CN-121990483-A - Intelligent optimization method for hoisting path of hinge sinking unit

CN121990483ACN 121990483 ACN121990483 ACN 121990483ACN-121990483-A

Abstract

The invention relates to the technical field of underwater engineering construction and discloses an intelligent optimization method for a hoisting path of a hinge sinking unit, which comprises the steps of dividing a predicted hoisting total period into a plurality of discrete time slices and constructing a three-dimensional prediction environment model based on multi-level water flow profile data; the method comprises the steps of combining a hydrodynamic resistance model and a three-dimensional prediction environment model to calculate the predicted tension of each grid node of the submerged array to generate a flexible deformation energy state matrix, carrying out inspection on the flexible deformation energy state matrix to judge local tension peaks, introducing rigid hanger inclination angle control variables to slices with peaks, distributing unequal rate instructions to generate a global optimal equipment workflow scheduling table, executing according to the scheduling table, extracting the sum of the predicted tensions, carrying out deviation check on the equivalent mean value of the actual physical load and the sum of the predicted tensions, which are obtained by feedback data calculation of a variable frequency driver, cutting off the instructions when the deviation exceeds the limit, and triggering resampling to re-plan the rest time slices.

Inventors

MIAO PU
LIN WENFENG
ZHANG HUISU
Du Tianchi
DONG CHAOMING
YU TAO
ZHANG BINYU
YIN SIHE
ZHANG RUI
CHEN JUAN
WANG JUAN
XU JUN

Assignees

长江南京航道工程局

Dates

Publication Date: 20260508
Application Date: 20260410

Claims (10)

1. An intelligent optimization method for a hoisting path of a hinge sinking unit is characterized by comprising the following steps: dividing the estimated total hoisting period into a plurality of equal-length discrete time slices, and constructing a three-dimensional prediction environment model corresponding to the discrete time slices based on multi-level water flow velocity and water flow direction profile data fed back by an acoustic Doppler flow velocity profiler; Calculating the predicted tension of each grid node of the hinge sink row by combining the hydrodynamic resistance model and the three-dimensional predicted environment model, and generating a flexible deformation energy state matrix corresponding to the discrete time slice; Carrying out inspection on the flexible deformation energy state matrix to judge a local tension peak value, introducing a rigid hanger inclination angle control variable aiming at the discrete time slice with the local tension peak value, and distributing an unequal rate instruction to generate a global optimal equipment workflow schedule; And executing actions according to the global optimal equipment workflow schedule, extracting the predicted tension sum of the corresponding slice in the flexible deformation energy state matrix, carrying out deviation check on the actual physical load equivalent mean value obtained by the feedback data of the variable frequency driver through nonlinear mechanical loss compensation calculation and the predicted tension sum, intercepting the current instruction when the deviation exceeds the limit, and triggering resampling to re-plan the residual time slice.
2. The intelligent optimization method for the hoisting path of the hinge sinking unit according to claim 1, wherein the step of dividing the expected hoisting total period into a plurality of equal-length discrete time slices and constructing the three-dimensional prediction environment model corresponding to the discrete time slices based on the multi-level water flow rate and water flow direction profile data fed back by the acoustic doppler flow profiler specifically comprises the following steps: Dividing the vertical space span by a larger value of the rated paying-off speed and the safety linear speed lower limit to obtain a predicted total hoisting period by adopting anti-overflow calculation logic, and equally dividing the predicted total hoisting period according to a preset slicing time resolution to generate discrete time slices; Analyzing multi-level water flow speed and water flow direction profile data fed back by an acoustic Doppler flow velocity profiler, dividing a water body from a water surface to the bottom of a target falling bed into a plurality of discrete water deep layers, extracting actual measurement water flow speed values and actual measurement water flow direction values of the discrete water deep layers at the current sampling moment, and synchronously aligning an acquisition time stamp with a corresponding discrete time slice in a time axis manner to synthesize an initial flow velocity vector; the historical flow velocity vector sequence of each discrete water depth layer is called and spliced end to end with the initial flow velocity vector to serve as a time sequence prediction input sequence, and a time sequence prediction model is input to calculate a predicted flow velocity vector corresponding to each discrete water depth layer; and establishing a multidimensional mapping relation by taking the corresponding predicted flow velocity vector as a state characteristic value, and constructing a three-dimensional prediction environment model corresponding to the discrete time slice by means of the multidimensional mapping relation.
3. The intelligent optimization method for the hoisting path of the hinge sinking unit according to claim 1, wherein the step of calculating the predicted tension of each grid node of the hinge sinking unit by combining the hydrodynamic resistance model and the three-dimensional predicted environment model specifically comprises the following steps: the physical size data of the hinge sinking row to be hoisted is called to establish a topological constraint model, continuous physical rows are discretized into a two-dimensional grid system formed by rows and columns according to inherent hinge connection characteristics, and grid node coordinates corresponding to each physical connection block are given; Mapping the grid node coordinates into a reference space coordinate system according to the expected uniform-speed lowering depth, and calculating to obtain the space depth parameters of each node in different time dimensions; performing interpolation addressing in the three-dimensional prediction environment model according to the spatial depth parameter, extracting a prediction flow velocity vector of the corresponding depth, and taking the amplitude of the prediction flow velocity vector as a prediction flow velocity scalar; And extracting physical parameters related to the hydrodynamic resistance model, and calculating the predicted tension of each grid node of the hinge sinker by adopting a node tension iterative deduction formula in combination with the hydrodynamic resistance model and the predicted flow rate scalar.
4. A method for intelligently optimizing a lifting path of a hinge row unit according to claim 3, wherein the step of generating the flexible deformation energy matrix corresponding to the discrete time slices specifically comprises: And using a row mark and a column mark of the two-dimensional grid system as two-dimensional indexes, and carrying out arrayed arrangement and recombination on the predicted tensions of all grid nodes calculated under the same discrete time slice to generate a flexible deformation energy state matrix which reflects the stress mapping distribution condition under the impact of underwater shear flow in a digital array form and corresponds to the discrete time slice.
5. The intelligent optimization method for the hoisting path of the hinge sinking unit according to claim 1, wherein the step of inspecting the flexible deformation energy state matrix to judge the local tension peak value specifically comprises the following steps: A preset safety tension threshold value is called, the predicted tension of each grid node in the flexible deformation energy state matrix is preliminarily compared with the safety tension threshold value, and grid nodes with predicted tension larger than the safety tension threshold value are screened out; Introducing a connected domain statistical algorithm, and evaluating the number of space aggregation adjacent to grid nodes with the predicted tension greater than the safety tension threshold to form a space aggregation degree; If the space aggregation degree exceeds a preset space aggregation degree tolerance parameter, marking a corresponding region so as to judge a local tension peak value; Wherein the safety tension threshold is determined based on the material yield limit of the hinge sinker physical connection multiplied by a safety reduction coefficient.
6. The intelligent optimization method for the hoisting path of the hinge sinking unit according to claim 5, wherein the step of introducing a rigid hanger inclination angle control variable and distributing unequal rate instructions for the discrete time slices with local tension peaks specifically comprises the following steps: Introducing a rigid hanger inclination angle control variable to break a rigid posture of horizontal suspension aiming at the discrete time slice with the local tension peak value, and artificially manufacturing a relative water attack angle of a sinking plane and a water flow impact direction; triggering a shear force dispersion iteration program based on the change of the attack angle of the water, and calculating to obtain the unequal rate offset for the current high-risk slice by adopting a tension peak value reduction compensation formula; And independently decoupling the left auxiliary winch and the right auxiliary winch according to the unequal rate offset, setting the expected rate of the left auxiliary winch as the rated paying-off speed minus the unequal rate offset, and setting the expected rate of the right auxiliary winch as the rated paying-off speed plus the unequal rate offset, thereby distributing the unequal rate instruction.
7. The intelligent optimization method for the hoisting path of the hinge sinking row unit according to claim 6, wherein the step of generating the global optimal equipment workflow schedule specifically comprises the following steps: The method comprises the steps of performing physical integration on accumulated lateral sliding distances generated by each discrete time slice on a time axis, and when the accumulated lateral sliding distances approach a set space track deviation allowable value, forcibly recalling a rigid hanger inclination angle control variable to restore a horizontal posture and synchronously reducing the overall lowering rate of a main winch, so as to generate an asymmetric adjustment instruction under space track constraint; and compiling and generating a global optimal equipment workflow schedule covering the whole period by integrating a conventional symmetrical downloading instruction under the safety energy state without a peak value and an asymmetric adjusting instruction under the space track constraint.
8. The intelligent optimization method for the hoisting path of the hinge sinking row unit according to claim 6, wherein the step of extracting the predicted tension sum of the corresponding slices in the flexible deformation energy state matrix and calculating the actual physical load equivalent mean value of the feedback data of the variable frequency drive through nonlinear mechanical loss compensation specifically comprises the following steps: Acquiring feedback data of a variable frequency driver comprising armature current in the operation process of a driving motor of a main winch and the operation linear speed of the main winch, introducing a dynamic electromechanical conversion function into the feedback data of the variable frequency driver to perform nonlinear mechanical loss compensation, and calculating an actual physical load equivalent mean value by adopting an actual physical load conversion formula; and carrying out transverse summation operation on the predicted tension of the topmost grid node in the flexible deformation energy state matrix, and extracting to obtain a predicted tension sum for carrying out subsequent target alignment with the equivalent mean value of the actual physical load.
9. The intelligent optimization method for the hoisting path of the hinge sinking unit according to claim 8, wherein the step of performing deviation verification on the equivalent mean value of the actual physical load and the predicted tension sum specifically comprises the following steps: performing difference calculation on the calculated equivalent mean value of the actual physical load and the extracted predicted tension sum to perform deviation verification, and obtaining real-time dynamic deviation; judging whether the absolute value of the real-time dynamic deviation exceeds a set load check deviation tolerance, and starting an internal timer to monitor the duration of an overrun state when the absolute value exceeds the set load check deviation tolerance; When the duration time continuously maintains to exceed a preset filtering time window, confirming that the current actual stress state is substantially deviated from a predicted environment reference, and judging that the deviation exceeds the limit.
10. The intelligent optimization method for the hoisting path of the hinge sinking row unit according to claim 9, wherein the steps of intercepting the current instruction and triggering resampling to reprogram the remaining time slices when the deviation exceeds the limit specifically comprise: when the deviation exceeds the limit, immediately issuing an interrupt instruction to a programmable logic controller to intercept the current instruction and triggering a follow-up force release mechanism; The hoisting mechanism is controlled to release the locking state of the brake through the follow-up force release mechanism, the driving motor is allowed to generate controlled reversal yielding under the fluid impact tension exceeding the safety tension threshold, and the brake of the hoisting machine is locked again when the accumulation of the reversal yielding distance reaches the maximum yielding travel limit value so as to complete the emergency risk avoidance action; After the emergency risk avoidance action is completed, a high-frequency wake-up signal is sent to an underwater sensing system, and an acoustic Doppler flow profiler is triggered to resample so as to acquire the latest environmental data of the current water area; And reconstructing a three-dimensional prediction environment model by taking the latest environment data as initial input, and rescheduling the residual time slice by utilizing a new environment characteristic matrix.

Description

Intelligent optimization method for hoisting path of hinge sinking unit Technical Field The invention relates to the technical field of underwater engineering construction, in particular to an intelligent optimization method for a hoisting path of a hinge sinking unit. Background In water conservancy and channel renovation projects, hinged sinkers are often used for river bed bottoming and embankment reinforcement. The lifting and lowering process of the submerged rows needs to pass through a water body with a certain depth. The discharging body is subjected to the combined action of water flow scouring and self gravity in the descending stage, so that the fluid-solid coupling effect is realized. In an actual underwater operating environment, there are typically multiple levels of flow velocity and direction changes in the body of water, creating water flow shear. The current sinking and hoisting method is usually operated by means of fixed lowering speed or manual experience, and cannot acquire and model the flow field changes of different depths under water in advance. The control mode is difficult to predict the local stress state of each node of the submerged row under different water depths. When the drainage body is locally impacted by water flow to generate a tension peak value, the system cannot actively adjust the water facing posture of the drainage body to disperse impact force, so that the connection part of the drainage body is subjected to excessive stress to generate physical tearing. Meanwhile, the sinking bar can generate lateral sliding under the continuous action of the water flow thrust and deviate from a preset descending track. The existing operation control means often disconnect the force control from the track control. If the horizontal lowering posture is maintained in the hoisting process, the risk of overrun of stress is increased, and if the discharging posture is changed only for avoiding the water flow impact, the requirement of space displacement is difficult to be met. This can lead to deviation in the positioning of the sinking bed, and the positioning requirements of engineering cannot be met. In addition, existing lifting devices lack the ability to respond and avoid sudden water flow loads in control logic. When the underwater flow field environment is suddenly changed and the water flow impact force exceeds the yield limit of the drainage material, driving equipment such as a winch still keeps a conventional descending or locking state, and active force release cannot be realized. The lack of a controlled yielding mechanism causes the lifting process to lack safety margin when facing abrupt changes of the water flow environment, thereby causing overload or body-displacement damage of equipment. Disclosure of Invention Aiming at the defects of the prior art, the invention provides an intelligent optimization method for a lifting path of a hinge sinking unit, which solves the problems that the existing underwater sinking lifting operation is difficult to predict a local stress state in a complex water flow environment, and active force release cannot be realized in the face of sudden water flow shearing force, so that a row body is physically torn and deviates from a preset falling bed track. In order to achieve the purpose, the intelligent optimization method for the lifting path of the hinge sinking unit comprises the following steps: dividing the estimated total hoisting period into a plurality of equal-length discrete time slices, and constructing a three-dimensional prediction environment model corresponding to the discrete time slices based on multi-level water flow velocity and water flow direction profile data fed back by an acoustic Doppler flow velocity profiler; Calculating the predicted tension of each grid node of the hinge sink row by combining the hydrodynamic resistance model and the three-dimensional predicted environment model, and generating a flexible deformation energy state matrix corresponding to the discrete time slices; Carrying out inspection on the flexible deformation energy state matrix to judge local tension peaks, introducing rigid hanger inclination angle control variables aiming at discrete time slices with the local tension peaks, and distributing unequal rate instructions to generate a global optimal equipment workflow schedule; And executing actions according to a global optimal equipment workflow schedule, extracting the predicted tension sum of the corresponding slice in the flexible deformation energy state matrix, carrying out deviation checking on the actual physical load equivalent mean value and the predicted tension sum, which are obtained by carrying out nonlinear mechanical loss compensation calculation on feedback data of the variable frequency driver, cutting off the current instruction when the deviation exceeds the limit, and triggering resampling to re-plan the residual time slice. Preferably, the step of dividing the est