CN117708562A - Road surface flatness evaluation method, device, computer equipment and storage medium - Google Patents
Road surface flatness evaluation method, device, computer equipment and storage medium Download PDFInfo
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Abstract
The application relates to a road surface flatness evaluation method, a device, computer equipment and a storage medium, and belongs to the technical field of road surface detection, wherein the method comprises the following steps: acquiring and preprocessing the wheel speed of a target vehicle to obtain first characteristic data; based on the first characteristic data, calculating and acquiring second characteristic data by using a preset function to determine whether to trigger a road surface flatness assessment mechanism; determining a first road surface flatness evaluation level based on the second characteristic data in response to detecting that the road surface flatness evaluation mechanism is triggered; and checking the preset road surface flatness evaluation level by using the first road surface flatness evaluation level, and determining the target road surface flatness evaluation level according to a checking result to realize evaluation of the road surface flatness. The method and the device reduce corresponding cost while ensuring timely and accurate evaluation of the road surface flatness.
Description
Technical Field
The present disclosure relates to the field of road surface detection technologies, and in particular, to a road surface flatness evaluation method, a device, a computer device, and a storage medium.
Background
The road surface is often subjected to the load of the vehicle with larger mass and fast movement and is in a natural environment, so that the road surface is easy to damage, the road surface condition after damage can be accelerated to deteriorate, the uneven road surface can influence riding comfort, vehicle damage, tyre abrasion, fuel consumption increase and accident rate increase can also be caused, at present, along with the increasing demands of users on the comfort, dynamic property and other aspects of new energy automobiles, how to timely and accurately evaluate the road surface flatness so as to facilitate the real-time and accurate response of a vehicle-machine system to an evaluation result, and the improvement of the comfort of the new energy automobiles is a problem to be solved urgently.
The related data are usually collected by adding a plurality of sensors to calculate the evenness of the pavement, and the method has no universality and is high in cost.
Therefore, there is a need to propose a road surface flatness evaluation method, apparatus, computer device, and storage medium that can reduce costs while ensuring high accuracy.
Disclosure of Invention
Based on this, it is necessary to provide a road surface flatness evaluation method, apparatus, computer device, and storage medium that can reduce costs on the basis of ensuring high accuracy, in view of the above-described technical problems.
In one aspect, a method for evaluating road surface flatness is provided, the method comprising:
acquiring the wheel speed of a target vehicle, and preprocessing the wheel speed to obtain first characteristic data;
based on the first characteristic data, calculating and acquiring second characteristic data by using a preset function to determine whether to trigger a road surface flatness assessment mechanism;
determining a first road surface flatness assessment level based on the second characteristic data in response to detecting that the road surface flatness assessment mechanism is triggered;
and checking the preset road surface flatness evaluation level by using the first road surface flatness evaluation level, and determining the target road surface flatness evaluation level according to a checking result to realize evaluation of the road surface flatness.
Optionally, the wheel speed at least includes a wheel speed, and preprocessing the wheel speed to obtain first feature data includes:
acquiring the wheel speed of at least one wheel on the target vehicle;
based on a low-pass filtering mechanism, preprocessing the wheel speed for one time to obtain a target data value;
and carrying out first-order derivation on the target data value to obtain the wheel acceleration of the target vehicle, wherein the wheel acceleration is the first characteristic data.
Optionally, the preset function includes at least a first preset function, a second preset function and a third preset function, the second feature data includes at least a root mean square value, a variance value and a peak-to-peak value, and based on the first feature data, the calculating to obtain the second feature data by using the preset function includes:
acquiring first characteristic data in N operation periods;
calculating a root mean square value corresponding to the first characteristic data by using the first preset function, wherein the first preset function comprises the following steps:
wherein X is Rms Represents root mean square value, N represents number of operation cycles, X i Representing the wheel acceleration corresponding to the ith operating period;
calculating a variance value corresponding to the first feature data by using the second preset function, wherein the second preset function comprises:
wherein sigma represents a variance value, and mu represents a wheel acceleration average value;
calculating a peak-to-peak value corresponding to the first characteristic data by using the third preset function, wherein the third preset function comprises:
X Delta ＝X i max -X i min
wherein X is Delta Representing peak-to-peak value, X i max Indicating maximum value of wheel acceleration, X i min Representing the minimum value of wheel acceleration.
Optionally, based on the second feature data, determining whether to trigger a road surface flatness assessment mechanism includes:
And triggering a road surface flatness assessment mechanism in response to detecting that the number of times the root mean square value is larger than a first preset value is larger than a preset number of times in N operation periods, and/or that the number of times the variance value is larger than a second preset value is larger than a preset number of times, and/or that the number of times the peak-to-peak value is larger than a third preset value is larger than a preset number of times.
Optionally, determining the first road surface flatness evaluation level based on the second feature data includes:
respectively acquiring the root mean square value, the variance value and the road surface flatness evaluation grade corresponding to the peak-to-peak value based on a preset mapping table;
calculating and determining the first road surface flatness evaluation grade based on a fourth preset function, wherein the fourth preset function comprises:
wherein τ 1 、τ 2 、τ 3 All represent weight coefficients, and are 1, alpha represents a first road surface flatness evaluation level, mu Rms Represents the road surface flatness evaluation level corresponding to the root mean square value,represents the road surface flatness evaluation grade, mu corresponding to the variance value Delta And the road surface flatness evaluation grade corresponding to the peak value is represented.
Optionally, the first road surface flatness evaluation level is used for checking a preset road surface flatness evaluation level, and determining the target road surface flatness evaluation level according to the checking result includes:
Determining the first road surface flatness evaluation level as the target road surface flatness evaluation level in response to detecting that the absolute value of the difference between the first road surface flatness evaluation level and the preset road surface flatness evaluation level is larger than a fourth preset value;
and determining the preset road surface flatness evaluation level as the target road surface flatness evaluation level in response to detecting that the absolute value of the difference between the first road surface flatness evaluation level and the preset road surface flatness evaluation level is smaller than or equal to a fourth preset value.
Optionally, the method further comprises:
and in response to detecting that the root mean square value is smaller than a first preset value, the variance value is smaller than a second preset value and the peak-to-peak value is smaller than a third preset value in the target time period, exiting the road surface flatness assessment mechanism.
In another aspect, there is provided a road surface flatness evaluation device, the device including:
the preprocessing module is used for acquiring the wheel speed of the target vehicle and preprocessing the wheel speed to obtain first characteristic data;
the first determining module is used for calculating and acquiring second characteristic data by utilizing a preset function based on the first characteristic data so as to determine whether to trigger a road surface flatness assessment mechanism;
The second determining module is used for determining a first road surface flatness evaluation level based on the second characteristic data in response to detecting that the road surface flatness evaluation mechanism is triggered;
and the evaluation result verification module is used for verifying the preset pavement evenness evaluation level by utilizing the first pavement evenness evaluation level, and determining the target pavement evenness evaluation level according to the verification result so as to realize evaluation of pavement evenness.
In yet another aspect, a computer device is provided comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, the processor implementing the steps of:
acquiring the wheel speed of a target vehicle, and preprocessing the wheel speed to obtain first characteristic data;
based on the first characteristic data, calculating and acquiring second characteristic data by using a preset function to determine whether to trigger a road surface flatness assessment mechanism;
determining a first road surface flatness assessment level based on the second characteristic data in response to detecting that the road surface flatness assessment mechanism is triggered;
and checking the preset road surface flatness evaluation level by using the first road surface flatness evaluation level, and determining the target road surface flatness evaluation level according to a checking result to realize evaluation of the road surface flatness.
In yet another aspect, a computer readable storage medium is provided, having stored thereon a computer program which when executed by a processor performs the steps of:
acquiring the wheel speed of a target vehicle, and preprocessing the wheel speed to obtain first characteristic data;
based on the first characteristic data, calculating and acquiring second characteristic data by using a preset function to determine whether to trigger a road surface flatness assessment mechanism;
determining a first road surface flatness assessment level based on the second characteristic data in response to detecting that the road surface flatness assessment mechanism is triggered;
and checking the preset road surface flatness evaluation level by using the first road surface flatness evaluation level, and determining the target road surface flatness evaluation level according to a checking result to realize evaluation of the road surface flatness.
The road surface flatness evaluation method, the device, the computer equipment and the storage medium, wherein the method comprises the following steps: acquiring the wheel speed of a target vehicle, and preprocessing the wheel speed to obtain first characteristic data; based on the first characteristic data, calculating and acquiring second characteristic data by using a preset function to determine whether to trigger a road surface flatness assessment mechanism; determining a first road surface flatness assessment level based on the second characteristic data in response to detecting that the road surface flatness assessment mechanism is triggered; the first road surface flatness evaluation level is utilized to check the preset road surface flatness evaluation level, and the target road surface flatness evaluation level is determined according to the checking result, so that the evaluation of the road surface flatness is realized.
Drawings
FIG. 1 is an application environment diagram of a road surface flatness assessment method in one embodiment;
FIG. 2 is a flow chart of a road surface flatness assessment method according to an embodiment;
FIG. 3 is a block diagram of a road surface flatness assessment device in one embodiment;
fig. 4 is an internal structural diagram of a computer device in one embodiment.
Detailed Description
For the purposes of making the objects, technical solutions and advantages of the present application more apparent, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application, and it is apparent that the described embodiments are only some embodiments of the present application, but not all embodiments. All other embodiments, which can be made by one of ordinary skill in the art without undue burden from the present disclosure, are within the scope of the present disclosure.
It should be understood that throughout this description, unless the context clearly requires otherwise, the words "comprise," "comprising," and the like are to be construed in an inclusive sense as opposed to an exclusive or exhaustive sense; that is, it is the meaning of "including but not limited to".
It should also be appreciated that the terms "first," "second," and the like are used for descriptive purposes only and are not to be construed as indicating or implying relative importance. Furthermore, in the description of the present application, unless otherwise indicated, the meaning of "a plurality" is two or more.
It should be noted that the terms "S1", "S2", and the like are used for the purpose of describing steps only, and are not intended to be limited to the order or sequence of steps or to limit the present application, but are merely used for convenience in describing the method of the present application and are not to be construed as indicating the sequence of steps. In addition, the technical solutions of the embodiments may be combined with each other, but it is necessary to base that the technical solutions can be realized by those skilled in the art, and when the technical solutions are contradictory or cannot be realized, the combination of the technical solutions should be regarded as not exist and not within the protection scope of the present application.
The road surface flatness evaluation method provided by the application can be applied to the vehicle 100 shown in fig. 1, and the vehicle 100 can include the vehicle-mounted terminal 120. The in-vehicle terminal 120 includes at least one memory and at least one processor, the at least one memory storing a computer program therein, which when executed by the at least one processor, performs the road surface flatness evaluation method according to the exemplary embodiment of the present disclosure. Here, the in-vehicle terminal 120 is not necessarily a single electronic device, but may be any assembly of devices or circuits capable of executing the above-described computer programs alone or in combination.
In the in-vehicle terminal 120, the processor may include a Central Processing Unit (CPU), a Graphics Processor (GPU), a programmable logic device, a special purpose processor system, a microcontroller, or a microprocessor. By way of example, and not limitation, processors may also include analog processors, digital processors, microprocessors, multi-core processors, processor arrays, network processors, and the like; in the in-vehicle terminal 120, the processor may run a computer program stored in a memory, which may be divided into one or more modules/units (e.g., computer program 1, computer program 2, … …) stored in the memory and executed by the processor to complete the present invention. The one or more modules/units may be a series of computer program instruction segments capable of performing the specified functions, which instruction segments are used for describing the execution of the computer program in the terminal device. The memory may be integrated with the processor, for example, RAM or flash memory disposed within an integrated circuit microprocessor or the like. In addition, the memory may include a stand-alone device, such as an external disk drive, a storage array, or any other storage device usable by a database system. The memory and the processor may be operatively coupled or may communicate with each other, for example, through an I/O port, a network connection, etc., such that the processor is able to read files stored in the memory.
In addition, the in-vehicle terminal 120 may further include a display device (such as a liquid crystal display, etc.) and a user interaction interface (such as a keyboard, a mouse, a touch input device, etc.), and all components of the in-vehicle terminal 120 may be connected to each other via a bus and/or a network.
In one embodiment, as shown in fig. 2, a road surface flatness evaluation method is provided, and the method is applied to the terminal in fig. 1 for illustration, and includes the following steps:
s1: and acquiring the wheel speed of the target vehicle, and preprocessing the wheel speed to obtain first characteristic data.
The related data of the target vehicle to be acquired includes a vehicle speed in addition to a wheel speed, the wheel speed is obtained by a wheel speed sensor carried by the vehicle, and the first characteristic data is a wheel acceleration.
In some embodiments, the obtaining the wheel speed of the target vehicle, and preprocessing the wheel speed to obtain the first characteristic data includes:
acquiring the wheel speed of at least one wheel on the target vehicle;
based on a low-pass filtering mechanism, preprocessing the wheel speed for one time to obtain a target data value, namely removing useless high-frequency components in the collected wheel speed data set through low-pass filtering so as to ensure the accuracy of subsequent road surface flatness assessment;
And carrying out first-order derivation on the target data value to obtain the wheel acceleration of the target vehicle, wherein the wheel acceleration is the first characteristic data, and the target data value is the wheel speed obtained after low-pass filtering.
In the embodiment, the accuracy of the subsequent road surface flatness evaluation can be improved by preprocessing the collected related data.
S2: and calculating and acquiring second characteristic data by using a preset function based on the first characteristic data so as to determine whether to trigger a road surface flatness assessment mechanism.
The preset function at least comprises a first preset function, a second preset function and a third preset function, and the second characteristic data at least comprises a root mean square value, a variance value and a peak-to-peak value, wherein the peak-to-peak value refers to a value of a difference between a highest value and a lowest value of a signal in one period, namely a range between the maximum value and the minimum value, and the range describes the size of a signal value variation range.
In some embodiments, based on the first feature data, obtaining the second feature data using a preset function calculation includes:
acquiring first characteristic data in N operation periods, wherein the operation periods can be set according to actual requirements and can be single time nodes, and the first characteristic data is the wheel acceleration obtained in the steps;
Calculating a root mean square value corresponding to the first characteristic data by using the first preset function, wherein the first preset function comprises the following steps:
wherein X is Rms Represents root mean square value, N represents number of operation cycles, X i Representing the wheel acceleration corresponding to the ith operating period;
further, calculating a variance value corresponding to the first feature data by using the second preset function, where the second preset function includes:
wherein sigma represents a variance value, and mu represents a wheel acceleration average value;
calculating a peak-to-peak value corresponding to the first characteristic data by using the third preset function, wherein the third preset function comprises:
X Delta ＝X i max -X i min
wherein X is Delta Representing peak-to-peak value, X i max Indicating maximum value of wheel acceleration, X i min Representing the minimum value of wheel acceleration.
In some embodiments, determining whether to trigger a road surface flatness assessment mechanism based on the second characteristic data comprises:
and triggering a road surface flatness assessment mechanism in response to detecting that the number of times that the root mean square value is larger than a first preset value is larger than a preset number of times in N operation periods, and/or the number of times that the variance value is larger than a second preset value is larger than a preset number of times, and/or the number of times that the peak-to-peak value is larger than a third preset value is larger than a preset number of times, wherein the first preset value, the second preset value, the third preset value and the preset number of times can be set according to actual requirements.
Specifically, evaluating the road surface flatness by means of the root mean square value, the variance value, and the peak-to-peak value includes: the more the root mean square value is greater, the more obvious the excited vibration of the tire is, the worse the road surface condition is, the same, the more the excited vibration of the tire is, the worse the road surface condition is, and the more the number of times the peak value is greater than the third preset value is, the more obvious the excited vibration of the tire is, the worse the road surface condition is, therefore, if the root mean square value is greater than the first preset value in N operation periods, the road surface is uneven, when the road surface is uneven, the road surface flatness assessment mechanism is triggered, if the variance value is greater than the second preset value in N operation periods, the road surface is uneven, when the road surface is uneven, the number of times the peak value is greater than the third preset value in N operation periods, the road surface is uneven, the road surface flatness assessment mechanism is triggered, wherein, in order to distinguish different road surface flatness grades, the different thresholds are preset, the preset number of times is increased, the road surface flatness assessment mechanism is triggered, if the number of times is preset, the road surface flatness is combined, the number of times is increased, the road surface flatness assessment mechanism is determined to meet any one of the more conditions when the road surface flatness assessment mechanism is more than the number of times.
In the above embodiment, the root mean square value, the variance value and the peak-to-peak value are obtained through the wheel acceleration calculation, and the corresponding constraint conditions are set to determine whether to trigger the road surface flatness assessment mechanism, so as to further improve the accuracy of road surface flatness assessment.
S3: and determining a first road surface flatness evaluation level based on the second characteristic data in response to detecting that the road surface flatness evaluation mechanism is triggered.
It should be noted that this step specifically includes:
respectively acquiring road surface flatness evaluation grades corresponding to the root mean square value, the variance value and the peak-to-peak value based on a preset mapping table, wherein the preset mapping table comprises at least one mapping relation between the root mean square value and the road surface flatness evaluation grade, between the variance value and the road surface flatness evaluation grade and between the peak-to-peak value and the road surface flatness evaluation grade, and the road surface flatness evaluation grade is a numerical value obtained by multiple tests and expert scoring;
calculating and determining the first road surface flatness evaluation grade based on a fourth preset function, wherein the fourth preset function comprises:
wherein τ 1 、τ 2 、τ 3 All represent weight coefficients, and are 1, alpha represents a first road surface flatness evaluation level, mu Rms Representing road surface flatness corresponding to root mean square valueEvaluation of degree the grade of the product is that,represents the road surface flatness evaluation grade, mu corresponding to the variance value Delta The road surface flatness evaluation grade corresponding to the peak value is represented, wherein the higher the first road surface flatness evaluation grade is, the more uneven the road surface is, and the weight coefficient is obtained by a plurality of tests and expert scores;
in the embodiment, the corresponding weight coefficient is determined according to the root mean square value, the variance value and the importance degree of the peak-to-peak value, so that the final road surface flatness evaluation grade is calculated and determined, and the accuracy of road surface flatness evaluation is further improved.
S4: and checking the preset road surface flatness evaluation level by using the first road surface flatness evaluation level, and determining the target road surface flatness evaluation level according to a checking result to realize evaluation of the road surface flatness.
It should be noted that, the preset road surface flatness is the flatness grade of the road surface determined by the pre-aiming information of the camera, which is determined by the image processing result, and the image processing and flatness grade determining process is a common means, which is not described herein.
In some embodiments, the verifying the preset road surface flatness evaluation level by using the first road surface flatness evaluation level, and determining the target road surface flatness evaluation level according to the verification result includes:
Determining the first road surface flatness evaluation level as the target road surface flatness evaluation level in response to detecting that the absolute value of the difference between the first road surface flatness evaluation level and the preset road surface flatness evaluation level is larger than a fourth preset value, wherein the fourth preset value can be set according to actual requirements;
determining the preset road surface flatness evaluation level as the target road surface flatness evaluation level in response to detecting that the absolute value of the difference between the first road surface flatness evaluation level and the preset road surface flatness evaluation level is smaller than or equal to a fourth preset value;
specifically, the road surface flatness grade judgment is realized based on calculated road surface flatness and camera pre-aiming information, namely the road surface flatness is preferentially judged in a feedforward way by using the camera pre-aiming information, then the road surface flatness is calculated by combining with wheel acceleration for verification, if the grade difference between the calculated road surface flatness and the pre-aiming road surface flatness is smaller than or equal to a fourth preset value, the first road surface flatness evaluation grade is adopted as a final result, and if the grade difference is larger than the fourth preset value, the preset road surface flatness evaluation grade is adopted as a final result.
In some embodiments, in order to avoid that the vehicle is on an uneven road surface again and affects the whole vehicle control, it is also required that the root mean square value, the variance value and the peak-to-peak value of the vehicle are determined whether to exit the road surface evenness assessment mechanism, that is, when the root mean square value is smaller than the first preset value, the variance value is smaller than the second preset value and the peak-to-peak value is smaller than the third preset value in the target time period, the road surface evenness assessment mechanism is exited, wherein frequent jump of the assessment mechanism is not avoided, the root mean square value, the variance value and the peak-to-peak value are respectively kept at a certain interval with the corresponding value of the road surface evenness assessment mechanism, if the interval value is A, the absolute value of the difference value of the root mean square value when the road surface evenness value is subtracted by the exit mechanism is A, the target time period can be set according to the actual requirement, the time node of the start time and the time node when the road surface evenness assessment mechanism is triggered keeps a time interval, the interval value is related to the road surface evenness grade and the vehicle speed, if the road surface evenness grade is high, and the road surface evenness grade is considered to be restored to be the default grade if the interval value and the target time period is longer.
In the embodiment, the final road surface flatness evaluation grade is determined by the verification method, the accuracy of road surface flatness evaluation is further improved, the road surface flatness evaluation mechanism exit rule is set, the influence on the whole vehicle control caused by the fact that the vehicle is on an uneven road surface again is avoided, and the experience of the vehicle user is improved.
In some embodiments, when a road surface flatness assessment mechanism is triggered, vehicle operation parameters need to be adjusted according to a road surface flatness assessment level, for example, a damping coefficient adjustment value corresponding to the road surface flatness assessment level is determined based on a mapping relation table of the road surface flatness assessment level and a damping coefficient adjustment value, so as to adjust a current damping coefficient of a vehicle downwards, thereby improving the comfort of the whole vehicle, and the current damping coefficient is exemplified by X, the damping coefficient adjustment value is exemplified by Y, and because the higher the road surface flatness level is the more uneven the road surface, the value obtained by X-Y needs to be used as a target damping coefficient, wherein the mapping relation table comprises at least one mapping relation between the road surface flatness assessment level and the damping coefficient adjustment value, the damping coefficient adjustment value is a value obtained by multiple tests and combining expert scores, further, when the vehicle is on a flat road surface, the self-adaptive adjustment of the vehicle is stopped, thereby realizing intelligent control of the comfort of the vehicle, and improving the user experience of the vehicle.
In the above road surface flatness evaluation method, the method includes: acquiring the wheel speed of a target vehicle, and preprocessing the wheel speed to obtain first characteristic data; based on the first characteristic data, calculating and acquiring second characteristic data by using a preset function to determine whether to trigger a road surface flatness assessment mechanism; determining a first road surface flatness assessment level based on the second characteristic data in response to detecting that the road surface flatness assessment mechanism is triggered; the first road surface flatness evaluation level is utilized to check the preset road surface flatness evaluation level, and the target road surface flatness evaluation level is determined according to the checking result, so that the evaluation of the road surface flatness is realized.
It should be understood that, although the steps in the flowchart of fig. 2 are shown in sequence as indicated by the arrows, the steps are not necessarily performed in sequence as indicated by the arrows. The steps are not strictly limited to the order of execution unless explicitly recited herein, and the steps may be executed in other orders. Moreover, at least some of the steps in fig. 2 may include multiple sub-steps or stages that are not necessarily performed at the same time, but may be performed at different times, nor do the order in which the sub-steps or stages are performed necessarily performed in sequence, but may be performed alternately or alternately with at least a portion of the sub-steps or stages of other steps or other steps.
In one embodiment, as shown in fig. 3, there is provided a road surface evenness assessment apparatus including: the device comprises a preprocessing module, a first determining module, a second determining module and an evaluation result checking module, wherein:
the preprocessing module is used for acquiring the wheel speed of the target vehicle and preprocessing the wheel speed to obtain first characteristic data;
the first determining module is used for calculating and acquiring second characteristic data by utilizing a preset function based on the first characteristic data so as to determine whether to trigger a road surface flatness assessment mechanism;
the second determining module is used for determining a first road surface flatness evaluation level based on the second characteristic data in response to detecting that the road surface flatness evaluation mechanism is triggered;
and the evaluation result verification module is used for verifying the preset pavement evenness evaluation level by utilizing the first pavement evenness evaluation level, and determining the target pavement evenness evaluation level according to the verification result so as to realize evaluation of pavement evenness.
As a preferred implementation manner, in the embodiment of the present invention, the preprocessing module is specifically configured to:
acquiring the wheel speed of at least one wheel on the target vehicle;
Based on a low-pass filtering mechanism, preprocessing the wheel speed for one time to obtain a target data value;
and carrying out first-order derivation on the target data value to obtain the wheel acceleration of the target vehicle, wherein the wheel acceleration is the first characteristic data.
As a preferred implementation manner, in the embodiment of the present invention, the first determining module is specifically configured to:
defining the preset function to at least comprise a first preset function, a second preset function and a third preset function, wherein the second characteristic data at least comprise a root mean square value, a variance value and a peak-to-peak value, and calculating and acquiring the second characteristic data by using the preset function based on the first characteristic data comprises the following steps:
acquiring first characteristic data in N operation periods;
calculating a root mean square value corresponding to the first characteristic data by using the first preset function, wherein the first preset function comprises the following steps:
wherein X is Rms Represents root mean square value, N represents number of operation cycles, X i Representing the wheel acceleration corresponding to the ith operating period;
calculating a variance value corresponding to the first feature data by using the second preset function, wherein the second preset function comprises:
wherein sigma represents a variance value, and mu represents a wheel acceleration average value;
Calculating a peak-to-peak value corresponding to the first characteristic data by using the third preset function, wherein the third preset function comprises:
X Delta ＝X i max -X i min
wherein X is Delta Representing peak-to-peak value, X i max Indicating maximum value of wheel acceleration, X i min Indicating wheel accelerationMinimum value.
As a preferred implementation manner, in the embodiment of the present invention, the first determining module is specifically further configured to:
and triggering a road surface flatness assessment mechanism in response to detecting that the number of times the root mean square value is larger than a first preset value is larger than a preset number of times in N operation periods, and/or that the number of times the variance value is larger than a second preset value is larger than a preset number of times, and/or that the number of times the peak-to-peak value is larger than a third preset value is larger than a preset number of times.
As a preferred implementation manner, in the embodiment of the present invention, the second determining module is specifically configured to:
respectively acquiring the root mean square value, the variance value and the road surface flatness evaluation grade corresponding to the peak-to-peak value based on a preset mapping table;
calculating and determining the first road surface flatness evaluation grade based on a fourth preset function, wherein the fourth preset function comprises:
wherein τ 1 、τ 2 、τ 3 All represent weight coefficients, and are 1, alpha represents a first road surface flatness evaluation level, mu Rms Represents the road surface flatness evaluation level corresponding to the root mean square value,represents the road surface flatness evaluation grade, mu corresponding to the variance value Delta And the road surface flatness evaluation grade corresponding to the peak value is represented.
As a preferred implementation manner, in the embodiment of the present invention, the evaluation result checking module is specifically configured to:
determining the first road surface flatness evaluation level as the target road surface flatness evaluation level in response to detecting that the absolute value of the difference between the first road surface flatness evaluation level and the preset road surface flatness evaluation level is larger than a fourth preset value;
and determining the preset road surface flatness evaluation level as the target road surface flatness evaluation level in response to detecting that the absolute value of the difference between the first road surface flatness evaluation level and the preset road surface flatness evaluation level is smaller than or equal to a fourth preset value.
As a preferred implementation manner, in the embodiment of the present invention, the apparatus further includes an exit module, where the exit module is specifically configured to:
and in response to detecting that the root mean square value is smaller than a first preset value, the variance value is smaller than a second preset value and the peak-to-peak value is smaller than a third preset value in the target time period, exiting the road surface flatness assessment mechanism.
For the specific definition of the road surface flatness evaluation device, reference may be made to the definition of the road surface flatness evaluation method hereinabove, and the description thereof will not be repeated here. The respective modules in the road surface flatness evaluation device described above may be realized in whole or in part by software, hardware, and a combination thereof. The above modules may be embedded in hardware or may be independent of a processor in the computer device, or may be stored in software in a memory in the computer device, so that the processor may call and execute operations corresponding to the above modules.
In one embodiment, a computer device is provided, which may be a terminal, and the internal structure of which may be as shown in fig. 4. The computer device includes a processor, a memory, a network interface, a display screen, and an input device connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program is executed by a processor to implement a road surface flatness assessment method. The display screen of the computer equipment can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer equipment can be a touch layer covered on the display screen, can also be keys, a track ball or a touch pad arranged on the shell of the computer equipment, and can also be an external keyboard, a touch pad or a mouse and the like.
Those skilled in the art will appreciate that the structures shown in FIG. 4 are block diagrams only and do not constitute a limitation of the computer device on which the present aspects apply, and that a particular computer device may include more or less components than those shown, or may combine some of the components, or have a different arrangement of components.
In one embodiment, a computer device is provided comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, the processor implementing the steps of when executing the computer program:
s1: acquiring the wheel speed of a target vehicle, and preprocessing the wheel speed to obtain first characteristic data;
s2: based on the first characteristic data, calculating and acquiring second characteristic data by using a preset function to determine whether to trigger a road surface flatness assessment mechanism;
s3: determining a first road surface flatness assessment level based on the second characteristic data in response to detecting that the road surface flatness assessment mechanism is triggered;
s4: and checking the preset road surface flatness evaluation level by using the first road surface flatness evaluation level, and determining the target road surface flatness evaluation level according to a checking result to realize evaluation of the road surface flatness.
In one embodiment, the processor when executing the computer program further performs the steps of:
acquiring the wheel speed of at least one wheel on the target vehicle;
based on a low-pass filtering mechanism, preprocessing the wheel speed for one time to obtain a target data value;
and carrying out first-order derivation on the target data value to obtain the wheel acceleration of the target vehicle, wherein the wheel acceleration is the first characteristic data.
In one embodiment, the processor when executing the computer program further performs the steps of:
defining the preset function to at least comprise a first preset function, a second preset function and a third preset function, wherein the second characteristic data at least comprise a root mean square value, a variance value and a peak-to-peak value, and calculating and acquiring the second characteristic data by using the preset function based on the first characteristic data comprises the following steps:
acquiring first characteristic data in N operation periods;
calculating a root mean square value corresponding to the first characteristic data by using the first preset function, wherein the first preset function comprises the following steps:
wherein X is Rms Represents root mean square value, N represents number of operation cycles, X i Representing the wheel acceleration corresponding to the ith operating period;
calculating a variance value corresponding to the first feature data by using the second preset function, wherein the second preset function comprises:
Wherein sigma represents a variance value, and mu represents a wheel acceleration average value;
calculating a peak-to-peak value corresponding to the first characteristic data by using the third preset function, wherein the third preset function comprises:
X Delta ＝X i max -X i min
wherein X is Delta Representing peak-to-peak value, X i max Indicating maximum value of wheel acceleration, X i min Representing the minimum value of wheel acceleration.
In one embodiment, the processor when executing the computer program further performs the steps of:
and triggering a road surface flatness assessment mechanism in response to detecting that the number of times the root mean square value is larger than a first preset value is larger than a preset number of times in N operation periods, and/or that the number of times the variance value is larger than a second preset value is larger than a preset number of times, and/or that the number of times the peak-to-peak value is larger than a third preset value is larger than a preset number of times.
In one embodiment, the processor when executing the computer program further performs the steps of:
respectively acquiring the root mean square value, the variance value and the road surface flatness evaluation grade corresponding to the peak-to-peak value based on a preset mapping table;
calculating and determining the first road surface flatness evaluation grade based on a fourth preset function, wherein the fourth preset function comprises:
wherein τ 1 、τ 2 、τ 3 All represent weight coefficients, and are 1, alpha represents a first road surface flatness evaluation level, mu Rms Represents the road surface flatness evaluation level corresponding to the root mean square value,represents the road surface flatness evaluation grade, mu corresponding to the variance value Delta And the road surface flatness evaluation grade corresponding to the peak value is represented.
In one embodiment, the processor when executing the computer program further performs the steps of:
determining the first road surface flatness evaluation level as the target road surface flatness evaluation level in response to detecting that the absolute value of the difference between the first road surface flatness evaluation level and the preset road surface flatness evaluation level is larger than a fourth preset value;
and determining the preset road surface flatness evaluation level as the target road surface flatness evaluation level in response to detecting that the absolute value of the difference between the first road surface flatness evaluation level and the preset road surface flatness evaluation level is smaller than or equal to a fourth preset value.
In one embodiment, the processor when executing the computer program further performs the steps of:
and in response to detecting that the root mean square value is smaller than a first preset value, the variance value is smaller than a second preset value and the peak-to-peak value is smaller than a third preset value in the target time period, exiting the road surface flatness assessment mechanism.
In one embodiment, a computer readable storage medium is provided having a computer program stored thereon, which when executed by a processor, performs the steps of:
S1: acquiring the wheel speed of a target vehicle, and preprocessing the wheel speed to obtain first characteristic data;
s2: based on the first characteristic data, calculating and acquiring second characteristic data by using a preset function to determine whether to trigger a road surface flatness assessment mechanism;
s3: determining a first road surface flatness assessment level based on the second characteristic data in response to detecting that the road surface flatness assessment mechanism is triggered;
s4: and checking the preset road surface flatness evaluation level by using the first road surface flatness evaluation level, and determining the target road surface flatness evaluation level according to a checking result to realize evaluation of the road surface flatness.
In one embodiment, the computer program when executed by the processor further performs the steps of:
acquiring the wheel speed of at least one wheel on the target vehicle;
based on a low-pass filtering mechanism, preprocessing the wheel speed for one time to obtain a target data value;
and carrying out first-order derivation on the target data value to obtain the wheel acceleration of the target vehicle, wherein the wheel acceleration is the first characteristic data.
In one embodiment, the computer program when executed by the processor further performs the steps of:
Defining the preset function to at least comprise a first preset function, a second preset function and a third preset function, wherein the second characteristic data at least comprise a root mean square value, a variance value and a peak-to-peak value, and calculating and acquiring the second characteristic data by using the preset function based on the first characteristic data comprises the following steps:
acquiring first characteristic data in N operation periods;
calculating a root mean square value corresponding to the first characteristic data by using the first preset function, wherein the first preset function comprises the following steps:
wherein X is Rms Represents root mean square value, N represents number of operation cycles, X i Representing the wheel acceleration corresponding to the ith operating period;
calculating a variance value corresponding to the first feature data by using the second preset function, wherein the second preset function comprises:
wherein sigma represents a variance value, and mu represents a wheel acceleration average value;
calculating a peak-to-peak value corresponding to the first characteristic data by using the third preset function, wherein the third preset function comprises:
X Delta ＝X i max -X i min
wherein X is Delta Representing peak-to-peak value, X i max Indicating maximum value of wheel acceleration, X i min Representing the minimum value of wheel acceleration.
In one embodiment, the computer program when executed by the processor further performs the steps of:
and triggering a road surface flatness assessment mechanism in response to detecting that the number of times the root mean square value is larger than a first preset value is larger than a preset number of times in N operation periods, and/or that the number of times the variance value is larger than a second preset value is larger than a preset number of times, and/or that the number of times the peak-to-peak value is larger than a third preset value is larger than a preset number of times.
In one embodiment, the computer program when executed by the processor further performs the steps of:
respectively acquiring the root mean square value, the variance value and the road surface flatness evaluation grade corresponding to the peak-to-peak value based on a preset mapping table;
calculating and determining the first road surface flatness evaluation grade based on a fourth preset function, wherein the fourth preset function comprises:
wherein τ 1 、τ 2 、τ 3 All represent weight coefficients, and are 1, alpha represents a first road surface flatness evaluation level, mu Rms Represents the road surface flatness evaluation level corresponding to the root mean square value,represents the road surface flatness evaluation grade, mu corresponding to the variance value Delta And the road surface flatness evaluation grade corresponding to the peak value is represented.
In one embodiment, the computer program when executed by the processor further performs the steps of:
determining the first road surface flatness evaluation level as the target road surface flatness evaluation level in response to detecting that the absolute value of the difference between the first road surface flatness evaluation level and the preset road surface flatness evaluation level is larger than a fourth preset value;
and determining the preset road surface flatness evaluation level as the target road surface flatness evaluation level in response to detecting that the absolute value of the difference between the first road surface flatness evaluation level and the preset road surface flatness evaluation level is smaller than or equal to a fourth preset value.
In one embodiment, the computer program when executed by the processor further performs the steps of:
and in response to detecting that the root mean square value is smaller than a first preset value, the variance value is smaller than a second preset value and the peak-to-peak value is smaller than a third preset value in the target time period, exiting the road surface flatness assessment mechanism.
Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed, may comprise the steps of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in the various embodiments provided herein may include non-volatile and/or volatile memory. The nonvolatile memory can include Read Only Memory (ROM), programmable ROM (PROM), electrically Programmable ROM (EPROM), electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double Data Rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous Link DRAM (SLDRAM), memory bus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), among others.
The technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The above examples merely represent a few embodiments of the present application, which are described in more detail and are not to be construed as limiting the scope of the invention. It should be noted that it would be apparent to those skilled in the art that various modifications and improvements could be made without departing from the spirit of the present application, which would be within the scope of the present application. Accordingly, the scope of protection of the present application is to be determined by the claims appended hereto.
Claims (10)
1. A method for evaluating road surface flatness, the method comprising:
acquiring the wheel speed of a target vehicle, and preprocessing the wheel speed to obtain first characteristic data;
based on the first characteristic data, calculating and acquiring second characteristic data by using a preset function to determine whether to trigger a road surface flatness assessment mechanism;
determining a first road surface flatness assessment level based on the second characteristic data in response to detecting that the road surface flatness assessment mechanism is triggered;
And checking the preset road surface flatness evaluation level by using the first road surface flatness evaluation level, and determining the target road surface flatness evaluation level according to a checking result to realize evaluation of the road surface flatness.
2. The road surface flatness evaluation method according to claim 1, wherein the obtaining wheel speed of the target vehicle and preprocessing the wheel speed to obtain the first characteristic data includes:
acquiring the wheel speed of at least one wheel on the target vehicle;
based on a low-pass filtering mechanism, preprocessing the wheel speed for one time to obtain a target data value;
and carrying out first-order derivation on the target data value to obtain the wheel acceleration of the target vehicle, wherein the wheel acceleration is the first characteristic data.
3. The road surface evenness assessment method according to claim 1, wherein the preset functions include at least a first preset function, a second preset function, and a third preset function, the second characteristic data includes at least a root mean square value, a variance value, and a peak-to-peak value, and calculating, based on the first characteristic data, second characteristic data using the preset function includes:
acquiring first characteristic data in N operation periods;
Calculating a root mean square value corresponding to the first characteristic data by using the first preset function, wherein the first preset function comprises the following steps:
wherein X is Rms Represents root mean square value, N represents number of operation cycles, X i Representing the wheel acceleration corresponding to the ith operating period;
calculating a variance value corresponding to the first feature data by using the second preset function, wherein the second preset function comprises:
wherein sigma represents a variance value, and mu represents a wheel acceleration average value;
calculating a peak-to-peak value corresponding to the first characteristic data by using the third preset function, wherein the third preset function comprises:
X Delta ＝X imax -X imin
wherein X is Delta Representing peak-to-peak value, X imax Indicating maximum value of wheel acceleration, X imin Representing the minimum value of wheel acceleration.
4. The road surface flatness assessment method according to claim 3, characterized in that determining whether to trigger a road surface flatness assessment mechanism based on the second characteristic data comprises:
and triggering a road surface flatness assessment mechanism in response to detecting that the number of times the root mean square value is larger than a first preset value is larger than a preset number of times in N operation periods, and/or that the number of times the variance value is larger than a second preset value is larger than a preset number of times, and/or that the number of times the peak-to-peak value is larger than a third preset value is larger than a preset number of times.
5. The road surface flatness evaluation method according to claim 3, wherein determining a first road surface flatness evaluation level based on the second characteristic data includes:
respectively acquiring the root mean square value, the variance value and the road surface flatness evaluation grade corresponding to the peak-to-peak value based on a preset mapping table;
calculating and determining the first road surface flatness evaluation grade based on a fourth preset function, wherein the fourth preset function comprises:
wherein τ 1 、τ 2 、τ 3 All represent weight coefficients, and are 1, alpha represents a first road surface flatness evaluation level, mu Rms Represents the road surface flatness evaluation level corresponding to the root mean square value,represents the road surface flatness evaluation grade, mu corresponding to the variance value Delta And the road surface flatness evaluation grade corresponding to the peak value is represented.
6. The road surface flatness evaluation method according to claim 1, wherein verifying a preset road surface flatness evaluation level using the first road surface flatness evaluation level, and determining a target road surface flatness evaluation level based on a verification result comprises:
determining the first road surface flatness evaluation level as the target road surface flatness evaluation level in response to detecting that the absolute value of the difference between the first road surface flatness evaluation level and the preset road surface flatness evaluation level is larger than a fourth preset value;
And determining the preset road surface flatness evaluation level as the target road surface flatness evaluation level in response to detecting that the absolute value of the difference between the first road surface flatness evaluation level and the preset road surface flatness evaluation level is smaller than or equal to a fourth preset value.
7. A road surface evenness assessment method according to claim 3, further comprising:
and in response to detecting that the root mean square value is smaller than a first preset value, the variance value is smaller than a second preset value and the peak-to-peak value is smaller than a third preset value in the target time period, exiting the road surface flatness assessment mechanism.
8. A road surface flatness evaluation device, characterized in that the device comprises:
the preprocessing module is used for acquiring the wheel speed of the target vehicle and preprocessing the wheel speed to obtain first characteristic data;
the first determining module is used for calculating and acquiring second characteristic data by utilizing a preset function based on the first characteristic data so as to determine whether to trigger a road surface flatness assessment mechanism;
the second determining module is used for determining a first road surface flatness evaluation level based on the second characteristic data in response to detecting that the road surface flatness evaluation mechanism is triggered;
And the evaluation result verification module is used for verifying the preset pavement evenness evaluation level by utilizing the first pavement evenness evaluation level, and determining the target pavement evenness evaluation level according to the verification result so as to realize evaluation of pavement evenness.
9. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the method of any of claims 1 to 7 when executing the computer program.
10. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the method of any of claims 1 to 7.
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