High pier verticality intelligent monitoring and control system

Abstract

The invention relates to the technical field of civil engineering construction, and discloses an intelligent monitoring and control system for the verticality of a high pier. The real-time data acquisition module is used for acquiring attitude data and environmental data of the high pier; the edge calculation module is used for receiving the attitude data and the environment data; the cloud decision and control instruction generation module is used for generating a target deviation correction amount according to the real perpendicularity deviation; the prediction execution and collaborative optimization module is used for performing prediction according to the target deviation correction amount and the attitude data; the deviation correction execution module is used for responding to the cooperative control sequence; and the feedback verification module is used after the deviation correction execution module completes the physical adjustment. By constructing a self-adaptive data fusion algorithm fused with the physical characteristics of the high pier, the real perpendicularity deviation of the structure can be accurately recognized, elastic deformation interference caused by sunlight and wind load environmental factors is effectively stripped, and the problem that misjudgment on recoverable elastic deformation is insufficient is solved.

Description

Technical Field

[0001] This invention relates to the field of civil engineering construction technology, specifically to an intelligent monitoring and control system for the verticality of high piers. Background Technology

[0002] Verticality control during the construction of high piers is a crucial aspect of bridge construction, as its accuracy directly impacts structural safety and construction quality. Current technologies for monitoring the verticality of high piers typically employ total stations as the primary measurement method, combined with manual or semi-automated data recording to discretely measure the spatial position of the pier. However, this approach has significant shortcomings.

[0003] Current monitoring data is sourced from a single source and fails to effectively differentiate between pier deformations caused by different factors. During construction, piers are affected not only by accumulated structural errors but also by environmental factors such as solar radiation, temperature differences, and wind loads, resulting in instantaneous or periodic elastic deformation. Traditional methods treat all measured deviations as structural deviations requiring correction, leading to misjudgments of normal elastic deflection by system or human decision-makers, resulting in unnecessary corrective actions. This not only increases construction costs but may also introduce new deviations through over-correction, causing resource waste and inefficiency.

[0004] At the execution level of deviation correction, existing technologies generally employ open-loop control methods that rely on human experience. When monitoring data indicates that the deviation exceeds the threshold, on-site personnel typically rely on experience to judge and manually or through simple commands operate actuators such as hydraulic jacks. This process lacks in-depth consideration of the pier structure's mechanical properties and fails to achieve coordinated cooperation among multiple actuators. This crude control method is prone to generating sudden impacts and vibrations during the correction process, potentially causing irreversible secondary damage to the pier's main structure or the jacking system under construction, seriously threatening construction safety.

[0005] Existing verticality control systems generally lack adaptive and self-learning capabilities. The control models or rules they use are static and fixed, unable to be dynamically adjusted based on actual correction effects. Throughout the construction period, the structural stiffness, environmental response characteristics, and actuator performance of the high pier will change slightly over time. Because existing systems cannot compare the actual feedback of each correction action with the theoretical model and update the model parameters, their control accuracy gradually decreases as construction progresses, making it impossible to achieve long-term, high-precision, and stable control of the high pier's verticality. Summary of the Invention

[0006] To address the shortcomings of existing technologies, this invention provides an intelligent monitoring and control system for the verticality of high piers. This system solves the problems of inaccurate monitoring decisions caused by the inability to accurately distinguish between structural deviations and environmental elastic deformation during the construction of high piers, as well as the low accuracy and tendency to generate secondary structural disturbances in traditional correction methods.

[0007] To achieve the above objectives, the present invention provides the following technical solution: an intelligent monitoring and control system for the verticality of high piers, comprising; The real-time data acquisition module is used to collect the attitude data and environmental data of the high pier; The edge computing module and the real-time data acquisition module are used to receive attitude data and environmental data, and based on the data fusion algorithm that uses the physical characteristics of the high pier as a priori constraint, the attitude data is processed to remove the elastic deformation component caused by environmental factors, thereby determining the true verticality deviation of the high pier. The cloud-based decision and control command generation module, connected to the edge computing layer, is used to generate target correction values ​​based on the actual verticality deviation. The predictive execution and collaborative optimization module is connected to the cloud-based decision and control command generation module and the real-time data acquisition module. It is used to perform forward calculations based on the target correction amount and attitude data, calling the dynamic response model of the actuator and the mechanical model of the pier structure to generate a collaborative control sequence that suppresses secondary disturbances in the correction process. The deviation correction execution module, connected to the predictive execution and collaborative optimization module, is used to physically adjust the high piers in response to the collaborative control sequence; The feedback verification module, connected to the real-time data acquisition module and the prediction execution and collaborative optimization module, is used to compare the actual attitude change with the predicted attitude change after the deviation correction execution module completes the physical adjustment, so as to update the dynamic response model of the actuator and the mechanical model of the pier structure.

[0008] Preferably, the real-time data acquisition module includes: An inertial attitude sensing unit is used to collect the angular velocity and acceleration of the high pier at high frequency to form part of the attitude data; An optical reference calibration unit is used to acquire the absolute spatial coordinates of the high pier with high precision to form another part of the attitude data; an environmental state sensor is used to acquire environmental data synchronously.

[0009] Furthermore, the inertial attitude sensing unit can capture the instantaneous dynamic response of the high pier under wind load or other disturbances, providing highly dynamic real-time feedback for subsequent predictive control. The optical reference calibration unit provides an absolute spatial reference that does not drift over time and periodically corrects the accumulated error of the inertial sensing unit.

[0010] Preferably, the real-time data acquisition module acquires the attitude data through the following steps: The inertial attitude sensing unit performs continuous high-frequency measurements of the attitude of the pier to generate a real-time attitude information stream. The optical reference calibration unit measures the absolute spatial coordinates of the pier at preset time intervals to generate an absolute position reference point; The cumulative error caused by integration in the real-time attitude information stream is periodically corrected using an absolute position reference point, thereby generating the final attitude data.

[0011] Furthermore, by using the high-frequency data stream of the inertial attitude sensing unit as the main component, the continuity and density of the data on the time axis are ensured. At the same time, the measurement results of the optical reference calibration unit are used as the absolute truth value to periodically reset and calibrate the high-frequency data stream. By using low-frequency high-precision data to anchor high-frequency high-dynamic data, the drift problem common in inertial navigation systems is eliminated from the algorithm level.

[0012] Preferably, the data fusion algorithm embedded in the edge computing module is an adaptive Kalman fusion algorithm, and the physical characteristics of the high pier serve as constraints on the state transition matrix. The edge computing module dynamically adjusts the state transition matrix according to the collected environmental data in order to separate the true verticality deviation during the filtering process.

[0013] Furthermore, the physical laws governing thermal expansion and contraction and wind-induced vibration of high-pier structures under different environments are solidified into prior constraints in the algorithm model. By using environmental data as real-time input for dynamically adjusting model parameters, the algorithm is able to proactively predict and eliminate temporary elastic deformations, thereby accurately separating the permanent true verticality deviation of the structure itself that needs to be corrected.

[0014] Preferably, the edge computing module specifically includes the following steps: Receive the collected attitude data and environmental data; By utilizing environmental data and the physical characteristics of the pier, the state transition matrix of the adaptive Kalman fusion algorithm is dynamically adjusted to predict the pier's attitude state at the next moment. The predicted attitude state is updated by filtering the attitude data, thereby separating the elastic deformation component caused by environmental factors and determining the true verticality deviation.

[0015] Furthermore, the standard Kalman filter prediction-update loop has been deeply optimized. In the prediction phase, a more accurate physical prediction that takes into account environmental influences is made based on real-time environmental data and the embedded structural physics model. In the update phase, this physical prediction is then corrected using actual attitude measurement data. In this way, temporary deformations caused by the environment are naturally separated during the filtering process, ultimately outputting a high-purity true verticality deviation.

[0016] Preferably, the cloud-based decision and control command generation module compares the actual verticality deviation with a preset multi-level control threshold to determine whether to generate a target correction amount; The received actual verticality deviation is compared with the first warning threshold. If the actual verticality deviation is greater than or equal to the first warning threshold, a warning signal is generated. The actual verticality deviation is compared with the second correction threshold. When the actual verticality deviation is greater than or equal to the second correction threshold, a target correction amount containing the specific displacement is generated.

[0017] Furthermore, complex construction control specifications are transformed into clear, executable machine instructions, avoiding the subjectivity and delays of human judgment. By setting different response thresholds, the system can achieve differentiated management of deviation states, providing early warnings when deviations are small and decisively initiating automatic correction procedures when deviations exceed the allowable range, ensuring the timeliness, accuracy, and standardization of control responses.

[0018] Preferably, the prediction execution and collaborative optimization module specifically includes the following steps; Based on the target correction amount, real-time attitude data, dynamic response model of the actuator, and mechanical model of the pier structure; A cooperative control sequence that suppresses secondary disturbances during the correction process is generated by solving the optimal control problem. The objective function of the optimal control problem includes optimization terms that make the pier attitude change smoothly approximate the target correction amount and optimization terms that suppress the vibration generated during the operation of the actuator.

[0019] Furthermore, a time-series cooperative control sequence is generated by solving an optimal control problem with a clearly defined optimization objective. The design of this objective function is particularly crucial; it not only requires the final result to approximate the target correction amount but also demands that the entire process be smooth and stable, actively suppressing potential secondary disturbances such as vibrations. This transforms the correction action from a rigid push-pull maneuver into a precisely calculated optimal path adjustment that minimizes the impact on the structure.

[0020] Preferably, the deviation correction execution module includes a cluster of multiple hydraulic jacks, which is used to parse the coordinated control sequence into thrust and displacement commands for each hydraulic jack and perform synchronous or asynchronous drive control to physically adjust the high pier.

[0021] Furthermore, the cluster of multiple hydraulic jacks has the ability to accurately analyze the complex time-sequential collaborative control sequence output by the upper-level module, and can convert it into the precise thrust, speed and displacement that each independent hydraulic jack should output at different times. It supports synchronous or asynchronous drive control modes, which means that the jacks in the cluster can act in a highly coordinated manner according to the planning of the collaborative control sequence, thereby achieving precise and stable physical attitude adjustment of the high pier structure, ensuring that the intent of the upper-level optimization algorithm can be physically reproduced with high fidelity.

[0022] Preferably, the feedback verification module specifically includes the following steps: Acquire the actual attitude change after the deviation correction execution module completes the physical adjustment; Calculate the prediction error between the actual attitude change and the attitude change predicted when the cooperative control sequence is generated by the prediction execution and cooperative optimization module; Based on the prediction error, the optimization algorithm is invoked to update the model parameters of the dynamic response model of the actuator and the mechanical model of the pier structure online, thereby reducing the prediction error of subsequent correction actions.

[0023] Furthermore, after each correction action, it acquires the actual attitude change of the pier from the real-time data acquisition module. Then, it precisely compares this actual change with the predicted attitude change made by the predictive execution and collaborative optimization module when generating the control sequence, calculating the prediction error. This error, as a crucial feedback signal, is used to invoke an optimization algorithm to update and optimize the parameters of the actuator's dynamic response model and the pier's structural mechanics model online.

[0024] A device for intelligent monitoring and control of the verticality of a high pier includes: a support base; a support frame fixedly connected to the upper surface of the support base; a laser tracker fixedly connected to the upper surface of the support frame; a support rod fixedly connected to the upper surface of the support frame; an anemometer fixedly connected to one end of the support rod; a solar intensity meter fixedly connected to the upper surface of the anemometer; a support column fixedly connected to the upper surface of the support base; a fixing frame fixedly connected to the outer wall of the support column; the lower surface of the fixing frame fixedly connected to the upper surface of the support base; and a hydraulic jack fixedly connected to the upper surface of the support base. A connecting frame three is fixedly connected to the outlet end. A connecting rod is rotatably connected inside the connecting frame three. A rotating block is fixedly connected to the outer wall of the connecting rod. The outer wall of the rotating block is rotatably connected to the inner wall of the support column. A connecting frame one is rotatably connected to one end of the connecting rod. A rolling frame is fixedly connected to the outer wall of the connecting frame one. A fixing plate is fixedly connected to the upper surface of the support column. A sliding rod is slidably connected inside the fixing plate. A connecting frame two is rotatably connected to one end of the sliding rod. A three-axis gyroscope sensor is fixedly connected to the outer wall of the connecting frame two. A spring is fixedly connected to the outer wall of the sliding rod. One end of the spring is fixedly connected to the outer wall of the fixing plate.

[0025] This invention provides an intelligent monitoring and control system for the verticality of high piers. It has the following beneficial effects: 1. This invention, by constructing an adaptive data fusion algorithm that integrates the physical characteristics of high piers, can accurately identify structural true verticality deviations and effectively eliminate elastic deformation interference caused by environmental factors such as sunlight and wind load. Compared with existing technologies that rely solely on raw measurement data for judgment, this invention solves the problem of misjudging recoverable elastic deformation, avoids unnecessary correction actions, and significantly improves the effectiveness of monitoring results and the accuracy of decision-making.

[0026] 2. This invention, through a predictive execution and collaborative optimization control method, transforms the correction process from a simple open-loop command into a forward-looking closed-loop optimization solution process based on model prediction. This achieves stable and precise adjustment of the pier's attitude, effectively suppressing secondary disturbances. It changes the traditional reliance on manual experience and the crude control of jacks, solving the problems of stiff correction actions, easy generation of impact vibrations, and potential secondary damage to the pier structure.

[0027] 3. After the correction action, the present invention uses a feedback verification module to compare the actual results with the model predictions and updates the internal dynamic and mechanical model parameters online according to the error. Compared with the static control scheme in the prior art where the model parameters are fixed and cannot be adaptively adjusted with the construction process and environmental changes, the present invention solves the problem that its control accuracy will gradually deteriorate over time and cannot guarantee long-term stability. Attached Figure Description

[0028] Figure 1 This is a flowchart of the method of the present invention; Figure 2 This is a schematic diagram of the data fusion process of the real-time data acquisition module of the present invention; Figure 3 A schematic diagram illustrating the process of separating the true verticality deviation in the edge computing module of this invention; Figure 4 This is a schematic diagram of the decision logic of the cloud-based decision and control instruction generation module of the present invention; Figure 5 A schematic diagram illustrating the generation of a collaborative control sequence by the predictive execution and collaborative optimization module of this invention; Figure 6 This is a schematic diagram illustrating the closed-loop process of the feedback verification module of the present invention for implementing online model updates. Figure 7 This is a perspective view of the device of the present invention; Figure 8 This is a schematic diagram of the support frame of the present invention; Figure 9 This is a schematic diagram of the support column of the present invention.

[0029] The components include: 1. Support base; 2. Support frame; 3. Laser tracker; 4. Support rod; 5. Anemometer; 6. Solar intensity meter; 7. Fixing frame; 8. Hydraulic jack; 9. Fixing plate; 10. Three-axis gyroscope sensor; 11. Spring; 12. Sliding rod; 13. Connecting frame one; 14. Rolling frame; 15. Connecting rod; 16. Support column; 17. Connecting frame two; 18. Rotating block; 19. Connecting frame three. Detailed Implementation

[0030] The technical solutions in 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.

[0031] Please see the appendix Figure 1 - Appendix Figure 6 This invention provides an intelligent monitoring and control system for the verticality of high piers, including: a real-time data acquisition module for acquiring the attitude data and environmental data of the high piers; Specifically, the real-time data acquisition module, as the sensing foundation of the entire intelligent monitoring and control system, has the core task of providing high-precision, high-frequency, and multi-dimensional real-time data input for subsequent modules. The real-time data acquisition module achieves comprehensive and synchronous acquisition of the dynamic attitude, absolute spatial position, and surrounding environmental parameters of the high pier by deploying a collaborative network of multiple sensors at key parts of the high pier structure and in its surrounding environment.

[0032] The real-time data acquisition module physically comprises one or more inertial attitude sensing units, one or more optical reference calibration units, and one or more environmental state sensors. The inertial attitude sensing units, optical reference calibration units, and environmental state sensors are connected to a data aggregation and preprocessing unit to achieve synchronous data acquisition and timestamp alignment.

[0033] The inertial attitude sensing unit is preferably a microelectromechanical system (MEMS) inertial measurement unit (IMU) with a built-in three-axis gyroscope and a three-axis accelerometer. The inertial attitude sensing unit continuously measures the angular velocity and linear acceleration of the high pier measuring point at a sampling frequency of not less than 100Hz. These high-frequency raw data form the basis for subsequent attitude calculation.

[0034] The optical reference calibration unit is preferably a total station or laser tracker with automatic target recognition and tracking functions. The optical reference calibration unit is set up at a stable reference point within the construction site and is used to perform three-dimensional spatial coordinate measurements on the prism reflection target installed on the top of the high pier or a specific monitoring section at a preset, low time frequency.

[0035] The environmental condition sensors include a high-precision digital thermometer, a three-dimensional ultrasonic anemometer and wind vane, and a solar radiation meter. These sensors are installed at different heights and orientations of the pier to acquire environmental data such as temperature, wind speed, wind direction, and solar radiation that affect the pier's deformation, all in strict synchronization with attitude data acquisition.

[0036] The technical solution for acquiring final attitude data in the real-time data acquisition module is implemented through a fusion processing flow. The flow first involves the inertial attitude sensing unit performing continuous high-frequency measurements to generate a real-time attitude information stream containing dynamic information.

[0037] Specifically, by integrating the measured angular velocity values, the attitude quaternions or Euler angles can be preliminarily calculated, and the dynamic equation can be expressed as: In the formula, Let q(t) denote the derivative of the state quaternion at time t, and let p(ω(t)) denote the state quaternion at time t. The symbol for quaternion multiplication is represented. It represents a constant coefficient.

[0038] The optical reference calibration unit measures the absolute spatial coordinates of the pier at a preset time interval, such as every five minutes, thereby generating a discrete time series of absolute position reference points.

[0039] The cumulative error caused by integration in the real-time attitude information stream is periodically corrected using an absolute position reference point. In one implementation, this correction process is based on an extended Kalman filter framework, using the attitude integrated by the inertial measurement unit as the state prediction and the absolute position measured by the optical reference calibration unit as the observation value at each correction time t. k The system calculates the predicted position of the prism target based on the predicted attitude and compares it with the measured value of the absolute position reference point to obtain the observation residual. The system uses this observation residual to update the attitude state, thereby eliminating accumulated errors.

[0040] The edge computing module and the real-time data acquisition module are used to receive attitude data and environmental data, and based on the data fusion algorithm that uses the physical characteristics of the high pier as a priori constraint, the attitude data is processed to remove the elastic deformation component caused by environmental factors, thereby determining the true verticality deviation of the high pier. Specifically, the edge computing module ensures low-latency response in data processing. Its core function is to perform in-depth processing on the received raw attitude data containing various disturbances to separate and determine the true verticality deviation of the piers.

[0041] The core algorithm embedded in the edge computing module is an adaptive Kalman fusion algorithm, which uses the physical characteristics of the high pier as prior knowledge to strongly constrain the algorithm model.

[0042] The physical properties of the high piers are pre-established through finite element analysis or empirical formulas, using mathematical models to describe the elastic deformation of the high piers under different temperature fields, wind loads, and solar radiation intensities. This model takes environmental data as input and outputs the corresponding elastic deformation components.

[0043] The specific workflow of the edge computing module is as follows: First, it receives attitude data and environmental data from the real-time data acquisition module. Then, the edge computing module uses the environmental data to drive the physical characteristic model of the high pier and calculates the expected elastic deformation under the current environmental conditions.

[0044] The calculation results were used to dynamically adjust the state transition matrix of the adaptive Kalman fusion algorithm, which already included the prediction of elastic deformation caused by environmental factors in the state prediction stage.

[0045] During the state update phase of the algorithm, the predicted state is corrected using measured attitude data. Since the predicted state already includes elastic deformation components, the updated state estimate can effectively separate the structural, permanent true perpendicularity deviation from the temporary, recoverable elastic deformation components.

[0046] This process can be described by the following state-space model, whose state prediction equation is: In the formula, k represents the discrete-time index, X k This represents the system state vector at time k. This represents the optimal estimate of the system state at time k-1. E represents the predicted value of the state at time k based on the state at time k-1. k Let A(E) represent the environmental data vector collected at time k. k ) indicates that the environmental data E k Dynamically adjusted state transition matrix.

[0047] By executing a complete Kalman filter prediction and update loop, the edge computing module ultimately obtains the state vector... Extract the true verticality deviation component θ true,k .

[0048] The cloud-based decision and control command generation module, connected to the edge computing layer, is used to generate target correction values ​​based on the actual verticality deviation. Specifically, the edge computing module can be physically deployed in edge computing gateways or industrial computers at the construction site to ensure low-latency response for data processing. Its core function is to perform in-depth processing on the received raw attitude data containing various disturbances to separate and determine the true verticality deviation of the high piers.

[0049] Specifically, the cloud-based decision-making and control command generation module can be physically deployed on a remote cloud server to perform high-level intelligent decision-making and command generation tasks. Its core function is to determine whether and how to perform correction actions based on the received actual vertical deviation and according to preset control strategies.

[0050] The decision-making logic of the cloud-based decision and control command generation module is based on a preset multi-level control threshold system. This multi-level control threshold system is set according to the construction stage, structural safety specifications, and design requirements, and supports online adjustment.

[0051] The cloud-based decision and control command generation module first compares the received actual verticality deviation with a first warning threshold. When the absolute value of the actual verticality deviation is greater than or equal to the first warning threshold, the module generates a warning signal, which is then communicated to management personnel via the project management platform or mobile terminal.

[0052] The cloud-based decision and control command generation module compares the magnitude of the actual verticality deviation with a second correction threshold. This second correction threshold is numerically greater than the first warning threshold. When the absolute value of the actual verticality deviation is greater than or equal to the second correction threshold, the module determines that physical correction must be performed.

[0053] The cloud-based decision and control command generation module will generate a target correction value. The target correction value is a command containing a specific value, which defines not only the magnitude of the displacement to be corrected, but also the direction of the correction.

[0054] This hierarchical decision-making logic can be formally expressed through the following decision function: In the formula, O represents the output of the cloud-based decision and control command generation module, f(·) represents the decision function, and D true Indicates the true perpendicularity deviation of the input, |D true | Indicates the magnitude or absolute value of the actual perpendicularity deviation, T warn T represents the preset first warning threshold. corr This indicates the preset second correction threshold; NULL indicates no output. warn Indicates the generated warning signal, C target (D true ) represents the generated target correction amount.

[0055] The predictive execution and collaborative optimization module is connected to the cloud-based decision and control command generation module and the real-time data acquisition module. It is used to perform forward calculations based on the target correction amount and attitude data, calling the dynamic response model of the actuator and the mechanical model of the pier structure to generate a collaborative control sequence that suppresses secondary disturbances in the correction process. Specifically, the predictive execution and collaborative optimization module receives the target correction data generated by the cloud-based decision and control command generation module and simultaneously acquires real-time attitude data provided by the real-time data acquisition module. Physically, the module can be deployed at the edge or in the cloud, depending on computational needs.

[0056] A static, discrete target correction quantity is transformed into a dynamic, time-sequential cooperative control sequence. This transformation process is not a simple command mapping, but a forward-looking, model-based optimal control solution process aimed at suppressing secondary disturbances that may arise in the physical correction actions.

[0057] The predictive execution and collaborative optimization module incorporates two core mathematical models: a dynamic response model for the actuator and a structural mechanics model for the pier. The dynamic response model describes the nonlinear response characteristics of the deviation correction actuator after receiving a control signal. The structural mechanics model describes the structural deformation and vibration characteristics of the pier under external forces.

[0058] The specific workflow of the predictive execution and collaborative optimization module is as follows: First, a target attitude state is defined based on the received target correction amount, and the current initial attitude state is determined using real-time attitude data.

[0059] Subsequently, the predictive execution and collaborative optimization module constructs the correction process as an optimal control problem. By solving this optimal control problem, a collaborative control sequence is generated that can smoothly drive the pier's attitude from its initial state to the target state.

[0060] The goal of the optimal control problem is to find an optimal sequence of control inputs that minimizes a cost function containing multiple optimization indices. This cost function can be formally expressed as: In the formula, J(U) represents the total cost to be minimized, i represents the discrete time step index in the prediction time domain, N represents the total number of steps in the prediction time domain, and x i Let x represent the system state vector predicted at time step i. ref,i U represents the reference state at time step i. i Let represent the control input vector applied at time step i, U represent the entire control sequence from the current time to the next N-1 time steps, Q be a positive semi-definite state weight matrix, and R be a positive definite control weight matrix. T This represents the transpose operation of a matrix or vector.

[0061] The first term of the objective function (x) i -x ref,i ) T Q(x i -x ref,i ) is an optimization term whose function is to enable the attitude change of the high pier to smoothly and accurately approximate the final attitude defined by the target correction amount.

[0062] The second term of the objective function It is another optimization term that punishes the magnitude of the control input to suppress excessive impact or high-frequency vibration that may be generated during the operation of the actuator, thereby ensuring the smoothness of the correction process.

[0063] By solving this optimal control problem, the predictive execution module ultimately generates an optimal control sequence U. * This sequence is the cooperative control sequence.

[0064] The deviation correction execution module, connected to the predictive execution and collaborative optimization module, is used to physically adjust the high piers in response to the collaborative control sequence; Specifically, the deviation correction execution module is the physical execution end of the entire intelligent control system. Its function is to accurately convert the optimized control strategy calculated and generated by the upper module into physical adjustment actions on the high pier.

[0065] The deviation correction execution module physically comprises a cluster of multiple hydraulic jacks, which are high-performance servo hydraulic jacks equipped with high-precision displacement sensors and pressure sensors, and are installed in an array at the bottom of the high pier.

[0066] The core task of the deviation correction execution module is to accurately respond to the cooperative control sequence. This sequence is a time-sequential set of instruction vectors that specifies the comprehensive control quantity to be applied at each discrete time step over a future period.

[0067] The deviation correction execution module has a built-in controller that parses the abstract cooperative control sequence into directly executable thrust and displacement commands for each hydraulic jack in the cluster.

[0068] The analytical process can be formally described as an instruction assignment mapping. For any time step i in the cooperative control sequence, the control input vector u... i The underlying drive command c generated for the j-th hydraulic jack i j can be determined through the following relationship; c i,j =M j u i ; In the formula, i represents the index of the discrete time step, j represents the unique number of the hydraulic jack in the cluster, and u i M represents the control input quantity of the cooperative control sequence generated by the predictive execution module at time step i. j Represents the instruction allocation matrix, c i,j This indicates that the underlying drive command is issued to the j-th hydraulic jack at time step i.

[0069] The controller, defined at each time step in the coordinated control sequence, can drive the cluster of hydraulic jacks synchronously or asynchronously. Synchronous control is used to achieve overall translation or rotation of the pier, while asynchronous control is used to achieve more complex, non-rigid attitude fine-tuning.

[0070] The feedback verification module, connected to the real-time data acquisition module and the prediction execution and collaborative optimization module, is used to compare the actual attitude change with the predicted attitude change after the deviation correction execution module completes the physical adjustment, so as to update the dynamic response model of the actuator and the mechanical model of the pier structure. Specifically, the function of the feedback verification module is to update and optimize the internal model of the system online after the deviation correction execution module completes a physical adjustment, so as to continuously improve the accuracy of subsequent control.

[0071] The feedback verification module is activated after the deviation correction execution module completes physical adjustments and stabilizes. It first acquires high-precision attitude data before and after the correction action through the real-time data acquisition module. By comparing the difference between the two sets of attitude data, the feedback verification module calculates the actual attitude change caused by the correction action. This actual attitude change represents the true response in the physical world.

[0072] The feedback verification module retrieves the theoretically predicted attitude changes that are generated synchronously when the cooperative control sequence is generated.

[0073] The feedback verification module precisely compares the actual attitude change with the predicted attitude change to calculate the vector difference between them. This vector difference is the prediction error for the current control cycle. The prediction error is used as input to an optimization algorithm to update the model parameters of the actuator dynamic response model and the pier structure mechanical model online. In one embodiment, this online update process can be described by the following iterative optimization formula: In the formula, k1 represents the index of the number of times the corrective action is executed. This indicates that before the k1th correction action is executed, This indicates that after this feedback verification, η represents the learning rate, and J(P) k ) represents a model parameter vector P k The cost function is the independent variable. Represents the cost function Relative to parameter vector The gradient.

[0074] By executing the above optimization algorithm, the system obtains a set of updated model parameters. This set of updated parameters was used to replace the old parameters in the dynamic response model of the actuator and the mechanical model of the pier structure within the predictive execution module.

[0075] The device for intelligent monitoring and control of the verticality of high piers described below can be referred to in correspondence with the intelligent monitoring and control system for the verticality of high piers described above.

[0076] Please see the appendix Figure 7 - Appendix Figure 9 The present invention also provides a device for intelligent monitoring and control of the verticality of high piers, comprising: a support base 1, a support frame 2 fixedly connected to the upper surface of the support base 1, a laser tracker 3 fixedly connected to the upper surface of the support frame 2, a support rod 4 fixedly connected to the upper surface of the support frame 2, an anemometer 5 fixedly connected to one end of the support rod 4, a solar intensity meter 6 fixedly connected to the upper surface of the anemometer 5, a support column 16 fixedly connected to the upper surface of the support base 1, a fixing frame 7 fixedly connected to the outer wall of the support column 16, the lower surface of the fixing frame 7 fixedly connected to the upper surface of the support base 1, a hydraulic jack 8 fixedly connected to the upper surface of the support base 1, and the output end of the hydraulic jack 8 fixedly connected to... There is a connecting frame 3 19, and a connecting rod 15 is rotatably connected inside the connecting frame 3 19. A rotating block 18 is fixedly connected to the outer wall of the connecting rod 15. The outer wall of the rotating block 18 is rotatably connected to the inner wall of the support column 16. One end of the connecting rod 15 is rotatably connected to a connecting frame 1 13. A rolling frame 14 is fixedly connected to the outer wall of the connecting frame 1 13. A fixing plate 9 is fixedly connected to the upper surface of the support column 16. A sliding rod 12 is slidably connected inside the fixing plate 9. One end of the sliding rod 12 is rotatably connected to a connecting frame 2 17. A three-axis gyroscope sensor 10 is fixedly connected to the outer wall of the connecting frame 2 17. A spring 11 is fixedly connected to the outer wall of the sliding rod 12. One end of the spring 11 is fixedly connected to the outer wall of the fixing plate 9.

[0077] Specifically, firstly, the support base 1 is installed on the periphery of the high pier. By starting the hydraulic jack 8, the connecting frame 19 is moved, which pushes the connecting rod 15 to drive the rotating block 18 to rotate on the support column 16. The movement of the connecting rod 15 causes it to rotate on the connecting frame 13, and pushes the rolling frame 14 to contact the high pier. Then, the sliding rod 12 is pushed by the spring 11 to slide inside the fixed plate 9, so that the sliding rod 12 rotates on the connecting frame 17 and pushes the connecting frame 17 and the three-axis gyroscope sensor 10 to move, so that the three-axis gyroscope sensor 10 is attached to the high pier, and then the inertial attitude data of the high pier is collected and transmitted to the inertial attitude sensing unit. The three-dimensional spatial coordinates of the high pier are measured by the laser tracker 3 fixed on the support frame 2, and the data is transmitted to the optical reference calibration unit. Temperature, wind speed, wind direction and solar radiation are collected by the anemometer 5 and the solar radiation intensity meter 6 at the top of the support rod 4, respectively. When the deviation correction execution module receives the collaborative control sequence, it controls and starts the hydraulic jack 8, causing the hydraulic jack 8 to push the connecting frame 19 to move the connecting rod 15, causing the rotating block 18 to rotate on the support column 16, and at the same time pushing the connecting frame 13 and the rolling frame 14 to move, so that the rolling frame 14 applies a thrust to the high pier, thereby correcting the deviation of the high pier.

Claims

1. A high pier verticality intelligent monitoring and control system, characterized in that, Comprise; Real-time data acquisition module for collecting the attitude data and environmental data of the high pier; Edge computing module, with real-time data acquisition module, for receiving attitude data and environmental data, and processing attitude data based on a data fusion algorithm with high pier physical characteristics as prior constraints to eliminate elastic deformation components caused by environmental factors, thereby determining the true verticality deviation of the high pier; Cloud decision and control instruction generation module connected with the edge computing layer for generating target deviation correction amount according to the true verticality deviation; Predictive execution and collaborative optimization module connected with the cloud decision and control instruction generation module and the real-time data acquisition module for generating a collaborative control sequence to suppress secondary disturbance in the deviation correction process according to the target deviation correction amount and the attitude data by calling the execution mechanism dynamic response model and the pier structure mechanics model for prospective calculation; Deviation correction execution module connected with the predictive execution and collaborative optimization module for physically adjusting the high pier in response to the collaborative control sequence; Feedback verification module connected with the real-time data acquisition module and the predictive execution and collaborative optimization module for comparing the actual attitude change with the predicted attitude change after the deviation correction execution module completes the physical adjustment to update the execution mechanism dynamic response model and the pier structure mechanics model.

2. The high pier verticality intelligent monitoring and control system according to claim 1, characterized in that, The real-time data acquisition module comprises: Inertial attitude sensing unit for high-frequency acquisition of angular velocity and acceleration of the high pier to constitute part of the attitude data; Optical reference calibration unit for high-precision acquisition of absolute spatial coordinates of the high pier to constitute another part of the attitude data; Environment state sensor for synchronous acquisition of environmental data.

3. The high pier verticality intelligent monitoring and control system according to claim 2, characterized in that, The real-time data acquisition module acquires the attitude data by the following steps: The inertial attitude sensing unit continuously measures the attitude of the high pier at a high frequency to generate a real-time attitude information stream; The optical reference calibration unit measures the absolute spatial coordinates of the high pier at a preset time interval to generate an absolute position reference point; The absolute position reference point is used to periodically correct the cumulative error in the real-time attitude information stream caused by integration, thereby generating the final attitude data.

4. The high pier verticality intelligent monitoring and control system according to claim 1, characterized in that, The data fusion algorithm embedded in the edge computing module is an adaptive Kalman fusion algorithm, and the high pier physical characteristics are used as constraint conditions of the state transition matrix. The edge computing module dynamically adjusts the state transition matrix according to the collected environmental data to separate the true verticality deviation in the filtering process.

5. The high pier verticality intelligent monitoring and control system according to claim 1, wherein, The edge computing module specifically comprises the following steps: Receiving collected attitude data and environmental data; Using environmental data and high pier physical characteristics, dynamically adjusting the state transition matrix of the adaptive Kalman fusion algorithm to predict the pier attitude state at the next time; Using attitude data to filter and update the predicted attitude state, thereby separating the elastic deformation components caused by environmental factors and determining the true verticality deviation.

6. The high pier verticality intelligent monitoring and control system according to claim 1, wherein, The cloud decision and control instruction generation module compares the true verticality deviation with the preset multi-level control threshold to determine whether to generate the target deviation correction amount; comparing the received real verticality deviation with the first early-warning threshold, and generating an early-warning signal when the real verticality deviation is greater than or equal to the first early-warning threshold; comparing the real verticality deviation with the second correction threshold, and generating a target correction amount including a specific displacement amount when the real verticality deviation is greater than or equal to the second correction threshold.

7. The high pier verticality intelligent monitoring and control system according to claim 1, wherein, The prediction execution and collaborative optimization module specifically includes the following steps: based on the target correction amount, real-time attitude data, an actuator dynamic response model, and a pier structure mechanics model; generating a collaborative control sequence for suppressing secondary disturbance in the correction process by solving an optimal control problem; wherein the objective function of the optimal control problem includes an optimization term for making the pier attitude change smoothly approach the target correction amount and an optimization term for suppressing vibration during actuator action.

8. The high pier verticality intelligent monitoring and control system according to claim 1, wherein, The deviation correction execution module includes a cluster of multiple hydraulic jacks, which is used to parse the collaborative control sequence into thrust and displacement instructions of each hydraulic jack, and to perform synchronous or asynchronous driving control to physically adjust the high pier.

9. The high pier verticality intelligent monitoring and control system according to claim 1, wherein, The feedback checking module specifically includes the following steps: obtaining the actual attitude change after the deviation correction execution module completes the physical adjustment; calculating the prediction error between the actual attitude change and the attitude change predicted when the collaborative control sequence is generated by the prediction execution and collaborative optimization module; According to the prediction error, calling an optimization algorithm to update the model parameters of the actuator dynamic response model and the pier structure mechanics model online to reduce the prediction error of subsequent correction actions.

10. The device for intelligent monitoring and control of high pier verticality, applied to the system for intelligent monitoring and control of high pier verticality according to any one of claims 1-9, characterized in that, including; Support base (1), the upper surface of the support base (1) is fixedly connected with support frame (2), the upper surface of the support frame (2) is fixedly connected with laser tracker (3), the upper surface of the support frame (2) is fixedly connected with support rod (4), one end of the support rod (4) is fixedly connected with wind speed and direction instrument (5), the upper surface of the wind speed and direction instrument (5) is fixedly connected with sunshine intensity meter (6), the upper surface of the support base (1) is fixedly connected with support column (16), the outer wall of the support column (16) is fixedly connected with fixed frame (7), the lower surface of the fixed frame (7) is fixedly connected on the upper surface of support base (1), the upper surface of the support base (1) is fixedly connected with hydraulic jack (8), the output end of the hydraulic jack (8) is fixedly connected with connecting frame three (19), the inside of the connecting frame three (19) is rotatably connected with connecting rod (15), the outer wall of the connecting rod (15) is fixedly connected with rotating block (18), the outer wall of the rotating block (18) is rotatably connected in the inner wall of support column (16), one end of the connecting rod (15) is rotatably connected with connecting frame one (13), the outer wall of the connecting frame one (13) is fixedly connected with rolling frame (14), the upper surface of the support column (16) is fixedly connected with fixed plate (9), the inside of the fixed plate (9) is slidably connected with sliding rod (12), one end of the sliding rod (12) is rotatably connected with connecting frame two (17), the outer wall of the connecting frame two (17) is fixedly connected with three-axis gyroscope sensor (10), the outer wall of the sliding rod (12) is fixedly connected with spring (11), one end of the spring (11) is fixedly connected in the outer wall of fixed plate (9).

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