Sightseeing vehicle smoothness optimization method and system based on big data

Abstract

The invention provides a big data-based sightseeing vehicle smoothness optimization method and system, and relates to the technical field of vehicle control, and the method comprises the steps: obtaining the operation data of a sightseeing vehicle, the operation data of the sightseeing vehicle comprising the actual state vector and the actual control signal of each moment in the operation process of the sightseeing vehicle; constructing a dynamic model of the suspension seat of the sightseeing vehicle; a PID controller is constructed; substituting the actual state vector and the actual control signal into a sightseeing vehicle suspension seat dynamic model for solving, and calculating a corresponding driving smoothness index, a suspension dynamic deflection value and a tire dynamic displacement value; by taking the minimum driving smoothness index, the suspension dynamic deflection value and the tire dynamic displacement value as targets, carrying out smoothness optimization on the PID controller; acquiring a current state vector of the sightseeing vehicle; according to the current state vector, determining a current control signal of the sightseeing vehicle through the PID controller after smoothness optimization; and controlling the sightseeing vehicle through the current control signal.

Description

Technical Field

[0001] The present invention relates to the field of vehicle control technology, and in particular to a sightseeing vehicle ride comfort optimization method and system based on big data. Background Art

[0002] Sightseeing buses are usually used for travel and sightseeing, and the main demand of passengers is to enjoy a comfortable journey. Factors such as uneven roads and bumps will directly affect the passenger experience. Therefore, it is particularly important to optimize the ride comfort of sightseeing buses. By optimizing the ride comfort of the suspension system, the vibration and impact caused by uneven roads can be significantly reduced, the vehicle's driving stability and comfort can be improved, and the handling and driving safety of sightseeing buses can be improved.

[0003] At present, the smoothness optimization of sightseeing vehicles is mainly achieved by adjusting structural parameters such as spring stiffness and damping coefficient in the suspension system. Softer springs can better absorb vibrations caused by uneven roads, thereby improving the smoothness of the vehicle and are suitable for sightseeing vehicles to travel on low-speed and stable roads. A larger damping coefficient can limit the movement of the suspension system, allowing the vehicle to quickly return to a stable state after bumps, which helps to improve the stability of the vehicle.

[0004] However, the optimization method of structural parameters such as spring stiffness and damping coefficient in the entire suspension system can only be optimized before leaving the factory, and cannot be adjusted in real time according to different driving conditions and road conditions. It may perform well under certain road conditions, but it cannot flexibly adapt to changes under complex, irregular road surfaces or different load conditions. This results in its optimization effect often performing well only under certain fixed conditions, and cannot take into account various complex scenarios. At the same time, even under the optimal structural parameters, the response of the suspension is still passive and cannot respond quickly to instantaneous changes (such as sudden bumps or obstacles). Therefore, passengers may still feel obvious bumps, especially in more complex road conditions. Summary of the invention

[0005] In order to solve the technical problems that the prior art cannot make real-time adjustments according to different driving conditions and road conditions, cannot flexibly adapt to changes, resulting in the inability to take into account various complex scenarios in the optimization effect, and cannot respond quickly to instantaneous changes, and passengers may still feel obvious bumps, the present invention provides a sightseeing car smoothness optimization method and system based on big data.

[0006] The technical solution provided by the embodiment of the present invention is as follows:

[0007] First aspect:

[0008] An embodiment of the present invention provides a sightseeing vehicle ride comfort optimization method based on big data, comprising:

[0009] S1: Acquire sightseeing car operation data, wherein the sightseeing car operation data includes the actual state vector and the actual control signal at each moment during the sightseeing car operation;

[0010] S2: Construct the dynamics model of the sightseeing car suspension seat;

[0011] S3: construct PID controller;

[0012] S4: Substituting the actual state vector and the actual control signal into the sightseeing vehicle suspension seat dynamics model for solving, and calculating the corresponding driving smoothness index, suspension dynamic deflection value and tire dynamic displacement value;

[0013] S5: optimizing the ride comfort of the PID controller with the goal of minimizing the ride comfort index, the suspension dynamic deflection value, and the tire dynamic displacement value;

[0014] S6: Get the current state vector of the sightseeing car;

[0015] S7: Determine the current control signal of the sightseeing car according to the current state vector through the PID controller after the smoothness optimization;

[0016] S8: Controlling the sightseeing vehicle through the current control signal.

[0017] Second aspect:

[0018] An embodiment of the present invention provides a sightseeing vehicle ride comfort optimization system based on big data, comprising:

[0019] processor;

[0020] A memory having computer-readable instructions stored thereon, wherein when the computer-readable instructions are executed by the processor, the method for optimizing the smoothness of a sightseeing vehicle based on big data as described in the first aspect is implemented.

[0021] The third aspect:

[0022] An embodiment of the present invention provides a computer-readable storage medium, on which a computer program is stored. When the program is executed by a processor, the method for optimizing the smoothness of a sightseeing vehicle based on big data as described in the first aspect is implemented.

[0023] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least:

[0024] In the present invention, the PID controller is optimized for smoothness with the goal of minimizing the driving smoothness index, the dynamic deflection value of the suspension and the dynamic displacement value of the tire. Then, the real-time control signal of the sightseeing car is determined by the PID controller after the smoothness optimization, and the sightseeing car is actually controlled. It can be adjusted in real time according to different driving conditions and road conditions, flexibly adapt to changes in driving conditions and road conditions, and can respond quickly to instantaneous changes. By minimizing the dynamic deflection of the suspension and the dynamic displacement of the tire, the body shaking and vibration of the vehicle on uneven roads can be reduced, the bumpy feeling of passengers can be reduced, smooth driving can be guaranteed, and the smoothness optimization effect can be improved. By controlling the dynamic displacement of the tire and the road surface, it can ensure that the tire has a more stable contact with the road surface, avoid the loss of grip due to the hysteresis reaction of the suspension system, and thus improve the overall driving safety of the vehicle. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0026] Figure 1 A schematic diagram of a flow chart of a sightseeing vehicle ride comfort optimization method based on big data provided by an embodiment of the present invention;

[0027] Figure 2 A schematic structural diagram of a sightseeing vehicle ride comfort optimization system based on big data provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0028] The technical solution of the present invention is described below in conjunction with the accompanying drawings.

[0029] In the embodiments of the present invention, words such as "exemplarily" and "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "example" in the present invention should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of the word "example" is intended to present the concept in a specific way. In addition, in the embodiments of the present invention, the meaning expressed by "and / or" can be both, or it can be either of the two.

[0030] In the embodiments of the present invention, "image" and "picture" can sometimes be used interchangeably. It should be noted that when the difference between them is not emphasized, the meanings they intend to express are the same. "of", "corresponding, relevant" and "corresponding" can sometimes be used interchangeably. It should be noted that when the difference between them is not emphasized, the meanings they intend to express are the same.

[0031] In the embodiments of the present invention, sometimes a subscript such as W1 may be written as a non-subscript such as W1. When the difference is not emphasized, the meanings to be expressed are the same.

[0032] In order to make the technical problems, technical solutions and advantages to be solved by the present invention more clear, a detailed description will be given below with reference to the accompanying drawings and specific embodiments.

[0033] Reference Manual Attached Figure 1 , showing a flow chart of a sightseeing vehicle smoothness optimization method based on big data provided in an embodiment of the present invention.

[0034] The embodiment of the present invention provides a sightseeing car ride comfort optimization method based on big data, which can be implemented by a sightseeing car ride comfort optimization device based on big data, and the sightseeing car ride comfort optimization device based on big data can be a terminal or a server. The processing flow of the sightseeing car ride comfort optimization method based on big data can include the following steps:

[0035] S1: Obtain sightseeing bus operation data.

[0036] The sightseeing car operation data includes the actual state vector and the actual control signal at each moment during the sightseeing car operation.

[0037] Optionally, the actual state vector includes: the vertical displacement of the center of mass of the vehicle body, the vertical displacement of the seat, the vertical displacement of the human body-seat module, the pitch angle and the roll angle.

[0038] Optionally, most of the seats of sightseeing vehicles on the market are made of plastic and fixed to the vehicle body. In this case, the actual control signal includes: the control force at each suspension-body connection point.

[0039] Furthermore, for a sightseeing car with seats having a built-in adjustment function, the actual control signal at this time includes: the control force at each suspension-body connection point and the seat control force.

[0040] S2: Construct a dynamic model of the sightseeing car suspension seat.

[0041] In a possible implementation, for sightseeing vehicles on the market, most of whose seats are made of plastic and fixed to the vehicle body, the dynamic model of the sightseeing vehicle suspension seat is constructed in the following manner. S2 specifically includes sub-steps S201 to S206:

[0042] S201: Determine the body-suspension-seat displacement equation:

[0043]

[0044] Among them, z si represents the vertical displacement of the i-th suspension-body connection point, i=1,2,3,4 respectively represent the left front connection point, right front connection point, left rear connection point, right rear connection point, z b represents the vertical displacement of the center of mass of the vehicle body, l1 represents the distance between the center of mass of the vehicle body and the front axle, l2 represents the distance between the center of mass of the vehicle body and the rear axle, l3 represents the distance between the center of mass of the vehicle body and the left axle, l4 represents the distance between the center of mass of the vehicle body and the right axle, θ represents the pitch angle, is the roll angle, z xy represents the vertical displacement of the seat, r x The horizontal coordinate representing the position of the human body-seat module, r y The vertical coordinate representing the position of the human body-seat module.

[0045] S202: Determine the center of mass motion equation of the human body-seat module:

[0046]

[0047] Among them, m c represents the mass of the human body-seat module, z c represents the vertical displacement of the human body-seat module, k c represents the stiffness of the human body-seat module, c c Represents the damping of the body-seat module.

[0048] S203: Determine the vehicle body center of mass motion equation:

[0049]

[0050] Among them, m b Indicates the vehicle body mass, k si represents the stiffness of the ith suspension-body connection point, z ui represents the vertical displacement of the tire at the i-th suspension-body connection point, c si represents the damping of the ith suspension-body connection point, u i represents the control force at the i-th suspension-body connection point.

[0051] S204: Determine the suspension pitch rotation differential equation:

[0052]

[0053] Among them, J θ Represents the pitching inertia of the suspension.

[0054] S205: Determine the differential equation for suspension roll rotation:

[0055]

[0056] in, Represents the roll inertia of the suspension.

[0057] S206: Determine the tire center of mass motion equation:

[0058]

[0059] Among them, m ui represents the tire mass at the i-th suspension-body connection point, k ui represents the tire stiffness at the i-th suspension-body connection point, z r Represents the vertical displacement of the road surface excitation.

[0060] Furthermore, for sightseeing vehicles with seats having self-adjusting functions, the actual control signals at this time include: control forces at various suspension-body connection points and seat control forces, and therefore, the dynamics model of the sightseeing vehicle suspension seat will also be adjusted.

[0061] The center of mass motion equation of the human body-seat module is adjusted to:

[0062]

[0063] Among them, u c Indicates seat control signal.

[0064] The vehicle body center of mass motion equation is adjusted to:

[0065]

[0066] The suspension pitch rotation differential equation is adjusted to:

[0067]

[0068] The differential equation for suspension roll rotation is adjusted to:

[0069]

[0070] In the present invention, by constructing the dynamic model of the sightseeing car suspension and seat, the movement law of the vehicle under different road conditions can be understood in detail. The model can accurately describe the vertical movement of the vehicle body, the response of the suspension system, the vibration of the seat, and the interaction between the tire and the road surface. This provides a theoretical basis for subsequent optimization design and control strategy.

[0071] S3: Construct a PID controller.

[0072] Among them, the PID controller (Proportional-Integral-Derivative Controller) is one of the most common and widely used feedback controllers in control engineering. It controls the system error to generate the corresponding control signal, thereby achieving system regulation.

[0073] In a possible implementation manner, the control signal output by the PID controller is specifically:

[0074]

[0075] e(t)=0-e c (t)

[0076] Among them, u(t) represents the control signal at time t, e(t) represents the vertical vibration velocity error of the seat at time t, K p Represents the proportional gain coefficient, K i Indicates the integral gain coefficient, K d represents the differential gain coefficient, d represents the differential sign, e c (t) represents the vertical vibration velocity of the seat at time t.

[0077] It should be noted that the vertical vibration velocity of the seat should be 0 in an ideal state. Therefore, the vertical vibration velocity error of the seat is determined by subtracting the vertical vibration velocity of the seat from 0.

[0078] S4: Substitute the actual state vector and the actual control signal into the sightseeing car suspension seat dynamics model for solution, and calculate the corresponding driving smoothness index, suspension dynamic deflection value and tire dynamic displacement value.

[0079] Optionally, the driving comfort index is specifically:

[0080]

[0081] Where SI is the driving comfort index, V is the vibration amplitude measurement value, and V i represents the vibration amplitude measurement value at the i-th suspension-body connection point, V c represents the vibration amplitude measurement value at the human body-seat module, λi represents the weight coefficient of the i-th suspension-body connection point, λ c represents the weight coefficient of the human body-seat module, a represents acceleration, a(t) represents the acceleration at time t, d represents the differential sign, and T represents the length of the time window.

[0082] Among them, those skilled in the art can set the weight coefficients of the suspension-body connection point and the human body-seat module according to actual conditions, and the present invention does not limit this.

[0083] It should be noted that the vibration amplitude measurement value is obtained by averaging the fourth power integral of the acceleration and taking the fourth root. The fourth power integral is more sensitive to vibration events with large amplitudes, which means that the formula can more effectively capture and reflect larger bumps or instabilities.

[0084] In the present invention, by introducing the vibration amplitudes of multiple suspension points and seat points and weighting them, the overall and local ride comfort of the vehicle can be better captured and optimized, and the vibration behavior of the entire vehicle can be more comprehensively evaluated. At the same time, the vibration of the seat is taken into account, the passenger experience is paid attention to, and the passenger comfort is improved.

[0085] Optionally, the suspension dynamic deflection value is specifically:

[0086]

[0087] Among them, z p Indicates the dynamic deflection value of the suspension, z si represents the vertical displacement of the ith suspension-body connection point, z ui represents the vertical displacement of the tire at the i-th suspension-body connection point, i=1,2,3,4 represents the left front connection point, right front connection point, left rear connection point, and right rear connection point, respectively.

[0088] In the present invention, by calculating the sum of the dynamic deflection values ​​of the four suspension-body connection points, the overall dynamic performance of the entire suspension system can be comprehensively evaluated. By reducing the dynamic deflection value of the suspension, the PID controller is optimized to ensure the accuracy and stability of the dynamic response of the suspension system, so that the PID controller can more effectively adjust the suspension working state and further improve the vehicle's handling and comfort.

[0089] Optionally, the tire dynamic displacement value is specifically:

[0090]

[0091] Among them, z l Indicates the dynamic displacement value of the tire, z ui represents the vertical displacement of the tire at the i-th suspension-body connection point, z rRepresents the vertical displacement of the road surface excitation, i=1,2,3,4 represent the left front connection point, right front connection point, left rear connection point, and right rear connection point respectively.

[0092] In the present invention, by calculating the dynamic displacement of the four tires, the contact between the tires and the road surface during the driving process of the sightseeing car can be fully reflected. By reducing the dynamic displacement value of the tires and optimizing the PID controller, it is helpful for the vehicle to maintain better grip when turning, accelerating or braking, thereby enhancing the vehicle's handling performance, ensuring the vehicle's stability and responsiveness, and improving driving safety.

[0093] S5: Optimize the ride comfort of the PID controller with the goal of minimizing the ride comfort index, suspension dynamic deflection value, and tire dynamic displacement value.

[0094] In a possible implementation, S5 specifically includes:

[0095] S501: Construct an objective function with the goal of minimizing the driving smoothness index, the suspension dynamic deflection value, and the tire dynamic displacement value.

[0096] Optionally, the objective function is specifically:

[0097] minf(σ)=β1SI+β2z p +β3z l

[0098] Where min means minimization, f means objective function, σ means PID controller parameter set, SI means driving comfort index, z p Indicates the dynamic deflection value of the suspension, z l represents the dynamic displacement value of the tire, β1 represents the weight coefficient of the driving smoothness index, β2 represents the weight coefficient of the dynamic deflection value of the suspension, and β3 represents the weight coefficient of the dynamic displacement value of the tire.

[0099] Among them, those skilled in the art can set the weight coefficient β1 of the driving smoothness index, the weight coefficient β2 of the suspension dynamic deflection value, and the weight coefficient β3 of the tire dynamic displacement value according to actual conditions, and the present invention does not limit this.

[0100] Furthermore, the ride smoothness index measures the riding comfort of passengers. By optimizing this index, passengers can experience a smoother ride during vehicle driving.

[0101] Furthermore, the suspension dynamic deflection value measures the dynamic response of the suspension system. Optimizing this value can improve the dynamic stability of the suspension and ensure the vehicle's handling and safety under different road conditions.

[0102] Furthermore, the tire dynamic displacement value measures the contact condition between the tire and the road surface. Optimizing this value helps improve the tire's grip and enhance the vehicle's overall driving stability.

[0103] In the present invention, by optimizing the ride smoothness index, suspension dynamic deflection and tire dynamic displacement simultaneously, a multi-dimensional comprehensive optimization of the overall performance of the vehicle is achieved. This approach can better balance the comfort, handling and safety of the vehicle.

[0104] S502: According to the objective function, the PID controller is optimized for smoothness by using the firefly optimization algorithm to determine the optimal PID controller parameters.

[0105] Among them, the Firefly Algorithm (FA) is a heuristic optimization algorithm based on the behavior of fireflies in nature. It performs global optimization by simulating the behavior of fireflies based on brightness and attraction. It is mainly used to solve complex, nonlinear, and non-convex optimization problems.

[0106] Specifically, initialize the firefly individuals, each firefly individual represents a feasible PID controller parameter set, and the PID controller parameters include: proportional gain coefficient, integral gain coefficient, and differential gain coefficient. Each firefly individual is composed of multiple dimensional components, and each component represents a certain PID controller parameter.

[0107] Compare each firefly individual with other individuals in the population, generate a random number u, and move according to the following adaptive movement rules:

[0108]

[0109] in, represents the position of the i-th firefly individual at the t+1th iteration, ω represents the inertia weight factor, represents the position of the i-th firefly individual at the t-th iteration, represents the position of the j-th firefly individual at the t-th iteration, β represents the attraction, α represents the step factor, sign represents the sign function, r represents a random number in the range of 0 to 1, Levy represents the Levy flight random step, exp represents the exponential function with the natural constant e as the base, b represents the logarithmic spiral shape parameter, l represents a uniform random number between -1 and 1, R t represents the adaptive switching factor at the tth iteration, and u represents a uniform random number between 0 and 1.

[0110] In the present invention, different mobile strategies are dynamically selected according to the random number and the adaptive switching factor, and different search methods can be used at different stages. When the random number is greater than the adaptive switching factor, a long-distance Levy flight is selected to search with a large step size to help the algorithm escape the local optimum and conduct extensive exploration in the solution space. This strategy is suitable for rapidly expanding the search area in the early stages. When the random number is less than the adaptive switching factor, a fine local search based on a logarithmic spiral trajectory is selected to conduct a more refined exploration around fireflies with greater brightness with a small step size. This strategy is suitable for refining the approximation to the optimal solution in the later stages.

[0111] Optionally, the adaptive switching factor is specifically:

[0112]

[0113] in, represents the fitness value of the global best position at the t-1th iteration, Represents the fitness value of the global best position at the t-2th iteration.

[0114] In the present invention, by calculating the switching factor based on the global best fitness value of the first two iterations, the algorithm can dynamically adjust the search strategy. If the optimal fitness value between two iterations changes greatly, it means that the global search space still has the potential to be improved, and the algorithm tends to perform a global search. If the difference between the two fitness values ​​is small, it means that the algorithm is close to the optimal solution, and the algorithm tends to choose a local search for fine-tuning. In the early iterations, the algorithm usually needs to explore a wider search space to ensure that the global optimal solution is found. In the later stages, as the fitness value converges, more local searches help improve the accuracy of the solution. The adaptive switching factor ensures the flexibility of searching at different optimization stages.

[0115] Optionally, the attraction is specifically:

[0116]

[0117] Among them, β0 represents the attraction between two fireflies when the distance between them is 0, exp represents the exponential function with the natural constant e as the base, γ represents the light intensity attraction coefficient, and r ij represents the Euclidean distance between the i-th firefly individual and the j-th firefly individual.

[0118] In the present invention, relatively close individuals have stronger attraction, which can accelerate their approach to each other and quickly converge to the area where the fireflies with higher brightness are located, thereby enhancing the local search capability.

[0119] Optionally, the Levy flight random step size is specified as:

[0120]

[0121] Among them, u obeys The normal distribution of v follows The normal distribution of , λ represents the exponential parameter.

[0122]

[0123] Among them, τ is the standard Gamma function.

[0124] In the present invention, the Levy flight step size follows a long-tail distribution, which can generate a larger random step size, allowing individuals to perform longer-distance jump searches in the solution space. This gives the algorithm a powerful global search capability, especially in the early stages, where the Levy flight can help the algorithm jump out of the local optimal solution and increase the chance of exploring the global optimal solution.

[0125] After the firefly individual moves to a new location, update the individual fitness.

[0126] Determine whether the current number of iterations has reached the maximum number of iterations. If so, output the PID controller parameter set representing the firefly individual with the highest current fitness. Otherwise, return to continue iterating.

[0127] In the present invention, the smoothness of the PID controller is optimized by the firefly optimization algorithm, which can effectively improve the performance of the PID controller and enable it to globally optimize the driving smoothness, suspension dynamic response and tire dynamic displacement.

[0128] S6: Get the current state vector of the sightseeing car.

[0129] S7: According to the current state vector, the current control signal of the sightseeing car is determined by the PID controller after the smoothness optimization.

[0130] S8: Control the sightseeing car through the current control signal.

[0131] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least:

[0132] In the present invention, the PID controller is optimized for smoothness with the goal of minimizing the driving smoothness index, the dynamic deflection value of the suspension and the dynamic displacement value of the tire. Then, the real-time control signal of the sightseeing car is determined by the PID controller after the smoothness optimization, and the sightseeing car is actually controlled. It can be adjusted in real time according to different driving conditions and road conditions, flexibly adapt to changes in driving conditions and road conditions, and can respond quickly to instantaneous changes. By minimizing the dynamic deflection of the suspension and the dynamic displacement of the tire, the body shaking and vibration of the vehicle on uneven roads can be reduced, the bumpy feeling of passengers can be reduced, smooth driving can be guaranteed, and the smoothness optimization effect can be improved. By controlling the dynamic displacement of the tire and the road surface, it can ensure that the tire has a more stable contact with the road surface, avoid the loss of grip due to the hysteresis reaction of the suspension system, and thus improve the overall driving safety of the vehicle.

[0133] Reference Manual Attached Figure 2 , showing a structural schematic diagram of a sightseeing car smoothness optimization system based on big data provided by the present invention.

[0134] The present invention further provides a sightseeing vehicle ride comfort optimization system 20 based on big data, which is applied to the above-mentioned sightseeing vehicle ride comfort optimization method based on big data, and comprises:

[0135] Processor 201;

[0136] The memory 202 stores computer-readable instructions, and when the computer-readable instructions are executed by the processor 201, the sightseeing vehicle smoothness optimization method based on big data as described in the method embodiment is implemented.

[0137] The sightseeing vehicle smoothness optimization system 20 based on big data provided by the present invention can execute the above-mentioned sightseeing vehicle smoothness optimization method based on big data and achieve the same or similar technical effects. To avoid repetition, the present invention will not go into details.

[0138] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least:

[0139] In the present invention, the PID controller is optimized for smoothness with the goal of minimizing the driving smoothness index, the dynamic deflection value of the suspension and the dynamic displacement value of the tire. Then, the real-time control signal of the sightseeing car is determined by the PID controller after the smoothness optimization, and the sightseeing car is actually controlled. It can be adjusted in real time according to different driving conditions and road conditions, flexibly adapt to changes in driving conditions and road conditions, and can respond quickly to instantaneous changes. By minimizing the dynamic deflection of the suspension and the dynamic displacement of the tire, the body shaking and vibration of the vehicle on uneven roads can be reduced, the bumpy feeling of passengers can be reduced, smooth driving can be guaranteed, and the smoothness optimization effect can be improved. By controlling the dynamic displacement of the tire and the road surface, it can ensure that the tire has a more stable contact with the road surface, avoid the loss of grip due to the hysteresis reaction of the suspension system, and thus improve the overall driving safety of the vehicle.

[0140] It should be understood that the processor in the embodiment of the present invention may be a central processing unit (CPU), and the processor may also be other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), field programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0141] It should also be understood that the memory in the embodiments of the present invention may be a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memories. Among them, the non-volatile memory may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory may be a random access memory (RAM), which is used as an external cache. By way of example and not limitation, many forms of random access memory (RAM) are available, such as static RAM (SRAM), dynamic random access memory (DRAM), synchronous DRAM (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link DRAM (SLDRAM), and direct rambus RAM (DR RAM).

[0142] The above embodiments can be implemented in whole or in part by software, hardware (such as circuits), firmware or any other combination. When implemented by software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present invention is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center by wired (such as infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (for example, a floppy disk, a hard disk, a tape), an optical medium (for example, a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0143] It should be understood that the term "and / or" in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. A and B can be singular or plural. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship, but it may also indicate an "and / or" relationship. Please refer to the context for specific understanding.

[0144] In the present invention, "at least one" means one or more, and "more than one" means two or more. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b, or c can be represented by: a, b, c, ab, ac, bc, or abc, where a, b, c can be single or multiple.

[0145] It should be understood that in various embodiments of the present invention, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0146] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0147] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described equipment, devices and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0148] In the several embodiments provided by the present invention, it should be understood that the disclosed devices, apparatuses and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another device, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0149] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0150] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0151] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, and other media that can store program codes.

[0152] An embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein when the program is executed by a processor, the method for optimizing the smoothness of a sightseeing vehicle based on big data as described in the method embodiment is implemented.

[0153] A computer-readable storage medium provided by the present invention can implement the steps and effects of the sightseeing vehicle smoothness optimization method based on big data in the above method embodiment. To avoid repetition, the present invention will not go into details.

[0154] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least:

[0155] In the present invention, the PID controller is optimized for smoothness with the goal of minimizing the driving smoothness index, the dynamic deflection value of the suspension and the dynamic displacement value of the tire. Then, the real-time control signal of the sightseeing car is determined by the PID controller after the smoothness optimization, and the sightseeing car is actually controlled. It can be adjusted in real time according to different driving conditions and road conditions, flexibly adapt to changes in driving conditions and road conditions, and can respond quickly to instantaneous changes. By minimizing the dynamic deflection of the suspension and the dynamic displacement of the tire, the body shaking and vibration of the vehicle on uneven roads can be reduced, the bumpy feeling of passengers can be reduced, smooth driving can be guaranteed, and the smoothness optimization effect can be improved. By controlling the dynamic displacement of the tire and the road surface, it can ensure that the tire has a more stable contact with the road surface, avoid the loss of grip due to the hysteresis reaction of the suspension system, and thus improve the overall driving safety of the vehicle.

[0156] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art who is familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

[0157] There are a few points to note:

[0158] (1) The drawings of the embodiments of the present invention only relate to the structures related to the embodiments of the present invention, and other structures may refer to the general design.

[0159] (2) For the sake of clarity, in the drawings used to describe the embodiments of the present invention, the thickness of the layers or regions is exaggerated or reduced, that is, these drawings are not drawn according to the actual scale. It is understood that when an element such as a layer, film, region or substrate is referred to as being "on" or "under" another element, the element may be "directly" "on" or "under" the other element or there may be intermediate elements.

[0160] (3) In the absence of conflict, the embodiments of the present invention and the features therein may be combined with each other to obtain new embodiments.

[0161] The above are only specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. The protection scope of the present invention shall be based on the protection scope of the claims.

Claims

1. A sightseeing car ride comfort optimization method based on big data, characterized in that: include: S1: Acquire sightseeing car operation data, wherein the sightseeing car operation data includes the actual state vector and the actual control signal at each moment during the sightseeing car operation; S2: Construct the dynamics model of the sightseeing car suspension seat; S3: build PID controller; S4: Substituting the actual state vector and the actual control signal into the sightseeing vehicle suspension seat dynamics model for solving, and calculating the corresponding driving smoothness index, suspension dynamic deflection value and tire dynamic displacement value; S5: optimizing the ride comfort of the PID controller with the goal of minimizing the ride comfort index, the suspension dynamic deflection value, and the tire dynamic displacement value; S6: Get the current state vector of the sightseeing car; S7: Determine the current control signal of the sightseeing car according to the current state vector through the PID controller after the smoothness optimization; S8: Controlling the sightseeing vehicle through the current control signal.

2. The sightseeing vehicle ride comfort optimization method based on big data according to claim 1 is characterized in that: The actual state vector includes: the vertical displacement of the center of mass of the vehicle body, the vertical displacement of the seat, the vertical displacement of the human body-seat module, the pitch angle and the roll angle; The actual control signal includes: control force at each suspension-body connection point.

3. The sightseeing vehicle ride comfort optimization method based on big data according to claim 1 is characterized in that: The S2 specifically includes: S201: Determine the body-suspension-seat displacement equation; S202: Determine the center of mass motion equation of the human body-seat module; S203: Determine the motion equation of the center of mass of the vehicle body; S204: Determine the suspension pitch rotation differential equation; S205: Determine a differential equation of suspension roll rotation; S206: Determine the tire center of mass motion equation.

4. The method for optimizing the ride comfort of a sightseeing vehicle based on big data according to claim 1 is characterized in that: The control signal output by the PID controller is specifically: e(t)=0-e c (t) Among them, u(t) represents the control signal at time t, e(t) represents the vertical vibration velocity error of the seat at time t, K p Represents the proportional gain coefficient, K i Indicates the integral gain coefficient, K d represents the differential gain coefficient, d represents the differential sign, e c (t) represents the vertical vibration velocity of the seat at time t.

5. The method for optimizing the ride comfort of a sightseeing vehicle based on big data according to claim 1 is characterized in that: The S5 specifically includes: S501: constructing an objective function with the goal of minimizing the driving smoothness index, the suspension dynamic deflection value, and the tire dynamic displacement value; S502: According to the objective function, the PID controller is optimized for smoothness by using the firefly optimization algorithm to determine the optimal PID controller parameters.

6. The method for optimizing the ride comfort of a sightseeing vehicle based on big data according to claim 5 is characterized in that: The objective function is specifically: minf(σ)=β1SI+β2z p +β3z l Where min means minimization, f means objective function, σ means PID controller parameter set, SI means driving comfort index, z p Indicates the dynamic deflection value of the suspension, z l represents the dynamic displacement value of the tire, β1 represents the weight coefficient of the driving smoothness index, β2 represents the weight coefficient of the dynamic deflection value of the suspension, and β3 represents the weight coefficient of the dynamic displacement value of the tire.

7. The method for optimizing the ride comfort of a sightseeing vehicle based on big data according to claim 6 is characterized in that: The driving comfort index is specifically: Where SI is the driving comfort index, V is the vibration amplitude measurement value, and V i represents the vibration amplitude measurement value at the i-th suspension-body connection point, V c represents the vibration amplitude measurement value at the human body-seat module, λ i represents the weight coefficient of the i-th suspension-body connection point, λ c represents the weight coefficient of the human body-seat module, a represents acceleration, a(t) represents the acceleration at time t, d represents the differential sign, and T represents the length of the time window.

8. The method for optimizing the ride comfort of a sightseeing vehicle based on big data according to claim 6 is characterized in that: The suspension dynamic deflection value is specifically: Among them, z p Indicates the dynamic deflection value of the suspension, z si represents the vertical displacement of the ith suspension-body connection point, z ui represents the vertical displacement of the tire at the i-th suspension-body connection point, i=1,2,3,4 represents the left front connection point, right front connection point, left rear connection point, and right rear connection point, respectively.

9. The method for optimizing the ride comfort of a sightseeing vehicle based on big data according to claim 6 is characterized in that: The tire dynamic displacement value is specifically: Among them, z l Indicates the dynamic displacement value of the tire, z ui represents the vertical displacement of the tire at the i-th suspension-body connection point, z r Represents the vertical displacement of the road surface excitation, i=1,2,3,4 represent the left front connection point, right front connection point, left rear connection point, and right rear connection point respectively.

10. A sightseeing car ride comfort optimization system based on big data, characterized in that: include: processor; A memory having computer-readable instructions stored thereon, wherein when the computer-readable instructions are executed by the processor, the method for optimizing the smoothness of a sightseeing vehicle based on big data as described in any one of claims 1 to 9 is implemented.

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