CN-121978945-A - Buoyancy balance overturning safety protection method for amphibious emergency vehicle

CN121978945ACN 121978945 ACN121978945 ACN 121978945ACN-121978945-A

Abstract

The application relates to a buoyancy balance overturning safety protection method for an amphibious emergency vehicle. The method comprises the steps of carrying out state analysis fusion on motion state data and buoyancy state data of an amphibious emergency vehicle to obtain tool state quantity data ‌, carrying out on-line updating on a buoyancy stability digital twin model according to the tool state quantity data to obtain stability index data, carrying out safety constraint analysis on stability margin parameter data and/or righting capacity characterization quantity data in the stability index data to obtain capsizing risk grade data and safety constraint set data, carrying out safety constraint predictive control solving on target buoyancy distribution according to the capsizing risk grade data, the safety constraint set data and the stability index data to obtain tool protection control data, and controlling the position relation of buoyancy centers relative to gravity centers of the amphibious emergency vehicle to generate anti-capsizing righting moment. The method can improve the utilization efficiency of stability margin and the anti-overturning safety.

Inventors

ZHOU JINGYI
SHENG XINGHUA
WANG JIE
HE XIAOBING
ZHANG JIE

Assignees

重庆市国兴宜民智能装备科技有限公司

Dates

Publication Date: 20260505
Application Date: 20260204

Claims (10)

1. A method of buoyancy-balanced capsizing safety protection for an amphibious emergency vehicle, the method comprising: carrying out state analysis and fusion on the motion state data and the buoyancy state data of the amphibious emergency transportation means to obtain tool state quantity data ‌; according to the tool state quantity data, carrying out online updating on the buoyancy stability digital twin model of the amphibious emergency transportation tool to obtain stability index data; Safety constraint analysis is carried out on the stability margin parameter data and/or the righting capacity characterization quantity data in the stability index data to obtain overturning risk level data and safety constraint set data; According to the capsizing risk level data, the safety constraint set data and the stability index data, carrying out safety constraint prediction control solution on the target buoyancy distribution of the amphibious emergency vehicle to obtain tool protection control data; the tool protection control data is used for controlling the position relation of the buoyancy center of the amphibious emergency vehicle relative to the gravity center so as to generate anti-overturning centralizing moment.
2. The method of claim 1, wherein said solving the target buoyancy profile of the amphibious emergency vehicle for safety restraint predictive control based on the overturning risk level data, the safety restraint set data, and the stability index data, to obtain tool protection control data, comprises: performing stability sensitivity analysis on the target buoyancy distribution according to the overturning risk level data and the stability index data to obtain target buoyancy distribution candidate set data; model prediction control calculation is carried out on each candidate buoyancy distribution in the target buoyancy distribution candidate set data, so that optimal unconstrained control sequence data are obtained; And carrying out safety constraint projection correction on the optimal unconstrained control sequence data according to the safety constraint set data to obtain the tool protection control data.
3. The method according to claim 2, wherein performing model predictive control calculation on each candidate buoyancy distribution in the target buoyancy distribution candidate set data to obtain optimal unconstrained control sequence data comprises: Performing control parameterization conversion on buoyancy variables of the buoyancy units corresponding to each candidate buoyancy distribution to obtain candidate control parameter vector data; Performing risk self-adaptive shaping on the prediction time domain parameters and/or the cost weight parameters in the model prediction control calculation according to the stability index data and the overturning risk level data to obtain prediction control shaping parameter data; Carrying out unconstrained model prediction control calculation on the candidate control parameter vector data under a prediction model corresponding to the prediction control shaping parameter data to obtain unconstrained control sequence data; and carrying out multimode consistency fusion analysis on each candidate control sequence in the unconstrained control sequence data to obtain the optimal unconstrained control sequence data.
4. A method according to claim 3, wherein said performing unconstrained model predictive control calculation on said candidate control parameter vector data under a predictive model corresponding to said predictive control shaping parameter data to obtain unconstrained control sequence data comprises: Performing on-line discretization processing on the state transition matrix data and/or the control gain matrix data of the prediction model according to the candidate control parameter vector data and the prediction control shaping parameter data to obtain preset discrete prediction model data; Performing similarity retrieval on the historical optimal control parameters and/or adjacent candidate control parameters in the candidate control parameter vector data to obtain hot start initial value data; performing unconstrained model prediction control rolling optimization calculation on the preset discrete prediction model data and the hot start initial value data to obtain initial control sequence data; and performing actuator accessibility and dynamic response correction on the control increment data in the initial control sequence data to obtain the unconstrained control sequence data.
5. The method of claim 4, wherein performing unconstrained model predictive control rolling optimization calculations on the preset discrete predictive model data and the hot start initial value data to obtain initial control sequence data comprises: Performing feedforward injection processing on external disturbance items and/or load bias items in the preset discrete prediction model data according to the stability index data and the overturning risk level data to obtain disturbance enhancement prediction model data; Performing risk morphological shaping on the control sequence initial value in the hot start initial value data to obtain shaped hot start sequence data; performing barrier equivalent transformation on the objective function of the unconstrained model predictive control rolling optimization calculation according to the safety constraint set data and the predictive control shaping parameter data to obtain unconstrained equivalent objective function data; performing event-triggered segmented rolling recursion solution on the disturbance enhancement prediction model data, the shaping hot start sequence data and the unconstrained equivalent objective function data to obtain candidate initial control sequence data; And performing span consistency rearrangement on the control increment sequence in the candidate initial control sequence data to obtain the initial control sequence data.
6. The method according to claim 1, wherein the performing a safety constraint analysis on the stability margin parameter data and/or the righting ability characterization data in the stability index data to obtain the capsizing risk level data and the safety constraint set data includes: threshold comparison is carried out on stability margin parameter data and/or righting capacity characterization quantity data in the stability index data to obtain basic risk characteristic data; performing barrier function construction on the safety constraint analyzed by the safety constraint according to the basic risk characteristic data and the stability index data to obtain dynamic barrier constraint data; according to the change rate of the stability index data, predicting and tightening the dynamic barrier constraint data to obtain the safety constraint set data; And carrying out feasibility assessment analysis on the stability index data according to the safety constraint set data to obtain the overturning risk level data.
7. The method of claim 6, wherein the performing a prediction tightening process on the dynamic barrier constraint data according to the rate of change of the stability index data to obtain the safety constraint set data comprises: carrying out differential calculation on the change rate of the stability index data to obtain risk trend characterization data; according to the risk trend representation data and the stability index data, constraint budget allocation is carried out on a plurality of constraint items in the dynamic barrier constraint data, so that constraint tightening weight data are obtained; according to preset discrete prediction model data, predicting propagation analysis is carried out on the risk trend characterization data to obtain future time domain risk track data; the preset discrete prediction model data are obtained by carrying out analog control solution on the target buoyancy distribution through the buoyancy stability digital twin model; according to the future time domain risk track data and the constraint tightening weight data, performing time domain segment tightening processing on the dynamic barrier constraint data to obtain segment tightening barrier constraint data; And compiling a constraint codebook for the segmented tightening barrier constraint data to obtain the security constraint set data.
8. The method according to claim 7, wherein the predicting propagation analysis is performed on the risk trend characterization data according to the preset discrete prediction model data to obtain future time domain risk track data, including: Performing risk coordinate mapping on the risk trend characterization data to obtain risk propagation state vector data; Performing risk self-adaptive injection on an uncertainty structure in the preset discrete prediction model data according to the stability index data and the overturning risk level data to obtain uncertainty enhanced prediction model data; according to the uncertainty enhanced prediction model data, constructing a scene tree skeleton of the risk propagation state vector data to obtain risk propagation scene tree data; Performing reachable set risk bundle propagation on the risk propagation scene tree data under the uncertainty enhanced prediction model data to obtain future time domain risk track data; and extracting the worst case risk boundary and/or quantile risk boundary in the future time domain risk track data to obtain the future time domain risk track data.
9. An amphibious emergency vehicle buoyancy-balanced capsizing safety protection device, the device comprising: The data fusion module is used for carrying out state analysis and fusion on the motion state data and the buoyancy state data of the amphibious emergency vehicle to obtain tool state quantity data ‌; The model updating module is used for carrying out online updating on the buoyancy stability digital twin model of the amphibious emergency transportation means according to the tool state quantity data to obtain stability index data; the safety analysis module is used for carrying out safety constraint analysis on the stability margin parameter data and/or the righting capacity characterization quantity data in the stability index data to obtain overturning risk level data and safety constraint set data; The control solving module is used for carrying out safety constraint prediction control solving on the target buoyancy distribution of the amphibious emergency transportation means according to the overturning risk level data, the safety constraint set data and the stability index data to obtain tool protection control data; the tool protection control data is used for controlling the position relation of the buoyancy center of the amphibious emergency vehicle relative to the gravity center so as to generate anti-overturning centralizing moment.
10. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor implements the steps of the method of any one of claims 1 to 8 when the computer program is executed.

Description

Buoyancy balance overturning safety protection method for amphibious emergency vehicle Technical Field The application relates to the technical field of intelligent control, in particular to a buoyancy balance overturning safety protection method, device and computer equipment for an amphibious emergency vehicle. Background In the prior art, the buoyancy balance overturning safety protection of an amphibious emergency vehicle aims at the buoyancy balance overturning safety protection of the amphibious emergency vehicle, the initial stability and the residual buoyancy after cabin breakage are improved through low gravity center arrangement, bilateral symmetry load channels, transverse stability (GM) margin design, multi-cabin/foam filling and other cabin anti-sinking measures, the dynamic correction of longitudinal and transverse inclination and draught is realized through a sealed buoyancy tank, an adjustable ballast (water cabin pump/valve), a variable buoyancy cabin (inflation and deflation) or a transferable liquid cabin on the buoyancy balance level, the rolling and yaw are reduced in cooperation with propulsion and rudder efficiency control, the speed limiting and navigation strategies of a side anti-tilting buoy/expanding floating body, an automatic inflatable anti-overturning air bag, a folding type stabilizing wing/supporting floating body and a surge working condition are commonly adopted on the anti-overturning protection level, and the safety protection level is provided with an IMU/inclinometer, a liquid level and cabin pressure sensor, load detection and water detection to realize real-time stability assessment and threshold value alarming, and parallel action automatic water drainage, emergency leveling, emergency high-risk leveling, floating power and the like. However, the traditional technology often depends on passive stability and a single and dispersed protection device, and lacks of rapid self-adaptive leveling and closed-loop linkage control on load change and sudden water inlet/wind wave working conditions, so that the stability margin utilization efficiency and anti-overturning safety of the buoyancy balance overturning safety protection are insufficient. Disclosure of Invention Based on the above, there is a need to provide an amphibious emergency vehicle buoyancy balance capsizing safety protection method, device and computer equipment capable of improving stability margin utilization efficiency and anti-capsizing safety. In a first aspect, the application provides a buoyancy-balanced capsizing safety protection method for an amphibious emergency vehicle, comprising: carrying out state analysis and fusion on the motion state data and the buoyancy state data of the amphibious emergency transportation means to obtain tool state quantity data ‌; according to the tool state quantity data, carrying out online updating on the buoyancy stability digital twin model of the amphibious emergency transportation tool to obtain stability index data; Safety constraint analysis is carried out on the stability margin parameter data and/or the righting capacity characterization quantity data in the stability index data to obtain overturning risk level data and safety constraint set data; According to the capsizing risk level data, the safety constraint set data and the stability index data, carrying out safety constraint prediction control solution on the target buoyancy distribution of the amphibious emergency vehicle to obtain tool protection control data; the tool protection control data is used for controlling the position relation of the buoyancy center of the amphibious emergency vehicle relative to the gravity center so as to generate anti-overturning centralizing moment. In a second aspect, the application also provides an amphibious emergency vehicle buoyancy-balanced capsizing safety protection device comprising: The data fusion module is used for carrying out state analysis and fusion on the motion state data and the buoyancy state data of the amphibious emergency vehicle to obtain tool state quantity data ‌; The model updating module is used for carrying out online updating on the buoyancy stability digital twin model of the amphibious emergency transportation means according to the tool state quantity data to obtain stability index data; the safety analysis module is used for carrying out safety constraint analysis on the stability margin parameter data and/or the righting capacity characterization quantity data in the stability index data to obtain overturning risk level data and safety constraint set data; The control solving module is used for carrying out safety constraint prediction control solving on the target buoyancy distribution of the amphibious emergency transportation means according to the overturning risk level data, the safety constraint set data and the stability index data to obtain tool protection control data; the tool protection control data is used for controlling