Tire radial deformation measurement method and system, electronic equipment and storage medium

Abstract

The invention discloses a tire radial deformation quantity measurement method and system, electronic equipment and a storage medium, an acceleration sensor is arranged at a tire crown of a tire, measurement data of the tire in each rolling period are obtained through the acceleration sensor, and the measurement data comprise time and radial acceleration; constructing a relation curve of time and radial acceleration in a rolling period based on the measurement data; acquiring curve characteristic quantity from the relation curve according to the change trend of the relation curve; the curve characteristic quantity is input into a trained neural network model, the radial deformation quantity of the tire is obtained, and the neural network model is used for outputting the radial deformation quantity according to the curve characteristic quantity. The change trend of the radial acceleration is related to the change trend of the radial deformation quantity, the extracted curve characteristic quantity is also the characteristic expression of the radial deformation quantity, the radial deformation quantity of the tire is obtained based on the curve characteristic quantity, the accuracy is high, and the applicability is wide.

Description

Technical Field

[0001] The present invention relates to the technical field of tire radial deformation measurement, and in particular to a tire radial deformation measurement method, system, electronic equipment and storage medium. Background Art

[0002] With the development of the automobile industry, the trend of unmanned driving and intelligent driving is becoming more and more obvious, and the performance requirements for tires are getting higher and higher. The intelligence of tires has become a trend. Among them, the measurement of the radial deformation (compression) of the tire under load is a relatively important issue, which has a significant impact on the safety of the tire.

[0003] In the prior art, as shown in patent CN107430185B, the radial deformation of the tire is mainly determined by deformation detection of the axle, but this method requires the installation of a high-precision sensor and the polishing of the axle. The installation cost is high, and the detection accuracy is easily affected by the humidity and dust of the environment, which leads to inaccurate detection of the radial deformation of the tire. Summary of the invention

[0004] In order to solve the above technical problem, the present invention provides a method for measuring radial deformation of a tire.

[0005] In a first aspect, the present invention provides a method for measuring radial deformation of a tire, wherein an acceleration sensor is provided at a crown of the tire, and the method comprises:

[0006] Obtaining measurement data of the tire in each rolling cycle through the acceleration sensor, wherein the measurement data includes time and radial acceleration;

[0007] constructing a relationship curve between time and radial acceleration within the rolling period based on the measurement data;

[0008] Acquiring a curve feature quantity from the relationship curve according to a change trend of the relationship curve;

[0009] The curve feature quantity is input into a trained neural network model to obtain the radial deformation quantity of the tire. The neural network model is used to output the radial deformation quantity according to the curve feature quantity.

[0010] Optionally, the curve feature includes a first curve feature, and obtaining the curve feature from the relationship curve according to a change trend of the relationship curve includes:

[0011] Determine the maximum and minimum values ​​of the radial acceleration in the relationship curve, as well as a stable value within a period, wherein the stable value refers to a value whose change amplitude is less than a preset amplitude within a preset continuous time length;

[0012] A first curve feature quantity is constructed based on the maximum value, the minimum value, and the plateau value.

[0013] Optionally, the curve feature includes a second curve feature, and the acquiring the curve feature from the relationship curve according to the change trend of the relationship curve further includes:

[0014] Set the value of the y-axis direction to be equal to the straight line of the stable value to obtain the dividing line;

[0015] Acquire a first area and a second area enclosed by the dividing line and the relationship curve, wherein the first area is an area above the dividing line, and the second area is an area below the dividing line;

[0016] The area of ​​the first region and the area of ​​the second region are used as second curve feature quantities.

[0017] Optionally, the curve feature includes a third curve feature, and the acquiring the curve feature from the relationship curve according to the change trend of the relationship curve further includes:

[0018] Determine the time points of the minimum value and the stable value respectively to obtain a first time point and a second time point;

[0019] Calculate the absolute value of the difference between the second time point and the first time point to obtain the deformation time;

[0020] The deformation time is used as the third curve feature quantity.

[0021] Optionally, after obtaining the curve feature quantity from the relationship curve according to the change trend of the relationship curve, the method further includes:

[0022] Acquire the curve feature quantities corresponding to a plurality of the scrolling cycles;

[0023] For each of the curve feature quantities, an average value of the curve feature quantities in a plurality of the scrolling cycles is taken as a final value of the curve feature quantity.

[0024] Optionally, after inputting the curve feature into a trained neural network model to obtain the radial deformation of the tire, the method further includes:

[0025] Obtaining the radial deformation of each tire of the vehicle;

[0026] The load distribution of the vehicle is determined based on the radial deformation amount of each tire.

[0027] Optionally, after inputting the curve feature into a trained neural network model to obtain the radial deformation of the tire, the method further includes:

[0028] Obtaining a radial deformation threshold of the tire;

[0029] Determine whether the difference between the radial deformation amount and the radial deformation amount threshold is less than a preset difference lower limit;

[0030] If so, an early warning is issued to the user's terminal.

[0031] In a second aspect, the present invention provides a tire radial deformation measurement system, including a vehicle computer and a module terminal, wherein the module terminal includes an acceleration sensor and an analysis module, wherein the acceleration sensor is arranged at the tire crown, and the analysis module includes:

[0032] A data measurement submodule, used to obtain measurement data of the tire in each rolling cycle through the acceleration sensor, wherein the measurement data includes time and radial acceleration;

[0033] A relationship curve construction submodule, used for constructing a relationship curve between time and radial acceleration within the rolling period based on the measurement data;

[0034] A curve feature extraction submodule, used for obtaining a curve feature from the relationship curve according to a change trend of the relationship curve;

[0035] The radial deformation output submodule is used to input the curve feature into a trained neural network model to obtain the radial deformation of the tire. The neural network model is used to output the radial deformation according to the curve feature.

[0036] In a third aspect, the present invention provides an electronic device, the electronic device comprising:

[0037] at least one processor; and

[0038] a memory communicatively connected to the at least one processor; wherein,

[0039] The memory stores a computer program executable by the at least one processor. The computer program is executed by the at least one processor so that the at least one processor can perform the tire radial deformation measurement method according to the first aspect of the present invention.

[0040] In a fourth aspect, the present invention provides a computer-readable storage medium storing computer instructions, wherein the computer instructions are used to enable a processor to implement the tire radial deformation measurement method described in the first aspect of the present invention when executed.

[0041] In the tire radial deformation measurement method of the embodiment of the present invention, an acceleration sensor is provided at the tire crown, and the measurement data of the tire in each rolling cycle is obtained through the acceleration sensor, and the measurement data includes time and radial acceleration; a relationship curve between time and radial acceleration in the rolling cycle is constructed based on the measurement data; a curve feature quantity is obtained from the relationship curve according to the change trend of the relationship curve; the curve feature quantity is input into the trained neural network model to obtain the radial deformation of the tire, and the neural network model is used to output the radial deformation according to the curve feature quantity. The change trend of the relationship curve is related to the change trend of the radial acceleration, and the change trend of the radial acceleration is also related to the change trend of the radial deformation. Therefore, the extracted curve feature quantity is also a characteristic expression of the radial deformation. The radial deformation of the tire can be obtained based on the curve feature quantity, and there is no need to install a high-precision sensor and polish the axle, which reduces the installation cost, and the detection accuracy is not easily affected by the humidity and dust of the environment, and the applicability is relatively wide.

[0042] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0044] Figure 1 is a flow chart of a tire radial deformation measurement method provided in Embodiment 1 of the present invention;

[0045] Figure 2 is a relationship curve between time and radial acceleration after smooth fitting provided in the first embodiment of the present invention;

[0046] Figure 3 is a flow chart of a tire radial deformation measurement method provided in Embodiment 2 of the present invention;

[0047] Figure 4 This is a schematic diagram of obtaining curve characteristic quantities in a relationship curve diagram provided by the second embodiment of the present invention;

[0048] Figure 5 It is a schematic diagram of a neural network model structure provided by the second embodiment of the present invention;

[0049] Figure 6This is a schematic diagram of a tire radial deformation measurement and application process provided by the second embodiment of the present invention;

[0050] Figure 7 is a structural schematic diagram of an analysis module in a tire radial deformation measurement system provided in a third embodiment of the present invention;

[0051] Figure 8 It is a schematic diagram of the structure of an electronic device provided in Embodiment 4 of the present invention. DETAILED DESCRIPTION

[0052] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0053] Embodiment 1

[0054] Figure 1 This is a flow chart of a tire radial deformation measurement method provided in the first embodiment of the present invention. This embodiment is applicable to the situation of tire radial deformation measurement. This method can be performed by a tire radial deformation measurement system. The tire radial deformation measurement system can be implemented in the form of hardware and / or software. The tire radial deformation measurement system can be configured in an electronic device. An acceleration sensor is provided at the crown of the tire. Generally speaking, the acceleration sensor is a three-way sensor, that is, the acceleration sensor can measure acceleration in three directions: radial, longitudinal and lateral. In this embodiment, the radial acceleration data is mainly used.

[0055] like Figure 1 As shown, the tire radial deformation measurement method includes:

[0056] S101. Obtain measurement data of the tire in each rolling cycle through an acceleration sensor, where the measurement data includes time and radial acceleration.

[0057] Specifically, the measurement data of the tire in multiple rolling cycles can be obtained. One rolling cycle is the time consumed by the tire to roll one circle. Generally speaking, the rotation speed of the tire is the same in multiple rolling cycles, that is, the rolling cycles are the same. Then, the measurement data in any one or more rolling cycles can be taken to perform calculations in subsequent steps.

[0058] S102: construct a relationship curve between time and radial acceleration within a rolling period based on the measurement data.

[0059] The horizontal axis of the relationship curve is time, and the vertical axis is radial acceleration. The data can be smoothed first. Specifically, low-pass filter, polynomial fitting, sliding average method and other methods can be used to achieve smooth fitting. The main functions of smooth fitting of data include reducing noise, improving data quality, improving analysis results, and making data easier to analyze and visualize. For example, Figure 2 As shown, Figure 2 It is a relationship curve between time and radial acceleration after smooth fitting.

[0060] S103. Obtaining a curve feature quantity from the relationship curve according to a change trend of the relationship curve.

[0061] The measurement data detected by the acceleration sensor is the measurement data of the tire site where the acceleration sensor is located. During the operation of the vehicle, the non-grounded part of the tire is not compressed, while the grounded part is compressed. The radial acceleration of the compressed part is large, while the radial acceleration of the non-compressed part is small. Therefore, the radial acceleration measured by the acceleration sensor in a rolling cycle will also change significantly. For details, please refer to Figure 2 The relationship curve shown, therefore, the curve feature can be obtained from the relationship curve according to the changing trend of the curve, which can be specifically the maximum value, minimum value or rolling cycle time of the radial acceleration, etc. It can be known that the greater the radial acceleration, the greater the radial deformation of the tire will be (but it is not a linear relationship), the smaller the rolling cycle time, the faster the tire rotation speed, and the radial deformation of the tire will also increase accordingly (the diameter will decrease). Therefore, the variable related to the radial deformation of the tire can be used as the curve feature, and further, the variable positively correlated with the radial deformation of the tire can be used as the curve feature. For the curve feature that is negatively correlated with the radial deformation of the tire, the curve feature that is negatively correlated with the radial deformation of the tire can be added to the combined feature in the form of constructing a combined feature, so that the combined feature is positively correlated with the radial deformation of the tire.

[0062] In an optional embodiment, after obtaining the curve feature quantity from the relationship curve according to the change trend of the relationship curve, it also includes: obtaining the curve feature quantities corresponding to multiple rolling cycles; for each curve feature quantity, taking the average value of the curve feature quantities in multiple rolling cycles as the final value of the curve feature quantity. This can improve data accuracy and avoid the presence of abnormal data in a single cycle affecting the detection of radial deformation.

[0063] S104, inputting the curve feature quantity into the trained neural network model to obtain the radial deformation quantity of the tire.

[0064] Wherein, the neural network model is used to output the radial deformation amount according to the curve characteristic amount. The neural network model can be trained according to the training data, and the training data can be obtained in the tire testing laboratory. In the laboratory, the radial deformation amount generated by the tire during the rolling process under the load of the vehicle can be simulated, and the radial deformation amount can be directly measured. The curve characteristic amount can also be obtained by setting an acceleration sensor. Therefore, the curve characteristic amount and the corresponding radial deformation amount can be obtained as training data. When the vehicle is put into actual use, directly measuring the radial deformation amount of the tire will lead to high measurement cost and great technical difficulty, so it is difficult to achieve. In addition, the radial deformation amount of the tire is not a simple linear relationship with the load, the radial acceleration of the tire, etc., and it cannot be directly calculated. Therefore, the present invention uses test data to train the neural network model to learn the complex relationship between the curve characteristic amount and the radial deformation amount, and uses the neural network model to detect the radial deformation amount of the vehicle tire.

[0065] Optionally, the neural network model includes an input layer, a hidden layer and an output layer, wherein the hidden layer is provided with initial model parameters, and the model parameters are related to the output results of the model, therefore, the training process of the neural network model is the process of continuously adjusting the model parameters in the hidden layer so that the output meets the requirements. Specifically, the neural network model can be a convolutional neural network, including an input layer, a hidden layer and an output layer, wherein the hidden layer includes a convolutional layer, a pooling layer and a fully connected layer, and the hidden layer can also include a convolutional layer, an activation layer, a pooling layer, and a fully connected layer. On the basis of a multi-layer neural network, a more effective feature learning part is added, specifically, a convolutional layer and a pooling layer are added before the fully connected layer, and a convolutional neural network appears, so that the number of neural network layers can be deepened, and the correlation between the characteristic quantity of the curve and the radial deformation quantity can be deeply learned. The structure of the specific neural network model can be set according to actual needs, and the present invention is not limited to this.

[0066] In an optional embodiment, the training process of the neural network model is as follows (without corresponding figures):

[0067] Acquire training data, the training data includes curve feature quantities and actual radial deformation quantities corresponding to the curve feature quantities; input the curve feature quantities in the input layer; process the curve feature quantities in the hidden layer, and output the predicted radial deformation quantities in the output layer; calculate the difference between the predicted radial deformation variables and the actual radial deformation variables; determine whether the difference is within a preset difference range; if so, determine that the neural network model training is completed; if not, adjust the model parameters in the hidden layer based on the difference, and return to the step of inputting the curve feature quantities in the input layer. After determining that the neural network model training is completed, the prediction effect of the model (the accuracy of the output result) can also be tested using test data. When the test meets the standard, it is determined that the model can be put into use. If it does not meet the standard, return to the step of inputting the curve feature quantities in the input layer to continue training the model.

[0068] In the tire radial deformation measurement method of the embodiment of the present invention, an acceleration sensor is provided at the tire crown, and the measurement data of the tire in each rolling cycle is obtained through the acceleration sensor, and the measurement data includes time and radial acceleration; a relationship curve between time and radial acceleration in the rolling cycle is constructed based on the measurement data; a curve feature quantity is obtained from the relationship curve according to the change trend of the relationship curve; the curve feature quantity is input into the trained neural network model to obtain the radial deformation of the tire, and the neural network model is used to output the radial deformation according to the curve feature quantity. The change trend of the relationship curve is related to the change trend of the radial acceleration, and the change trend of the radial acceleration is also related to the change trend of the radial deformation. Therefore, the extracted curve feature quantity is also a characteristic expression of the radial deformation. The radial deformation of the tire can be obtained based on the curve feature quantity, and there is no need to install a high-precision sensor and polish the axle, which reduces the installation cost, and the detection accuracy is not easily affected by the humidity and dust of the environment, and the applicability is relatively wide.

[0069] Embodiment 2

[0070] Figure 3 This is a flow chart of a tire radial deformation measurement method provided in the second embodiment of the present invention. The embodiment of the present invention is optimized based on the above-mentioned first embodiment. Figure 3 As shown, the tire radial deformation measurement method includes:

[0071] S301. Obtain measurement data of the tire in each rolling cycle through an acceleration sensor, where the measurement data includes time and radial acceleration.

[0072] S302: construct a relationship curve between time and radial acceleration within a rolling period based on the measurement data.

[0073] S301-S302 are similar to S101-S202 in the first embodiment, and the details may refer to the relevant description of S101-S202.

[0074] S303: Determine the maximum value and the minimum value of the radial acceleration and the stable value within the cycle in the relationship curve.

[0075] A stable value refers to a value whose change amplitude is less than a preset amplitude within a preset continuous time length.

[0076] S304: Construct a first curve feature value based on the maximum value, the minimum value and the plateau value.

[0077] Specific as Figure 4 As shown, Figure 4 It is a schematic diagram of obtaining curve characteristic quantities in a relationship curve diagram. It can be seen that the maximum value of radial acceleration is a1, the minimum value is a2, and the stable value is a3. The preset amplitude can be set according to actual needs. Then the first curve characteristic quantity can include a first acceleration difference and a second acceleration difference. The first acceleration difference is the maximum radial acceleration difference of the upper part with the stable value as the limit, and the second acceleration difference is the maximum radial acceleration difference of the lower part with the stable value as the limit, h1=a1-a3, h2=a3-a2.

[0078] It should be noted that when a tire rolls, it will go through the process of ground contact and ground separation. Figure 2 or Figure 4 As shown, the moment of the first maximum radial acceleration may correspond to the touchdown moment, and the moment of the second maximum radial acceleration may correspond to the lift-off moment, and the radial accelerations at the touchdown moment and the lift-off moment are close.

[0079] It can be known that a3 is a relatively stable value. When the radial deformation amount is larger, the radial acceleration is also larger. Then, the larger the value of a1 is and the smaller the value of a2 is, the larger the values ​​of the first acceleration difference h1 and the second acceleration difference h2 are. Therefore, the first acceleration difference h1 and the second acceleration difference h2 are positively correlated with the radial deformation amount.

[0080] S305, setting the straight line whose value in the y-axis direction is equal to the stable value to obtain a dividing line.

[0081] Specific as Figure 4 As shown, a straight line L is set, and the expression of L is y=a3. L is the dividing line.

[0082] S306: Acquire a first region and a second region enclosed by the boundary line and the relationship curve.

[0083] The first area is the area above the dividing line, and the second area is the area below the dividing line, then the first area is S1, and the second area is S2. It should be noted that there are two first areas above the dividing line, and the first area can also be all the first areas above the dividing line, but because the areas of the two first areas are similar, only one of the first areas can be taken as a representative.

[0084] It can be known that when the radial deformation is larger, the radial acceleration is also larger, then the value of a1 is larger and the value of a2 is smaller, then the values ​​of the first area S1 and the second area S2 are larger, therefore, the first area S1 and the second area S2 are positively correlated with the radial deformation.

[0085] S307: Use the area of ​​the first region and the area of ​​the second region as the second curve feature value.

[0086] S308. Determine the time points of the minimum value and the stable value respectively to obtain a first time point and a second time point.

[0087] S309: Calculate the absolute value of the difference between the second time point and the first time point to obtain the deformation time.

[0088] like Figure 4 As shown, the second time point may be the time point when the radial acceleration is equal to a3 (there may be two such time points, and any one can be selected), the first time point is the time point when the radial acceleration is equal to a2, and the deformation time is t r .

[0089] It can be known that the greater the radial acceleration, the greater the radial deformation of the tire (but it is not a linear relationship). Therefore, the deformation time can be used as a curve feature quantity related to the radial deformation of the tire.

[0090] S310: Taking the deformation time as the characteristic quantity of the third curve.

[0091] S311, inputting the curve feature quantity into the trained neural network model to obtain the radial deformation quantity of the tire.

[0092] The neural network model is used to output the radial deformation amount according to the curve feature quantity. The curve feature quantity specifically includes the first curve feature quantity, the second curve feature quantity and the third curve feature quantity.

[0093] Figure 5 is a schematic diagram of a neural network model structure, such as Figure 5 As shown in FIG. 1 , the curve feature is input into the input layer of the neural network model, and the neural network model can output the corresponding radial deformation.

[0094] In this embodiment, by constructing the above curve feature quantities, which are all positively correlated with the radial deformation amount, the above curve feature quantities are input into the neural network model, and accurate radial deformation amounts can be quickly obtained without being affected by the state of the tire.

[0095] In an optional embodiment, after inputting the curve feature into the trained neural network model to obtain the radial deformation of the tire, the method further includes: obtaining the radial deformation of each tire of the vehicle; and determining the load distribution of the vehicle based on the radial deformation of each tire. The radial deformation of the tire is closely related to the load of the vehicle. Therefore, the load distribution of the vehicle can be determined based on the radial deformation of each tire. For example, if the number of tires of the vehicle is 4, when the distribution of the load on the vehicle is different, the deformation of each tire is also different, and the radial deformation of the tire near the position with greater load is also greater.

[0096] In an optional embodiment, after the curve feature quantity is input into the trained neural network model to obtain the radial deformation of the tire, it also includes: obtaining the radial deformation threshold of the tire; judging whether the difference between the radial deformation and the radial deformation threshold is less than a preset lower limit of the difference; if so, issuing an early warning to the user's terminal.

[0097] Specifically, the vehicle may be provided with a vehicle computer (a human-computer interaction product), and when the radial deformation amount exceeds a radial deformation amount threshold, the vehicle computer may issue an early warning to the user.

[0098] In general, the solution of this embodiment is as follows Figure 6 As shown, Figure 6 A schematic diagram of a tire radial deformation measurement and application process is shown in FIG. Figure 6 As shown, the tire radial deformation measurement and application mainly include the following steps:

[0099] S601, reading acceleration sensor data;

[0100] S602, further obtain curve feature quantities S1, S2, h1, h2, t through acceleration sensor data r ;

[0101] The specific meaning of the above curve characteristic quantity can be referred to the above description and will not be elaborated here.

[0102] S603, obtaining the radial deformation amount δ of the tire through the curve feature and the neural network, and starting an early warning when the difference between δ and the radial deformation amount threshold δ0 is within a certain range.

[0103] That is, the radial acceleration of the tire in the rolling cycle is measured by an acceleration sensor, and the waveform of the radial acceleration in one cycle is obtained. The radial deformation of the tire is obtained by the neural network algorithm using the characteristic values ​​of some defined acceleration waveforms. Real-time monitoring and early warning of tire compression can be achieved to improve the safety of vehicle driving. The load of the tire can be obtained from the radial compression and radial stiffness. The load of the whole vehicle is obtained according to the compression information of the four tires. And for the different compression situations of the four tires, the load distribution of the vehicle can be obtained, so that the driver can adjust the driving strategy in time, thereby improving driving safety. It can be used for tires of different specifications and vehicles. And it can be applied to different tire types and brands of tires, and different vehicle speeds and tire wear conditions do not affect the measurement.

[0104] Embodiment 3

[0105] A tire radial deformation measurement system is provided in a third embodiment of the present invention. The tire radial deformation measurement system includes a vehicle computer and a module terminal. The module terminal includes an acceleration sensor and an analysis module. The acceleration sensor is arranged at the tire crown. Figure 7 This is a schematic diagram of the structure of an analysis module in a tire radial deformation measurement system provided in Embodiment 3 of the present invention. Figure 7 As shown, the analysis module includes:

[0106] A data measurement submodule 701, configured to obtain measurement data of the tire in each rolling cycle through the acceleration sensor, wherein the measurement data includes time and radial acceleration;

[0107] A relationship curve construction submodule 702, configured to construct a relationship curve between time and radial acceleration within the rolling period based on the measurement data;

[0108] The curve feature extraction submodule 703 is used to obtain the curve feature from the relationship curve according to the change trend of the relationship curve;

[0109] The radial deformation output submodule 704 is used to input the curve feature into the trained neural network model to obtain the radial deformation of the tire. The neural network model is used to output the radial deformation according to the curve feature.

[0110] Optionally, the curve feature includes a first curve feature, and the curve feature extraction submodule 703 includes:

[0111] A characteristic value acquisition unit, used to determine the maximum value and the minimum value of the radial acceleration in the relationship curve, and the stable value within the period, wherein the stable value refers to a value whose change amplitude is less than a preset amplitude within a preset continuous time length;

[0112] The first curve feature quantity determining unit is used to construct a first curve feature quantity based on the maximum value, the minimum value and the plateau value.

[0113] Optionally, the curve feature includes a second curve feature, and the curve feature extraction submodule 703 includes:

[0114] A dividing line setting unit, used for setting a straight line with a value in the y-axis direction equal to the stable value to obtain a dividing line;

[0115] A feature area determination unit, used to obtain a first area and a second area enclosed by the dividing line and the relationship curve, wherein the first area is an area above the dividing line, and the second area is an area below the dividing line;

[0116] The second curve feature value determining unit is configured to use the area of ​​the first region and the area of ​​the second region as the second curve feature value.

[0117] Optionally, the curve feature includes a third curve feature, and the curve feature extraction submodule 703 includes:

[0118] A time point determination unit, used to respectively determine the time points of the minimum value and the stable value to obtain a first time point and a second time point;

[0119] a deformation time determining unit, configured to calculate an absolute value of a difference between the second time point and the first time point to obtain a deformation time;

[0120] The third curve feature value determining unit is used to use the deformation time as the third curve feature value.

[0121] Optionally, the analysis module further includes:

[0122] A curve feature acquisition submodule, used to acquire the curve feature corresponding to a plurality of the scrolling periods;

[0123] The curve feature value final value determination submodule is used to take, for each of the curve feature values, an average value of the curve feature values ​​within a plurality of the rolling cycles as the final value of the curve feature value.

[0124] Optionally, the tire radial deformation measurement system, the analysis module further includes:

[0125] A radial deformation acquisition submodule is used to acquire the radial deformation of each tire of the vehicle;

[0126] The load distribution acquisition submodule is used to determine the load distribution of the vehicle based on the radial deformation amount of each tire.

[0127] Optionally, the tire radial deformation measurement system also includes a strategy module:

[0128] The strategy module is used to obtain a radial deformation threshold of the tire; determine whether the difference between the radial deformation and the radial deformation threshold is less than a preset lower limit of the difference, and if so, issue an early warning to the user's terminal.

[0129] The tire radial deformation measurement system provided by the embodiment of the present invention can execute the tire radial deformation measurement method provided by any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0130] Embodiment 4

[0131] Figure 8 A block diagram of an electronic device 40 that can be used to implement an embodiment of the present invention is shown. The electronic device is intended to represent various forms of digital computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or claimed herein.

[0132] like Figure 8 As shown, the electronic device 40 includes at least one processor 41, and a memory connected to the at least one processor 41, such as a read-only memory (ROM) 42, a random access memory (RAM) 43, etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor 41 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 42 or the computer program loaded from the storage unit 48 to the random access memory (RAM) 43. In the RAM 43, various programs and data required for the operation of the electronic device 40 can also be stored. The processor 41, the ROM 42, and the RAM 43 are connected to each other through a bus 44. An input / output (I / O) interface 45 is also connected to the bus 44.

[0133] A number of components in the electronic device 40 are connected to the I / O interface 45, including: an input unit 46, such as a keyboard, a mouse, etc.; an output unit 47, such as various types of displays, speakers, etc.; a storage unit 48, such as a disk, an optical disk, etc.; and a communication unit 49, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 49 allows the electronic device 40 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0134] The processor 41 may be a variety of general and / or dedicated processing components with processing and computing capabilities. Some examples of the processor 41 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 41 executes the various methods and processes described above, such as a tire radial deformation measurement method.

[0135] In some embodiments, the tire radial deformation amount measurement method may be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 48. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 40 via the ROM 42 and / or the communication unit 49. When the computer program is loaded into the RAM 43 and executed by the processor 41, one or more steps of the tire radial deformation amount measurement method described above may be performed. Alternatively, in other embodiments, the processor 41 may be configured to execute the tire radial deformation amount measurement method in any other appropriate manner (e.g., by means of firmware).

[0136] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0137] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that when the computer program is executed by the processor, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer program may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0138] In the context of the present invention, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in combination with an instruction execution system, device or equipment. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0139] To provide interaction with a user, the systems and techniques described herein may be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0140] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0141] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The client and server relationship is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services.

[0142] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and this document does not limit this.

[0143] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for measuring radial deformation of a tire, characterized in that: An acceleration sensor is provided at the crown of the tire, and the method comprises: Obtaining measurement data of the tire in each rolling cycle through the acceleration sensor, wherein the measurement data includes time and radial acceleration; constructing a relationship curve between time and radial acceleration within the rolling period based on the measurement data; Acquiring a curve feature quantity from the relationship curve according to a change trend of the relationship curve; The curve feature quantity is input into a trained neural network model to obtain the radial deformation quantity of the tire. The neural network model is used to output the radial deformation quantity according to the curve feature quantity.

2. The method according to claim 1, characterized in that The curve feature quantity includes a first curve feature quantity, and the acquiring the curve feature quantity from the relationship curve according to the change trend of the relationship curve includes: Determine the maximum and minimum values ​​of the radial acceleration in the relationship curve, as well as a stable value within a period, wherein the stable value refers to a value whose change amplitude is less than a preset amplitude within a preset continuous time length; A first curve feature quantity is constructed based on the maximum value, the minimum value, and the plateau value.

3. The method according to claim 2, characterized in that The curve feature quantity includes a second curve feature quantity, and the obtaining of the curve feature quantity from the relationship curve according to the change trend of the relationship curve further includes: Set the value of the y-axis direction to be equal to the straight line of the stable value to obtain the dividing line; Acquire a first area and a second area enclosed by the dividing line and the relationship curve, wherein the first area is an area above the dividing line, and the second area is an area below the dividing line; The area of ​​the first region and the area of ​​the second region are used as second curve feature quantities.

4. The method according to claim 2, characterized in that The curve feature quantity includes a third curve feature quantity, and the obtaining of the curve feature quantity from the relationship curve according to the change trend of the relationship curve further includes: Determine the time points of the minimum value and the stable value respectively to obtain a first time point and a second time point; Calculate the absolute value of the difference between the second time point and the first time point to obtain the deformation time; The deformation time is used as the third curve feature quantity.

5. The method according to claim 1, characterized in that After obtaining the curve feature quantity from the relationship curve according to the change trend of the relationship curve, the method further includes: Acquire the curve feature quantities corresponding to a plurality of the scrolling cycles; For each of the curve feature quantities, an average value of the curve feature quantities in a plurality of the scrolling cycles is taken as a final value of the curve feature quantity.

6. The method according to any one of claims 1 to 5, characterized in that: After the curve feature is input into the trained neural network model to obtain the radial deformation of the tire, the method further includes: Obtaining the radial deformation of each tire of the vehicle; The load distribution of the vehicle is determined based on the radial deformation amount of each tire.

7. The method according to any one of claims 1 to 5, characterized in that: After the curve feature is input into the trained neural network model to obtain the radial deformation of the tire, the method further includes: Obtaining a radial deformation threshold of the tire; Determine whether the difference between the radial deformation amount and the radial deformation amount threshold is less than a preset difference lower limit; If so, an early warning is issued to the user's terminal.

8. A tire radial deformation measurement system, characterized in that: It includes a vehicle computer and a module terminal, wherein the module terminal includes an acceleration sensor and an analysis module, wherein the acceleration sensor is arranged at the crown of the tire, and the analysis module includes: A data measurement submodule, used to obtain measurement data of the tire in each rolling cycle through the acceleration sensor, wherein the measurement data includes time and radial acceleration; A relationship curve construction submodule, used for constructing a relationship curve between time and radial acceleration within the rolling period based on the measurement data; A curve feature extraction submodule, used for obtaining a curve feature from the relationship curve according to a change trend of the relationship curve; The radial deformation output submodule is used to input the curve feature into a trained neural network model to obtain the radial deformation of the tire. The neural network model is used to output the radial deformation according to the curve feature.

9. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can perform the tire radial deformation measurement method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the tire radial deformation measurement method according to any one of claims 1 to 7 when the processor executes the computer instructions.

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