CN116587791A

CN116587791A - Damping control method, device and system for life support cabin

Info

Publication number: CN116587791A
Application number: CN202310568210.XA
Authority: CN
Inventors: 苏至钒; 潘晶; 夏知拓; 刘鹏
Original assignee: Shanghai Timi Robot Co ltd
Current assignee: Shanghai Timi Robot Co ltd
Priority date: 2023-05-18
Filing date: 2023-05-18
Publication date: 2023-08-15

Abstract

The application provides a damping control method, a damping control device and a damping control system for a life support cabin, wherein the life support cabin is provided with a damping device, and the damping control method comprises the following steps: acquiring ground heave information and vehicle state information; inputting the ground fluctuation information and the vehicle state information into a trained neural network model to obtain damping parameter information output by the neural network model; and controlling the damping device to perform damping treatment according to the damping parameter information. The damping control method of the life support cabin has the capability of acquiring the ground fluctuation information in advance, and parameter information such as the suspension length, the suspension coefficient, the suspension damping coefficient and the like corresponding to the damping device is calculated and output through the neural network model, so that the damping device is correspondingly adjusted according to the parameter information, and the vehicle can still keep stably running on the fluctuation ground.

Description

Damping control method, device and system for life support cabin

Technical Field

The application relates to the technical field of robots, in particular to a damping method and device for a life support cabin, a damping system for the life support cabin, electronic equipment and a storage medium.

Background

On-site emergency and transport of critically ill patients has always been an important research context in the field of medical emergency. The medical emergency emphasizes the golden treatment time, and if the serious patients are effectively treated in the golden treatment time, the treatment rate is obviously improved.

The life medical cabin can bear the task of transferring patients and is used as a small rescue room for the help of the patients. However, in the running process of the life medical cabin trolley, if the ground is uneven, larger jolt can be generated, discomfort and even secondary injury are brought to patients, and the normal operation of some medical equipment can be influenced. Therefore, in the traditional damping method, the mechanical damper is arranged on the life medical cabin, and the elasticity and damping of the vehicle body are controlled through the spring, the damper and the like of the damper so as to achieve the purpose of damping, but the mechanical damper cannot timely adjust the parameters according to the road condition change, so that the damping effect is poor.

Disclosure of Invention

The embodiment of the application aims to provide a damping control method for a life support cabin, which calculates damping parameters through a model, so that a damping device is controlled to adjust according to the damping parameters, the life support cabin can be always stable when passing through a wavy bottom surface, and the problem of road surface jolt vibration is reduced.

In a first aspect, the present application provides a method for controlling vibration damping of a life support compartment on which a vibration damping device is mounted, the method comprising:

acquiring ground heave information and vehicle state information;

inputting the ground fluctuation information and the vehicle state information into a trained neural network model to obtain damping parameter information output by the neural network model;

and controlling the damping device to perform damping treatment according to the damping parameter information.

In one embodiment, the vehicle state information includes: the system comprises vehicle attitude information and vehicle speed information, wherein a speed measuring sensor, an inertial sensor and an image acquisition device are arranged on a life support cabin; the obtaining ground heave information and vehicle state information includes:

acquiring original speed information of the vehicle, which is acquired by the speed measuring sensor, original posture information of the vehicle, which is acquired by the inertial sensor, and original fluctuation information of the ground, which is acquired by the image acquisition device;

and preprocessing the original speed information, the original posture information and the original ground fluctuation information of the vehicle to obtain the processed speed information, the processed posture information and the processed ground fluctuation information, wherein the preprocessing comprises data filtering, downsampling and standardization processing.

In one embodiment, prior to said inputting the ground heave information and the vehicle state information into a trained neural network model, the method further comprises:

and carrying out parameter adjustment on the initial neural network according to the sample training set to obtain the neural network model.

In an embodiment, before the parameter adjustment is performed on the initial neural network according to the sample training set to obtain the neural network model, the method further includes:

acquiring the mapping relation between the ground fluctuation information, the vehicle state information and the damping parameter information;

and determining the sample training set according to the mapping relation.

In one embodiment, the damping parameter information includes: the suspension length of the damping device; the damping device is provided with a driving element; according to the damping parameter information, controlling the damping device to perform damping treatment, including:

and controlling the driving element to adjust according to a preset voltage value and a preset current value so as to adjust the damping device to the suspension length.

In an embodiment, the damping parameter information further includes: the suspension coefficient and the suspension damping coefficient of the damping device; according to the damping parameter information, controlling the damping device to perform damping treatment, including:

and controlling the driving element to adjust according to a preset rotation value, so that the damping device is adjusted to the suspension coefficient and the suspension damping coefficient.

In a second aspect, the present application provides a shock-absorbing control device for a life support compartment, comprising:

the acquisition module is used for acquiring ground fluctuation information and vehicle state information;

the output module is used for inputting the ground fluctuation information and the vehicle state information into the trained neural network model to obtain damping parameter information output by the neural network model;

and the control module is used for controlling the damping device to perform damping treatment according to the damping parameter information.

In a third aspect, the present application provides a shock attenuation control system for a life support chamber, comprising:

the data acquisition system is provided with a plurality of sensors and an image acquisition device, wherein the sensors are used for acquiring vehicle state information, and the image acquisition device is used for acquiring ground fluctuation information;

the data processing system is connected with the data acquisition system, and is used for inputting the acquired vehicle state information and the ground heave information into a trained neural network model and outputting damping parameter information;

the damping system comprises a damping device, the damping device is connected with the data processing system, and the damping system controls the damping device to perform damping treatment according to the damping parameter information;

and the cloud storage system is connected with the data acquisition system and the data processing system and is used for storing the vehicle state information, the ground fluctuation information and the damping parameter information.

In a fourth aspect, the present application provides an electronic device, including: a processor; a memory for storing processor-executable instructions; wherein the processor is configured to implement the method for controlling damping of a life support compartment according to any one of the embodiments of the first aspect of the present application.

In a fifth aspect, the present application provides a computer readable storage medium storing a computer program, which when executed by a processor, is configured to perform a method for controlling vibration damping of a life support compartment according to any one of the embodiments of the first aspect of the present application.

According to the scheme, the damping control method of the life support cabin has the capability of acquiring the ground fluctuation information in advance, and parameter information such as the suspension length, the suspension coefficient, the suspension damping coefficient and the like corresponding to the damping device is calculated and output through the neural network model, so that the damping device is correspondingly adjusted according to the parameter information, and a vehicle can still stably run on the fluctuation ground.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are needed in the embodiments will be briefly described below, it being understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered as limiting the scope, and other related drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.

Fig. 1 is a schematic structural diagram of an electronic device according to an embodiment of the present application;

FIG. 2 is a flow chart of a method for damping control of a life support cabin according to an embodiment of the present application;

FIG. 3 is a flow chart of a method for damping control of a life support cabin according to another embodiment of the present application;

FIG. 4 is a schematic diagram of a damping control system for a life support cabin according to an embodiment of the present application;

fig. 5 is a schematic structural diagram of a damping control device for a life support cabin according to an embodiment of the present application.

Detailed Description

The technical solutions in the embodiments of the present application will be described below with reference to the accompanying drawings in the embodiments of the present application.

Like reference numerals and letters denote like items in the following figures, and thus once an item is defined in one figure, no further definition or explanation thereof is necessary in the following figures. Meanwhile, in the description of the present application, the terms "first", "second", and the like are used only to distinguish the description, and are not to be construed as indicating or implying relative importance.

Fig. 1 is a schematic structural diagram of an electronic device 1 according to an embodiment of the application, including at least one processor 11 and a memory 12, and one processor is taken as an example in fig. 1. The processor 11 and the memory 12 are connected through the bus 10, and the memory 12 stores instructions executable by the at least one processor 11, the instructions being executed by the at least one processor 11 to cause the at least one processor 11 to perform a method of damping control of a life support compartment in the embodiments described below.

In one embodiment, processor 11 may be a general-purpose processor including, but not limited to, a central processing unit (Central Processing Unit, CPU), a network processor (Network Processor, NP), etc., a digital signal processor (Digital Signal Processor, DSP), an application specific integrated circuit (Application Specific Integrated Circuit, ASIC), an off-the-shelf programmable gate array (Field Programmable Gate Array, FPGA) or other programmable logic device, discrete gate or transistor logic device, discrete hardware components, etc. The general purpose processor may be a microprocessor or the processor may be any conventional processor or the like, and the processor 11 is a control center of the electronic device 1, and connects the various parts of the entire electronic device 1 using various interfaces and lines. The processor 11 may implement or perform the methods, steps and logic blocks disclosed in the embodiments of the present application.

In one embodiment, memory 12 may be implemented by any type of volatile or non-volatile Memory device or combination thereof, including, but not limited to, random access Memory (Random Access Memory, RAM), read Only Memory (ROM), static random access Memory (Static Random Access Memory, SRAM for short), programmable Read Only Memory (Programmable Read-Only Memory, PROM), erasable Read Only Memory (Erasable Programmable Read-Only Memory, EPROM), electrically erasable Read Only Memory (Electric Erasable Programmable Read-Only Memory, EEPROM), and the like.

The structure of the electronic device shown in fig. 1 is only illustrative, and the electronic device 1 may also comprise more or fewer components than shown in fig. 1, or have a different configuration than shown in fig. 1. The components shown in fig. 1 may be implemented in hardware, software, or a combination thereof.

Specifically, the electronic device 1 in the present application may be a service device such as a computer, a notebook computer, a smart phone, an industrial personal computer, etc.

Fig. 2 is a flowchart of a damping control method for a life support cabin according to an embodiment of the application. The life support compartment, which may also be referred to as a life medical compartment, is an ambulance equipped with medical treatment equipment for patient first aid. The life support cabin needs enough space to meet the requirements of patients and medical staff, and a wide vehicle type is generally adopted to form a rescue site in the medical cabin. The medical cabin is internally provided with not only medical cabinet and other treatment necessities, but also intelligent instruments required by rescuing patients, and meanwhile, the vehicle body is provided with an inverter, so that the power supply of medical equipment is ensured, the medical cabin is internally provided with stretcher of different types to meet the requirements because of the serious and unfavorable patients with large action and jolt.

When the life support cabin is used for transporting patients, if the life support cabin encounters bumpy and hollow uneven pavement, the life support cabin can jolt and vibrate, and secondary injury is easily brought to the patients. Therefore, the present application is to provide a shock absorbing device mounted on a life support compartment, which is generally mounted on four wheels, and to perform a shock absorbing process by the shock absorbing device, so that the vehicle is allowed to reduce shock during transportation of a patient.

The damping control method of the life support cabin can enable the life support cabin to be stable when passing through an uneven road surface, reduce jolt vibration and avoid secondary injury of patients. The method of the present application has steps S210 to S230:

step S210: ground heave information and vehicle state information are acquired.

Ground heave means the height of the ground topography and can be understood as the absolute height of the ground topography and the relative height or degree of steepness of the slope. The ground heave condition may include: the pavement has a pothole section, the pavement has foreign matters, the road edge is convex, and broken stone is generated to form an uneven pavement. The ground relief situation is relative to a flat ground.

The vehicle state represents the running speed of the vehicle and the posture exhibited by the vehicle as the vehicle passes over the rough terrain. For example, when the vehicle runs on a depressed road, the vehicle brakes and takes a posture in which the vehicle body leans forward, and when the vehicle passes through the depressed road, the vehicle is jolted by the road and takes a posture in which the vehicle head lifts upward.

Optionally, the vehicle state information includes: vehicle attitude information and vehicle speed information. The life support cabin is provided with a speed measuring sensor, an inertial sensor and an image acquisition device.

The speed sensor detects the vehicle speed by converting the detected rotation speed signal into a voltage signal. Inertial sensors are used to detect and measure acceleration, tilt, shock, vibration, rotation, and multi-degree of freedom motion. The image acquisition device can be a detection instrument composed of a depth camera and a laser radar sensor and is used for acquiring ground fluctuation information, specifically, pits, bulges and foreign matters which appear on the ground are acquired through the depth camera, detection signals (such as laser beams) are transmitted to the road surface through the laser radar, received signals (namely target echoes) reflected by a target are compared with the transmission signals, target distances, orientations, heights, postures, shapes and the like of the pits, bulges, the foreign matters and the like of the road surface are obtained, and the acquired ground fluctuation information can be images or image information representing ground forms.

The above-mentioned various types of sensors may be connected to the electronic device 1 such as a computer or an industrial personal computer through a communication port such as a network cable or a USB, and the image acquisition apparatus may also be connected to the electronic device 1 such as a computer or an industrial personal computer through a communication port such as a network cable or a USB.

In step S210, the original speed information of the vehicle may be collected by the speed sensor, the original posture information of the vehicle may be collected by the inertial sensor, and the original heave information of the ground may be collected by the image collecting device. After the processor 11 acquires the original vehicle speed information, the original vehicle posture information and the original ground heave information, data preprocessing is performed to obtain processed vehicle speed information, vehicle posture information and ground heave information.

The manner of pretreatment may include: filtering, downsampling, and normalizing. The noise influence is removed by filtering the original speed information, the original posture information and the original fluctuation information of the ground. The downsampling refers to the process of proportionally reducing the width and height of the characteristic image, and the ground relief image acquired by the image acquisition device is filtered and then downsampled. The downsampling process is described in detail with reference to the downsampling process of an image in the prior art. The normalization processing refers to dimensionless normalization processing of the original speed information, the original posture information and the original fluctuation information of the ground after filtering and downsampling processing, so that indexes of different units or orders can be compared and weighted conveniently, and the comparability of data is solved.

Step S220: and inputting the ground fluctuation information and the vehicle state information into the trained neural network model to obtain damping parameter information output by the neural network model.

The preprocessed vehicle speed information, vehicle posture information and ground heave information in the step S210 are input into a trained and optimized neural network model, calculated through the neural network model, and finally damping parameter information is output. The damping parameter information is used for representing specific parameters of parts which need to be adjusted or changed when the damping device is subjected to damping treatment, so that the damping device achieves a damping effect, adapts to the ground fluctuation condition, and keeps the vehicle stable, namely, the parameter adjustment strategy of the damping device.

Neural Networks (NNs) are complex network systems formed by a large number of simple processing units (called neurons) widely interconnected. The basic principle of the neural network is: each neuron multiplies the initial input value by a certain weight, adds other values input into the neuron (and combines other information values), calculates a sum finally, adjusts the deviation of the neuron, and finally normalizes the output value by using an excitation function. The computational units of neural networks are called neurons, and these networks can classify data processing as output. The neural network model is described based on a mathematical model of neurons.

The neural network model is represented by a network topology, node characteristics, and learning rules. Training a neural network is an optimization problem, i.e., finding which parameters make the model best. Optionally, before step S220, the neural network is continuously trained to obtain a neural network model with optimized performance. Specifically, the initial neural network can be subjected to parameter adjustment according to the sample training set, and a neural network model is obtained.

In this embodiment, the initial neural network refers to a neural network that has not been trained. Training of the neural network is done based on a large sample training set. The sample training set may be obtained specifically by:

and obtaining the mapping relation between the ground fluctuation information and the vehicle state information and the damping parameter information, and determining a sample training set according to the mapping relation.

As described above, the collected ground heave information, vehicle state information and damping parameters are labeled, and a mapping relation between the ground heave information, the vehicle state information and the damping parameters is established. The image acquisition device acquires an image of a bumpy road section when the vehicle runs, the speed sensor detects the vehicle speed when the vehicle passes through the bumpy road section, the inertial sensor acquires the vehicle posture when the vehicle passes through the bumpy road section, and the damping parameters of the damping device are calculated according to the mapping function, so that whether the vehicle passes through the bumpy road section or not is known according to the damping parameters, and a pothole road section is known.

The ground fluctuation information, the vehicle state information and the damping parameters after the mapping relation is established are input into a neural network, and the neural network outputs the damping parameters of the corresponding damping device according to the input ground fluctuation information, the vehicle state information and the damping parameters, namely the adjustment strategy of the damping parameters.

The parameter adjustment of the initial neural network can be specifically that the structure, the activation function, the loss function and the like of the neural network are adjusted, so that the generalization capability and the robustness of the neural network model are improved.

The neural network architecture includes three layers, namely an input layer, an output layer, and an intermediate layer (also called a hidden layer). The input layer has 3 input units, the hidden layer has 4 units, and the output layer has 2 units. When designing a neural network, the node numbers of the input layer and the output layer are always fixed, and the middle layer can be freely designated, so that the adjustment of the neural network structure can be realized.

The activation function is a function added to the artificial neural network, which ultimately determines what is to be transmitted to the next neuron. There are several types of activation functions, including: sigmoid activation function, hyperbolic tangent activation function, reLU activation function, etc., different functions may be selected for adjustment during training.

The loss function may be expressed as L (y, f (x)) and is used to measure the degree of inconsistency between the true value y and the predicted value f (x), generally the smaller the better. In the training process, the loss degree is determined, and residual errors in the regression problem are analyzed, so that the neural network model is continuously optimized.

The neural network model is continuously optimized by continuously adjusting parameters of the neural network.

Step S230: and controlling the damping device to perform damping treatment according to the damping parameter information.

After step S220, the trained neural network model is embedded into the life support cabin, so that the damping device can adjust damping parameters in real time, and parameters of parts on the damping device can be adjusted in a self-adaptive manner in the running process of the vehicle, so that the vehicle can keep a stable running state, and the purpose of damping treatment is achieved.

Optionally, the damping parameter information includes: suspension length of the shock absorber. Damping devices are often adopted in a vehicle suspension system, and in the suspension system, vibration is generated due to impact of an elastic element, so that the running smoothness of an automobile is improved, and a damper is arranged in parallel with the elastic element in the suspension. In general, a shock absorbing device on a vehicle is composed of a spring and a shock absorber together.

In this embodiment, the shock absorbing means consists of a drive element and an electric suspension spring above the four wheels of the life support compartment. The drive element is for driving the adjustment of the electric suspension length, the drive element being, for example, an electric motor.

Further, step S230 may include: the control driving element is adjusted according to the preset voltage value and the preset current value, so that the damping device is adjusted to be in a hanging length.

After the neural network model outputs the suspension length information of the damping device, the processor 11 may control the damping device to adjust according to the suspension length. Specifically, the suspension length of the shock absorber is changed by controlling the voltage value and the current value of the motor to be increased or decreased according to the preset current value and the voltage value, and it is noted that the changes of the voltage value and the current value are opposite.

Optionally, the damping parameter information may further include: suspension coefficient and suspension damping coefficient of the shock absorber. In the compression stroke (namely, the axle and the frame are close to each other), the damping force of the shock absorber is small, so that the elastic function of the spring is fully exerted, and the impact is alleviated. When the vehicle is in the suspension extension stroke (the axle and the frame are far away from each other), the damping force of the shock absorber is large, and the shock absorber rapidly absorbs the shock.

Further, step S230 may further include: the control driving element is adjusted according to a preset rotation value, so that the damping device is adjusted to be a suspension coefficient and a suspension damping coefficient.

In this embodiment, after the neural network model outputs the information of the suspension coefficient and the suspension damping coefficient of the damping device, the processor 11 may control the damping device to adjust according to the suspension coefficient and the suspension damping coefficient. Specifically, the spring is squeezed or relaxed by controlling the number of rotations or the number of rotations of the motor, and is in a squeezed state when the number of rotations increases and in a relaxed state when the number of rotations decreases. Depending on whether the spring is in a compressed or relaxed state, the suspension coefficient and suspension damping coefficient are varied.

The suspension length, the suspension coefficient and the suspension damping coefficient can be correspondingly adjusted according to the ground fluctuation condition, the vehicle speed and the vehicle posture, so that the damping device achieves the damping purpose, and the vehicle keeps stable when the ground fluctuates.

Fig. 3 is a flowchart of a damping control method for a life support cabin according to another embodiment of the application. The method comprises the steps of S310-S350:

step S310: vehicle state information and ground heave information are acquired.

The specific description of step S310 may refer to the description of the portion S210 in detail, and will not be repeated here.

Step S320: and judging whether the current state of the vehicle deviates from the target state.

The obtained state information of the current vehicle and the current ground fluctuation information are input into a neural network model for training and learning to obtain suspension lengths, suspension coefficients and suspension damping coefficients of four wheels of a life support cabin, continuous optimization is carried out based on the neural network model, so that the suspension lengths, the suspension coefficients and the suspension damping coefficients of the four wheels of the life support cabin after optimization are obtained, the suspension lengths, the suspension coefficients and the suspension damping coefficients of the four wheels of the life support cabin after optimization are output into a closed-loop PID controller for real-time control of a damping device, and after the damping device is adjusted according to the parameters, the vehicle state information and the ground fluctuation information are acquired in real time through an image acquisition device and various sensors, so that whether the current state of the vehicle deviates from a target state is judged. Wherein.

The target state is that the damping device adjusts the suspension length, the suspension coefficient and the suspension damping coefficient of four wheels of the life support cabin which are output after training according to the neural network model, so that the vehicle can theoretically change the state of the vehicle under the current adjustment parameters, namely the vehicle state when the vehicle can stably pass through a bumpy road surface and a pothole road surface. If deviation exists between the current state of the collected vehicle and the self-changed state of the vehicle simulated by the model, the neural network model is required to be optimized continuously until the output suspension length, the suspension coefficient and the suspension damping coefficient are more in line with the actual situation.

Step S330: if yes, the PID controller controls the damping device to adjust the voltage value and the current value of the motor so as to adjust the suspension length.

If the current state of the vehicle deviates from the target state in step S320, the PID controller needs to control the damping device to continuously adjust the voltage and current values of the motor to adjust the suspension length, so that the vehicle can adapt to the ground fluctuation condition smoothly.

The PID controller is a feedback loop component that compares the collected data to a reference value and then uses this difference to calculate a new input value that is intended to allow the data to reach or remain at the reference value. The PID controller can adjust the input value according to the historical data and the occurrence rate of the difference, so that the error between the real data value and the reference value is smaller and more stable.

Step S340: the PID controller controls the damping device to adjust the rotation number of the motor so as to adjust the suspension coefficient and the suspension damping coefficient.

Step S340 may refer to the description of step S230 in detail, and will not be described herein.

Step S350: the damping device adjusts according to the suspension length, the suspension coefficient and the suspension damping coefficient, so that the vehicle adjusts the posture of the vehicle to reach the target posture, otherwise, the step S320 is continuously executed.

Step S350 may refer to the description of step S230 in detail, and will not be described herein.

The damping control method of the life support cabin has the capability of acquiring the ground fluctuation information in advance, and parameter information such as the suspension length, the suspension coefficient, the suspension damping coefficient and the like corresponding to the damping device is calculated and output through the neural network model, so that the damping device is correspondingly adjusted according to the parameter information, and the vehicle can still keep stably running on the fluctuation ground.

Referring to fig. 4, a schematic structural diagram of a vibration damping control system for a life support cabin according to the present application is provided, where the vibration damping control system for a life support cabin includes: a data acquisition system 410, a data processing system 420, a shock absorption system 430 and a cloud storage system 440.

The data acquisition system 410 is provided with a plurality of sensors for acquiring vehicle state information and an image acquisition device for acquiring ground heave information. The method for collecting the vehicle status information and the ground heave information by the data collecting system 410 can refer to the description of step S210, and will not be described herein.

The data processing system 420 is connected with the data acquisition system 410, and the data processing system 420 inputs the acquired vehicle state information and ground heave information into the trained neural network model and outputs damping parameter information.

The step of training and calculating the neural network model by the data processing system 420 may refer to the description of step S220 in detail, and will not be described herein.

The damping system 430 comprises a damping device, the damping device is connected with the data processing system 420, and the damping system 430 controls the damping device to perform damping processing according to damping parameter information.

The manner in which the damping system 430 performs the damping process may be described in detail with reference to step S230, which is not described herein.

The cloud storage system 440 is connected to the data acquisition system 410 and the data processing system 420, and is used for storing vehicle state information, ground heave information and damping parameter information.

The cloud storage system 440 may upload locally collected ground heave information and vehicle state information to the cloud, and simultaneously download machine learning data of the cloud to the data processing system 420 for model optimization processing. It should be noted that, the cloud end may be a server end for storing various data information, or may be formed by a high-performance hard disk with a large storage capacity. Wherein each item of data information includes ground heave information, vehicle state information, damping parameter information of the damping device, and historical damping parameters of the damping device when passing through each heave ground. The cloud storage system 440 can be connected with the 5G module through a communication port when running on a computer or an industrial personal computer, so as to realize information interaction between the internet and a server.

Optionally, after the cloud end stores the ground relief information, the vehicle state information, the damping parameter information of the damping device, and other data, when the same life support cabin travels to the same relief road section again, the cloud end storage system 440 may download parameters such as a suspension length, a suspension coefficient, a suspension damping coefficient, and the like, corresponding to the damping device, which are calculated and output through the neural network model, from the cloud end when the life support cabin passes through the relief road section last time, so as to realize data sharing, and certainly, also realize sharing to the life support cabin that travels to the relief road section next time. When the next life support cabin encounters a similar fluctuation road section, parameter data such as the suspension length, the suspension coefficient, the suspension damping coefficient and the like corresponding to the damping device stored in the cloud can be directly called, so that the damping device can be correspondingly regulated according to the parameter, early warning can be realized, the local operation power consumption is reduced, and the execution speed of damping treatment is improved.

Referring to fig. 5, which is a schematic structural diagram of a vibration damping control device for a life support cabin according to an embodiment of the present application, the vibration damping control device for a life support cabin includes: an acquisition module 500, an output module 600, and a control module 700.

The acquisition module 500 is configured to acquire ground heave information and vehicle state information.

The output module 600 is configured to input the ground heave information and the vehicle state information into the trained neural network model, and obtain damping parameter information output by the neural network model.

And the control module 700 is used for controlling the damping device to perform damping treatment according to the damping parameter information.

The implementation process of the functions and roles of each module in the above device is specifically detailed in the implementation process of the corresponding steps in the above description, and will not be repeated here.

The embodiment of the application also provides an electronic device readable storage medium, which comprises: a program which, when run on an electronic device, causes the electronic device to perform all or part of the flow of the method in the above-described embodiments. The storage medium may be a magnetic Disk, an optical Disk, a Read-Only Memory (ROM), a random access Memory (Random Access Memory, RAM), a Flash Memory (Flash Memory), a Hard Disk (HDD), or a Solid State Drive (SSD), etc. The storage medium may also include a combination of the above-mentioned types of memory 12.

In the several embodiments provided in the present application, the disclosed apparatus and method may be implemented in other manners. The apparatus embodiments described above are merely illustrative, for example, of the flowcharts and block diagrams in the figures that illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s).

In some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.

In addition, functional modules in the embodiments of the present application may be integrated together to form a single part, or each module may exist alone, or two or more modules may be integrated to form a single part.

In this specification, each embodiment is described in a progressive manner, and identical and similar parts of each embodiment are all referred to each other, and each embodiment mainly describes differences from other embodiments. The foregoing description is only of the preferred embodiments of the present application, and is not intended to limit the scope of the application. Any modification, equivalent replacement, improvement, etc. which are within the spirit and principle of the present application, should be included in the protection scope of the present application, for those skilled in the art.

Claims

1. A method of damping control of a life support compartment, wherein a damping device is mounted on the life support compartment, the method comprising:

acquiring ground heave information and vehicle state information;

2. The shock absorbing control method for a life support compartment according to claim 1, wherein the vehicle state information includes: the system comprises vehicle attitude information and vehicle speed information, wherein a speed measuring sensor, an inertial sensor and an image acquisition device are arranged on a life support cabin; the obtaining ground heave information and vehicle state information includes:

3. The method of vibration damping control for a life support compartment of claim 1, wherein prior to said inputting said ground heave information and said vehicle state information into a trained neural network model, said method further comprises:

4. A method of damping control of a life support compartment according to claim 3, wherein prior to said parameter adjustment of an initial neural network from a sample training set to obtain said neural network model, the method further comprises:

and determining the sample training set according to the mapping relation.

5. The method for controlling the damping of a life support compartment according to claim 1, wherein the damping parameter information includes: the suspension length of the damping device; the damping device is provided with a driving element; according to the damping parameter information, controlling the damping device to perform damping treatment, including:

6. The method for damping control of a life support compartment according to claim 5, wherein the damping parameter information further comprises: the suspension coefficient and the suspension damping coefficient of the damping device; according to the damping parameter information, controlling the damping device to perform damping treatment, including:

7. A shock absorbing control device for a life support compartment, comprising:

8. A shock absorbing control system for a life support chamber, comprising:

9. An electronic device, the electronic device comprising:

a processor;

a memory for storing processor-executable instructions;

wherein the processor is configured to perform the method of damping control of a life support compartment of any of claims 1 to 6.

10. A computer-readable storage medium, characterized in that the storage medium stores a computer program for executing the method of damping control of a life support compartment according to any one of claims 1 to 6 when the computer program is run by a processor.