Seawall slope protection axis control system based on GNSS and infrared level

Abstract

The application relates to the technical field of automatic construction of large infrastructure, and discloses a seawall slope axis control system based on GNSS and an infrared level meter, which comprises a GNSS module, an infrared level meter module and a controller. The GNSS module is used for acquiring global position information of a construction machine. The infrared level meter module is used for providing a space reference plane signal. The controller is configured to establish a multi-dimensional state vector containing a position component and a posture component of the construction machine, update the state vector based on the global position information acquired by the GNSS module, and establish a geometric constraint equation related to the state vector by using the reference plane signal provided by the infrared level meter module. By establishing a multi-dimensional dynamic state space model containing position, posture and speed, the system can accurately describe and predict the kinematics and dynamics characteristics of the construction machine, the model can capture the motion inertia and nonlinear characteristics of the machine, and provides a prerequisite for a forward-looking control strategy.

Description

Technical Field

[0001] This invention relates to the field of automated construction technology for large-scale infrastructure, specifically a seawall slope protection axis control system based on GNSS and infrared level. Background Technology

[0002] In the construction of seawalls, water conservancy dikes, airport runways, and high-grade highways, precise control of the linearity, slope, and elevation of the final formed surface is a core requirement for ensuring project quality and long-term stability. To achieve this goal, automated construction machinery, such as pavers and graders, typically integrates advanced guidance and control systems. These systems generally employ a collaborative approach using Global Navigation Satellite Systems (GNSS) and laser or infrared reference systems. GNSS, particularly systems employing real-time dynamic differential technology, provides the construction machinery with three-dimensional position information in a global coordinate system, ensuring that the construction axis conforms to the overall design blueprint. Simultaneously, infrared levels or laser emitters establish a highly stable spatial reference plane at the construction site. Receivers mounted on the machinery's operating units (such as screeds) detect this reference plane, providing high-frequency references for precise elevation or slope control.

[0003] The automated control of existing construction machinery often employs simplified feedback control methods. These methods typically treat the machinery as a point without inertia, ignoring its inherent physical dynamic characteristics. This simplified model cannot accurately predict the future movement of the machinery, making it difficult for the control system to effectively compensate for the physical lag of the actuators, thus affecting the control accuracy during dynamic construction. Infrared levels are commonly used in existing high-precision construction to provide elevation correction; however, this approach only utilizes one-dimensional elevation information, failing to fully leverage the geometric constraints inherent in the signal. When the attitude of the construction machinery deviates, such as during roll or pitch, the correlation between this elevation information and the machine's attitude is ignored, resulting in an inability to effectively and synchronously correct attitude errors. This directly affects the flatness and slope accuracy of the final formed surface. Furthermore, relying solely on GNSS as a global positioning data source makes its performance susceptible to multipath effects. When GNSS signal quality deteriorates, existing systems lack a mechanism for dynamic evaluation and adaptive adjustment. In such cases, large errors in the GNSS data are directly used for state estimation, significantly reducing the overall system's positioning accuracy and robustness, and potentially even leading to misinterpretations of control commands. Meanwhile, existing methods typically employ hysteresis feedback control, meaning corrections are only made after the deviation between the current and target states is measured. Given the physical inertia of construction machinery and the inherent response delay of the actuators, this hysteresis correction mechanism is prone to overshoot and oscillations, making smooth trajectory tracking difficult. Its control performance deteriorates further, especially under conditions of varying slopes or requiring rapid response. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides a seawall slope protection axis control system based on GNSS and an infrared level. This system solves the problems of low dynamic control accuracy, poor robustness, and uneven trajectory tracking caused by neglecting the dynamic characteristics of construction machinery, failing to deeply integrate infrared signals as multi-dimensional geometric constraints, lacking an adaptive adjustment mechanism for changes in GNSS signal quality, and the difficulty in compensating for system delays using hysteresis feedback control.

[0005] To achieve the above objectives, the present invention provides the following technical solution:

[0006] The first aspect of the present invention provides a GNSS and infrared level-based control system for the axis of a seawall slope protection, the system comprising a GNSS module, an infrared level module, and a controller.

[0007] The GNSS module is used to acquire the global three-dimensional position information of the construction machinery.

[0008] The infrared level module includes an infrared laser transmitter and an infrared receiver installed on the construction machinery operating unit. The infrared laser transmitter is used to emit a laser reference plane signal that is fixed in space, and the infrared receiver is used to receive the reference plane signal.

[0009] The controller is configured to:

[0010] Establish a dynamic state-space model of the construction machinery, the model including multi-dimensional state vectors. and state transition equations The state vector Includes the global position components of the construction machinery attitude components And linear velocity and angular velocity components;

[0011] The global three-dimensional position information acquired by the GNSS module is used as the observation input;

[0012] The reference plane signal provided by the infrared level module is used to establish a relationship with the state vector based on the relative geometric relationship between the infrared receiver installed on the construction machinery and the machinery body. Related nonlinear algebraic geometric constraint equations ;

[0013] Using the global position information obtained by the GNSS module and the geometric constraint equations, the state vector is estimated using a state estimation algorithm. Perform a joint update to obtain the current optimal state estimate of the construction machinery. ;

[0014] The state estimation algorithm is configured such that: at each time step The state vector is predicted based on the state transition equation, and the fusion weight of the GNSS module observation data is dynamically adjusted based on the residual between the GNSS global position information and the predicted position. Then, the predicted state is corrected using the GNSS global position information and the geometric constraint equation.

[0015] Based on the optimal state estimation Based on the preset design axis, a model predictive control algorithm is used to solve the optimization problem of making the motion trajectory of the construction machinery fit the design axis in the future time domain, so as to generate control commands for the construction machinery.

[0016] The model predictive control algorithm is configured to: during the solution of the optimization problem, incorporate the geometric constraint equations It is incorporated into the cost function as a soft constraint term to prioritize satisfying the constraints of the reference plane.

[0017] Through the above configuration, the controller can deeply integrate the global position information provided by GNSS with the local high-precision geometric constraint information provided by the infrared level, and perform forward compensation for system delay during the control command generation stage through model predictive control strategy, thereby achieving precise control of the construction machinery operation unit.

[0018] A second aspect of the present invention provides a method for controlling the axis of a seawall revetment based on GNSS and an infrared level, the method comprising the following steps:

[0019] State Modeling and Prediction: Establish a multidimensional state vector for the construction machinery, including position, attitude, linear velocity, and angular velocity components. And the state transition equations describing its motion characteristics ; at each time step Based on the optimal state estimate of the previous time step and control input The state vector at the current moment is predicted using the state transition equation to obtain the prior state estimate. ;

[0020] Multi-source information fusion and status update: Receiving three-dimensional position measurements provided by the GNSS module The infrared level module provides a reference plane signal; the infrared level signal is modeled as a nonlinear geometric constraint equation related to the state vector. The GNSS measurements are processed using the extended Kalman filter algorithm. and the geometric constraint equations For the prior state estimation By performing joint corrections, we obtain the optimal state estimate for the current time step. ;

[0021] Adaptive weight adjustment: Compare the GNSS measurements before the state update. Compared with the prior state estimation The residuals between the predicted measurements are used to dynamically adjust the fusion weights of the GNSS measurement data and the geometric constraint equations in the extended Kalman filter algorithm based on the statistical characteristics of the residuals.

[0022] Control command generation: based on the aforementioned optimal state estimation Using a pre-defined design axis, a model predictive control algorithm is employed to solve an optimization problem in the future time domain that ensures the predicted trajectory aligns with the design axis, thereby obtaining the optimal control sequence. In the cost function of the optimization problem, the geometric constraint equations are included. This is included as a soft constraint in the calculation to ensure that the generated control commands can make the posture and motion of the construction machinery consistent with the reference plane constraint.

[0023] Control command execution: Only execute the optimal control sequence. The first control command in It is sent to the actuator of the construction machinery.

[0024] Through the steps described above, the method elevates the reference signal provided by the infrared level from an independent elevation measurement to a multidimensional geometric constraint on the mechanical position and attitude, realizing deep fusion of heterogeneous sensor data at the physical model level. Furthermore, through adaptive weight adjustment and model predictive control, it improves the robustness and control accuracy of the system in dynamic construction environments.

[0025] This invention provides a seawall slope protection axis control system based on GNSS and an infrared level. It has the following beneficial effects:

[0026] 1. This invention establishes a multi-dimensional dynamic state-space model that includes position, attitude, and velocity, enabling the system to accurately describe and predict the kinematic and dynamic characteristics of construction machinery, thus providing a solid physical foundation for subsequent control and estimation. This model can capture the inertia and nonlinear characteristics of the machinery, providing a prerequisite for forward-looking control strategies.

[0027] 2. This invention models the reference plane signal provided by the infrared level as a nonlinear geometric constraint equation related to the multidimensional state vector, and jointly updates it with GNSS position information within an extended Kalman filter framework, achieving high-frequency, high-precision correction of the position and attitude of construction machinery. This method creatively transforms the signal originally used for single-dimensional elevation correction into a collaborative constraint on multiple key state components (position, roll angle, and pitch angle), thereby improving the dimensionality and accuracy of state estimation.

[0028] 3. This invention dynamically adjusts the fusion weights of GNSS observation data based on the statistical characteristics of the GNSS measurement residuals before the update step of the extended Kalman filter. This enables the system to autonomously reduce its dependence on GNSS data and increase its confidence in infrared geometric constraints when GNSS signal quality deteriorates or is interfered with. This adaptive mechanism gives the system the ability to maintain robustness and accuracy of state estimation in complex construction environments.

[0029] 4. This invention incorporates the infrared geometric constraint equation as a high-weight soft constraint term in the cost function of the model predictive control algorithm, ensuring that the generated control commands prioritize the consistency between the mechanical attitude and the reference plane. This method places high-precision local geometric constraint information at the forefront of the control decision-making process, effectively guiding the controller's planning path and avoiding repeated overshoot and oscillations caused by delayed feedback, thereby achieving a smoother and more accurate control trajectory.

[0030] 5. This invention organically integrates dynamic state modeling, multi-source sensor adaptive fusion, and geometrically constrained model predictive control into a complete closed-loop control framework, forming a coordinated linkage from perception and estimation to decision-making. This tightly coupled system architecture enables each technical component to enhance and support each other, effectively overcoming the system performance bottlenecks caused by data processing separation and response delays in traditional control methods, and ensuring the coherence and stability of the overall control. Attached Figure Description

[0031] Figure 1 This is a schematic diagram illustrating the overall system architecture and application scenarios of an embodiment of the present invention;

[0032] Figure 2 This is a system control logic block diagram according to an embodiment of the present invention;

[0033] Figure 3 This is a schematic diagram illustrating the geometric constraint modeling principle of the infrared level in an embodiment of the present invention.

[0034] Figure 4 This is a schematic diagram illustrating the principle of Model Predictive Control (MPC) in an embodiment of the present invention. Detailed Implementation

[0035] 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. Example

[0036] Please see the appendix Figure 1 -Appendix Figure 4 This invention provides a GNSS and infrared level-based seawall slope protection axis control system, comprising:

[0037] The GNSS module includes one or more GNSS antennas and receivers mounted on the construction machinery body, used to acquire the three-dimensional position information of the construction machinery in a preset global coordinate system in real time.

[0038] An infrared level module includes an infrared laser emitter fixedly installed within the construction area and an infrared receiver mounted on the operating unit of construction machinery (e.g., a paver screed). The infrared laser emitter emits a highly stable laser reference plane in space; the infrared receiver is used to detect whether it is positioned on this laser reference plane.

[0039] The controller, which can be an industrial computer, a programmable logic controller (PLC), or an embedded system, is the core computing unit of this system, used to receive data from each module and execute the control methods described below.

[0040] Actuators, such as hydraulic cylinders or servo motors on construction machinery, are used to receive control commands from the controller and drive the working parts of the construction machinery to move.

[0041] The specific steps of the control method executed by the controller are as follows:

[0042] First, a precise mathematical model is developed for the motion characteristics of the construction machinery. This model serves as the cornerstone for subsequent state estimation and predictive control, aiming to overcome the limitations of traditional methods that treat machinery as a point without inertia.

[0043] In this embodiment, the system preferably employs a dynamic state-space model to describe the kinematic and dynamic characteristics of the construction machinery. Specifically, to comprehensively capture the motion and attitude of the machinery in three-dimensional space, this embodiment abstracts the construction machinery as a multi-degree-of-freedom rigid body and establishes a multi-dimensional state vector. , used to perform at any discrete time step It fully represents the motion state. This state vector contains not only the position information of the machine in the global coordinate system, but also its attitude and corresponding velocity information.

[0044] State vector Specifically defined as:

[0045] ;

[0046] in, The three-dimensional position of the construction machinery reference point in the preset inertial global coordinate system; These are the roll angle, pitch angle, and yaw angle of the mechanical body, used to describe its attitude in space; dotted variables. These represent the corresponding linear velocity and angular velocity components, respectively. By including this velocity and angular velocity information, the state vector can fully reflect the motion trend and inertia of the machine, providing basic data for subsequent predictive control.

[0047] Building upon this, this embodiment further establishes a state transition equation describing how the state vector evolves over time. This equation is based on the physical motion laws of construction machinery, such as the Newton-Euler equations, and correlates the current state of the system with the state and control input of the previous moment. The specific form of the state transition equation is as follows:

[0048] ;

[0049] in, This is a nonlinear state transition function that transforms the state vector from the previous time step... and control input The signal driving the hydraulic actuator of the machine is mapped to the current state. This function inherently captures the kinematic and dynamic characteristics of the machine, such as its mass, inertia, and the response characteristics of the hydraulic system, thus enabling accurate physical modeling of the machine's future trajectory.

[0050] This represents process noise, used to characterize model uncertainties, unmodeled dynamics, and external disturbances such as uneven ground or wind. Its covariance matrix is... By establishing this dynamic model, the controller can go beyond simple feedback logic and achieve forward-looking prediction of the future state of the machine, which is a prerequisite for the subsequent implementation of model predictive control.

[0051] This invention employs a multi-source sensor observation model to accurately perceive the real-time status of construction machinery. This model aims to overcome the inherent limitations of single sensors in complex construction environments by deeply fusing global positioning information provided by GNSS with high-precision local information provided by an infrared level, thus providing a comprehensive and robust data foundation for subsequent state estimation.

[0052] The multi-source observation model preferably includes a GNSS observation model and an infrared level geometric constraint model.

[0053] First, regarding the GNSS module, it provides three-dimensional position measurements of the construction machinery in the global coordinate system. This measurement can be viewed as a state vector. The linear observations can be represented by the following observation equation:

[0054] ;

[0055] in, It is a fixed selection matrix, and its function is to select from the 12-dimensional state vector. Extract the first three positional components .

[0056] The measurement noise of the GNSS module has the following covariance matrix: This model provides global position information for the machinery, giving the system a macroscopic location on the design axis.

[0057] Secondly, the core innovation of this embodiment lies in the modeling method of the infrared level signal. Unlike treating the infrared level output as a single elevation measurement value, this invention creatively models it as a nonlinear algebraic geometric constraint applied to a multidimensional state vector. The infrared level includes a laser emitter fixedly installed within the construction area, whose emitted laser beam forms a highly stable reference plane in space. This plane can be uniquely determined by its normal vector and intercept; specifically, its equation can be expressed as:

[0058] ;

[0059] in, Let be the coordinates of any point on the plane. Plane parameters. It can be obtained through calibration before construction.

[0060] The position of the infrared receiver installed on the construction machinery operating unit in the machine's coordinate system It is known. The receiver's real-time coordinates in the global coordinate system. This coordinate transformation will change with the position and orientation of the mechanical body. This coordinate transformation relationship can be expressed by the following formula:

[0061] ;

[0062] in, It is a rotation matrix whose elements are determined by the mechanical attitude angles. The decision is made when the infrared receiver detects a laser signal, signifying its global coordinates. The equations of the laser plane must be satisfied. Substituting the above coordinate transformation relationships into the plane equations yields the nonlinear geometric constraint equations, which are related to the state vector. The position and attitude components are related:

[0063] ;

[0064] This geometric constraint equation effectively transforms the originally isolated elevation measurement information into positional components of the construction machinery. Roll angle and pitch angle Joint constraints on multiple key state variables. This approach enables one-dimensional measurement signals to provide high-frequency, high-precision correction information for multiple interrelated state variables, with observation noise being... Its covariance matrix is Through the above modeling, this invention provides local, high-precision attitude and elevation constraints for GNSS global positioning information, laying the foundation for subsequent information fusion.

[0065] In this embodiment, to achieve effective fusion of the aforementioned multi-source sensor observation information and obtain accurate and robust estimates of the construction machinery's state, the present invention employs a state estimation and adaptive fusion method based on Extended Kalman Filter (EKF). The EKF is chosen because the system model, compared to the aforementioned infrared level's geometric constraint model, exhibits nonlinear characteristics.

[0066] In this embodiment, the method mainly includes a prediction step, an adaptive weight adjustment step, and a measurement update step.

[0067] First, in the prediction step of each control cycle, the controller makes a priori predictions about the state of the construction machinery based on the established state transition equations. Specifically, this step utilizes the previous time step... Optimal state estimation and the control inputs that have been applied Through the state transition function To predict the current moment Prior state estimation :

[0068] ;

[0069] Simultaneously, this step also propagates the uncertainty of the state estimate, i.e., updates its covariance matrix. :

[0070] ;

[0071] in, Let be the covariance matrix of the state estimate from the previous time step. Let be the process noise covariance matrix. It is a nonlinear state transition function exist The Jacobian matrix, obtained by taking the partial derivative with respect to the state vector, is used to perform local linearization on the nonlinear system. This prediction step provides a benchmark based on model predictions for subsequent measurement updates.

[0072] Next, before performing the measurement update, this embodiment preferably performs an adaptive weight adjustment step. This step aims to dynamically evaluate the quality of the incoming GNSS signal and adjust its weight in the fusion process accordingly, thereby improving the system's robustness in environments where the GNSS signal is interfered with (e.g., multipath effects). This step first calculates the residual of the GNSS measurement, i.e., the difference between the actual measured value and the predicted measured value based on the prior state estimation:

[0073] ;

[0074] The controller continuously monitors the statistical properties of the residual sequence, such as its variance or amplitude, within a sliding time window. When the residuals consistently and systematically deviate from their theoretical distribution, the controller determines that the current GNSS signal quality has deteriorated. In response, the controller dynamically increases the GNSS measurement noise covariance matrix at the current time. The numerical value. Within the framework of Kalman filtering, increasing the measurement noise covariance is equivalent to reducing the confidence in the measurement source, thereby reducing its influence weight in the final state update. Accordingly, the controller can reduce the noise covariance of the infrared geometric constraints. This enhances the system's reliance on internal high-precision geometric constraints.

[0075] Finally, in the measurement update step, the controller uses the sensor information, adjusted with adaptive weights, to estimate the prior state. Make corrections. Due to the infrared geometric constraint equations It is nonlinear, so we first need to calculate its prior state. Jacobian matrix at the location This is done to achieve local linearization. Subsequently, the linear observation matrix of the GNSS is... Jacobian matrix Combined into a total observation matrix .

[0076] The controller then calculates the Kalman gain. This gain determines the degree to which the measurement residual corrects the prior state estimate:

[0077] ;

[0078] in, The combined measurement noise covariance matrix has its internal values ​​dynamically updated by the aforementioned adaptive weight adjustment step.

[0079] Finally, the controller updates the state estimate and its covariance using the Kalman gain to obtain the optimal state estimate at the current time step. :

[0080] ;

[0081] Simultaneously update the state covariance matrix. :

[0082] ;

[0083] In this updated formula, the measurement vector is derived from the actual GNSS measurements. The system consists of the target value "0" of the geometric constraint equation. Through this step, the geometric constraint information provided by the infrared level is organically integrated with the global position information of the GNSS. This not only corrects the elevation but also performs high-frequency joint corrections on multiple state variables, including the position, roll angle, and pitch angle of the construction machinery. The final output is a smooth, accurate system state estimate that is adaptive to environmental changes.

[0084] In this embodiment, after obtaining an accurate estimate of the current optimal state, the controller preferably uses a model predictive control (MPC) algorithm to generate control commands for the construction machinery. This method differs from traditional lag feedback control; its core idea is to use the dynamic model of the machinery to predict future states and calculate the optimal control sequence by solving an optimization problem within a finite time domain, thereby achieving forward-looking compensation for the inherent physical delay of the system.

[0085] This method constructs an optimization problem to find a set of optimal control input sequences. This enables construction machinery to predict future time domains. The motion trajectory within the model can most closely track the preset design axis. The optimization problem aims to minimize the comprehensive cost function. :

[0086] ;

[0087] First, the first term of the cost function This is the trajectory tracking term. This term is used to penalize the controller's predicted future state trajectory. With respect to the preset design axis reference trajectory The deviation between them. Among them, This is a positive definite weighting matrix, used to assign different importance to the tracking errors of different components in the state vector. By minimizing this term, the controller can drive the construction machinery smoothly along the designed axis.

[0088] Secondly, the second term of the cost function This is a penalty term for controlling energy consumption. This term is used to constrain the control input. The amplitude is adjusted to prevent overly drastic or unstable changes in control commands, thereby achieving smooth and efficient control of the machinery. Among these, This is a weight matrix used to adjust the degree of penalty for different control input components.

[0089] Finally, the core innovation of this embodiment lies in the third term of the cost function. This section establishes the aforementioned geometric constraint equations for the infrared level. The soft constraint, with its high weight, is directly introduced into the cost function of MPC. This is achieved by setting a large weighting coefficient for this soft constraint term. The controller is configured to prioritize ensuring that the mechanical attitude and position in the prediction time domain always satisfy the constraints of the infrared laser reference plane when solving optimization problems. This allows the controller to not only comprehensively consider global GNSS trajectory information but also be guided by high-frequency, high-precision local geometric constraints provided by the infrared level. This enables fine-grained planning of the mechanical attitude and motion during the control command generation phase, effectively avoiding repeated overshoot and oscillations caused by hysteresis corrections.

[0090] In solving optimization problems, the constraints of the system's dynamic equations must be satisfied simultaneously. This includes the physical constraints of the actuators, such as the maximum stroke and maximum speed of the hydraulic cylinders. By taking these physical constraints into account, the controller can generate physically feasible and optimal control commands.

[0091] Based on the rolling time-domain principle of MPC, after solving the above optimization problem in each control cycle, the controller only obtains the optimal control sequence. The first control input in The data is sent to the actuators of the construction machinery. In the next time step, the controller will reconstruct and solve the optimization problem based on the new optimal state estimate, continuously performing a prediction-decision-execution cycle. This rolling decision-making mechanism enables the controller to continuously adjust future control strategies in real time based on the latest sensor data and dynamic models, thereby achieving high-precision, robust, and forward-looking control of the machinery.

Claims

1. A seawall slope axis control system based on GNSS and infrared level, characterized in that, The system comprises: a GNSS module for obtaining global position information of the construction machine; an infrared level module for providing a spatial reference plane signal; a controller configured to: establish a multi-dimensional state vector containing position components and attitude components of the construction machine; update the state vector based on the global position information obtained by the GNSS module; establish the reference plane signal provided by the infrared level module as a geometric constraint equation related to the state vector; jointly update the position components and attitude components in the state vector using the geometric constraint equation to obtain optimal state estimation; generate control instructions for the construction machine based on the optimal state estimation and a pre-set design axis; the controller implements the joint update of the state vector through an extended Kalman filtering algorithm; the controller establishes the geometric constraint equation by establishing a functional relationship between the spatial position of an infrared receiver mounted on the construction machine and the position components and attitude components in the state vector.

2. The seawall slope axis control system based on GNSS and infrared level according to claim 1, characterized in that, The controller uses a model predictive control algorithm to predict the system state in the future time domain based on the optimal state estimation and the dynamic model of the construction machine, and solves the optimal control instructions.

3. The seawall slope axis control system based on GNSS and infrared level according to claim 2, characterized in that, The cost function of the model predictive control algorithm contains the geometric constraint equation to preferentially meet the constraints of the reference plane during the process of solving the optimal control instructions.

4. The GNSS and infrared level based seawall slope axis control system of claim 1, wherein, The multi-dimensional state vector further contains linear velocity components and angular velocity components of the construction machine.

5. The GNSS and infrared level based seawall slope axis control system of claim 2, wherein, The controller is further configured to compare the residual between the GNSS measurement value predicted by the model predictive control algorithm and the actual GNSS measurement value.

6. The GNSS and infrared level based seawall slope axis control system of claim 5, wherein, The controller dynamically adjusts the fusion weight of the GNSS global position information and the geometric constraint equation in the state vector update process according to the statistical characteristics of the residual.

7. The GNSS and infrared level based seawall slope axis control system of claim 1, wherein, The controller predicts the state vector based on the state transition equation describing the kinematics and dynamics characteristics of the construction machine.

8. The GNSS and infrared level based seawall slope axis control system of claim 1, wherein, The system comprises: a construction machine, and a GNSS module and a receiver of an infrared level module mounted thereon; the controller receives the signals of the modules and outputs control instructions to the actuators of the construction machine.

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