Leveling method, device, medium, program product and system for underwater leveler

Abstract

The invention relates to the technical field of leveling control of underwater leveling machines, in particular to a leveling method and device of an underwater leveling machine, a medium, a program product and a system. The leveling method comprises the following steps: measuring state parameters of the leveling machine in real time; performing data fusion on different types of measurement data, and estimating the real-time state of the leveling machine; according to the estimated real-time state of the leveling machine, flow parameters of hydraulic oil in the hydraulic supporting legs are adjusted, stretching and retracting of the hydraulic supporting legs are adjusted, and therefore the supporting height of the leveling machine is adjusted, and leveling is achieved. The real-time state of the leveling machine is estimated by obtaining the state parameters of the leveling machine, then the hydraulic valve set is driven according to the real-time state of the leveling machine, stretching and retracting of the hydraulic supporting legs are regulated and controlled, and inclination of the leveling machine is eliminated. According to the invention, when the real-time state of the leveling machine is estimated, fusion analysis is carried out on various sensing data, and the influence of various factors on the balance of the leveling machine is considered, so that the estimated real-time state of the leveling machine is more accurate, and the leveling precision is improved.

Description

Technical Field

[0001] This invention relates to the field of underwater leveling machine leveling control technology, and particularly to leveling methods, equipment, media, program products and systems for underwater leveling machines. Background Technology

[0002] Underwater leveling machines are essential equipment in hydraulic engineering for leveling and compacting foundation beds, and are widely used in the construction of ports, docks, and offshore facilities. Their main function is to reduce the risk of settlement and uneven settlement by uniformly compacting and leveling riprap foundation beds, ensuring the stability and durability of the entire engineering structure. When using an underwater leveling machine to uniformly compact and level riprap foundation beds, the leveling of the machine has a significant impact on the compaction and leveling effect. If the leveling machine is not leveled or is significantly tilted, it will lead to uneven compaction and foundation bed tilting.

[0003] Currently, the main methods for leveling screeds include manual leveling, tamping with a hammer, vibratory leveling, and traditional single-point automatic control. Manual leveling relies on manual adjustment by operators, which is not only labor-intensive and inefficient, but also significantly affected by human factors, making it difficult to guarantee uniformity. While tamping with a hammer can achieve compaction of the subgrade through a large amount of impact energy, the leveling speed is slow and energy utilization is low due to the significant influence of buoyancy and sea conditions on the equipment. Vibratory leveling and single-point automatic control have problems such as limited adjustment accuracy and delayed control response, making it difficult to achieve comprehensive real-time control of the ship's attitude under complex sea conditions. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of existing leveling technology for underwater leveling machines and to provide a leveling method, equipment, medium, program product and system for underwater leveling machines, so that underwater leveling machines can achieve adaptive balance control, that is, achieve automatic leveling.

[0005] Firstly, this invention provides a leveling method for an underwater leveling machine, characterized by comprising the following steps: Step 1: Measure the status parameters of the leveling machine in real time; wherein, the status parameters include at least two of the following: the lateral tilt angle, the longitudinal tilt angle, and the vertical displacement of the leveling machine. Step 2: Perform data fusion on different types of measurement data to estimate the real-time status of the leveling machine; Step 3: Adjust the flow parameters of the hydraulic oil in the hydraulic outriggers according to the estimated real-time status of the leveling machine, and then adjust the extension and retraction of the hydraulic outriggers to adjust the support height of the leveling machine from the seabed, so as to eliminate the tilt error of the leveling machine and achieve leveling.

[0006] According to a preferred embodiment, step three includes: The tilt error of the leveler is determined based on the estimated real-time status of the leveler; Then, the height compensation amount of the hydraulic outrigger is determined based on the tilt error. Then, based on the height compensation amount, the opening degree of the servo valve in the hydraulic valve group is driven to change the flow parameters of the hydraulic oil in the hydraulic outrigger, thereby adjusting the extension and retraction of the hydraulic outrigger to achieve leveling.

[0007] According to a preferred embodiment, the state parameters in step one include: the tilt angle of the leveler chassis in the X and Y axis directions, the acceleration components of the leveler in the X, Y, and Z axis directions, the angular velocities of the leveler around the X, Y, and Z axes, the buoyancy change of the leveler, and the seawater depth where the leveler is located. Preferably, the X, Y, and Z axes are a three-dimensional coordinate system constructed with the center of the leveler chassis as the origin; the X, Y, and Z axes are mutually perpendicular; the X axis is along the transverse direction of the leveler, and the Y axis is along the longitudinal direction of the leveler.

[0008] According to a preferred embodiment, step two includes: Model the state vector and the sensor measurement vector; A state prediction equation is established using the time discrete model and the state vector; Establish a nonlinear relationship between the sensor measurement vector and the state vector; Initialize the state vector and the sensor measurement vector; Within each preset time step, state prediction and covariance prediction are performed using the state vector and the sensor measurement vector. In the case of the sensor measurement vector update, the influence of the sensor measurement on the leveling machine state is determined by the Jacobian matrix and Kalman gain, and the prediction results of the state prediction and covariance prediction are updated at the same time. The output prediction results provide the best estimate of the real-time state of the leveling machine; wherein the best estimate includes the leveling machine's tilt angle and angular velocity information. Preferably, the elements in the state vector include the leveling machine's yaw and pitch angles, and the corresponding angular velocities of the leveling machine's yaw and pitch angles; the elements in the sensor measurement vector include the leveling machine chassis's tilt angles in the X and Y axis directions, the leveling machine's acceleration components in the X, Y, and Z axis directions, the leveling machine's angular velocities around the X, Y, and Z axes, the leveling machine's buoyancy change, and the seawater depth where the leveling machine is located.

[0009] According to a preferred embodiment, in step three, a PID control algorithm is used to control the opening degree of the servo valves related to the hydraulic outriggers, so that the leveling machine reaches the control target range. Preferably, the control targets of the PID control algorithm are set to a lateral tilt angle of 0° and a pitch angle of 0°. Furthermore, in step three, a DCS algorithm is used to optimize the PID parameters of the PID control algorithm.

[0010] The present invention also provides an electronic device. The electronic device includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores a computer program executable by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the leveling method of the underwater leveling machine provided by the present invention.

[0011] The present invention also provides a computer-readable storage medium. The computer-readable storage medium stores computer instructions that, when executed by a processor, implement the leveling method of the underwater leveling machine provided by the present invention.

[0012] The present invention also provides a computer program product. The computer program product includes a computer program that, when executed by a processor, implements the leveling method for the underwater leveling machine provided by the present invention.

[0013] This invention also provides a leveling system for an underwater screed, comprising: a balancing device and a balancing control device signal-connected to the balancing device. The balancing device supports the screed. The balancing device includes a hydraulic valve assembly and hydraulic outriggers. The hydraulic valve assembly is connected to the hydraulic outriggers and is used to adjust the flow parameters of the hydraulic oil in the hydraulic outriggers, thereby adjusting the extension and retraction of the hydraulic outriggers, and thus adjusting the support height of the screed above the seabed. The balancing control device includes: an attitude measurement unit, a data processing unit, and a control optimization unit. The attitude measurement unit is used to measure the state parameters of the screed in real time. Preferably, the state parameters include at least two of the following: the lateral tilt angle, the longitudinal tilt angle, and the vertical displacement of the screed. The data processing unit acquires the measurement data from the attitude measurement unit and performs data fusion on different types of measurement data to estimate the real-time state of the screed. The control optimization unit drives the hydraulic valve assembly according to the real-time state of the screed estimated by the data processing unit, thereby regulating the extension and retraction of the hydraulic outriggers to eliminate tilt errors and achieve leveling.

[0014] According to a preferred embodiment, the control optimization unit determines the tilt error of the leveling machine based on the real-time state of the leveling machine estimated by the data processing unit; then determines the height compensation amount of the hydraulic outriggers based on the tilt error; and then drives the opening of the servo valve in the hydraulic valve group according to the height compensation amount to change the flow parameters of the hydraulic oil in the hydraulic outriggers, thereby adjusting the extension and retraction of the hydraulic outriggers to achieve leveling.

[0015] According to a preferred embodiment, the attitude measurement unit includes: two single-axis horizontal sensors, a three-axis accelerometer, a gyroscope, a buoyancy sensor, and a depth sensor. The two single-axis horizontal sensors are mounted on the leveling machine chassis and are used to measure the tilt angle of the leveling machine chassis in the X-axis and Y-axis directions, respectively. The three-axis accelerometer is mounted near the center of the leveling machine chassis and is used to measure the acceleration components of the leveling machine in the X-axis, Y-axis, and Z-axis directions. The gyroscope is mounted near the center of the leveling machine chassis and is used to measure the angular velocity of the leveling machine around the X-axis, Y-axis, and Z-axis. The buoyancy sensor is mounted on the leveling machine chassis and is used to measure the buoyancy changes of the leveling machine caused by uneven mudbed or differences in bottom hardness. The depth sensor is mounted on the leveling machine chassis and is used to measure the seawater depth where the leveling machine is located. Preferably, the X-axis, Y-axis, and Z-axis are a three-dimensional coordinate system constructed with the center of the leveling machine chassis as the origin; the X-axis, Y-axis, and Z-axis are perpendicular to each other; the X-axis is along the transverse direction of the leveling machine, and the Y-axis is along the longitudinal direction of the leveling machine.

[0016] According to a preferred embodiment, the data processing unit integrates the measurement data from the single-axis horizontal sensor, the three-axis accelerometer, the gyroscope, the buoyancy sensor, and the depth sensor using a data fusion algorithm to estimate the real-time state of the leveling machine. The data processing unit first models the state vector and the sensor measurement vector; then establishes a state prediction equation using a time-discrete model and the state vector; and finally establishes a nonlinear relationship between the sensor measurement vector and the state vector. Preferably, the elements in the state vector include the leveling machine's roll and pitch angles, and the angular velocities corresponding to these angles; the elements in the sensor measurement vector include the measurement data from the single-axis horizontal sensor, the three-axis accelerometer, the gyroscope, the buoyancy sensor, and the depth sensor.

[0017] According to a preferred embodiment, the data processing unit performs data fusion as follows: S11. Initialize the state vector and the sensor measurement vector using the initial measurement values ​​of the single-axis horizontal sensor, the three-axis accelerometer, the gyroscope, the buoyancy sensor, and the water depth sensor; S12. Within each preset time step, state prediction and covariance prediction are performed using the state vector and the sensor measurement vector. S13. When the sensor measurement vector is updated, the influence of the sensor measurement value on the leveling machine state is determined by the Jacobian matrix and Kalman gain, and the prediction results of the state prediction and covariance prediction are updated at the same time. S14. Output the prediction result to obtain the best estimate of the real-time status of the leveling machine by the data processing unit; wherein, the best estimate includes the tilt angle and angular velocity information of the leveling machine.

[0018] According to a preferred embodiment, the control optimization unit uses a PID controller to control the extension and retraction of the hydraulic outriggers, thereby controlling the lateral and longitudinal tilt angles of the leveling machine. Preferably, the control targets of the PID controller are set to 0° lateral tilt and 0° longitudinal tilt. Preferably, a stroke sensor is installed on the hydraulic outrigger to measure the extension and retraction amount of the hydraulic outrigger; and the control optimization unit is connected to the stroke sensor to obtain feedback on the extension and retraction amount of the hydraulic outrigger. The control optimization unit, upon obtaining the optimal estimate, calculates the tilt error between the optimal estimate and the control target, thus obtaining the PID parameters of the PID controller. The control optimization unit optimizes the PID parameters using a DCS algorithm and updates the optimized PID parameters to the PID controller. The PID controller determines the height compensation amount of the hydraulic outrigger within a control cycle based on the optimized PID parameters. The PID controller converts the height compensation amount of the hydraulic outrigger into a drive signal for the servo valve in the hydraulic valve group, driving the hydraulic outrigger to extend and retract.

[0019] According to a preferred embodiment, when the PID controller drives the hydraulic outriggers to extend and retract, the attitude measurement unit continuously measures the state parameters of the leveling machine; the data processing unit continuously estimates the real-time state of the leveling machine. If the obtained optimal estimate reaches the PID controller's control target or the error is within a preset range, the control optimization unit keeps the PID controller stable; otherwise, the control optimization unit continues to optimize the PID parameters of the PID controller.

[0020] According to a preferred embodiment, when the PID controller drives the hydraulic outrigger to extend and retract, if an abnormality or emergency occurs, the PID parameters of the PID controller are switched to safety parameters or the leveling system triggers a safety mechanism.

[0021] According to a preferred embodiment, the process of optimizing the PID parameters by the DCS algorithm includes: S21. PID parameter initialization; Initialize the DCS population, set the population size, and set the individual dimension to three-dimensional PID parameters; Define the initial search boundary for the PID parameters; Set the initial number of iterations and the termination condition; Define the fitness function; The fitness function is evaluated using the integral of the absolute value of the error during the leveling process; and the fitness value of each PID parameter combination is calculated in each iteration. S22, Perform iterative updates; S221. Calculate the knowledge acquisition rate for each PID parameter combination; S222. Adjust the parameters in each PID parameter combination; S223. Select and update PID parameter combinations through boundary treatment and retrospective evaluation; S224. Determine whether the maximum number of iterations has been reached. If yes, proceed to step S225; otherwise, return to step S221. S225. Update the PID parameters and select a new combination of PID parameters based on the decrease in the fitness function value to obtain the optimal solution.

[0022] The present invention also provides an underwater leveling machine, wherein the leveling machine includes the leveling system provided by the present invention.

[0023] Compared with the prior art, the beneficial effects of the present invention are as follows: The underwater leveling machine leveling system and method provided by this invention obtains the leveling machine's state parameters to estimate its real-time state, and then drives the hydraulic valve group based on the leveling machine's real-time state to regulate the extension and retraction of the hydraulic outriggers, thereby eliminating the leveling machine's tilt error and achieving leveling. This invention fuses and analyzes multiple sensor data when estimating the leveling machine's real-time state, considering the influence of various factors on the leveling machine's balance, making the estimated real-time state of the leveling machine more accurate, and thus improving the leveling accuracy of the leveling system. Attached Figure Description

[0024] Figure 1 This is a schematic diagram of the composition of a leveling system according to a preferred embodiment of the present invention; Figure 2 This is a schematic diagram comparing the estimated tilt angle of the leveling machine of the present invention with the actual tilt angle of the leveling machine; Figure 3 This is a schematic diagram comparing the estimated angular velocity around the X-axis of the leveling machine of the present invention with the actual angular velocity around the X-axis of the leveling machine. Figure 4 This is a schematic diagram illustrating the logic of adjusting the extension and retraction of the hydraulic outriggers based on the estimated real-time status of the leveling machine according to the present invention. Figure 5 This is a schematic diagram of the DCS algorithm of the present invention optimizing PID parameters; Figure 6 This is a schematic diagram of an underwater leveling layer thickness monitoring and recording device for an underwater leveling machine according to the present invention; Figure 7 This is a schematic diagram (radial sliding) of an underwater leveling layer thickness monitoring and recording device for an underwater leveling machine according to this application. Figure 8 This application provides a schematic diagram of the underwater leveling machine's operating interface displaying real-time water depth. Figure 9 This is a schematic diagram summarizing the water depth of the base bed of an underwater leveling machine according to this application.

[0025] Figure label: 7. Fabric tube, 8. Distance sensor, 81. Anti-collision box, 83. First line, 84. Gear, 85. Rack, 86. Cylindrical support, 87. Pipe structure, 88. Threading hole. Detailed Implementation

[0026] The present invention will now be described in further detail with reference to specific embodiments. However, this should not be construed as limiting the scope of the present invention to the following embodiments; all technologies implemented based on the content of the present invention fall within the scope of the present invention.

[0027] Unless otherwise specified, the use of terms such as "upper," "lower," "left," "right," "center," "inner," and "outer" to indicate orientation or positional relationships in the description of specific embodiments of the present invention is based on the orientation or positional relationships shown in the accompanying drawings, or the orientation or positional relationship in which the product / equipment / device is typically placed during use. These terms are merely for the purpose of facilitating the description of the present invention or simplifying the description in specific embodiments, enabling those skilled in the art to quickly understand the solution, and do not indicate or imply that a particular device / component / element must have a specific orientation, or be constructed and operated in a specific positional relationship. Therefore, they should not be construed as limitations on the present invention.

[0028] Furthermore, the use of terms such as "horizontal," "vertical," "suspended," and "parallel" does not imply that the corresponding device / component / element must be absolutely horizontal, vertical, suspended, or parallel, but rather that it can be slightly tilted or have a deviation. For example, "horizontal" merely means that its direction is more horizontal relative to "vertical," not that the structure must be completely horizontal, but can be slightly tilted. Alternatively, it can be simplified to mean that the corresponding device / component / element, when set in a "horizontal," "vertical," "suspended," or "parallel" direction, can have an error / deviation of ±10% relative to the corresponding direction, more preferably within ±8%, more preferably within ±6%, more preferably within ±5%, and more preferably within ±4%. As long as the corresponding device / component / element is within the error / deviation range, it can still achieve its function in the present invention.

[0029] Furthermore, the use of terms such as "first," "second," and "third" in terminology is merely for distinguishing descriptions of identical or similar components and should not be interpreted as emphasizing or implying the relative importance of a particular component.

[0030] Furthermore, in the description of the embodiments of the present invention, "several", "more than", and "a number of" represent at least two. The number can be any number, such as 2, 3, 4, 5, 6, 7, 8, or 9, and can even exceed nine.

[0031] Furthermore, in the description of the technical solution of this invention, unless otherwise explicitly specified / limited / restricted, the terms "set up," "install," "connect," "link," "provided with," "laid out," and "arranged" should be interpreted broadly. For example, they can refer to fixed connections, detachable connections, or integral connections; they can refer to common connection methods in the art, such as welding, riveting, bolting, and threaded connections. Such connections can be mechanical, electrical, or communication connections; they can be direct connections or indirect connections through an intermediate medium; and they can refer to the internal communication between two components.

[0032] Example 1 This embodiment provides a leveling system for an underwater leveling machine. See also... Figure 1The leveling system includes: a balancing device and a balancing control device connected to the balancing device via signals. The balancing device supports the leveling machine. The balancing device includes a hydraulic valve assembly and hydraulic outriggers. The hydraulic valve assembly is connected to the hydraulic outriggers and is used to adjust the flow parameters of the hydraulic oil in the hydraulic outriggers, thereby adjusting the extension and retraction of the hydraulic outriggers and thus adjusting the support height of the leveling machine from the seabed. The balancing control device includes: an attitude measurement unit, a data processing unit, and a control optimization unit. The attitude measurement unit is used to measure the state parameters of the leveling machine in real time. Preferably, the state parameters include at least two of the following: the leveling machine's lateral tilt angle, longitudinal tilt angle, and vertical displacement. The data processing unit acquires the measurement data from the attitude measurement unit and performs data fusion on different types of measurement data to estimate the real-time state of the leveling machine. The control optimization unit drives the hydraulic valve assembly according to the real-time state of the leveling machine estimated by the data processing unit, thereby regulating the extension and retraction of the hydraulic outriggers to eliminate the tilt error of the leveling machine and achieve leveling.

[0033] Example 2 This embodiment is a further improvement on Embodiment 1, and the repeated content will not be described again. The control optimization unit determines the tilt error of the leveler based on the real-time status of the leveler estimated by the data processing unit; then, it determines the height compensation amount of the hydraulic outriggers based on the tilt error; and then, based on the height compensation amount, it drives the opening of the servo valve in the hydraulic valve group to change the flow parameters of the hydraulic oil in the hydraulic outriggers, thereby adjusting the extension and retraction of the hydraulic outriggers to achieve leveling. Preferably, the adjustment of the opening of the servo valve in the hydraulic valve group can affect the flow direction, flow rate, and other flow parameters of the hydraulic oil in the hydraulic outriggers.

[0034] Preferably, the underwater leveling machine involved in this invention is, for example, the walking underwater leveling machine disclosed in patent application (publication number CN103132553A) that is a wirelessly remotely controlled walking underwater leveling machine, or other underwater leveling machines supported by hydraulic outriggers, and the leveling machine chassis is a rectangular chassis. Preferably, the underwater leveling machine is supported by four hydraulic outriggers.

[0035] Preferably, the attitude measurement unit consists of multiple sensors. Preferably, the data processing unit and the control optimization unit are mounted on a data processing terminal. Preferably, the data processing unit and the control optimization unit are mounted on a PLC.

[0036] Preferably, the attitude measurement unit includes: two single-axis horizontal sensors, a three-axis accelerometer, a gyroscope, a buoyancy sensor, and a water depth sensor.

[0037] Two single-axis level sensors are mounted on the leveling machine chassis to measure the tilt angle of the chassis in the X and Y axes, respectively. Preferably, the two single-axis level sensors can be mounted on the crossbeams and longitudinal beams of the leveling machine chassis, or at other locations capable of measuring the tilt angle of the chassis in the X and Y axes. Preferably, the single-axis level sensors transmit the collected tilt angle data to the PLC in the form of analog voltage (0~10V) or current (4~20mA).

[0038] A triaxial accelerometer is positioned near the center of the leveling machine chassis to measure the acceleration components of the leveling machine in the X, Y, and Z axes. Preferably, the triaxial accelerometer and the single-axis leveling sensor are on the same plane.

[0039] A gyroscope, positioned near the center of the leveling machine chassis, is used to measure the angular velocities of the leveling machine around the X, Y, and Z axes. Preferably, the gyroscope can be integrated with a three-axis accelerometer in the same inertial measurement unit module.

[0040] A buoyancy sensor, installed on the chassis of the leveling machine, is used to measure changes in the leveling machine's buoyancy caused by uneven mud beds or differences in bottom hardness. Preferably, the buoyancy sensor sends the collected buoyancy data to the PLC in the form of analog voltage (0~10V) or current (4~20mA). Preferably, the buoyancy data collected by the buoyancy sensor can be used to estimate the attitude deviation of the leveling machine caused by changes in buoyancy.

[0041] A depth sensor, mounted on the chassis of the leveling machine, is used to measure the seawater depth where the leveling machine is located. Preferably, the depth sensor is positioned at the bottom of the leveling machine chassis, near the front or sides, or at other locations capable of real-time detection of the seawater depth. Preferably, the data collected by the depth sensor can be used to estimate the impact of the water body on the forces acting on the leveling machine. Preferably, the data collected by the depth sensor is fed into the PLC in the form of analog voltage (0~10V) or current (4~20mA).

[0042] Preferably, the X-axis, Y-axis, and Z-axis form a three-dimensional coordinate system with the center of the leveling machine chassis as the origin; the X-axis, Y-axis, and Z-axis are perpendicular to each other; the X-axis is along the transverse direction of the leveling machine, and the Y-axis is along the longitudinal direction of the leveling machine.

[0043] Preferably, the data collected by each sensor is processed by a conversion module before being input into the PLC. The output signals of each sensor are different; some are analog voltage / current signals, while others are digital signals such as SPI or I²C bus signals. Therefore, the conversion module also includes commonly used digital-to-analog converters / analog-to-digital converters, or integrated ADC samplers, network converters, modular interfaces, etc.

[0044] Preferably, the data processing unit integrates measurement data from a single-axis horizontal sensor, a three-axis accelerometer, a gyroscope, a buoyancy sensor, and a depth sensor using a data fusion algorithm to estimate the real-time status of the leveling machine.

[0045] Preferably, the data processing unit mounted in the PLC uses a fusion algorithm based on an extended Kalman filter to fuse the measurement data from each sensor, thereby estimating the real-time status of the leveling machine.

[0046] The data processing unit first models the state vector and sensor measurement vector; then establishes a state prediction equation using a time-discrete model and the state vector; and finally establishes a nonlinear relationship between the sensor measurement vector and the state vector. Preferably, the elements in the state vector include the leveler's tilt and pitch angles, as well as the angular velocities corresponding to these angles; the elements in the sensor measurement vector include measurement data from a single-axis horizontal sensor, a three-axis accelerometer, a gyroscope, a buoyancy sensor, and a depth sensor.

[0047] Preferably, the data processing unit performs data fusion in the following steps: S11. Initialize the state vector and sensor measurement vector using the initial measurement values ​​of the single-axis horizontal sensor, three-axis accelerometer, gyroscope, buoyancy sensor, and water depth sensor.

[0048] S12. Within each preset time step, state prediction and covariance prediction are performed using the state vector and sensor measurement vector. S13. Under the condition of sensor measurement vector update, determine the influence of sensor measurement value on leveling machine state through Jacobi matrix and Kalman gain, and update the prediction results of state prediction and covariance prediction at the same time. S14. Output the prediction results to obtain the best estimate of the real-time status of the leveling machine by the data processing unit; wherein, the best estimate includes the tilt angle and angular velocity information of the leveling machine.

[0049] Preferably, the control optimization unit uses a PID controller to control the extension and retraction of the hydraulic outriggers, thereby controlling the lateral and longitudinal tilt angles of the leveling machine. Preferably, the control targets of the PID controller are set to 0° lateral tilt and 0° longitudinal tilt. Preferably, stroke sensors are installed on the hydraulic outriggers to measure the extension and retraction amount; and the control optimization unit is connected to the stroke sensors to obtain feedback on the extension and retraction amount of the hydraulic outriggers. After obtaining the best estimate, the control optimization unit calculates the tilt error between the best estimate and the control target, obtaining the PID parameters of the PID controller. The control optimization unit optimizes the PID parameters using a DCS algorithm and updates the optimized PID parameters to the PID controller. The PID controller determines the height compensation amount of the hydraulic outriggers within a control cycle based on the optimized PID parameters. The PID controller converts the height compensation amount of the hydraulic outriggers into a drive signal for the servo valve in the hydraulic valve group, driving the hydraulic outriggers to extend and retract.

[0050] Preferably, when the hydraulic outriggers are extended and retracted by the PID controller, the attitude measurement unit continuously measures the state parameters of the leveling machine; the data processing unit continuously estimates the real-time state of the leveling machine. If the obtained optimal estimate reaches the control target of the PID controller or the error is within the preset range, the control optimization unit keeps the PID controller stable; otherwise, the control optimization unit continues to optimize the PID parameters of the PID controller.

[0051] Preferably, when the hydraulic outrigger is extended or retracted by the PID controller, if an abnormality or emergency occurs (such as abnormal parameters, over-adjustment, oscillation, etc.), the PID parameters of the PID controller are switched to safe parameters or the leveling system triggers a safety mechanism.

[0052] Preferably, the process of optimizing PID parameters using the DCS algorithm includes: S21. PID parameter initialization; Initialize the DCS population, set the population size, and set the individual dimension to three-dimensional PID parameters; Define the initial search boundary for the PID parameters; Set the initial number of iterations and the termination condition; Define the fitness function; The fitness function is evaluated using the integral of the absolute value of the error during the leveling process; and the fitness value of each PID parameter combination is calculated in each iteration. S22, Perform iterative updates; S221. Calculate the knowledge acquisition rate for each PID parameter combination; S222. Adjust the parameters in each PID parameter combination; S223. Select and update PID parameter combinations through boundary treatment and retrospective evaluation; S224. Determine whether the maximum number of iterations has been reached. If yes, proceed to step S225; otherwise, return to step S221. S225. Update the PID parameters and select a new combination of PID parameters based on the decrease in the fitness function value to obtain the optimal solution.

[0053] Preferably, the control process for achieving automatic leveling using this embodiment is as follows: When the operator sends an "automatic leveling" command to the PLC via buttons or switches, the PLC immediately switches to automatic leveling mode. First, it invokes a multi-sensor fusion algorithm based on an extended Kalman filter (EKF) to fuse data from various sensors, including tilt angle data from a single-axis horizontal sensor, acceleration data from an accelerometer, and data from depth or buoyancy sensors, in real time. This results in an accurate real-time attitude estimate of the leveling machine (including estimates of the lateral and longitudinal tilt angles and angular velocities). Subsequently, the PID parameters obtained from the online optimization of the DCS algorithm are directly loaded into two PID control channels. The tilt error fused by the EKF is simultaneously input into the lateral and longitudinal PID controllers. Within each 50-100ms control cycle, the PLC calculates the height compensation for the four hydraulic outriggers and converts the results into servo valve drive signals to precisely drive the corresponding hydraulic cylinders to extend and retract, quickly eliminating the tilt error. When the error converges to a preset tolerance and the system has no tilt angle or oil pressure over-limit alarms, the leveling cycle automatically exits, completing a full one-button automatic leveling operation without any manual intervention.

[0054] Example 3 This embodiment provides an underwater leveling machine. Preferably, the underwater leveling machine provided in this embodiment has the same structure as the measuring tower and multi-degree-of-freedom adjustable underwater leveling machine described in patent application (publication number CN120273400A), and the underwater leveling machine provided in this embodiment is equipped with the leveling system of the underwater leveling machine involved in Embodiments 1 and 2.

[0055] like Figure 6-9As shown, this embodiment provides an underwater screed machine that also includes an underwater screed layer thickness monitoring and recording device. Preferably, the underwater screed layer thickness monitoring and recording device is disposed on the side wall of the material distribution pipe 7. Preferably, the underwater screed layer thickness monitoring and recording device includes a distance measuring sensor 8 and a collision avoidance box 81. The material distribution pipe 7 serves as a vertical channel for the stone material, and is disposed on the underwater screed machine. The material distribution pipe 7 is capable of moving laterally and longitudinally. The distance measuring sensor 8 is installed inside the material distribution pipe 7 and measures distance downwards. The collision avoidance box 81 is installed inside the material distribution pipe 7, and the collision avoidance box 81 is at least partially located above the distance measuring sensor 8. This embodiment describes an underwater leveling layer thickness monitoring and recording device. After the leveling machine completes its underwater positioning, it is adjusted to a suitable elevation. Subsequently, a distance sensor 8 located underwater measures the water depth data in real time. As the material placement pipe 7 moves, the distance sensor 8 continuously collects water depth information, ultimately establishing a correspondence between the entire working plane position and water depth information. This allows for the determination of the underwater leveling layer thickness, guiding the control of stone volume and the selection of specifications during subsequent leveling and material placement. The anti-collision box 81 is preferably made of iron.

[0056] When the leveling layer thickness is 30-40cm, use 8-15cm two-piece stones. When the leveling layer thickness is 20-30cm, use a mixture of 8-15cm two-piece stones and 20-40mm crushed stone, with a preferred mixing ratio of 6:4-4:6. When the base bed leveling thickness is 1-10cm, using 20-40mm crushed stone will achieve the best leveling effect.

[0057] In one preferred embodiment, the ranging sensor 8 is preferably an M50-M80 underwater ranging sensor. Its measurement principle is as follows: During distance measurement, an ultrasonic signal is emitted by an ultrasonic probe, reflected back by the liquid or other solid medium, and received by the same probe. The time difference between the ultrasonic wave emission and reception is measured to achieve the measurement of the material level. The relationship between the distance L between the ultrasonic probe and the measured material surface, the temperature-compensated sound velocity v, and the sound wave travel time t within the measurement range can be expressed by the following formula: L =0.5 vtL: Unit: m; v: Unit: m / s; t: Unit: s. The single-beam underwater ultrasonic ranging sensor is a high-response ranging sensor designed based on the ARM architecture. This sensor has a built-in RS485 chip for reading data and configuring internal parameters. Internal parameters can be modified via the ModBus-RTU protocol to adjust sensor performance and implement certain functions. Protection rating: IP68, equipped with RS485 communication, data is readable and writable, and address, baud rate, and other parameters can be modified via the ModBus-RTU protocol. It has a short response time and temperature compensation function. The output type is standard RS485 output with Modbus-RTU protocol. The error compensation value is represented by 2 bytes of unsigned data, unit: mm. When the ranging error exceeds the maximum allowable range, this value can be used for slight compensation. The default compensation value is 0. The compensation value is obtained by splitting the int16t type data into two bytes, with the high byte first and the low byte last, in the format of numerical storage in the computer. Negative compensation value = 65535 - error value.

[0058] In a preferred embodiment, the ranging sensor 8 is located inside the anti-collision box 81, and the bottom of the anti-collision box 81 is provided with a through hole, and the ranging sensor 8 is connected to the through hole.

[0059] In a preferred embodiment, the system further includes a signal receiver located above the water surface. The ranging sensor 8 is connected to a first line 83, which includes at least one of a power line and a signal line. The power line and the signal line are tied to the fabric tube 7 and led upwards out of the water surface. The upper end of the signal line is connected to the signal receiver.

[0060] The underwater leveling layer thickness monitoring and recording device described in this embodiment has an opening on the side wall of the lower part of the leveling machine's material distribution pipe 7 and an anti-collision box 81 is installed therein. A ranging sensor 8 with underwater ranging function is installed inside the anti-collision box 81, preferably an acoustic sensor. The power line and signal line connected to the acoustic sensor are led out of the material distribution pipe 7 and led out of the water surface along the material distribution pipe 7 and connected to the signal receiver of the working platform.

[0061] After the screed completes its underwater positioning, it is adjusted to a suitable elevation. Then, the material placement pipe 7 moves, and the underwater ranging sensor 8 measures the water depth data in real time and sends the data signal back to the signal receiver at the water surface of the screed. The data is displayed on the operating interface. By moving the screed, water depth information is continuously collected, and finally a chart with position and water depth information is generated. After simple processing, the thickness of the underwater screed layer can be determined to guide the control of the volume and selection of specifications of the stone material during subsequent screed placement.

[0062] Preferably, both the anti-collision box 81 and the distance sensor 8 are located inside the fabric tube 7, which makes their data measurement more accurate.

[0063] In a preferred embodiment, the top of the anti-collision box 81 is inclined, with the portion near the inner wall of the material distribution tube 7 being higher than the portion near the middle of the material distribution tube 7. This facilitates the stones rolling off the top of the anti-collision box 81 during descent, effectively reducing the probability of stones accumulating on the top of the anti-collision box 81.

[0064] like Figure 6 As shown, a threading hole 88 is provided through the side wall of the fabric tube 7. The threading hole 88 corresponds to the height of the anti-collision box 81 and is connected to the internal cavity of the anti-collision box 81.

[0065] The power cord and signal line pass through the third gap 741 and the wire hole 88 in sequence and then extend into the anti-collision box 81 to connect with the ranging sensor 8, thereby effectively avoiding direct contact between the power cord and signal line and the stone, thus effectively ensuring the service life of the power cord and signal line.

[0066] Preferably, the entire assembly formed by the ranging sensor 8 and the anti-collision box 81 is capable of moving radially along the fabric tube 7.

[0067] The underwater leveling layer thickness monitoring and recording device described in this embodiment involves the distance measuring sensor 8 and the anti-collision box 81 extending into the material distribution pipe 7 for measurement during the initial distance measurement. During the subsequent filling operation, the distance measuring sensor 8 and the anti-collision box 81 are moved radially along the material distribution pipe 7 to the outside of the material distribution pipe 7, thereby more effectively reducing the obstruction of the distance measuring sensor 8 and the anti-collision box 81 to the stone material and further improving the service life of the distance measuring sensor 8 and the anti-collision box 81.

[0068] See Figure 7 More preferably, the distance sensor 8 and the anti-collision box 81 are driven to move radially along the fabric tube 7 via a gear and rack mechanism. The rack 85 is connected to both the distance sensor 8 and the anti-collision box 81 via a cylindrical support 86. The rack 85 is arranged radially along the fabric tube 7. A motor capable of operating underwater drives the gear 84 to rotate, causing the gear 84 and rack 85 to engage. Holes are formed in the side wall of the fabric tube 7, and a pipe fitting structure 87 with a diameter adapted to the hole is welded to the outside of the holes. The engagement of the gear 84 and rack 85 drives the cylindrical support 86 to move radially relative to the pipe fitting structure 87 along the fabric tube 7, thereby enabling both the distance sensor 8 and the anti-collision box 81 to move radially along the fabric tube 7.

[0069] The underwater leveling layer thickness monitoring and recording device described in this embodiment has an opening on the side wall of the lower part of the leveling machine's material distribution pipe 7 and an anti-collision box 81 is installed therein. A ranging sensor 8 with underwater ranging function is installed inside the anti-collision box 81, preferably an acoustic sensor. The power line and signal line connected to the acoustic sensor are led out of the material distribution pipe 7 and led out of the water surface along the material distribution pipe 7 and connected to the signal receiver of the working platform.

[0070] After the screed completes its underwater positioning, it is adjusted to a suitable elevation. Then, the material placement pipe 7 moves, and the underwater ranging sensor 8 measures the water depth data in real time and sends the data signal back to the signal receiver at the water surface of the screed. The data is displayed on the operating interface. By moving the screed, water depth information is continuously collected, and finally a chart with position and water depth information is generated. This allows the thickness of the underwater screed layer to be determined, which can guide the control of the volume of stone and the selection of specifications during subsequent screed placement.

[0071] The fabric tube 7 can move laterally and longitudinally.

[0072] like Figure 8 and 9 As shown in the figure, the underwater leveling machine and the underwater leveling layer thickness monitoring and recording device described in this embodiment include the following steps during construction: (1) The distance sensor 8 moves along the horizontal and vertical sides of the leveling machine, and the distance sensor 8 collects water depth information in real time to form a correspondence between the working plane position of the leveling machine and the water depth information; (2) Based on the correspondence between the working plane position of the leveling machine and the water depth information, the required leveling layer thickness at each position of the working plane of the leveling machine is obtained; (3) Determine the specifications and volume of filling stone for each location based on the required leveling layer thickness; (4) Return the material distribution pipe 7 to its initial position, and fill the material distribution pipe 7 with stone according to the determined specifications and volume of the stone at each position and perform leveling operations.

[0073] like Figure 9 More preferably, the working plane of the leveling machine is divided into multiple areas, and the water depth information of each area is collected in real time by the distance sensor 8. After being collected, a correspondence table of the working plane position and water depth information of the leveling machine is formed. Figure 9 The outriggers #1, #2, #3, and #4 in the diagram represent the four hydraulic outriggers used to support the leveling machine.

[0074] Example 4 This embodiment provides a leveling method for an underwater leveling machine, and this embodiment is an explanation of the operation method of the leveling system involved in Embodiments 1 and 2.

[0075] The leveling method provided in this embodiment includes the following steps: Step 1: Measure the status parameters of the leveling machine in real time; the status parameters include at least two of the following: the lateral tilt angle, the longitudinal tilt angle, and the vertical displacement of the leveling machine. Step 2: Perform data fusion on different types of measurement data to estimate the real-time status of the leveling machine; Step 3: Adjust the flow parameters of the hydraulic oil in the hydraulic outriggers according to the estimated real-time status of the leveling machine, and then adjust the extension and retraction of the hydraulic outriggers to adjust the support height of the leveling machine from the seabed, so as to eliminate the tilt error of the leveling machine and achieve leveling.

[0076] Preferably, step three includes: The tilt error of the leveler is determined based on the estimated real-time status of the leveler; Then determine the height compensation amount of the hydraulic outriggers based on the tilt error; Then, based on the height compensation amount, the opening degree of the servo valve in the hydraulic valve group is driven to change the flow parameters of the hydraulic oil in the hydraulic outrigger, thereby adjusting the extension and retraction of the hydraulic outrigger and achieving leveling.

[0077] Preferably, the state parameters in step one include: the tilt angle of the leveler chassis in the X and Y axis directions, the acceleration components of the leveler in the X, Y, and Z axis directions, the angular velocities of the leveler around the X, Y, and Z axes, the buoyancy change of the leveler, and the seawater depth where the leveler is located. Preferably, the X, Y, and Z axes form a three-dimensional coordinate system with the center of the leveler chassis as the origin; the X, Y, and Z axes are mutually perpendicular; the X axis is along the transverse direction of the leveler, and the Y axis is along the longitudinal direction of the leveler.

[0078] Preferably, step two includes: modeling the state vector and sensor measurement vector; establishing a state prediction equation using a time-discrete model and state vector; establishing a nonlinear relationship between the sensor measurement vector and state vector; initializing the state vector and sensor measurement vector; performing state prediction and covariance prediction using the state vector and sensor measurement vector within each preset time step; determining the influence of sensor measurements on the leveling machine's state using the Jacobian matrix and Kalman gain when the sensor measurement vector is updated, and simultaneously updating the prediction results of state prediction and covariance prediction; outputting the prediction results to obtain the best estimate of the real-time state of the leveling machine by the data processing unit; wherein, the best estimate includes the leveling machine's tilt angle and angular velocity information.

[0079] Preferably, the elements in the state vector include the leveler's tilt angle and pitch angle, as well as the angular velocities corresponding to the leveler's tilt angle and pitch angle; the elements in the sensor measurement vector include the tilt angle of the leveler chassis in the X and Y axis directions, the acceleration components of the leveler in the X, Y, and Z axis directions, the angular velocities of the leveler around the X, Y, and Z axes, the buoyancy change of the leveler, and the seawater depth where the leveler is located.

[0080] Preferably, in step two: The state vector is defined as ;in , These are the horizontal and vertical tilt angles of the leveling machine, respectively. , This corresponds to the angular velocity. The system uses a discrete-time model with a time step of . The state prediction equation can then be written as:

[0081] in, Let be the state transition matrix, defined as follows: B is the control input matrix; k represents the time step. Indicates at time The system's control input vector, i.e. the control command of the leveling machine's balancing device, is considered a zero vector in this model as there is no active control input. The process noise is represented by a zero-mean Gaussian distribution with a covariance of . .

[0082] The information provided by each sensor is combined to form a sensor measurement vector: The elements in the sensor measurement vector represent the sensing data of each sensor. At this point, the nonlinear relationship between the sensor output data and the real-time state of the leveling machine is represented by a function:

[0083] in, It is a nonlinear measurement function. To measure the noise, it follows a zero-mean Gaussian distribution with a covariance of . .

[0084] Preferably, in step two, the process from initializing the state vector and sensor measurement vector to outputting the prediction result is the operation flow of the extended Kalman filter. Preferably, the operation flow of the extended Kalman filter includes: S31: Initialization phase, setting state estimation Initial state estimation (the values ​​of the elements in the state vector X are taken from the initial measurements of the sensor); initialization of the state covariance matrix. ; S32: Prediction Phase: For each time step Perform state prediction and covariance prediction:

[0085]

[0086] S33: Update Phase When new measurement values ​​are received First, calculate the Jacobian matrix, and then take the partial derivative of the nonlinear measurement function with respect to the state vector to obtain:

[0087] Calculate the Kalman gain:

[0088] Status Update:

[0089] Covariance update:

[0090] S34: Output the fusion result: the fused state estimate This is the best estimate of the real-time status of the leveler at the current moment, including accurate information on the leveler's tilt angle and angular velocity, which can provide feedback input for the leveling of the leveler.

[0091] The estimated tilt angle of the leveler obtained through the extended Kalman filter (estimated) ) and the true tilt angle of the leveling machine (true For example Figure 2 As shown; the estimated angular velocity of the leveler about the X-axis obtained through an extended Kalman filter (estimated). dot ) and the actual angular velocity of the leveling machine around the X-axis (real) dot For example Figure 3 As shown.

[0092] according to Figure 2 and Figure 3 It is known that the extended Kalman filter can effectively fuse multi-sensor data and quickly converge to the true state, providing feedback input for the leveling of the leveling machine.

[0093] See Figure 4 Preferably, the optimal estimate of the real-time state of the leveling machine output in step two provides input feedback for the control of the hydraulic outriggers in step three. Preferably, in step three, a PID control algorithm is used to control the opening degree of the relevant servo valves of the hydraulic outriggers, so that the leveling machine reaches the control target range. Preferably, the control target of the PID control algorithm is set to a lateral tilt angle of 0° and a pitch angle of 0°. Furthermore, in step three, a DCS algorithm is used to optimize the PID parameters of the PID control algorithm.

[0094] Preferably, in step three, the control target is first set, and the ideal tilt angle and pitch angle of the leveling machine are both set to be 1. , recorded as Based on the state estimation output from step two and Define the error:

[0095]

[0096] Preferably, PID controllers are used for the lateral tilt angle control and longitudinal tilt angle control of each leveling machine, and the control quantity is... The calculation formula is:

[0097] in, These are the proportional, integral, and differential coefficients, respectively.

[0098] Preferably, to optimize the PID parameters of the PID controller in real time, a Differentiated Creative Search (DCS) algorithm is introduced for real-time online optimization.

[0099] See Figure 5 Preferably, the specific implementation process of the DCS algorithm to optimize PID parameters includes: Step 1: Initialize the population, define the fitness function, define the PID parameter search boundary, set the number of iterations and termination conditions, the number of fitness function evaluations, probability constants, etc. Step 2: Calculate the knowledge acquisition rate of each individual and rank the population; where each individual represents a combination of PID parameters. Step 3: Use divergent thinking to explore and update individual positions, and use convergent thinking to fine-tune the combination of each PID parameter. Step 4: Select and update PID parameter combinations through boundary treatment and retrospective evaluation; Step 5: Determine if the maximum number of iterations has been reached; if yes, output the optimal PID parameter combination; if no, return to Step 2 and continue iterating.

[0100] Preferably, in process one, the DCS population is initialized, and the population size is set ( Individual dimensions are three-dimensional PID parameters ( Define the initial search boundary for the PID parameters, such as... Set the number of iterations and the termination condition.

[0101] Preferably, in process one, the fitness function is evaluated using the integral of the absolute value of the error (IAE) during the leveling process:

[0102] In each iteration, the fitness value for each combination of PID parameters is calculated.

[0103] Preferably, in process two, differentiated knowledge acquisition (DKA) is performed for each PID parameter combination, and the knowledge acquisition rate (KRA) is calculated based on individual performance. ;

[0104] in, This indicates rounding to the nearest integer. Indicates the individual in the first... During the next iteration The coefficient measures an individual's knowledge gaps or incompleteness, and the formula is as follows:

[0105] in, Indicates the individual in the first... Ranking at the next iteration; Indicates the population size in the current iteration; The smaller the value, the greater the individual's knowledge gap, meaning that the individual may need more learning and adjustment.

[0106] Preferably, in process three, divergent thinking is used to explore and update the individual position: according to the formula Update individual locations and conduct a global exploration; among them, Indicates that the individual is in The updated solution in terms of dimension The Linnik distribution controls the frequency and magnitude of jumps. The Linnik distribution has a heavy-tailed property, which generates a wide range of jumps in the search space, thus encouraging individuals to explore more extensively.

[0107] Preferably, in process three, a convergent approach is used to fine-tune the various PID parameter combinations: using... Fine-tune the parameters for each individual; in, It is a weighting factor, representing the influence of the current optimal solution; and Individual social cognition and learning intensity are controlled separately. Convergent thinking helps individuals make local adjustments based on known optimal solutions, thereby accelerating convergence.

[0108] Preferably, in process four, boundary handling is used to ensure that the PID parameters are always within a reasonable range, as shown in the following formula:

[0109] in, and Indicates that the individual is in The upper and lower bounds in the dimension ensure that the generated hyperparameter values ​​are always within the legal range, avoiding the generation of unrealistic solutions.

[0110] Preferably, in process four, the PID parameters are updated through retrospective evaluation, and new PID parameters are selected based on the decrease in the fitness function value.

[0111] Preferably, the quality of the solution is adjusted through retrospective evaluation. This allows for feedback adjustments to the entire population based on the optimal solution of each generation. The specific selection process and optimal solution tracking formula are as follows:

[0112] Best solution tracing:

[0113] Among them, the current solution The updated solution , This represents the value of the objective function. Preferably, the objective function of the DCS algorithm is the loss function value of the model, while the dimension... It consists of three dimensions (learning rate, number of hidden layer nodes, and regularization coefficient).

[0114] Preferably, after each optimization, the DCS algorithm updates the optimal PID parameters to the PID controller in real time, and calculates the control quantities of the leveler's yaw and pitch angles within the current control cycle.

[0115]

[0116] Based on the control amount of the tilt angle of the leveling machine Control of the tilt angle of the leveling machine Output control signals to adjust the corresponding balancing devices respectively; detect the control effect: simultaneously monitor and provide feedback on the actual leveling effect in real time. If the control target is achieved or the absolute value of the lateral tilt error and the absolute value of the longitudinal tilt error are less than the preset threshold, then maintain stability; otherwise, continue to optimize the PID parameters of the PID controller.

[0117] Preferably, the balancing device consists of hydraulic outriggers and a hydraulic valve assembly. The PID controller controls the tilt angle of the leveling machine. Control of the tilt angle of the leveling machine The height compensation amount of each hydraulic outrigger is determined, which in turn drives the opening of the servo valve in the hydraulic valve group, changes the flow parameters of the hydraulic oil in the hydraulic outrigger, and thus adjusts the extension and retraction of the hydraulic outrigger to achieve leveling.

[0118] Example 5 This embodiment provides an electronic device. The electronic device includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores a computer program executable by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the leveling method of the underwater leveling machine according to Embodiment 4.

[0119] Example 6 This embodiment provides a computer-readable storage medium. The computer-readable storage medium stores computer instructions that are used to cause a processor to execute and implement the leveling method of the underwater leveler involved in Embodiment 4.

[0120] Example 7 This embodiment provides a computer program product. The computer program product includes a computer program that, when executed by a processor, implements the leveling method of the underwater leveling machine involved in Embodiment 4.

[0121] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A leveling method for an underwater leveling machine, characterized in that, Includes the following steps: Step 1: Measure the status parameters of the leveling machine in real time; wherein, the status parameters include at least two of the following: the lateral tilt angle, the longitudinal tilt angle, and the vertical displacement of the leveling machine. Step 2: Perform data fusion on different types of measurement data to estimate the real-time status of the leveling machine; Step 3: Adjust the flow parameters of the hydraulic oil in the hydraulic outriggers according to the estimated real-time status of the leveling machine, and then adjust the extension and retraction of the hydraulic outriggers to adjust the support height of the leveling machine from the seabed, so as to eliminate the tilt error of the leveling machine and achieve leveling.

2. The leveling method for an underwater leveling machine according to claim 1, characterized in that, Step three includes: The tilt error of the leveler is determined based on the estimated real-time status of the leveler; Then, the height compensation amount of the hydraulic outrigger is determined based on the tilt error. Then, based on the height compensation amount, the opening degree of the servo valve in the hydraulic valve group is driven to change the flow parameters of the hydraulic oil in the hydraulic outrigger, thereby adjusting the extension and retraction of the hydraulic outrigger to achieve leveling.

3. The leveling method for an underwater leveling machine according to claim 2, characterized in that, The state parameters in step one include: the tilt angle of the leveler chassis in the X and Y axis directions, the acceleration components of the leveler in the X, Y and Z axis directions, the angular velocity of the leveler about the X, Y and Z axes, the buoyancy change of the leveler, and the seawater depth where the leveler is located. The X-axis, Y-axis, and Z-axis are a three-dimensional coordinate system constructed with the center of the leveling machine chassis as the origin; the X-axis, Y-axis, and Z-axis are perpendicular to each other; the X-axis is along the horizontal direction of the leveling machine, and the Y-axis is along the vertical direction of the leveling machine.

4. The leveling method for an underwater leveling machine according to claim 3, characterized in that, Step two includes: Model the state vector and the sensor measurement vector; A state prediction equation is established using the time discrete model and the state vector; Establish a nonlinear relationship between the sensor measurement vector and the state vector; Initialize the state vector and the sensor measurement vector; Within each preset time step, state prediction and covariance prediction are performed using the state vector and the sensor measurement vector. In the case of the sensor measurement vector update, the influence of the sensor measurement on the leveling machine state is determined by the Jacobian matrix and Kalman gain, and the prediction results of the state prediction and covariance prediction are updated at the same time. The prediction results are output to obtain the best estimate of the real-time state of the leveling machine; wherein, the best estimate includes the tilt angle and angular velocity information of the leveling machine; The elements in the state vector include the leveler's tilt and pitch angles, as well as the angular velocities corresponding to the tilt and pitch angles; the elements in the sensor measurement vector include the tilt angles of the leveler chassis in the X and Y axis directions, the acceleration components of the leveler in the X, Y, and Z axis directions, the angular velocities of the leveler around the X, Y, and Z axes, the buoyancy change of the leveler, and the seawater depth where the leveler is located.

5. The leveling method for an underwater leveling machine according to claim 4, characterized in that, In step three, the opening degree of the servo valves related to the hydraulic outriggers is controlled using a PID control algorithm, so that the leveling machine reaches the control target range; wherein, the control target of the PID control algorithm is set to 0° for the lateral tilt angle and 0° for the longitudinal tilt angle. Furthermore, in step three, the DCS algorithm is used to optimize the PID parameters of the PID control algorithm.

6. An electronic device, characterized in that, The electronic device includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores a computer program executable by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the leveling method according to any one of claims 1 to 5.

7. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that cause a processor to execute the leveling method according to any one of claims 1 to 5.

8. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements the leveling method according to any one of claims 1 to 5.

9. A leveling system for an underwater leveling machine, characterized in that, include: A balancing device and a balancing control device that is signal-connected to the balancing device; The balancing device is used to support the leveling machine; the balancing device includes a hydraulic valve group and hydraulic outriggers; the hydraulic valve group is connected to the hydraulic outriggers and is used to adjust the flow parameters of the hydraulic oil in the hydraulic outriggers, thereby adjusting the extension and retraction of the hydraulic outriggers, and thus adjusting the support height of the leveling machine from the seabed. The balance control device includes: an attitude measurement unit, a data processing unit, and a control optimization unit; The attitude measurement unit is used to measure the state parameters of the leveling machine in real time; wherein, the state parameters include at least two of the following: the lateral tilt angle, the longitudinal tilt angle, and the vertical displacement of the leveling machine. The data processing unit is used to acquire the measurement data of the attitude measurement unit, and to perform data fusion on different types of measurement data to estimate the real-time status of the leveling machine. The control optimization unit drives the hydraulic valve group according to the real-time status of the leveling machine estimated by the data processing unit, thereby regulating the extension and retraction of the hydraulic outriggers to eliminate the tilt error of the leveling machine and achieve leveling.

10. The leveling system of an underwater leveling machine according to claim 9, characterized in that, The control optimization unit determines the tilt error of the leveler based on the real-time status of the leveler estimated by the data processing unit. Then, the height compensation amount of the hydraulic outrigger is determined based on the tilt error. Then, based on the height compensation amount, the opening degree of the servo valve in the hydraulic valve group is driven to change the flow parameters of the hydraulic oil in the hydraulic outrigger, thereby adjusting the extension and retraction of the hydraulic outrigger to achieve leveling.

11. The leveling system for an underwater leveling machine according to claim 10, characterized in that, The attitude measurement unit includes: Two single-axis horizontal sensors are installed on the chassis of the leveling machine to measure the tilt angle of the chassis in the X and Y axes, respectively. The triaxial accelerometer is installed near the center of the leveling machine chassis to measure the acceleration components of the leveling machine in the X, Y and Z axis directions. The gyroscope is installed near the center of the leveling machine chassis to measure the angular velocity of the leveling machine around the X, Y and Z axes. A buoyancy sensor is installed on the chassis of the leveling machine to measure the buoyancy changes of the leveling machine caused by uneven mud bed or differences in the softness and hardness of the bottom. A water depth sensor is installed on the chassis of the leveling machine to measure the depth of the seawater where the leveling machine is located. The X-axis, Y-axis, and Z-axis are a three-dimensional coordinate system constructed with the center of the leveling machine chassis as the origin; the X-axis, Y-axis, and Z-axis are perpendicular to each other; the X-axis is along the horizontal direction of the leveling machine, and the Y-axis is along the vertical direction of the leveling machine.

12. The leveling system of an underwater leveling machine according to claim 11, characterized in that, The data processing unit integrates the measurement data from the single-axis horizontal sensor, the three-axis accelerometer, the gyroscope, the buoyancy sensor, and the water depth sensor through a data fusion algorithm to estimate the real-time status of the leveling machine. The data processing unit first models the state vector and the sensor measurement vector; A state prediction equation is established using the time discrete model and the state vector; Establish a nonlinear relationship between the sensor measurement vector and the state vector; The elements in the state vector include the tilt angle and pitch angle of the leveling machine, as well as the angular velocities corresponding to the tilt angle and pitch angle of the leveling machine; the elements in the sensor measurement vector include the measurement data of the single-axis horizontal sensor, the three-axis accelerometer, the gyroscope, the buoyancy sensor, and the depth sensor.

13. The leveling system for an underwater leveling machine according to claim 12, characterized in that, The data processing unit performs data fusion in the following steps: S11. Initialize the state vector and the sensor measurement vector using the initial measurement values ​​of the single-axis horizontal sensor, the three-axis accelerometer, the gyroscope, the buoyancy sensor, and the water depth sensor; S12. Within each preset time step, state prediction and covariance prediction are performed using the state vector and the sensor measurement vector. S13. When the sensor measurement vector is updated, the influence of the sensor measurement value on the leveling machine state is determined by the Jacobian matrix and Kalman gain, and the prediction results of the state prediction and covariance prediction are updated at the same time. S14. Output the prediction result to obtain the best estimate of the real-time status of the leveling machine by the data processing unit; wherein, the best estimate includes the tilt angle and angular velocity information of the leveling machine.

14. The leveling system for an underwater leveling machine according to claim 13, characterized in that, The control optimization unit uses a PID controller to control the extension and retraction of the hydraulic outriggers, thereby controlling the lateral and longitudinal tilt angles of the leveling machine; wherein, the control target of the PID controller is set to 0° lateral tilt angle and 0° longitudinal tilt angle. The hydraulic outrigger is equipped with a stroke sensor to measure the extension and retraction of the hydraulic outrigger; and the control optimization unit is connected to the stroke sensor to obtain feedback on the extension and retraction of the hydraulic outrigger. The control optimization unit, upon obtaining the optimal estimate, calculates the tilt error between the optimal estimate and the control target, and obtains the PID parameters of the PID controller. The control optimization unit uses the DCS algorithm to optimize the PID parameters and updates the optimized PID parameters to the PID controller; The PID controller determines the height compensation amount of the hydraulic outrigger within a control cycle based on the optimized PID parameters. The PID controller converts the height compensation of the hydraulic outrigger into a drive signal for the servo valve in the hydraulic valve group, thereby driving the hydraulic outrigger to extend and retract.

15. The leveling system of an underwater leveling machine according to claim 14, characterized in that, While the hydraulic outriggers are extended and retracted by the PID controller, the attitude measurement unit continuously measures the state parameters of the leveling machine; the data processing unit continuously estimates the real-time state of the leveling machine. If the best estimate obtained meets the control target of the PID controller or the error is within the preset range, the control optimization unit keeps the PID controller stable; otherwise, the control optimization unit continues to optimize the PID parameters of the PID controller.

16. The leveling system for an underwater leveling machine according to claim 14, characterized in that, When the hydraulic outrigger is extended or retracted by the PID controller, if an abnormality or emergency occurs, the PID parameters of the PID controller will switch to safety parameters or the leveling system will trigger a safety mechanism.

17. The leveling system for an underwater leveling machine according to claim 14, characterized in that, The process by which the DCS algorithm optimizes the PID parameters includes: S21. PID parameter initialization; Initialize the DCS population, set the population size, and set the individual dimension to three-dimensional PID parameters; Define the initial search boundary for the PID parameters; Set the initial number of iterations and the termination condition; Define the fitness function; The fitness function is evaluated using the integral of the absolute value of the error during the leveling process; and the fitness value of each PID parameter combination is calculated in each iteration. S22, Perform iterative updates; S221. Calculate the knowledge acquisition rate for each PID parameter combination; S222. Adjust the parameters in each PID parameter combination; S223. Select and update PID parameter combinations through boundary treatment and retrospective evaluation; S224. Determine whether the maximum number of iterations has been reached. If yes, proceed to step S225; otherwise, return to step S221. S225. Update the PID parameters and select a new combination of PID parameters based on the decrease in the fitness function value to obtain the optimal solution.

18. An underwater leveling machine, characterized in that, The leveling machine includes the leveling system as described in any one of claims 9 to 17.

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