Tire burst risk assessment method, terminal and storage medium

Abstract

The invention provides a tire burst risk assessment method, a terminal and a storage medium. The method comprises the following steps: when a vehicle is static, acquiring tire pressure and tire temperature of a target tire on the vehicle, and recording the tire pressure and the tire temperature as cold tire pressure and cold tire temperature; calculating a tire constant of the target tire according to the cold tire pressure and the cold tire temperature; when the vehicle runs, tire pressure of a target tire is obtained and recorded as thermal state tire pressure; according to the thermal state tire pressure and the tire constant, the tire temperature of the target tire is calculated and recorded as the thermal state air pressure compensation tire temperature; and the tire burst risk of the target tire is evaluated according to the thermal state air pressure compensation tire temperature. According to the method, the temperature of the gas in the target tire can be more accurately represented according to the thermal-state air pressure compensation tire temperature, so that the tire burst risk of the target tire can be better measured and evaluated according to the thermal-state air pressure compensation tire temperature.

Description

Technical Field

[0001] The present invention relates to the technical field of vehicle tires, and in particular to a tire blowout risk assessment method, a terminal and a storage medium. Background Art

[0002] A tire blowout (also called a blowout) is a rapid, explosive loss of air pressure in a pneumatic tire. A tire blowout, especially at high speeds, is the leading cause of accidents resulting from loss of vehicle control.

[0003] Research has found that high-risk factors for abnormal tire blowouts include: speeding, insufficient inflation pressure, overload, eccentric load, excessive inflation pressure, high temperature, sharp turns, physical damage to tires, and mixed use of tires. There are currently two main ways to assess the risk of tire blowouts: the first is to conduct regular offline inspections of tires, measure the tread depth of the tires, check the tire damage, and manually assess whether the tires are in poor condition and need to be replaced. The second is to monitor the tire temperature and pressure of the vehicle through the on-board tire pressure monitoring system (TPMS) equipment, and generate tire risk warnings by setting risk thresholds to prevent tire blowouts caused by excessive tire pressure or temperature.

[0004] However, the inventors found in the process of implementing the present invention that the current tire blowout risk assessment method strongly relies on empirical judgment and it is difficult to combine multiple factors. Moreover, the tire temperature and pressure sensors are usually installed at the valve position, and the measured tire temperature is close to the actual gas temperature in the tire only when the tire is cold. However, the gas temperature in the tire after the vehicle has been running for a period of time is significantly different from the tire temperature measured by the tire temperature and pressure sensors installed at the valve position, which makes it impossible to measure the risk of tire blowout caused by the change in gas temperature in the tire. Summary of the invention

[0005] The embodiments of the present invention provide a tire blowout risk assessment method, a terminal and a storage medium to solve the problem that the risk of tire blowout cannot be well measured at present.

[0006] In a first aspect, an embodiment of the present invention provides a tire blowout risk assessment method, comprising: When the vehicle is stationary, the tire pressure and tire temperature of the target tire on the vehicle are obtained, which are recorded as the cold tire pressure and cold tire temperature; Calculating a tire constant of the target tire according to the cold tire pressure and the cold tire temperature; When the vehicle is running, obtaining the tire pressure of the target tire and recording it as the hot tire pressure; Calculating the tire temperature of the target tire according to the hot tire pressure and the tire constant, and recording it as the hot air pressure compensated tire temperature; The tire blowout risk of the target tire is evaluated based on the hot air pressure compensating tire temperature.

[0007] In a possible implementation, calculating the tire constant of the target tire according to the cold tire pressure and the cold tire temperature includes: according to , calculate the tire constant of the target tire; in, is the tire constant of the target tire, is the cold tire pressure, is the cold tire temperature.

[0008] In a possible implementation, calculating the tire temperature of the target tire according to the hot tire pressure and the tire constant, recorded as hot air pressure compensated tire temperature, includes: according to , calculating the tire temperature of the target tire; in, compensating the hot air pressure for tire temperature, is the hot tire pressure, is the tire constant.

[0009] In a possible implementation, evaluating the risk of the target tire bursting according to the hot air pressure compensating tire temperature includes: Within a set statistical period, the fluctuation information of the hot air pressure-compensated tire temperature is obtained, recorded as tire temperature fluctuation information, and the eccentric load fluctuation information of the vehicle is obtained; The tire blowout risk of the target tire is evaluated according to the tire temperature fluctuation information and the eccentric load fluctuation information.

[0010] In a possible implementation, the obtaining of the eccentric load fluctuation information of the vehicle includes: The fluctuation information and the maximum fluctuation information of the difference between the hot tire pressure and the tire pressure of the coaxial opposite tire of the target tire are obtained.

[0011] In a possible implementation, the tire blowout risk assessment method further includes: Obtaining a minimum tread depth value of the target tire in a tire tread inspection closest to the set statistical period; The method of evaluating the risk of a tire blowout of the target tire according to the hot air pressure compensation tire temperature includes: The tire blowout risk of the target tire is evaluated according to the tire temperature fluctuation information, the eccentric load fluctuation information and the minimum inspection pattern depth.

[0012] In a possible implementation, evaluating the tire blowout risk of the target tire according to the tire temperature fluctuation information, the eccentric load fluctuation information, and the minimum inspection pattern depth includes: The tire temperature fluctuation information, the eccentric load fluctuation information and the minimum inspection pattern depth are input into a tire blowout risk assessment model to obtain the tire blowout risk of the target tire.

[0013] In a possible implementation, the training process of the tire blowout risk assessment model includes: Get historical usage data of multiple tires; Extracting the tire blowout risk characteristic data of the tire blowout in the target time period of the tire blowout and the set statistical period before the tire blowout according to the historical use data, and extracting the tire blowout risk characteristic data of the normal tire in the target time period of normal use and the set statistical period before normal use, wherein the tire blowout risk characteristic data includes the tire temperature fluctuation information, the eccentric load fluctuation information and the minimum value of the inspection pattern depth; The tire blowout risk characteristic data of the blowout tire and the tire blowout risk characteristic data of the normal tire are used to form a training set according to a set ratio; The tire blowout risk characteristic data of the flat tires in the training set within a set statistical period before the tire blowout and the tire blowout risk characteristic data of the normal tires within the set statistical period before normal use are used as input, and the tire blowout risk characteristic data of the flat tires in the target time period of the tire blowout and the tire blowout risk characteristic data of the normal tires in the target time period of normal use are used as output, and a preset supervised machine learning model is trained to obtain the tire blowout risk assessment model.

[0014] In a second aspect, an embodiment of the present invention provides a terminal, comprising a memory and a processor, wherein the memory is used to store a computer program, and the processor is used to call and run the computer program stored in the memory to perform the steps of the method described in the first aspect or any possible implementation of the first aspect.

[0015] In a third aspect, an embodiment of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the method described in the first aspect or any possible implementation of the first aspect are implemented.

[0016] The embodiment of the present invention provides a tire blowout risk assessment method, terminal and storage medium. When the vehicle is stationary, the tire pressure and tire temperature of a target tire on a vehicle are obtained, recorded as cold tire pressure and cold tire temperature, and then the tire constant of the target tire is calculated based on the cold tire pressure and cold tire temperature, thereby calculating the tire constant of the target tire based on the ideal gas state equation, and then based on the tire constant, according to the hot tire pressure of the target tire when the vehicle is driving, the hot air pressure-compensated tire temperature of the target tire is calculated, thereby more accurately characterizing the temperature of the gas in the target tire according to the hot air pressure-compensated tire temperature, and then better measuring and assessing the blowout risk of the target tire according to the hot air pressure-compensated tire temperature. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0018] Figure 1 is a flow chart of an implementation of a tire blowout risk assessment method provided by an embodiment of the present invention; Figure 2 is a flow chart of an implementation of a tire blowout risk assessment method provided by another embodiment of the present invention; Figure 3 is a schematic structural diagram of a tire blowout risk assessment device provided by an embodiment of the present invention; Figure 4 is a schematic diagram of a terminal provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0019] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present invention. However, it should be clear to those skilled in the art that the present invention may be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present invention.

[0020] In order to make the purpose, technical solutions and advantages of the present invention more clear, specific embodiments will be described below in conjunction with the accompanying drawings.

[0021] See also Figure 1 , which shows a flow chart of the implementation of the tire blowout risk assessment method provided by an embodiment of the present invention, and is described in detail as follows: In step 101 , when the vehicle is stationary, the tire pressure and tire temperature of a target tire on the vehicle are obtained, which are recorded as cold tire pressure and cold tire temperature.

[0022] In step 102, the tire constant of the target tire is calculated according to the cold tire pressure and the cold tire temperature.

[0023] In this embodiment, considering that the tire temperature and pressure sensors are usually installed at the air valve position, the measured tire temperature is close to the actual gas temperature inside the tire only when the tire is cold. However, there is a large difference between the gas temperature inside the tire after the vehicle has run for a period of time and the tire temperature measured by the tire temperature and pressure sensors installed at the air valve position. Therefore, it is considered to use the ideal gas state equation to measure the tire temperature during vehicle driving.

[0024] The ideal gas state equation is , is the air pressure, is the gas volume, which is the volume of the tire here, is the amount of substance, here the amount of gas in the tire, is the molar gas constant, is the absolute temperature (in Kelvin). and the volume of the tire It can be assumed to be constant, so the ideal gas state equation can be transformed into ,but It can be regarded as a constant, that is, the tire constant, which can be calculated through tire pressure and tire temperature.

[0025] Exemplarily, the tire constant of the target tire is calculated according to the cold tire pressure and the cold tire temperature, that is, according to , calculate the tire constant of the target tire.

[0026] in, is the tire constant of the target tire, For cold tire pressure, Cold tire temperature.

[0027] Exemplarily, the cold tire pressure and the cold tire temperature of the tire may be collected after the vehicle has been stationary for a period of time, for example, after the vehicle has been stationary for more than 30 minutes, the cold tire pressure and the cold tire temperature of the tire may be collected.

[0028] In step 103, when the vehicle is traveling, the tire pressure of the target tire is obtained and recorded as the hot tire pressure.

[0029] In step 104, the tire temperature of the target tire is calculated based on the hot tire pressure and the tire constant, and is recorded as the hot air pressure compensated tire temperature.

[0030] Combined with the above steps 101 and 102, due to ,and Can be regarded as a constant, also known as the tire constant , then after obtaining the tire pressure of the vehicle when it is running, that is, after obtaining the hot tire pressure, it can be calculated based on Calculate the tire temperature.

[0031] Among them, the tire pressure value of the vehicle running continuously for more than half an hour when the vehicle speed is greater than or equal to 10 km / h can be counted as the hot tire pressure.

[0032] Exemplarily, the tire temperature of the target tire is calculated based on the hot tire pressure and the tire constant, and is recorded as the hot air pressure compensated tire temperature, that is: , calculate the tire temperature of the target tire.

[0033] in, The tire temperature converted to degrees Celsius is the hot pressure compensated tire temperature. For hot tire pressure, is the tire constant.

[0034] This embodiment calculates the tire constant of the target tire based on the ideal gas state equation, and then calculates the hot air pressure-compensated tire temperature of the target tire based on the tire constant and the hot tire pressure of the target tire when the vehicle is running, so as to more accurately characterize the temperature of the gas in the target tire based on the hot air pressure-compensated tire temperature, and better measure the risk of tire blowout caused by the change of gas temperature in the tire by using the hot air pressure-compensated tire temperature.

[0035] In step 105, the risk of a target tire bursting is evaluated based on the thermal air pressure compensating tire temperature.

[0036] In this embodiment, after the hot air pressure compensated tire temperature during tire driving is calculated through the above process, the hot air pressure compensated tire temperature can be used as an influencing factor of tire blowout to evaluate the risk of tire blowout. For example, the hot air pressure compensated tire temperature is compared with the hot air pressure compensated tire temperature threshold when the tire blows out to evaluate the risk of tire blowout, or the hot air pressure compensated tire temperature is combined with influencing factors such as overspeed, abnormal tire pressure, overload, eccentric load, high temperature, sharp turn, physical damage to the tire, and mixed use of tires to evaluate the risk of tire blowout.

[0037] The embodiment of the present invention obtains the tire pressure and tire temperature of a target tire on a vehicle when the vehicle is stationary, recorded as cold tire pressure and cold tire temperature, and then calculates the tire constant of the target tire based on the cold tire pressure and cold tire temperature, thereby calculating the tire constant of the target tire based on the ideal gas state equation, and then based on the tire constant, calculates the hot air pressure-compensated tire temperature of the target tire according to the hot tire pressure of the target tire when the vehicle is driving, thereby more accurately characterizing the temperature of the gas in the target tire according to the hot air pressure-compensated tire temperature, and then better measuring and evaluating the risk of tire blowout of the target tire according to the hot air pressure-compensated tire temperature.

[0038] In one embodiment of the present invention, evaluating the risk of a target tire bursting according to the thermal air pressure compensation tire temperature may include: In a set statistical period, the fluctuation information of the tire temperature compensated by the hot air pressure is obtained, recorded as the tire temperature fluctuation information, and the eccentric load fluctuation information of the vehicle is obtained; The blowout risk of the target tire is assessed based on the tire temperature fluctuation information and the eccentric load fluctuation information.

[0039] In this embodiment, in order to design a more reasonable tire blowout risk index, considering that tire overload and unbalanced load will also increase the tire blowout risk, the eccentric load fluctuation information of the vehicle is obtained and combined with the tire temperature fluctuation information to better evaluate the tire blowout risk.

[0040] In one embodiment of the present invention, obtaining the eccentric load fluctuation information of the vehicle may include: The fluctuation information and the maximum fluctuation information of the difference between the hot tire pressure and the tire pressure of the coaxial opposite tire of the target tire are obtained.

[0041] In this embodiment, in order to measure the eccentric load fluctuation information of the vehicle, in the design stage of the tire blowout risk index, not only the hot tire pressure of the target tire is monitored, but also the digital technology can be used to find the tire pressure status of the coaxial opposite tire of the monitored target tire, and establish a comparison parameter with the status of the monitored target tire. In this way, the fluctuation information and maximum fluctuation information of the difference between the hot tire pressure of the target tire and the tire pressure of the coaxial opposite tire of the target tire are obtained, and the deviation of the vehicle load is reflected by the fluctuation of the difference between the tire pressure of the target tire and the coaxial opposite tire of the target tire, thereby better reflecting the problem of the eccentric load of the vehicle. For example, if the tire pressure of the monitored target tire (i.e., the hot tire pressure) is too high, while the tire pressure of the coaxial opposite tire is too low, it indicates that the load of the monitored target wheel is heavier and the probability of tire blowout risk is higher.

[0042] In an embodiment of the present invention, the fluctuation information of the thermal air pressure compensating the tire temperature may include: the standard deviation or variance of the thermal air pressure compensating the tire temperature.

[0043] The fluctuation information of the difference between the hot tire pressure and the tire pressure of the coaxial opposite tire of the target tire may include: a standard deviation or a variance of the difference between the hot tire pressure and the tire pressure of the coaxial opposite tire of the target tire.

[0044] The maximum fluctuation information of the difference between the hot tire pressure and the tire pressure of the coaxial opposite tire of the target tire may include: the maximum value of the absolute value of the difference between the hot tire pressure and the tire pressure of the coaxial opposite tire of the target tire.

[0045] In one embodiment of the present invention, the tire blowout risk assessment method may further include: Get the minimum tread depth of the target tire in the tire tread inspection closest to the set statistical period.

[0046] Based on the thermal air pressure compensation tire temperature, the target tire blowout risk is assessed, which may include: The blowout risk of the target tire is assessed based on tire temperature fluctuation information, eccentric load fluctuation information and the minimum tread depth of the inspection.

[0047] In this embodiment, considering that the tire factory generally has the periodic inspection data of tire tread depth, and the vehicle factory has the tire temperature and tire pressure monitoring data, it is generally difficult to combine the periodic inspection data of tire tread depth with the tire temperature and tire pressure monitoring data to systematically and quantitatively determine the risk of tire blowout on a daily basis. Therefore, by obtaining the minimum inspection tread depth of the tire in the most recent tire tread inspection within the set statistical period, combined with the tire temperature fluctuation information, the eccentric load fluctuation information and the minimum inspection tread depth, the risk of tire blowout is comprehensively evaluated.

[0048] Among them, since a tire surface generally has multiple patterns, the inspection pattern depth obtained from the tire pattern inspection includes the depths of multiple patterns of the tire, and the minimum inspection pattern depth (that is, the shallowest groove on the tire surface) can better represent the wear condition of the tire. Therefore, taking the minimum inspection pattern depth to assess the risk of tire blowout is also more representative of the risk of tire blowout.

[0049] In one embodiment of the present invention, tire temperature fluctuation information, eccentric load fluctuation information and minimum inspection pattern depth value may be input into a tire blowout risk assessment model to obtain the tire blowout risk of the target tire.

[0050] In this embodiment, in order to comprehensively evaluate the tire blowout risk by considering various factors affecting tire blowout, a supervised model training method may be used to train a tire blowout risk assessment model, so as to obtain the tire blowout risk by using the trained tire blowout risk assessment model.

[0051] Among them, in order to make the tire blowout risk assessment model more accurately consider the tire temperature and tire load imbalance, and combined with the tire inspection pattern depth, the tire temperature fluctuation information, eccentric load fluctuation information and inspection pattern depth minimum value obtained in the above steps can be used to train the tire blowout risk assessment model and evaluate the tire blowout risk.

[0052] In one embodiment of the present invention, the training process of the tire blowout risk assessment model may include: Get historical usage data for multiple tires.

[0053] Based on the historical usage data, the tire blowout risk characteristic data of the tires during the target period of blowout and the set statistical period before the blowout are extracted, and the tire blowout risk characteristic data of the normal tires during the target period of normal use and the set statistical period before normal use are extracted. The tire blowout risk characteristic data include tire temperature fluctuation information, eccentric load fluctuation information and minimum inspection pattern depth.

[0054] The tire blowout risk characteristic data of the blowout tire and the tire blowout risk characteristic data of the normal tire are combined into a training set according to a set ratio.

[0055] The tire blowout risk characteristic data of the flat tires in the training set within a set statistical period before the tire blowout and the tire blowout risk characteristic data of the normal tires within a set statistical period before normal use are used as input, and the tire blowout risk characteristic data of the flat tires in the target period of tire blowout and the tire blowout risk characteristic data of the normal tires in the target period of normal use are used as output, and the preset supervised machine learning model is trained to obtain a tire blowout risk assessment model.

[0056] Combination Figure 2 As shown, before training the tire blowout risk assessment model, tire data can be collected first, including tire and vehicle basic information data, tire inspection data, trajectory data, tire temperature and pressure sensor data, and tire blowout label data.

[0057] Among them, after collecting the basic information data of tires and vehicles, a commercial vehicle brand type and tire brand model information database can be established.

[0058] Exemplarily, the commercial vehicle brand type library may include the following three dimensions of information: vehicle type, number of axles, and rated load information. Among them, the vehicle type can be divided into five categories: tractor, van transporter, grid transporter, bus and other categories. The number of vehicle axles can be recorded in 6×4 and 6×2 formats. For example, the "6" in the axle number 6×4 means that the vehicle has a total of 6 axle heads, that is, 3 axles; "4" means that 4 axle heads are driven, that is, 2 drive axles. Rated load information can be expressed in "tons".

[0059] The tire brand and model information database can record the following two dimensions of information: tire brand and tire size. The tire size record format is as follows: 215 / 75R17.5, which is a conventional tire record format.

[0060] Afterwards, tire blowout risk feature data is extracted based on the collected tire data: When extracting tire blowout risk feature data, it can be determined that the statistical period is set to t, and the upload frequency of the tire temperature and pressure data sensor is generally 30 seconds / time. Based on this, the following features are extracted within the set statistical period t: 1. Extract the tire pressure of each tire within the set statistical period t (i.e. the hot tire pressure of the target tire) and the recommended tire pressure for this model of tire The absolute value of the difference is: ; 2. Extract the tire pressure of each tire within the set statistical period t Standard Deviation .

[0061] 3. Extract the tire pressure of each tire within the set statistical period t The maximum difference between the tire pressure of the tire on the opposite side of the same axle. For example, the tire being evaluated is the inner tube on the left side of the second axle, and the tire pressure is , the tire on the opposite side of the same axle is the inner tube on the right side of the second axle, and the tire pressure is The maximum difference between the tire pressure of this tire and the tire on the opposite side of the same axle is: ; 4. Extract the tire pressure of each tire within the set statistical period t The standard deviation of the difference between the tire pressure of the tire on the opposite side of the same axle. For example, the tire being evaluated is the inner tube on the left side of the second axle, and the tire pressure is , the tire on the opposite side of the same axle is the inner tube on the right side of the second axle, and the tire pressure is The standard deviation of the difference between the tire pressure of this tire and the tire on the opposite side of the same axle is: ; 5. Extract the standard deviation of the hot air pressure compensation tire temperature of each tire within the set statistical period t , where the thermal pressure compensation tire temperature of each tire can be calculated according to the formula in the above steps Calculated, within the set statistical period t, according to the hot tire pressure The acquisition frequency can calculate multiple hot air pressure compensation tire temperature In order to characterize the tire temperature fluctuation information during driving, multiple hot air pressure compensation tire temperature Standard Deviation .

[0062] 6. Count whether the brand and model of the target tire and the coaxial opposite side tire are consistent. When the brand of the target tire and the coaxial opposite side tire is consistent, it can be marked as 1, and when it is inconsistent, it can be marked as 0.

[0063] 7. Count the minimum inspection tread depth characteristics of the target tire. Each tire tread inspection will measure the tread groove depth of each tire of the vehicle. Suppose a tire has 4 inspection tread depth values. , then take the minimum inspection pattern depth : ; 8. Extract the number of drastic fluctuations in tire pressure within a short period of time for each tire within a set statistical period t. For example, drastic fluctuations in tire pressure can be defined as the number of times the tire pressure changes by more than 200 kPa.

[0064] 9. Extract the number of low tire pressure warnings within a set statistical period t (for example, extract the number of times the tire pressure is less than 700 kPa).

[0065] 10. Extract the number of high tire pressure warnings within a set statistical period t (for example, extract the number of times the tire pressure is greater than 1200 kPa).

[0066] 11. Extract whether the vehicle where the target tire is located is a tractor. If it is a tractor, it can be marked as 1, and if it is not a tractor, it can be marked as 0.

[0067] 12. Extract whether the vehicle where the target tire is located is a trailer. If it is a trailer, it can be marked as 1, and if it is not a trailer, it can be marked as 0.

[0068] 13. Extract the maximum vehicle speed within the set statistical period t .

[0069] 14. Extract the standard deviation of vehicle speed within the set statistical period t .

[0070] 15. Use Scikit-learn's OneHotEncoder to process the four categorical variables of vehicle type, number of axles, tire brand, and tire size into dummy variables.

[0071] After extracting the tire blowout risk feature data, a model training sample set can be established. For example, assuming that the statistical period t=6 days is set, after knowing the tire blowout date, the above features of the 6 days before the tire blowout and the above features of the tire blowout on the day can be obtained, and the above features of the normal tire on the day and the above features of the 6 days before the day can be obtained, and the data volume of the tire blowout and the normal tire data are matched with the sample volume of 1:3 as the model training sample set. In order to ensure the accuracy of the model, the data of the sample set can be updated daily.

[0072] For example, during training, the sample set can be randomly divided into 70% training set and 30% test set, and cross-validation is performed 5 times between the training set and the test set each time, and then the XGBoost algorithm is called for gradient boosting training. The trained model can use precision and F1-score as prediction effect judgment indicators. After the training is completed, the joblib package is called to package the trained model and deploy it to the server for subsequent tire blowout risk assessment applications.

[0073] When the tire blowout risk assessment model is applied, in order to promptly warn high-risk tires, the offline data warehouse can retrieve the historical data of the target tires for 6 days after 0:00 every day, and the XGBboost model trained on the server side outputs the risk probability value p of the tire blowout on that day (p is a probability value between 0-1), and notifies the vehicle through mobile applets or vehicle-mounted terminals to conduct targeted inspections on tires with higher risks before departure, so as to prevent adverse events such as tire blowouts from occurring when the vehicle is performing its departure mission on that day.

[0074] For example, when the risk probability value of a tire blowout on the same day is p>0.5, the vehicle can be notified to conduct a targeted inspection before leaving the vehicle.

[0075] This embodiment uses a supervised machine learning method to train a large amount of flat tire data and non-flat tire data during the model training stage, and adjusts the ratio of flat tire samples and non-flat tire model samples during the training process to increase the proportion of flat tire samples in the training. At the same time, the accuracy rate is used as the model effect evaluation index when evaluating the model effect, so that the model can better identify and capture tires with flat tire risks under extreme category imbalance data, and establish a model relationship between flat tire risk characteristics and flat tire labels. The flat tire risk assessment model trained based on this embodiment can push the tire flat tire risk assessment results based on historical data to the vehicle on a daily basis, overcoming the problem of subjective manual tire inspection and assessment methods in the previous tire maintenance process, and more comprehensively and quantitatively measuring the recent flat tire risk than the traditional TPMS setting thresholds to generate tire pressure and temperature alarms. In addition, the daily push of the tire flat tire risk of the day is also more timely for the management and control of tire risks.

[0076] It should be understood that the order of execution of the steps in the above embodiment does not necessarily mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiment of the present invention.

[0077] The following is an embodiment of the device of the present invention. For details not described in detail therein, reference may be made to the corresponding method embodiment described above.

[0078] Figure 3 The structure diagram of the tire blowout risk assessment device provided by the embodiment of the present invention is shown. For the convenience of description, only the part related to the embodiment of the present invention is shown, which is described in detail as follows: like Figure 3 As shown, the tire blowout risk assessment device includes: a first acquisition module 31 , a first calculation module 32 , a second acquisition module 33 , a second calculation module 34 and an assessment module 35 .

[0079] A first acquisition module 31 is used to acquire the tire pressure and tire temperature of a target tire on the vehicle when the vehicle is stationary, which are recorded as cold tire pressure and cold tire temperature; A first calculation module 32, used for calculating a tire constant of a target tire according to a cold tire pressure and a cold tire temperature; The second acquisition module 33 is used to acquire the tire pressure of the target tire when the vehicle is traveling, and record it as the hot tire pressure; A second calculation module 34 is used to calculate the tire temperature of the target tire according to the hot tire pressure and the tire constant, which is recorded as the hot air pressure compensated tire temperature; The evaluation module 35 is used to evaluate the risk of a target tire bursting according to the thermal air pressure compensating tire temperature.

[0080] The embodiment of the present invention obtains the tire pressure and tire temperature of a target tire on a vehicle when the vehicle is stationary, recorded as cold tire pressure and cold tire temperature, and then calculates the tire constant of the target tire based on the cold tire pressure and cold tire temperature, thereby calculating the tire constant of the target tire based on the ideal gas state equation, and then based on the tire constant, calculates the hot air pressure-compensated tire temperature of the target tire according to the hot tire pressure of the target tire when the vehicle is driving, thereby more accurately characterizing the temperature of the gas in the target tire according to the hot air pressure-compensated tire temperature, and then better measuring and evaluating the risk of tire blowout of the target tire according to the hot air pressure-compensated tire temperature.

[0081] In a possible implementation, the first calculation module 32 may be configured to calculate , calculate the tire constant of the target tire; in, is the tire constant of the target tire, For cold tire pressure, Cold tire temperature.

[0082] In a possible implementation, the second calculation module 34 may be used to calculate , calculate the tire temperature of the target tire; in, To compensate tire temperature for hot air pressure, For hot tire pressure, is the tire constant.

[0083] In a possible implementation, the evaluation module 35 may be used to obtain the fluctuation information of the hot air pressure compensated tire temperature within a set statistical period, recorded as tire temperature fluctuation information, and obtain the eccentric load fluctuation information of the vehicle; The blowout risk of the target tire is assessed based on the tire temperature fluctuation information and the eccentric load fluctuation information.

[0084] In a possible implementation, the evaluation module 35 may be used to obtain fluctuation information and maximum fluctuation information of the difference between the hot tire pressure and the tire pressure of the coaxial opposite tire of the target tire.

[0085] In a possible implementation, the second acquisition module 33 can also be used to obtain the minimum inspection pattern depth of the target tire in the tire pattern inspection closest to the set statistical period; the evaluation module 35 can be used to evaluate the tire blowout risk of the target tire based on tire temperature fluctuation information, eccentric load fluctuation information and the minimum inspection pattern depth.

[0086] In a possible implementation, the evaluation module 35 may be used to input tire temperature fluctuation information, eccentric load fluctuation information and the minimum value of the inspection pattern depth into a tire blowout risk evaluation model to obtain the tire blowout risk of the target tire.

[0087] In a possible implementation, the training process of the tire blowout risk assessment model includes: Get historical usage data of multiple tires; Based on the historical usage data, the tire blowout risk characteristic data of the tire blowout in the target period of the tire blowout and the set statistical period before the tire blowout are extracted, and the tire blowout risk characteristic data of the normal tire in the target period of normal use and the set statistical period before normal use are extracted. The tire blowout risk characteristic data includes tire temperature fluctuation information, eccentric load fluctuation information and the minimum value of the inspection pattern depth; The tire blowout risk characteristic data of the blowout tire and the tire blowout risk characteristic data of the normal tire are used to form a training set according to a set ratio; The tire blowout risk characteristic data of the flat tires in the training set within a set statistical period before the tire blowout and the tire blowout risk characteristic data of the normal tires within a set statistical period before normal use are used as input, and the tire blowout risk characteristic data of the flat tires in the target period of tire blowout and the tire blowout risk characteristic data of the normal tires in the target period of normal use are used as output, and the preset supervised machine learning model is trained to obtain a tire blowout risk assessment model.

[0088] Figure 4 is a schematic diagram of a terminal provided by an embodiment of the present invention. Figure 4 As shown, the terminal 4 of this embodiment includes: a processor 40, a memory 41, and a computer program 42 stored in the memory 41 and executable on the processor 40. When the processor 40 executes the computer program 42, the steps in the above-mentioned tire blowout risk assessment method embodiments are implemented, such as Figure 1 Steps 101 to 105 shown, or Figure 2 Alternatively, when the processor 40 executes the computer program 42, the functions of each module / unit in the above-mentioned device embodiments are implemented, for example Figure 3The functions of each module are shown.

[0089] Exemplarily, the computer program 42 may be divided into one or more modules / units, one or more modules / units are stored in the memory 41 and executed by the processor 40 to implement the present invention. One or more modules / units may be a series of computer program instruction segments capable of implementing specific functions, and the instruction segments are used to describe the execution process of the computer program 42 in the terminal 4. For example, the computer program 42 may be divided into Figure 3 The modules shown.

[0090] The terminal 4 may be a computing device such as a mobile terminal, a vehicle-mounted terminal, a desktop computer, a notebook, a PDA, a cloud server, etc. The terminal 4 may include, but is not limited to, a processor 40 and a memory 41. Those skilled in the art will appreciate that Figure 4 It is only an example of terminal 4 and does not constitute a limitation on terminal 4. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, the terminal may also include input and output devices, network access devices, buses, etc.

[0091] The processor 40 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0092] The memory 41 may be an internal storage unit of the terminal 4, such as a hard disk or memory of the terminal 4. The memory 41 may also be an external storage device of the terminal 4, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. equipped on the terminal 4. Further, the memory 41 may also include both an internal storage unit and an external storage device of the terminal 4. The memory 41 is used to store computer programs and other programs and data required by the terminal. The memory 41 may also be used to temporarily store data that has been output or is to be output.

[0093] The technicians in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0094] As another embodiment of the present invention, the present invention may also include a vehicle, including a terminal as described in any of the above embodiments, and having the same beneficial effects as the above terminals, which will not be described in detail herein.

[0095] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0096] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0097] In the embodiments provided by the present invention, it should be understood that the disclosed devices / terminals and methods can be implemented in other ways. For example, the device / terminal embodiments described above are only schematic, for example, the division of modules or units is only a logical function division, and there may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0098] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0099] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0100] If the integrated module / unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by the processor, the steps of the above-mentioned tire blowout risk assessment method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device that can carry computer program code, recording medium, U disk, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium.

[0101] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included in the protection scope of the present invention.

Claims

1. A tire blowout risk assessment method, characterized in that: include: When the vehicle is stationary, the tire pressure and tire temperature of the target tire on the vehicle are obtained, which are recorded as the cold tire pressure and cold tire temperature; Calculating a tire constant of the target tire according to the cold tire pressure and the cold tire temperature; When the vehicle is running, obtaining the tire pressure of the target tire and recording it as the hot tire pressure; Calculating the tire temperature of the target tire according to the hot tire pressure and the tire constant, and recording it as the hot air pressure compensated tire temperature; The tire blowout risk of the target tire is evaluated based on the hot air pressure compensating tire temperature.

2. The tire blowout risk assessment method according to claim 1, characterized in that: Calculating the tire constant of the target tire according to the cold tire pressure and the cold tire temperature includes: according to , calculate the tire constant of the target tire; in, is the tire constant of the target tire, is the cold tire pressure, is the cold tire temperature.

3. The tire blowout risk assessment method according to claim 1, characterized in that: Calculating the tire temperature of the target tire according to the hot tire pressure and the tire constant, recorded as hot air pressure compensated tire temperature, includes: according to , calculating the tire temperature of the target tire; in, compensating the hot air pressure for tire temperature, is the hot tire pressure, is the tire constant.

4. The tire blowout risk assessment method according to claim 1, characterized in that: The method of evaluating the risk of a tire blowout of the target tire according to the hot air pressure compensation tire temperature includes: Within a set statistical period, the fluctuation information of the hot air pressure-compensated tire temperature is obtained, recorded as tire temperature fluctuation information, and the eccentric load fluctuation information of the vehicle is obtained; The tire blowout risk of the target tire is evaluated according to the tire temperature fluctuation information and the eccentric load fluctuation information.

5. The tire blowout risk assessment method according to claim 4, characterized in that: The obtaining of the eccentric load fluctuation information of the vehicle includes: The fluctuation information and the maximum fluctuation information of the difference between the hot tire pressure and the tire pressure of the coaxial opposite tire of the target tire are obtained.

6. The tire blowout risk assessment method according to claim 4, characterized in that: Also includes: Obtaining a minimum tread depth value of the target tire in a tire tread inspection closest to the set statistical period; The method of evaluating the risk of a tire blowout of the target tire according to the hot air pressure compensation tire temperature includes: The tire blowout risk of the target tire is evaluated according to the tire temperature fluctuation information, the eccentric load fluctuation information and the minimum inspection pattern depth.

7. The tire blowout risk assessment method according to claim 6, characterized in that: According to the tire temperature fluctuation information, the eccentric load fluctuation information and the minimum inspection pattern depth, the blowout risk of the target tire is evaluated, including: The tire temperature fluctuation information, the eccentric load fluctuation information and the minimum inspection pattern depth are input into a tire blowout risk assessment model to obtain the tire blowout risk of the target tire.

8. The tire blowout risk assessment method according to claim 7, characterized in that: The training process of the tire blowout risk assessment model includes: Get historical usage data of multiple tires; Extracting the tire blowout risk characteristic data of the tire blowout in the target time period of the tire blowout and the set statistical period before the tire blowout according to the historical use data, and extracting the tire blowout risk characteristic data of the normal tire in the target time period of normal use and the set statistical period before normal use, wherein the tire blowout risk characteristic data includes the tire temperature fluctuation information, the eccentric load fluctuation information and the minimum value of the inspection pattern depth; The tire blowout risk characteristic data of the blowout tire and the tire blowout risk characteristic data of the normal tire are used to form a training set according to a set ratio; The tire blowout risk characteristic data of the flat tires in the training set within a set statistical period before the tire blowout and the tire blowout risk characteristic data of the normal tires within the set statistical period before normal use are used as input, and the tire blowout risk characteristic data of the flat tires in the target time period of the tire blowout and the tire blowout risk characteristic data of the normal tires in the target time period of normal use are used as output, and a preset supervised machine learning model is trained to obtain the tire blowout risk assessment model.

9. A terminal, characterized in that: The method comprises a memory and a processor, wherein the memory is used to store a computer program, and the processor is used to call and run the computer program stored in the memory to execute the method according to any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method as claimed in any one of claims 1 to 7 are implemented.

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