. The multi-parameter monitoring model here is a mathematical model for real-time monitoring of various key parameters in the bridge rotation process. It integrates data from multiple sensors to achieve comprehensive monitoring of the bridge status. Sensor parameters refer to various data collected by sensors to reflect the bridge rotation status, such as displacement, angle, stress, etc.

[0062] Specifically, the expression of this multi-parameter monitoring model is a complex mathematical formula, which involves two one-dimensional monitoring parameters, the number of sampling times N, and the number of sensor parameters p. The adjustment coefficient, error term and sampling value in the formula are all key components of the model.

[0063] More specifically, the adjustment coefficient is used to adjust the sensitivity of the model to different sensor parameters. The error term reflects the deviation between the actual sampled value and the expected value, while the sampled value is the data collected by the sensor at a specific moment. In practical applications, the setting of these parameters needs to be determined according to the specific bridge structure and rotation requirements to ensure the accuracy and practicality of the model.

[0064] Preferably, for the implementation of the multi-parameter monitoring model, advanced sensor technology, such as fiber optic sensors or wireless sensor networks, can be used to improve the accuracy and real-time performance of data acquisition. At the same time, in order to ensure the adaptability and flexibility of the model, machine learning algorithms can be used to optimize the model parameters so that it can automatically adapt to different bridge rotation conditions.

[0065] Furthermore, a dynamic adjustment mechanism can be set up to automatically adjust model parameters according to changes in real-time monitoring data to improve monitoring accuracy and response speed.

[0066] In some embodiments, the expression of the multi-parameter monitoring model is:

[0067]

[0068] Where X,Y∈R p , are two one-dimensional monitoring parameters; N is the number of sampling times; k=1,…N;

[0069] σi is the adjustment coefficient of the i-th sensor parameter, i=1,…p, p is the number of sensor parameters, e i (k) = x i (k)-y i (k) is the error of the i-th parameter at the k-th sampling, x i (k), y i (k) is the sampling value of the i-th parameter of the one-dimensional monitoring parameters X and Y at the k-th sampling.

[0070] It should be noted that the expression of the multi-parameter monitoring model mentioned in this system is a key mathematical formula, which is used to quantify and analyze various monitoring parameters in the bridge rotation process. In this expression, two one-dimensional monitoring parameters are involved, the number of sampling times N, and the number of sensor parameters p. The one-dimensional monitoring parameters here refer to single-dimensional data, such as displacement or angle, while the sensor parameters refer to the data collected by the sensor, which are used to monitor the key variables in the bridge rotation process.

[0071] Specifically, each parameter in this expression has its specific meaning and function. For example, the number of sampling times N determines the time resolution of the monitoring data, that is, the time interval between each sampling; the number of sensor parameters p determines the breadth of monitoring, that is, the number of parameters monitored simultaneously.

[0072] More specifically, the adjustment coefficient is used to adjust the weight of each sensor parameter on the final monitoring result, and the error term reflects the deviation between the actual sampling value and the expected value. In practical applications, the setting of these parameters needs to be determined according to the specific structure of the bridge, the rotation requirements, and the environmental conditions to ensure the accuracy and reliability of the monitoring model.

[0073] Preferably, in order to improve the accuracy and adaptability of the monitoring model, advanced sensor technology, such as fiber optic sensors or wireless sensor networks, can be used to improve the accuracy and real-time performance of data collection. At the same time, machine learning algorithms can be used to optimize the model parameters so that it can automatically adapt to different bridge rotation conditions.

[0074] Furthermore, a dynamic adjustment mechanism can be set up to automatically adjust the model parameters according to the changes in real-time monitoring data to improve the monitoring accuracy and response speed. For example, adaptive filters can be used to deal with noise and interference, or data fusion technology can be used to integrate data from different sensors to obtain more comprehensive and accurate monitoring results.

[0075] In some embodiments, the control model for establishing the rotation measurement and control of the rotating bridge is as follows:

[0076]

[0077] y=Cx+r

[0078] where x∈R n is the state vector of the rotation measurement and control of the rotating bridge, y∈R m is the observation vector of the rotating bridge rotation measurement and control, A is the state transfer matrix, B is the control input matrix, C is the observation matrix, q and r are interference terms assumed to be independent of each other; the interference measure of q is Q; the interference measure of r is R.

[0079] It should be noted that the control model of the rotating bridge rotation measurement and control mentioned in this system is a mathematical model used to describe and control the state and observation vector during the bridge rotation process. In this model, the state vector represents the internal state of the bridge during rotation, such as position, speed, etc., while the observation vector is the external observation data obtained by the sensor, such as displacement, angle, etc. The state transfer matrix, control input matrix and observation matrix are the key parameters in the model, which respectively describe the change of state, the influence of control input on state, and the relationship between observation data and state.

[0080] Specifically, the state vector, observation vector, state transfer matrix, control input matrix and observation matrix are all defined by mathematical equations. The state vector and observation vector are multidimensional vectors, and their dimensions depend on the complexity of the bridge rotation system. The state transfer matrix describes the evolution of the bridge rotation state over time, the control input matrix describes how the external control signal affects the state vector, and the observation matrix describes how the observation vector is obtained from the state vector.

[0081] More specifically, the parameter settings of these matrices need to be determined according to the specific design and operation requirements of the bridge rotation to ensure that the model can accurately describe and control the rotation process. Interference terms and interference measures are used to consider the uncertainty and noise in the model. They can be random variables used to simulate the errors and interferences that may occur in actual operations.

[0082] Preferably, in order to improve the accuracy and robustness of the control model, advanced control theories, such as adaptive control or optimal control, can be used to design the control input matrix. At the same time, filtering algorithms, such as Kalman filtering, can be used to estimate the state vector and the observation vector, thereby improving the estimation accuracy of the model.

[0083] Furthermore, data fusion technology can be used to comprehensively process data from different sensors to improve the accuracy and reliability of the observation vector. In practical applications, model parameters can also be adjusted and optimized through experiments and simulations to adapt to different bridge rotation conditions and requirements. For example, the parameters of the state transfer matrix can be adjusted to adapt to different rotation speeds, or the parameters of the observation matrix can be adjusted to adapt to different observation conditions.

[0084] In some embodiments, the control model for establishing the rotation measurement and control of the rotating bridge is described as follows:

[0085] x k+1 =Fx k +Gw k

[0086] z k =Hx k +v k

[0087] where I∈R n is the identity matrix, x k is x k A priori estimate of k yes:

[0088] w k ~N(0,W)

[0089] Interference term w k The interference measure is W;

[0090] Among them, P k is the prior error variance, B p and B r Obtained by Cholesky decomposition of Q and R;

[0091]

[0092] In P k Multiply both sides by B k Get P′ k ;

[0093]

[0094] Among them, P′ k is the posterior error variance; ξ k For white interference,

[0095] It should be noted that the control model in this system is further refined into a recursive form, that is, the state and observation of the bridge rotation are described in a time series manner. The recursive form here means using the state of the previous moment to predict the state of the next moment, and combining the observation data of the current moment to update the state estimate.

[0096] Preferably, the identity matrix is ​​a concept in linear algebra, which is a square matrix with 1 on the diagonal and 0 in the rest of the positions, used to keep the dimension of the vector or matrix unchanged. A priori estimation refers to the pre-estimation of the state in the absence of current observation data. Interference terms are random variables used to describe uncertain factors in the model, which affect the accuracy of the state and observation.

[0097] Specifically, the recursive control model involves two main steps: state prediction and state update. State prediction is based on the state and control input at the previous moment, and is achieved through the state transfer matrix. Observation update is to adjust the state estimate through the observation matrix based on the observed data and predicted state at the current moment.

[0098] More specifically, in this process, the a priori error variance and the a posteriori error variance are two important parameters for measuring the uncertainty of state estimation. The a priori error variance is an assessment of the uncertainty of state estimation before observation, while the a posteriori error variance is an assessment of the uncertainty of state estimation after observation. The interference measure of the interference term, such as white interference, is usually assumed to be a Gaussian distribution, and its parameters (such as mean and variance) need to be set according to the actual system characteristics and noise characteristics.

[0099] Preferably, in order to improve the accuracy and robustness of state estimation, a Kalman filter algorithm can be used to achieve state prediction and update. Kalman filtering is an effective recursive filtering algorithm that can estimate the state of a dynamic system from a series of incomplete and noisy measurements in the presence of noise.

[0100] Furthermore, adaptive filtering techniques can be used to adjust filtering parameters to adapt to changes in system characteristics. For example, if the interference characteristics of the system change, the parameters of the interference term, such as the variance of the interference, can be dynamically adjusted to maintain the performance of the filter.

[0101] Furthermore, in practical applications, experiments and simulations can be used to verify and optimize model parameters to ensure the stability and reliability of the system under different working conditions.

[0102] In some embodiments, the objective function of the adaptive adjustment algorithm under the multi-parameter monitoring model is:

[0103]

[0104] where θ is the parameter vector, z k is the observation value, H is the observation matrix, and N is the number of observations.

[0105] It should be noted that the objective function of the adaptive adjustment algorithm mentioned in this system is a mathematical expression that measures the accuracy of parameter estimation. In this expression, the objective function is used to quantify the error of parameter estimation, that is, the difference between the observed value and the model predicted value. The parameter vector represents the set of unknown parameters in the model, the observed value is the actual measurement data obtained by the sensor, and the observation matrix is ​​a mathematical tool to map the parameter vector to the observation space. The number of observations refers to the number of observations or samples made during the parameter estimation process.

[0106] Specifically, the parameter vector in the objective function contains all the parameters that need to be estimated, which may include various physical properties of the bridge during rotation, such as mass, stiffness, damping, etc. The observations are the data collected by sensors at a specific time, which may include displacement, velocity, acceleration, etc. The observation matrix defines how these parameters affect the observations, which is usually derived from the physical model.

[0107] More specifically, in practical applications, the design of the objective function needs to take into account the complexity of the model and the noise characteristics of the observed data. The setting of the number of observations depends on the required accuracy and the limitations of computing resources. Generally, increasing the number of observations can improve the accuracy of parameter estimation, but it will also increase the complexity and time of the calculation.

[0108] Preferably, in order to improve the efficiency and accuracy of parameter estimation, an iterative algorithm can be used to optimize the objective function. For example, a gradient descent method or Newton's method can be used to find the minimum value of the objective function to obtain the optimal parameter estimation. In addition, regularization techniques can be used to prevent overfitting, that is, a penalty term is added to the objective function to limit the complexity of the parameters.

[0109] Furthermore, in practical applications, appropriate regularization methods, such as L1 regularization or L2 regularization, can be selected according to the characteristics of the observed data. In addition, the Bayesian estimation method can be used to regard parameter estimation as a probabilistic inference problem, thereby introducing prior knowledge into the objective function and improving the reliability of the estimation.

[0110] Furthermore, in actual operations, techniques such as cross-validation can be used to evaluate and adjust model parameters to ensure accurate parameter estimates under different observation conditions.

[0111] In some embodiments, under the adaptive adjustment algorithm, the optimal parameter estimate is:

[0112]

[0113] Among them, θ represents the parameter vector, z k represents the observed value at the kth sampling, H represents the observation matrix, and N represents the total number of sampling times.

[0114] It should be noted that the optimal parameter estimator mentioned in this system refers to a set of parameter values ​​determined by minimizing the objective function in the adaptive adjustment algorithm. The optimal parameter estimator here is obtained through the optimization process, which minimizes the difference between the model prediction value and the actual observation value.

[0115] Preferably, the parameter vector contains all the model parameters that need to be estimated, the observations are the actual measured data, and the observation matrix links the parameter vector with the observations. The number of observations refers to the number of observations or samplings made during the parameter estimation process, which affects the accuracy and reliability of parameter estimation.

[0116] Specifically, the calculation of the optimal parameter estimator involves optimizing an objective function, which is usually a function of the parameter vector that measures the difference between the model's predictions and the actual observed values.

[0117] More specifically, in practical applications, the parameter vector may include various physical parameters during the bridge rotation process, such as mass, stiffness, damping, etc. The observations are the data collected by sensors at a specific moment, such as displacement, velocity, acceleration, etc. The observation matrix defines how these parameters affect the observations, which is usually derived from the physical model. The number of observations depends on the required accuracy and the limitations of computing resources. More observations can improve the accuracy of parameter estimation, but it will also increase the complexity and time of the calculation.

[0118] Preferably, in order to improve the efficiency and accuracy of parameter estimation, an iterative optimization algorithm, such as gradient descent or Newton's method, can be used to find the minimum value of the objective function. These algorithms gradually approach the optimal solution by iteratively updating the parameter vector.

[0119] Furthermore, regularization techniques can be used to prevent overfitting, that is, adding a penalty term to the objective function to limit the complexity of the parameters. In practical applications, appropriate regularization methods, such as L1 regularization or L2 regularization, can be selected according to the characteristics of the observed data.

[0120] Furthermore, the Bayesian estimation method can be used to treat parameter estimation as a probabilistic inference problem, thereby introducing prior knowledge into the objective function and improving the reliability of the estimation. In actual operation, the model parameters can also be evaluated and adjusted through cross-validation and other techniques to ensure that accurate parameter estimates can be obtained under different observation conditions.

[0121] In addition, one can consider using parallel computing or distributed computing to speed up the optimization process, especially when the parameter space is large or the number of observations is large.

[0122] In some embodiments, a parameter vector θ that satisfies the optimal solution;

[0123] in, P θ =(H T H) -1 ;

[0124] σ p =[σ1,σ2,...,σ n ] T is the process disturbance measure vector, σ r =[σ n+1 ,σ n+2 ,...,σ n+m ] T is the measured interference metric vector, e p is the process error, e r is the measurement error.

[0125] It should be noted that the parameter vector of the optimal solution mentioned in this system refers to a set of parameter values ​​calculated by a specific mathematical method in the adaptive adjustment algorithm, which enables the system to achieve the best performance in the measurement and control process of the rotating bridge. The optimal solution here means that the parameter vector can minimize the objective function, that is, reduce the error between the model prediction value and the actual observation value. The parameter vector contains all the model parameters that need to be estimated, and the process interference measurement vector and the measurement interference measurement vector represent the interference and error that the system may encounter in the process and measurement, respectively.

[0126] Specifically, the parameter vector of the optimal solution is obtained by solving a set of linear equations involving a process disturbance metric vector and a measurement disturbance metric vector, which correspond to the disturbances and errors that the system may encounter in the process and measurement, respectively.

[0127] More specifically, in practical applications, the setting of these interference measurement vectors needs to be determined according to the specific environment and conditions of the bridge rotation to ensure the accuracy and reliability of parameter estimation. Process errors and measurement errors are inevitable in the system during the rotation process, and they may be caused by a variety of factors, such as sensor accuracy, changes in environmental conditions, etc. When designing the system, it is necessary to consider the impact of these errors on parameter estimation and take appropriate measures to reduce their impact.

[0128] Preferably, in order to improve the accuracy of parameter estimation and the robustness of the system, advanced signal processing techniques can be used to handle interference in the process and measurement. For example, filters can be used to reduce the impact of noise, or adaptive filtering techniques can be used to dynamically adjust filtering parameters.

[0129] Furthermore, data fusion technology can be used to integrate data from different sensors to improve the accuracy and reliability of observations. In actual operation, experiments and simulations can also be used to verify and optimize model parameters to ensure the stability and reliability of the system under different working conditions.

[0130] Furthermore, machine learning algorithms can be considered to identify and predict potential interference sources, so that measures can be taken in advance to reduce their impact. In the parameter estimation process, parallel computing or distributed computing can also be used to improve computational efficiency, especially when the parameter space is large or the number of observations is large.

[0131] In some embodiments, the process of performing rotation measurement and control of a rotating bridge by using an adaptive adjustment algorithm includes: initializing the rotation measurement and control;

[0132] The established control model of the rotating bridge rotation measurement and control is used to predict the state;

[0133] Use the predicted state to update the state at the initial moment;

[0134] Enter the adaptive adjustment algorithm; determine whether the adjustment constraints are met according to the updated state; if not, execute the adjustment process of the adaptive adjustment algorithm under the multi-parameter monitoring model, and continue to determine whether the adjustment constraints are met for the adjusted state update; if not, continue to execute the adaptive adjustment algorithm process; if satisfied, proceed to the next step;

[0135] Update the posterior error and output the measurement and control results.

[0136] It should be noted that the process of rotation measurement and control of the rotating bridge through the adaptive adjustment algorithm mentioned in this system involves a series of steps, from initialization to state prediction and update, and then to the execution of the adaptive adjustment algorithm and the judgment of constraints. The adaptive adjustment algorithm here is a method of dynamically adjusting system parameters to adapt to changes in the bridge rotation process. Initialization refers to the necessary settings and calibration of the system before the rotation measurement and control begins. State prediction is to predict the state at the next moment based on the current system state and control input. State update is to correct the predicted state based on the actual observation data.

[0137] Specifically, when initializing the rotation measurement and control, it is necessary to set the initial state vector and error variance, as well as determine the parameters of the control model, such as the state transfer matrix and the observation matrix. State prediction and update involve algorithms such as Kalman filtering, which can estimate the state of the system based on the dynamic model and observation data of the system. The execution of the adaptive adjustment algorithm includes adjusting the parameter vector to minimize the objective function, that is, the difference between the observed value and the model predicted value. The judgment of the constraint condition is to ensure that the state update is within a reasonable range and prevent excessive fluctuations in the system parameters. In this process, the constraint threshold is a key parameter that defines the allowable range of state updates.

[0138] Preferably, when executing the adaptive adjustment algorithm, a variety of strategies can be used to optimize parameter estimates. For example, recursive least squares can be used to update parameter estimates in real time, or more advanced filtering techniques such as particle filtering can be used to handle nonlinear systems. In the process of state prediction and updating, machine learning techniques can be introduced to identify and predict potential system changes, so as to adjust parameters in advance.

[0139] Furthermore, in order to improve the robustness of the system, a variety of fault detection and diagnosis mechanisms can be designed to identify and handle possible system anomalies. In practical applications, the effectiveness of the adaptive adjustment algorithm can be verified through simulation and experiments, and the algorithm parameters can be adjusted according to the actual system performance to achieve the best measurement and control effect. In addition, it is possible to consider using a distributed computing framework to process large-scale data and complex computing tasks to improve the system's processing power and response speed.

[0140] In some embodiments, the adjustment process of the adaptive adjustment algorithm under the multi-parameter monitoring model includes:

[0141]

[0142] in, is the state update value at time t, is the state update value at time t-1, is the predicted value of the state, K k is the estimated gain at time t, P k is the prior error variance P k The estimated value of W k is the estimated value of the variance matrix W, M p is the estimated value of Q, M r is the estimated value of R, e p is the process error, e r is the measurement error.

[0143] It should be noted that the adjustment process of the adaptive adjustment algorithm mentioned in this system refers to a method of dynamically adjusting the state estimate according to real-time data during the measurement and control of the rotating bridge. The state update value here refers to the correction of the state estimate according to the observed data in each time step. The gain estimate is a key parameter for adjusting the state estimate, which determines the degree of influence of the observed data on the state estimate. The prior error variance and variance matrix are parameters that measure the uncertainty of the state estimate, and they are used to calculate the gain estimate.

[0144] Specifically, during the adjustment process of the adaptive adjustment algorithm, the state update value is calculated by combining the predicted state and the observed data. The predicted state is predicted by the state transfer matrix based on the state and control input at the previous moment. The observed data is collected by the sensor at the current moment. The gain estimate is used to adjust the weight between the predicted state and the observed data to obtain the optimal state update value. The prior error variance and variance matrix are determined by the uncertainty of the system model and the observation model, which reflect the uncertainty and noise level of the state estimation. In practical applications, the setting of these parameters needs to be determined according to the specific environment and conditions of the bridge swivel to ensure the accuracy and reliability of the state estimation.

[0145] Preferably, in the implementation of the adaptive adjustment algorithm, a variety of strategies can be used to optimize parameter estimates. For example, recursive least squares can be used to update parameter estimates in real time, or more advanced filtering techniques such as particle filtering can be used to handle nonlinear systems. In the process of state prediction and updating, machine learning techniques can be introduced to identify and predict potential system changes, so as to adjust parameters in advance.

[0146] Furthermore, in order to improve the robustness of the system, a variety of fault detection and diagnosis mechanisms can be designed to identify and handle possible system anomalies. In practical applications, the effectiveness of the adaptive adjustment algorithm can also be verified through simulation and experiments, and the algorithm parameters can be adjusted according to the actual system performance to achieve the best measurement and control effect.

[0147] Furthermore, it is possible to consider using a distributed computing framework to process large-scale data and complex computing tasks to improve the system's processing power and response speed. In the parameter estimation process, parallel computing or distributed computing can also be used to improve computing efficiency, especially when the parameter space is large or the number of observations is large.

[0148] In some embodiments, the constraints of the adaptive adjustment algorithm under the multi-parameter monitoring model are:

[0149]

[0150] in, is the state update value at time t, is the state update value at time t-1, δ is the constraint threshold, α and β are two small positive numbers, which are set at initialization.

[0151] It should be noted that the constraints of the adaptive adjustment algorithm mentioned in this system refer to a mechanism that limits the range of changes in the state update value in order to ensure system stability and accuracy during the state update process. The constraint threshold here is a pre-set parameter that defines the maximum allowed change in the state update value between two iterations. This constraint mechanism helps prevent excessive fluctuations in state estimation and ensures stable operation of the system.

[0152] Specifically, the state update value in the constraint condition refers to the new state estimate calculated based on the observed data and the prediction model at each time step. The constraint threshold is a parameter set according to the system characteristics and operating requirements, which defines the maximum change allowed in the state update value between two iterations. The setting of this threshold needs to comprehensively consider the dynamic characteristics, noise level and control accuracy requirements of the system. If the change in the state update value exceeds this threshold, the system will not accept the update to avoid system instability due to excessive changes. In practical applications, the setting of the constraint threshold needs to be optimized through experiments and simulations to ensure that the stability and accuracy of the system can be maintained under different operating conditions.

[0153] Preferably, when implementing the adaptive adjustment algorithm, a variety of strategies can be used to refine the setting and execution of constraint conditions. For example, a method of dynamically adjusting the constraint threshold can be used to adjust the threshold value according to the real-time performance of the system and changes in the external environment. In addition, adaptive filtering techniques such as extended Kalman filtering or unscented Kalman filtering can be introduced to handle nonlinear and non-Gaussian noise. In the state update process, a variety of fault detection and diagnosis mechanisms can also be used to identify and handle possible system anomalies.

[0154] Furthermore, in actual operation, the validity of the constraints can be verified through simulation and experiment, and the constraint threshold can be adjusted according to the actual system performance to achieve the best measurement and control effect. In addition, it is possible to consider using a distributed computing framework to process large-scale data and complex computing tasks to improve the processing power and response speed of the system. In the parameter estimation process, parallel computing or distributed computing can also be used to improve computing efficiency, especially when the parameter space is large or the number of observations is large.

[0155] The above-mentioned embodiments of the present invention have the following beneficial effects: The rotating bridge rotation measurement and control system described in the present invention can provide a comprehensive and accurate monitoring and control framework for bridge rotation construction. By establishing a multi-parameter monitoring model and a control model, the system can monitor the key parameters in the bridge rotation process in real time, and dynamically adjust the control strategy according to the monitoring data, thereby ensuring the accuracy and safety of the rotation operation. This real-time monitoring and adaptive adjustment capability can significantly improve the efficiency and quality of the bridge rotation project, reduce errors and potential risks, and ensure the smooth progress of the construction process.

[0156] In addition, the system's adaptive adjustment algorithm can optimize parameter estimation and improve the accuracy of monitoring data, thereby providing a more reliable control basis for bridge rotation. Through accurate state prediction and update, the system can effectively handle process interference and measurement interference to ensure the stability and reliability of the rotation process. This high degree of automation and intelligence can not only reduce the dependence on the professional skills of operators, but also improve the visualization of the construction process, providing a more scientific and efficient management method for bridge rotation projects.

[0157] Furthermore, the storage medium of the embodiment of the present application stores program instructions that can implement all the above methods, wherein the program instructions can be stored in the above storage medium in the form of a software product, including several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) or a processor to execute all or part of the steps of the methods described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, or terminal devices such as a computer, a server, a mobile phone, and a tablet.

[0158] The above descriptions are only some preferred embodiments of the present invention and an explanation of the technical principles used. Those skilled in the art should understand that the scope of the invention involved in the embodiments of the present invention is not limited to the technical solutions formed by a specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the above inventive concept. For example, the above features are replaced with (but not limited to) technical features with similar functions disclosed in the embodiments of the present invention to form a technical solution.

Claims

1. A rotating bridge rotation measurement and control system, characterized in that: The system performs the following steps: Establishing a multi-parameter monitoring model during the bridge rotation process; the multi-parameter monitoring model is a monitoring model including p sensor parameters based on N samplings; Establish a control model for the rotation measurement and control of the rotating bridge; For the control model, an adaptive adjustment algorithm is established under a multi-parameter monitoring model; The rotation measurement and control of the rotating bridge is carried out through an adaptive adjustment algorithm.

2. The rotating bridge rotation measurement and control system according to claim 1 is characterized in that: The expression of the multi-parameter monitoring model is: Where X,Y∈R p , are two one-dimensional monitoring parameters; N is the number of sampling times; k=1,…N; σi is the adjustment coefficient of the i-th sensor parameter, i=1,…p, p is the number of sensor parameters, e i (k) = x i (k)-y i (k) is the error of the i-th parameter at the k-th sampling, x i (k), y i (k) is the sampling value of the i-th parameter of the one-dimensional monitoring parameters X and Y at the k-th sampling.

3. The rotating bridge rotation measurement and control system according to claim 1 is characterized in that: The control model for the rotation measurement and control of the rotating bridge is established as follows: y=Cx+r where x∈R n is the state vector of the rotation measurement and control of the rotating bridge, y∈R m is the observation vector of the rotating bridge rotation measurement and control, A is the state transfer matrix, B is the control input matrix, C is the observation matrix, q and r are interference terms assumed to be independent of each other; the interference measure of q is Q; the interference measure of r is R.

4. The rotating bridge rotation measurement and control system according to claim 3 is characterized in that: The control model for establishing the rotation measurement and control of the rotating bridge is described as: x k+1 =Fx k + See k z k =Hx k +v k where I∈R n is the identity matrix, x k is x k A priori estimate of k yes: w k ~N(0,W) Interference term w k The interference measure is W; Among them, P k is the prior error variance, B p and B r It is obtained by Cholesky decomposition of Q and R; In P k Multiply both sides by B k Get P′ k ; Among them, P′ k is the posterior error variance; ξ k For white interference, 5. The rotating bridge rotation measurement and control system according to claim 4 is characterized in that: The objective function of the adaptive adjustment algorithm under the multi-parameter monitoring model is: where θ is the parameter vector, z k is the observation value, H is the observation matrix, and N is the number of observations.

6. The rotating bridge rotation measurement and control system according to claim 5 is characterized in that: Under the adaptive adjustment algorithm, the optimal parameter estimator is: Among them, θ represents the parameter vector, z k represents the observed value at the kth sampling, H represents the observation matrix, and N represents the total number of sampling times.

7. The rotating bridge rotation measurement and control system according to claim 6 is characterized in that: The parameter vector θ that satisfies the optimal solution; in, σ p =[σ1,σ2,...,σ n ] T is the process disturbance measure vector, σ r =[σ n+1 ,σ n+2 ,...,σ n+m ] T is the measured interference metric vector, e p is the process error, e r is the measurement error.

8. The rotating bridge rotation measurement and control system according to claim 7 is characterized in that: The process of performing rotation measurement and control of a rotating bridge by using an adaptive adjustment algorithm includes: initializing the rotation measurement and control; The established control model of the rotating bridge rotation measurement and control is used to predict the state; Use the predicted state to update the state at the initial moment; Enter the adaptive adjustment algorithm; determine whether the adjustment constraints are met according to the updated state; if not, execute the adjustment process of the adaptive adjustment algorithm under the multi-parameter monitoring model, and continue to determine whether the adjustment constraints are met for the adjusted state update; if not, continue to execute the adaptive adjustment algorithm process; if satisfied, proceed to the next step; Update the posterior error and output the measurement and control results.

9. The rotating bridge rotation measurement and control system according to claim 8, characterized in that: The adjustment process of the adaptive adjustment algorithm under the multi-parameter monitoring model includes: in, is the state update value at time t, is the state update value at time t-1, is the predicted value of the state, K k is the estimated gain at time t, P k is the prior error variance P k The estimated value of W k is the estimated value of the variance matrix W, M p is the estimated value of Q, M r is the estimated value of R, e p is the process error, e r is the measurement error.

10. The rotating bridge rotation measurement and control system according to claim 8, characterized in that: The constraints of the adaptive adjustment algorithm under the multi-parameter monitoring model are: in, is the state update value at time t, is the state update value at time t-1, δ is the constraint threshold, α and β are two small positive numbers, which are set at initialization.

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