Tire pressure monitoring method and system based on wireless transmission

Abstract

The invention discloses a tire pressure monitoring method and system based on wireless transmission, and the method comprises the steps: optimizing the installation position of a sensor in a tire, and carrying out the vibration reduction processing and temperature compensation processing of the sensor; a wireless transmission antenna scheme is designed in the tire to transmit tire pressure data collected by a sensor; receiving tire pressure data based on a wireless receiver in combination with an adaptive signal processing algorithm; performing data loss diagnosis on the tire pressure data based on the central processing unit, and if data loss exists, compensating the tire pressure data to obtain corrected tire pressure data; and performing feature extraction on the corrected tire pressure data to obtain key feature parameters, and comparing the key feature parameters with a preset working threshold to obtain the working state of the tire. According to the invention, the measurement precision, anti-interference capability and reliability of tire pressure monitoring are obviously improved, long-term stable operation in a severe environment is ensured, and a powerful guarantee is provided for driving safety.

Description

Technical Field

[0001] The present invention belongs to the technical field of tire pressure monitoring, and particularly relates to a tire pressure monitoring method and system based on wireless transmission. Background Art

[0002] In wireless tire pressure monitoring, the sensor assembly installed inside the tire needs to operate stably for a long time in a harsh working environment. When the tire is running at high speed, it will generate large vibrations and impacts, and at the same time, the temperature will also change greatly. These factors may affect the measurement accuracy and service life of the sensor. In addition, the sensor assembly also needs to have strong waterproof and dustproof performance to avoid being affected by the external environment.

[0003] The tire pressure and temperature data collected by the sensor need to be transmitted wirelessly to the receiver or central control unit inside the vehicle. However, the wireless signal inside the tire is weak and is easily shielded and interfered by the metal parts of the vehicle body. How to design an efficient and reliable wireless transmission antenna in a limited space and ensure that the data can be stably transmitted in a complex electromagnetic environment is a technical problem to be solved urgently. In addition, after the receiver inside the vehicle receives the wireless signal sent by the sensor, how to process it to obtain accurate tire pressure and temperature data is also important.

[0004] In view of the above problems, there is an urgent need to propose a tire pressure monitoring method and system based on wireless transmission. Summary of the Invention

[0005] To solve the above technical problems, the present invention proposes a tire pressure monitoring method and system based on wireless transmission to solve the problems existing in the above prior art.

[0006] To achieve the above object, the present invention provides a tire pressure monitoring method based on wireless transmission, including the following steps:

[0007] Optimize the installation position of the sensor inside the tire, and perform vibration damping treatment and temperature compensation treatment on the sensor;

[0008] Design a wireless transmission antenna scheme inside the tire to transmit the tire pressure data collected by the sensor;

[0009] Receive the tire pressure data based on a wireless receiver combined with an adaptive signal processing algorithm;

[0010] Based on the central processing unit, perform data loss diagnosis on the tire pressure data. If there is data loss, compensate the tire pressure data to obtain the corrected tire pressure data;

[0011] Extract features from the corrected tire pressure data to obtain key feature parameters, and compare the key feature parameters with preset working thresholds to obtain the working state of the tire.

[0012] Optionally, the process of optimizing the installation position of the sensor inside the tire includes:

[0013] Based on the technical parameters and installation requirements of the sensor, determine the optimal installation position of the sensor through finite element analysis and simulation.

[0014] Optionally, the process of performing vibration damping treatment and temperature compensation treatment on the sensor includes:

[0015] Set a vibration damping device around the installation position of the sensor to perform vibration damping treatment on the sensor; construct a temperature-error model, obtain the temperature data measured by the sensor in real time, and based on the pre-established temperature-error model, obtain the measurement error compensation value under the current temperature condition, and perform real-time correction on the measurement result of the sensor.

[0016] Optionally, the process of designing a wireless transmission antenna scheme inside the tire to transmit the tire pressure data collected by the sensor includes:

[0017] Based on the size and shape of the internal space of the tire, design the structure and layout position of the wireless transmission antenna, optimize the electromagnetic performance of the wireless transmission antenna based on simulation technology, and obtain the optimal structure and layout position of the wireless transmission antenna; based on the impedance characteristics of the wireless transmission antenna, design a matching circuit, and use the Smith chart and optimization algorithm to adjust the component parameters of the matching circuit; add filtering, shielding and isolation measures to the wireless transmission antenna and the matching circuit to complete the wireless transmission antenna scheme for a single tire; form an antenna array with the wireless transmission antennas of multiple tires, use beamforming and diversity technologies to dynamically adjust the radiation pattern and gain of the antenna array, and complete the wireless transmission of data by tracking the movement trajectory of the vehicle.

[0018] Optionally, the process of performing data loss diagnosis on the tire pressure data based on the central processing unit and compensating the tire pressure data if data loss exists to obtain the corrected tire pressure data includes:

[0019] The central processing unit judges whether there is data loss in each sensor based on a preset sensor threshold. If data loss occurs in one of the sensors, the data of the other sensors is used to perform data compensation on the sensor with data loss by using the Kalman filter algorithm, and the compensated data is fused with the original data of the sensor to obtain the corrected tire pressure data.

[0020] Optionally, the process of extracting features from the corrected tire pressure data to obtain key feature parameters and comparing the key feature parameters with preset working thresholds to obtain the working state of the tire includes:

[0021] Denoise and remove outliers from the corrected tire pressure data, and then use a data analysis algorithm to extract features to obtain key feature parameters reflecting the tire state; compare the key feature parameters with preset working thresholds. When the preset working thresholds are exceeded, trigger corresponding tire abnormality alarms, and the central processing unit generates an abnormality report and sends it to the mobile terminal.

[0022] The present invention also provides a tire pressure monitoring system based on wireless transmission, based on the method described above, including: a sensor optimization module, a data transmission module, a data reception module, a data compensation module, and a data processing module;

[0023] The sensor optimization module is used to optimize the installation position of the sensor inside the tire, and perform vibration damping processing and temperature compensation processing on the sensor;

[0024] The data transmission module is used to design a wireless transmission antenna scheme inside the tire to transmit the tire pressure data collected by the sensor;

[0025] The data reception module is used to receive the tire pressure data based on a wireless receiver combined with an adaptive signal processing algorithm;

[0026] The data compensation module is used to perform data loss diagnosis on the tire pressure data based on the central processing unit. If there is data loss, compensate the tire pressure data to obtain corrected tire pressure data;

[0027] The data processing module is used to extract features from the corrected tire pressure data to obtain key feature parameters, and compare the key feature parameters with preset working thresholds to obtain the working state of the tire.

[0028] The present invention also provides a computer device, including a memory, a processor, and a computer program stored on the memory. The processor executes the computer program to implement the steps of the method.

[0029] The present invention also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, it implements the steps of the method.

[0030] The present invention also provides a computer program product, including a computer program. When the computer program is executed by a processor, it implements the steps of the method.

[0031] Compared with the prior art, the present invention has the following advantages and technical effects:

[0032] The present invention optimizes the installation position of sensors and takes vibration damping measures, while using a temperature compensation algorithm. To solve the problems of shielding and electromagnetic interference of body metal components, the present invention designs a miniaturized high-gain antenna inside the tire, and optimizes the layout and matching circuit. The present invention also adopts a high-sensitivity and low-power receiver and an adaptive signal processing algorithm to improve the detection ability of weak signals. The central processing unit of the present invention uses a high-performance processor and a real-time system, optimizes the data processing algorithm, and through redundant design and fault diagnosis, when individual sensors fail, other sensor data can be used for compensation. The present invention significantly improves the measurement accuracy, anti-interference ability and reliability of tire pressure monitoring, ensures long-term stable operation in harsh environments, and provides a strong guarantee for driving safety. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] The drawings constituting a part of this application are used to provide a further understanding of this application. The schematic embodiments of this application and their descriptions are used to explain this application and do not constitute an improper limitation to this application. In the drawings:

[0034] Figure 1 It is a schematic flowchart of the method of the embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0035] It should be noted that, without conflict, the embodiments in this application and the features in the embodiments can be combined with each other. The following will refer to the drawings and combine the embodiments to detail this application.

[0036] It should be noted that the steps shown in the flowchart of the drawings can be executed in a computer system such as a set of computer executable instructions, and although the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in a different order than here.

[0037] Embodiment 1

[0038] As Figure 1 shown, this embodiment provides a tire pressure monitoring method based on wireless transmission, including the following steps:

[0039] Optimize the installation position of the sensor inside the tire, and perform vibration damping treatment and temperature compensation treatment on the sensor;

[0040] Design a wireless transmission antenna scheme inside the tire to transmit the tire pressure data collected by the sensor;

[0041] Based on a wireless receiver combined with an adaptive signal processing algorithm, receive the tire pressure data;

[0042] Based on the central processing unit, data loss diagnosis is performed on the tire pressure data. If data loss exists, the tire pressure data is compensated to obtain corrected tire pressure data.

[0043] Feature extraction is performed on the corrected tire pressure data to obtain key feature parameters, and the key feature parameters are compared with preset working thresholds to obtain the working state of the tire.

[0044] As a specific implementation manner, the tire pressure monitoring system of this embodiment adopts devices and packaging processes with high reliability, wide temperature range, anti-vibration, waterproof and dustproof, to improve the measurement accuracy and service life of the sensor, and ensure that the sensor can operate stably for a long time.

[0045] Specifically, the sensor of the tire pressure monitoring system needs to meet strict working environment requirements. For example, the temperature range may be from -40°C to 125°C, the vibration frequency can reach 2000 Hz, and the protection level needs to reach IP67. Therefore, a ceramic capacitive pressure sensor can be selected, which has good temperature stability and anti-vibration performance. In terms of packaging, a combination of metal packaging and silicone potting is adopted, which not only ensures the sealing performance but also provides a certain buffering effect. To evaluate the sensor performance, high and low temperature cycle tests, vibration tests, salt spray tests, etc. can be carried out. For example, in the high and low temperature cycle test, the sensor is placed in the temperature range of -40°C to 125°C and cycled 100 times, with each cycle lasting 2 hours, to test its output stability. Through these tests, the most suitable devices and packaging processes are selected.

[0046] As a specific implementation manner, for the selected sensor, its installation position is optimized and vibration damping measures are taken to reduce the influence of the vibration and impact generated by the tire during high-speed driving on the sensor. At the same time, a temperature compensation algorithm is adopted to reduce the influence of temperature change on the measurement accuracy.

[0047] Specifically, according to the technical parameters and installation requirements of the sensor, through finite element analysis and simulation, the optimal installation position of the sensor is determined to minimize the influence of vibration and impact on the sensor. A vibration damping device is set around the installation position of the sensor, and damping materials and vibration isolation structures are used to absorb and attenuate the vibration energy generated by the tire during high-speed driving, reducing the interference of vibration on the measurement accuracy of the sensor. The real-time measured temperature data of the sensor is obtained, and according to the pre-established temperature-error model, the measurement error compensation value under the current temperature condition is calculated, and the measurement result of the sensor is corrected in real time to improve the measurement accuracy. The Kalman filter algorithm is used to fuse and optimize the measurement data of the sensor, comprehensively considering the influence of multiple factors such as temperature and vibration, and dynamically adjusting the filtering parameters to obtain more accurate and stable measurement results.

[0048] Further, the process of establishing a temperature-error model through a machine learning algorithm, predicting the measurement error of the sensor based on the real-time collected environmental parameters, and performing adaptive compensation to further improve the measurement accuracy includes:

[0049] Obtain the measurement data of the sensor at different temperatures and the corresponding standard reference values, and construct a historical data set containing measurement errors and temperature parameters. Preprocess the historical data, remove outliers, interpolate or delete missing values, and normalize the data to unify parameters with different dimensions to the same scale. Select a suitable machine learning algorithm, such as support vector machine, neural network, or random forest, etc., and use the preprocessed historical data set for training to establish a non-linear mapping model between the measurement error and environmental factors. During the model training process, evaluate the model using methods such as cross-validation, and optimize the model performance and improve the prediction accuracy by adjusting the model hyperparameters and feature selection. Deploy the trained measurement error prediction model to the sensor data acquisition and processing system to obtain the temperature parameters of the environment where the sensor is located in real time. Input the real-time collected environmental parameters into the prediction model to obtain the predicted value of the measurement error of the sensor under the current environmental conditions, and perform adaptive compensation and correction on the measurement results of the sensor according to the predicted value. Continuously monitor the changes in the measurement data of the sensor and environmental parameters, and regularly update and optimize the prediction model using newly collected data to adapt to the long-term changes in environmental conditions and maintain the stability of the measurement accuracy.

[0050] Specifically, in the sensor measurement error compensation system, constructing a historical dataset is a key step. For example, for a tire pressure sensor, data can be collected at different temperatures (such as -20°C to 60°C) and vibration frequencies (such as 10 Hz to 1000 Hz), while recording the readings of a standard barometer as reference values. In this way, a multi-dimensional dataset containing measurement errors, temperature, and vibration parameters can be obtained. Data preprocessing is crucial for model training. Taking temperature data as an example, there may be outliers such as -100°C or 200°C, and these data that clearly exceed the normal range need to be removed. For missing values in vibration data, linear interpolation can be used for filling. Normalization processing can scale the temperature range to between 0 and 1, and the same applies to vibration frequencies, making parameters with different dimensions comparable. The selection of machine learning algorithms needs to consider data characteristics and model complexity. Support vector machines are suitable for dealing with non-linear relationships and can effectively capture the complex effects of temperature and vibration on measurement errors. Neural networks can automatically learn features and are suitable for large-scale datasets. Random forests have good anti-noise capabilities and are suitable for dealing with sensor data with certain noise. During the model training process, cross-validation is an effective method to evaluate the generalization ability of the model. For example, 5-fold cross-validation can be adopted, dividing the dataset into 5 parts, and taking turns using 4 parts as the training set and 1 part as the validation set. By adjusting the kernel function parameters of the support vector machine or the number of layers and neurons of the neural network, the optimal model structure can be found. When deploying the trained model to an actual system, real-time performance and computing resources need to be considered. The model can be converted into a lightweight format, such as TensorFlowLite, to adapt to the limitations of embedded systems. Environment parameters obtained in real time may include the temperature value measured by the temperature sensor and the vibration intensity measured by the acceleration sensor. Adaptive compensation and correction are the keys to improving measurement accuracy. Suppose the model predicts that when the current temperature is 30°C and the vibration frequency is 100 Hz, the measurement error of the sensor is 0.5%. Then the actual measurement value can be divided by 1.005 to obtain the corrected result. This method can dynamically adapt to environmental changes and maintain the accuracy of measurement. Regularly updating the model is an important means to maintain long-term performance. It can be set to update the model once a month or a quarter, and retrain the model using newly collected data. This can adapt to possible performance changes of the sensor over time, such as error changes caused by aging or wear. Through continuous optimization, the system can always maintain a high-precision measurement ability and provide reliable support for key applications such as tire pressure monitoring.

[0051] As a specific implementation manner, a miniaturized, high-gain, broadband wireless transmission antenna is designed in the internal space of the tire, and the antenna layout and matching circuit are optimized to improve the transmission efficiency and reliability of wireless signals and reduce the shielding and electromagnetic interference effects of vehicle body metal components.

[0052] According to the size and shape of the internal space of the tire, design a miniaturized, high-gain, broadband wireless transmission antenna structure and layout scheme that meets the requirements. Optimize the electromagnetic performance of the antenna through simulation software to obtain the best antenna parameters and layout positions. For the impedance characteristics of the wireless transmission antenna, design a matching circuit. Using the Smith chart and optimization algorithm, adjust the component parameters of the matching circuit to achieve impedance matching of the antenna within the operating frequency band, reduce reflection loss, and improve transmission efficiency. Adopt electromagnetic compatibility design technology, and add anti-interference measures such as filtering, shielding, and isolation to the antenna and the matching circuit to reduce the electromagnetic interference of in-vehicle electronic devices and ensure the reliability and stability of the wireless transmission signal. Composed of wireless transmission antennas inside multiple tires to form an antenna array, using beamforming and diversity techniques, dynamically adjust the radiation pattern and gain of the antenna array, track the movement trajectory of the vehicle, maintain a stable wireless link, and improve the transmission coverage and reliability. Build a test platform for wireless transmission inside the tire, test the antenna performance and transmission quality under different working conditions, and conduct long-term reliability tests. Optimize the antenna design and the matching circuit to finally obtain a tire internal wireless transmission antenna solution with high reliability, strong anti-interference ability, and high transmission efficiency.

[0053] Specifically, the internal space size and shape of the tire limit the antenna design, and an antenna structure with miniaturization, high gain, and wide bandwidth is required. For example, a microstrip antenna or a planar inverted-F antenna can be used, and these antenna structures are compact and easy to integrate. Through simulation software such as HFSS or CST, antenna parameters such as the feed point position and patch size can be optimized to obtain the best performance. In practical applications, a microstrip antenna with a working frequency of 2.4 GHz and a size of 20 mm × 15 mm may have a gain of 5 dBi and a bandwidth of 200 MHz. Antenna impedance matching is crucial for improving transmission efficiency. The Smith chart can be used to visually analyze the impedance characteristics and design a matching circuit. For example, for a system with a characteristic impedance of 50 Ω, if the antenna presents an impedance of 75 + j25 Ω at the operating frequency, a matching can be achieved by adding an L network with a series inductor and a parallel capacitor. By adjusting the component values through an optimization algorithm, the reflection loss can be reduced to below -20 dB, ensuring that most of the energy is radiated. Antenna performance testing is the key to screening the optimal solution. A vector network analyzer can be used to measure the S parameters of the antenna, and the radiation pattern and gain can be measured in an anechoic chamber. For example, a qualified internal tire antenna may have a gain variation of no more than 3 dB within the 360° azimuth range and a bandwidth covering 2.4 - 2.5 GHz, meeting the Bluetooth or Wi-Fi communication requirements. The metal components of the vehicle body have a significant impact on wireless signals. By arranging multiple test points around the tire, a signal strength heat map can be drawn to identify areas with weak signals. For example, it may be found that the signal attenuation is severe near the wheel hub. At this time, the antenna can be considered to be arranged at a position far from the wheel hub, or a directional antenna can be used to point in the direction of the weak signal. Electromagnetic compatibility design is crucial for ensuring communication reliability. A band-pass filter can be added at the front end of the antenna to filter out interference signals generated by in-vehicle electronic devices. For example, a band-pass filter with a center frequency of 2.45 GHz and a bandwidth of 100 MHz can effectively suppress interference from the in-vehicle entertainment system. At the same time, a metal shielding cover can be set around the antenna to reduce environmental electromagnetic interference. Multi-antenna technology can significantly improve communication performance. For example, by adopting a 2×2 MIMO (Multiple Input Multiple Output) system, spatial diversity can be utilized to increase the channel capacity. Through an adaptive beamforming algorithm, the antenna radiation pattern can be adjusted in real time according to the vehicle movement to maintain the best connection with the receiving end. When driving at high speed, this technology can expand the signal coverage range to more than twice that of a traditional single-antenna system. Finally, long-term reliability testing is the key to verifying the antenna solution. Temperature cycling, vibration, shock, etc. tests can be carried out on a test platform that simulates the internal environment of the tire. For example, cycle 1000 times within the temperature range of -40°C to 85°C, with each vibration test lasting 8 hours, simulating a driving mileage of 100,000 km. Through these tests, the stability and durability of the antenna performance can be verified to ensure long-term reliable operation in actual use.

[0054] As a specific implementation, the tire pressure monitoring system adopts a high-sensitivity and low-power wireless receiver, combined with an adaptive signal processing algorithm, to improve the detection and demodulation capabilities of the receiver for weak signals.

[0055] Obtain the tire pressure signal collected by the tire pressure monitoring system and transmit the tire pressure signal to a high-sensitivity and low-power wireless receiver; after receiving the tire pressure signal, the wireless receiver processes the tire pressure signal using an adaptive signal processing algorithm to improve the signal-to-noise ratio of weak signals.

[0056] As a specific implementation, the tire pressure monitoring system improves the system fault tolerance through redundant design and fault diagnosis technology. When an individual sensor fails or a signal is lost, other sensor data is used for compensation or estimation to ensure that the reliability of the entire system is not affected.

[0057] Obtain the tire pressure data collected by each sensor in the tire pressure monitoring system and transmit the data to the central processing unit. The central processing unit receives the sensor data and determines whether each sensor has failed or a signal is lost through a preset threshold. If it is determined that a certain sensor has failed or a signal is lost, the data of the faulty sensor is compensated or estimated using the Kalman filtering algorithm based on the data of other sensors. The data obtained through compensation or estimation is fused with the original data of the faulty sensor to obtain the corrected tire pressure data.

[0058] Specifically, the tire pressure monitoring system collects tire pressure data through multiple sensors and transmits the data to the central processing unit for analysis. Taking a four-wheel vehicle as an example, each tire is equipped with a pressure sensor to collect pressure data in real time and transmit it through a wireless signal. After receiving these data, the central processing unit will first perform sensor fault detection. Assuming that the normal tire pressure should be in the range of 200 - 250 kPa during normal operation, if a certain sensor outputs 0 or an abnormally high value for a long time, it may be determined that the sensor has failed. When a sensor fault is detected, the system will activate the data compensation mechanism. For example, if the left front wheel sensor fails, the system can estimate the pressure of the left front wheel using the data of the right front wheel, left rear wheel, and right rear wheel with the Kalman filtering algorithm. The Kalman filtering algorithm can make a relatively accurate prediction of the missing data by establishing a mathematical model and combining historical data and current observations. This method can keep the system running continuously in the case of sensor faults. After data compensation, the system will fuse the estimated value with the original data to obtain the corrected tire pressure data. For example, if the estimated value is 220 kPa and the original faulty data is 0 kPa, the system may use a weighted average method to obtain a corrected value closer to the actual situation. This data fusion can improve the robustness of the system and reduce the impact of single-point faults.

[0059] As a specific implementation, a high-performance embedded processor and a real-time operating system are adopted in the central processing unit to improve the data processing speed and real-time performance, optimize the data parsing and status judgment algorithms, and quickly and accurately complete tire status monitoring and alarming.

[0060] Preprocess the corrected tire pressure data to remove noise and outliers in the data, and obtain the cleaned tire status data. For the cleaned tire status data, use the optimized data parsing algorithm for feature extraction to obtain the key feature parameters reflecting the tire status, such as tire pressure, temperature, vibration, etc. According to the extracted tire status feature parameters, conduct comprehensive analysis through the status judgment algorithm, compare the feature parameters with the preset normal working thresholds, and judge whether the tire is in a normal working state. If the tire status feature parameters exceed the normal working threshold range, trigger the corresponding tire abnormality alarm, and display the location and specific abnormality of the abnormal tire in real time through the human-machine interface. At the same time as the tire abnormality alarm is triggered, the central processing unit automatically generates an abnormality report, records information such as the time of the abnormality, the tire location, and the type of abnormality, and uploads it to the remote server through the wireless communication module. After receiving the abnormality report, the remote server automatically pushes the report to the mobile terminal of the maintenance personnel to notify the maintenance personnel to check and maintain the abnormal tire in time to ensure the safety of vehicle driving. The central processing unit continuously monitors the tire status, updates the tire status data in real time, and regularly uploads the monitoring data to the remote server to form a complete tire status monitoring history record, providing data support for subsequent big data analysis and predictive maintenance.

[0061] Furthermore, the optimization of the data parsing algorithm is crucial for accurately extracting tire state features. Methods such as wavelet transform can be used to perform time-frequency analysis on the cleaned data to extract key parameters such as tire pressure and temperature. For example, by analyzing the spectral characteristics of vibration signals, problems such as tire imbalance or abnormal wear can be identified. The state judgment algorithm needs to comprehensively consider multiple characteristic parameters. For instance, normal tire pressure but abnormally high temperature may indicate potential tire faults. The normal range of tire pressure can be set at 220 - 240 kPa, and the normal range of temperature can be set at 40 - 60 °C. If the detected tire pressure is 230 kPa but the temperature reaches 75 °C, although the tire pressure is within the normal range, an abnormal alarm should still be triggered. The design of the human-machine interface should be intuitive and clear. The vehicle top view can be used to display the position of the abnormal tire, and different colors can be used to identify the type of abnormality. For example, red indicates too low tire pressure, and yellow indicates too high temperature, etc. At the same time, the specific abnormal parameter values should be displayed in real-time on the interface to facilitate the driver to quickly understand the situation. The function of automatically generating and uploading abnormal reports can greatly improve the maintenance efficiency. The report should include information such as vehicle identification number, abnormal occurrence time, tire position, type of abnormality, and specific parameter values. For example: "Vehicle VIN: LSVAU6C46EN123456, Time: 2023-05-20 14:30:25, Left front tire pressure too low (180 kPa), it is recommended to check in time". After receiving the push, maintenance personnel can quickly locate the problem and respond. Continuous monitoring and data uploading provide the basis for predictive maintenance. By analyzing the change trends of historical data, potential tire problems can be predicted. For example, if it is found that the pressure of a certain tire is continuously and slowly decreasing, even if it has not reached the alarm threshold, an inspection can be arranged in advance to prevent problems before they occur. This predictive maintenance based on big data can significantly reduce vehicle failure rates and improve driving safety. The implementation of the entire system not only improves driving safety but also optimizes vehicle maintenance strategies. Through real-time monitoring and timely warning, accidents caused by tire problems can be avoided. At the same time, predictive maintenance can reduce unnecessary inspections and lower maintenance costs. In addition, the long-term accumulated data can also provide valuable insights for tire design and production, promoting the technological progress of the entire industry.

[0062] Furthermore, for the cleaned tire state data, the process of using the optimized data parsing algorithm for feature extraction to obtain key feature parameters reflecting the tire state, such as tire pressure, temperature, vibration, etc., includes:

[0063] Adopt machine learning algorithms such as support vector machines and random forests to establish a tire status evaluation model. Through training and optimization, improve the accuracy and generalization ability of the model. Input the extracted key feature parameters into the tire status evaluation model, and through model inference and calculation, judge the real-time status of the tire, such as normal, abnormal, dangerous, etc. According to the judgment result of the tire status, take corresponding measures, such as adjusting tire pressure, reducing speed, replacing tires, etc., to ensure driving safety and the service life of the tires. Continuously monitor the status change of the tires, and through data visualization and alarm mechanisms, timely discover potential problems and risks, and provide predictive maintenance and fault diagnosis services. Continuously optimize and update the tire status evaluation model, and through collecting more actual data and user feedback, improve the adaptability and robustness of the model, and provide more intelligent and personalized services for users.

[0064] Specifically, after feature extraction is completed, the system will use machine learning algorithms to establish a tire status evaluation model. Support Vector Machine (SVM) is a commonly used classification algorithm suitable for processing high-dimensional feature data. By collecting a large amount of tire data with known status for training, SVM can learn the feature boundaries of tires in different states. For example, the tire status can be divided into four categories: "normal", "slightly worn", "severely worn", and "dangerous". SVM will find the optimal hyperplane in the feature space to distinguish these categories. After the model training is completed, the system will input the real-time extracted feature parameters into the model for inference. Suppose the inference result shows that the tire is in the "slightly worn" state. The system will take corresponding measures according to the preset strategy. This may include sending a warning to the driver, suggesting reducing the vehicle speed or adjusting the tire pressure. If the system detects that the tire is in a "dangerous" state, more stringent measures may be triggered, such as forced speed limit or notifying the nearest repair point. The system will also continuously monitor the change of the tire status and visually display the tire health status through data visualization technology. As the usage time increases, the system will continuously collect new data to optimize and update the evaluation model. For example, by analyzing a large amount of user data, the system may find that the tire wear speed accelerates under certain specific road conditions. This information can be used to improve the model and enhance its accuracy in different environments. At the same time, the system can provide personalized tire maintenance suggestions for users according to their driving habits and vehicle usage conditions, such as recommending the best tire replacement time. Through this intelligent tire status monitoring system, not only can driving safety be improved, but also the service life of the tires can be optimized, and the vehicle maintenance cost can be reduced. The predictive maintenance function of the system can help users avoid accidents caused by sudden tire failures, and at the same time extend the service life of the tires through timely maintenance suggestions.

[0065] This embodiment also provides a tire pressure monitoring system based on wireless transmission, based on the above method, including: a sensor optimization module, a data transmission module, a data receiving module, a data compensation module, and a data processing module;

[0066] The sensor optimization module is used to optimize the installation position of the sensor inside the tire, and perform vibration damping treatment and temperature compensation treatment on the sensor;

[0067] The data transmission module is used to design a wireless transmission antenna scheme inside the tire and transmit the tire pressure data collected by the sensor;

[0068] The data receiving module is used to receive the tire pressure data based on a wireless receiver combined with an adaptive signal processing algorithm;

[0069] The data compensation module is used to perform data loss diagnosis on the tire pressure data based on a central processing unit. If data loss exists, the tire pressure data is compensated to obtain corrected tire pressure data;

[0070] The data processing module is used to extract features from the corrected tire pressure data to obtain key feature parameters, and compare the key feature parameters with preset working thresholds to obtain the working state of the tire.

[0071] Embodiment 2

[0072] This embodiment also provides a computer device, including a memory, a processor, and a computer program stored on the memory. The processor executes the computer program to implement the steps of the method.

[0073] Embodiment 3

[0074] This embodiment also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the method are implemented.

[0075] Embodiment 4

[0076] This embodiment also provides a computer program product, including a computer program. When the computer program is executed by a processor, the steps of the method are implemented.

[0077] The above is only a preferred specific embodiment of the present application, but the protection scope of the present application is not limited thereto. Any changes or substitutions that can be easily thought of by those skilled in the technical field of the present application within the technical scope disclosed in the present application should be covered by the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A tire pressure monitoring method based on wireless transmission, characterized in that, Including the following steps: Optimize the installation position of the sensor inside the tire, and perform vibration damping treatment and temperature compensation treatment on the sensor; Design a wireless transmission antenna solution inside the tire to transmit the tire pressure data collected by the sensor; Based on the wireless receiver combined with the adaptive signal processing algorithm, receive the tire pressure data; Based on the central processing unit, perform data loss diagnosis on the tire pressure data. If data loss exists, compensate the tire pressure data to obtain the corrected tire pressure data; Extract features from the corrected tire pressure data to obtain key feature parameters, and compare the key feature parameters with the preset working threshold to obtain the working state of the tire.

2. The method according to claim 1, wherein The process of optimizing the installation position of the sensor inside the tire includes: Based on the technical parameters and installation requirements of the sensor, determine the optimal installation position of the sensor through finite element analysis and simulation.

3. The method according to claim 2, wherein The process of performing vibration damping treatment and temperature compensation treatment on the sensor includes: Set a vibration damping device around the installation position of the sensor to perform vibration damping treatment on the sensor; construct a temperature-error model, obtain the temperature data measured by the sensor in real time, and based on the pre-established temperature-error model, obtain the measurement error compensation value under the current temperature condition, and perform real-time correction on the measurement result of the sensor.

4. The method according to claim 1, wherein The process of designing a wireless transmission antenna solution inside the tire to transmit the tire pressure data collected by the sensor includes: Based on the size and shape of the internal space of the tire, design the structure and layout position of the wireless transmission antenna, optimize the electromagnetic performance of the wireless transmission antenna based on simulation technology to obtain the optimal structure and layout position of the wireless transmission antenna; based on the impedance characteristics of the wireless transmission antenna, design a matching circuit, and use the Smith chart and optimization algorithm to adjust the component parameters of the matching circuit; add filtering, shielding and isolation measures to the wireless transmission antenna and the matching circuit to complete the wireless transmission antenna solution for a single tire; form an antenna array with the wireless transmission antennas of multiple tires, use beamforming and diversity technology to dynamically adjust the radiation pattern and gain of the antenna array, and complete the wireless transmission of data by tracking the movement trajectory of the vehicle.

5. The method according to claim 1, wherein The process of performing data loss diagnosis on the tire pressure data based on the central processing unit, and if data loss exists, compensating the tire pressure data to obtain the corrected tire pressure data includes: The central processing unit judges whether each sensor has data loss based on the preset sensor threshold. If one of the sensors has data loss, the data of the other sensors is used to perform data compensation on the sensor with data loss by using the Kalman filter algorithm, and the compensated data is fused with the original data of the sensor to obtain the corrected tire pressure data.

6. The method according to claim 1, wherein The process of extracting features from the corrected tire pressure data to obtain key feature parameters and comparing the key feature parameters with preset working thresholds to obtain the working state of the tire includes: Denoise and remove outliers from the corrected tire pressure data, and then use a data parsing algorithm for feature extraction to obtain key feature parameters reflecting the tire state; compare the key feature parameters with preset working thresholds. When exceeding the preset working thresholds, trigger corresponding tire anomaly alarms, and generate anomaly reports by the central processing unit and send them to the mobile terminal.

7. A tire pressure monitoring system based on wireless transmission, characterized in that, The method according to any one of claims 1-6 includes: a sensor optimization module, a data transmission module, a data reception module, a data compensation module, and a data processing module; The sensor optimization module is used to optimize the installation position of the sensor inside the tire, and perform vibration damping processing and temperature compensation processing on the sensor; The data transmission module is used to design a wireless transmission antenna scheme inside the tire to transmit the tire pressure data collected by the sensor; The data reception module is used to receive the tire pressure data based on a wireless receiver combined with an adaptive signal processing algorithm; The data compensation module is used to perform data loss diagnosis on the tire pressure data based on the central processing unit. If there is data loss, compensate the tire pressure data to obtain corrected tire pressure data; The data processing module is used to extract features from the corrected tire pressure data to obtain key feature parameters, and compare the key feature parameters with preset working thresholds to obtain the working state of the tire.

8. A computer device, comprising a memory, a processor, and a computer program stored on the memory, characterized in that, The processor executes the computer program to implement the steps of the method according to any one of claims 1-6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the method according to any one of claims 1-6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the method according to any one of claims 1-6.

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