CN114088422A - Vehicle fault diagnosis method and device and electronic equipment - Google Patents
Vehicle fault diagnosis method and device and electronic equipment Download PDFInfo
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Abstract
The invention provides a vehicle fault diagnosis method, a vehicle fault diagnosis device and electronic equipment, wherein the vehicle fault diagnosis method comprises the following steps: acquiring sound information to be detected of a vehicle to be detected; identifying the sound information to be detected based on the brain wave rhythm characteristics of the target driver, and determining target noise information; and determining fault information of the vehicle to be tested based on the target noise information. According to the fault diagnosis method for the vehicle, the sound information to be detected is identified and subjected to noise reduction through the brain wave rhythm characteristics of the target driver to obtain the required target noise information, fault diagnosis is carried out based on the target noise information, objective parameters and subjective evaluation results can be combined, noise entering the vehicle can be effectively screened and checked, and noise reduction coupling processing is avoided; and fault diagnosis under different user preferences can be met, and the flexibility and the personalization degree are high.
Description
Technical Field
The present invention relates to the field of fault diagnosis technologies, and in particular, to a method and an apparatus for diagnosing a fault of a vehicle, and an electronic device.
Background
In actual running of the vehicle, the engine and tires are constantly vibrating, thereby radiating noise. In the related art, fault identification can be performed based on noise, but the technology cannot effectively screen and examine the noise transmitted into the vehicle, and error noise reduction coupling processing is easily caused.
Disclosure of Invention
The invention provides a vehicle fault diagnosis method, a vehicle fault diagnosis device and electronic equipment, which are used for solving the defect that noise transmitted into a vehicle cannot be effectively screened and checked during fault diagnosis in the prior art, and realizing efficient screening and checking.
The invention provides a fault diagnosis method of a vehicle, which comprises the following steps:
acquiring sound information to be detected of a vehicle to be detected;
identifying the sound information to be detected based on the brain wave rhythm characteristics of the target driver, and determining target noise information;
and determining fault information of the vehicle to be tested based on the target noise information.
According to a fault diagnosis method of a vehicle provided by the present invention, the identifying the sound information to be measured and determining target noise information based on the electroencephalogram rhythm characteristics of a target driver, includes:
performing feature extraction on the sound information to be detected to generate a plurality of pieces of noise information to be selected;
and determining target noise information corresponding to the target driver from the plurality of pieces of noise information to be selected based on the brain wave rhythm characteristics of the target driver.
According to a failure diagnosis method of a vehicle provided by the present invention, the determining target noise information corresponding to the target driver from the plurality of noise information to be selected based on the electroencephalogram rhythm characteristic of the target driver, includes:
acquiring brain wave rhythm sub-features of the target driver on each piece of noise information to be selected;
and under the condition that the brain wave rhythm sub-feature is in a target frequency range, determining the noise information to be selected corresponding to the brain wave rhythm sub-feature as the target noise information corresponding to the target driver.
According to the fault diagnosis method for the vehicle provided by the invention, the step of determining the fault information of the vehicle to be tested based on the target noise information comprises the following steps:
performing parameter pre-emphasis processing on the target noise information;
and determining fault information of the vehicle to be tested based on the processing result.
According to the fault diagnosis method for the vehicle provided by the invention, the parameter pre-emphasis processing is performed on the target noise information, and the method comprises the following steps:
performing parameter pre-emphasis processing on at least one of rear vehicle whistle information, chassis abnormal sound information of the vehicle to be tested, brake pad abnormal sound information of the vehicle to be tested, suspension abnormal sound information of the vehicle to be tested and engine abnormal sound information of the vehicle to be tested in the target noise information to obtain a first processing result;
and performing parameter pre-emphasis processing on at least one of the magnetic field information, the weather information, the wind resistance information, the current speed information and the self-weight information of the vehicle to be tested in the current environment of the vehicle to be tested based on the first processing result to obtain a second processing result.
According to the fault diagnosis method for the vehicle provided by the invention, the step of acquiring the sound information to be detected of the vehicle to be detected comprises the following steps:
and acquiring the in-vehicle noise value of the vehicle to be detected and the in-vehicle and out-vehicle noise difference value of the vehicle to be detected.
According to the present invention, there is provided a failure diagnosis device for a vehicle, comprising:
the first acquisition module is used for acquiring sound information to be detected of a vehicle to be detected;
the first determining module is used for identifying the sound information to be detected based on the brain wave rhythm characteristics of the target driver and determining target noise information;
and the second determination module is used for determining the fault information of the vehicle to be tested based on the target noise information.
The invention also provides an electronic device, which comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor executes the computer program to realize the steps of the vehicle fault diagnosis method.
The present invention also provides a non-transitory computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of the method for diagnosing a malfunction of a vehicle as in any one of the above.
The present invention also provides a computer program product comprising a computer program which, when executed by a processor, carries out the steps of the method for diagnosing a malfunction of a vehicle of an electric vehicle as described in any one of the above.
According to the fault diagnosis method, the fault diagnosis device and the electronic equipment for the vehicle, the sound information to be detected is identified and subjected to noise reduction through the brain wave rhythm characteristics of the target driver, the required target noise information is obtained, fault diagnosis is carried out based on the target noise information, objective parameters and subjective evaluation results can be combined, noise entering the vehicle can be effectively screened and checked, and noise reduction coupling processing is avoided; and fault diagnosis under different user preferences can be met, and the flexibility and the personalization degree are high.
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In order to more clearly illustrate the technical solutions of the present invention or the prior art, the drawings needed for the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and those skilled in the art can also obtain other drawings according to the drawings without creative efforts.
FIG. 1 is a schematic flow chart of a method for diagnosing a fault of a vehicle according to the present invention;
FIG. 2 is a second schematic flow chart of a vehicle fault diagnosis method provided by the present invention;
fig. 3 is a schematic structural view of a failure diagnosis apparatus of a vehicle provided by the present invention;
fig. 4 is a schematic structural diagram of an electronic device provided in the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings, and it is obvious that the described embodiments are some, but not all embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The method of diagnosing a failure of a vehicle of the present invention is described below with reference to fig. 1 to 2.
The main body of the vehicle fault diagnosis method may be a vehicle, a fault diagnosis device provided in the vehicle, or a server communicatively connected to the vehicle.
As shown in fig. 1, the method for diagnosing a failure of a vehicle includes: step 110, step 120 and step 130.
in this step, the sound information to be measured is time-varying sound information generated by the vehicle to be measured during actual driving.
It is understood that the vehicle, as it travels, is affected by the speed of the vehicle and by the powertrain, and radiates noise that varies in real time. The sources of vehicle noise currently known are engine noise, body vibration noise, chassis noise, aerodynamic noise, and the like.
For example, when the same vehicle runs on different road surfaces (cement road surface, asphalt road surface or mud ground), the noise generated by the brake pads is different; and for example, when the same vehicle runs in different environments (sunny days, rainy days, windy days or snowy days), the radiated noise is different.
In the actual execution process, the sound information to be detected of the vehicle to be detected is obtained, and the sound information to be detected is represented as real-time sound information radiated by the vehicle to be detected in the running process.
The sound information to be measured includes the above-described noise radiated from the vehicle itself, the noise outside the vehicle, the voice information generated by the driver, and the like.
In the fault identification process, the acquired sound information to be detected needs to be processed, unnecessary sound signals are filtered, and the required noise signals are reserved.
In the actual execution process, the sound information to be tested can be acquired by a sound intensity test method. The accuracy of the sound intensity collecting method is improved through the matrix processing of the holographic method, the sound field distribution of the surface of the free vibration sound field is reconstructed through the sound pressure value measured outside the free vibration sound field, the whole external sound field is predicted, the sound pressure is subjected to two-position Fourier change, the rapid change of a space domain and a wave number domain is realized, and the sound pressure field of the source surface is reconstructed.
In some embodiments, step 110 comprises:
and acquiring the in-vehicle noise value of the vehicle to be detected and the in-vehicle and out-vehicle noise difference value of the vehicle to be detected.
The in-vehicle noise value includes, but is not limited to, rear car whistle, car chassis sound, brake pad sound, car suspension sound, engine sound, and other information.
The noise difference between the inside and outside of the vehicle includes a white noise difference between the inside and outside of the vehicle.
White noise is a sound in which the power spectral density of a wavelength component in a piece of sound is uniform over the entire audible range. By acquiring the white noise difference between the inside and the outside of the vehicle, the phenomenon that the white noise value outside the vehicle is mistakenly absorbed in the subsequent noise identification process can be avoided.
In the actual execution process, the acquired sound information to be tested may be input to the car machine system to execute step 120.
in this step, the target noise information includes the type and the eigenvalue of the noise.
The target noise information is the noise information which is subjectively perceived by the target driver, and the target noise information and the target driver have a corresponding relation.
The target driver is a driver driving the vehicle to be tested.
The target noise information is determined by the electroencephalogram rhythm characteristics of the driver, and the target noise information may be the same or different for different drivers.
Among them, the brain waves (EEG) are spontaneous rhythmic neuroelectrical activities generated by voltage fluctuations caused by ionic currents in neurons of the brain, which record the changes of electric waves during brain activities, and are an overall reflection of electrophysiological activities of brain neurons on the surface of the cerebral cortex or scalp. The electroencephalogram signal has continuous and rhythmic potential changes and local high potential changes caused by external stimulation.
The brain wave rhythm includes 5 rhythms, such as a, b, c, d, and e, according to the frequency range, and the characteristics of the respective rhythms are different in different physiological states.
Wherein, the frequency of the rhythm a is 0-4Hz, and in the rhythm, the normal phenomenon that the infants and children appear in deep sleep is indicated.
The frequency of the b rhythm is 4-8Hz, and in the rhythm, the b rhythm indicates that normal infants, children and adults are in drowsiness, the b rhythm is less appeared in a normal waking state, and when a large number of b rhythms appear in high amplitude, the b rhythm belongs to an abnormal active pathological change phenomenon.
The frequency of the rhythm c is 8-13Hz, the rhythm is the basic rhythm of normal adults, the rhythm is obvious in a quiet eye-closing state, the rhythm is blocked when eyes are opened or the rhythm is blocked when the mind is in a state that the cerebral cortex is clear and relaxed.
The frequency of the d rhythm is 13-30Hz, and the rhythm is the main expression of the brain cortex in a waking state and a relaxing state.
The frequency of the e rhythm is 30-50Hz, which is visible during wakefulness and REM dreams (rapid eye movement sleep) or anesthesia.
Different individuals have different perceptions of noise, and the corresponding brain wave rhythm characteristics of the individuals are different under the condition of facing different noises, so that the sound information to be detected can be identified through the brain wave rhythm characteristics of the drivers, and the target noise information corresponding to different drivers can be identified.
The inventor finds that in the research and development process, in the related technology, the noise transmitted into the vehicle cannot be screened and checked by a plurality of noise reduction technologies, and the noise is easily mistakenly reduced and coupled, so that the fault identification result is influenced.
In the method, the sound information to be detected is identified based on the brain wave rhythm characteristics of the target driver, and objective parameters and subjective evaluation can be combined, so that noise is effectively screened and identified, the accuracy of an identification result is improved, and the accuracy of subsequent fault diagnosis is improved.
The inventor also finds that in the related art, only noise can be classified simply, and in the application, different noises can be learned and identified by combining with the brain wave rhythm characteristics of the target driver, so that the intelligent degree of vehicle fault diagnosis is improved remarkably.
In some embodiments, step 120 may include:
performing feature extraction on the sound information to be detected to generate a plurality of pieces of noise information to be selected;
and determining target noise information corresponding to the target driver from the plurality of pieces of noise information to be selected based on the brain wave rhythm characteristics of the target driver.
In this embodiment, the noise information to be selected is information having noise characteristics for all drivers, which is extracted from the sound information to be tested.
For example, the candidate noise information may include rear car whistle, abnormal sound of a car chassis, abnormal sound of a brake pad, abnormal sound of a car suspension, abnormal sound of an engine, sound wave of the engine, and the like.
It should be noted that different drivers may have different perceptions of the same candidate noise information.
For example, if the driver a likes the sound wave of the automobile engine, and the driver B does not like the sound wave of the engine, the sound wave information of the engine in the noise information to be selected is not the noise information corresponding to the driver a for the driver a; for the driver B, the sound wave information of the engine in the noise information to be selected is the noise information corresponding to the driver B.
The driver's perception of noise information may be reflected in his brainwave rhythm characteristics.
And based on the brain wave rhythm characteristics of the driver, determining the target noise information corresponding to the driver from the noise information to be selected.
In some embodiments, determining target noise information corresponding to the target driver from among a plurality of noise information to be selected based on the brain wave rhythm characteristic of the target driver includes:
acquiring brain wave rhythm sub-characteristics of each noise information to be selected by a target driver;
and under the condition that the brain wave rhythm sub-feature is in the target frequency range, determining the noise information to be selected corresponding to the brain wave rhythm sub-feature as the target noise information corresponding to the target driver.
In this embodiment, the electroencephalogram rhythmic sub-feature is electroencephalogram information of a driver for one piece of noise information to be selected from among a plurality of pieces of noise information to be selected, and is used for representing a perception situation of the driver for the piece of noise information to be selected.
The electroencephalogram rhythm characteristic corresponding to one driver may include a plurality of electroencephalogram rhythm sub-characteristics, wherein each electroencephalogram rhythm sub-characteristic corresponds to one piece of noise information to be selected.
It can be understood that when the electroencephalogram rhythm sub-feature is in the target frequency range, the driver can be approximately considered to be in the counter-attitude to the current noise information to be selected, and the noise information to be selected is determined as the target noise information.
Wherein the target frequency range can be customized based on the user, such as setting the target frequency range to be
In the actual implementation process, after a plurality of pieces of noise information to be selected are extracted and obtained based on the sound information to be detected, the brain wave information of the target driver facing each piece of noise information to be selected can be respectively obtained, and the brain wave information is marked as the brain wave rhythm sub-feature corresponding to the noise information to be selected corresponding to the target driver.
And determining the noise information to be selected corresponding to the brain wave rhythm sub-features within the target frequency range as the target noise information corresponding to the target driver by comparing the brain wave rhythm sub-features with the target frequency range.
According to the vehicle fault diagnosis method provided by the embodiment of the invention, the sound information to be detected is identified based on the brain wave rhythm characteristics of the target driver, and objective parameters and subjective evaluation can be combined, so that noise is effectively screened and identified, the accuracy of an identification result is improved, and the accuracy of subsequent fault diagnosis is improved. In actual implementation, step 120 may be performed by a noise identification model.
In some embodiments, step 120 comprises: and inputting the voice information to be detected into a noise identification model corresponding to the target driver to obtain target noise information corresponding to the voice information to be detected.
In this embodiment, a noise identification model is used to identify the target noise information. The input value of the noise identification model is the sound information to be detected, and the output value is the target noise information corresponding to the target driver.
It should be noted that the noise recognition model is obtained by training based on subjective perception of noise by a driver, the noise recognition model has a corresponding relationship with the driver, and different drivers may have different noise recognition models.
Based on the sound sampling frequency of the driver, a noise recognition model corresponding to the target driver can be matched.
The following describes a training method of the noise recognition model.
In some embodiments, the noise identification model corresponding to the target driver is obtained by training using the sample to-be-tested sound information as a sample and using the sample target noise information corresponding to the sample to-be-tested sound information as a sample label, wherein the sample target noise information is determined based on the electroencephalogram rhythm characteristics of the target driver.
In this embodiment, in an actual implementation process, different noise recognition models are designed and trained based on different drivers, respectively.
The sound information to be measured of the sample includes but is not limited to: the information of the range of the audition Hertz of the driver, the information of the sampling frequency of the sound of the driver, the noise value in the vehicle of the vehicle, the white noise difference value between the outside of the vehicle and the inside of the vehicle, and the like.
The sample sound information to be tested input into the noise identification model is sound information recognizable to the driver, and can be determined based on the Hertz range of the hearing of the driver.
The sample target noise information is the noise information perceived by the driver, including but not limited to rear car whistle, car chassis sound, brake pad sound, car suspension sound, and engine sound.
In the actual implementation process, the sound data and the noise data in the sound information to be detected of the sample may be marked, for example, the target noise information of the sample is marked, so as to distinguish the sound to be transmitted.
Wherein the voice data is information to be transmitted (such as rear car whistle, car chassis sound, brake pad sound, car suspension sound, engine sound, etc. as described above); the noise data is information to be filtered, and noise reduction processing is performed by filtering the noise data, so that the accuracy of subsequent fault diagnosis results is improved.
In addition, the noise difference value in the vehicle needs to be matched to determine the range of the noise difference value in the vehicle which continuously changes in the later driving process, so that the data marking of multiple wheels is prevented.
According to the vehicle fault diagnosis method provided by the embodiment of the invention, the noise recognition model is trained based on the brain wave rhythm characteristics of the target driver, and objective parameters and subjective evaluation can be combined, so that noise is effectively screened and recognized, the accuracy of a recognition result is improved, and the accuracy of subsequent fault diagnosis is improved.
In some embodiments, the sample target noise information is determined by:
acquiring brain wave rhythm characteristics of the target driver on the sound information to be detected of the sample;
and determining sample target noise information corresponding to the target driver from the sample sound information to be detected based on the electroencephalogram rhythm characteristics.
In this embodiment, it can be appreciated that the subjective perception of different noise by different individuals is different.
The psychoacoustics-based sound quality objective quantification model solves the problem of poor repeatability of a subjective evaluation method, and a statistical prediction model is established through psychoacoustics objective parameters and subjective evaluation results, so that the purpose of predicting sound quality is achieved.
The psychoacoustic objective parameters include loudness, roughness, sharpness, jitter, tone scheduling, comfort, and the like.
The existing psychoacoustic objective parameters cannot accurately describe the sound quality, many parameters are only suitable for steady-state acoustic signals and have no international unified marking, most of vehicle noises are time-varying noises, and more importantly, subjective evaluation results in the model establishing process are greatly influenced by ethnic groups, regions, culture and the like.
For example, the sound waves of an automobile engine are acceptable to people who prefer loud sounds, and may be noise to people who prefer delicate sounds.
It will be appreciated that in actual implementation, there are situations where the same objective parameter values obtained present distinct results from the subjective perception of the human body.
The sound quality is comprehensively affected by the intrinsic characteristics of sound and the physiological and psychological aspects of the evaluator, and is not perceived in some senses, but there is a certain burden on the physiological state.
According to research, the brain wave rhythm characteristics can objectively describe subjective feelings of an individual on sound, and can be used for analyzing time-varying signals.
In the actual implementation process, the electroencephalogram rhythm characteristics of the driver under various sound information are collected, and Mark is carried out on the noise information perceived by the driver aiming at the electroencephalogram rhythm characteristics of different drivers, so that sample target noise information corresponding to the target driver can be determined from the sample sound information to be detected.
In the model training process, the sound information to be detected of the sample and the noise information of the target of the sample are used as input quantities and input into the noise recognition model so as to train the noise recognition model.
For example, when the user a and the user B are subjected to model customization, the hearing hertz ranges of the user a and the user B and the sampling frequencies of the sounds are respectively collected, and the in-vehicle noise value and the in-vehicle white noise difference value of the vehicle of the user a and the vehicle of the user B are collected, wherein the in-vehicle noise value and the in-vehicle white noise difference value can be used as sample to-be-detected sound information.
And respectively testing the perceptions of the user A and the user B to the sound information to be tested of the sample, and performing Mark on the sound information to be tested of the sample according to the electroencephalogram rhythm characteristics of different individuals so as to respectively Mark the noise information of the sample target corresponding to the user A and the user B.
The user A likes the sound wave of the automobile engine, and the user B considers that the sound wave of the engine is noise, so that the sample target noise information corresponding to the user A does not include the sound wave information of the engine; the sample target noise information corresponding to the user B includes the sound wave information of the engine.
And inputting the sound information to be detected of the sample and the sample target noise information corresponding to the user A as input quantities into the noise recognition model so as to train the noise recognition model, thus obtaining the noise recognition model corresponding to the user A.
And inputting the sound information to be detected of the sample and the sample target noise information corresponding to the user B into the noise recognition model as input quantities to train the noise recognition model, so that the noise recognition model corresponding to the user B can be obtained.
In the actual driving process, the identity information of the driver can be confirmed based on the sound frequency of the driver, so that the noise identification model corresponding to the current driver is matched.
It should be noted that, in some embodiments, after the training of the noise recognition model, the driver may be prompted to check and modify the parameters of the noise recognition model, so that the noise recognition model is more matched with the driver.
According to the vehicle fault diagnosis method provided by the embodiment of the invention, the noise identification model is customized based on the brain wave rhythm characteristics of the target driver, and objective parameters and subjective evaluation can be combined, so that noise is effectively screened and identified, the accuracy of an identification result is improved, and the accuracy of subsequent fault diagnosis is improved.
And step 130, determining fault information of the vehicle to be tested based on the target noise information.
In this step, the target noise information includes a plurality of types of noise information.
And respectively carrying out fault diagnosis on the noise information of each type to determine fault information.
Wherein, the fault information includes but is not limited to: engine faults, brake faults, chassis faults, and other faults.
It should be noted that the noise information generated by the vehicle during the driving process includes general noise and special noise, wherein the general noise includes sound information containing a large amount of noise characteristics, such as wind noise, rain noise, and tire noise; the special noise comprises sound information such as rear vehicle whistling, abnormal sound of an automobile chassis, abnormal sound of a brake pad, abnormal sound of an automobile suspension, abnormal sound of an engine and the like.
When fault diagnosis is performed, diagnosis is mainly performed for special noise.
It can be understood that the environment of the vehicle is constantly changed during the running process, and the noise information is also in a real-time changing state.
For example, when a vehicle runs on a cement road, an asphalt road and a mud land, noise information generated by braking is different; in addition, when the vehicle is driven in sunny days, rainy days or snowy ground, noise information generated by braking is different.
In this embodiment, by identifying and denoising the current sound information to be detected of the vehicle to be detected, generating target noise information in the current environment, and performing fault identification based on the target noise information, it can be determined whether the vehicle to be detected fails in the current environment.
It should be noted that each time the collected sound information to be detected and the output target noise information can be stored as historical data, and the collected sound information and the output target noise information can be used as training samples of the noise identification model in the next model training, so that the sample volume is enlarged, and the intelligent degree of the noise identification model is improved.
This step is illustrated below by way of specific examples.
For example, if the user a likes the sound wave of the automobile engine, and the user B considers that the sound wave of the engine is noise, the noise information finally output by the noise identification model corresponding to the user a does not include the sound wave information of the engine through the previous model training; the noise information finally output by the noise identification model corresponding to the user B comprises the sound wave information of the engine.
In the process that a user A and a user B drive a vehicle, a vehicle machine system collects the noise value in the vehicle and the white noise difference value between the inside and the outside of the vehicle in real time, and outputs target noise information corresponding to the user A to the vehicle of the user A after a noise identification model corresponding to the user is obtained through matching, wherein the target noise information does not include the sound wave of an engine; and outputting target noise information corresponding to the user B to the vehicle of the user B, wherein the target noise information comprises the sound waves of the engine.
And sending the target noise information to a fault monitoring system for analysis so as to respectively obtain the vehicle fault information of the user A and the vehicle fault information of the user B.
Under the condition that the vehicle of the user A is detected to be in fault, alarm information is output to the user A to prompt the user A to process the fault in time; and under the condition that the vehicle of the user B is detected to run normally, the alarm information is not output, and the next round of fault monitoring is carried out.
According to the vehicle fault diagnosis method provided by the embodiment of the invention, the sound information to be detected is identified and subjected to noise reduction through the brain wave rhythm characteristics of the target driver to obtain the required target noise information, fault diagnosis is carried out based on the target noise information, and the objective parameters and the subjective evaluation results can be combined to effectively screen and check the noise entering the vehicle, so that the noise reduction coupling processing is avoided; and fault diagnosis under different user preferences can be met, and the flexibility and the personalization degree are high.
As shown in fig. 2, in some embodiments, step 130 comprises:
carrying out parameter pre-emphasis processing on target noise information;
and determining fault information of the vehicle to be tested based on the processing result.
In this embodiment, the target noise information is subjected to parameter pre-emphasis processing to obtain noise coefficient values under different scenes.
And inputting the noise coefficient value into a system for detection, comparing the noise coefficient value under different scenes with a standard value to determine abnormal data, and checking and feeding back the abnormal data.
In some embodiments, the performing the parameter pre-emphasis processing on the target noise information includes:
performing parameter pre-emphasis processing on at least one of rear vehicle whistle information, chassis abnormal sound information of the vehicle to be tested, brake pad abnormal sound information of the vehicle to be tested, suspension abnormal sound information of the vehicle to be tested and engine abnormal sound information of the vehicle to be tested in the target noise information to obtain a first processing result;
and performing parameter pre-emphasis processing on at least one of the magnetic field information, the weather information, the wind resistance information, the current speed information and the self-weight information of the vehicle to be detected of the current environment of the vehicle to be detected based on the first processing result to obtain a second processing result.
In this embodiment, it can be understood that the rear vehicle whistle information, the vehicle chassis abnormal sound information of the vehicle to be tested, the brake pad abnormal sound information of the vehicle to be tested, the vehicle suspension abnormal sound information of the vehicle to be tested, and the engine abnormal sound information of the vehicle to be tested are all noises radiated by the vehicle itself;
the magnetic field information, the weather information, the wind resistance information, the current vehicle speed information and the self-weight information of the vehicle of the current environment of the vehicle to be detected are noise information caused by external factors of the vehicle.
In actual implementation, fault diagnosis is first performed based on noise information radiated by the vehicle itself to obtain different types of noise coefficient values.
And then, bringing different types of noise coefficient values into different external environments, and performing a second round of parameter pre-emphasis processing to obtain the noise coefficient values under different environments.
And comparing the noise coefficient value with a standard value, determining that the vehicle has a fault if the noise coefficient value and the standard value are inconsistent, outputting alarm information based on fault information to inform a driver, and assisting the driver in safety prejudgment and inspection.
And under the condition that the noise information is consistent with the normal noise, judging that the vehicle does not break down, and not outputting alarm information.
According to the vehicle fault diagnosis method provided by the embodiment of the invention, the target noise information is subjected to parameter pre-emphasis processing, the parameters obtained by the pre-emphasis processing are fed back to the system for detection, checking and feedback are carried out aiming at each abnormality, and the fault information is output under the condition of confirming the occurrence of the fault, so that on one hand, fault diagnosis can be effectively carried out, and the accuracy of a diagnosis result is improved; on the other hand, the system can also effectively assist a driver to carry out safety prejudgment and fault detection, and improve the driving safety.
The following describes a vehicle failure diagnosis apparatus provided by the present invention, and the vehicle failure diagnosis apparatus described below and the vehicle failure diagnosis method described above may be referred to in correspondence with each other.
As shown in fig. 3, the vehicle failure diagnosis device includes: a first acquisition module 310, a first determination module 320, and a second determination module 330.
The first obtaining module 310 is configured to obtain sound information to be detected of a vehicle to be detected;
the first determining module 320 is configured to identify sound information to be detected based on a brain wave rhythm characteristic of a target driver, and determine target noise information;
and a second determining module 330, configured to determine fault information of the vehicle to be tested based on the target noise information.
According to the vehicle fault diagnosis device provided by the embodiment of the invention, the sound information to be detected is identified and subjected to noise reduction through the brain wave rhythm characteristics of the target driver to obtain the required target noise information, fault diagnosis is carried out based on the target noise information, and the objective parameters and the subjective evaluation results can be combined to effectively screen and check the noise entering the vehicle, so that the noise reduction coupling processing is avoided; and fault diagnosis under different user preferences can be met, and the flexibility and the personalization degree are high.
In some embodiments, the first determining module 320 is further configured to:
performing feature extraction on the sound information to be detected to generate a plurality of pieces of noise information to be selected;
and determining target noise information corresponding to the target driver from the plurality of pieces of noise information to be selected based on the brain wave rhythm characteristics of the target driver.
In some embodiments, the first determining module 320 is further configured to:
acquiring brain wave rhythm sub-characteristics of each noise information to be selected by a target driver;
and under the condition that the brain wave rhythm sub-feature is in the target frequency range, determining the noise information to be selected corresponding to the brain wave rhythm sub-feature as the target noise information corresponding to the target driver.
In some embodiments, the first determining module 320 is further configured to:
inputting the voice information to be detected into a noise identification model corresponding to a target driver to obtain target noise information corresponding to the voice information to be detected; wherein the content of the first and second substances,
the noise identification model corresponding to the target driver is obtained by training by taking the sound information to be detected as a sample and the sample target noise information corresponding to the sound information to be detected as a sample label, wherein the sample target noise information is determined based on the brain wave rhythm characteristics of the target driver.
According to the vehicle fault diagnosis device provided by the embodiment of the invention, the noise recognition model is trained based on the brain wave rhythm characteristics of the target driver, and objective parameters and subjective evaluation can be combined, so that noise is effectively screened and recognized, the accuracy of a recognition result is improved, and the accuracy of subsequent fault diagnosis is improved.
In some embodiments, the sample target noise information is determined by:
acquiring brain wave rhythm characteristics of the target driver on the sound information to be detected of the sample;
and determining sample target noise information corresponding to the target driver from the sample sound information to be detected based on the electroencephalogram rhythm characteristics.
According to the vehicle fault diagnosis device provided by the embodiment of the invention, the noise identification model is customized based on the brain wave rhythm characteristics of the target driver, and objective parameters and subjective evaluation can be combined, so that noise is effectively screened and identified, the accuracy of an identification result is improved, and the accuracy of subsequent fault diagnosis is improved.
In some embodiments, the second determining module 330 is further configured to:
carrying out parameter pre-emphasis processing on target noise information;
and determining fault information of the vehicle to be tested based on the processing result.
In some embodiments, the second determining module 330 is further configured to:
performing parameter pre-emphasis processing on at least one of rear vehicle whistle information, chassis abnormal sound information of the vehicle to be tested, brake pad abnormal sound information of the vehicle to be tested, suspension abnormal sound information of the vehicle to be tested and engine abnormal sound information of the vehicle to be tested in the target noise information to obtain a first processing result;
and performing parameter pre-emphasis processing on at least one of the magnetic field information, the weather information, the wind resistance information, the current speed information and the self-weight information of the vehicle to be detected of the current environment of the vehicle to be detected based on the first processing result to obtain a second processing result.
In some embodiments, the first obtaining module 310 is further configured to:
acquiring sound information to be tested of a vehicle to be tested, comprising:
and acquiring the in-vehicle noise value of the vehicle to be detected and the in-vehicle and out-vehicle noise difference value of the vehicle to be detected.
According to the vehicle fault diagnosis device provided by the embodiment of the invention, the target noise information is subjected to parameter pre-emphasis processing, the parameters obtained by the pre-emphasis processing are fed back to the system for detection, checking and feedback are carried out aiming at each abnormality, and the fault information is output under the condition of confirming the occurrence of the fault, so that on one hand, fault diagnosis can be effectively carried out, and the accuracy of a diagnosis result is improved; on the other hand, the system can also effectively assist a driver to carry out safety prejudgment and fault detection, and improve the driving safety.
Fig. 4 illustrates a physical structure diagram of an electronic device, which may include, as shown in fig. 4: a processor (processor)410, a communication Interface 420, a memory (memory)430 and a communication bus 440, wherein the processor 410, the communication Interface 420 and the memory 430 are communicated with each other via the communication bus 440. The processor 410 may invoke logic instructions in the memory 430 to perform a method of fault diagnosis for a vehicle, the method comprising: acquiring sound information to be detected of a vehicle to be detected; identifying the sound information to be detected based on the brain wave rhythm characteristics of the target driver, and determining target noise information; and determining fault information of the vehicle to be tested based on the target noise information.
In addition, the logic instructions in the memory 430 may be implemented in the form of software functional units and stored in a computer readable storage medium when the software functional units are sold or used as independent products. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
In another aspect, the present invention also provides a computer program product comprising a computer program stored on a non-transitory computer-readable storage medium, the computer program comprising program instructions which, when executed by a computer, enable the computer to perform a method of diagnosing a fault of a vehicle provided by the above methods, the method comprising: acquiring sound information to be detected of a vehicle to be detected; identifying the sound information to be detected based on the brain wave rhythm characteristics of the target driver, and determining target noise information; and determining fault information of the vehicle to be tested based on the target noise information.
In yet another aspect, the present invention also provides a non-transitory computer-readable storage medium having stored thereon a computer program, which when executed by a processor, is implemented to perform the above-provided fault diagnosis method for a vehicle, the method including: acquiring sound information to be detected of a vehicle to be detected; identifying the sound information to be detected based on the brain wave rhythm characteristics of the target driver, and determining target noise information; and determining fault information of the vehicle to be tested based on the target noise information.
The above-described embodiments of the apparatus are merely illustrative, and the units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.
Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware. With this understanding in mind, the above-described technical solutions may be embodied in the form of a software product, which can be stored in a computer-readable storage medium such as ROM/RAM, magnetic disk, optical disk, etc., and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the methods described in the embodiments or some parts of the embodiments.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (10)
1. A fault diagnosis method of a vehicle, characterized by comprising:
acquiring sound information to be detected of a vehicle to be detected;
identifying the sound information to be detected based on the brain wave rhythm characteristics of the target driver, and determining target noise information;
and determining fault information of the vehicle to be tested based on the target noise information.
2. The method according to claim 1, wherein the identifying the sound information to be measured based on the electroencephalogram rhythm characteristic of the target driver, and determining target noise information, comprises:
performing feature extraction on the sound information to be detected to generate a plurality of pieces of noise information to be selected;
and determining target noise information corresponding to the target driver from the plurality of pieces of noise information to be selected based on the brain wave rhythm characteristics of the target driver.
3. The method according to claim 2, wherein the determining target noise information corresponding to the target driver from the plurality of noise information to be selected based on the electroencephalogram rhythm characteristic of the target driver includes:
acquiring brain wave rhythm sub-features of the target driver on each piece of noise information to be selected;
and under the condition that the brain wave rhythm sub-feature is in a target frequency range, determining the noise information to be selected corresponding to the brain wave rhythm sub-feature as the target noise information corresponding to the target driver.
4. The method according to claim 1, wherein the determining the fault information of the vehicle under test based on the target noise information includes:
performing parameter pre-emphasis processing on the target noise information;
and determining fault information of the vehicle to be tested based on the processing result.
5. The method according to claim 4, wherein the performing parameter pre-emphasis processing on the target noise information includes:
performing parameter pre-emphasis processing on at least one of rear vehicle whistle information, chassis abnormal sound information of the vehicle to be tested, brake pad abnormal sound information of the vehicle to be tested, suspension abnormal sound information of the vehicle to be tested and engine abnormal sound information of the vehicle to be tested in the target noise information to obtain a first processing result;
and performing parameter pre-emphasis processing on at least one of the magnetic field information, the weather information, the wind resistance information, the current speed information and the self-weight information of the vehicle to be tested in the current environment of the vehicle to be tested based on the first processing result to obtain a second processing result.
6. The method according to any one of claims 1 to 5, wherein the acquiring sound information to be tested of the vehicle to be tested includes:
and acquiring the in-vehicle noise value of the vehicle to be detected and the in-vehicle and out-vehicle noise difference value of the vehicle to be detected.
7. A failure diagnosis device of a vehicle, characterized by comprising:
the first acquisition module is used for acquiring sound information to be detected of a vehicle to be detected;
the first determining module is used for identifying the sound information to be detected based on the brain wave rhythm characteristics of the target driver and determining target noise information;
and the second determination module is used for determining the fault information of the vehicle to be tested based on the target noise information.
8. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor, when executing the program, carries out the steps of the method for diagnosing a malfunction of a vehicle according to any one of claims 1 to 6.
9. A non-transitory computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the fault diagnosis method for a vehicle according to any one of claims 1 to 6.
10. A computer program product comprising a computer program, characterized in that the computer program, when being executed by a processor, carries out the steps of a method for fault diagnosis of a vehicle according to any one of claims 1 to 6.
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