Non-contact real-time monitoring system for rotation angle of steering shaft of motor vehicle

Abstract

The invention discloses a motor vehicle steering shaft rotation angle non-contact real-time monitoring system, and belongs to the technical field of motor vehicle steering system monitoring, and the system comprises an encoder module which is used for generating an electric signal related to a steering shaft rotation angle; the signal processing module is used for processing the received electric signal and calculating a rotation angle value; the artificial intelligence analysis module is used for performing anomaly detection and predictive maintenance on the angle data, analyzing time sequence features of the angle data based on a machine learning model, and realizing abnormal behavior detection and potential fault prediction; and the wireless communication module is used for transmitting the rotation angle data to a vehicle control system. The non-contact real-time monitoring system for the rotation angle of the steering shaft of the motor vehicle can meet the requirement for high-precision measurement, is suitable for real-time monitoring in a dynamic driving environment, and provides durability and stability of the system.

Description

Technical Field

[0001] The present invention relates to the technical field of motor vehicle steering system monitoring, in particular to a non-contact real-time monitoring system for the rotation angle of a steering shaft of a motor vehicle. Background Art

[0002] In modern motor vehicles, precise control of the steering system is crucial to driving safety. Traditional methods for monitoring steering angles typically rely on mechanical sensors or contact-based measurement devices, which have the following problems:

[0003] High risk of wear and failure: Contact sensors are prone to wear due to friction during long-term use, resulting in reduced measurement accuracy or even failure.

[0004] Slow response speed: Traditional sensors have slow signal acquisition and processing speeds and cannot meet the real-time monitoring needs under high-speed dynamic driving conditions.

[0005] Complex installation: Contact sensors require precise installation and calibration, which increases assembly costs and maintenance difficulties.

[0006] To solve the above problems, the present invention proposes a steering shaft rotation angle monitoring system based on non-contact technology, which can achieve high precision, fast response and reliable operation. Summary of the Invention

[0007] The purpose of the present invention is to provide a non-contact real-time monitoring system for the steering shaft rotation angle of a motor vehicle, which can meet the high-precision measurement requirements, is suitable for real-time monitoring in dynamic driving environments, and provides durability and stability of the system.

[0008] To achieve the above object, the present invention provides a non-contact real-time monitoring system for the steering shaft rotation angle of a motor vehicle, comprising:

[0009] An encoder module for generating an electrical signal related to the rotation angle of the steering shaft;

[0010] A signal processing module is used to process the received electrical signal and calculate the rotation angle value;

[0011] The AI ​​analysis module is used to perform anomaly detection and predictive maintenance on angle data. It analyzes the time series characteristics of angle data based on machine learning models to detect abnormal behavior and predict potential failures.

[0012] The wireless communication module is used to transmit the rotation angle data to the vehicle control system.

[0013] Preferably, the encoder module uses a combination of neodymium iron boron rare earth permanent magnets and a three-axis Hall sensor array to perform six-degree-of-freedom posture tracking through magnetic field coupling.

[0014] Preferably, the encoder is a magnetic encoder or an optical encoder, and the encoder module adopts a self-cleaning design, with a built-in piezoelectric ceramic driven scraper to regularly remove contaminants from the grating surface.

[0015] Preferably, the grating scanning system of the encoder module adopts a combination of a moving grating with a black-white ratio of n:1 (n is an odd number ≥3) and (n+1) / 2 fixed gratings, and the fixed grating light-transmitting slit width decreases in a geometric sequence.

[0016] Preferably, the Hall sensor array adopts an 8×8 matrix layout, with a spatial resolution of 0.1° and a dynamic response delay of <2ms.

[0017] Preferably, the signal processing module includes an operational amplifier, a filtering circuit and a microcontroller, and adopts the following formula:

[0018] Noise filtering formula:

[0019]

[0020] Among them, V filtered is the signal value after filtering; V i is the signal value of the i-th sampling; N is the number of sampling points.

[0021] Preferably, the artificial intelligence analysis module performs time series analysis on the angle data based on a machine learning model, specifically including:

[0022] Anomaly detection formula:

[0023] If |θ t -θ t-1 |>Δθ threshold , it is considered abnormal behavior.

[0024] Among them, θ t is the angle value between the current moment and the previous moment; θ t-1 is the angle value at the previous moment; Δθ threshold is the set threshold;

[0025] Fault prediction formula:

[0026] P f =f(θ t ,θ t-1 ,θ t-2 ,…θ t-N );

[0027] Among them, P f is the failure probability; f is the prediction function trained based on historical data.

[0028] Preferably, the anomaly detection in the artificial intelligence analysis module implements a three-level early warning mechanism:

[0029] Primary warning: single angle deviation >5°;

[0030] Intermediate warning: 3 consecutive deviations >3°;

[0031] Emergency warning: instantaneous angular velocity>100° / s.

[0032] Preferably, the wireless communication module supports CAN bus, LIN bus or Wi-Fi communication protocol, can achieve 10Mbps high-speed transmission, adopts AES-256 encryption and SHA-3 check, and adopts the following formula:

[0033] Data encryption formula:

[0034] D e =E(K,D r );

[0035] Among them, D e is the encrypted data; D r is the original data; K is the key; E is the encryption algorithm;

[0036] Error checking formula:

[0037] C c =H(D r );

[0038] Among them, C c is the check code; H is the hash function.

[0039] Preferably, the wireless communication module has a built-in dual-channel redundant design, the main channel adopts CAN bus, and the backup channel adopts LoRa wireless communication.

[0040] Preferably, the artificial intelligence module deploys a federated learning framework to support collaborative training of multi-vehicle data without leaking local data.

[0041] Preferably, the system further comprises a mechanical overload protection mechanism, which automatically triggers the electromagnetic clutch to disconnect the transmission when it detects that the steering torque is continuously greater than 50N·m;

[0042] The system integrates a GNSS positioning module, and the steering angle data is bound and stored with geographic coordinates to form a spatiotemporal trajectory database.

[0043] Therefore, the present invention adopts the above-mentioned non-contact real-time monitoring system for the steering shaft rotation angle of a motor vehicle, and the technical effects are as follows:

[0044] High precision: The resolution of the magnetic encoder can reach 0.01°, which can meet the needs of high-precision measurement;

[0045] Strong real-time performance: The system response time is less than 1 millisecond, suitable for real-time monitoring in dynamic driving environments;

[0046] High reliability: non-contact design reduces mechanical wear and improves system durability and stability;

[0047] Easy to install: The magnetic encoder module is small and can be easily integrated into existing steering systems without major changes to the vehicle structure.

[0048] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 The present invention is a structural schematic diagram of a non-contact real-time monitoring system for the rotation angle of a steering shaft of a motor vehicle according to an embodiment of the present invention. DETAILED DESCRIPTION

[0050] The technical solution of the present invention is further described below with reference to the accompanying drawings and embodiments.

[0051] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention. Unless otherwise defined, the technical terms or scientific terms used in the present invention should be the common meanings understood by people with ordinary skills in the field to which the present invention belongs.

[0052] like Figure 1 As shown, the present invention provides a non-contact real-time monitoring system for the steering shaft rotation angle of a motor vehicle, comprising:

[0053] An encoder module for generating an electrical signal related to the rotation angle of the steering shaft;

[0054] A signal processing module is used to process the received electrical signal and calculate the rotation angle value;

[0055] The AI ​​analysis module is used to perform anomaly detection and predictive maintenance on angle data. It analyzes the time series characteristics of angle data based on machine learning models to detect abnormal behavior and predict potential failures.

[0056] The wireless communication module is used to transmit the rotation angle data to the vehicle control system.

[0057] This embodiment improves measurement accuracy through a composite grating design and integrates federated learning to protect data privacy. Its three-level warning mechanism effectively prevents steering system failures. A spatiotemporal database supports retrospective analysis of driving behavior, providing a data foundation for optimizing autonomous driving systems.

[0058] The details are as follows: encoder module:

[0059] It uses a magnetic encoder or optical encoder with a built-in self-cleaning design, using piezoelectric ceramics to drive the scraper to regularly remove contaminants from the grating surface.

[0060] The grating scanning system uses a moving grating with a black-white ratio of 2:1 and 1.5 fixed gratings. The fixed gratings have light slits that decrease in geometric sequence.

[0061] The Hall sensor array adopts an 8×8 matrix layout with a spatial resolution of 0.1° and a dynamic response delay of less than 2ms.

[0062] Signal processing module:

[0063] Includes operational amplifiers, filtering circuits and a microcontroller.

[0064] Noise filtering formula:

[0065]

[0066] Among them, V filtered is the signal value after filtering; V i is the signal value of the i-th sampling; N is the number of sampling points.

[0067] Artificial intelligence analysis module:

[0068] Perform time series analysis on angle data based on machine learning models.

[0069] Anomaly detection formula: If |θ t -θ t-1 |>Δθ threshold , it is considered abnormal behavior.

[0070] Among them, θ t is the angle value between the current moment and the previous moment; θ t-1 is the angle value at the previous moment; Δθ threshold is the set threshold;

[0071] Fault prediction formula:

[0072] P f =f(θ t ,θ t-1 ,θ t-2 ,…θ t-N );

[0073] Among them, P f is the failure probability; f is the prediction function trained based on historical data.

[0074] Three-level warning mechanism for anomaly detection:

[0075] Primary warning: Single angle deviation is greater than 5°;

[0076] Intermediate warning: deviation greater than 3° for three consecutive times;

[0077] Emergency warning: instantaneous angular velocity is greater than 100° / s.

[0078] Wireless communication module:

[0079] Supports CAN bus, LIN bus, or Wi-Fi communication protocols, achieving 10Mbps high-speed transmission. Built-in dual-channel redundancy design, the main channel uses CAN bus, and the backup channel uses LoRa wireless communication.

[0080] Data encryption formula:

[0081] D e =E(K,D r );

[0082] Among them, D e is the encrypted data; D r is the original data; K is the key; E is the encryption algorithm;

[0083] Error checking formula:

[0084] C c =H(D r );

[0085] Among them, C c is the check code; H is the hash function.

[0086] Other functional modules:

[0087] The artificial intelligence module deploys a federated learning framework to support collaborative training of multi-vehicle data without leaking local data.

[0088] The integrated mechanical overload protection mechanism automatically triggers the electromagnetic clutch to disconnect the transmission when it detects that the steering torque is continuously greater than 50N·m.

[0089] Integrating the GNSS positioning module, the steering angle data is bound and stored with the geographic coordinates to form a spatiotemporal trajectory database.

[0090] Workflow:

[0091] The encoder module generates an electrical signal related to the steering shaft rotation angle.

[0092] The signal processing module performs noise filtering on the received electrical signal and calculates the rotation angle value.

[0093] The artificial intelligence analysis module performs anomaly detection and predictive maintenance on angle data.

[0094] The wireless communication module transmits the rotation angle data to the vehicle control system and ensures data security through AES-256 encryption and SHA-3 verification.

[0095] During the steering process, if abnormal behavior or potential failure is detected, the corresponding early warning mechanism will be triggered.

[0096] If the steering torque continues to exceed the set threshold (50N·m), the electromagnetic clutch is automatically triggered to disconnect the transmission.

[0097] Steering angle data is stored in conjunction with geographic coordinates for subsequent data analysis and trajectory playback.

[0098] Example 1: Laboratory Test

[0099] 1. Hardware Installation:

[0100] Mount the magnetic encoder near the steering shaft, ensuring that its sensing area covers the full rotation range of the steering shaft. Avoid direct contact between the magnetic encoder and metal parts to prevent interference.

[0101] Connect the signal processing module and magnetic encoder using a shielded cable to reduce electromagnetic interference. Connect the wireless communication module to the signal processing module to ensure stable data transmission. Check that all connection points are secure to avoid loose connections that could cause data loss.

[0102] 2. Software debugging:

[0103] Calibrate the zero point position of the magnetic encoder to ensure the accuracy of the measurement data. Verify that the measurement data is accurate and without offset after zero point calibration.

[0104] Configure the wireless communication protocol and select the appropriate wireless communication protocol (such as Wi-Fi, Bluetooth, or Zigbee) based on the test platform. Test the communication latency and packet loss rate to ensure that it is compatible with the test platform and meets the system requirements.

[0105] 3. Testing and verification:

[0106] The system's measurement accuracy and response speed are tested in a laboratory environment to verify its anti-interference ability and long-term stability under different temperature, humidity and vibration conditions.

[0107] Example 2: Actual vehicle road test

[0108] 1. Installation and deployment:

[0109] The monitoring system was integrated into an autonomous driving test vehicle, replacing the existing contact-based steering angle sensor. The installation location ensured it would not affect the vehicle's structural strength and safety, and the connecting cables were wrapped with waterproof tape to prevent moisture intrusion.

[0110] 2. Data Collection and Analysis:

[0111] Record steering angle data for urban, highway, and off-road conditions. Ensure the sampling frequency is high enough to capture rapidly changing steering movements.

[0112] Use artificial intelligence analysis modules to analyze data in real time and evaluate the system's performance under complex working conditions.

[0113] 3. Result evaluation:

[0114] Test results show that the system exhibits excellent performance under all working conditions, with a measurement accuracy of 0.01° and a response time of less than 1 millisecond.

[0115] Example 3: Commercial Vehicle Steering Angle Monitoring

[0116] 1. Background:

[0117] Commercial vehicles (such as trucks and buses) typically have long wheelbases and wide steering angle ranges, which place higher demands on steering system monitoring. Traditional contact sensors are unable to meet the requirements of long-term use in harsh working conditions.

[0118] 2. Implementation method:

[0119] 2.1 Hardware Installation:

[0120] Fix the magnetic encoder near the steering axis of the commercial vehicle and ensure that its sensing area covers the maximum steering angle (±50°).

[0121] Use a protective cover to protect the magnetic encoder module to prevent external factors such as dust and moisture from affecting its performance.

[0122] 2.2 Software debugging:

[0123] Calibrate the zero point position of the magnetic encoder to ensure the accuracy of the measurement data.

[0124] Configure the wireless communication protocol to CAN bus for seamless integration with the vehicle control system. Check the CAN bus communication rate and signal quality to ensure stable data transmission.

[0125] 3. Testing and verification:

[0126] The system's performance was tested on urban roads and highways, and steering angle data was recorded.

[0127] Evaluate system reliability in high and low temperature (-40°C to 85°C) and high humidity environments.

[0128] 4. Results:

[0129] Test results show that the system demonstrates excellent performance on commercial vehicles, with a measurement accuracy of 0.02° and a response time of less than 1 millisecond.

[0130] The system can operate stably under harsh working conditions and meet the needs of commercial vehicles for steering system monitoring.

[0131] Example 4: Steering Angle Optimization in Racing

[0132] 1. Background:

[0133] In motorsports, accurate steering angle monitoring is crucial for optimizing driving strategies and improving performance. Traditional steering angle sensors, due to their insufficient accuracy and slow response speed, cannot meet the requirements of motorsports.

[0134] 2. Implementation method:

[0135] 2.1 Hardware Installation:

[0136] Integrating a magnetic encoder into a racing car's steering system allows for multi-turn measurement to record absolute steering position. Lightweight design materials are also used to reduce system weight and minimize impact on racing car performance.

[0137] Use high-performance shielded cable to connect the magnetic encoder and the signal processing module to ensure stable data transmission. Check that all connection points are secure to avoid loose connections that could cause data loss.

[0138] 2.2 Software debugging:

[0139] An artificial intelligence analysis module is configured to analyze steering angle data in real time and identify the driver's operating habits. The analysis results are fed back to the racing control system to optimize the suspension system and power distribution.

[0140] Monitor system operating status and identify potential failure risks in advance. Automatically generate maintenance reminders to reduce the probability of car failure during the race.

[0141] 3. Testing and verification:

[0142] Test the system's performance in a racetrack environment, record steering angle data, and analyze the results.

[0143] Evaluate the system's effectiveness in improving the car's performance.

[0144] 4. Results:

[0145] Test results show that the system can monitor the steering angle of the car in real time with a resolution of 0.01°, helping the team optimize driving strategy.

[0146] The system's predictive maintenance function effectively reduces the risk of car failure during the race and improves competition results.

[0147] Example 5: Industrial Robot Joint Rotation Angle Monitoring

[0148] 1. Background and demand analysis:

[0149] In modern manufacturing, industrial robots have become a key component in improving production efficiency and product quality. Whether in automotive manufacturing, electronics assembly, or logistics handling, industrial robots meet diverse needs with their high precision, repeatability, and flexibility. However, with the increasing degree of industrial automation, the performance requirements for industrial robots are becoming increasingly stringent, particularly in terms of joint motion control.

[0150] Monitoring the rotation angle of industrial robot joints is a critical step in ensuring their proper operation. Accurate angle measurement not only ensures the robot's operational accuracy but also extends equipment life and reduces unplanned downtime through predictive maintenance. This is crucial for improving operational accuracy and extending service life. While traditional contact sensors (such as point meters or encoders) offer some measurement capabilities, they are susceptible to loss of accuracy due to mechanical wear over time and struggle to adapt to complex industrial environments. These issues become particularly acute in complex industrial environments (such as those characterized by high temperatures, humidity, and dust).

[0151] 2. System design:

[0152] 2.1 Hardware Architecture:

[0153] 2.1.1 Encoder module:

[0154] The encoder module is one of the core components of the entire system, responsible for generating electrical signals related to the joint rotation angle. To meet the high precision and reliability requirements of industrial scenarios, this embodiment uses a magnetic encoder as a core component. The magnetic encoder has the following characteristics:

[0155] Non-contact measurement: avoids the problem of accuracy loss caused by mechanical wear of traditional contact sensors.

[0156] Built-in self-cleaning design: piezoelectric ceramic driven scrapers regularly remove contaminants from the grating surface, ensuring long-term stable operation.

[0157] High resolution: Using an 8×8 Hall sensor array layout, the spatial resolution reaches 0.1° and the dynamic response delay is less than 2ms.

[0158] In addition, the grating scanning system adopts a combination design of a dynamic grating with a black-white ratio of 2:1 and 1.5 fixed gratings. The width of the fixed grating's light-transmitting slit decreases in a geometric sequence, further improving the measurement accuracy.

[0159] 2.1.2 Protection design:

[0160] To adapt to complex industrial environments, the encoder module adopts the following protection measures:

[0161] Waterproof and dustproof design: Meets IP67 protection level and can work normally in environments with high moisture and dust content.

[0162] High temperature resistant material: The shell is made of high temperature resistant composite material to ensure stable operation in the range of -40℃ to 85℃.

[0163] Anti-vibration structure: internal components are fixed by shock-absorbing brackets, effectively reducing the impact of external vibration on measurement accuracy.

[0164] 2.2 Signal processing unit:

[0165] The signal processing unit is responsible for filtering the received electrical signal and calculating the rotation angle. It includes the following modules:

[0166] Operational amplifier: used to amplify weak electrical signals to ensure the accuracy of subsequent processing.

[0167] Filter circuit: Use digital filtering algorithm to remove noise interference generated during the measurement process. The filtering formula is as follows:

[0168]

[0169] Among them, V filtered is the signal value after filtering; V i is the signal value of the i-th sampling; N is the number of sampling points.

[0170] Microcontroller: responsible for executing the core algorithm and calculating the final rotation angle value.

[0171] 2.3 Artificial Intelligence Analysis Module:

[0172] The AI ​​analysis module performs time series analysis on angle data based on machine learning models to detect abnormal behavior and predict potential faults. Specific functions include:

[0173] Anomaly detection: Determine whether abnormal behavior occurs based on the set threshold. The anomaly detection formula is as follows:

[0174] If |θ t -θ t-1 |>Δθ threshold , it is considered abnormal behavior.

[0175] Among them, θ t is the angle value between the current moment and the previous moment; θ t-1 is the angle value at the previous moment; Δθ threshold is the set threshold.

[0176] Fault prediction: Based on the prediction function trained with historical data, the probability of future faults is calculated. The fault prediction formula is as follows:

[0177] P f =f(θ t ,θ t-1 ,θ t-2 ,…θ t-N );

[0178] Among them, P f is the failure probability; f is the prediction function trained based on historical data.

[0179] Three-level early warning mechanism:

[0180] Primary warning: Single angle deviation is greater than 5°.

[0181] Intermediate warning: Deviation greater than 3° for three consecutive times.

[0182] Emergency warning: instantaneous angular velocity is greater than 100° / s.

[0183] 2.4 Wireless Communication Module:

[0184] The wireless communication module is responsible for transmitting the rotation angle data to the vehicle control system and ensures data security through AES-256 encryption and SHA-3 verification. Specific features include:

[0185] Dual-channel redundant design: The main channel uses CAN bus, and the backup channel uses LoRa wireless communication to ensure normal communication when the main channel fails.

[0186] High-speed transmission: supports 10Mbps high-speed transmission to meet real-time requirements.

[0187] Data encryption and verification: AES-256 encryption algorithm is used to encrypt data, and a verification code is generated through the SHA-3 hash function to ensure data integrity and security.

[0188] 3. Testing and verification:

[0189] 3.1 Hardware Installation:

[0190] Install the magnetic encoder at the joint of the industrial robot to ensure that its sensing area covers the full rotation range (0° to 360°). The specific steps are:

[0191] 1) Determine the installation location: Choose a location near the joint axis to ensure that the encoder's sensing area covers the maximum rotation angle;

[0192] 2) Install a protective cover: Use a waterproof and dustproof design to protect the encoder module and prevent external factors from affecting its performance;

[0193] 3) Calibrate the zero point position: Calibrate the zero point position of the encoder through dedicated software to ensure the accuracy of the measurement data.

[0194] 3.2 Software debugging:

[0195] After completing the hardware installation, enter the software debugging phase, which includes:

[0196] 1) Calibrate the zero point position: Manually adjust the robot joint angle, record and calibrate the zero point position of the magnetic encoder to ensure the accuracy of the measurement data.

[0197] 2) Configure the communication protocol: Configure the wireless communication protocol to Ethernet to achieve seamless connection with the industrial control network.

[0198] 3) Test communication stability: Test the stability and response speed of the communication module under different network loads.

[0199] 3.3 Testing and Verification:

[0200] The system's performance was tested on an industrial production line, and joint rotation angle data was recorded.

[0201] Evaluate the system's effectiveness in improving the robot's operational accuracy and service life.

[0202] 4. Results and Application Cases:

[0203] 4.1 Test Results Summary

[0204] Test results show that the system exhibits excellent performance in the field of industrial robot joint rotation angle monitoring:

[0205] Ability to monitor the rotation angle of industrial robot joints in real time with a resolution of 0.01°, significantly improving operational accuracy;

[0206] Response time: less than 1 millisecond, meeting the needs of high-speed industrial scenarios;

[0207] Strong environmental adaptability, able to operate stably in harsh environments such as -40℃ to 85℃ and high humidity;

[0208] The system's predictive maintenance function effectively reduces the robot's downtime caused by joint failures and improves production efficiency.

[0209] 4.2 Application Cases:

[0210] Case 1: Automobile manufacturing industry:

[0211] On a welding production line at an automobile manufacturer, the system was integrated into the joints of an industrial robot to monitor the rotation angle of the welding head in real time. Test results showed that welding accuracy increased by approximately 10%, significantly improving welding quality.

[0212] Case 2: Electronic product assembly:

[0213] On an automated electronics assembly line, this system is used to monitor the rotation angles of collaborative robot joints. Through predictive maintenance, downtime due to joint failures has been reduced by approximately 80%, increasing production efficiency by approximately 20%.

[0214] Case 3: Logistics handling robot:

[0215] In a logistics warehouse, this system was applied to the steering system of a handling robot. Test results showed that steering accuracy increased by approximately 15% and handling efficiency increased by approximately 20%.

[0216] This example proposes a system for monitoring the joint rotation angle of industrial robots using a non-contact magnetic encoder. This system, combined with artificial intelligence technology, implements anomaly detection and predictive maintenance. Test results demonstrate that this system exhibits excellent performance in measurement accuracy, response time, and environmental adaptability, significantly improving the operation of industrial robots.

[0217] Therefore, the present invention adopts the above-mentioned non-contact real-time monitoring system for the steering shaft rotation angle of a motor vehicle, which can meet the high-precision measurement requirements, is suitable for real-time monitoring in dynamic driving environments, and provides durability and stability of the system.

[0218] It is worth noting that the contents not elaborated in detail in the present invention are all prior art and are well known to those skilled in the art.

[0219] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit the same. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that they can still modify or replace the technical solutions of the present invention with equivalents, and these modifications or equivalent replacements cannot cause the modified technical solutions to deviate from the spirit and scope of the technical solutions of the present invention.

Claims

1. A non-contact real-time monitoring system for the steering axis rotation angle of a motor vehicle, characterized in that: include: An encoder module for generating an electrical signal related to the rotation angle of the steering shaft; A signal processing module is used to process the received electrical signal and calculate the rotation angle value; The AI ​​analysis module is used to perform anomaly detection and predictive maintenance on angle data. It analyzes the time series characteristics of angle data based on machine learning models to detect abnormal behavior and predict potential failures. The wireless communication module is used to transmit the rotation angle data to the vehicle control system.

2. The non-contact real-time monitoring system for the steering shaft rotation angle of a motor vehicle according to claim 1, characterized in that: The encoder module uses a combination of NdFeB rare earth permanent magnets and a three-axis Hall sensor array to perform six-degree-of-freedom posture tracking through magnetic field coupling; The encoder module has a built-in piezoelectric ceramic driven scraper to regularly remove pollutants from the grating surface. The encoder is a magnetic encoder or an optical encoder.

3. The non-contact real-time monitoring system for the steering shaft rotation angle of a motor vehicle according to claim 2, characterized in that: The grating scanning system of the encoder module adopts a combination of a dynamic grating with a black-white ratio of n:1 and (n+1) / 2 fixed gratings, and the width of the fixed grating light transmission slit decreases in a geometric sequence; the Hall sensor array adopts an 8×8 matrix layout, with a spatial resolution of 0.1° and a dynamic response delay of <2ms.

4. The non-contact real-time monitoring system for the steering shaft rotation angle of a motor vehicle according to claim 3, characterized in that: The signal processing module includes an operational amplifier, a filtering circuit, and a microcontroller, and uses the following formula: Noise filtering formula: Among them, V filtered is the signal value after filtering; V i is the signal value of the i-th sampling; N is the number of sampling points.

5. The non-contact real-time monitoring system for the steering shaft rotation angle of a motor vehicle according to claim 4, characterized in that: The artificial intelligence analysis module performs time series analysis on the angle data based on the machine learning model, specifically including: Anomaly detection formula: If |θ t -θ t-1 |>Δθ threshold , it is considered abnormal behavior. Among them, θ t is the angle value between the current moment and the previous moment; θ t-1 is the angle value at the previous moment; Δθ threshold is the set threshold; Fault prediction formula: P f =f(θ t ,i t-1 ,i t-2 ,…θ t-N ); Among them, P f is the failure probability; f is the prediction function trained based on historical data.

6. The non-contact real-time monitoring system for the steering shaft rotation angle of a motor vehicle according to claim 5, characterized in that: The artificial intelligence analysis module implements a three-level early warning mechanism for anomaly detection: Primary warning: single angle deviation >5°; Intermediate warning: 3 consecutive deviations >3°; Emergency warning: instantaneous angular velocity>100° / s.

7. The non-contact real-time monitoring system for the steering shaft rotation angle of a motor vehicle according to claim 6, characterized in that: The wireless communication module supports CAN bus, LIN bus or Wi-Fi communication protocols and uses the following formula: Data encryption formula: D e =E(K,D r ); Among them, D e is the encrypted data; D r is the original data; K is the key; E is the encryption algorithm; Error checking formula: C c =H(D r ); Among them, C c is the check code; H is the hash function.

8. The non-contact real-time monitoring system for the steering shaft rotation angle of a motor vehicle according to claim 7, characterized in that: The wireless communication module has a built-in dual-channel redundant design, with the main channel using CAN bus and the backup channel using LoRa wireless communication.

9. The non-contact real-time monitoring system for the steering shaft rotation angle of a motor vehicle according to claim 8, characterized in that: The artificial intelligence module deploys a federated learning framework to support collaborative training of multi-vehicle data without leaking local data.

10. The non-contact real-time monitoring system for the steering shaft rotation angle of a motor vehicle according to claim 9, characterized in that: The system also includes a mechanical overload protection mechanism that automatically triggers the electromagnetic clutch to disconnect the transmission when it detects that the steering torque is continuously greater than 50N·m; The system integrates a GNSS positioning module, and the steering angle data is bound and stored with geographic coordinates to form a spatiotemporal trajectory database.

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