CN117057219A - Tire damage prediction method, device, equipment, storage medium and vehicle - Google Patents
Tire damage prediction method, device, equipment, storage medium and vehicle Download PDFInfo
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Abstract
本申请涉及一种轮胎受损预测方法、装置、设备、存储介质及车辆，涉及汽车技术领域。包括在目标车辆行驶的过程中，获取目标车辆的第一车况参数和第一运行参数，第一车况参数包括以下至少一项：路况信息、轮胎型号、轮胎当前的磨损度，第一运行参数包括目标车辆中的至少一个传感器采集到的运行参数；基于目标车辆的第一车况参数和受损预测模型，确定目标车辆的运行参数对应的受损阈值；基于受损阈值和目标车辆的第一运行参数，确定目标车辆的轮胎的受损参数，受损参数用于指示目标车辆的轮胎受损的概率。由此，可以基于第一车况参数和第一运行参数，实现从多个角度对轮胎的综合受损预测，提高轮胎受损预测的准确性。
The present application relates to a tire damage prediction method, device, equipment, storage medium and vehicle, and relates to the field of automotive technology. This includes obtaining the first vehicle condition parameter and the first operating parameter of the target vehicle while the target vehicle is driving. The first vehicle condition parameter includes at least one of the following: road condition information, tire model, and current wear degree of the tire. The first operating parameter includes Operating parameters collected by at least one sensor in the target vehicle; based on the first vehicle condition parameters of the target vehicle and the damage prediction model, determining the damage threshold corresponding to the operating parameters of the target vehicle; based on the damage threshold and the first operation of the target vehicle Parameter, determine the damage parameter of the tire of the target vehicle, and the damage parameter is used to indicate the probability that the tire of the target vehicle is damaged. Thus, based on the first vehicle condition parameter and the first operating parameter, comprehensive tire damage prediction can be achieved from multiple angles, thereby improving the accuracy of tire damage prediction.
Description
Technical Field
The application relates to the technical field of automobiles, in particular to a tire damage prediction method, a device, equipment, a storage medium and a vehicle.
Background
With the rapid development of vehicle technology, the service life of vehicles is gradually increasing. With the increase of the service time of the vehicle, the damage condition of the tires of the vehicle during running gradually becomes a key factor affecting the running safety of the vehicle. In the related technology of damage to a vehicle tire, real-time parameter data of each sensor of the vehicle is mainly used for analysis to obtain the wear degree of the tire, so that damage prediction of the tire is realized.
However, in the above method, the tire damage predicted value is generally determined directly based on real-time data of each sensor of the vehicle, the obtained tire damage predicted value is completely dependent on parameter information of each sensor of the current vehicle, the obtained result value only represents the current damage degree of the tire, and the association relationship between the damage predicted value and the running safety of the vehicle cannot be determined, so that the accuracy of the tire damage predicted result cannot be guaranteed, and thus the accuracy of the current tire damage prediction is poor.
Disclosure of Invention
The application provides a tire damage prediction method, a device, equipment, a storage medium and a vehicle, which at least solve the technical problem of poor accuracy of tire damage prediction of the vehicle in the related art. The technical scheme of the application is as follows:
According to a first aspect of the present application, there is provided a tire damage prediction method comprising: during the driving of the target vehicle, acquiring a first vehicle condition parameter and a first operation parameter of the target vehicle, wherein the first vehicle condition parameter comprises at least one of the following: the road condition information, the tire model and the current wear degree of the tire, and the first operation parameters comprise operation parameters acquired by at least one sensor in the target vehicle; determining a damage threshold corresponding to the operation parameter of the target vehicle based on the first vehicle condition parameter and the damage prediction model of the target vehicle, wherein each operation parameter of at least one operation parameter included in the first operation parameter corresponds to one damage threshold; based on the damage threshold and the first operating parameter of the target vehicle, a damage parameter of the tire of the target vehicle is determined, the damage parameter being indicative of a probability of damage to the tire of the target vehicle.
According to the technical means, the damage threshold corresponding to the operation parameter of the target vehicle can be determined based on the first vehicle condition parameter of the target vehicle and the damage prediction model, and then the damage parameter of the tire of the target vehicle is determined according to the damage threshold and the first operation parameter of the target vehicle, so that damage prediction of the target tire is realized based on the damage parameter. Based on the method, the method and the device can realize comprehensive damage prediction of the tire from multiple angles based on the first vehicle condition parameter and the first operation parameter, and improve the accuracy of the damage prediction of the tire.
In one possible embodiment, before determining the damage threshold corresponding to the operating parameter of the target vehicle based on the first condition parameter of the target vehicle and the damage prediction model, the method further includes: acquiring a second vehicle condition parameter and a second operating parameter transmitted when a tire of each of a plurality of training vehicles is damaged, the second vehicle condition parameter including at least one of: the road condition information, the tire model and the current wear degree of the tire, and the second operation parameters comprise at least one of the following: steering angle, steering speed, training tire pressure, temperature, pedal depth; determining an influence weight value of each vehicle condition parameter in the second vehicle condition parameters and each operation parameter in the second operation parameters on damage of the tire respectively; a damage prediction model is determined based on each of the second vehicle condition parameters and an impact weight value of each of the second operating parameters on damage to the tire.
According to the technical means, the second vehicle condition parameters and the second running parameters when a plurality of training vehicles are damaged are used as training data, so that the influence weight value of each running parameter on the damage of the tire is determined through the training data, and the damage prediction model is obtained, so that the damage prediction model is determined based on the plurality of running parameters and the vehicle condition parameters, the prediction accuracy of the damage prediction model is improved, and the accuracy of the damage prediction of the tire is improved.
In one possible embodiment, determining the damage parameter of the tire of the target vehicle based on the damage threshold and the first operating parameter of the target vehicle comprises: determining a damaged parameter of a tire of the target vehicle through a decision tree algorithm based on the damaged threshold value of the operating parameter of the target vehicle and the first operating parameter; the method further comprises the steps of: and sending first damage warning information when the damage parameter of the tire of the target vehicle is larger than a first preset threshold value, wherein the first damage warning information is used for indicating the damage parameter of the tire of the target vehicle.
According to the technical means, the method and the device can realize the prediction of the damaged parameters of the tire based on the decision tree algorithm, and send the first damage warning information under the condition that the damaged parameters of the tire of the target vehicle are larger than the first preset threshold value, so that the prediction of the damaged parameters of the tire based on the running data of the target vehicle is realized, and the efficiency of the tire damage prediction is improved through the prediction of the damaged parameters of the tire by the decision tree algorithm.
In one possible embodiment, the method further comprises: determining a speed-up value of each first operation parameter based on a plurality of groups of first operation parameters corresponding to a plurality of time points in a target time period in the running process of the target vehicle, wherein each time point corresponds to one group of operation parameters; and sending second damage warning information under the condition that the acceleration value of any one of at least one operation parameters included in the first operation parameters is larger than a second preset threshold value, wherein the second damage warning information is used for indicating that the operation parameters of the target vehicle are abnormal.
According to the technical means, the method can realize the prediction of the damage of the tire based on the speed increasing value of each operation parameter based on the plurality of groups of first operation parameters at a plurality of time points in the target time period so as to predict the damage of the tire from multiple aspects.
In one possible embodiment, the method further comprises: before the target vehicle runs, acquiring a running distance of a current journey and journey parameters corresponding to a last journey, wherein the journey parameters comprise at least one of the following: the method comprises the steps of journey average speed and historical damage parameters, wherein the historical damage parameters are used for indicating damage parameters of tires of a target vehicle at the end of a last journey; determining estimated damage parameters of the target tire when the target vehicle runs to the destination based on the running distance estimated value and the running evaluation reference value; and sending third damage warning information when the estimated damage parameter of the target tire is larger than a third preset threshold value, wherein the third damage warning information is used for indicating the estimated damage parameter corresponding to the current journey.
According to the technical means, the estimated damage parameters of the target tire when the vehicle runs to the destination can be determined based on the historical travel parameters and the travel distance of the current travel before the vehicle runs, and then the damage condition of the tire when the vehicle runs to the destination is predicted based on the estimated damage parameters, so that the damage prediction of the tire before the vehicle runs is realized, the damage of the tire during the running is avoided, and the running safety is improved.
According to a second aspect of the present application, there is provided a tire damage prediction apparatus comprising: an acquisition unit and a determination unit; the system comprises an acquisition unit, a control unit and a control unit, wherein the acquisition unit is used for acquiring a first vehicle condition parameter and a first operation parameter of a target vehicle in the process of driving the target vehicle, and the first vehicle condition parameter comprises at least one of the following components: the road condition information, the tire model and the current wear degree of the tire, and the first operation parameters comprise operation parameters acquired by at least one sensor in the target vehicle; the determining unit is used for determining a damage threshold value corresponding to the operation parameter of the target vehicle based on the first condition parameter and the damage prediction model of the target vehicle, wherein each operation parameter of at least one operation parameter included in the first operation parameter corresponds to one damage threshold value; the determining unit is further configured to determine a damage parameter of the tire of the target vehicle based on the damage threshold and the first operating parameter of the target vehicle, the damage parameter being used to indicate a probability that the tire of the target vehicle is damaged.
In a possible embodiment, the obtaining unit is further configured to obtain, when the tire of each of the plurality of training vehicles is damaged, a second vehicle condition parameter and a second operating parameter that are transmitted, the second vehicle condition parameter including at least one of: the road condition information, the tire model and the current wear degree of the tire, and the second operation parameters comprise at least one of the following: steering angle, steering speed, training tire pressure, temperature, pedal depth; the determining unit is further used for determining an influence weight value of each vehicle condition parameter in the second vehicle condition parameters and each operation parameter in the second operation parameters on damage of the tire respectively; the determining unit is further configured to determine a damage prediction model based on each of the second vehicle condition parameters and an influence weight value of each of the second operating parameters on damage to the tire.
In one possible embodiment, the tire damage prediction apparatus further includes: an alarm unit; the determining unit is further used for determining damaged parameters of tires of the target vehicle through a decision tree algorithm based on the damaged threshold value of the operation parameters of the target vehicle and the first operation parameters; and the warning unit is used for sending first damage warning information when the damage parameter of the tire of the target vehicle is larger than a first preset threshold value, wherein the first damage warning information is used for indicating the damage parameter of the tire of the target vehicle.
In a possible implementation manner, the determining unit is further configured to determine a speed-up value of each first operation parameter based on a plurality of sets of first operation parameters corresponding to a plurality of time points in a target time period during the driving process of the target vehicle, where each time point corresponds to a set of operation parameters; and the alarm unit is used for sending second damaged alarm information when the speed-up value of any one of at least one operation parameter included in the first operation parameter is larger than a second preset threshold value, wherein the second damaged alarm information is used for indicating that the operation parameter of the target vehicle is abnormal.
In one possible implementation manner, the obtaining unit is further configured to obtain, before the target vehicle travels, a travel distance of a current trip and a trip parameter corresponding to a last trip, where the trip parameter includes at least one of: the method comprises the steps of journey average speed and historical damage parameters, wherein the historical damage parameters are used for indicating damage parameters of tires of a target vehicle at the end of a last journey; the determining unit is further used for determining estimated damage parameters of the target tire when the target vehicle runs to the destination based on the running distance predicted value and the running evaluation reference value; the warning unit is used for sending third damage warning information when the estimated damage parameter of the target tire is larger than a third preset threshold value, and the third damage warning information is used for indicating the estimated damage parameter corresponding to the current journey.
According to a third aspect to which the present application relates, there is provided an electronic device comprising: a processor; a memory for storing processor-executable instructions; wherein the processor is configured to execute instructions to implement the method of the first aspect and any of its possible embodiments described above.
According to a fourth aspect of the application, there is provided a computer readable storage medium, which when executed by a processor of an electronic device, enables the electronic device to perform the method of any one of the above-mentioned first aspects and any one of its possible embodiments.
According to a fifth aspect of the application, there is provided a vehicle for carrying out the method of the first aspect and any one of its possible embodiments.
According to a sixth aspect of the application, there is provided a computer program product comprising computer instructions which, when run on an electronic device, cause the electronic device to perform the method of the first aspect and any of its possible embodiments.
Therefore, the technical characteristics of the application have the following beneficial effects:
(1) And determining a damage threshold corresponding to the operation parameter of the target vehicle based on the first vehicle condition parameter of the target vehicle and the damage prediction model, and further determining the damage parameter of the tire of the target vehicle according to the damage threshold and the first operation parameter of the target vehicle so as to realize damage prediction of the target tire based on the damage parameter. Based on the method, the method and the device can realize comprehensive damage prediction of the tire from multiple angles based on the first vehicle condition parameter and the first operation parameter, and improve the accuracy of the damage prediction of the tire.
(2) And taking the second vehicle condition parameters and the second running parameters when the plurality of training vehicles are damaged as training data, determining the influence weight value of each running parameter on the damage of the tire according to the training data, and obtaining a damage prediction model so as to determine the damage prediction model based on the plurality of running parameters and the vehicle condition parameters, and improving the prediction accuracy of the damage prediction model so as to improve the accuracy of the damage prediction of the tire.
(3) The method comprises the steps of realizing prediction of damaged parameters of the tire based on a decision tree algorithm, and sending first damage warning information under the condition that the damaged parameters of the tire of the target vehicle are larger than a first preset threshold value, so that the prediction of the damaged parameters of the tire based on the running data of the target vehicle is realized, and the efficiency of tire damage prediction is improved.
(4) Based on a plurality of sets of first operation parameters at a plurality of time points in a target time period, prediction of damage to the tire based on a speed-up value of each operation parameter is realized to predict damage to the tire from multiple aspects.
(5) Before the vehicle runs, the estimated damage parameters of the target tires are determined based on the historical travel parameters and the running distance of the current travel, and then the damage condition of the tires when the vehicle runs to the destination is predicted based on the estimated damage parameters, so that the damage prediction of the tires before the vehicle runs is realized, the damage of the tires in the running process is avoided, and the running safety is improved.
It should be noted that, the technical effects caused by any implementation manner of the second aspect to the sixth aspect may refer to the technical effects caused by the corresponding implementation manner in the first aspect, which is not described herein.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the application as claimed.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the application and together with the description, serve to explain the principles of the application and do not constitute a undue limitation on the application.
FIG. 1 is a schematic diagram of a tire damage prediction system according to an exemplary embodiment;
FIG. 2 is a flowchart illustrating a method of predicting tire damage according to an exemplary embodiment;
FIG. 3 is a flowchart illustrating yet another tire damage prediction method according to an exemplary embodiment;
FIG. 4 is a flowchart illustrating yet another tire damage prediction method according to an exemplary embodiment;
FIG. 5 is a flowchart illustrating yet another tire damage prediction method according to an exemplary embodiment;
FIG. 6 is a flowchart illustrating yet another tire damage prediction method according to an exemplary embodiment;
FIG. 7 is a flowchart illustrating yet another tire damage prediction method according to an exemplary embodiment;
FIG. 8 is a block diagram illustrating a tire damage prediction device according to an exemplary embodiment;
fig. 9 is a block diagram of an electronic device, according to an example embodiment.
Detailed Description
In order to enable a person skilled in the art to better understand the technical solutions of the present application, the technical solutions of the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings.
It should be noted that the terms "first," "second," and the like in the description and the claims of the present application and the above figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the application described herein may be implemented in sequences other than those illustrated or otherwise described herein. The implementations described in the following exemplary examples do not represent all implementations consistent with the application. Rather, they are merely examples of apparatus and methods consistent with aspects of the application as detailed in the accompanying claims.
In traffic safety, more and more traffic accidents are caused by broken tires of vehicles, particularly in specific road conditions such as expressways, single-lane roads and the like, the broken tires of a single vehicle not only threatens the safety of personnel in the vehicle, but also affects the safety of surrounding vehicles. In the related art, the safety of the forum is determined mainly by monitoring the wear degree of the tire, or whether the tire is normal is determined by conventional manual inspection. Common inspection methods determine the safety level of a tire by inspecting the tire pressure of the tire, mainly from the following aspects: checking frequency: the tire pressure, including the spare tire, is checked at least once monthly or before each long trip. The reading mode is as follows: the tire pressure test should be performed in a cooled state of the tire, i.e., after at least three hours of parking. After the tire is warmed up due to driving, it is normal that the tire pressure is higher than the recommended cold inflation pressure. Inflation and deflation: after accurate readings, the tire pressure should be replenished to the prescribed pressure on the automobile billboard if necessary. Because the readings of the cold tyre are most accurate, the vehicle should be started as far as possible until the nearest inflation point is inflated to the standard air pressure. The hot tire prohibits deflation or decompression. If the tire pressure of the hot tire is reduced, the tire pressure is insufficient after the tire is cooled. Inspection apparatus: the tire pressure is checked with a high quality barometer. We cannot determine from the appearance whether the inflation pressure of the tire is correct. Standard barometric pressure value: the tire pressure should be adjusted according to the recommended standard air pressure value of the vehicle manufacturer. Normal tire pressure can be seen at the driver's seat door of the vehicle, or at the driver's manual, fuel tank cap, or glove box. If the problem is found after inspection, the vehicle should be started to the nearest inflation point as much as possible for inflation.
The tire damage prediction method provided by the embodiment of the application can be applied to a tire damage prediction system. Fig. 1 shows a schematic configuration of the tire damage prediction system. As shown in fig. 1, the tire damage prediction system 10 includes: an information acquisition module 11, an electronic device 12 and an alarm module 13.
The tire damage prediction system 10 may be used in the internet of things, and the tire damage prediction system 10 (such as the information acquisition module 11, the electronic device 12, the alarm module 13, etc.) may include a plurality of central processing units (central processing unit, CPU), a plurality of memories, a storage device storing a plurality of operating systems, and other hardware.
The information obtaining module 11 is configured to obtain various status parameters of the vehicle, such as information of a current ambient temperature of the vehicle, a vehicle speed, a road condition, a tire pressure, a steering speed, a steering angle, a tire model, a tire wear degree, and the like.
Alternatively, the information acquisition module 11 may include a plurality of sensors, such as a temperature sensor, a speed sensor, etc., for acquiring various state parameters of the vehicle.
Optionally, the information acquisition module 11 may further include an image acquisition device, so as to acquire and analyze information such as road condition information and tire model.
Alternatively, the tire model information may also be manually selected based on a user to input the tire model of the vehicle.
The electronic device 12 may be a vehicle controller (vehicle control unit, VCU), an electronic control unit (electronic control unit, ECU), etc. and is configured to analyze and process information, such as determining damaged parameters of a tire of a vehicle based on parameter information of the vehicle acquired by the information acquisition module 11.
Optionally, the electronic device 12 may include a cloud big data platform to enable determination of damage parameters based on the cloud big data platform.
The alarm module 13 is configured to send alarm information, for example, based on the damage parameter determined by the electronic device 12, and send different alarm information based on an alarm information sending policy.
For easy understanding, the following describes the tire damage prediction method provided by the application with reference to the accompanying drawings.
Fig. 2 is a flowchart illustrating a tire damage prediction method according to an exemplary embodiment, and as shown in fig. 2, the tire damage prediction method includes the following S201 to S203:
s201, acquiring a first vehicle condition parameter and a first operation parameter of a target vehicle in the process of driving the target vehicle.
Wherein the first vehicle condition parameter comprises at least one of: the road condition information, the tire model and the current wear degree of the tire, and the first operating parameters comprise operating parameters acquired by at least one sensor in the target vehicle.
Optionally, in the driving process of the target vehicle, each sensor of the vehicle body may be set to transmit data (such as the first vehicle condition parameter and the first operation parameter) to the electronic device in real time according to a certain frequency, so as to obtain the first vehicle condition parameter and the first operation parameter of the target vehicle.
Alternatively, the tire model may be understood as the brand, type, etc. of the tire, and the model of the tire may be determined by the tire identification.
Alternatively, in combination with the tire damage prediction system 10 described above, the first condition parameter and the first operation parameter of the target vehicle may be acquired by the information acquisition module 11.
Optionally, the road surface image can be obtained through the image acquisition device, and the road condition grade corresponding to the road surface image is determined through operations such as feature extraction and the like, so that the road condition information is obtained.
The current wear degree of the tire is understood to be information such as the current wear degree of the tire, the thickness variation of the tire, and the like.
It is understood that as the service life of the vehicle increases, the thickness of the tires of the vehicle gradually decreases, i.e., wear occurs.
Optionally, the vehicle condition parameter may be understood as vehicle-machine data of the vehicle, and the road condition information refers to the road condition of the road on which the vehicle is running; the operating parameters are understood to be driving data of the vehicle, i.e. based on operating parameter information which varies over time as the vehicle travels.
It should be noted that, the damage in the embodiment of the present application may refer to damage that affects normal use of the tire, such as tire breakage (e.g. tire burst).
S202, determining a damage threshold corresponding to the operation parameter of the target vehicle based on the first condition parameter of the target vehicle and the damage prediction model.
Wherein each of the at least one operating parameter included in the first operating parameter corresponds to a damage threshold.
Optionally, a plurality of damage prediction models may be established, and further, based on a first vehicle condition parameter of the target vehicle, a damage prediction model corresponding to the first vehicle condition parameter is determined.
Alternatively, the damage prediction model may be a model that is determined by a machine learning algorithm based on training data and is capable of characterizing the association between each operating parameter and the damage to the tire.
S203, determining damage parameters of tires of the target vehicle based on the damage threshold and the first operation parameters of the target vehicle.
Wherein the damage parameter is used to indicate a probability of damage to a tire of the target vehicle.
Alternatively, the damaged parameter of the tire of the target vehicle may be comprehensively determined based on the damaged threshold value corresponding to each of the at least one first operating parameter and the actual value of the first operating parameter.
It should be noted that, since a vehicle generally includes a plurality of tires, for the same vehicle, there may be a difference between damage monitoring models corresponding to different tires due to different brands of different tires, different wear degrees of different tires, and different road conditions of different tires.
In one possible implementation, a large amount of training data may be collected, the collected training data is compared and analyzed through an algorithm to obtain a damaged model of the tire, the damaged model may include various parameter thresholds (i.e., critical values) when the tire is damaged, and a trained damage prediction model is obtained by training the damaged model, so that damage parameters of the tire of the target vehicle are determined based on the damage prediction model.
In the embodiment of the application, the damage threshold corresponding to the running parameter of the target vehicle is determined based on the first vehicle condition parameter of the target vehicle and the damage prediction model, and then the damage parameter of the tire of the target vehicle is determined according to the damage threshold and the first running parameter of the target vehicle, so that the damage prediction of the target tire is realized based on the damage parameter. Based on the method, the method and the device can realize comprehensive damage prediction of the tire from multiple angles based on the first vehicle condition parameter and the first operation parameter, and improve the accuracy of the damage prediction of the tire.
In some embodiments, in order to obtain the damage prediction model more accurately, as shown in fig. 3, in the method for predicting damage to a tire provided in the embodiment of the present application, before S202, S301 to S303 are further included:
s301, acquiring a second vehicle condition parameter and a second operation parameter which are transmitted when tires of each training vehicle in the plurality of training vehicles are damaged.
Wherein the second vehicle condition parameter comprises at least one of: the road condition information, the tire model and the current wear degree of the tire, and the second operation parameters comprise at least one of the following: steering angle, steering speed, training tire pressure, temperature, pedal depth.
Optionally, after the second vehicle condition parameter and the second operation parameter are obtained, the data needs to be preprocessed.
Optionally, preprocessing of the data may include feature screening, removing outlier data, filling in missing values, and the like.
Alternatively, for the second operating parameter, since both are numerical variables, a data normalization algorithm may be used to eliminate the magnitude effects of the data.
For example, assuming that the numerical variable before data normalization (i.e., the operation parameter) is shown in the following formula one, and the numerical variable after data normalization is shown in the following formula two, the relationship of data normalization may be shown in the following formula three.
X＝[x 1 ,x 2 ,…,x t ]Equation one
Y＝[y 1 ,y 2 ,…,y t ]Formula II
Alternatively, for the second vehicle condition parameters, since they are all classification variables, the dummy variable generation algorithm may be used to perform preprocessing of the data.
For example, assume that the classification variable X before preprocessing has t classifications, and the value of each classification is X (t). Based on the dummy variable generation algorithm, t-1 dummy variables can be generated, and the conversion relationship of the new variable (Y) and the original variable (X) can be shown as the following formula four.
S302, determining an influence weight value of each vehicle condition parameter in the second vehicle condition parameters and each operation parameter in the second operation parameters on damage of the tire.
S303, determining a damage prediction model based on each of the second vehicle condition parameters and the influence weight value of each of the second operating parameters on the damage of the tire.
Alternatively, because the dependent variables are classified variables and the interactions between the independent variables need to be eliminated, the independent variables can be one by one in a regression relationship with the dependent variables, such as a logic (logics) regression, and the variable coefficients Beta and R in the logics regression can be calculated 2 And the association relation between the two.
Since there is only one independent variable in the present application, R can be directly passed through 2 The confidence level of the argument is measured.
The prediction function of the logistic regression formula may be shown in the following formula five, so as to obtain a classification expression of the target variable shown in the formula six and the formula seven according to the formula five, and further integrate the classification expression of the target variable shown in the formula six and the formula seven to obtain a log likelihood function shown in the following formula eight; further, solving the minimum value of the function shown in the formula eight to obtain the value of Beta, and obtaining R through a method shown in the formula nine after solving the Beta value 2 Is a value of (2).
P(y＝1|x：θ)＝g(θ T *[1，x]) Formula six
P(y＝0|x：θ)＝1-g(θ T *[1，x]) Equation seven
The Beta is a coefficient of variation, and in the present application, it is understood that the degree of support of each variable on the occurrence of severe abrasion (damage) of the tire, R 2 It is understood that the degree of trust of Beta. y is the tag variable, θ represents the vector combination of intercept and variable coefficient, i.e., θ= [ α, beta ]]Where α represents the intercept. In formula nine, y t The value of the true tag is represented,representing predictive tag value,/->Representing the label average.
Optionally, in determining Beta and R 2 The variable screening can be performed by setting a threshold value (i.e., a threshold value of an operation parameter) according to specific use requirements.
Alternatively, when calculating the relevant row analysis of each parameter, the principal component analysis method (Principal Components analysis, PCA) may be used to perform the aggregation treatment on the variables (such as tire pressure, etc.) that have a relatively large influence on the damage probability of the tire.
Based on the above method, the correlation (i.e. the weight influence value) between the vehicle running parameters corresponding to different vehicle condition parameters and the damage of the vehicle can be determined, that is, a plurality of damage prediction models are determined, and then the damage prediction model corresponding to the vehicle condition parameters is determined according to the vehicle condition parameters of the vehicle.
In the embodiment of the application, the second vehicle condition parameters and the second running parameters when a plurality of training vehicles are damaged are used as training data, so that the influence weight value of each running parameter on the damage of the tire is determined through the training data, and the damage prediction model is obtained, so that the damage prediction model is determined based on the plurality of running parameters and the vehicle condition parameters, the prediction accuracy of the damage prediction model is improved, and the accuracy of the damage prediction of the tire is improved.
In some embodiments, in order to more accurately predict the damage of the tire, as shown in fig. 4, in the method for predicting the damage of the tire provided in the embodiment of the present application, S203 includes S401, and the method further includes S402:
S401, determining damaged parameters of tires of the target vehicle through a decision tree algorithm based on the damaged threshold value of the operation parameters of the target vehicle and the first operation parameters.
Alternatively, a decision tree algorithm model may be first established, and then, based on the decision tree algorithm model and the operating parameter damage threshold value and the first operating parameter of the target vehicle, the damage parameter of the tire of the target vehicle may be determined.
For example, a training set D, a feature set a and a threshold epsilon may be input, and a decision tree T may be output by the following steps (1) - (6).
(1) If all instances in D belong to the same class C k Setting T as a single junction tree and setting C k Returning T as the class of the node; (2) If it isThen T is set as a single junction tree and class C with the largest number of instances in D k Returning T as the class of the node; (3) Otherwise, calculating the information gain ratio of each feature in A to D, and selecting the feature Ag with the largest information gain ratio; (4) If the information gain ratio of Ag is smaller than the threshold epsilon, setting T as a single-node tree, and setting class C with the largest number of instances in D k As the nodeClass, return T; (5) Otherwise, for each possible value a of Ag i At ag=a i Dividing D into subsets and non-empty D i D is to i The class with the largest instance number is used as a mark to construct sub-nodes, the nodes and the sub-nodes form a tree T, and the tree T is returned; (6) For node i, with D i And (3) recursively calling the steps (1) - (5) by taking A- { Ag } as a feature set to obtain a subtree Ti and returning the subtree Ti.
In some embodiments, the impairment prediction model and the decision tree T may also be integrated into a decision tree classification prediction model.
Specifically, the obtained training data (such as the second vehicle condition parameter and the second operating parameter that are sent when the tire of each of the plurality of training vehicles is damaged) may be determined according to the tag variable: whether the tire is damaged or not is divided into a training set test set, for example, the training set and the test set are divided according to the proportion of 7:3.
Further, preprocessing the training data, such as data screening, data normalization, correlation analysis, PCA processing, and the like, and performing model training based on the preprocessed training data, such as according to tag variables: whether the tire is damaged (defining y= -1 as damaged, y = 1 as normal), training and testing the model based on a decision tree; and outputting the model when the test accuracy of the test set of the model is greater than a preset threshold (such as 70%), thereby obtaining a trained decision tree model.
Alternatively, the trained model may be deployed by using a springboot architecture, or may be deployed by using a flash interface.
Optionally, after the model is deployed and online, the model may be iteratively optimized periodically (e.g., weekly) based on real-time data monitored by the model, and the optimized result may be updated (e.g., each parameter threshold) to the database.
S402, sending first damage warning information when damage parameters of tires of the target vehicle are larger than a first preset threshold value.
Wherein the first damage warning information is used for indicating damage parameters of tires of the target vehicle.
Optionally, if the damage parameter of the tire of the target vehicle is greater than the first preset threshold, it may be considered that the tire of the target vehicle has a greater risk of damage (such as a possible tire burst), and at this time, a strong early warning may be performed to prompt the user (such as a driver in the vehicle) that the risk exists, and corresponding precautions are performed.
Optionally, when the damage parameter of the tire of the target vehicle is greater than the first preset threshold, the damage cause may be determined and displayed based on the first operation parameter of the target vehicle and the magnitude of the damage threshold of the operation parameter, so that the driver can perform targeted processing.
In the embodiment of the application, the prediction of the damaged parameters of the tire is realized based on the decision tree algorithm, and the first damage warning information is sent under the condition that the damaged parameters of the tire of the target vehicle are larger than the first preset threshold value, so that the prediction of the damaged parameters of the tire through the decision tree algorithm based on the running data of the target vehicle is realized, and the efficiency of the damage prediction of the tire is improved.
In some embodiments, in order to improve efficiency of predicting damage to a target tire, as shown in fig. 5, in the method for predicting damage to a tire provided in the embodiment of the present application, S501 to S502 are further included:
s501, determining a speed increasing value of each first operation parameter based on a plurality of groups of first operation parameters corresponding to a plurality of time points in a target time period in the driving process of the target vehicle.
Wherein each time point corresponds to a set of operating parameters.
Alternatively, the target time period may be determined according to specific usage requirements, for example, the target time period may be the first ten minutes, the first five minutes, and so on of the current driving time of the target vehicle.
Alternatively, the first operating parameters of the target vehicle may be periodically obtained, so as to obtain multiple sets of first operating parameters corresponding to multiple time points in the target time period.
For example, the electronic device may set the retention requirements of two fields (the last data, the current data, i.e., the first operation parameters of the last time point and the current time point) for each first operation parameter uploaded by the vehicle, and determine the quasi-real-time acceleration of each first parameter based on the two field data each time.
It can be understood that, since there are a plurality of (at least one) first operation parameters that remain each time, for each first operation parameter, the acceleration value of the first operation parameter can be determined based on the value of the last time point of the first operation parameter and the value of the current time point.
S502, sending second damage warning information under the condition that the acceleration value of any one of at least one operation parameters included in the first operation parameters is larger than a second preset threshold value.
The second damage alarm information is used for indicating that the operation parameters of the target vehicle are abnormal.
It should be noted that, because the first operating parameters of different items have different influences on the damage of the tire, the second preset thresholds corresponding to the first operating parameters of different items may also be different, and the specific second preset thresholds may be determined in combination with the use requirement and the experimental data.
Alternatively, the second damage warning information may be understood as warning information indicating a specific operating parameter type of the target vehicle.
For example, when the speed of the vehicle increases too fast, the warning can be performed through the second damaged warning information to prompt the driver that the current speed of the vehicle affects the safety of the tire, and the driver is guided to reduce the speed of the vehicle.
In a possible implementation manner, based on the above steps, early warning can be performed when the acceleration value of any one of the first operation parameters is greater than the second preset threshold value, so that the driver is prompted that the current vehicle tire state is poor, the possibility of damage of tires such as tire burst exists, and the like is provided, so that the driver is guided to reduce the vehicle speed, pay attention to road running with few platforms in road condition selection, and the like, and the risk of damage of the tires is reduced.
In the embodiment of the application, the prediction of the damage of the tire based on the acceleration value of each operation parameter is realized based on a plurality of groups of first operation parameters at a plurality of time points in a target time period, so that the damage of the tire is predicted from multiple aspects.
In some embodiments, in order to further expand the scene of predicting the damage to the tire, as shown in fig. 6, in the method for predicting the damage to the tire provided by the embodiment of the present application, S601 to S603 are further included:
s601, acquiring a travel distance of a current travel and travel parameters corresponding to a last travel before a target vehicle travels.
Wherein the travel parameters include at least one of: travel average vehicle speed, historical damage parameters, the historical damage parameters being used to indicate damage parameters of the tires of the target vehicle at the end of the last travel.
Alternatively, the average vehicle speed during the last vehicle driving and the last generated tire damage parameter can be obtained through statistical calculation based on the last uploaded data of each sensor of the vehicle after the last vehicle driving (such as before the vehicle is powered down).
Illustratively, the statistically calculated data may be: the average speed per hour of the last-run vehicle was 60 km/h, and the last-produced tire damage parameter was 0.06%.
S602, determining estimated damage parameters of the target tire when the target vehicle runs to the destination based on the running distance predicted value and the running evaluation reference value.
Alternatively, the travel distance estimated value may be determined based on the travel route selected by the user based on the destination of the navigation before the vehicle travels.
Optionally, after determining the driving route selected by the user, determining the damage degree (i.e. the driving evaluation reference value) of the tires of other vehicles passing through the driving route based on the cloud big data platform, so as to analyze the first condition parameter based on the target vehicle, and the damage degree (i.e. the estimated damage parameter) of the tires generated by the driving route.
The cloud big data platform can determine tire damage parameters when other vehicles pass through the route according to the navigation route after acquiring the driving destination input by a driver through modes such as vehicle navigation and the like, and further calculate the tire damage parameters of the vehicle if the vehicle drives the navigation route based on the route average speed and the tire damage parameters calculated at the last time of the route, namely, the estimated damage parameters.
S603, sending third damage warning information under the condition that the estimated damage parameter of the target tire is larger than a third preset threshold value.
The third damage alarm information is used for indicating estimated damage parameters corresponding to the current journey.
Alternatively, when the third preset threshold value may be determined based on the usage requirement, for example, the third preset threshold value may be a tire remaining damaging threshold value, such as a damage threshold value of the tire—a historical damage parameter of the tire.
For example, the estimated damage parameters of the tire of the vehicle can be determined based on the damage prediction model, when the current damage parameters of the vehicle and the estimated damage parameters are greater than the damage parameter threshold of the tire, the vehicle gives an alarm to the driver, and prompts the driver to run on the current navigation route with the risk of tire damage so as to guide the user to replace the navigation route or reduce the speed of the vehicle during running, and the like, so that the estimated damage parameters of the tire are reduced, and the driver can smoothly reach the destination.
In one possible implementation, it is also possible to predict whether the driver is allowed to run normally the next time based on the last uploaded sensor parameters of the vehicle when the vehicle is powered down.
For example, after the driver arrives at the destination and selects the vehicle to power down, the vehicle control end may upload the current state parameters of the current sensors to the cloud big data platform to perform power down calculation, so as to obtain the average damage parameters of the current day.
Further, the big data platform may add the average damage parameter to the current damage parameter of the vehicle tyre, i.e. the estimated damage parameter of the vehicle tyre obtained according to the current driving habit in the open day. If the estimated damage parameter is larger than the damage parameter threshold value of the tire, the driver is considered to be unable to normally reach the destination when using the vehicle next time, the driver is warned, and the tire is guided to be replaced in time.
In the embodiment of the application, before the vehicle runs, the estimated damage parameter of the target tire is determined based on the historical travel parameter and the running distance of the current travel, and then the tire damage condition of the vehicle when the vehicle runs to the destination is predicted based on the estimated damage parameter, so that the damage prediction of the tire before the vehicle runs is realized, the damage of the tire during running is avoided, and the running safety is improved.
In a possible implementation manner, as shown in fig. 7, the tire damage prediction method provided by the application can collect vehicle-machine data, then perform data analysis and comparison on the vehicle-machine data, and further obtain a prediction model through decision tree modeling. When the model is used, the prediction can be performed when the vehicle is electrified, the quasi-real-time prediction can be performed during the running of the vehicle, the prediction can be performed when the vehicle is electrified, and the corresponding warning information can be sent when the tire is predicted to be possibly damaged, so that the damage prediction of the tire is realized.
The foregoing description of the solution provided by the embodiments of the present application has been mainly presented in terms of a method. In order to achieve the above functions, the tire damage prediction apparatus or the electronic device includes hardware structures and/or software modules that perform the respective functions. Those of skill in the art will readily appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as hardware or combinations of hardware and computer software. Whether a function is implemented as hardware or computer software driven hardware 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 application.
The method according to the embodiment of the present application may be used for performing functional module division on the tire damage prediction device or the electronic device, for example, the tire damage prediction device or the electronic device may include each functional module corresponding to each functional division, or two or more functions may be integrated into one processing module. The integrated modules may be implemented in hardware or in software functional modules. It should be noted that, in the embodiment of the present application, the division of the modules is schematic, which is merely a logic function division, and other division manners may be implemented in actual implementation.
Fig. 8 is a block diagram illustrating a tire damage prediction apparatus according to an exemplary embodiment. Referring to fig. 8, the tire damage prediction apparatus 800 includes: an acquisition unit 801, a determination unit 802, and an alarm unit 803.
An obtaining unit 801, configured to obtain, during running of the target vehicle, a first vehicle condition parameter and a first operation parameter of the target vehicle, where the first vehicle condition parameter includes at least one of: the road condition information, the tire model and the current wear degree of the tire, and the first operating parameters comprise operating parameters acquired by at least one sensor in the target vehicle.
A determining unit 802, configured to determine, based on the first vehicle condition parameter and the damage prediction model of the target vehicle, a damage threshold corresponding to an operation parameter of the target vehicle, where each operation parameter of the at least one operation parameter included in the first operation parameter corresponds to one damage threshold.
The determining unit 802 is further configured to determine a damage parameter of the tire of the target vehicle based on the damage threshold and the first operation parameter of the target vehicle, the damage parameter being used to indicate a probability that the tire of the target vehicle is damaged.
In some embodiments, the obtaining unit 801 is further configured to obtain, when a tire of each of the plurality of training vehicles is damaged, a second vehicle condition parameter and a second operating parameter that are transmitted, where the second vehicle condition parameter includes at least one of: the road condition information, the tire model and the current wear degree of the tire, and the second operation parameters comprise at least one of the following: steering angle, steering speed, training tire pressure, temperature, pedal depth.
The determining unit 802 is further configured to determine an impact weight value of each of the second vehicle condition parameters and each of the second operating parameters on the damage of the tire, respectively.
The determining unit 802 is further configured to determine a damage prediction model based on the impact weight value of each of the second vehicle condition parameters and each of the second operating parameters on the damage of the tire.
In some embodiments, the determining unit 802 is further configured to determine, by a decision tree algorithm, a damage parameter of the tire of the target vehicle based on the first operating parameter and the damage threshold of the operating parameter of the target vehicle.
And an alarm unit 803 for transmitting first damage alarm information indicating damage parameters of tires of the target vehicle in case that the damage parameters of tires of the target vehicle are greater than a first preset threshold.
In some embodiments, the determining unit 802 is further configured to determine a speed-up value of each first operation parameter based on a plurality of sets of first operation parameters corresponding to a plurality of time points in a target time period during the driving of the target vehicle, where each time point corresponds to a set of operation parameters.
And an alarm unit 803 for sending second damaged alarm information, where the acceleration value of any one of the at least one operation parameter included in the first operation parameter is greater than a second preset threshold, the second damaged alarm information being used to indicate that the operation parameter of the target vehicle is abnormal.
In some embodiments, the obtaining unit 801 is further configured to obtain, before the target vehicle travels, a travel distance of a current trip and a trip parameter corresponding to a last trip, where the trip parameter includes at least one of: travel average vehicle speed, historical damage parameters, the historical damage parameters being used to indicate damage parameters of the tires of the target vehicle at the end of the last travel.
The determining unit 802 is further configured to determine an estimated damage parameter of the target tire when the target vehicle is traveling to the destination, based on the travel distance estimated value and the travel evaluation reference value.
And the warning unit 803 is configured to send third damage warning information when the estimated damage parameter of the target tire is greater than a preset threshold value, where the third damage warning information is used to indicate the estimated damage parameter corresponding to the current journey.
The specific manner in which the various modules perform the operations in the apparatus of the above embodiments have been described in detail in connection with the embodiments of the method, and will not be described in detail herein.
Fig. 9 is a block diagram of an electronic device, according to an example embodiment. As shown in fig. 9, electronic device 900 includes, but is not limited to: a processor 901 and a memory 902.
The memory 902 is configured to store executable instructions of the processor 901. It will be appreciated that the processor 901 is configured to execute instructions to implement the tire damage prediction method in the above embodiment.
It should be noted that the electronic device structure shown in fig. 9 is not limited to the electronic device, and the electronic device may include more or less components than those shown in fig. 9, or may combine some components, or may have different arrangements of components, as will be appreciated by those skilled in the art.
The processor 901 is a control center of the electronic device, connects various parts of the entire electronic device using various interfaces and lines, and performs various functions of the electronic device and processes data by running or executing software programs and/or modules stored in the memory 902 and calling data stored in the memory 902, thereby performing overall monitoring of the electronic device. The processor 901 may include one or more processing units. Alternatively, the processor 901 may integrate an application processor that primarily handles operating systems, user interfaces, applications, etc., with a modem processor that primarily handles wireless communications. It will be appreciated that the modem processor described above may not be integrated into the processor 901.
The memory 902 may be used to store software programs as well as various data. The memory 902 may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, application programs required for at least one functional module, and the like. In addition, the memory 902 may include high-speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other volatile solid-state storage device.
In an exemplary embodiment, a computer readable storage medium is also provided, such as a memory 902, comprising instructions executable by the processor 901 of the electronic device 900 to implement the tire damage prediction method of the above embodiments.
In actual implementation, the functions of the acquisition unit 801, the determination unit 802, and the alarm unit 803 in fig. 8 may be implemented by the processor 901 in fig. 9 calling a computer program stored in the memory 902. For specific execution, reference is made to the description of the tire damage prediction method in the above embodiment, and details thereof will not be repeated here.
Alternatively, the computer readable storage medium may be a non-transitory computer readable storage medium, for example, a Read-Only Memory (ROM), a random access Memory (Random Access Memory, RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, and the like.
In an exemplary embodiment, the present application further provides a vehicle for implementing the tire damage prediction method described above.
In an exemplary embodiment, embodiments of the application also provide a computer program product comprising one or more instructions executable by the processor 901 of the electronic device 900 to perform the tire damage prediction method of the above-described embodiments.
It should be noted that, when the instructions in the computer readable storage medium or one or more instructions in the computer program product are executed by the processor of the electronic device, the respective processes of the embodiments of the tire damage prediction method are implemented, and the same technical effects as those of the tire damage prediction method can be achieved, so that repetition is avoided, and further description is omitted here.
From the foregoing description of the embodiments, it will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-described division of functional modules is illustrated, and in practical application, the above-described functional allocation may be performed by different functional modules according to needs, i.e. the internal structure of the apparatus is divided into different functional modules, so as to perform all the classification parts or part of the functions described above.
In the several embodiments provided by the present application, it should be understood that the disclosed apparatus and method may be implemented in other manners. For example, the apparatus embodiments described above are merely illustrative, e.g., the division of modules or units is merely a logical function division, and there may be additional divisions when actually implemented, e.g., multiple units or components may be combined or integrated into another apparatus, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, which may be in electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and the parts shown as units may be one physical unit or a plurality of physical units, may be located in one place, or may be distributed in a plurality of different places. The purpose of the embodiment scheme can be achieved by selecting part or all of the classification part units according to actual needs.
In addition, each functional unit in the embodiments of the present application 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 units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a readable storage medium. Based on such understanding, the technical solution of the embodiments of the present application, or the portion contributing to the prior art or the whole classification portion or portion of the technical solution, may be embodied in the form of a software product stored in a storage medium, where the software product includes several instructions to cause a device (may be a single-chip microcomputer, a chip or the like) or a processor (processor) to execute the whole classification portion or part of the steps of the method of the embodiments of the present application. And the aforementioned storage medium includes: a usb disk, a removable hard disk, a ROM, a RAM, a magnetic disk, or an optical disk, etc.
The present application is not limited to the above embodiments, and any changes or substitutions within the technical scope of the present application should be covered by the scope of the present application. Therefore, the protection scope of the application is subject to the protection scope of the claims.
Claims (9)
1. A method of predicting tire damage, the method comprising:
during the running process of a target vehicle, acquiring a first vehicle condition parameter and a first running parameter of the target vehicle, wherein the first vehicle condition parameter comprises at least one of the following components: the first operation parameters comprise operation parameters acquired by at least one sensor in the target vehicle;
determining a damage threshold corresponding to the operation parameters of the target vehicle based on the first vehicle condition parameters and the damage prediction model of the target vehicle, wherein each operation parameter of at least one operation parameter included in the first operation parameters corresponds to one damage threshold;
a damage parameter of a tire of the target vehicle is determined based on the damage threshold and a first operating parameter of the target vehicle, the damage parameter being indicative of a probability of damage to the tire of the target vehicle.
2. The method of claim 1, wherein prior to determining the damage threshold corresponding to the operating parameter of the target vehicle based on the first vehicle condition parameter and the damage prediction model of the target vehicle, the method further comprises:
acquiring a second vehicle condition parameter and a second operating parameter transmitted when a tire of each of a plurality of training vehicles is damaged, the second vehicle condition parameter including at least one of: road condition information, tire model, tire current wear degree, the second operation parameter includes at least one of the following: steering angle, steering speed, training tire pressure, temperature, pedal depth;
determining an influence weight value of each vehicle condition parameter in the second vehicle condition parameters and each operation parameter in the second operation parameters on damage of the tire respectively;
the damage prediction model is determined based on each of the second vehicle condition parameters and an impact weight value of each of the second operating parameters on tire damage.
3. The method according to claim 1 or 2, wherein the determining the damage parameter of the tire of the target vehicle based on the damage threshold and the first operating parameter of the target vehicle comprises:
Determining, by a decision tree algorithm, a damage parameter of a tire of the target vehicle based on the first operating parameter and an operating parameter damage threshold of the target vehicle;
the method further comprises the steps of:
and sending first damage warning information when the damage parameter of the tire of the target vehicle is larger than a first preset threshold value, wherein the first damage warning information is used for indicating the damage parameter of the tire of the target vehicle.
4. The method according to claim 1 or 2, characterized in that the method further comprises:
determining a speed-up value of each first operation parameter based on a plurality of groups of first operation parameters corresponding to a plurality of time points in a target time period in the running process of the target vehicle, wherein each time point corresponds to one group of operation parameters;
and sending second damage warning information under the condition that the speed-up value of any one of at least one operation parameter included in the first operation parameters is larger than a second preset threshold value, wherein the second damage warning information is used for indicating that the operation parameters of the target vehicle are abnormal.
5. The method according to claim 1 or 2, characterized in that the method further comprises:
before the target vehicle runs, acquiring a running distance of a current journey and journey parameters corresponding to a last journey, wherein the journey parameters comprise at least one of the following: the vehicle comprises a trip average speed and a historical damage parameter, wherein the historical damage parameter is used for indicating damage parameters of tires of the target vehicle when the last trip is finished;
Determining estimated damage parameters of the target tire when the target vehicle travels to the destination based on the travel distance estimated value and the travel evaluation reference value;
and sending third damage warning information when the estimated damage parameter of the target tire is larger than a third preset threshold value, wherein the third damage warning information is used for indicating the estimated damage parameter corresponding to the current journey.
6. A tire damage prediction apparatus, characterized by comprising: an acquisition unit and a determination unit;
the acquisition unit is used for acquiring a first vehicle condition parameter and a first operation parameter of a target vehicle in the process of driving the target vehicle, wherein the first vehicle condition parameter comprises at least one of the following: the first operation parameters comprise operation parameters acquired by at least one sensor in the target vehicle;
the determining unit is used for determining a damage threshold value corresponding to the operation parameter of the target vehicle based on a first vehicle condition parameter and a damage prediction model of the target vehicle, wherein each operation parameter of at least one operation parameter included in the first operation parameter corresponds to one damage threshold value;
The determining unit is further configured to determine a damage parameter of a tire of the target vehicle based on the damage threshold and a first operation parameter of the target vehicle, the damage parameter being used to indicate a probability that the tire of the target vehicle is damaged.
7. An electronic device, comprising: a processor and a memory; wherein the memory is configured to store one or more programs, the one or more programs comprising computer-executable instructions that, when executed by the electronic device, cause the electronic device to perform the method of any of claims 1-5.
8. A computer readable storage medium, characterized in that, when computer-executable instructions stored in the computer readable storage medium are executed by a processor of an electronic device, the electronic device is capable of performing the method of any one of claims 1 to 5.
9. A vehicle for implementing the method according to any one of claims 1 to 5.
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| CN202310878205.9A CN117057219A (en) | 2023-07-17 | 2023-07-17 | Tire damage prediction method, device, equipment, storage medium and vehicle |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN118277720A (en) * | 2024-02-01 | 2024-07-02 | 托马斯(杭州)汽车科技服务有限公司 | Tire burst prediction method, device, terminal and storage medium |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN118277720A (en) * | 2024-02-01 | 2024-07-02 | 托马斯(杭州)汽车科技服务有限公司 | Tire burst prediction method, device, terminal and storage medium |
| CN118277720B (en) * | 2024-02-01 | 2024-09-17 | 托马斯(杭州)汽车科技服务有限公司 | Tire burst prediction method, device, terminal and storage medium |
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