CN115052761A - Method and device for detecting tire abnormity - Google Patents
Method and device for detecting tire abnormity Download PDFInfo
- Publication number
- CN115052761A CN115052761A CN202180000477.8A CN202180000477A CN115052761A CN 115052761 A CN115052761 A CN 115052761A CN 202180000477 A CN202180000477 A CN 202180000477A CN 115052761 A CN115052761 A CN 115052761A
- Authority
- CN
- China
- Prior art keywords
- tire
- tire pressure
- pressure data
- period
- data
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000000034 method Methods 0.000 title claims abstract description 53
- 230000002159 abnormal effect Effects 0.000 claims abstract description 117
- 238000004458 analytical method Methods 0.000 claims abstract description 21
- 230000005856 abnormality Effects 0.000 claims description 46
- 230000004807 localization Effects 0.000 claims description 14
- 238000012549 training Methods 0.000 claims description 12
- 238000012545 processing Methods 0.000 claims description 8
- 230000003213 activating effect Effects 0.000 claims description 5
- 230000004913 activation Effects 0.000 claims description 4
- 238000001514 detection method Methods 0.000 description 30
- 238000004891 communication Methods 0.000 description 10
- 230000008569 process Effects 0.000 description 8
- 230000009467 reduction Effects 0.000 description 8
- 230000006870 function Effects 0.000 description 7
- 238000001228 spectrum Methods 0.000 description 6
- 239000000203 mixture Substances 0.000 description 5
- 230000005236 sound signal Effects 0.000 description 5
- 238000005516 engineering process Methods 0.000 description 4
- 238000000605 extraction Methods 0.000 description 3
- 238000012544 monitoring process Methods 0.000 description 3
- 206010039203 Road traffic accident Diseases 0.000 description 2
- 238000004364 calculation method Methods 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 238000009432 framing Methods 0.000 description 2
- 238000003709 image segmentation Methods 0.000 description 2
- 238000009434 installation Methods 0.000 description 2
- 238000005259 measurement Methods 0.000 description 2
- 230000035945 sensitivity Effects 0.000 description 2
- 230000001131 transforming effect Effects 0.000 description 2
- 230000008859 change Effects 0.000 description 1
- 238000007405 data analysis Methods 0.000 description 1
- 238000013480 data collection Methods 0.000 description 1
- 230000003247 decreasing effect Effects 0.000 description 1
- 230000009365 direct transmission Effects 0.000 description 1
- 238000005265 energy consumption Methods 0.000 description 1
- 230000037433 frameshift Effects 0.000 description 1
- 230000002045 lasting effect Effects 0.000 description 1
- 239000000463 material Substances 0.000 description 1
- 239000011159 matrix material Substances 0.000 description 1
- 238000010606 normalization Methods 0.000 description 1
- 230000037361 pathway Effects 0.000 description 1
- 238000003672 processing method Methods 0.000 description 1
- 230000011664 signaling Effects 0.000 description 1
- 238000010183 spectrum analysis Methods 0.000 description 1
- 230000003068 static effect Effects 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 238000012546 transfer Methods 0.000 description 1
- 230000001755 vocal effect Effects 0.000 description 1
Images
Classifications
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60C—VEHICLE TYRES; TYRE INFLATION; TYRE CHANGING; CONNECTING VALVES TO INFLATABLE ELASTIC BODIES IN GENERAL; DEVICES OR ARRANGEMENTS RELATED TO TYRES
- B60C23/00—Devices for measuring, signalling, controlling, or distributing tyre pressure or temperature, specially adapted for mounting on vehicles; Arrangement of tyre inflating devices on vehicles, e.g. of pumps or of tanks; Tyre cooling arrangements
- B60C23/02—Signalling devices actuated by tyre pressure
- B60C23/04—Signalling devices actuated by tyre pressure mounted on the wheel or tyre
- B60C23/0401—Signalling devices actuated by tyre pressure mounted on the wheel or tyre characterised by the type of alarm
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60C—VEHICLE TYRES; TYRE INFLATION; TYRE CHANGING; CONNECTING VALVES TO INFLATABLE ELASTIC BODIES IN GENERAL; DEVICES OR ARRANGEMENTS RELATED TO TYRES
- B60C23/00—Devices for measuring, signalling, controlling, or distributing tyre pressure or temperature, specially adapted for mounting on vehicles; Arrangement of tyre inflating devices on vehicles, e.g. of pumps or of tanks; Tyre cooling arrangements
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60C—VEHICLE TYRES; TYRE INFLATION; TYRE CHANGING; CONNECTING VALVES TO INFLATABLE ELASTIC BODIES IN GENERAL; DEVICES OR ARRANGEMENTS RELATED TO TYRES
- B60C23/00—Devices for measuring, signalling, controlling, or distributing tyre pressure or temperature, specially adapted for mounting on vehicles; Arrangement of tyre inflating devices on vehicles, e.g. of pumps or of tanks; Tyre cooling arrangements
- B60C23/02—Signalling devices actuated by tyre pressure
- B60C23/04—Signalling devices actuated by tyre pressure mounted on the wheel or tyre
- B60C23/0408—Signalling devices actuated by tyre pressure mounted on the wheel or tyre transmitting the signals by non-mechanical means from the wheel or tyre to a vehicle body mounted receiver
- B60C23/0481—System diagnostic, e.g. monitoring battery voltage, detecting hardware detachments or identifying wireless transmission failures
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01L—MEASURING FORCE, STRESS, TORQUE, WORK, MECHANICAL POWER, MECHANICAL EFFICIENCY, OR FLUID PRESSURE
- G01L17/00—Devices or apparatus for measuring tyre pressure or the pressure in other inflated bodies
Abstract
The embodiment of the application provides a method for detecting tire abnormity, which can be applied to an intelligent driving automobile, a new energy automobile and an automatic driving automobile, and comprises the following steps: the method comprises the steps of periodically acquiring tire pressure data of a target tire through a pressure sensor; analyzing the tire pressure data acquired periodically, and identifying that the tire pressure of the target tire is continuously reduced; collecting audio data through a sound-sensitive sensor; carrying out abnormal sound identification on the audio data, and identifying that the audio data has abnormal sound; carrying out sound source positioning analysis on the audio data to identify an abnormal sound source; and matching the pointing position of the abnormal sound source with the target tire, wherein the matching of the pointing position of the abnormal sound source with the target tire indicates that the target tire is abnormal.
Description
Technical Field
The application relates to the field of automobiles, in particular to a method for detecting tire abnormity.
Background
In recent years, the number of automobiles in China is 2.6 hundred million, the number is still rapidly increased, and the automobile safety is more and more emphasized by people. During the running process of the vehicle, the automobile tire failure is most worried by the driver and is most difficult to prevent. According to statistics, about 30% of traffic accidents in China each year are caused by tire burst due to too low or too high tire pressure, and up to 50% of high-speed traffic accidents are caused by abnormal tire pressure of automobile tires, so how to monitor, prevent and detect abnormal tires is an important subject of automobile safety.
The Tire Pressure Monitoring System (TPMS) applied to the automobile can detect the Tire Pressure of the Tire in real time, and can timely send out alarm and even early warning when detecting that the Tire Pressure is abnormal, thereby ensuring the safety of the vehicle and passengers. The basic principle of the TPMS is to set a reasonable range of the tire pressure of the vehicle tire firstly, ensure that the set value is consistent with the parameters of the vehicle tire, and send an alarm when the tire pressure deviates from the set value and is not in the set reasonable range of the tire pressure of the vehicle tire. The TPMS can timely give an alarm when the tire pressure of the tire is too low or gas leaks.
TPMS currently on the market can be roughly divided into two categories: indirect TPMS and direct TPMS. The indirect TPMS is a system for measuring other parameters of the vehicle, such as four-wheel rotation speed, instead of directly measuring the tire pressure of the vehicle, but the indirect TPMS cannot measure the instantaneous air pressure of the vehicle tire, and has low sensitivity. Direct type TPMS indicates to carry out the direct measurement to the tire pressure through installing the pressure sensor in vehicle tyre inside to with the system on data direct transmission receiver, direct type TMPS monitoring accuracy is high, no matter the vehicle can both monitor under what kind of operating mode, and sensitivity is high, the false positive can not appear, but this kind of direct type TPMS installation cost is higher, and battery power consumption is very fast.
The conventional TPMS usually gives an alarm when the tire pressure value of a tire is lower than a certain preset value, and cannot give an alarm in time to prompt a driver to have potential driving safety.
Disclosure of Invention
The embodiment of the application provides a tire abnormity detection method and device aiming at the problem that in the prior art, when a tire is abnormal, a TPMS cannot give an alarm in time.
In a first aspect, embodiments of the present application provide a method for detecting tire anomalies, which can be used to detect whether a tire has anomalies, including whether the tire is flat or not. The method comprises the following specific steps: the method comprises the steps that pressure sensors built in vehicle wheels are used for periodically collecting tire pressure data of a target tire; analyzing the tire pressure data acquired periodically, and identifying the condition that the tire pressure of the target tire is continuously reduced; collecting audio data through a sound-sensitive sensor configured for the vehicle; carrying out abnormal sound identification on the audio data, and identifying that the audio data has abnormal sound; carrying out sound source positioning analysis on the audio data to identify an abnormal sound source; and matching the pointing position of the abnormal sound source with the target tire, wherein the fact that the pointing position of the abnormal sound source is matched with the target tire indicates that the target tire is abnormal.
The tire pressure detection, abnormal sound identification and sound source positioning technology are combined, the tire of the vehicle can be detected to be abnormal in time, an alarm can be given in time, and compared with the technical scheme that the judgment is only carried out according to the tire pressure threshold value in the traditional TPMS, the tire abnormal detection accuracy rate is higher.
One possible implementation, the occurrence of the continuous decrease in the tire pressure of the target tire includes: the first tire pressure data is larger than the second tire pressure data, and the difference value of subtracting the second tire pressure data from the first tire pressure data is not larger than a preset value;
the first tire pressure data is the tire pressure data of the target tire acquired in a first period, the second tire pressure data is the tire pressure data of the target tire acquired in a second period, the first period and the second period are any two adjacent periods in a plurality of continuous periods, and the first period is located before the second period.
In one possible implementation, the method for detecting tire anomalies further includes: analyzing the tire pressure data acquired periodically, and identifying that the target tire has air leakage; and sending an alarm to prompt a user that the target tire is flat.
One possible implementation manner, wherein the occurrence of the leakage of the target tire comprises: the third tire pressure data is larger than fourth tire pressure data, the difference value of the third tire pressure data minus the fourth tire pressure data is larger than a preset value, the third tire pressure data is the tire pressure data of the target tire acquired in a third period, the fourth tire pressure data of the target tire acquired in a fourth period, the first period and the second period are two adjacent periods, and the third period is located before the fourth period.
One possible implementation manner, where the performing abnormal sound identification on the audio data and identifying that there is an abnormal sound in the audio data includes: processing the audio data to obtain sound characteristic parameters corresponding to the audio data; and comparing the sound characteristic parameters corresponding to the audio data with an audio template library obtained by pre-training, and outputting abnormal sounds existing in the audio data.
In one possible implementation, the method for detecting tire anomalies further includes: sending out a tire abnormity alarm to prompt the user that the target tire is abnormal; receiving a feedback result of the user for the tire abnormality warning; and updating the audio template library based on the feedback result and the sound characteristic parameters corresponding to the audio data.
One possible implementation manner, in which the number of the acoustic sensors is greater than 1, and the audio data is subjected to sound source localization analysis to identify an abnormal sound source, includes: acquiring audio energy collected by the sound-sensitive sensor based on the audio data; and calculating the distance of each sound sensitive sensor relative to the sound source at the unknown position based on the audio energy, wherein the position of the sound sensitive sensor corresponding to the minimum distance relative to the sound source at the unknown position represents the abnormal sound source.
One possible implementation manner, before the acquiring the audio data by the acoustic sensor, further includes: activating the acoustic sensor associated with the target tire.
In a second aspect, the present embodiments provide an apparatus for detecting tire anomalies, for implementing the method for detecting tire anomalies provided in the first aspect, where the apparatus for detecting tire anomalies includes: the tire pressure data acquisition module is used for periodically acquiring the tire pressure data of the target tire through the pressure sensor; the tire pressure analysis module is used for analyzing the tire pressure data acquired periodically and identifying that the tire pressure of the target tire is continuously reduced; the audio data acquisition module is used for acquiring audio data through the sound-sensitive sensor; the abnormal sound identification module is used for identifying abnormal sounds of the audio data and identifying the abnormal sounds of the audio data; the sound source positioning module is used for carrying out sound source positioning analysis on the audio data and identifying an abnormal sound source; and the matching module is used for matching the pointing position of the abnormal sound source with the target tire, and the matching of the pointing position of the abnormal sound source with the target tire indicates that the target tire is abnormal.
One possible implementation, the occurrence of the continuous decrease in the tire pressure of the target tire includes: the first tire pressure data is larger than the second tire pressure data, and the difference value of subtracting the second tire pressure data from the first tire pressure data is not larger than a preset value;
the first tire pressure data is the tire pressure data of the target tire acquired in a first period, the second tire pressure data is the tire pressure data of the target tire acquired in a second period, the first period and the second period are any two adjacent periods in a plurality of continuous periods, and the first period is located before the second period.
In one possible implementation, the tire pressure analysis module is further configured to: analyzing the tire pressure data acquired periodically, and identifying that the target tire has air leakage; the device further comprises: and the tire air leakage alarm module is used for giving an alarm to prompt a user that the target tire is air leakage.
One possible implementation manner, wherein the occurrence of the leakage of the target tire comprises: the third tire pressure data is larger than fourth tire pressure data, the difference value of the third tire pressure data minus the fourth tire pressure data is larger than a preset value, the third tire pressure data is the tire pressure data of the target tire acquired in a third period, the fourth tire pressure data of the target tire acquired in a fourth period, the first period and the second period are two adjacent periods, and the third period is located before the fourth period.
In one possible implementation manner, the abnormal sound identification module is specifically configured to: processing the audio data to obtain sound characteristic parameters corresponding to the audio data; and comparing the sound characteristic parameters corresponding to the audio data with an audio template library obtained by pre-training, and outputting abnormal sounds existing in the audio data.
In one possible implementation, the apparatus for detecting tire abnormality further includes: the tire abnormity warning module is used for sending out a tire abnormity warning to prompt a user that the target tire is abnormal; and the audio template library updating module is used for receiving a feedback result of the user for the tire abnormity alarm and updating the audio template library based on the feedback result and the sound characteristic parameters corresponding to the audio data.
A possible implementation manner, where the number of the acoustic sensors is greater than 1, the sound source localization module is specifically configured to: acquiring audio energy collected by the sound-sensitive sensor based on the audio data; and calculating the distance of each sound sensitive sensor relative to the sound source with the unknown position based on the audio energy, wherein the position of the sound sensitive sensor corresponding to the minimum distance relative to the sound source with the unknown position represents the abnormal sound source.
In one possible implementation, the apparatus for detecting a tire abnormality further includes: an acoustic sensor activation module to activate the acoustic sensor associated with the target tire.
In a third aspect, embodiments of the present application provide a control unit for tire anomaly detection, the control unit being configured with programmable instructions that, when executed, may implement the method for detecting tire anomalies provided in the first aspect.
In a fourth aspect, embodiments of the present application provide a system for detecting tire anomalies that includes a tire pressure sensor, an acoustic sensor, and a control unit. The tire pressure sensor is arranged in each wheel and used for collecting tire pressure data of each wheel. The acoustic sensor is positioned on a vehicle capable of collecting audio data at the tire for collecting the audio data. The control unit is coupled to the tire pressure sensor and the acoustic sensor for performing the method for detecting tire anomalies provided in the first aspect.
In a fifth aspect, the present application provides a storage medium storing instructions, which when executed, may implement the method for detecting tire anomalies provided in the first aspect.
The method for detecting the tire abnormity provided by the embodiment of the application combines the tire pressure detection, the abnormal sound identification and the sound source positioning technology, can give an alarm in time when the tire is abnormal (for example, the tire pricks into a foreign matter, the tire leaks air and the like), improves the driving safety, and can continuously increase the identification accuracy through an incremental learning mode.
Drawings
FIG. 1 is a schematic view of a tire anomaly detection system provided in an embodiment of the present application;
fig. 2 is a schematic view of a microphone installation position provided in an embodiment of the present application;
FIG. 3 is a flow chart of a method for detecting tire anomalies according to an embodiment of the present disclosure;
fig. 4 is a flowchart of an abnormal sound identification method according to an embodiment of the present application;
fig. 5 is a schematic diagram of a sound source localization principle provided in an embodiment of the present application;
FIG. 6 is a schematic view of a tire anomaly detection device according to an embodiment of the present application;
fig. 7 is a schematic view of another tire abnormality detection device according to an embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application clearer, the technical solutions in the embodiments of the present application will be described in further detail below with reference to the accompanying drawings. It is to be understood that the embodiments described are only some of the embodiments of the present application and not all of them. 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 application.
An embodiment of the present application provides a tire abnormality detection system, as shown in fig. 1, a tire abnormality detection system 100 includes: a data acquisition module 101, a control module 102, a display module 103, and a feedback module 104.
The data acquisition module 101 includes a pressure sensor module 1011 and an acoustic sensor module 1012. Specifically, the pressure sensor module 1011 includes pressure sensors built in each wheel, and in general, one pressure sensor is disposed on each wheel, and specifically, the pressure sensor is disposed on the hub of each wheel, and is used for acquiring the tire pressure data of the north part of the tire of the corresponding wheel. Optionally, a wireless communication component is built in each pressure sensor, the pressure sensor can convert the collected tire pressure signal into an analog signal, the collected tire pressure data is sent to the control module 102 through the wireless communication component, and the control module 102 performs data analysis and abnormal judgment. Specifically, the acoustic sensor module 1012 includes one or more acoustic sensors, which can be disposed at the inner side of the door or near the wheel, respectively, for collecting audio data of the vehicle during driving. Acoustic sensors generally refer to devices capable of audio data acquisition, such as microphones, microphones. As shown in fig. 2, the acoustic sensor module uses 4 microphones as acoustic sensors, and the microphones are respectively disposed at positions close to the wheels outside the vehicle for audio data during the driving process of the vehicle. It should be noted that the number and the disposition position of the acoustic sensors are not particularly limited. Alternatively, a speech recognition microphone in the vehicle may be reused. Optionally, one or more acoustic sensors send the collected audio data to the control module 102 in a wireless manner or a wired manner.
The control module 102 includes a storage unit 1021, an abnormal sound recognition unit 1022, and a sound source localization unit 1023. The storage unit 1021 stores the tire pressure data, the audio data, and the audio database received from the data acquisition module 101. The abnormal sound identification unit 1022 performs abnormal sound analysis on the audio data received from the data collection module 101. The sound source localization unit 1023 performs sound source localization analysis on the audio data received from the data acquisition module 101. When the control module 102 determines that there is an abnormality in the tire based on the analysis results of the abnormal sound recognition unit 1022 and the sound source positioning unit 1023, a signal is sent to the display module 103, and the display module 103 is used for displaying the abnormality in the tire to prompt the user that there is an abnormality in the tire. Besides the display module 103 can prompt the user that the tire is abnormal, the user can also prompt the user that the tire is abnormal through sound, vibration, vision and the like.
The audio template library updating module 104 is used for receiving feedback information of the user for prompting the display module 103 that the tire has the abnormality, for example, positive feedback that the user really checks that the abnormality exists after the display module 103 prompts that the tire has the abnormality, or negative feedback that the user does not check that the abnormality exists after the display module 103 prompts that the tire has the abnormality. The audio template library update module 104 is further configured to update the audio template library according to the user feedback to improve the performance of the tire anomaly detection system 100.
An embodiment of the present application provides a tire anomaly detection method, and as shown in fig. 3, a tire anomaly detection method 200 may include the following steps:
and S100, when the vehicle is started, activating each pressure sensor arranged in each wheel hub and acquiring tire pressure data of a corresponding wheel. In the complete tire anomaly detection method flow, the pressure sensor periodically collects the tire pressure data of the corresponding wheel, for example, the tire pressure data is collected every preset time (such as 20 seconds, 30 seconds, etc.), and the tire pressure data can be collected once or multiple times. When the tire pressure data is collected for a plurality of times at every preset time, the average tire pressure data may be taken as the tire pressure data collected for one cycle.
And S200, comparing and analyzing the tire pressure data acquired by each pressure sensor in the current period with the tire pressure data acquired by each pressure sensor in the previous period so as to detect the tire pressure state of each pressure sensor corresponding to the wheel. For example, the tire pressure data of the wheel a collected by the pressure sensor a in the current period is compared with the tire pressure data of the wheel a collected by the pressure sensor a in the previous period, and the tire pressure data of the wheel B collected by the pressure sensor B in the current period is compared with the tire pressure data of the wheel B collected by the pressure sensor B in the previous period. It should be noted that if the current cycle is the first cycle, that is, after the vehicle is started, each pressure sensor collects the tire pressure data of the corresponding wheel for the first time, in this case, S200 may not be performed, that is, the comparison analysis of the tire pressure data collected during the cycle is not performed. The tire pressure data acquired by each pressure sensor in the current period can also be used as the tire pressure data acquired by each pressure sensor in the previous period of the first period, that is, the tire pressure data acquired by each pressure sensor in the current period and the tire pressure data acquired by each pressure sensor in the current period are compared and analyzed. The tire pressure data acquired by each pressure sensor in the current period can also be compared and analyzed with the tire pressure data acquired by each pressure sensor most recently before the vehicle is started.
The tire pressure state can include that the tire pressure reduces fast, the non-fast reduction of tire pressure, the tire pressure increases and the tire pressure is normal, and wherein the tire pressure reduces fast and shows that there is quick gas leakage in the tire of wheel, and the non-fast reduction of tire pressure shows that there is not quick gas leakage of tire but the reduction appears in the tire pressure in the wheel, and the tire pressure that the tire pressure increased shows the wheel takes place to rise, and the tire pressure that the tire pressure normally shows the wheel does not change.
Optionally, S200 further includes:
s201, if the tire pressure state is that the tire pressure is rapidly reduced and indicates that the tire is leaked, alarming is carried out in the modes of displaying, sound, vibration and the like, and a user is prompted that the tire is leaked. Optionally, the user is prompted to finish the tire pressure abnormality detection method flow after the tire is leaked.
S202, if the tire pressure state is that the tire pressure is not rapidly reduced, whether the numerical value of the timer reaches a preset numerical value is judged. If the value of the timer does not reach the preset value, the value of the timer is increased by 1, and the step S100 is executed. If the value of the timer reaches the preset value, the value of the timer is cleared, and the following step S300 is performed. The value of the timer reaching the preset value indicates that the tire is abnormal. The timer is used for recording the times of non-rapid reduction of the tire pressure, and the numerical value of the timer represents the times of non-rapid reduction of the tire pressure, namely, the continuous increase of the numerical value of the timer represents that the tire pressure is in a continuous reduction state. It should be noted that the number of times that the tire pressure of each tire is not rapidly reduced is counted separately, and may be counted by one timer or by a plurality of timers. The timer is a device capable of counting, and the specific structure and composition of the timer are not limited. Alternatively, the preset value may be set according to actual requirements, for example, 10 or 20, and is used to indicate that the preset tire pressure state is the number of times of non-rapid continuous occurrences of tire pressure reduction.
S203, if the tire pressure state is tire pressure increase, clearing the timer value, and further returning to perform step S100.
And S204, if the tire pressure state is that the tire pressure is normal, clearing the value of the timer, and further returning to execute the step S100.
For example, assuming that the pressure sensor a is built in the hub of the wheel a, the tire data of the wheel a collected by the pressure sensor a in the current period n is P n In kilopascals (Kpa). In the last period n-1, the tire data of the wheel A collected by the pressure sensor A is P n-1 . Step S200 specifically includes:
if P n-1 -P n If the pressure is more than P, the tire pressure state is that the tire pressure is rapidly reduced, and the tire of the wheel A is rapidly deflated.
If 0 < P n-1 -P n P is less than or equal to the preset value, the tire pressure state is that the tire pressure is not rapidly reduced, which means that although the tire of the wheel A does not have rapid leakage, the tire pressure is reduced.
If P n-1 -P n If < 0, the tire pressure state is tire pressure increase, indicating that the tire pressure of the wheel A is increased.
If P n-1 -P n When the tire pressure is 0, the tire pressure state is normal, which indicates that the tire pressure of the wheel a has not changed. It should be noted that in practical application, the tire pressure will generally fluctuate, and P will not occur n -P n-1 0, is merely an example of the present application. It is true P n And P n-1 The tire pressure state may be judged to be normal if the difference is not large, for example, between-0.5 and 0.5. Likewise, -0.5 < P n-1 -P n If the tire pressure is less than 0.5, it cannot be determined that the tire pressure state is a non-rapid decrease in tire pressure or an increase in tire pressure.
P is a preset value, which is a positive number, and is used to indicate a threshold value of the tire leakage, where the preset value may be a fixed threshold value (e.g., 5kpa), or may be a dynamic threshold value, for example, a threshold value dynamically adjusted based on information such as ambient temperature, driving environment, weather, tire material, and road condition.
And S300, activating the sound-sensitive sensor and collecting audio data. The description of the deployment position of the acoustic sensor can refer to the description of the acoustic sensor module and the acoustic sensor in the corresponding embodiment of fig. 1. Generally speaking, acoustic sensors are used to collect audio data from tires during the running of a vehicle. Alternatively, the acoustic sensor may periodically collect audio data for a period of time, for example, collecting audio data every minute, each time lasting 10 s. Optionally, the number of activated sound-sensitive sensors may be determined according to requirements, preset rules, and the like, and only one or more sound-sensitive sensors, which are corresponding to or near the preset value when the tire pressure state of the wheel is that the tire pressure is not rapidly reduced and the value of the timer reaches the preset value, may be activated, and all the sound-sensitive sensors may be activated. In addition to the acoustic sensor corresponding to the condition that the tire pressure of the activated wheel is not rapidly reduced and the value of the timer reaches the preset value, other acoustic sensors (for example, all or nearby sensors) are also activated, so that the audio data can be more comprehensively collected for abnormal sound identification. As shown in fig. 2, for example, if it is detected in step S200 that the tire pressure of the wheel corresponding to the microphone 1 (the upper left wheel in the figure) is not rapidly decreased and the value of the timer reaches the preset value, then in step S300, the microphone 1, the microphone 2, and the microphone 3 may be activated, and there is no need to activate the microphone 4 farther from the wheel corresponding to the microphone 1, because the microphone 1 is relatively farther from the wheel corresponding to the microphone 4 (the lower right wheel in the figure), the influence of the sound at the wheel corresponding to the microphone 4 on the audio data collected by the microphone 1 is small, and the microphone 4 is not activated, so that the energy consumption may be reduced, and the occupation of computing resources may be reduced.
S400, abnormal sound identification is carried out on the collected audio data so as to identify whether abnormal sound exists or not. If there is an abnormal sound, step S500 is executed. If no abnormal sound exists, the value of the timer is cleared, and the process returns to the step S100. How to identify abnormal sounds in audio data may adopt an abnormal sound identification method in the prior art, and refer to the following description of embodiments, which is not described herein again.
S500, performing sound source localization on the abnormal sound by using a sound source localization technique to identify the tire with the abnormality, specifically, locating which sound sensitive sensor the abnormal sound identified in step S400 originates from, and further determining that the tire of the wheel corresponding to the located sound sensitive sensor has the abnormality, for example, by approximating the source of the abnormal sound and the microphone 1 in combination with fig. 2, it is identified that the tire with the abnormality is the tire of the wheel corresponding to the microphone 1 (the upper left wheel in the figure).
And S600, matching the tire with the abnormality identified based on the sound source positioning with the tire with the abnormality identified based on the tire pressure state.
S601, if the matching is successful, a tire abnormality warning is issued to the user, for example, the user is prompted by display, sound, vibration, or the like that there is an abnormality in the tire.
For example, if the tire with abnormality identified based on the sound source localization is a and the tire with abnormality identified based on the tire pressure status is also a, the matching is successful, and a tire abnormality warning is issued to the user: the tire A has abnormity, and can be used for prompting a user to get off to detect whether the discourse tone is abnormal.
S602, if the matching is not successful, namely the tire with the abnormality identified based on the sound source positioning is not the same as the tire with the abnormality identified based on the tire pressure state, the sound-sensitive sensor is closed. Further, the value of the timer is cleared.
And S700, receiving feedback information of the user for the tire abnormity warning. For example, the feedback information that the user detects that the tire is actually abnormal or the feedback information that the user detects that the tire is not abnormal.
And S800, updating the audio database based on the feedback information of the user and the audio data corresponding to the tire abnormity warning aimed at by the feedback information so as to improve the accuracy of the abnormal sound identification algorithm in an incremental learning mode.
In the prior art, when the monitored tire pressure exceeds the threshold value of the tire leakage, a warning is sent out when the tire leakage is judged, and the tire abnormity detection method provided by the embodiment can timely identify the tire abnormity and send out the warning through continuous tire pressure reduction and abnormal tire sound monitoring under the condition that the tire leakage does not occur, so that the driving safety of a vehicle is ensured.
In combination with the foregoing embodiments, the embodiments of the present application provide an abnormal sound identification method, which may be used to implement step S400 in the embodiment corresponding to fig. 3. As shown in fig. 4, the abnormal sound identification method includes the steps of:
s301, extracting sound characteristic parameters of the collected audio data. Optionally, before extracting the sound characteristic parameters of the audio data, a valid sound segment is separated from the audio data, which is also called end point detection, and a start point and an end point of an abnormal sound are extracted from the audio data. After the endpoint detection, the sound characteristic parameters are extracted from the valid sound segment. Accurate endpoint detection can improve abnormal sound identification accuracy. The endpoint detection can adopt methods based on short-time energy, short-time zero crossing rate, short-time amplitude, short-time dynamic threshold rate and the like in the prior art, and the methods related to the endpoint detection are not described in detail herein.
The sound characteristic parameters may be selected according to different requirements, such as short-time amplitude, Mel-Frequency Cepstrum Coefficient (MFCC), MFCC first-order difference Coefficient, etc. for reflecting the audio data. Optionally, characterizing the audio data with a MFCC, and the method of calculating the MFCC substantially comprises: decomposing an audio signal (i.e., audio data or a valid sound segment, the same applies below) into a plurality of frames; pre-emphasizing the audio signal, and passing the audio signal through a high-pass filter; then, carrying out Fourier transform on the audio signal passing through the high-pass filter, and transforming the audio signal to a frequency domain; passing the obtained frequency spectrum of each frame through a Mel filter (triangular overlapping window) to obtain Mel scale; extracting logarithmic energy on each mel scale; and performing inverse discrete Fourier transform on the obtained result, and transforming the result into an inverse spectrum domain. MFCC is the magnitude (amplitudes) of this cepstral plot. Typically 12 coefficients are used, superimposed with the frame energy to obtain 13-dimensional coefficients. Further, the method for extracting the audio characteristic parameters corresponding to the audio data in the abnormal sound identification stage adopts the same characteristic parameters as the audio characteristic parameters corresponding to the audio samples extracted in the stage of training the audio template library for abnormal sound identification.
S302, comparing the sound characteristic parameters corresponding to the extracted audio data with an audio template library, and outputting an identification result. The specific content comprises the following steps:
the sound characteristic parameter corresponding to the audio data to be identified
The maximum posterior probability of the j-th class audio in the audio template library is P (lambda) j | X). According to Bayesian criterion, the maximum posterior probability can be expressed as:
it is assumed that the audio data to be recognized are equal in probability for each type of sound in the audio template library, i.e.
And P (X) is constant and the same. Therefore, the maximum posterior probability is obtained to find the lambda j Is such that P (X | λ) j ) And maximum. Since the likelihood probability of a piece of audio is multiplied by the likelihood probability of each frame, the likelihood probability of a piece of audio is multiplied by the likelihood probability of each frame
In logarithmic form of
The recognition result is
That is, the probability that the audio data to be recognized is the jth class of audio is the largest, and if the jth class of audio is an abnormal sound in the audio template library, the audio data to be recognized is output as the abnormal sound.
The recognition rate of the abnormal sound depends on the accuracy of the trained audio template library. The audio template library may be trained using acoustic models such as Hidden Markov Models (HMMs), Gaussian Mixture Models (GMMs), and the like. Optionally, the GMM is used for audio template library training, and the gaussian distribution combination of different parameters can be used to represent different audios, that is, the characteristic parameter of each audio corresponds to one GMM.
The audio template library training process comprises the following steps: the method comprises a sound characteristic parameter extraction stage and an audio template library training stage of the audio sample. In the examples of the present application, the sound characteristic parameter employs MFCC. The sound characteristic parameter extraction stage of the audio sample comprises the following specific steps:
1) normalization: it is possible to make each type of audio sample have a uniform standard, and the audio amplitude is normalized between [ -1,1], so as to eliminate the difference between different audio samples, i.e. to divide each sampled value by the amplitude peak of the signal segment. The calculation formula is as follows:
where x (i) is the original audio sample,
is the normalized audio sample and n is the audio sample length.
2) Pre-emphasis: the pre-emphasis may boost the high frequency components to flatten the spectrum of the audio samples for spectral analysis or vocal tract parameter analysis. The transfer function of the pre-emphasis filter z is:
H(z)＝1-μ·z -1 ，
where μ is a constant, usually 0.97.
3) Windowing and framing: in order to ensure the short-time stationarity of the audio samples, a Hamming window function is selected for framing. A typical window size is 25ms and the frame shift is 10 ms. The window function is:
w(n)＝(1-α)-α·cos(2πn/(N-1)),0≤n≤N-1，
wherein, the length of the window sequence is N, and alpha is 0.4.
4) MFCC extraction: taking a frame length N as 256 points, performing Fast Fourier Transform (FFT) on each frame to obtain a spectrum parameter, performing convolution operation on the spectrum parameter of each frame of data through a Mel frequency filter consisting of a group of N triangular band-pass filters, and then taking a logarithm of the output of each frequency band to obtain the logarithm energy S (m) of each output. And finally, performing discrete cosine transform on the N parameters to obtain a Mel cepstrum coefficient as an audio characteristic parameter. The formula is expressed as:
wherein n is the number of the MFCCs; c i (n) is the nth MFCC coefficient of the ith frame, S (M) is the log power spectrum of the audio sample, and M is the number of triangular filters.
It should be noted that the processing method for extracting the sound characteristic parameters of the audio samples in the training process of the audio template library may be applied to the step S301 of extracting the sound characteristic parameters of the acquired audio data.
The audio template library training stage can adopt GMM, and the specific steps comprise the following steps: aiming at the abnormal sound recognition scene of the tire, storing audio samples in the driving process of the vehicle under a plurality of scene road conditions and audio samples in the driving process of the tire stuck with foreign matters in an audio template library, extracting sound characteristic parameters, and training by adopting GMM (Gaussian mixture model) to obtain GMM (Gaussian mixture model) corresponding to each type of audio samples respectively, wherein lambda can be used j To mean, a ternary equation describing the GMM of each type of audio sample is finally obtained:
λ j ＝{P j ,μ j ,Σ j },j＝1,2,…,N，
wherein, P j Is the weight of the mixture component, mu j As mean vector, sigma j Is a covariance matrix, N is the mixing order, j is the sample number, and the ternary combination of GMMs describing each class of audio samples is the audio template library.
In combination with the foregoing embodiments of the present application, an embodiment of the present application provides a sound source localization analysis method, which may be used to implement step S500 in the embodiment corresponding to fig. 5, and includes the following steps:
s501, the audio data in which the abnormal sound is recognized is taken as an input.
S502: according to the audio data with abnormal sound in S501, the audio energy collected by a certain sound-sensitive sensor (e.g., a microphone) at a certain time is obtained.
S503: the distance between the microphone and the sound source position is calculated based on the two-dimensional position coordinates of the corresponding microphone.
With reference to fig. 5, possible implementations are as follows:
the diagram contains n +1 undifferentiated microphones and an unknown position sound source, and the expression of the energy received by the ith microphone at the time t is as follows:
s (t) is the energy value of the sound source, g i Is the gain coefficient of the ith microphone, d i Is the distance, ε, between the ith microphone and the sound source at the unknown location i (t) is the superimposed background noise energy value. For simplicity of calculation,. epsilon i (t) is 0, gi is set to 1, and the energy value of the sound source S (t) can be set to some fixed constant (e.g., 1000). Can be based on energy measurements E i (t) calculating the distance from the ith microphone to the sound source at the unknown position
S504: and identifying the sound source of the abnormal sound by adopting a least square method. Specifically, the probability of the position at which the sound source of the abnormal sound is directed is the greatest at which sound sensitive sensor (e.g., microphone).
Optionally, taking the acoustic sensor as a microphone as an example, the implementation is specifically as follows:
let the two-dimensional position coordinate of the ith microphone be r i ＝(x i ,y i ) I is 1,2, … n, the distance between each microphone i and the sound source at the unknown position being
The formula can be obtained:
wherein 1 is less than or equal to iN is less than or equal to n. The least square method can be used for obtaining:
the above equation can be expressed as:
namely, it is
Wherein
To estimate the pointing position of a sound source at an unknown position, based on
The minimum distance between the value of (a) and the four microphones is obtained, and the minimum value is taken, so that a certain microphone i pointed by the sound source at the unknown position at the time t can be obtained.
S505: and identifying the tire with the abnormality according to the estimation result of the last step.
Further, based on a certain microphone i pointed by the sound source at the unknown position at the time t obtained in the step S504, the microphones pointed by the sound sources at the unknown positions at other times are continuously calculated, and finally, the probability of which microphone the sound source at the unknown position points to is the largest in the whole audio data is calculated, and then, based on the position relationship between the microphones and the wheels, the wheel corresponding to the microphone with the largest probability is found, so that the tire with the abnormality can be positioned. The sound source localization is to find the position pointed by the sound source of the unknown position of the audio data, so as to identify the tire with the abnormality. In step S600 described in the embodiment of fig. 3, the tire with abnormality identified based on the sound source location is matched with the tire with abnormality identified based on the tire pressure status, that is, whether the tire with the location pointed by the sound source with unknown location found based on the sound source location method is consistent with the tire with abnormality identified based on the tire pressure status is actually determined. It should be noted that the sound source at the unknown position, i.e., the abnormal sound source, corresponds to the abnormal sound recognized by the abnormal sound.
Traditional TPMS can't in time report to the police when the tire pressure takes place to reduce, and the nail is inlayed for a long time in the tire, threatens passenger's life safety, and often when taking place to report to the police, and the tire pressure has crossed too low and can't go. Aiming at the problems, the audio data of the running of the vehicle is collected through the microphone, the tire pressure data collected by the tire pressure sensor and the audio data collected by the sound sensitive sensor are analyzed through the incremental learning type abnormal sound identification technology and the sound source positioning technology based on the GMM, the alarm can be given in time when the tire is abnormal (for example, the tire pricks into a foreign body), the driving safety is improved, and the identification accuracy can be continuously increased through the incremental learning mode.
The embodiment of the present application provides an apparatus for detecting tire abnormality, as shown in fig. 6, an apparatus 1000 for detecting tire abnormality includes: the system comprises a tire pressure data acquisition module 1010, a tire pressure analysis module 1020, an audio data acquisition module 1030, an abnormal sound identification module 1040, a sound source positioning module 1050, a matching module 1060, a tire air leakage alarm module 1070, a tire abnormal alarm module 1080, an audio template library updating module 1090 and an acoustic sensor activation module 1110.
The tire pressure data acquisition module 1010 is used for periodically acquiring the tire pressure data of the target tire through the pressure sensor; the tire pressure analysis module 1020 is configured to analyze the tire pressure data acquired periodically and identify that the tire pressure of the target tire is continuously reduced; the audio data acquisition module 1030 is used for acquiring audio data through the acoustic sensor; the abnormal sound identification module 1040 is configured to perform abnormal sound identification on the acquired audio data, and identify that the audio data has an abnormal sound; the sound source positioning module 1050 is configured to perform sound source positioning analysis on the acquired audio data to identify an abnormal sound source; a matching module 1060, configured to match the pointed position of the abnormal sound source with the target tire, where matching the pointed position of the abnormal sound source with the target tire indicates that the target tire is abnormal. The occurrence of the continuous decrease in the tire pressure of the target tire includes: the first tire pressure data is larger than the second tire pressure data, and the difference value of the first tire pressure data minus the second tire pressure data is not larger than a preset value; the first tire pressure data is the tire pressure data of the target tire acquired in the first period, the second tire pressure data of the target tire acquired in the second period, the first period and the second period are any two adjacent periods in a plurality of continuous periods, and the first period is positioned before the second period. The plurality of cycles includes at least 3 acquisition cycles.
Further, the tire pressure analyzing module 1020 is further configured to: and analyzing the tire pressure data acquired periodically, and identifying that the target tire has air leakage.
Optionally, the apparatus 1000 further comprises: and the tire air leakage alarm module is used for giving an alarm to prompt a user that the target tire is air leakage. The occurrence of the target tire leakage includes: the third tire pressure data is larger than the fourth tire pressure data, the difference value of the third tire pressure data minus the fourth tire pressure data is larger than the preset value, the third tire pressure data is the tire pressure data of the target tire acquired in the third period, the fourth tire pressure data of the target tire acquired in the fourth period, the first period and the second period are two adjacent periods, and the third period is located before the fourth period.
The abnormal sound identification module 1040 is specifically configured to: processing the audio data to obtain sound characteristic parameters corresponding to the audio data; and comparing the sound characteristic parameters corresponding to the audio data with an audio template library obtained by pre-training, and outputting abnormal sounds existing in the audio data.
The apparatus 1000 further comprises:
the tire abnormity warning module is used for sending out a tire abnormity warning to prompt a user that the target tire is abnormal; and an audio template library updating module 1090 which is used for receiving a feedback result of the user for the tire abnormity warning and updating the audio template library based on the feedback result and the sound characteristic parameter corresponding to the audio data.
Optionally, the number of the acoustic sensors is greater than 1, and the sound source localization module 1050 is specifically configured to: acquiring audio energy collected by a sound-sensitive sensor based on audio data; and calculating the distance of each sound sensitive sensor relative to the sound source with the unknown position based on the audio energy, wherein the position of the sound sensitive sensor corresponding to the minimum distance relative to the sound source with the unknown position represents an abnormal sound source.
Device 1000 further comprises an acoustic sensor activation module 1110 for activating said acoustic sensor associated with said target tire.
The present embodiment provides another tire abnormality detection apparatus, and referring to fig. 7, the tire abnormality detection apparatus 200 may implement the tire abnormality detection method described in the embodiment corresponding to fig. 3. The tire abnormality detection device 200 includes: memory 201, processor 202, communication interface 203, and bus 204. The memory 201, the processor 202 and the communication interface 203 are connected to each other through a bus 204.
The memory 201 may be a read-only memory, a static memory device, a dynamic memory device, or a random access memory. The memory 201 may store a program, and when the program stored in the memory 201 is executed by the processor 202, the processor 202 is configured to execute the tire abnormality detecting method described in the embodiment of the present application corresponding to fig. 3.
The processor 202 may be a general-purpose central processing unit, a microprocessor, an application specific integrated circuit, a Graphics Processing Unit (GPU), or one or more integrated circuits, and is configured to execute a relevant program to implement the functions that are required to be executed by the units in the signaling analysis apparatus according to the embodiment of the present application, or to execute the image segmentation method according to the embodiment of the present application. The processor may implement the functions of the blocks in fig. 6.
The processor 202 may also be an integrated circuit chip having signal processing capabilities. In implementation, the steps of the image segmentation method of the present application may be performed by integrated logic circuits of hardware or instructions in the form of software in the processor 202. The processor 202 may also be a general purpose processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic device, or discrete hardware components. The various methods, steps, and logic blocks disclosed in the embodiments of the present application may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of the method disclosed in connection with the embodiments of the present application may be directly implemented by a hardware decoding processor, or implemented by a combination of hardware and software modules in the decoding processor. The software modules may be located in ram, flash, rom, prom, or eprom, registers, etc. as is well known in the art. The storage medium is located in the memory 201, and the processor 202 reads information in the memory 201, and performs, in combination with hardware (e.g., a display screen) thereof, functions that are required to be performed by modules included in the tire abnormality detection apparatus 100 according to the embodiment of the present application, or performs the tire abnormality detection method 100 according to the embodiment of the method of the present application.
The communication interface 203 enables communication between the tire abnormality detecting device 200 and other devices or communication networks using a transceiver device such as, but not limited to, a transceiver. For example, data of actual parking spaces around the vehicle to be parked may be received through the communication interface 203.
Bus 204 may include a pathway to communicate information between various components of tire anomaly detection device 200 (e.g., memory 201, processor 202, communication interface 203).
It should be noted that although the tire anomaly detection apparatus 200 shown in fig. 7 shows only a memory, a processor, and a communication interface, in a specific implementation process, those skilled in the art will understand that the tire anomaly detection apparatus 200 also includes other devices necessary for normal operation. Meanwhile, it will be understood by those skilled in the art that the tire abnormality detecting apparatus 200 may further include hardware devices for implementing other additional functions, according to specific needs. Further, those skilled in the art will appreciate that the tire abnormality detecting apparatus 200 may also include only the components necessary to implement the embodiments of the present application, and need not include all of the components shown in fig. 7.
The above description is only for the specific embodiments of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily think of the changes or substitutions within the technical scope of the present invention, and shall cover the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the appended claims.
Claims (17)
1. A method of detecting tire anomalies, comprising:
the method comprises the steps of periodically acquiring tire pressure data of a target tire through a pressure sensor;
analyzing the tire pressure data acquired periodically, and identifying that the tire pressure of the target tire is continuously reduced;
collecting audio data through a sound-sensitive sensor;
carrying out abnormal sound identification on the audio data, and identifying that the audio data has abnormal sound;
carrying out sound source positioning analysis on the audio data to identify an abnormal sound source;
and matching the pointing position of the abnormal sound source with the target tire, wherein the matching of the pointing position of the abnormal sound source with the target tire indicates that the target tire is abnormal.
2. The method of claim 1, wherein the occurrence of the continuous decrease in the tire pressure of the target tire comprises: the first tire pressure data is larger than the second tire pressure data, and the difference value of the first tire pressure data minus the second tire pressure data is not larger than a preset value;
the first tire pressure data is the tire pressure data of the target tire acquired in a first period, the second tire pressure data is the tire pressure data of the target tire acquired in a second period, the first period and the second period are any two adjacent periods in a plurality of continuous periods, and the first period is located before the second period.
3. The method of claim 1 or 2, further comprising:
analyzing the tire pressure data acquired periodically to identify that the target tire has air leakage;
and sending an alarm to prompt a user that the target tire is flat.
4. The method of claim 3, wherein the occurrence of a leak in the target tire comprises: the third tire pressure data is greater than the fourth tire pressure data, a difference value obtained by subtracting the fourth tire pressure data from the third tire pressure data is greater than a preset value, the third tire pressure data is the tire pressure data of the target tire acquired in a third period, the fourth tire pressure data is the tire pressure data of the target tire acquired in a fourth period, the third period and the fourth period are two adjacent periods, and the third period is located before the fourth period.
5. The method according to any one of claims 1-4, wherein the performing abnormal sound identification on the audio data, and identifying that the abnormal sound exists in the audio data, comprises:
processing the audio data to obtain sound characteristic parameters corresponding to the audio data;
and comparing the sound characteristic parameters corresponding to the audio data with an audio template library obtained by pre-training, and outputting abnormal sounds existing in the audio data.
6. The method of claim 5, further comprising:
sending out a tire abnormity alarm to prompt the user that the target tire is abnormal;
receiving a feedback result of the user for the tire abnormality warning;
and updating the audio template library based on the feedback result and the sound characteristic parameters corresponding to the audio data.
7. The method according to any one of claims 1-6, wherein the number of acoustic sensors is greater than 1, and wherein performing a sound source localization analysis on the audio data to identify an abnormal sound source comprises:
acquiring audio energy collected by the sound-sensitive sensor based on the audio data;
and calculating the distance of each sound sensitive sensor relative to the sound source with the unknown position based on the audio energy, wherein the position of the sound sensitive sensor corresponding to the minimum distance relative to the sound source with the unknown position represents the abnormal sound source.
8. The method of any of claims 1-7, further comprising, prior to the acquiring audio data by the acoustic sensor:
activating the acoustic sensor associated with the target tire.
9. An apparatus for detecting tire anomalies, comprising:
the tire pressure data acquisition module is used for periodically acquiring the tire pressure data of the target tire through the pressure sensor;
the tire pressure analysis module is used for analyzing the tire pressure data acquired periodically and identifying that the tire pressure of the target tire is continuously reduced;
the audio data acquisition module is used for acquiring audio data through the sound-sensitive sensor;
the abnormal sound identification module is used for identifying abnormal sounds of the audio data and identifying the abnormal sounds of the audio data;
the sound source positioning module is used for carrying out sound source positioning analysis on the audio data and identifying an abnormal sound source;
and the matching module is used for matching the pointing position of the abnormal sound source with the target tire, and the matching of the pointing position of the abnormal sound source with the target tire indicates that the target tire is abnormal.
10. The apparatus of claim 9, wherein the occurrence of the continuous decrease in the tire pressure of the target tire comprises: the first tire pressure data is larger than the second tire pressure data, and the difference value of subtracting the second tire pressure data from the first tire pressure data is not larger than a preset value;
the first tire pressure data is the tire pressure data of the target tire acquired in a first period, the second tire pressure data is the tire pressure data of the target tire acquired in a second period, the first period and the second period are any two adjacent periods in a plurality of continuous periods, and the first period is located before the second period.
11. The apparatus of claim 9 or 10, wherein the tire pressure analysis module is further configured to: analyzing the tire pressure data acquired periodically to identify that the target tire has air leakage;
the device further comprises:
and the tire air leakage alarm module is used for giving an alarm to prompt a user that the target tire is air leakage.
12. The apparatus of claim 11, wherein the occurrence of a leak in the target tire comprises: the third tire pressure data is greater than the fourth tire pressure data, the difference value of the third tire pressure data minus the fourth tire pressure data is greater than a preset value, the third tire pressure data is the tire pressure data of the target tire acquired in a third period, the fourth tire pressure data is the tire pressure data of the target tire acquired in a fourth period, the third period and the fourth period are two adjacent periods, and the third period is located before the fourth period.
13. The apparatus according to any one of claims 9-12, wherein the abnormal sound identification module is specifically configured to:
processing the audio data to obtain sound characteristic parameters corresponding to the audio data;
and comparing the sound characteristic parameters corresponding to the audio data with an audio template library obtained by pre-training, and outputting abnormal sounds existing in the audio data.
14. The apparatus of claim 13, further comprising:
the tire abnormity warning module is used for sending out a tire abnormity warning to prompt a user that the target tire is abnormal;
and the audio template library updating module is used for receiving a feedback result of the user for the tire abnormity alarm and updating the audio template library based on the feedback result and the sound characteristic parameters corresponding to the audio data.
15. The apparatus according to any one of claims 9-14, wherein the number of acoustic sensors is greater than 1, and the sound source localization module is specifically configured to:
acquiring audio energy collected by the sound-sensitive sensor based on the audio data;
and calculating the distance of each sound sensitive sensor relative to the sound source at the unknown position based on the audio energy, wherein the position of the sound sensitive sensor corresponding to the minimum distance relative to the sound source at the unknown position represents the abnormal sound source.
16. The apparatus of any one of claims 9-15, further comprising:
an acoustic sensor activation module to activate the acoustic sensor associated with the target tire.
17. A control unit, characterized in that it comprises programmable instructions for performing the method of any one of claims 1-8 when called.
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| PCT/CN2021/071370 WO2022150999A1 (en) | 2021-01-13 | 2021-01-13 | Tire abnormality detection method and apparatus |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN115052761A true CN115052761A (en) | 2022-09-13 |
| CN115052761B CN115052761B (en) | 2023-11-03 |
Family
ID=82446479
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202180000477.8A Active CN115052761B (en) | 2021-01-13 | 2021-01-13 | Method and device for detecting tire abnormality |
Country Status (2)
| Country | Link |
|---|---|
| CN (1) | CN115052761B (en) |
| WO (1) | WO2022150999A1 (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117589480A (en) * | 2024-01-18 | 2024-02-23 | 山东中亚轮胎试验场有限公司 | Tire abnormal state detecting system |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116051897B (en) * | 2023-01-28 | 2023-06-27 | 合肥中科类脑智能技术有限公司 | Door opening and closing state detection method and device, edge processing equipment and storage medium |
Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102717671A (en) * | 2012-07-10 | 2012-10-10 | 江苏科技大学 | Vehicle tyre burst detection emergency method |
| US20140070935A1 (en) * | 2011-05-20 | 2014-03-13 | Northeastern University | Real-time wireless dynamic tire pressure sensor and energy harvesting system |
| CN104908645A (en) * | 2015-06-08 | 2015-09-16 | 上海斐讯数据通信技术有限公司 | Auxiliary vehicle driving system and working method thereof |
| KR20160066755A (en) * | 2014-12-03 | 2016-06-13 | 현대모비스 주식회사 | Apparatus and Method for detecting error of wheel alignment |
| US20170320363A1 (en) * | 2016-05-06 | 2017-11-09 | Mobiletron Electronics Co., Ltd. | Method for monitoring tire condition |
| CN107458383A (en) * | 2016-06-03 | 2017-12-12 | 法拉第未来公司 | Use the vehicle trouble automatic detection of audio signal |
| CN107499069A (en) * | 2017-09-08 | 2017-12-22 | 江苏金坛绿能新能源科技有限公司 | Vehicle tyre flat tire prewarning device |
| CN109017166A (en) * | 2018-08-16 | 2018-12-18 | 杭州容大智造科技有限公司 | A kind of equipment using sound detection tire pressure |
| CN111890850A (en) * | 2020-09-10 | 2020-11-06 | 李光明 | Tire noise monitoring and alarming system and method for preventing tire burst of automobile |
Family Cites Families (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH09145501A (en) * | 1995-11-20 | 1997-06-06 | Yokohama Rubber Co Ltd:The | Method and apparatus for measuring tire fitting pressure |
| CN101973192B (en) * | 2010-10-03 | 2013-01-30 | 燕山大学 | Method for monitoring tire pressure during corning process of automobile |
| CN203580528U (en) * | 2013-11-29 | 2014-05-07 | 山东永泰化工有限公司 | Tire abnormity monitoring device |
| CN107933212A (en) * | 2017-10-23 | 2018-04-20 | 深圳市招科智控科技有限公司 | Harbour container is horizontal to carry the quick-fried detecting system of unmanned vehicle tire tire and method |
| CN110789278A (en) * | 2018-08-02 | 2020-02-14 | 郑州宇通客车股份有限公司 | Tire pressure monitoring method and device |
-
2021
- 2021-01-13 WO PCT/CN2021/071370 patent/WO2022150999A1/en active Application Filing
- 2021-01-13 CN CN202180000477.8A patent/CN115052761B/en active Active
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20140070935A1 (en) * | 2011-05-20 | 2014-03-13 | Northeastern University | Real-time wireless dynamic tire pressure sensor and energy harvesting system |
| CN102717671A (en) * | 2012-07-10 | 2012-10-10 | 江苏科技大学 | Vehicle tyre burst detection emergency method |
| KR20160066755A (en) * | 2014-12-03 | 2016-06-13 | 현대모비스 주식회사 | Apparatus and Method for detecting error of wheel alignment |
| CN104908645A (en) * | 2015-06-08 | 2015-09-16 | 上海斐讯数据通信技术有限公司 | Auxiliary vehicle driving system and working method thereof |
| US20170320363A1 (en) * | 2016-05-06 | 2017-11-09 | Mobiletron Electronics Co., Ltd. | Method for monitoring tire condition |
| CN107458383A (en) * | 2016-06-03 | 2017-12-12 | 法拉第未来公司 | Use the vehicle trouble automatic detection of audio signal |
| CN107499069A (en) * | 2017-09-08 | 2017-12-22 | 江苏金坛绿能新能源科技有限公司 | Vehicle tyre flat tire prewarning device |
| CN109017166A (en) * | 2018-08-16 | 2018-12-18 | 杭州容大智造科技有限公司 | A kind of equipment using sound detection tire pressure |
| CN111890850A (en) * | 2020-09-10 | 2020-11-06 | 李光明 | Tire noise monitoring and alarming system and method for preventing tire burst of automobile |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117589480A (en) * | 2024-01-18 | 2024-02-23 | 山东中亚轮胎试验场有限公司 | Tire abnormal state detecting system |
Also Published As
| Publication number | Publication date |
|---|---|
| CN115052761B (en) | 2023-11-03 |
| WO2022150999A1 (en) | 2022-07-21 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| JP5837341B2 (en) | Road surface condition determination method and apparatus | |
| KR100870889B1 (en) | Sound signal processing method, sound signal processing apparatus and recording medium | |
| KR102272622B1 (en) | Deep learning based abnormal symptom detection system using mechanical sound of mechanical equipment | |
| CN115052761B (en) | Method and device for detecting tire abnormality | |
| CN109632086A (en) | Interior noise measuring method, apparatus, readable storage medium storing program for executing and computer equipment | |
| US9311930B2 (en) | Audio based system and method for in-vehicle context classification | |
| US9733346B1 (en) | Method for providing sound detection information, apparatus detecting sound around vehicle, and vehicle including the same | |
| CN108630221A (en) | Audio signal quality based on quantization SNR analyses and adaptive wiener filter enhances | |
| Oettle et al. | The effects of unsteady on-road flow conditions on cabin noise | |
| CN112560822A (en) | Road sound signal classification method based on convolutional neural network | |
| EP3712020B1 (en) | System for monitoring an acoustic scene outside a vehicle | |
| Kemalkar et al. | Engine fault diagnosis using sound analysis | |
| Lee et al. | Acoustic hazard detection for pedestrians with obscured hearing | |
| Harlow et al. | Automated accident detection system | |
| CN113345443A (en) | Marine mammal vocalization detection and identification method based on mel-frequency cepstrum coefficient | |
| CN112420074A (en) | Method for diagnosing abnormal sound of motor of automobile rearview mirror | |
| CN116364108A (en) | Transformer voiceprint detection method and device, electronic equipment and storage medium | |
| CN112230208B (en) | Automobile running speed detection method based on smart phone audio perception | |
| CN115346514A (en) | Intelligent driving evaluation-oriented audio early warning intelligent identification method | |
| JP2002538514A (en) | Speech detection method using stochastic reliability in frequency spectrum | |
| CN111968671B (en) | Low-altitude sound target comprehensive identification method and device based on multidimensional feature space | |
| CN110579274A (en) | Vehicle chassis fault sound diagnosis method and system | |
| JP7112909B2 (en) | Tire type discrimination method and tire type discrimination device | |
| CN114093385A (en) | Unmanned aerial vehicle detection method and device | |
| Harlow et al. | Acoustic accident detection system |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |