CN112902946A - Tire condition detection method, tire condition detection device, computer device, and storage medium - Google Patents
Tire condition detection method, tire condition detection device, computer device, and storage medium Download PDFInfo
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Abstract
The application relates to a tire condition detection method, a tire condition detection device, a computer device and a storage medium. The method comprises the following steps: acquiring geomagnetic field data at a tire position of a target vehicle; performing data fitting on the geomagnetic field data, and drawing a tire state curve of the target vehicle; and matching the tire state curve with a reference tire state curve in a database, determining a target tire state curve according to a similarity index between the tire state curve and the reference tire state curve, and taking a motion state corresponding to the target tire state curve as the tire state of the target vehicle. The method can improve the accuracy of tire condition detection.
Description
Technical Field
The present application relates to the field of automotive electronics, and in particular, to a method and an apparatus for detecting a tire condition, a computer device, and a storage medium.
Background
A tire Pressure Monitoring system tpms (tire Pressure Monitoring system) is a system for Monitoring the internal air Pressure of a vehicle tire, which monitors the Pressure information of the tire by a Pressure sensor in the tire, and at the same time, the tire Pressure Monitoring system can also monitor the tire state information, and the methods for Monitoring the tire state include an acceleration detection method and a geomagnetic detection method.
However, in the two methods of monitoring the tire condition information by the conventional tire pressure monitoring system, the acceleration sensor related to the acceleration detection method includes a movable element, which is easily affected by vibration, and thus the tire condition is detected inaccurately, while the geomagnetic sensor related to the geomagnetic detection method is easily affected by external interference, such as interference of metal objects, ac motors, magnetic objects, and the like, and thus the tire condition is detected inaccurately.
Disclosure of Invention
In view of the above, it is necessary to provide a tire condition detection method, apparatus, computer device and storage medium for solving the above technical problems.
A tire condition detection method, the method comprising:
acquiring geomagnetic field data at a tire position of a target vehicle;
performing data fitting on the geomagnetic field data, and drawing a tire state curve of the target vehicle;
and matching the tire state curve with a reference tire state curve in a database, determining a target tire state curve according to a similarity index between the tire state curve and the reference tire state curve, and taking a motion state corresponding to the target tire state curve as the tire state of the target vehicle.
In one embodiment, the method further comprises the following steps:
acquiring initial geomagnetic field data at a tire position of a target vehicle through a geomagnetic sensor;
and carrying out threshold cleaning processing and data standardization processing on the initial geomagnetic field data to obtain processed geomagnetic field data, and using the processed geomagnetic field data as the geomagnetic field data at the tire position of the target vehicle.
In one embodiment, the data fitting of the geomagnetic field data to draw a tire condition curve of the target vehicle includes:
and based on a least square fitting algorithm, drawing a magnetic field intensity curve which changes along with time according to the sequence of corresponding time data of the magnetic field intensity contained in the geomagnetic field data, wherein the magnetic field intensity curve represents a tire state curve of the target vehicle.
In one embodiment, the matching the tire condition curve with a reference tire condition curve in a database, determining a target tire condition curve according to a similarity index between the tire condition curve and the reference tire condition curve, and using a motion state corresponding to the target tire condition curve as the tire condition of the target vehicle includes:
extracting data characteristics contained in the geomagnetic field data corresponding to the tire state curve; the data features comprise at least one of statistical features, time domain features and frequency domain features;
traversing each reference tire state curve in the database, and performing similarity matching according to the data characteristics and the reference data characteristics corresponding to each reference tire state curve to obtain a similarity matching result;
determining a reference tire state curve with the highest similarity to the tire state curve in the database as the target tire state curve in each similarity matching result;
and determining the tire state of the target vehicle according to the motion state label carried by the target tire state curve.
In one embodiment, the generation of the reference tire condition curve includes:
obtaining historical geomagnetic field data samples at vehicle tire locations;
carrying out threshold cleaning processing and data standardization processing on the historical geomagnetic field data sample to obtain a processed historical geomagnetic field data sample;
performing cluster analysis on the processed historical geomagnetic field data, obtaining sample classification results by updating each type of initial centroid, and taking each obtained sample classification result as a motion state sample;
performing feature extraction on each motion state sample to obtain data features corresponding to each motion state sample;
drawing a motion state curve corresponding to each motion state sample according to corresponding data characteristics in each motion state sample and the corresponding relation between the magnetic field intensity and time contained in the geomagnetic field data;
and marking a motion state label on the corresponding motion state curve according to the data characteristics corresponding to each motion state sample to be used as a reference tire state curve, and storing the reference tire state curve into a database.
In one embodiment, the geomagnetic field data is geomagnetic field data collected by a three-axis geomagnetic sensor, and the method further includes:
acquiring geomagnetic field data in three axial directions at a tire position of the target vehicle;
performing data fitting on the geomagnetic field data in each axial direction, and drawing a tire state curve of the target vehicle in each axial direction;
matching the tire state curve in each axial direction with a reference tire state curve in a database, determining a target tire state curve according to a similarity index between the tire state curve and the reference tire state curve, and taking a motion state corresponding to the target tire state curve as a candidate tire state of the target vehicle;
and carrying out weighted average processing on the candidate tire state corresponding to each axial direction to obtain the final tire state of the target vehicle.
A tire condition detecting device, the device comprising:
acquiring geomagnetic field data at a tire position of a target vehicle;
performing data fitting on the geomagnetic field data, and drawing a tire state curve of the target vehicle;
and matching the tire state curve with a reference tire state curve in a database, determining a target tire state curve according to a similarity index between the tire state curve and the reference tire state curve, and taking a motion state corresponding to the target tire state curve as the tire state of the target vehicle.
A computer device comprising a memory and a processor, the memory storing a computer program, the processor implementing the following steps when executing the computer program:
acquiring geomagnetic field data at a tire position of a target vehicle;
performing data fitting on the geomagnetic field data, and drawing a tire state curve of the target vehicle;
and matching the tire state curve with a reference tire state curve in a database, determining a target tire state curve according to a similarity index between the tire state curve and the reference tire state curve, and taking a motion state corresponding to the target tire state curve as the tire state of the target vehicle.
A computer-readable storage medium, on which a computer program is stored which, when executed by a processor, carries out the steps of:
acquiring geomagnetic field data at a tire position of a target vehicle;
performing data fitting on the geomagnetic field data, and drawing a tire state curve of the target vehicle;
and matching the tire state curve with a reference tire state curve in a database, determining a target tire state curve according to a similarity index between the tire state curve and the reference tire state curve, and taking a motion state corresponding to the target tire state curve as the tire state of the target vehicle.
The tire state detection method, the tire state detection device, the computer equipment and the storage medium are used for acquiring geomagnetic field data at the tire position of the target vehicle; performing data fitting on the geomagnetic field data, and drawing a tire state curve of the target vehicle; and matching the tire state curve with a reference tire state curve in a database, determining a target tire state curve according to a similarity index between the tire state curve and the reference tire state curve, and taking a motion state corresponding to the target tire state curve as the tire state of the target vehicle. By adopting the method, the tire state of the target vehicle is determined by utilizing the characteristic matching among the curves by reflecting the motion state of the tire of the target vehicle as the corresponding tire state curve, so that the problem that the tire detection is inaccurate because the single geomagnetic field data is easily interfered is avoided.
Drawings
FIG. 1 is a diagram illustrating an exemplary environment in which a tire condition detection method may be used;
FIG. 2 is a schematic flow chart of a tire condition detection method according to one embodiment;
FIG. 3 is a schematic flow chart of the step of acquiring geomagnetic field data in one embodiment;
FIG. 4 is a schematic flow chart diagram illustrating the tire condition curve feature matching step in one embodiment;
FIG. 5 is a schematic view of a tire condition reference curve in a tire static state according to one embodiment;
FIG. 6 is a schematic view of a tire condition reference curve in a tire static state according to one embodiment;
FIG. 7 is a schematic view showing a reference curve of a tire condition under a rolling condition of the tire in one embodiment;
FIG. 8 is a schematic view showing a reference curve of a tire condition under a rolling condition of the tire in one embodiment;
FIG. 9 is a schematic flow chart diagram illustrating a process for generating a reference tire condition curve according to one embodiment;
FIG. 10 is a flowchart illustrating a method for tire condition detection by data acquisition using a triaxial geomagnetic sensor according to an embodiment;
FIG. 11 is a block diagram showing the construction of a tire condition detecting apparatus according to an embodiment;
FIG. 12 is a diagram illustrating an internal structure of a computer device according to an embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.
The tire condition detection method provided by the application can be applied to the application environment shown in fig. 1. The in-vehicle terminal 102 communicates with the geomagnetic sensor 104 through a network or communicates through an electronic circuit. The vehicle-mounted terminal 102 acquires geomagnetic field data at a tire position of a target vehicle; performing data fitting on the geomagnetic field data, and drawing a tire state curve of the target vehicle; and matching the tire state curve with a reference tire state curve in a database, determining a target tire state curve according to a similarity index between the tire state curve and the reference tire state curve, and taking a motion state corresponding to the target tire state curve as the tire state of the target vehicle. The terminal 102 may be, but is not limited to, various computer devices that may be used for data processing.
In one embodiment, as shown in fig. 2, a tire condition detection method is provided, which is exemplified by the application of the method to the terminal in fig. 1, and includes the following steps:
The TPMS system of the target vehicle comprises data acquisition equipment such as a pressure sensor and a geomagnetic sensor, wherein the geomagnetic sensor can be mounted on a tire of the target vehicle and is used for acquiring geomagnetic field data at a certain position of the tire, and the geomagnetic field data is a geomagnetic field intensity data sequence corresponding to a time sequence.
In implementation, a processor of the in-vehicle terminal acquires geomagnetic field data at a tire position of a target vehicle acquired by a geomagnetic sensor.
Optionally, the geomagnetic sensor may be a single-axis, two-axis, or three-axis type magnetic field sensor, and for different types of geomagnetic sensors, the geomagnetic field data in each axial direction included in the geomagnetic sensor may be sent to the processor of the vehicle-mounted terminal for processing, and therefore, this embodiment is not limited to the geomagnetic sensor type.
And step 202, performing data fitting on the geomagnetic field data, and drawing a tire state curve of the target vehicle.
In implementation, the vehicle-mounted terminal performs data fitting on the geomagnetic field data to draw a tire state curve of the target vehicle.
Specifically, based on the relationship of the variation of the geomagnetic field intensity with time in the collected geomagnetic field data of a certain fixed position point of a wheel, a magnetic field intensity variation curve of the point of the target vehicle is drawn, and the curve reflects the tire state curve of the target vehicle.
And step 203, matching the tire state curve with a reference tire state curve in a database, determining a target tire state curve according to the similarity index between the tire state curve and the reference tire state curve, and taking the motion state corresponding to the target tire state curve as the tire state of the target vehicle.
In implementation, the vehicle-mounted terminal performs feature matching on a tire state curve drawn by the collected real-time geomagnetic field data and reference tire state curves stored in a database to obtain a matching result of the tire state curve and each reference tire state curve based on the similarity index, determines a target tire state curve in the reference tire state curves according to the matching result, and takes a motion state corresponding to the determined target tire state curve as the tire state of the target vehicle at the moment.
In the tire condition detection method, geomagnetic field data at a tire position of a target vehicle is acquired; performing data fitting on the geomagnetic field data, and drawing a tire state curve of the target vehicle; and matching the tire state curve with a reference tire state curve in a database, determining a target tire state curve according to a similarity index between the tire state curve and the reference tire state curve, and taking a motion state corresponding to the target tire state curve as the tire state of the target vehicle. By adopting the method, the tire state of the target vehicle is determined by utilizing the characteristic matching among the curves by reflecting the motion state of the tire of the target vehicle as the corresponding tire state curve, so that the problem that the tire detection is inaccurate because the single geomagnetic field data is easily interfered is avoided.
In one embodiment, as shown in FIG. 3, step 201 comprises:
in step 301, initial geomagnetic field data at a tire position of a target vehicle is acquired by a geomagnetic sensor.
In implementation, the geomagnetic sensor acquires initial geomagnetic field data at a certain fixed position of a tire of the target vehicle. Wherein the initial geomagnetic field data comprises a magnetic field strength at the location and a corresponding acquisition time.
Optionally, the precision of the geomagnetic sensor collecting the geomagnetic field data may be 3mGs, which is not limited in the embodiment of the present application.
And step 302, performing threshold cleaning processing and data standardization processing on the initial geomagnetic field data to obtain processed geomagnetic field data, and using the processed geomagnetic field data as the geomagnetic field data at the tire position of the target vehicle.
In implementation, the vehicle-mounted terminal performs data preprocessing on initial geomagnetic field data acquired by the geomagnetic sensor, wherein the data preprocessing comprises threshold cleaning processing and data standardization processing,
specifically, the threshold cleaning process is as follows: determining a value range of the geomagnetic field data according to a preset data threshold (for example, the geomagnetic field intensity is generally 0.5-0.6 Gs, so that the geomagnetic field data threshold can be selected to be 2Gs), further eliminating data which exceed the value range of the data (for example, 0-2 Gs) as abnormal data, and then supplementing the vacancy of the eliminated data by the median of the front data and the rear data of the vacancy.
The formula for the data normalization calculation is:
wherein the content of the first and second substances,
the earth magnetic field data which are not processed in a standardized way;
for normalized geomagnetic field data, minx(j)For the minimum value in the magnetic field data sequence in the current time period j (i.e. for the minimum value in the magnetic field data sequence in the current time period j)
)，maxx(j)Is the maximum value in the magnetic field data sequence in the current time period j (i.e. the maximum value in the magnetic field data sequence
)；
Is a normalized geomagnetic field data sequence; after normalization, the samples
All attribute values of (2) are [0,1 ]]In the meantime.
And the vehicle-mounted terminal preprocesses the initial geomagnetic field data to obtain the geomagnetic field data which can be used for detecting the tire state.
Alternatively, the method of data normalization is not limited to using the min-max normalization method (i.e., equation (1) above), but may also be used with a z-score normalization method, which scales the data to fall within a particular interval (typically the (0,1) interval). I.e. the mean value mu is 0,
the data may also be subjected to regularization, and the like, and therefore, the data preprocessing method is not limited in this embodiment.
In this embodiment, through preprocessing the geomagnetic field data, the geomagnetic field data abnormal due to interference is screened out, the accuracy of the drawn tire state curve is improved, and the current tire state of the target vehicle is accurately determined.
In one embodiment, step 202, i.e. the process of plotting the tire condition curve of the target vehicle specifically includes: based on a least square fitting algorithm, a magnetic field intensity curve which changes along with time is drawn according to the sequence of corresponding time data of the magnetic field intensity contained in the geomagnetic field data, and the magnetic field intensity curve represents a tire state curve of a target vehicle.
In implementation, the vehicle-mounted terminal performs point tracing on the acquired geomagnetic field data of the target vehicle according to the corresponding time sequence (namely, the time sequence is used as an abscissa) and the magnitude of the magnetic field intensity, and then performs data fitting on the magnetic field intensity data corresponding to the time sequence through a least square fitting algorithm to obtain a fitted magnetic field intensity curve changing along with time, wherein the fitted magnetic field intensity curve is a tire state curve representing the target vehicle.
In one embodiment, as shown in FIG. 4, the specific process of step 203 is as follows;
In implementation, the vehicle-mounted terminal extracts data characteristics contained in geomagnetic field data corresponding to a tire state curve, wherein the data characteristics include: at least one of a statistical feature, a time domain feature, and a frequency domain feature.
Specifically, the statistical features include: median, variance, mean, coefficient of variation, etc. of the geomagnetic field data; the time domain characteristics comprise autocorrelation coefficients, information entropy, peak factors, pulse factors, margin factors, kurtosis factors, waveform factors and skewness; the frequency domain features include: wavelet energy spectrum, etc., therefore, the present embodiment does not limit the types of data features that can be extracted.
In implementation, the vehicle-mounted terminal traverses each reference tire state curve in the database, and performs similarity matching according to the data characteristics and the reference data characteristics corresponding to each reference tire state curve to obtain a similarity matching result.
Specifically, the reference tire state curve in the database includes a plurality of tire state types, for example, the reference tire state curve may include a tire state curve of the tire in a stationary state (as shown in fig. 5 or fig. 6), a tire state curve of the tire in a rolling state (as shown in fig. 7 or fig. 8), and the like, and meanwhile, the tire state curve of the tire in the rolling state may be further classified into a tire rolling state curve when the vehicle travels straight, a tire rolling state curve when the vehicle turns, a tire rolling state curve when the vehicle meets, and the like, wherein the subdivided types of the tire rolling states are not limited in this embodiment.
Fig. 5 is a reference tire state curve of a tire stationary state in a conventional case, and fig. 6 is also a reference tire state curve corresponding to the tire stationary state, but fig. 6 shows a case that can correspond to a case where the geomagnetic sensor is disturbed, and since the disturbance source is difficult to determine, the other curve forms are defined as the reference tire state curve in the stationary state except for the reference tire state curve corresponding to the tire rolling state.
And then, when the vehicle-mounted terminal traverses each reference tire state curve in the database, simultaneously obtaining data characteristics (at least one type of statistical characteristics, time domain characteristics and frequency domain characteristics) corresponding to each reference tire state curve, and carrying out similarity calculation on the data characteristics corresponding to the tire state curve of the current wheel obtained by implementing data acquisition and the data characteristics of the reference tire state curve in the database to obtain a similarity matching result of the data characteristics of each reference tire state curve and the data characteristics corresponding to the current tire state curve.
In step 403, in each similarity matching result, the reference tire state curve with the highest similarity to the tire state curve in the database is determined as the target tire state curve.
In implementation, the vehicle-mounted terminal determines the reference tire state curve with the highest data characteristic similarity with the current tire state curve in the database as the target tire state curve in each similarity matching result.
And step 404, determining the tire state of the target vehicle according to the motion state label carried by the target tire state curve.
In implementation, since the reference tire state curve defines the corresponding tire motion state label in the generation process to be used for representing the tire state of the reference curve, the vehicle-mounted terminal determines the tire state of the target vehicle at that time according to the motion state label carried by the target tire state curve (i.e. the determined reference tire state curve).
In the embodiment, the tire state curve is matched with the data characteristics of the reference tire state curves in the database, the reference tire state curve with the highest similarity to the tire state curve is screened and determined to serve as the target tire state curve, and then the current tire state of the target vehicle is judged according to the target tire state curve, so that the condition that single magnetic field data is easily interfered is reduced, and the tire state detection accuracy is improved.
In one embodiment, as shown in fig. 9, the generation process of the reference tire condition curve includes:
In implementation, historical geomagnetic field data samples at vehicle tire locations are acquired.
Specifically, the historical geomagnetic field data sample may include geomagnetic field data in different axial directions of different vehicles, different tires, and even different geomagnetic sensors, and the embodiment is not limited thereto. Optionally, the data amount of the historical geomagnetic field data sample may be set according to requirements such as required accuracy of wheel state detection, and the embodiment is not limited.
And step 902, performing threshold cleaning processing and data standardization processing on the historical geomagnetic field data sample to obtain a processed historical geomagnetic field data sample.
In implementation, threshold cleaning processing and data standardization processing are also required to be carried out on the historical geomagnetic field data samples to obtain the processed historical geomagnetic field data samples, so that the standard property of the reference tire state curve is ensured. Specifically, the threshold cleaning process and the data normalization process are the same as the process of step 302, and are not described again in this embodiment.
And 903, performing cluster analysis on the processed historical geomagnetic field data, obtaining a sample classification result by updating each type of initial centroid, and taking each obtained sample classification result as a motion state sample.
In the implementation, the processed historical geomagnetic field data is subjected to cluster analysis (for example, a Kmeans clustering method), and the clustering analysis is performed by continuously updating the centroid values according to the specified initial centroids of a plurality of types until the clustering result is not changed any more, so that the clustering result corresponding to each type, that is, the sample classification result is obtained, and each sample classification result is used as a wheel motion state type, that is, a motion state sample corresponding to the wheel motion state type is obtained.
And 904, performing feature extraction on each motion state sample to obtain data features corresponding to each motion state sample.
In implementation, each motion state sample is subjected to feature extraction, so as to obtain a data feature corresponding to each motion state sample, where the data feature may include at least one of a statistical feature, a time domain feature, and a frequency domain feature.
In implementation, a motion state curve corresponding to the motion state sample is drawn according to the corresponding data characteristics in each motion state sample and the corresponding relationship between the magnetic field strength and the time included in the geomagnetic field data, and the process of fitting and drawing the motion state curve is the same as the process in step 202, which is not described in detail in this embodiment.
In implementation, according to the data characteristics corresponding to each motion state sample, the motion state curve obtained from the motion state sample is marked with a corresponding motion state label, the motion state label is used as a reference tire state curve, and the reference tire state curve carrying the data characteristics and the motion state label is stored in a database for calling the reference tire state curve in a later application process.
In one embodiment, as shown in fig. 10, there is provided an example of a tire condition detection method in which a collecting device is a triaxial geomagnetic sensor, geomagnetic field data being geomagnetic field data collected by the triaxial geomagnetic sensor, the method including:
As the selected geomagnetic sensor is a three-axis geomagnetic sensor, geomagnetic field data in three axial directions of XYZ based on the three-axis geomagnetic sensor can be correspondingly obtained,
in implementation, the in-vehicle terminal processor acquires geomagnetic field data in three axial directions at a tire position of a target vehicle, which is acquired by the triaxial geomagnetic sensor.
In implementation, for each axial direction of the geomagnetic field data, the time-series-based magnetic field intensity data can be fitted to obtain a time-varying magnetic field intensity curve in each axial direction, so as to characterize the tire condition curve of the target vehicle in each axial direction. That is, the method of step 201 and step 202 can be applied to each axial geomagnetic field data to draw the tire condition curve of the target vehicle.
In implementation, the onboard processor matches the tire state curve in each axial direction with a reference tire state curve in the database, determines a target tire state curve according to a similarity index between the tire state curve and the reference tire state curve, and takes a motion state corresponding to the target tire state curve as a candidate tire state of the target vehicle.
Specifically, the process of matching the tire state curve in each axial direction with the reference tire state curve pre-stored in the database is the same as the matching process in step 203 or step 402, and is not repeated in this embodiment. According to the obtained similarity matching result, the tire state of the target vehicle determined by the geomagnetic field data in each axial direction can be obtained, and the tire state can be used as a candidate tire state of the target vehicle.
Optionally, in a general case, the determined tire conditions candidate are the same for the geomagnetic field data in each axial direction of the geomagnetic sensor corresponding to the same target vehicle, that is, the tire condition corresponding to the target vehicle at that time may be independently obtained for a single axial data.
And 1004, performing weighted average processing on the candidate tire states corresponding to each axial direction to obtain the final tire state of the target vehicle.
In implementation, the vehicle-mounted terminal performs weighted average processing on the candidate tire states corresponding to each axial direction to obtain the final tire state of the target vehicle.
Specifically, when the candidate wheel states determined in the respective axial directions of the geomagnetic sensors are not identical, the candidate wheel states may be subjected to numerical processing, for example, the tire stationary state is assigned to 0, the tire rolling state is assigned to 1, then, the tire state assignment results in the respective axial directions are subjected to weighted average processing to obtain weighted average results, then, the distance between the weighted average results and the difference between 0 and 1 (tire state type value) is determined according to the weighted average results, and the tire state corresponding to the data end having a shorter distance is determined as the final tire state of the target vehicle at that time.
In this embodiment, the geomagnetic field data at the tire position of the target vehicle is collected by the triaxial geomagnetic sensor, the installation position of the geomagnetic sensor is not limited, and the geomagnetic field data can be collected, and meanwhile, the tire state detection is performed on the geomagnetic field data in each axial direction, so that the tire state detection result in each axial direction is comprehensively judged, the final tire state result is determined, and the accuracy of the tire state detection result is improved.
It should be understood that although the various steps in the flow charts of fig. 2-4,9-10 are shown in order as indicated by the arrows, the steps are not necessarily performed in order as indicated by the arrows. The steps are not performed in the exact order shown and described, and may be performed in other orders, unless explicitly stated otherwise. Moreover, at least some of the steps in fig. 2-4,9-10 may include multiple steps or multiple stages, which are not necessarily performed at the same time, but may be performed at different times, which are not necessarily performed in sequence, but may be performed alternately or at least partially with other steps or other steps.
In one embodiment, as shown in fig. 11, there is provided a tire-state detecting device 1100 including: an acquisition module 1110, a rendering module 1120, and a determination module 1130, wherein:
an acquisition module 1110 for acquiring geomagnetic field data at a tire position of a target vehicle;
the drawing module 1120 is used for performing data fitting on the geomagnetic field data and drawing a tire state curve of the target vehicle;
the determining module 1130 is configured to match the tire state curve with a reference tire state curve in the database, determine a target tire state curve according to a similarity index between the tire state curve and the reference tire state curve, and use a motion state corresponding to the target tire state curve as a tire state of the target vehicle.
In one embodiment, the obtaining module 1110 is specifically configured to acquire initial geomagnetic field data at a tire position of the target vehicle through a geomagnetic sensor;
and performing threshold cleaning processing and data standardization processing on the initial geomagnetic field data to obtain processed geomagnetic field data, and using the processed geomagnetic field data as the geomagnetic field data at the tire position of the target vehicle.
In one embodiment, the drawing module 1120 is specifically configured to draw a magnetic field strength curve varying with time according to a sequence of corresponding time data of a magnetic field strength included in the geomagnetic data based on a least square fitting algorithm, where the magnetic field strength curve represents a tire state curve of a target vehicle.
In one embodiment, the determination module 1130 is specifically configured to extract data features included in the geomagnetic field data corresponding to the tire condition curve; the data features comprise at least one of statistical features, time domain features and frequency domain features;
traversing each reference tire state curve in the database, and performing similarity matching according to the data characteristics and the reference data characteristics corresponding to each reference tire state curve to obtain a similarity matching result;
in each similarity matching result, determining a reference tire state curve with the highest similarity with the tire state curve in the database as a target tire state curve;
and determining the tire state of the target vehicle according to the motion state label carried by the target tire state curve.
In one embodiment, the tire-state detecting device 1100 further includes:
the acquisition module is used for acquiring historical geomagnetic field data samples at positions of vehicle tires;
the preprocessing module is used for carrying out threshold value cleaning processing and data standardization processing on the historical geomagnetic field data sample to obtain a processed historical geomagnetic field data sample;
the classification module is used for carrying out cluster analysis on the processed historical geomagnetic field data, obtaining a sample classification result by updating each type of initial centroid, and taking each obtained sample classification result as a motion state sample;
the characteristic extraction module is used for extracting the characteristics of each motion state sample to obtain the data characteristics corresponding to each motion state sample;
the drawing module is used for drawing a motion state curve corresponding to the motion state sample according to the corresponding data characteristics in each motion state sample and the corresponding relation between the magnetic field intensity and the time contained in the geomagnetic field data;
and the storage module is used for marking the corresponding motion state curve with a motion state label according to the data characteristics corresponding to each motion state sample, using the motion state label as a reference tire state curve, and storing the reference tire state curve into the database.
For specific limitations of the tire condition detection device 1100, reference may be made to the above limitations of the tire condition detection method, which are not described in detail herein. The respective modules in the tire-state detecting device 1100 described above may be entirely or partially implemented by software, hardware, and a combination thereof. The modules can be embedded in a hardware form or independent from a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.
In one embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as shown in fig. 12. The computer device includes a processor, a memory, a communication interface, a display screen, and an input device connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The communication interface of the computer device is used for carrying out wired or wireless communication with an external terminal, and the wireless communication can be realized through WIFI, an operator network, NFC (near field communication) or other technologies. The computer program is executed by a processor to implement a tire condition detection method. The display screen of the computer equipment can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer equipment can be a touch layer covered on the display screen, a key, a track ball or a touch pad arranged on the shell of the computer equipment, an external keyboard, a touch pad or a mouse and the like.
Those skilled in the art will appreciate that the architecture shown in fig. 12 is merely a block diagram of some of the structures associated with the disclosed aspects and is not intended to limit the computing devices to which the disclosed aspects apply, as particular computing devices may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.
In one embodiment, a computer device is provided, comprising a memory and a processor, the memory having a computer program stored therein, the processor implementing the following steps when executing the computer program:
acquiring geomagnetic field data at a tire position of a target vehicle;
performing data fitting on the geomagnetic field data, and drawing a tire state curve of the target vehicle;
and matching the tire state curve with a reference tire state curve in a database, determining a target tire state curve according to a similarity index between the tire state curve and the reference tire state curve, and taking a motion state corresponding to the target tire state curve as the tire state of the target vehicle.
In one embodiment, the processor, when executing the computer program, further performs the steps of:
acquiring initial geomagnetic field data at a tire position of a target vehicle through a geomagnetic sensor;
and carrying out threshold cleaning processing and data standardization processing on the initial geomagnetic field data to obtain processed geomagnetic field data, and using the processed geomagnetic field data as the geomagnetic field data at the tire position of the target vehicle.
In one embodiment, the processor, when executing the computer program, further performs the steps of:
and based on a least square fitting algorithm, drawing a magnetic field intensity curve which changes along with time according to the sequence of corresponding time data of the magnetic field intensity contained in the geomagnetic field data, wherein the magnetic field intensity curve represents a tire state curve of the target vehicle.
In one embodiment, the processor, when executing the computer program, further performs the steps of:
extracting data characteristics contained in the geomagnetic field data corresponding to the tire state curve; the data features comprise at least one of statistical features, time domain features and frequency domain features;
traversing each reference tire state curve in the database, and performing similarity matching according to the data characteristics and the reference data characteristics corresponding to each reference tire state curve to obtain a similarity matching result;
determining a reference tire state curve with the highest similarity to the tire state curve in the database as the target tire state curve in each similarity matching result;
and determining the tire state of the target vehicle according to the motion state label carried by the target tire state curve.
In one embodiment, the processor, when executing the computer program, further performs the steps of:
obtaining historical geomagnetic field data samples at vehicle tire locations;
carrying out threshold cleaning processing and data standardization processing on the historical geomagnetic field data sample to obtain a processed historical geomagnetic field data sample;
performing cluster analysis on the processed historical geomagnetic field data, obtaining sample classification results by updating each type of initial centroid, and taking each obtained sample classification result as a motion state sample;
performing feature extraction on each motion state sample to obtain data features corresponding to each motion state sample;
drawing a motion state curve corresponding to each motion state sample according to corresponding data characteristics in each motion state sample and the corresponding relation between the magnetic field intensity and time contained in the geomagnetic field data;
and marking a motion state label on the corresponding motion state curve according to the data characteristics corresponding to each motion state sample to be used as a reference tire state curve, and storing the reference tire state curve into a database.
In one embodiment, the processor, when executing the computer program, further performs the steps of:
acquiring geomagnetic field data in three axial directions at a tire position of the target vehicle;
performing data fitting on the geomagnetic field data in each axial direction, and drawing a tire state curve of the target vehicle in each axial direction;
matching the tire state curve in each axial direction with a reference tire state curve in a database, determining a target tire state curve according to a similarity index between the tire state curve and the reference tire state curve, and taking a motion state corresponding to the target tire state curve as a candidate tire state of the target vehicle;
and carrying out weighted average processing on the candidate tire state corresponding to each axial direction to obtain the final tire state of the target vehicle.
In one embodiment, a computer-readable storage medium is provided, having a computer program stored thereon, which when executed by a processor, performs the steps of:
acquiring geomagnetic field data at a tire position of a target vehicle;
performing data fitting on the geomagnetic field data, and drawing a tire state curve of the target vehicle;
and matching the tire state curve with a reference tire state curve in a database, determining a target tire state curve according to a similarity index between the tire state curve and the reference tire state curve, and taking a motion state corresponding to the target tire state curve as the tire state of the target vehicle.
In one embodiment, the computer program when executed by the processor further performs the steps of:
acquiring initial geomagnetic field data at a tire position of a target vehicle through a geomagnetic sensor;
and carrying out threshold cleaning processing and data standardization processing on the initial geomagnetic field data to obtain processed geomagnetic field data, and using the processed geomagnetic field data as the geomagnetic field data at the tire position of the target vehicle.
In one embodiment, the computer program when executed by the processor further performs the steps of:
and based on a least square fitting algorithm, drawing a magnetic field intensity curve which changes along with time according to the sequence of corresponding time data of the magnetic field intensity contained in the geomagnetic field data, wherein the magnetic field intensity curve represents a tire state curve of the target vehicle.
In one embodiment, the computer program when executed by the processor further performs the steps of:
extracting data characteristics contained in the geomagnetic field data corresponding to the tire state curve; the data features comprise at least one of statistical features, time domain features and frequency domain features;
traversing each reference tire state curve in the database, and performing similarity matching according to the data characteristics and the reference data characteristics corresponding to each reference tire state curve to obtain a similarity matching result;
determining a reference tire state curve with the highest similarity to the tire state curve in the database as the target tire state curve in each similarity matching result;
and determining the tire state of the target vehicle according to the motion state label carried by the target tire state curve.
In one embodiment, the computer program when executed by the processor further performs the steps of:
obtaining historical geomagnetic field data samples at vehicle tire locations;
carrying out threshold cleaning processing and data standardization processing on the historical geomagnetic field data sample to obtain a processed historical geomagnetic field data sample;
performing cluster analysis on the processed historical geomagnetic field data, obtaining sample classification results by updating each type of initial centroid, and taking each obtained sample classification result as a motion state sample;
performing feature extraction on each motion state sample to obtain data features corresponding to each motion state sample;
drawing a motion state curve corresponding to each motion state sample according to corresponding data characteristics in each motion state sample and the corresponding relation between the magnetic field intensity and time contained in the geomagnetic field data;
and marking a motion state label on the corresponding motion state curve according to the data characteristics corresponding to each motion state sample to be used as a reference tire state curve, and storing the reference tire state curve into a database.
In one embodiment, the computer program when executed by the processor further performs the steps of:
acquiring geomagnetic field data in three axial directions at a tire position of the target vehicle;
performing data fitting on the geomagnetic field data in each axial direction, and drawing a tire state curve of the target vehicle in each axial direction;
matching the tire state curve in each axial direction with a reference tire state curve in a database, determining a target tire state curve according to a similarity index between the tire state curve and the reference tire state curve, and taking a motion state corresponding to the target tire state curve as a candidate tire state of the target vehicle;
and carrying out weighted average processing on the candidate tire state corresponding to each axial direction to obtain the final tire state of the target vehicle.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, storage, database or other medium used in the embodiments provided herein can include at least one of non-volatile and volatile memory. Non-volatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical storage, or the like. Volatile Memory can include Random Access Memory (RAM) or external cache Memory. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM), among others.
The technical features of the above embodiments can be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the above embodiments are not described, but should be considered as the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present patent shall be subject to the appended claims.
Claims (10)
1. A tire condition detection method, the method comprising:
acquiring geomagnetic field data at a tire position of a target vehicle;
performing data fitting on the geomagnetic field data, and drawing a tire state curve of the target vehicle;
and matching the tire state curve with a reference tire state curve in a database, determining a target tire state curve according to a similarity index between the tire state curve and the reference tire state curve, and taking a motion state corresponding to the target tire state curve as the tire state of the target vehicle.
2. The method of claim 1, wherein the acquiring geomagnetic field data at a tire location of a target vehicle comprises:
acquiring initial geomagnetic field data at a tire position of a target vehicle through a geomagnetic sensor;
and carrying out threshold cleaning processing and data standardization processing on the initial geomagnetic field data to obtain processed geomagnetic field data, and using the processed geomagnetic field data as the geomagnetic field data at the tire position of the target vehicle.
3. The method of claim 1, wherein said data fitting the geomagnetic field data to draw a tire condition curve for a target vehicle comprises:
and based on a least square fitting algorithm, drawing a magnetic field intensity curve which changes along with time according to the sequence of corresponding time data of the magnetic field intensity contained in the geomagnetic field data, wherein the magnetic field intensity curve represents a tire state curve of the target vehicle.
4. The method according to claim 1, wherein the matching the tire condition curve with a reference tire condition curve in a database, determining a target tire condition curve according to a similarity index between the tire condition curve and the reference tire condition curve, and regarding a motion state corresponding to the target tire condition curve as the tire condition of the target vehicle comprises:
extracting data characteristics contained in the geomagnetic field data corresponding to the tire state curve; the data features comprise at least one of statistical features, time domain features and frequency domain features;
traversing each reference tire state curve in the database, and performing similarity matching according to the data characteristics and the reference data characteristics corresponding to each reference tire state curve to obtain a similarity matching result;
determining a reference tire state curve with the highest similarity to the tire state curve in the database as the target tire state curve in each similarity matching result;
and determining the tire state of the target vehicle according to the motion state label carried by the target tire state curve.
5. The method of claim 1, wherein the generating of the reference tire condition curve comprises:
obtaining historical geomagnetic field data samples at vehicle tire locations;
carrying out threshold cleaning processing and data standardization processing on the historical geomagnetic field data sample to obtain a processed historical geomagnetic field data sample;
performing cluster analysis on the processed historical geomagnetic field data, obtaining sample classification results by updating each type of initial centroid, and taking each obtained sample classification result as a motion state sample;
performing feature extraction on each motion state sample to obtain data features corresponding to each motion state sample;
drawing a motion state curve corresponding to each motion state sample according to corresponding data characteristics in each motion state sample and the corresponding relation between the magnetic field intensity and time contained in the geomagnetic field data;
and marking a motion state label on the corresponding motion state curve according to the data characteristics corresponding to each motion state sample to be used as a reference tire state curve, and storing the reference tire state curve into a database.
6. The method of claim 1, wherein the geomagnetic field data is geomagnetic field data acquired by a three-axis geomagnetic sensor, the method further comprising:
acquiring geomagnetic field data in three axial directions at a tire position of the target vehicle;
performing data fitting on the geomagnetic field data in each axial direction, and drawing a tire state curve of the target vehicle in each axial direction;
matching the tire state curve in each axial direction with a reference tire state curve in a database, determining a target tire state curve according to a similarity index between the tire state curve and the reference tire state curve, and taking a motion state corresponding to the target tire state curve as a candidate tire state of the target vehicle;
and carrying out weighted average processing on the candidate tire state corresponding to each axial direction to obtain the final tire state of the target vehicle.
7. A tire condition detecting device, characterized in that the device comprises:
an acquisition module for acquiring geomagnetic field data at a tire position of a target vehicle;
the drawing module is used for performing data fitting on the geomagnetic field data and drawing a tire state curve of the target vehicle;
and the determining module is used for matching the tire state curve with a reference tire state curve in a database, determining a target tire state curve according to a similarity index between the tire state curve and the reference tire state curve, and taking a motion state corresponding to the target tire state curve as the tire state of the target vehicle.
8. The apparatus according to claim 7, wherein the determining module is specifically configured to extract data features contained in the geomagnetic field data corresponding to the tire condition curve; the data features comprise at least one of statistical features, time domain features and frequency domain features;
traversing each reference tire state curve in the database, and performing similarity matching according to the data characteristics and the reference data characteristics corresponding to each reference tire state curve to obtain a similarity matching result;
determining a reference tire state curve with the highest similarity to the tire state curve in the database as the target tire state curve in each similarity matching result;
and determining the tire state of the target vehicle according to the motion state label carried by the target tire state curve.
9. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor, when executing the computer program, implements the steps of the method of any of claims 1 to 6.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method of any one of claims 1 to 6.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113391244A (en) * | 2021-06-13 | 2021-09-14 | 河海大学 | VMD-based transformer switching-on vibration signal characteristic frequency calculation method |
Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20020166373A1 (en) * | 1999-11-18 | 2002-11-14 | Federico Mancosu | Method and device for monitoring the instantaneous behaviour of a tyre during the running of a motor vehicle |
| DE102009044981A1 (en) * | 2009-09-24 | 2011-03-31 | Robert Bosch Gmbh | Method for determining driving route of vehicle, has comparing set of acceleration profiles stored in navigation device with actual acceleration profile recorded by navigation device for automatic detection of driver-vehicle-combination |
| CN102855757A (en) * | 2012-03-05 | 2013-01-02 | 浙江大学 | Identification method based on queuing detector information bottleneck state |
| CN107727110A (en) * | 2017-09-11 | 2018-02-23 | 上海斐讯数据通信技术有限公司 | The statistical method and device of a kind of step number |
| US20180292471A1 (en) * | 2017-04-06 | 2018-10-11 | Intel Corporation | Detecting a mechanical device using a magnetometer and an accelerometer |
| US20190118822A1 (en) * | 2017-10-25 | 2019-04-25 | Robert Bosch Gmbh | Method and device for determining a state of a roadway of a vehicle |
| CN109900295A (en) * | 2017-12-11 | 2019-06-18 | 上海交通大学 | The detection method and system of state of motion of vehicle based on autonomic sensor |
| CN110415377A (en) * | 2019-06-24 | 2019-11-05 | 天津五八到家科技有限公司 | Driving status determines method, apparatus and electronic equipment |
| CN110891840A (en) * | 2017-07-27 | 2020-03-17 | 大陆汽车有限公司 | Method and device for monitoring the behaviour of a tyre of a vehicle |
| CN111402329A (en) * | 2020-03-24 | 2020-07-10 | 上海眼控科技股份有限公司 | Vehicle line pressing detection method and device, computer equipment and storage medium |
-
2021
- 2021-01-14 CN CN202110047287.3A patent/CN112902946B/en active Active
Patent Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20020166373A1 (en) * | 1999-11-18 | 2002-11-14 | Federico Mancosu | Method and device for monitoring the instantaneous behaviour of a tyre during the running of a motor vehicle |
| DE102009044981A1 (en) * | 2009-09-24 | 2011-03-31 | Robert Bosch Gmbh | Method for determining driving route of vehicle, has comparing set of acceleration profiles stored in navigation device with actual acceleration profile recorded by navigation device for automatic detection of driver-vehicle-combination |
| CN102855757A (en) * | 2012-03-05 | 2013-01-02 | 浙江大学 | Identification method based on queuing detector information bottleneck state |
| US20180292471A1 (en) * | 2017-04-06 | 2018-10-11 | Intel Corporation | Detecting a mechanical device using a magnetometer and an accelerometer |
| CN110891840A (en) * | 2017-07-27 | 2020-03-17 | 大陆汽车有限公司 | Method and device for monitoring the behaviour of a tyre of a vehicle |
| CN107727110A (en) * | 2017-09-11 | 2018-02-23 | 上海斐讯数据通信技术有限公司 | The statistical method and device of a kind of step number |
| US20190118822A1 (en) * | 2017-10-25 | 2019-04-25 | Robert Bosch Gmbh | Method and device for determining a state of a roadway of a vehicle |
| CN109900295A (en) * | 2017-12-11 | 2019-06-18 | 上海交通大学 | The detection method and system of state of motion of vehicle based on autonomic sensor |
| CN110415377A (en) * | 2019-06-24 | 2019-11-05 | 天津五八到家科技有限公司 | Driving status determines method, apparatus and electronic equipment |
| CN111402329A (en) * | 2020-03-24 | 2020-07-10 | 上海眼控科技股份有限公司 | Vehicle line pressing detection method and device, computer equipment and storage medium |
Non-Patent Citations (1)
| Title |
|---|
| 谢仕民;李邦清;李文耀;王黎斌;周祖洋;: "地磁匹配技术及其基本匹配算法仿真研究", 航天控制, no. 05, pages 55 - 59 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113391244A (en) * | 2021-06-13 | 2021-09-14 | 河海大学 | VMD-based transformer switching-on vibration signal characteristic frequency calculation method |
| CN113391244B (en) * | 2021-06-13 | 2024-01-12 | 河海大学 | VMD-based transformer closing vibration signal characteristic frequency calculation method |
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|---|---|
| CN112902946B (en) | 2024-01-30 |
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