Tire radial deformation amount measurement method, system, electronic device, and storage medium

Abstract

This invention discloses a method, system, electronic device, and storage medium for measuring tire radial deformation. An acceleration sensor is installed at the tire crown to obtain measurement data of the tire during each rolling cycle. The measurement data includes time and radial acceleration. A relationship curve between time and radial acceleration during the rolling cycle is constructed based on the measurement data. Curve feature quantities are extracted from the relationship curve based on its changing trend. These curve feature quantities are input into a trained neural network model to obtain the tire's radial deformation. The neural network model outputs the radial deformation based on the curve feature quantities. The changing trend of radial acceleration is related to the changing trend of radial deformation, and the extracted curve feature quantities also represent the characteristics of radial deformation. Obtaining the tire's radial deformation based on these curve feature quantities is highly accurate and widely applicable.

Description

Technical Field

[0001] This invention relates to the field of tire radial deformation measurement technology, and in particular to a tire radial deformation measurement method, system, electronic device and storage medium. Background Technology

[0002] With the development of the automotive industry, the trends of autonomous and intelligent driving are becoming increasingly apparent, placing higher demands on tire performance. Tire intelligence has become a trend. Among these advancements, measuring the radial deformation (compression) of tires under load is a crucial issue, significantly impacting tire safety.

[0003] In existing technologies, such as patent CN107430185B, the radial deformation of the tire is mainly determined by detecting the deformation of the axle. However, this method requires the installation of high-precision sensors and the polishing of the axle. The installation cost is high, and the detection accuracy is easily affected by environmental humidity and dust, leading to inaccurate detection of the tire's radial deformation. Summary of the Invention

[0004] To address the aforementioned technical problems, this invention provides a method for measuring tire radial deformation.

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

[0006] The acceleration sensor obtains measurement data of the tire during each rolling cycle, including time and radial acceleration.

[0007] Based on the measurement data, a curve showing the relationship between time and radial acceleration during the rolling cycle is constructed.

[0008] Based on the changing trend of the relationship curve, obtain the curve feature quantity from the relationship curve;

[0009] The curve features are input 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 based on the curve features.

[0010] Optionally, the curve feature quantity includes a first curve feature quantity, and the step of obtaining the curve feature quantity from the relationship curve based on the changing trend of the relationship curve includes:

[0011] The maximum and minimum values ​​of radial acceleration, as well as the stationary values ​​within the period, are determined from the relationship curve. The stationary values ​​refer to values ​​whose variation amplitude is less than a preset amplitude within a preset continuous time length.

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

[0013] Optionally, the curve feature quantity includes a second curve feature quantity, and the step of obtaining the curve feature quantity from the relationship curve based on the changing trend of the relationship curve further includes:

[0014] A straight line whose y-axis value is equal to the stationary value is used to obtain the dividing line;

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

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

[0017] Optionally, the curve feature quantity includes a third curve feature quantity, and the step of obtaining the curve feature quantity from the relationship curve based on the changing trend of the relationship curve further includes:

[0018] The time points of the minimum value and the stationary value are determined respectively to obtain the first time point and the 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 characteristic quantity of the third curve.

[0021] Optionally, after obtaining the curve feature quantity from the relationship curve based on its changing trend, the method further includes:

[0022] Obtain the curve feature quantities corresponding to multiple rolling cycles;

[0023] For each of the curve feature quantities, the average value of the curve feature quantities over multiple rolling cycles is taken as the final value of the curve feature quantity.

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

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

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

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

[0028] Obtain the radial deformation threshold of the tire;

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

[0030] If so, issue a warning to the user's terminal.

[0031] Secondly, the present invention provides a tire radial deformation measurement system, including a vehicle-mounted unit and a module terminal, wherein the module terminal includes an acceleration sensor and an analysis module, the acceleration sensor being disposed at the tire crown, and the analysis module comprising:

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

[0033] The relationship curve construction submodule is used to construct a relationship curve between time and radial acceleration within the rolling cycle based on the measurement data.

[0034] The curve feature extraction submodule is used to obtain curve feature quantities from the relationship curve based on the changing trend of the relationship curve;

[0035] The radial deformation output submodule is used to input the curve feature quantity 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 based on the curve feature quantity.

[0036] Thirdly, 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 that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the tire radial deformation measurement method according to the first aspect of the present invention.

[0040] Fourthly, the present invention provides a computer-readable storage medium storing computer instructions that, when executed by a processor, implement the tire radial deformation measurement method described in the first aspect of the present invention.

[0041] The tire radial deformation measurement method of this invention involves an acceleration sensor installed at the tire crown. The acceleration sensor obtains measurement data of the tire during each rolling cycle, including time and radial acceleration. A relationship curve between time and radial acceleration within the rolling cycle is constructed based on the measurement data. Curve feature quantities are extracted from the relationship curve based on its changing trend. These curve feature quantities are then input into a trained neural network model to obtain the tire's radial deformation. The neural network model outputs the radial deformation based on the curve feature quantities. Since the changing trend of the relationship curve is related to the changing trend of radial acceleration, and the changing trend of radial acceleration is also related to the changing trend of radial deformation, the extracted curve feature quantities also represent the characteristic expression of radial deformation. Therefore, the tire's radial deformation can be obtained based on these curve feature quantities. This eliminates the need for high-precision sensors and axle polishing, reducing installation costs. Furthermore, the detection accuracy is less affected by environmental humidity and dust, making it widely applicable.

[0042] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0043] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

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

[0045] Figure 2 This is a smoothed fitting curve showing the relationship between time and radial acceleration, provided in Embodiment 1 of the present invention.

[0046] Figure 3 This is a flowchart 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 feature quantities in a relation curve graph according to Embodiment 2 of the present invention;

[0048] Figure 5 This is a schematic diagram of a neural network model structure provided in Embodiment 2 of the present invention;

[0049] Figure 6This is a schematic diagram of a tire radial deformation measurement and application process provided in Embodiment 2 of the present invention;

[0050] Figure 7 This is a schematic diagram of the structure of the analysis module in a tire radial deformation measurement system provided in Embodiment 3 of the present invention;

[0051] Figure 8 This is a schematic diagram of the structure of the electronic device provided in Embodiment 4 of the present invention. Detailed Implementation

[0052] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0053] Example 1

[0054] Figure 1 This is a flowchart of a tire radial deformation measurement method provided in Embodiment 1 of the present invention. This embodiment is applicable to the measurement of tire radial deformation. The method can be executed by a tire radial deformation measurement system, which can be implemented in hardware and / or software and can be configured in an electronic device. An acceleration sensor is installed at the tire crown. Typically, the acceleration sensor is a three-dimensional sensor, meaning it can measure acceleration in the radial, longitudinal, and lateral directions. In this embodiment, radial acceleration data is primarily 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. The measurement data includes time and radial acceleration.

[0057] Specifically, this can involve obtaining measurement data of the tire over multiple rolling cycles. A rolling cycle is the time it takes for the tire to roll one revolution. Generally speaking, the tire rotates at the same speed over multiple rolling cycles, meaning the rolling cycle is the same. Therefore, measurement data from any one or more rolling cycles can be used for calculations in subsequent steps.

[0058] S102. Construct a curve showing the relationship between time and radial acceleration within the rolling cycle based on the measurement data.

[0059] The horizontal axis of the relationship curve represents time, and the vertical axis represents radial acceleration. Data can be smoothed and fitted first. Specifically, methods such as low-pass filtering, polynomial fitting, and moving average can be used to achieve smooth fitting. The main benefits of smoothing the data include reducing noise, improving data quality, enhancing analytical results, and making the data easier to analyze and visualize. For example, ... Figure 2 As shown, Figure 2 This is a curve showing the relationship between time and radial acceleration after a smooth fit.

[0060] S103. Obtain curve characteristic quantities from the relationship curve based on the changing trend of the relationship curve.

[0061] The measurement data detected by the accelerometer is the measurement data at the tire position where the accelerometer is located. Because the tire is not compressed on the non-contact portion during vehicle movement, while the contact portion is compressed, the radial acceleration of the compressed portion is greater than that of the non-compressed portion. Therefore, the radial acceleration measured by the accelerometer within one rolling cycle will also change significantly. For details, please refer to [reference needed]. Figure 2 The relationship curve shown can be used to extract curve characteristic quantities based on the trend of the curve. These can be the maximum or minimum value of radial acceleration, or the rolling cycle time, etc. It is known that the greater the radial acceleration, the greater the radial deformation of the tire (but this is not a linear relationship). The shorter the rolling cycle time, the faster the tire speed, and the greater the radial deformation of the tire (the diameter will decrease). Therefore, variables related to the radial deformation of the tire can be used as curve characteristic quantities. Furthermore, variables positively correlated with the radial deformation of the tire can be used as curve characteristic quantities. For curve characteristic quantities negatively correlated with the radial deformation of the tire, a combination of curve characteristic quantities can be constructed to add the negatively correlated curve characteristic quantities to the combination of characteristic quantities, so that the combination of characteristic quantities is positively correlated with the radial deformation of the tire.

[0062] In an optional embodiment, after obtaining curve feature quantities from the relationship curve based on its changing trend, the method further includes: obtaining curve feature quantities corresponding to multiple rolling cycles; and for each curve feature quantity, taking the average value of the curve feature quantities over multiple rolling cycles as the final value of the curve feature quantity. This can improve data accuracy and avoid anomalies in data within a single cycle that could affect the detection of radial deformation.

[0063] S104. Input the curve features into the trained neural network model to obtain the radial deformation of the tire.

[0064] The neural network model is used to output radial deformation based on curve features. This model can be trained using training data obtained in a tire testing laboratory. This laboratory can simulate the radial deformation of a tire under load during rolling, and this radial deformation can be directly measured. Curve features can also be obtained using an acceleration sensor. Therefore, the curve features and corresponding radial deformation can be used as training data. However, directly measuring the radial deformation of a tire in actual vehicle use is costly and technically challenging, making it difficult to implement. Furthermore, the radial deformation of a tire is not a simple linear relationship with load or radial acceleration, and cannot be directly calculated. Therefore, this invention uses test data to train the neural network model to learn the complex relationship between curve features and radial deformation, and then uses the neural network model to detect the radial deformation of a vehicle's tires.

[0065] Optionally, the neural network model includes an input layer, hidden layers, and an output layer. The hidden layers have initial model parameters, which are related to the model's output. Therefore, the training process of this neural network model is the continuous adjustment of the model parameters in the hidden layers to ensure the output meets the requirements. Specifically, the neural network model can be a convolutional neural network, including an input layer, hidden layers, and an output layer. The hidden layers include convolutional layers, pooling layers, and fully connected layers. The hidden layers can also include convolutional layers, activation layers, pooling layers, and fully connected layers. Based on the multi-layer neural network, a more effective feature learning component is added. Specifically, convolutional and pooling layers are added before the fully connected layers. The convolutional neural network deepens the number of layers, enabling deep learning of the relationship between curve features and radial deformation. The specific structure of the neural network model can be set according to actual needs, and this invention does not impose any limitations on it.

[0066] In an optional embodiment, the training process of the neural network model is as follows (no corresponding figure):

[0067] The process involves acquiring training data, including curve features and their corresponding actual radial deformations. The curve features are then input into the input layer. These features are processed in the hidden layer, and the predicted radial deformations are output in the output layer. The difference between the predicted and actual radial deformations is calculated. It is then determined whether the difference falls within a preset range. If so, the neural network model training is complete. If not, the model parameters in the hidden layer are adjusted based on the difference, and the process returns to the step of inputting curve features into the input layer. After confirming the neural network model training is complete, test data can be used to test the model's predictive performance (accuracy of the output). If the test meets the standards, the model is ready for use. If it does not meet the standards, the process returns to the step of inputting curve features into the input layer to continue training the model.

[0068] The tire radial deformation measurement method of this invention involves an acceleration sensor installed at the tire crown. The acceleration sensor obtains measurement data of the tire during each rolling cycle, including time and radial acceleration. A relationship curve between time and radial acceleration within the rolling cycle is constructed based on the measurement data. Curve feature quantities are extracted from the relationship curve based on its changing trend. These curve feature quantities are then input into a trained neural network model to obtain the tire's radial deformation. The neural network model outputs the radial deformation based on the curve feature quantities. Since the changing trend of the relationship curve is related to the changing trend of radial acceleration, and the changing trend of radial acceleration is also related to the changing trend of radial deformation, the extracted curve feature quantities also represent the characteristic expression of radial deformation. Therefore, the tire's radial deformation can be obtained based on these curve feature quantities. This eliminates the need for high-precision sensors and axle polishing, reducing installation costs. Furthermore, the detection accuracy is less affected by environmental humidity and dust, making it widely applicable.

[0069] Example 2

[0070] Figure 3 This is a flowchart of a tire radial deformation measurement method provided in Embodiment 2 of the present invention. This embodiment of the present invention is an optimization based on Embodiment 1 described above, such as... 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. The measurement data includes time and radial acceleration.

[0072] S302. Construct a curve showing the relationship between time and radial acceleration within the rolling cycle based on the measurement data.

[0073] S301-S302 are similar to S101-S202 in Embodiment 1. For details, please refer to the relevant descriptions of S101-S202.

[0074] S303. Determine the maximum and minimum values ​​of radial acceleration, as well as the stationary values ​​within the period, from the relationship curve.

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

[0076] S304. Construct the first curve characteristic quantity based on the maximum value, minimum value and stationary value.

[0077] Specifically, such as Figure 4 As shown, Figure 4 This diagram illustrates how to obtain curve characteristic quantities from a relationship curve graph. It shows that the maximum radial acceleration is a1, the minimum is a2, and the stationary value is a3. The preset amplitude can be set according to actual needs. 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 in the upper part, bounded by the stationary value, and the second acceleration difference is the maximum radial acceleration difference in the lower part, bounded by the stationary value. h1 = a1 - a3, h2 = a3 - a2.

[0078] It should be noted that a tire experiences both contact with the ground and lift-off when rolling, such as... Figure 2 or Figure 4 As shown, the moment of the first maximum radial acceleration corresponds to the moment of grounding, and the moment of the second maximum radial acceleration corresponds to the moment of grounding. The radial acceleration at the moment of grounding and the moment of grounding are close.

[0079] It is known that a3 is a relatively stable value. 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 acceleration difference h1 and the second acceleration difference h2 are larger. Therefore, the first acceleration difference h1 and the second acceleration difference h2 are positively correlated with the radial deformation.

[0080] S305. Set the y-axis value to a straight line equal to the stationary value to obtain the dividing line.

[0081] Specifically, such as Figure 4 As shown, let's set a straight line L, with the expression y = a3. L is the dividing line.

[0082] S306. Obtain the first and second regions enclosed by the boundary line and the relationship curve.

[0083] The first area is the region above the boundary line, and the second area is the region below the boundary line. Therefore, the first area is S1, and the second area is S2. It should be noted that there are two first regions above the boundary line, and the first area could also be all the first regions above the boundary line. However, since the areas of the two first regions are similar, only one of them can be used as a representative area.

[0084] It is known that when the radial deformation is larger, the radial acceleration is also larger, the value of a1 is larger and the value of a2 is smaller, and 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 characteristic quantity of the second curve.

[0086] S308. Determine the time points of the minimum value and the stationary value respectively to obtain the first time point and the 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 can be the time point when the radial acceleration equals a3 (there may be two such time points, and either one can be chosen), the first time point is the time point when the radial acceleration equals a2, and the deformation time is t. r .

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

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

[0091] S311. Input the curve features into the trained neural network model to obtain the radial deformation of the tire.

[0092] The neural network model is used to output radial deformation based on curve features. These curve features specifically include a first curve feature, a second curve feature, and a third curve feature.

[0093] Figure 5 This is a schematic diagram of a neural network model structure, such as... Figure 5 As shown, when curve features are input into the input layer of a neural network model, the neural network model can output the corresponding radial deformation.

[0094] In this embodiment, by constructing the above-mentioned curve features, which are all positively correlated with radial deformation, the above-mentioned curve features can be input into the neural network model to quickly obtain accurate radial deformation, and are not affected by the state of the tire.

[0095] In an optional embodiment, after inputting the curve features into a trained neural network model to obtain the radial deformation of the tires, 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 tires is closely related to the vehicle's load; therefore, the load distribution of the vehicle can be determined based on the radial deformation of each tire. For example, if the vehicle has four tires, the deformation of each tire will differ depending on the load distribution on the vehicle, with tires near areas with higher loads exhibiting larger radial deformations.

[0096] In an optional embodiment, after inputting the curve features 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; determining 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, issuing a warning to the user's terminal.

[0097] Specifically, vehicles can be equipped with in-vehicle infotainment systems (human-computer interaction products) that can issue warnings to users when the radial deformation exceeds the radial deformation threshold.

[0098] In summary, the solution in this embodiment is as follows: Figure 6 As shown, Figure 6 This is a schematic diagram of a process for measuring and applying tire radial deformation, as shown below. Figure 6 As shown, the measurement and application of tire radial deformation mainly includes the following steps:

[0099] S601, Read acceleration sensor data;

[0100] S602, Further obtain curve characteristic quantities S1, S2, h1, h2, t using acceleration sensor data. r ;

[0101] The specific meanings of the aforementioned curve characteristic quantities can be found in the description above, and will not be repeated here.

[0102] S603. Obtain the radial deformation δ of the tire through curve features and neural network. When the difference between δ and the radial deformation threshold δ0 is within a certain range, start the warning.

[0103] This method uses an accelerometer to measure the radial acceleration of the tire during its rolling cycle, obtaining a waveform of the radial acceleration over one cycle. Using defined characteristic values ​​from this acceleration waveform, a neural network algorithm is employed to derive the tire's radial deformation. This enables real-time monitoring and early warning of tire compression, improving vehicle safety. The tire load can be determined from the radial compression and radial stiffness. The overall vehicle load is calculated based on the compression information of the four tires. Furthermore, for different compression levels of the four tires, the load distribution of the vehicle can be obtained, allowing the driver to adjust their driving strategy in a timely manner, thereby improving driving safety. It is applicable to tires of different specifications and vehicles, and is suitable for different tire types and brands. Different vehicle speeds and tire wear conditions do not affect the measurement.

[0104] Example 3

[0105] Embodiment 3 of the present invention provides a tire radial deformation measurement system, which includes a vehicle-mounted unit and a module terminal. The module terminal includes an acceleration sensor and an analysis module. The acceleration sensor is located at the tire crown. Figure 7 This is a schematic diagram of the 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] The data measurement submodule 701 is used to obtain measurement data of the tire in each rolling cycle through the acceleration sensor, the measurement data including time and radial acceleration;

[0107] The relationship curve construction submodule 702 is used to construct a relationship curve between time and radial acceleration within the rolling cycle based on the measurement data;

[0108] The curve feature extraction submodule 703 is used to obtain curve feature quantities from the relationship curve based on the changing trend of the relationship curve;

[0109] The radial deformation output submodule 704 is used to input the curve feature quantity 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 based on the curve feature quantity.

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

[0111] The feature value acquisition unit is used to determine the maximum and minimum values ​​of radial acceleration, as well as the stationary value within the period, in the relationship curve. The stationary 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 determination unit is used to construct the first curve feature based on the maximum value, the minimum value and the stationary value.

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

[0114] The boundary line setting unit is used to set a straight line whose value in the y-axis direction is equal to the stable value, thereby obtaining the boundary line;

[0115] A feature region determination unit is used to obtain a first region and a second region enclosed by the boundary line and the relationship curve, wherein the first region is the region above the boundary line and the second region is the region below the boundary line.

[0116] The second curve feature quantity determination unit is used to use the area of ​​the first region and the area of ​​the second region as the second curve feature quantity.

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

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

[0119] The deformation time determination unit is used to calculate the absolute value of the difference between the second time point and the first time point to obtain the deformation time.

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

[0121] Optionally, the analysis module further includes:

[0122] The curve feature acquisition submodule is used to acquire the curve feature corresponding to multiple rolling cycles;

[0123] The final value determination submodule for curve feature quantity is used to take the average value of the curve feature quantity over multiple rolling cycles as the final value of the curve feature quantity for each of the curve feature quantities.

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

[0125] The 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 of each tire.

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

[0128] The strategy module is used to obtain the 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; if so, issue a warning to the user's terminal.

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

[0130] Example 4

[0131] Figure 8 A schematic diagram of an electronic device 40 that can be used to implement embodiments of the present invention is shown. The electronic device is intended to represent various forms of digital computers. It can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the 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, such as a read-only memory (ROM) 42 or a random access memory (RAM) 43, communicatively connected to the at least one processor 41. The memory stores computer programs executable by the at least one processor. The processor 41 can perform various appropriate actions and processes based on the computer program stored in the ROM 42 or loaded into the RAM 43 from storage unit 48. The RAM 43 may also store various programs and data required for the operation of the electronic device 40. The processor 41, ROM 42, and RAM 43 are interconnected via a bus 44. An input / output (I / O) interface 45 is also connected to the bus 44.

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

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

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

[0136] Various embodiments 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-a-chip (SoCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0137] Computer programs used to implement 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, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

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

[0139] To provide interaction with a user, the systems and techniques described herein can 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 pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0140] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or computing systems that include middleware components (e.g., application servers), or computing systems that include frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0141] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0142] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0143] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A method for measuring tire radial deformation, characterized in that, An acceleration sensor is installed at the tread of the tire, and the method includes: The acceleration sensor obtains measurement data of the tire during each rolling cycle, including time and radial acceleration. Based on the measurement data, a curve showing the relationship between time and radial acceleration during the rolling cycle is constructed. Based on the changing trend of the relationship curve, obtain the curve feature quantity from the relationship curve; The curve features are input 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 based on the curve features. The curve feature quantities include a first curve feature quantity, a second curve feature quantity, and a third curve feature quantity. The step of obtaining the curve feature quantities from the relationship curve based on its changing trend includes: The maximum and minimum values ​​of radial acceleration, as well as the stationary values ​​within the period, are determined from the relationship curve. The stationary values ​​refer to values ​​whose variation 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 stationary value; wherein, the first curve feature quantity includes a first acceleration difference and a second acceleration difference, the first acceleration difference being the difference between the maximum value and the stationary value, and the second acceleration difference being the difference between the stationary value and the minimum value; A straight line whose y-axis value is equal to the stationary value is used to obtain the dividing line; Obtain the first region and the second region enclosed by the dividing line and the relationship curve, wherein the first region is the region above the dividing line and the second region is the region below the dividing line; The areas of the first region and the second region are used as the second curve feature quantities; The time points of the minimum value and the stationary value are determined respectively to obtain the first time point and the 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 characteristic quantity of the third curve.

2. The method as described in claim 1, characterized in that, After obtaining the curve feature quantities from the relationship curve based on its changing trend, the process further includes: Obtain the curve feature quantities corresponding to multiple rolling cycles; For each of the curve feature quantities, the average value of the curve feature quantities over multiple rolling cycles is taken as the final value of the curve feature quantity.

3. The method according to any one of claims 1-2, characterized in that, After inputting the curve features into the trained neural network model to obtain the radial deformation of the tire, the process further includes: Obtain the radial deformation of each tire of the vehicle; The load distribution of the vehicle is determined based on the radial deformation of each tire.

4. The method according to any one of claims 1-2, characterized in that, After inputting the curve features into the trained neural network model to obtain the radial deformation of the tire, the process further includes: Obtain the 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; If so, issue a warning to the user's terminal.

5. A tire radial deformation measurement system, characterized in that, The system includes a vehicle-mounted infotainment system and a module terminal. The module terminal includes an acceleration sensor and an analysis module. The acceleration sensor is located at the tire crown. The analysis module includes: The data measurement submodule is used to obtain measurement data of the tire in each rolling cycle through the acceleration sensor, the measurement data including time and radial acceleration; The relationship curve construction submodule is used to construct a relationship curve between time and radial acceleration within the rolling cycle based on the measurement data. The curve feature extraction submodule is used to obtain curve feature quantities from the relationship curve based on the changing trend of the relationship curve; The radial deformation output submodule is used to input the curve feature quantity 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 based on the curve feature quantity. The curve features include a first curve feature, a second curve feature, and a third curve feature; The curve feature extraction submodule includes: The feature value acquisition unit is used to determine the maximum and minimum values ​​of radial acceleration, as well as the stationary value within the period, in the relationship curve. The stationary 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 determination unit is used to construct a first curve feature quantity based on the maximum value, the minimum value, and the stationary value; wherein, the first curve feature quantity includes a first acceleration difference and a second acceleration difference, the first acceleration difference being the difference between the maximum value and the stationary value, and the second acceleration difference being the difference between the stationary value and the minimum value; The curve feature extraction submodule also includes: The boundary line setting unit is used to set a straight line whose value in the y-axis direction is equal to the stable value, thereby obtaining the boundary line; The feature region determination unit is used to obtain a first region and a second region enclosed by the boundary line and the relationship curve, wherein the first region is the region above the boundary line and the second region is the region below the boundary line. The second curve feature determination unit is used to use the area of ​​the first region and the area of ​​the second region as the second curve feature. The curve feature extraction submodule also includes: A time point determination unit is used to determine the time points of the minimum value and the stationary value respectively, so as to obtain a first time point and a second time point; The deformation time determination unit is used to calculate the absolute value of the difference between the second time point and the first time point to obtain the deformation time. The third curve feature quantity determination unit is used to use the deformation time as the third curve feature quantity.

6. An electronic device, characterized in that, The electronic device includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor to enable the at least one processor to perform the tire radial deformation measurement method according to any one of claims 1-4.

7. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that, when executed by a processor, implement the tire radial deformation measurement method according to any one of claims 1-4.

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