CN118277720B - Tire burst prediction method, device, terminal and storage medium - Google Patents
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Abstract
The invention provides a tire burst prediction method, a device, a terminal and a storage medium. The method comprises the following steps: in the using process of the tire, acquiring key factor data influencing tire burst and additional factor data influencing the tire burst together with the key factor data; counting the usage data of the tire when the key factor data meets a first threshold value and the additional factor data meets a second threshold value, wherein the first threshold value is a value representing that the tire is in a fatigue working condition under the key factor, and the second threshold value is a value representing that the tire is in the fatigue working condition under the additional factor corresponding to the key factor; calculating equivalent fatigue use data of the tire under a key factor according to the use data; and carrying out tire burst prediction on the tire according to the equivalent fatigue use data. The invention can more accurately predict the tire burst, thereby being beneficial to the early prevention of the tire burst of the commercial vehicle and further reducing traffic accidents and property loss.
Description
Technical Field
The present invention relates to the field of vehicle tires, and in particular, to a tire burst prediction method, device, terminal, and storage medium.
Background
The tire is an important component part of the vehicle, in traffic accidents caused by the vehicle, the traffic accidents caused by tire burst account for a large proportion, and particularly for commercial vehicles, the commercial vehicles are forced to run under higher running speed and saturated loading conditions along with the compression of logistics timeliness and logistics cost, so that the probability of tire burst is further increased.
The national standard GB9744 specifies that the truck tires must be run for 47 hours under cumulative load at a corresponding fixed speed in a particular environment without breaking the tires. Accordingly, during the tire development design phase, truck tire products are typically tested for tire failure under this standard, resulting in tire endurance times. Tire products of generally consistent materials, processes, and construction have consistent tire endurance times. And the tire burst probability in the use process of the tire is evaluated according to the duration of the tire obtained in the tire design process, and the limit value of tire burst of the tire product is measured.
In addition, in order to reduce the occurrence rate of tire burst accidents, a tire pressure monitoring system (Tire Pressure Monitoring System, TPMS) is generally introduced into the logistics vehicles at present, so as to measure tire temperature data (or tire temperature data), tire pressure data (or tire pressure data), tire load data and the like through sensors installed on the tires, and further perform tire burst early warning on a fleet or a driver according to the tire temperature data, the tire pressure data and the like and corresponding thresholds.
However, in the actual fleet tire management process, most tires often have a puncture accident when the tire temperature or the tire pressure threshold set by the TPMS has not been reached. Therefore, how to more accurately predict tire burst is a problem to be solved.
Disclosure of Invention
The embodiment of the invention provides a tire burst prediction method, a device, a terminal and a storage medium, which are used for solving the problem that the existing tire burst prediction is inaccurate.
In a first aspect, an embodiment of the present invention provides a tire puncture prediction method, including:
In the using process of the tire, acquiring key factor data influencing tire burst and additional factor data influencing the tire burst together with the key factor data;
Counting the usage data of the tire when the key factor data meets a first threshold value and the additional factor data meets a second threshold value, wherein the first threshold value is a value representing that the tire is in a fatigue working condition under the key factor, and the second threshold value is a value representing that the tire is in the fatigue working condition under the additional factor corresponding to the key factor;
calculating equivalent fatigue use data of the tire under a key factor according to the use data;
and carrying out tire burst prediction on the tire according to the equivalent fatigue use data.
In one possible implementation, counting usage data of the tire when the key factor data meets a first threshold and the additional factor data meets a second threshold includes:
Dividing the key factor data into a plurality of subintervals;
and counting the usage data of the tire in each subinterval of the key factor data when the key factor data meets a first threshold value and the additional factor data meets a second threshold value.
In one possible implementation, calculating equivalent fatigue usage data of the tire under a key factor from the usage data includes:
acquiring fatigue weights corresponding to the key factor data in each subinterval;
And calculating equivalent fatigue use data of the tire under the key factors according to the fatigue weights corresponding to the key factor data in each subinterval and the use data of the tire when the key factor data are in each subinterval.
In one possible implementation manner, calculating the equivalent fatigue usage data of the tire under the key factor according to the fatigue weight of the key factor data corresponding to each subinterval and the usage data of the tire when the key factor data are in each subinterval includes:
Calculating equivalent fatigue usage data of the tire under a key factor according to the Y= Σa iyi;
Wherein Y is equivalent fatigue use data of the tire under a key factor, ai is fatigue weight corresponding to the key factor data in the ith subinterval, and yi is use data of the tire when the key factor data is in the ith subinterval.
In one possible implementation, the key factor data includes at least one of load factor data, tire temperature factor data, high tire pressure factor data, low tire pressure factor data, and speed factor data;
When the key factor data is the loading factor data, the additional factor data which affects tire burst together with the key factor data comprises the tire temperature factor data, the high tire pressure factor data and the speed factor data;
when the key factor data is the tire temperature factor data, the additional factor data which affects tire burst together with the key factor data comprises the loading factor data, the high tire pressure factor data and the speed factor data;
when the key factor data is the high tire pressure factor data, the additional factor data which affects tire burst together with the key factor data comprises the load factor data, the tire temperature factor data and the speed factor data;
When the key factor data is the speed factor data, the additional factor data which affects the tire burst together with the key factor data comprises the loading factor data, the tire temperature factor data and the high tire pressure factor data;
When the key factor data is the low tire pressure factor data, the additional factor data which affects tire burst together with the key factor data comprises the loading factor data.
In one possible implementation, performing tire burst prediction on the tire according to the equivalent fatigue usage data includes:
Judging whether the equivalent fatigue use data of the tire under each key factor exceeds a corresponding fatigue use threshold value or not;
And if the equivalent fatigue use data of the tire under one of the key factors exceeds the corresponding fatigue use threshold value, sending a tire burst prompt message to a user.
In one possible implementation, the usage data includes usage mileage data and/or usage time data.
In a second aspect, an embodiment of the present invention provides a tire puncture prediction apparatus, including:
The acquisition module is used for acquiring key factor data influencing tire burst and additional factor data influencing the tire burst together with the key factor data in the use process of the tire;
The first processing module is used for counting the usage data of the tire when the key factor data meets a first threshold value and the additional factor data meets a second threshold value, wherein the first threshold value is a value representing that the tire is in a fatigue working condition under the key factor, and the second threshold value is a value representing that the tire is in the fatigue working condition under the additional factor corresponding to the key factor;
the second processing module is used for calculating equivalent fatigue use data of the tire under a key factor according to the use data;
and the prediction module is used for predicting tire burst of the tire according to the equivalent fatigue use data.
In a third aspect, an embodiment of the present invention provides a terminal, including a memory for storing a computer program and a processor for calling and running the computer program stored in the memory, to perform the steps of the method as described above in the first aspect or any possible implementation manner of the first aspect.
In a fourth aspect, embodiments of the present invention provide a computer readable storage medium storing a computer program which, when executed by a processor, implements the steps of the method as described above in the first aspect or any one of the possible implementations of the first aspect.
The embodiment of the invention provides a tire burst prediction method, a device, a terminal and a storage medium, wherein key factor data influencing tire burst and additional factor data influencing tire burst together with the key factor data are obtained in the use process of a tire, so that the use data of the tire when the key factor data meet a first threshold value and the additional factor data meet a second threshold value is counted, the use condition of the tire under the fatigue working condition is measured by the use data of the tire when the key factor data meet the first threshold value and the additional factor data meet the second threshold value, and the equivalent fatigue use data of the tire under the corresponding key factor and the corresponding additional factor are obtained, so that the tire burst prediction is carried out on the tire according to the equivalent fatigue use data more accurately, thereby being beneficial to the early prevention of the tire burst of commercial vehicles, and further reducing traffic accidents and property losses.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the embodiments or the description of the prior art will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flowchart of a tire burst prediction method according to an embodiment of the present invention;
fig. 2 is a schematic structural diagram of a tire burst prediction device according to an embodiment of the present invention;
Fig. 3 is a schematic diagram of a terminal according to an embodiment of the present invention.
Detailed Description
In the following description, for purposes of explanation and not limitation, specific details are set forth such as the particular system architecture, techniques, etc., in order to provide a thorough understanding of the embodiments of the present invention. It will be apparent, however, to one skilled in the art that the present invention may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present invention with unnecessary detail.
For the purpose of making the objects, technical solutions and advantages of the present invention more apparent, the following description will be made by way of specific embodiments with reference to the accompanying drawings.
Referring to fig. 1, a flowchart for implementing a tire burst prediction method provided by an embodiment of the present invention is shown, and details are as follows:
in step 101, during use of the tire, key factor data affecting a tire puncture and additional factor data affecting the tire puncture in combination with the key factor data are obtained.
In this embodiment, considering that there are many factors affecting tire burst, for example, burst caused by overload, burst caused by overspeed, burst caused by too high or too low tire pressure, etc., it is not enough to predict tire burst by simply using certain factor data, and the effect of directly combining various factor data to predict tire burst is also not ideal. Therefore, it is considered that a factor is used as a key factor and a factor having a synergistic effect with the factor as a key factor is used as an additional factor of the key factor, so that tire puncture prediction is performed based on the key factor data affecting tire puncture and the additional factor data corresponding to the key factor data.
For example, various factor data of the same type of tire from the start of use to the blowout may be collected, such as load data, tire temperature data, tire pressure data, speed data, and the like of the same type of tire from the start of use to the blowout. And then taking one of the factor data as key factor data, inputting the key factor data and other factor data into a preset model for training so as to determine an additional factor with a synergistic effect with the key factor. Or the additional factors with the synergistic effect with the key factors can be determined empirically, so that the subsequent tire burst prediction can be more accurately performed based on the key factor data of the tire and the additional factor data corresponding to the key factor data.
Alternatively, the key factor data trained by the preset model or empirically determined may include at least one of loading factor data, tire temperature factor data, high tire pressure factor data, low tire pressure factor data, and speed factor data. Including, for example, loading factor data, tire temperature factor data, high tire pressure factor data, low tire pressure factor data, and speed factor data. Or only comprises loading factor data, tire temperature factor data and high tire pressure factor data.
Each item of factor data affecting tire burst is used as key factor data, and additional factor data corresponding to the key factor data is determined, so that tire burst prediction can be performed more accurately when a certain item of key factor data and the additional factor data corresponding to the key factor data meet the corresponding conditions of tire burst.
By way of example, training through a pre-set model or empirically may determine: when the key factor data is load factor data, the additional factor data that affects the tire burst together with the key factor data may include tire temperature factor data, high tire pressure factor data, and speed factor data.
When the key factor data is tire temperature factor data, the additional factor data that affects tire burst together with the key factor data may include load factor data, high tire pressure factor data, and speed factor data.
When the key factor data is high tire pressure factor data, the additional factor data that affects tire burst together with the key factor data may include load factor data, tire temperature factor data, and speed factor data.
When the key factor data is speed factor data, the additional factor data that affects the tire burst together with the key factor data may include load factor data, tire temperature factor data, and high tire pressure factor data.
When the key factor data is low tire pressure factor data, the additional factor data that affects tire puncture in conjunction with the key factor data may include loading factor data.
The comparison of damaged tires in low tire pressure is considered, so that when the key factor data is low tire pressure factor data, tire burst prediction can be performed only by combining load factor data with strong synergism with the tire pressure factor. That is, only the loading factor data is used as the additional factor data of the low tire pressure factor data.
In step 102, statistics of usage data of the tire when the key factor data meets a first threshold and the additional factor data meets a second threshold.
The first threshold value is a value representing that the tire is in a fatigue working condition under the key factors, and the second threshold value is a value representing that the tire is in the fatigue working condition under the additional factors corresponding to the key factors.
In this embodiment, considering that the tire is more likely to be burst when the tire is in the fatigue working condition, the value of the tire in the fatigue working condition under the key factor is obtained as the first threshold corresponding to the key factor, and the value of the tire in the fatigue working condition under the additional factor corresponding to the key factor is obtained as the second threshold corresponding to the additional factor, so that the use data of the tire when the key factor data of the tire meets the first threshold and the additional factor data meets the second threshold is counted, so as to measure the use condition of the tire in the fatigue working condition, and further, whether the tire is burst is predicted more accurately based on the use condition of the tire in the fatigue working condition.
For example, if the tire is more likely to cause a puncture when the load of the tire is greater than 110% of the standard load, and at the same time, the tire is more likely to cause a puncture when the tire temperature is greater than 70 ℃ and the tire pressure is greater than 1150kpa, and the speed is greater than 70km/h, it may be determined that when the key factor data is the load factor data, the first threshold value corresponding to the key factor is 110% of the standard load, and the second threshold values corresponding to the key factors are 70 ℃ and 1150kpa and 70km/h, respectively.
For example, if the tire is more likely to cause a puncture when the tire temperature is > 80 ℃, and at the same time, the tire is more likely to cause a puncture when the load is > 80%, the tire pressure is > 1150kpa, and the speed is > 80km/h, it may be determined that when the key factor data is the tire temperature factor data, the first threshold value corresponding to the key factor is 80 ℃, and the second threshold values of the additional factors corresponding to the key factors are respectively 80% standard load, 1150kpa, and 80km/h.
For example, if the tire is more likely to cause a puncture when the high tire pressure is greater than 1250kpa, and at the same time, the tire is more likely to cause a puncture when the load is greater than 80%, the tire temperature is greater than 75 ℃, and the speed is greater than 80km/h, it may be determined that when the key factor data is the high tire pressure factor data, the first threshold value corresponding to the key factor is 1250kpa, and the second threshold values corresponding to the additional factors are 80% standard load, 75 ℃ and 80km/h, respectively.
For example, if the tire is more likely to cause a puncture when the speed of the tire is greater than 80km/h, and at the same time, the tire is more likely to cause a puncture when the load is greater than 80%, the tire temperature is greater than 65 ℃, and the high tire pressure is greater than 1150kpa, it may be determined that when the key factor data is the speed factor data, the first threshold value corresponding to the key factor is 80km/h, and the second threshold values corresponding to the additional factors are 80% standard load, 65 ℃ and 1150kpa, respectively.
For example, if the tire is more likely to cause a blowout when the low tire pressure is less than 600kpa, and at the same time, the tire is more likely to cause a blowout when the load is greater than 80%, it may be determined that the first threshold value corresponding to the key factor is 600kpa and the second threshold value corresponding to the additional factor is 80% of the standard load when the key factor data is the low tire pressure factor data.
When the key factor and the additional factor corresponding to the key factor are other factors, the determination process of the first threshold of the key factor and the second threshold of the corresponding additional factor is similar to the above process, and will not be described herein.
On this basis, because the tire burst is caused by the use of the tire under the action of factors of corresponding high load, high temperature and high pressure, the statistics of the usage data of the tire when the key factor data meets a first threshold value and the additional factor data meets a second threshold value is needed to predict the tire burst. For example, the statistical load is more than 110% of standard load, and the tire temperature is more than 70 ℃, the tire pressure is more than 1150kpa, and the speed is more than 70km/h, so as to predict the tire burst.
Wherein the usage data of the tire may characterize a usage experience of the tire. For example, the usage data of the tire may include usage mileage data and/or usage time data of the tire. And counting the accumulated use condition of the tire under the action of factors of corresponding high load, high temperature and high pressure through at least one of the use mileage data and the use time data of the tire.
For example, the accumulated use mileage data and/or the accumulated use time data of the tire are counted when the load is more than 110% of the standard load, and the tire temperature is more than 70 ℃, the tire pressure is more than 1150kpa, and the speed is more than 70 km/h.
In step 103, equivalent fatigue usage data for the tire under the key factors is calculated from the usage data.
In step 104, a puncture prediction is performed for the tire based on the equivalent fatigue usage data.
In combination with the description in step 102, due to the difference of the corresponding first threshold value or the second threshold value when the factor affecting the tire burst is taken as the key factor or the additional factor, and the difference of the corresponding second threshold value when the additional factor is taken as the additional factor of different key factors, the usage data of the tire when the key factor data meets the first threshold value and the corresponding additional factor data meets the second threshold value is counted, so as to calculate the equivalent fatigue usage data of the tire under the key factor according to the usage data counted when the key factor and the corresponding additional factor meet the condition, and perform the tire burst prediction on the tire according to the equivalent fatigue usage data of the tire under the key factor, thereby obtaining a more accurate tire burst prediction result.
According to the embodiment of the invention, the key factor data influencing tire burst and the additional factor data influencing tire burst together with the key factor data are obtained in the use process of the tire, so that the use data of the tire when the key factor data meets a first threshold value and the additional factor data meets a second threshold value is counted, the use condition of the tire under the fatigue working condition is measured by the use data of the tire when the key factor data meets the first threshold value and the additional factor data meets the second threshold value, and the equivalent fatigue use data of the tire under the corresponding key factor and the corresponding additional factor are obtained, so that the tire burst is predicted more accurately according to the equivalent fatigue use data, the early prevention of the tire burst of a commercial vehicle is facilitated, and the traffic accidents and the property loss are reduced.
Optionally, for the purpose of calculating the equivalent fatigue usage data more accurately later to more accurately perform tire burst prediction, the usage data of the tire when the statistical key factor data satisfies the first threshold and the additional factor data satisfies the second threshold may include: dividing the key factor data into a plurality of subintervals, and counting the usage data of the tire in each subinterval when the key factor data meets a first threshold value and the additional factor data meets a second threshold value.
For example, as shown in table 1, for example, the tire temperature factor data, the tire pressure factor data, the speed factor data, and the load factor data may be divided into a plurality of subintervals according to the set interval values, and the corresponding levels of the subintervals may be determined, so as to facilitate accurate statistics of usage data of the tire when the critical factor data satisfies the first threshold value and the additional factor data satisfies the second threshold value.
TABLE 1
| Rank sequence | Tire Wen | Tire pressure kpa | Speed km/h | Mark load% |
| 0 | ＞95 | ＞1400 | ＞140 | ＞200 |
| 1 | (90，95) | (1350，1400) | (135，140) | (190，200) |
| 2 | (85，90) | (1300，1350) | (130，135) | (180，190) |
| 3 | (80，85) | (1250，1300) | (125，130) | (170，180) |
| 4 | (75，80) | (1200，1250) | (120，125) | (160，170) |
| 5 | (70，75) | (1150，1200) | (115，120) | (150，160) |
| 6 | (65，70) | (1100，1150) | (110，115) | (140，150) |
| 7 | (60，65) | (1050，1100) | (105，110) | (130，140) |
| 8 | (55，60) | (1000，1050) | (100，105) | (120，130) |
| 9 | (50，55) | (950，1000) | (95，100) | (110，120) |
| 10 | (45，50) | (900，950) | (90，95) | (100，110) |
| 11 | (40，45) | (850，900) | (85，80) | (90，100) |
| 12 | (35，40) | (800，850) | (80，85) | (80，90) |
| 13 | (30，35) | (750，800) | (75，80) | (70，80) |
| … | … | … | … | … |
| 17 | (10，15) | (550，600) | (55，60) | (30，40) |
| 18 | (5，10) | (500，550) | (50，55) | (20，30) |
| 19 | (0，5) | (450，500) | (45，50) | (10，20) |
| 20 | (-5，0) | (400，450) | (40，45) | (0，10) |
| 21 | … | … | … |
As shown in table 1, the levels corresponding to the subintervals divided for each key factor data may be ordered in order from small to large or from large to small, which is not limited in this embodiment.
In this embodiment, considering that the probability of tire puncture is different when the key factor data falls in each subinterval, in order to predict tire puncture more accurately, the usage data of the tire at each subinterval is counted when the key factor data satisfies the first threshold and the additional factor data satisfies the second threshold.
Alternatively, calculating equivalent fatigue usage data for the tire under the key factors from the usage data may include: and acquiring fatigue weights corresponding to the key factor data in each subinterval, and calculating equivalent fatigue use data of the tire under the key factors according to the fatigue weights corresponding to the key factor data in each subinterval and the use data of the tire when the key factor data is in each subinterval.
In this embodiment, on the basis of dividing the key factor data into a plurality of subintervals, according to the possibility that the tire bursts when the key factor data falls in different subintervals, the fatigue weight corresponding to each subinterval of the key factor data is obtained, so that the equivalent fatigue use data of the tire under the key factor can be measured more accurately according to the fatigue weight corresponding to each subinterval of the key factor data and the use data of the tire when the key factor data is in each subinterval.
For example, the key factor data is divided into several sub-intervals and the fatigue weights of the key factor data corresponding to the respective sub-intervals may be determined by model training. As shown in table 1, for example, when the key factor data is load factor data, the corresponding first threshold may be 110% of the standard load (or nominal load), and the data with load factor data > 110% of the nominal load may be divided into 10 subintervals. When the key factor data is speed factor data, the corresponding first threshold value can be 100km/h, and the data of the speed factor data > 100km/h can be divided into 9 subintervals. When the key factor data is high tire pressure factor data, the corresponding first threshold value may be 1250kpa, and the data of the high tire pressure factor data > 1250kpa may be divided into 4 subintervals. When the key factor data is low tire pressure factor data, the corresponding first threshold value may be 600kpa, and the data of low tire pressure factor data < 600kpa may be divided into 12 subintervals. When the key factor data is the tire temperature factor data, the corresponding first threshold value can be 80 ℃, and the data of the tire temperature factor data > 80 ℃ can be divided into 4 subintervals.
Optionally, calculating the equivalent fatigue usage data of the tire under the key factors according to the fatigue weights corresponding to the key factor data in each subinterval and the usage data of the tire when the key factor data is in each subinterval may include:
From y= Σa iyi, equivalent fatigue usage data for the tire under the key factor is calculated.
Wherein Y is equivalent fatigue use data of the tire under the key factors, ai is fatigue weight corresponding to the ith subinterval of the key factor data, and yi is use data of the tire when the key factor data is in the ith subinterval.
Illustratively, in connection with table 1, the key factor data includes load factor data, tire temperature factor data, high tire pressure factor data, low tire pressure factor data, and speed factor data as examples:
For the load factor, statistics is carried out on the load factor data of more than 110 percent, and the tire usage data when the tire temperature is more than 70 ℃, the high tire pressure is more than 1150kpa, and the speed is more than 70km/h, wherein the load factor data of more than 110 percent can be divided into 10 subintervals according to the table 1, the equivalent fatigue usage data of the tire under the load factor is marked as Y 1, and the equivalent fatigue usage data of the tire under the load factor can be calculated according to Y 1＝a0y0+a1y1+…+a9y9.
For the tire temperature factor, the statistical tire temperature factor data is more than 80 ℃, the load is more than 80% standard load, the high tire pressure is more than 1150kpa, and the speed is more than 80km/h, wherein the tire temperature factor data can be divided into 4 subintervals according to table 1, the equivalent fatigue use data of the tire under the tire temperature factor is recorded as Y 2, and the equivalent fatigue use data of the tire under the tire temperature factor can be calculated according to Y 2＝a0y0+a1y1+…+a3y3.
For the high tire pressure factor, statistics is carried out on data of the high tire pressure factor which is more than 1250kpa, the load is more than 80 percent of standard load, the tire temperature is more than 75 ℃, and the speed is more than 80km/h, wherein the data of the high tire pressure factor which is more than 1250kpa can be divided into 4 subintervals according to the table 1, the equivalent fatigue use data of the tire under the high tire pressure factor is recorded as Y 3, and the equivalent fatigue use data of the tire under the high tire pressure factor can be calculated according to Y 3＝a0y0+a1y1+…+a3y3.
For the speed factor, the statistical speed factor data is more than 100km/h, the load is more than 80% standard load, the tire temperature is more than 65 ℃, and the tire usage data when the high tire pressure is more than 1150kpa, wherein the data of the speed factor data more than 100km/h can be divided into 9 subintervals according to the table 1, the equivalent fatigue usage data of the tire under the speed factor is recorded as Y 4, and the equivalent fatigue usage data of the tire under the speed factor can be calculated according to Y 4＝a0y0+a1y1+…+a8y8.
For the low tire pressure factor, statistics is carried out on the usage data of the tire with the low tire pressure factor data being less than 600kpa and the load being more than 80% of the standard load, wherein the data with the low tire pressure factor data being less than 600kpa can be divided into 12 subintervals according to the table 1, the equivalent fatigue usage data of the tire with the low tire pressure factor is recorded as Y 5, and the equivalent fatigue usage data of the tire with the tire temperature factor can be calculated according to Y 5＝a17y17+a18y18+…+a28y28.
Wherein, the fatigue weight a 0、a1……a9 used when the equivalent fatigue using data Y 1 of the tire under the loading factor is calculated, the fatigue weight a 0、a1……a3 used when the equivalent fatigue using data Y 2 of the tire under the tire temperature factor is calculated, calculating the fatigue weight a 0、a1……a3 used when the equivalent fatigue using data Y 3 of the tire under the high tire pressure factor, calculating the fatigue weight a 0、a1……a8 used when the equivalent fatigue using data Y 4 of the tire under the speed factor, And the fatigue weight a 17、a18……a28 used for calculating the equivalent fatigue use data Y 5 of the tire under the low tire pressure factor can be the same or different, and can be specifically determined through model training. In the model training process, the equivalent fatigue use data Y 1 of the tire under the load factor, the equivalent fatigue use data Y 2 of the tire under the tire temperature factor, the equivalent fatigue use data Y 3 of the tire under the high tire pressure factor can also be determined, The tire burst prediction method comprises the steps of performing tire burst prediction on tires based on the fatigue use thresholds corresponding to the equivalent fatigue use data Y 4 of the tires under the speed factors and the fatigue use data Y 5 of the tires under the low tire pressure factors.
Optionally, performing tire burst prediction on the tire according to the equivalent fatigue usage data may include:
Judging whether the equivalent fatigue use data of the tire under each key factor exceeds a corresponding fatigue use threshold value, and if the equivalent fatigue use data of the tire under one key factor exceeds the corresponding fatigue use threshold value, sending a tire burst prompt message to a user.
For example, as described above, when the key factor data includes the load factor data, the tire temperature factor data, the high tire pressure factor data, the low tire pressure factor data, and the speed factor data, the equivalent fatigue use data Y 1 of the tire under the load factor, the equivalent fatigue use data Y 2 of the tire under the tire temperature factor, the equivalent fatigue use data Y 3 of the tire under the high tire pressure factor, the equivalent fatigue use data Y 4 of the tire under the speed factor, and the equivalent fatigue use data Y 5 of the tire under the low tire pressure factor may be simultaneously calculated in parallel, thereby predicting whether the tire is flat or not in parallel based on the fatigue use thresholds corresponding to Y 1、Y2、Y3、Y4、Y5 and Y 1、Y2、Y3、Y4、Y5, respectively. When any one of the Y 1、Y2、Y3、Y4、Y5 exceeds the corresponding fatigue use threshold, a tire burst prompt message is sent to the user, so that the tire burst prompt can be timely and accurately given to the user, the user can make preventive measures in advance, and traffic accidents and property loss can be reduced.
It should be understood that the sequence number of each step in the foregoing embodiment does not mean that the execution sequence of each process should be determined by the function and the internal logic, and should not limit the implementation process of the embodiment of the present invention.
The following are device embodiments of the invention, for details not described in detail therein, reference may be made to the corresponding method embodiments described above.
Fig. 2 is a schematic structural diagram of a tire puncture prediction apparatus according to an embodiment of the present invention, and for convenience of explanation, only the portions related to the embodiment of the present invention are shown, and the details are as follows:
As shown in fig. 2, the tire burst prediction apparatus includes: an acquisition module 21, a first processing module 22, a second processing module 23 and a prediction module 24.
An obtaining module 21, configured to obtain, during use of the tire, key factor data that affects tire puncture and additional factor data that affects tire puncture together with the key factor data;
A first processing module 22, configured to count usage data of the tire when the key factor data meets a first threshold and the additional factor data meets a second threshold, where the first threshold is a value indicating that the tire is in a fatigue condition under the key factor, and the second threshold is a value indicating that the tire is in a fatigue condition under the additional factor corresponding to the key factor;
a second processing module 23 for calculating equivalent fatigue usage data of the tire under a key factor from the usage data;
and the prediction module 24 is used for carrying out tire burst prediction on the tire according to the equivalent fatigue use data.
According to the embodiment of the invention, the key factor data influencing tire burst and the additional factor data influencing tire burst together with the key factor data are obtained in the use process of the tire, so that the use data of the tire when the key factor data meets a first threshold value and the additional factor data meets a second threshold value is counted, the use condition of the tire under the fatigue working condition is measured by the use data of the tire when the key factor data meets the first threshold value and the additional factor data meets the second threshold value, and the equivalent fatigue use data of the tire under the corresponding key factor and the corresponding additional factor are obtained, so that the tire burst is predicted more accurately according to the equivalent fatigue use data, the early prevention of the tire burst of a commercial vehicle is facilitated, and the traffic accidents and the property loss are reduced.
In one possible implementation, the first processing module 22 may be configured to divide the key factor data into a plurality of subintervals; and counting the usage data of the tire in each subinterval of the key factor data when the key factor data meets a first threshold value and the additional factor data meets a second threshold value.
In a possible implementation manner, the second processing module 23 may be configured to obtain fatigue weights corresponding to the key factor data in each subinterval; and calculating equivalent fatigue use data of the tire under the key factors according to the fatigue weights corresponding to the key factor data in each subinterval and the use data of the tire when the key factor data are in each subinterval.
In one possible implementation, the second processing module 23 may be configured to calculate the equivalent fatigue usage data of the tire under the key factor according to y= Σa iyi; wherein Y is equivalent fatigue use data of the tire under a key factor, ai is fatigue weight corresponding to the key factor data in the ith subinterval, and yi is use data of the tire when the key factor data is in the ith subinterval.
In one possible implementation, the key factor data includes at least one of load factor data, tire temperature factor data, high tire pressure factor data, low tire pressure factor data, and speed factor data;
When the key factor data is the loading factor data, the additional factor data which affects tire burst together with the key factor data comprises the tire temperature factor data, the high tire pressure factor data and the speed factor data;
when the key factor data is the tire temperature factor data, the additional factor data which affects tire burst together with the key factor data comprises the loading factor data, the high tire pressure factor data and the speed factor data;
when the key factor data is the high tire pressure factor data, the additional factor data which affects tire burst together with the key factor data comprises the load factor data, the tire temperature factor data and the speed factor data;
When the key factor data is the speed factor data, the additional factor data which affects the tire burst together with the key factor data comprises the loading factor data, the tire temperature factor data and the high tire pressure factor data;
When the key factor data is the low tire pressure factor data, the additional factor data which affects tire burst together with the key factor data comprises the loading factor data.
In one possible implementation, prediction module 24 may be configured to determine whether the equivalent fatigue usage data for the tire at each key factor exceeds a corresponding fatigue usage threshold; and if the equivalent fatigue use data of the tire under one of the key factors exceeds the corresponding fatigue use threshold value, sending a tire burst prompt message to a user.
In one possible implementation, the usage data includes usage mileage data and/or usage time data.
Fig. 3 is a schematic diagram of a terminal according to an embodiment of the present invention. As shown in fig. 3, the terminal 3 of this embodiment includes: a processor 30, a memory 31 and a computer program 32 stored in the memory 31 and executable on the processor 30. The steps of the various embodiments of the tire puncture prediction method described above, such as steps 101 through 104 shown in fig. 1, are implemented by the processor 30 when executing the computer program 32. Or the processor 30, when executing the computer program 32, performs the functions of the modules/units of the apparatus embodiments described above, such as the functions of the modules/units 21 to 24 shown in fig. 2.
By way of example, the computer program 32 may be partitioned into one or more modules/units that are stored in the memory 31 and executed by the processor 30 to complete the present invention. One or more of the modules/units may be a series of computer program instruction segments capable of performing a specific function for describing the execution of the computer program 32 in the terminal 3. For example, the computer program 32 may be split into the modules/units 21 to 24 shown in fig. 2.
The terminal 3 may be a computing device such as a desktop computer, a notebook computer, a palm computer, and a cloud server. The terminal 3 may include, but is not limited to, a processor 30, a memory 31. It will be appreciated by those skilled in the art that fig. 3 is merely an example of the terminal 3 and is not limiting of the terminal 3, and may include more or fewer components than shown, or may combine some components, or different components, e.g., the terminal may further include an input-output device, a network access device, a bus, etc.
The Processor 30 may be a central processing unit (Central Processing Unit, CPU), other general purpose Processor, digital signal Processor (DIGITAL SIGNAL Processor, DSP), application SPECIFIC INTEGRATED Circuit (ASIC), field-Programmable gate array (Field-Programmable GATE ARRAY, FPGA) or other Programmable logic device, discrete gate or transistor logic device, discrete hardware components, or the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory 31 may be an internal storage unit of the terminal 3, such as a hard disk or a memory of the terminal 3. The memory 31 may also be an external storage device of the terminal 3, such as a plug-in hard disk provided on the terminal 3, a smart memory card (SMART MEDIA CARD, SMC), a Secure Digital (SD) card, a flash memory card (FLASH CARD), or the like. Further, the memory 31 may also include both an internal storage unit and an external storage device of the terminal 3. The memory 31 is used to store computer programs and other programs and data required by the terminal. The memory 31 may also be used to temporarily store data that has been output or is to be output.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-described division of the functional units and modules is illustrated, and in practical application, the above-described functional distribution may be performed by different functional units and modules according to needs, i.e. the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-described functions. The functional units and modules in the embodiment may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit, where the integrated units may be implemented in a form of hardware or a form of a software functional unit. In addition, the specific names of the functional units and modules are only for distinguishing from each other, and are not used for limiting the protection scope of the present application. The specific working process of the units and modules in the above system may refer to the corresponding process in the foregoing method embodiment, which is not described herein again.
In the foregoing embodiments, the descriptions of the embodiments are emphasized, and in part, not described or illustrated in any particular embodiment, reference is made to the related descriptions of other embodiments.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
In the embodiments provided in the present invention, it should be understood that the disclosed apparatus/terminal and method may be implemented in other manners. For example, the apparatus/terminal embodiments described above are merely illustrative, e.g., the division of modules or units is merely a logical functional division, and there may be additional divisions when actually implemented, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection via interfaces, devices or units, which may be in electrical, mechanical or other forms.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed over a plurality of 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.
In addition, each functional unit in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.
The integrated modules/units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the present invention may implement all or part of the flow of the method of the above embodiment, or may be implemented by a computer program instructing related hardware, and the computer program may be stored in a computer readable storage medium, where the computer program, when executed by a processor, may implement the steps of each of the embodiments of the method of predicting tire puncture. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, executable files or in some intermediate form, etc. The computer readable medium may include: any entity or device capable of carrying computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer Memory, a Read-Only Memory (ROM), a random access Memory (Random Access Memory, RAM), an electrical carrier signal, a telecommunications signal, a software distribution medium, and so forth.
The above embodiments are only for illustrating the technical solution of the present invention, and are not limiting; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention, and are intended to be included in the scope of the present invention.
Claims (7)
1. A tire puncture prediction method, comprising:
In the using process of the tire, acquiring key factor data influencing tire burst and additional factor data influencing the tire burst together with the key factor data;
Counting the usage data of the tire when the key factor data meets a first threshold value and the additional factor data meets a second threshold value, wherein the first threshold value is a value representing that the tire is in a fatigue working condition under the key factor, and the second threshold value is a value representing that the tire is in the fatigue working condition under the additional factor corresponding to the key factor;
calculating equivalent fatigue use data of the tire under a key factor according to the use data;
performing tire burst prediction on the tire according to the equivalent fatigue use data;
Wherein counting usage data of the tire when the key factor data satisfies a first threshold and the additional factor data satisfies a second threshold comprises:
Dividing the key factor data into a plurality of subintervals;
counting the usage data of the tire in each subinterval of the key factor data when the key factor data meets a first threshold value and the additional factor data meets a second threshold value;
calculating equivalent fatigue usage data of the tire under a key factor according to the usage data, wherein the equivalent fatigue usage data comprises:
acquiring fatigue weights corresponding to the key factor data in each subinterval;
Calculating equivalent fatigue use data of the tire under the key factors according to the fatigue weights corresponding to the key factor data in each subinterval and the use data of the tire when the key factor data are in each subinterval;
According to the fatigue weight corresponding to the key factor data in each subinterval and the use data of the tire when the key factor data is in each subinterval, calculating the equivalent fatigue use data of the tire under the key factor, wherein the method comprises the following steps:
Calculating equivalent fatigue usage data of the tire under a key factor according to the Y= Σa iyi;
Wherein Y is equivalent fatigue use data of the tire under a key factor, a i is fatigue weight corresponding to the key factor data in the ith subinterval, and Y i is use data of the tire when the key factor data is in the ith subinterval.
2. The tire puncture prediction method according to claim 1, wherein the key factor data includes at least one of load factor data, tire temperature factor data, high tire pressure factor data, low tire pressure factor data, and speed factor data;
When the key factor data is the loading factor data, the additional factor data which affects tire burst together with the key factor data comprises the tire temperature factor data, the high tire pressure factor data and the speed factor data;
when the key factor data is the tire temperature factor data, the additional factor data which affects tire burst together with the key factor data comprises the loading factor data, the high tire pressure factor data and the speed factor data;
when the key factor data is the high tire pressure factor data, the additional factor data which affects tire burst together with the key factor data comprises the load factor data, the tire temperature factor data and the speed factor data;
When the key factor data is the speed factor data, the additional factor data which affects the tire burst together with the key factor data comprises the loading factor data, the tire temperature factor data and the high tire pressure factor data;
When the key factor data is the low tire pressure factor data, the additional factor data which affects tire burst together with the key factor data comprises the loading factor data.
3. The tire puncture prediction method according to claim 2, wherein the tire puncture prediction is performed on the basis of the equivalent fatigue use data, comprising:
Judging whether the equivalent fatigue use data of the tire under each key factor exceeds a corresponding fatigue use threshold value or not;
And if the equivalent fatigue use data of the tire under one of the key factors exceeds the corresponding fatigue use threshold value, sending a tire burst prompt message to a user.
4. The method of claim 1, wherein the usage data comprises usage mileage data and/or usage time data.
5. A tire puncture prediction apparatus, comprising:
The acquisition module is used for acquiring key factor data influencing tire burst and additional factor data influencing the tire burst together with the key factor data in the use process of the tire;
The first processing module is used for counting the usage data of the tire when the key factor data meets a first threshold value and the additional factor data meets a second threshold value, wherein the first threshold value is a value representing that the tire is in a fatigue working condition under the key factor, and the second threshold value is a value representing that the tire is in the fatigue working condition under the additional factor corresponding to the key factor;
the second processing module is used for calculating equivalent fatigue use data of the tire under a key factor according to the use data;
the prediction module is used for predicting tire burst of the tire according to the equivalent fatigue use data;
the first processing module is specifically configured to:
Dividing the key factor data into a plurality of subintervals;
counting the usage data of the tire in each subinterval of the key factor data when the key factor data meets a first threshold value and the additional factor data meets a second threshold value;
The second processing module is specifically configured to:
acquiring fatigue weights corresponding to the key factor data in each subinterval;
Calculating equivalent fatigue use data of the tire under the key factors according to the fatigue weights corresponding to the key factor data in each subinterval and the use data of the tire when the key factor data are in each subinterval;
According to the fatigue weight corresponding to the key factor data in each subinterval and the use data of the tire when the key factor data is in each subinterval, calculating the equivalent fatigue use data of the tire under the key factor, wherein the method comprises the following steps:
Calculating equivalent fatigue usage data of the tire under a key factor according to the Y= Σa iyi;
Wherein Y is equivalent fatigue use data of the tire under a key factor, a i is fatigue weight corresponding to the key factor data in the ith subinterval, and Y i is use data of the tire when the key factor data is in the ith subinterval.
6. A terminal comprising a memory for storing a computer program and a processor for invoking and running the computer program stored in the memory to perform the method of any of claims 1 to 4.
7. A computer readable storage medium storing a computer program, characterized in that the computer program when executed by a processor implements the steps of the method according to any of the preceding claims 1 to 4.
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Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115157938A (en) * | 2022-08-17 | 2022-10-11 | 梅赛德斯-奔驰集团股份公司 | Method and system for predicting vehicle flat tire |
| CN117057219A (en) * | 2023-07-17 | 2023-11-14 | 深蓝汽车科技有限公司 | Tire damage prediction method, device, equipment, storage medium and vehicle |
Family Cites Families (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107963018A (en) * | 2017-11-23 | 2018-04-27 | 广东菲柯特电子科技有限公司 | Tire pressure monitoring device with fatigue driving early warning function |
| US11865875B2 (en) * | 2020-08-18 | 2024-01-09 | The Goodyear Tire & Rubber Company | Tire high temperature forecasting system |
| DE102021208435A1 (en) * | 2021-08-04 | 2023-02-09 | Continental Reifen Deutschland Gmbh | Method for predicting a profile depth profile for a vehicle tire |
| CN114953860B (en) * | 2021-08-23 | 2025-01-07 | 长城汽车股份有限公司 | Tire monitoring method, device, vehicle and storage medium |
| CN116973136A (en) * | 2023-07-26 | 2023-10-31 | 东风商用车有限公司 | Tire state prediction method and device |
| CN117094163A (en) * | 2023-08-25 | 2023-11-21 | 中国重汽集团济南动力有限公司 | Commercial vehicle braking height Wen Baotai prediction method, system, device and medium |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN115157938A (en) * | 2022-08-17 | 2022-10-11 | 梅赛德斯-奔驰集团股份公司 | Method and system for predicting vehicle flat tire |
| CN117057219A (en) * | 2023-07-17 | 2023-11-14 | 深蓝汽车科技有限公司 | Tire damage prediction method, device, equipment, storage medium and vehicle |
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