A vehicle-mounted tire pressure sensor self-calibration and dynamic matching method and system

Abstract

The present application relates to the technical field of sensor calibration, and discloses a vehicle-mounted tire pressure sensor self-calibration and dynamic matching method and system.The method comprises the following steps: sending an extended diagnostic service request frame to an electronic control unit through a VCI device to read a reference pressure value and a sensor ID list; selecting a target sensor ID based on the sensor ID list, transmitting a wake-up data frame containing the target sensor ID through the VCI device to activate the original sensor on the target tire, and receiving the working state data returned by the original sensor; determining the network connection state according to the vehicle identification code, obtaining the calibration parameters from the cloud server in the online mode, and reading the calibration parameters from the local storage path in the offline mode; writing the calibration parameters into the new sensor, calculating the calibration pressure value of the new sensor based on the reference pressure value and the working state data, and updating the new sensor.The present application reduces the maintenance cost and simplifies the operation process.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of sensor calibration, in particular to a vehicle-mounted tire pressure sensor self-calibration and dynamic matching method and system. BACKGROUND

[0002] Traditional vehicle-mounted tire pressure sensor (TPMS) calibration and matching rely on manual checking of vehicle manual or special equipment to obtain reference parameters, which is cumbersome and inefficient, and is difficult to adapt to large-scale maintenance scenarios. After replacing the existing sensor, the wheel position matching is usually completed by manual checking or simple signal matching, which is easily affected by tire transposition and sensor individual differences, resulting in deviation between the display position and the actual wheel position, affecting the accuracy of driving safety warning. In addition, it is difficult to obtain calibration data in offline scenarios, and there is a lack of dynamic verification mechanism, which cannot timely detect the wheel position deviation caused by sensor aging, replacement or vehicle working condition change. SUMMARY

[0003] The main purpose of the present application is to provide a vehicle-mounted tire pressure sensor self-calibration and dynamic matching method and system, which effectively solves the problem of traditional TPMS calibration and matching relying on manual operation and poor adaptability, reduces maintenance cost and simplifies operation process.

[0004] To achieve the above purpose, the present application provides a vehicle-mounted tire pressure sensor self-calibration and dynamic matching method, comprising the following steps:

[0005] Sending an extended diagnostic service request frame to the electronic control unit through the VCI device to read the reference pressure value and the sensor ID list;

[0006] Selecting a target sensor ID based on the sensor ID list, activating the original sensor on the target tire through the VCI device by transmitting a wake-up data frame containing the target sensor ID, and receiving the working state data returned by the original sensor;

[0007] According to the vehicle identification code, the network connection state is determined, the calibration parameters are obtained from the cloud server in online mode, and the calibration parameters are read from the local storage path in offline mode;

[0008] Write the calibration parameters to the new sensor, calculate the calibration pressure value of the new sensor based on the reference pressure value and the working state data, and update the new sensor.

[0009] Optionally, in the first implementation manner of the first aspect of the present application, the step of sending an extended diagnostic service request frame to the electronic control unit through the VCI device to read the reference pressure value and the sensor ID list comprises:

[0010] Establish a diagnostic communication link between the VCI device and the electronic control unit;

[0011] sending an extended diagnostic service request frame to the electronic control unit based on the diagnostic communication link, triggering the electronic control unit to read a reference pressure value, a sensor factory gain coefficient, and four wheel positions corresponding to sensor ID numbers, obtaining a diagnostic response frame returned by the electronic control unit;

[0012] performing data analysis on the diagnostic response frame to extract the reference pressure value and the sensor ID number list.

[0013] Optionally, in a second implementation manner of the first aspect of the present application, the target sensor ID is selected based on the sensor ID list, the original sensor on the target tire is activated by transmitting a wake-up data frame containing the target sensor ID through the VCI device, and working state data returned by the original sensor is received, including:

[0014] selecting a target sensor ID from the sensor ID list and constructing a wake-up data frame, and transmitting a low-frequency radio frequency signal to activate the original sensor through the VCI device according to the wake-up data frame;

[0015] The original sensor on the target tire receives the low-frequency radio frequency signal and collects pressure ADC raw values, temperature ADC raw values, and battery voltage raw values, obtaining working state data returned by the original sensor.

[0016] Optionally, in a third implementation manner of the first aspect of the present application, the network connection state is determined according to the vehicle identification code, calibration parameters are obtained from a cloud server in an online mode, and calibration parameters are read from a local storage path in an offline mode, including:

[0017] sending a diagnostic request to read the vehicle identification code to the electronic control unit through the VCI device to obtain the vehicle identification code;

[0018] detecting the network connection state based on the vehicle identification code, determining an online mode if there is an effective wireless network connection or mobile network connection in the network connection state, and determining an offline mode if there is no effective wireless network connection or mobile network connection in the network connection state;

[0019] in the online mode, sending a request containing the vehicle identification code and the sensor model to a cloud calibration data server, and obtaining calibration parameters and returning from the cloud calibration data server;

[0020] in the offline mode, constructing a local storage file name according to the vehicle identification code and reading calibration parameters from a preset local storage path.

[0021] Optionally, in a fourth implementation form of the first aspect of the present application, the writing the calibration parameters into the new sensor comprises:

[0022] dividing the calibration parameters into a plurality of data blocks and adding a block serial number and a target EEPROM address offset to each data block, sending a data block writing instruction to the new sensor through the VCI device in sequence, and calculating a cyclic redundancy check code for each data block by the new sensor and returning an acknowledgement frame;

[0023] calculating a temperature correction factor according to a temperature ADC raw value in the working state data by the new sensor;

[0024] calculating a calibration pressure value based on a pressure ADC raw value in the working state data, the temperature correction factor and the reference pressure value;

[0025] calculating a gain deviation rate based on the reference pressure value and the calibration pressure value, and updating a gain register value of the new sensor and writing the gain register value into a gain register address of an EEPROM of the new sensor when the gain deviation rate exceeds a preset deviation threshold, to obtain the new sensor that has completed self-calibration.

[0026] Optionally, in a fifth implementation form of the first aspect of the present application, the calculating a calibration pressure value based on a pressure ADC raw value in the working state data, the temperature correction factor and the reference pressure value comprises:

[0027] multiplying the pressure ADC raw value in the working state data by the reference pressure value and dividing the product by a preset ADC reference value to obtain a first pressure value, and performing non-linear correction on the first pressure value to obtain a linear pressure value;

[0028] calculating a voltage deviation value of a battery voltage raw value in the working state data from a preset standard working voltage, and dividing the voltage deviation value by the standard working voltage to obtain a voltage compensation coefficient;

[0029] calculating a second pressure value based on the linear pressure value and the temperature correction factor, and multiplying the second pressure value by the voltage compensation coefficient to obtain the calibration pressure value.

[0030] Optionally, in a sixth implementation form of the first aspect of the present application, the calculating a gain deviation rate based on the reference pressure value and the calibration pressure value, and updating a gain register value of the new sensor and writing the gain register value into a gain register address of an EEPROM of the new sensor when the gain deviation rate exceeds the preset deviation threshold, to obtain the new sensor that has completed self-calibration, comprises:

[0031] Subtracting the calibration pressure value from the reference pressure value obtains a pressure deviation value, and dividing the pressure deviation value by the reference pressure value obtains a gain deviation rate;

[0032] When the gain deviation rate exceeds the preset deviation threshold, a gain update trigger signal is generated, and when the absolute value of the gain deviation rate is less than or equal to the preset deviation threshold, the original gain register value is kept unchanged;

[0033] Based on the gain update trigger signal, a gain correction coefficient is calculated, the sensor factory gain coefficient is multiplied by the gain correction coefficient to obtain an updated gain register value, and the updated gain register value is written into the gain register address of the new sensor EEPROM to obtain a new sensor that has completed self-calibration.

[0034] Optionally, in a seventh implementation manner of the first aspect of the present application, the vehicle-mounted tire pressure sensor self-calibration and dynamic matching method further includes:

[0035] When the vehicle is running at a constant speed, three-axis acceleration data of the four sensors are collected to calculate radial acceleration and extract a peak time;

[0036] Based on the wheelbase and the vehicle speed, a theoretical time delay difference is calculated and compared with the peak time of each sensor to obtain a front wheel sensor group and a rear wheel sensor group;

[0037] When the vehicle is turning, radial acceleration data are collected and a time average value is calculated, and the front wheel sensor group and the rear wheel sensor group are distinguished based on the time average value and the turning direction to obtain a wheel position distinguishing result;

[0038] The wheel position distinguishing result is organized as a first mapping relationship between a sensor ID number and four wheel positions, and is written into the electronic control unit through an extended diagnostic service.

[0039] Optionally, in an eighth implementation manner of the first aspect of the present application, the vehicle-mounted tire pressure sensor self-calibration and dynamic matching method further includes:

[0040] The electronic control unit monitors the cumulative mileage, and when the cumulative mileage reaches a preset verification period, sensor radial acceleration data are collected and a real-time root mean square value is calculated;

[0041] The feature similarity between the real-time root mean square value and a reference root mean square value is calculated;

[0042] It is judged whether the feature similarity is lower than a preset similarity threshold, and when the feature similarity is lower than the similarity threshold, a wheel position re-learning trigger signal is generated and the first mapping relationship is updated to obtain a second mapping relationship.

[0043] The application also provides a vehicle-mounted tire pressure sensor self-calibration and dynamic matching system, comprising:

[0044] The sending module is configured to send an extended diagnostic service request frame to the electronic control unit through the VCI device to read a reference pressure value and a sensor ID list;

[0045] The wake-up module is configured to select a target sensor ID based on the sensor ID list, activate an original sensor on a target tire by transmitting a wake-up data frame containing the target sensor ID through the VCI device, and receive working state data returned by the original sensor;

[0046] The reading module is configured to determine a network connection state according to a vehicle identification code, obtain calibration parameters from a cloud server in an online mode, and read calibration parameters from a local storage path in an offline mode;

[0047] The updating module is configured to write the calibration parameters into a new sensor, calculate a calibration pressure value of the new sensor based on the reference pressure value and the working state data, and update the new sensor.

[0048] In summary, the application directly reads a factory-calibrated reference pressure value and a sensor factory gain coefficient from a vehicle ECU through VCI-OBD bidirectional communication, uses these authoritative calibration data as a reference for self-calibration of a new sensor, avoids the dependence on external standard pressure sources and special calibration equipment in traditional methods, reduces maintenance costs and simplifies the operation process. A radio frequency dual-frequency interaction mechanism using low-frequency directional activation and high-frequency data return is adopted to realize non-contact copying of original sensor working state data in a tire-free state, an online and offline dual-mode calibration data acquisition mechanism is established, the online mode guarantees real-time data update, and the offline mode ensures availability in a network-free environment. A multi-stage self-calibration algorithm integrated in the new sensor eliminates the influence of temperature drift through temperature segmentation correction, compensates for the inherent non-linear characteristics of MEMS sensors through pressure non-linear correction, offsets power fluctuation interference through battery voltage compensation, realizes long-term stable measurement performance through gain adaptive update, and solves the problem of sensor precision decay over time. Based on the accelerometer data of the sensor itself, the front and rear wheel time delay difference caused by the wheelbase of the vehicle and the centrifugal force difference in the turning condition are used for automatic wheel position recognition, without the need for external positioning equipment or manual intervention, thereby reducing the complexity. The established dynamic continuous verification mechanism periodically monitors the similarity of acceleration characteristics, automatically detects wheel position changes and triggers re-learning, and guarantees the reliability and intelligent level after tire position change or sensor replacement. BRIEF DESCRIPTION OF DRAWINGS

[0049] Figure 1 is a vehicle-mounted tire pressure sensor self-calibration and dynamic matching method step schematic diagram in an embodiment of the application;

[0050] Figure 2 is a structure block diagram of a vehicle-mounted tire pressure sensor self-calibration and dynamic matching system in an embodiment of the present application.

[0051] The implementation, functional features and advantages of the present application will be further described with reference to the embodiments and the accompanying drawings. DETAILED DESCRIPTION

[0052] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application will be further described in detail below in combination with the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and do not limit the present application.

[0053] Referring to Figure 1 The present embodiment provides a vehicle-mounted tire pressure sensor self-calibration and dynamic matching method, comprising the following steps:

[0054] S1, sending an extended diagnostic service request frame to the electronic control unit through the VCI device to read the reference pressure value and the sensor ID list;

[0055] In the present embodiment, the diagnostic communication link between the VCI device and the electronic control unit is established through the diagnostic service initialization process, the diagnostic communication link is based on the UDS protocol or the ISO 14229 standard, and the communication handshake is achieved through CAN or LIN bus transmission. Based on the diagnostic communication link, the VCI device acts as a diagnostic requestor, constructs an extended diagnostic service request frame and initiates a service call to the electronic control unit, wherein the request frame needs to carry a specific service identifier and a sub-function parameter, which is used to instruct the electronic control unit to perform the operations of reading the reference pressure value, reading the sensor factory gain coefficient and querying the wheel binding relationship. After receiving the extended diagnostic service request frame, the electronic control unit parses the information type to be returned according to the request parameter, and extracts the currently recorded reference pressure value data, the stored sensor factory gain correction coefficient and the unique sensor identification code (i.e. sensor ID number) corresponding to each wheel position from its internal EEPROM or RAM storage structure, and assembles the above data into a diagnostic response frame and returns it to the VCI device end. After receiving the diagnostic response frame, the VCI device parses the frame structure byte by byte through the preset data parsing module, extracts the reference pressure value information contained therein and converts it into valid engineering units (such as kPa), and extracts the sensor ID numbers bound on the four wheels from the specific bit segment in the data frame.

[0056] S2, selecting a target sensor ID based on the sensor ID list, activating the original sensor on the target tire through the VCI device to transmit a wake-up data frame containing the target sensor ID, and receiving the working state data returned by the original sensor;

[0057] In this embodiment, based on the sensor ID number list, the operator selects the target sensor ID to be activated according to the actual position of the tire or the preset wheel position strategy, and the system control module constructs a special format of wake-up data frame according to the target sensor ID, wherein the wake-up data frame contains the unique identification code of the target sensor ID and the frame header, frame tail and wake-up control byte conforming to the communication protocol specification. The wake-up data frame is sent to the vehicle-mounted low-frequency excitation antenna through the low-frequency communication interface of the VCI device, and the vehicle-mounted low-frequency excitation antenna converts the digital wake-up instruction into a low-frequency radio frequency excitation signal and directionally radiates to the physical area corresponding to the target tire. After the original sensor on the target tire receives the wake-up signal, it automatically switches from the sleep state to the working state and enters the data acquisition process, and the sampling module inside the sensor starts the pressure detection unit, temperature detection unit and power supply monitoring unit to synchronously collect the pressure ADC original value, temperature ADC original value and battery voltage original value under the current transient environment, while the sensor state word (including activation state bit, battery health state, etc.) is encapsulated into a standard format of data response frame. The data response frame is uplink returned to the vehicle-mounted antenna through the high-frequency radio frequency module built-in the tire pressure sensor and received by the VCI device. The data response frame is subjected to frame format verification, content decoding and field analysis, and three working state data of the original sensor are extracted.

[0058] S3, judging the network connection state according to the vehicle identification code, obtaining the calibration parameters from the cloud server in online mode, and reading the calibration parameters from the local storage path in offline mode;

[0059] In this embodiment, a diagnostic service request to read the vehicle identification number (VIN) is initiated by the VCI device through a standardized diagnostic process. The diagnostic service request uses the 0x1A or 0x22 service identifier of the UDS protocol. After establishing a communication link with the electronic control unit (ECU), the VIN is obtained by sending a diagnostic frame command containing the VIN request identifier. The VIN serves as the unique identifier of the vehicle. Network connectivity is detected based on the VIN. Network status detection assesses signal strength and connectivity through the communication module (such as WiFi, 4G, or 5G) of the VCI device or its supporting service terminal. If a valid wireless network connection or mobile cellular network channel exists and an external address is successfully accessed, the current mode is determined to be online. If no connection is established or the detection fails, the mode is determined to be offline. In online mode, the VIN and the model information of the new sensor to be written are packaged into a data request body in JSON or binary format and sent to a remote cloud calibration data server via HTTP or MQTT protocol. Upon receiving the request, the server retrieves a matching set of calibration parameters from the central database based on the historical maintenance records, original assembly parameters, and sensor specifications corresponding to the VIN index. After data encapsulation and encryption, the data is returned to the local VCI terminal, where the local control module extracts the calibration parameters. In offline mode, since a connection to the remote server is unavailable, a local redundant data strategy is adopted. A corresponding local storage filename is constructed based on the obtained VIN number, in the form of "VIN number + file extension" (e.g., ".cfg" or ".bin"). File retrieval and reading operations are performed in the preset local storage path. If the reading is successful, the calibration parameters are directly extracted from the local file.

[0060] S4. Write the calibration parameters into the new sensor, calculate the calibration pressure value of the new sensor based on the reference pressure value and operating status data, and update the new sensor.

[0061] In this embodiment, the calibration parameter content is divided into blocks according to the preset data structure format, each data block contains parameter main data, and is attached with block serial number, target EEPROM address offset information and necessary frame control information. The VCI device initiates a data block level write instruction, each instruction corresponds to a data block, and is sent to the new sensor in sequence to ensure that the write operation conforms to the page write specification of EEPROM. After receiving each data block, the new sensor starts the verification mechanism, calculates the cyclic redundancy check (CRC) code of the received data segment, and compares the calculation result with the CRC field attached to the data block. If the verification is passed, the VCI device returns a confirmation frame containing the block serial number, status flag and verification success bit to the VCI device; if the verification fails, the retransmission process is triggered, and the data block is resent until the write is successfully confirmed, ensuring that the data is complete and stored. After completing all parameter writing, the new sensor enters the self-calibration calculation stage, extracts the temperature ADC original value from the working state data returned from the original sensor, and calculates the temperature correction factor according to the temperature-coefficient curve or empirical correction function. Then, combined with the original pressure ADC value, the calculated temperature correction factor and the reference pressure value returned by the electronic control unit, the multi-factor linear or nonlinear regression relationship establishment process is executed to obtain the new sensor calibration pressure value under the current environment. By comparing the calculated calibration pressure value of the new sensor with the reference pressure value, the gain deviation rate is calculated according to the proportional difference between the two. If the gain deviation rate exceeds the set allowable error threshold, the gain register adjustment logic is triggered. In the adjustment logic, the new gain value to be written is recalculated according to the deviation rate correction factor, and is written into the corresponding gain register address through a special EEPROM write instruction to complete a gain compensation operation. After all write operations and calibration parameter calculation are confirmed to be correct, the current sensor is marked as complete self-calibration state, and the status frame is returned to the control terminal through the VCI device.

[0062] In one example, the VCI device sends an extended diagnostic service request frame to the electronic control unit to read the reference pressure value and the sensor ID list, including:

[0063] The VCI device establishes a diagnostic communication link with the electronic control unit;

[0064] The VCI device sends an extended diagnostic service request frame to the electronic control unit based on the diagnostic communication link, triggers the electronic control unit to read the reference pressure value, the sensor factory gain coefficient and the sensor ID number corresponding to the four wheel positions, and obtains the diagnostic response frame returned by the electronic control unit;

[0065] Data analysis is performed on the diagnostic response frame to extract the reference pressure value and the sensor ID list.

[0066] In this example, the physical communication channel between the VCI device and the electronic control unit is initialized when the vehicle power supply is in the on state and the CAN bus is in the idle state, and a diagnostic communication link is established using a compatible diagnostic protocol stack such as ISO 15765-4 or SAE J1979. After the communication link is successfully established, the VCI device sends an extended diagnostic service request frame to the electronic control unit, which conforms to the UDS protocol structure, with a 0x22 service identifier and a related DID (Data Identifier) field to instruct the electronic control unit to read multiple key data elements. The DID list carried in the request frame includes a DID representing the current tire reference pressure, a DID representing the factory gain parameter of each sensor, and a DID representing the sensor ID bound to the front left, front right, rear left, and rear right wheel positions. After receiving the extended diagnostic request frame, the electronic control unit performs legality verification on the frame content and queries the corresponding storage address in its internal registers or EEPROM area. After the data is read, a multi-segment or compressed diagnostic response frame is returned to the VCI device, which contains multiple data fields, each consisting of a start identifier, data length, and data content, with the data content presented in byte stream format. After receiving the diagnostic response frame, the VCI device performs protocol layer decoding on the response frame to verify the legality and integrity of the frame structure, and then extracts the data according to the pre-set field offset. During the parsing process, the reference pressure value field is extracted from the specified position and parsed as a floating point value; then the sensor ID fields corresponding to the four wheel positions are read in order, each ID field being 4 bytes in fixed length and expressed in hexadecimal or BCD encoding format; if the factory gain coefficient field is included, the gain coefficient is parsed and its corresponding EEPROM original value is recorded. After the data extraction is completed, the VCI device caches the parsed reference pressure value and sensor ID list to an intermediate cache area.

[0067] In one example, a target sensor ID is selected based on the sensor ID list, and a wake-up data frame containing the target sensor ID is transmitted by the VCI device to activate the original sensor on the target tire, and the working state data returned by the original sensor is received, including:

[0068] The target sensor ID is selected from the sensor ID list and a wake-up data frame is constructed, and the VCI device transmits a low-frequency radio frequency signal to activate the original sensor according to the wake-up data frame;

[0069] The original sensor on the target tire receives the low-frequency radio frequency signal and collects the pressure ADC original value, temperature ADC original value, and battery voltage original value, obtaining the working state data returned by the original sensor.

[0070] In this example, the effective parsing and caching of the sensor ID list in the VCI device is completed, the four wheel position binding sensor IDs extracted based on the diagnostic response frame are combined with the current state of the vehicle and the user's operation intention to determine the target wheel position that needs to be self-calibrated and dynamically matched at present, and the target sensor ID is obtained. After selecting the target sensor ID, a frame of wake-up data frame is generated in the main control logic, and the unique identifier of the target sensor and the frame control field, the check field and the effective load content required by the communication protocol are encapsulated in the wake-up data frame. The wake-up data frame is encoded and modulated using a predefined low-frequency (125 kHz or below) modulation format, such as ASK or FSK, for matching the low-power wake-up receiver mechanism of the tire pressure sensor. Based on the content of the wake-up data frame, the VCI device drives the built-in low-frequency transmission module to release low-frequency radio frequency signals in the target tire circumferentially using electromagnetic induction principle. In order to ensure the effective coverage and frequency stability of the signal, the low-frequency signal controls the transmission voltage, modulation depth and duty cycle during transmission, and continuously transmits for several cycles to ensure that the original sensor successfully receives the wake-up instruction in the low-power listening state. After the original sensor in the target tire receives the low-frequency wake-up signal through the induction coil, the low-frequency receiving module inside the sensor decodes and analyzes the target ID. If the received ID is consistent with the pre-set ID, it is considered as an effective wake-up trigger. The sensor will automatically wake up the main MCU, start the ADC sampling circuit and collect the pressure value in the current cavity, convert the pressure value to an analog signal through a high-precision pressure sensing element, and hand over the analog signal to the built-in ADC module for digital conversion to obtain the pressure ADC raw value. At the same time, the sensor collects the voltage of the temperature point thermal element and completes the temperature ADC conversion to record the current temperature environment state; at the same time, the internal battery voltage is sampled and converted by ADC to form the battery voltage ADC raw value. After the three groups of ADC data are collected, the sensor packs the ADC data into a working state data frame, adds a frame header, a frame tail and a CRC check, and transmits it back through a high-frequency (e.g. 315 MHz or 433 MHz) radio frequency communication link. The VCI device captures the high-frequency signal returned by the original sensor through the receiving end in real time and completes frame synchronization, channel demodulation and data decoding, extracts the pressure ADC raw value, temperature ADC raw value and battery voltage raw value, and marks them as the online working state data of the current sensor.

[0071] In one example, the network connection state is determined according to the vehicle identification code, the calibration parameters are obtained from the cloud server in online mode, and the calibration parameters are read from the local storage path in offline mode, including:

[0072] The vehicle identification code is obtained by sending a diagnostic request to the electronic control unit through the VCI device to read the vehicle identification code;

[0073] Detecting the network connection state based on the vehicle identification code, if the network connection state exists a valid wireless network connection or mobile network connection, it is determined as online mode, if the network connection state does not exist a valid wireless network connection or mobile network connection, it is determined as offline mode;

[0074] In online mode, send a request containing the vehicle identification code and sensor model to the cloud calibration data server, the cloud calibration data server obtains the calibration parameters and returns;

[0075] In offline mode, construct a local storage file name according to the vehicle identification code and read the calibration parameters from the preset local storage path.

[0076] In this example, the VCI device establishes a stable UDS (Unified Diagnostic Services) communication link with the electronic control unit of the target vehicle, including initializing CAN bus communication of the physical layer, negotiating the communication rate, and completing Session control switching to the extended diagnostic mode. After the communication link is successfully established, the VCI device sends a diagnostic request frame to the ECU according to the UDS protocol standard to read the vehicle identification code (VIN), and the diagnostic request frame corresponds to service 0x22 (ReadDataByIdentifier) and carries the data identifier corresponding to the VIN, such as 0xF190. After the ECU receives the request frame, the data identifier is parsed and the unique VIN code burned at the factory is retrieved from the EEPROM or memory, and then encapsulated into a diagnostic response frame and returned to the VCI device. After successfully parsing the diagnostic response frame, the VCI device extracts the vehicle identification code and transmits it to the network connection state detection module. The network connection state detection module detects whether there is a usable network connection method in the current running environment, including Wi-Fi network connection or cellular mobile network connection (such as 4G / 5G), and judges whether the network has effective communication capability, such as whether it can successfully access the external IP gateway or whether it can complete the DNS resolution operation. If the detection result shows that the network connection is effective, it is determined to be in online mode; if it cannot establish a stable connection with the external server, it is switched to offline mode. In online mode, the vehicle identification code is used as the key index, and the cloud request message is generated combined with the new sensor model information (such as sensor chip model, protocol version, factory batch, etc.), and a POST or GET request is initiated to the cloud calibration data server through the HTTPS protocol, and the cloud server retrieves the sensor calibration parameters corresponding to the VIN in the internal database after receiving the request, including pressure temperature gain coefficient, correction factor, linear compensation parameter, etc. After verification and encryption signature processing, it is returned to the VCI device for parsing and subsequent writing. If it is currently in offline mode, the vehicle identification code is used to automatically build a local file name for query, and the VIN is used as the main index to form a complete path after splicing and formatting the suffix, and file access operation is performed in the preset local storage path. If the file exists and the data is valid, the calibration parameters encapsulated in the local JSON or BIN format file are read and decoded.

[0077] In one example, the calibration parameters are written to the new sensor, the calibration pressure value of the new sensor is calculated based on the reference pressure value and the working state data, and the new sensor is updated, including:

[0078] The calibration parameters are divided into multiple data blocks, and a block serial number and a target EEPROM address offset are added to each data block. The VCI device sends data block write instructions to the new sensor in sequence, and the new sensor calculates a cyclic redundancy check code for each data block and returns an acknowledgement frame.

[0079] The new sensor calculates a temperature correction factor according to a temperature ADC raw value in the working state data;

[0080] A calibration pressure value is calculated based on a pressure ADC raw value in the working state data, the temperature correction factor, and a reference pressure value;

[0081] A gain deviation rate is calculated based on the reference pressure value and the calibration pressure value, and when the gain deviation rate exceeds a preset deviation threshold, a gain register value of the new sensor is updated and written into a gain register address of an EEPROM of the new sensor, so that the new sensor is completed self-calibration.

[0082] In this example, data preprocessing operations are performed on the calibration parameters, and the entire set of calibration parameters is divided into a plurality of data blocks according to a preset block size. Each data block contains block sequence number information and an offset of a target EEPROM address, so that the new sensor can receive and write into a storage area according to the correct address according to the block sequence number order. A stable communication channel is established between the VCI device and the new sensor, and the encapsulated calibration data write instruction is sent to the new sensor in a serialized manner block by block. Each data block is calculated by the internal logic of the new sensor after receiving to obtain a cyclic redundancy check code (CRC), and the corresponding confirmation frame is fed back to the VCI device end to confirm the data integrity and consistency in the writing process. After the writing of all data blocks is completed and verified without error, a temperature compensation operation is performed based on the temperature ADC raw value in the working state data received by the new sensor to construct a temperature correction factor for correcting the non-linear drift of the pressure sensor in different temperature environments. The calibration pressure value perceived by the current new sensor is calculated by using a mapping formula after temperature compensation, combining the pressure ADC raw value and the temperature correction factor, and superimposing the standard tire reference pressure value obtained from the VCI device. To determine whether there is a gain error between the sensor output and the reference, a gain deviation rate between the reference pressure value and the calibration pressure value is calculated, that is, a difference index is established by the ratio of the two and the ideal gain relationship. When the gain deviation rate exceeds the preset deviation threshold, it indicates that the current amplification multiple of the new sensor deviates from the standard, and the gain deviation rate is converted into a compensation factor and used to update the gain register value. The VCI device constructs a gain register write instruction according to the updated value, and writes the new gain register value into the corresponding gain register address of the EEPROM of the new sensor, completing the self-calibration process of the sensor.

[0083] In one example, a calibration pressure value is calculated based on a pressure ADC raw value in the working state data, a temperature correction factor, and a reference pressure value, including:

[0084] The first pressure value is obtained by multiplying the original pressure ADC value in the working status data by the reference pressure value and then dividing it by the preset ADC reference value. The first pressure value is then nonlinearly corrected to obtain a linear pressure value.

[0085] Calculate the voltage deviation between the original battery voltage value and the preset standard working voltage in the working status data, and divide the voltage deviation value by the standard working voltage to obtain the voltage compensation coefficient;

[0086] The second pressure value is calculated based on the linear pressure value and the temperature correction factor. Then, the second pressure value is multiplied by the voltage compensation coefficient to obtain the calibration pressure value.

[0087] In this example, the raw pressure ADC value in the operating status data is parsed and correlated with the reference pressure value provided by the electronic control unit. To eliminate the influence of sensor hardware manufacturing differences and ADC resolution errors, the raw pressure ADC value is multiplied by the reference pressure value and then divided by a preset ADC reference value to calculate the first pressure value, which is a preliminary pressure estimate without nonlinear compensation. Considering the nonlinear response characteristics of the sensor in the atmospheric pressure to high pressure range, a nonlinear correction function or polynomial fitting expression corresponding to the sensor process model is introduced into the first pressure value for value-by-value compensation, outputting a linear pressure value. The raw battery voltage value is extracted from the operating status data and compared with a preset standard operating voltage; the difference between the two is the voltage deviation value. Since the analog signal amplification and analog-to-digital conversion inside the sensor are greatly affected by the stability of the supply voltage, to compensate for the disturbance effect of the deviation on the pressure value estimation, the voltage deviation value is divided by the standard operating voltage to calculate the voltage compensation coefficient. The voltage compensation coefficient, as a multiplicative adjustment factor, is between slightly less than 1 and slightly greater than 1, and is used to fine-tune the actual output of the sensor. The linear pressure value, after nonlinear correction, is coupled with a temperature correction factor derived from the original temperature ADC value to correct measurement errors caused by temperature drift, resulting in a second pressure value. This second pressure value is then multiplied by a voltage compensation coefficient to output the calibrated pressure value.

[0088] In one example, the gain deviation rate is calculated based on the reference pressure value and the calibration pressure value. When the gain deviation rate exceeds a preset deviation threshold, the gain register value of the new sensor is updated and written to the gain register address of the new sensor's EEPROM, resulting in a new sensor that has completed self-calibration, including:

[0089] Subtracting the calibration pressure value from the reference pressure value yields the pressure deviation value. Dividing the pressure deviation value by the reference pressure value yields the gain deviation rate.

[0090] When the gain deviation rate exceeds the preset deviation threshold, a gain update trigger signal is generated; when the absolute value of the gain deviation rate is less than or equal to the preset deviation threshold, the original value of the gain register remains unchanged.

[0091] The gain correction coefficient is calculated based on the gain update trigger signal, the original sensor factory gain coefficient is multiplied by the gain correction coefficient to obtain the updated gain register value, and the updated gain register value is written into the gain register address of the new sensor EEPROM to obtain the new sensor after self-calibration.

[0092] In this example, the reference pressure value is subtracted from the calibration pressure value to obtain the pressure deviation in the current environment. The pressure deviation value is divided by the reference pressure value to normalize the gain deviation rate reflecting the current pressure deviation of the sensor. The gain deviation rate is logically judged. If the absolute value of the gain deviation rate is less than or equal to the preset gain deviation threshold, it is considered that the current sensor factory gain parameter still has validity, and the register value in the existing EEPROM gain register is maintained unchanged. If the gain deviation rate exceeds the preset deviation threshold, the gain update operation is triggered. In the gain update path, the gain update trigger signal is generated according to the current gain deviation rate, the gain correction coefficient is calculated by the gain correction algorithm, the gain correction coefficient is constructed according to the sign direction and amplitude of the gain deviation rate, and is expressed in the form of 1 plus the correction proportion factor multiplied by the gain deviation rate. The original sensor factory gain coefficient is multiplied by the gain correction coefficient to obtain a new gain register value, which ensures that the new coefficient is expressed within the storage range and resolution supported by the EEPROM register structure. The updated gain register value is written into the specified register address related to the gain control in the new sensor EEPROM through the VCI device in the form of data block writing, and the online self-calibration configuration of the new sensor gain parameter is completed.

[0093] The new sensor starts the built-in MEMS pressure sensor to perform the first measurement under the current ambient pressure, converts the pressure ADC raw value by applying the sensor factory gain coefficient stored in the current EEPROM, to obtain the sensor first measurement pressure value; extracts the current vehicle calibration reference pressure value stored in the electronic control unit from the reference pressure value as the calibration target pressure value, calculates the pressure deviation value of the calibration target pressure value and the sensor first measurement pressure value, divides the pressure deviation value by the calibration target pressure value and takes the absolute value to obtain the sensor measurement gain deviation rate; judge whether the sensor measurement gain deviation rate is greater than the preset gain deviation threshold, when the sensor measurement gain deviation rate is greater than the preset gain deviation threshold, calculate the gain correction coefficient as 1 minus the sensor measurement gain deviation rate, when the sensor measurement gain deviation rate is less than or equal to the preset gain deviation threshold, set the gain correction coefficient to 1, obtain the sensor gain correction coefficient; multiply the sensor factory gain coefficient by the sensor gain correction coefficient to obtain the adaptive updated gain coefficient value, send the gain write instruction to the new sensor through the radio frequency uplink of the VCI device, and write the adaptive updated gain coefficient value to the gain register address of the new sensor EEPROM.

[0094] In one example, the vehicle-mounted tire pressure sensor self-calibration and dynamic matching method further comprises:

[0095] When the vehicle is driving at a constant speed, three-axis acceleration data of the four sensors are collected to calculate the radial acceleration and extract the peak time;

[0096] Based on the wheelbase and vehicle speed, the theoretical time delay difference is calculated and compared with the peak time of each sensor to obtain the front wheel sensor group and the rear wheel sensor group;

[0097] When the vehicle is turning, the radial acceleration data is collected and the time average value is calculated, and the left and right wheel positions in the front wheel sensor group and the rear wheel sensor group are distinguished based on the time average value and the turning direction to obtain the wheel position discrimination result;

[0098] The wheel position discrimination result is organized as a first mapping relationship between the sensor ID number and the four wheel positions, and is written into the electronic control unit through the extended diagnostic service.

[0099] In this example, when the vehicle is in a state of uniform straight-line driving, the tire pressure monitoring module is controlled by the vehicle control system to sequentially collect three-axis acceleration data of each sensor installed on the four wheel positions, and select the acceleration component perpendicular to the ground from it, and calculate the radial acceleration data sequence of each sensor. Analyze the radial acceleration curve in the continuous time domain, identify the obvious periodic characteristics and extract the acceleration peak time of each wheel position sensor as the timing feature point of the tire dynamic response. According to the known wheelbase parameters of the vehicle and the current uniform driving speed of the vehicle, the theoretical delay difference between the front and rear wheels is derived. By comparing and cross-verifying the theoretical delay difference with the difference between the peak time of each sensor, it is determined which two groups of sensors belong to the front wheels and which two groups belong to the rear wheels. After the front and rear sensor groups are divided, the left and right wheel positions are distinguished through the vehicle turning scene. During the vehicle entering the turning trajectory at a certain angular velocity, the radial acceleration data of each sensor is collected again, and the time average of the acceleration during the turning is calculated for each wheel position. According to the known steering direction (such as left turn or right turn), combined with the centrifugal force mechanism, it is inferred that under the same steering condition, the acceleration response of the outer wheel position is higher than that of the inner wheel position, so the front sensor group and the rear sensor group are subdivided into left front, right front, left rear and right rear four specific wheel positions based on the size of the acceleration time average. The four identified sensor IDs and the specific wheel positions (left front, right front, left rear, right rear) form a one-to-one correspondence relationship, and a first mapping relationship table is constructed. The mapping relationship is packaged as a diagnostic data frame through the write instruction of the extended diagnostic service as the wheel position discrimination result, and is sent to the electronic control unit (ECU) through the vehicle-mounted VCI device, and is analyzed and updated by the ECU. Internal sensor wheel position mapping table.

[0100] The method comprises the following steps: collecting three-axis acceleration data of four sensors to calculate radial acceleration and extract peak time when the vehicle travels at a constant speed, calculating the theoretical time delay difference based on the wheelbase and vehicle speed, and comparing the peak time of each sensor to obtain the front wheel sensor group and the rear wheel sensor group, including: when the vehicle travels at a preset constant speed for a preset time, the electronic control unit broadcasts an acceleration data request frame to the four sensors at a preset sampling frequency, continuously receives the three-axis acceleration data packets reported by the four sensors, squares and sums the X-axis acceleration component and Y-axis acceleration component reported by each sensor to obtain a radial acceleration instantaneous value sequence, performs sliding window peak detection on the radial acceleration instantaneous value sequence to extract peak points greater than a preset acceleration threshold, records the time stamp corresponding to each peak point, and obtains the acceleration peak time sequence of each of the four sensors; read the vehicle wheelbase parameter and the current speed parameter from the reference pressure value, divide the vehicle wheelbase parameter by the current speed parameter, and obtain the theoretical time delay difference of the front and rear wheels passing through the same road feature point; traverse the acceleration peak time sequence of the four sensors, calculate the time difference of the peak time of each sensor and the peak time of the other three sensors, and construct a time difference matrix, find the time difference pair close to the theoretical time delay difference in the time difference matrix, determine the two sensors with the time difference pair close to the theoretical time delay difference as the front and rear wheel relationship, divide the four sensors into two groups according to the front and rear wheel relationship, and obtain the front wheel sensor group and the rear wheel sensor group; calculate the average value of the acceleration peak amplitude of the front wheel sensor group and the rear wheel sensor group, mark the sensor group with larger average value of acceleration peak amplitude as the front wheel group, mark the sensor group with smaller average value of acceleration peak amplitude as the rear wheel group, and obtain the front wheel sensor group and the rear wheel sensor group of the front and rear wheel position confirmation.

[0101] The wheel position discrimination result is obtained by collecting radial acceleration data and calculating time average value when the vehicle turns, combining the time average value and the turning direction to discriminate the left and right wheel positions in the front wheel sensor group and the rear wheel sensor group, including: the electronic control unit monitors the steering wheel angle sensor value through the CAN bus, and determines that the vehicle enters the turning working condition when the absolute value of the steering wheel angle is greater than the preset steering angle threshold and the duration exceeds the preset duration; the turning direction is determined to be left turn or right turn according to the positive and negative signs of the steering wheel angle value, and the turning direction identifier is obtained; during the turning working condition, the electronic control unit continuously collects the radial acceleration data reported by each sensor in the front wheel sensor group and the rear wheel sensor group, and calculates the arithmetic mean of the radial acceleration data sequence collected within the preset sampling time to obtain the front wheel radial acceleration time average value of each sensor in the front wheel sensor group and the rear wheel radial acceleration time average value of each sensor in the rear wheel sensor group; for the two sensors in the front wheel sensor group, the size relationship of the front wheel radial acceleration time average value of each sensor is compared, when the turning direction identifier is left turn, the sensor with larger front wheel radial acceleration time average value is determined as the left front wheel, and the sensor with smaller front wheel radial acceleration time average value is determined as the right front wheel, when the turning direction identifier is right turn, the sensor with larger front wheel radial acceleration time average value is determined as the right front wheel, and the sensor with smaller front wheel radial acceleration time average value is determined as the left front wheel, and the front wheel left and right position discrimination result is obtained; for the two sensors in the rear wheel sensor group, the same discrimination logic as the front wheel sensor group is used to compare the rear wheel radial acceleration time average value of each sensor and combine the turning direction identifier to determine the left and right rear wheel positions, and the rear wheel left and right position discrimination result is obtained; the front wheel left and right position discrimination result and the rear wheel left and right position discrimination result are combined to obtain the wheel position discrimination result corresponding to the left front wheel, the right front wheel, the left rear wheel and the right rear wheel respectively.

[0102] The three-axis acceleration data reported by the four sensors is continuously collected by the VCI device when the vehicle travels at a preset speed for a preset time, the X-axis acceleration and Y-axis acceleration of each sensor are squared and square-rooted to obtain a radial acceleration sequence, and the time stamp corresponding to the acceleration peak value is extracted from the radial acceleration sequence; the wheelbase parameter and the vehicle speed parameter are read from the ECU reference calibration data, and the wheelbase parameter is divided by the vehicle speed parameter to obtain a theoretical time delay difference; the time difference between the acceleration peak time of each sensor and the acceleration peak time of the other sensors is calculated and compared with the theoretical time delay difference; the sensors with a time difference close to the theoretical time delay difference are divided into two groups, i.e., a front wheel sensor group and a rear wheel sensor group; under the turning condition of the vehicle, the turning direction is determined by reading the steering wheel angle sensor value through the CAN bus, and the left and right wheel discrimination process is triggered; the radial acceleration data of each sensor in the front wheel sensor group and the rear wheel sensor group is continuously collected, and the time average of the radial acceleration data within a preset sampling time is calculated to obtain the radial acceleration time average of each sensor; the size relationship of the radial acceleration time averages of the two sensors in the front wheel sensor group is compared, and the positions of the left front wheel and the right front wheel are determined in combination with the turning direction; the positions of the left rear wheel and the right rear wheel are determined by the same method for the two sensors in the rear wheel sensor group, and the wheel position discrimination results of the four sensors ID numbers corresponding to the left front wheel, the right front wheel, the left rear wheel and the right rear wheel are obtained; the four sensor ID numbers in the wheel position discrimination results are organized in the order of left front wheel, right front wheel, left rear wheel and right rear wheel as the mapping relationship between the sensor ID number and the four wheel positions, and the VCI device sends an extended diagnostic service write frame containing a write data service code and a wheel position matching parameter identifier to the electronic control unit, and writes the mapping relationship into the wheel position configuration address segment of the non-volatile memory of the electronic control unit.

[0103] In one example, the vehicle-mounted tire pressure sensor self-calibration and dynamic matching method further comprises:

[0104] The electronic control unit monitors the cumulative mileage, and when the cumulative mileage reaches a preset verification period, the sensor radial acceleration data is collected and the real-time root mean square value is calculated;

[0105] The characteristic similarity of the real-time root mean square value and the reference root mean square value is calculated;

[0106] It is judged whether the characteristic similarity is lower than a preset similarity threshold, and if the similarity is lower than the similarity threshold, a wheel position relearning trigger signal is generated and the first mapping relationship is updated to obtain a second mapping relationship.

[0107] In this example, the electronic control unit (ECU) continuously monitors the cumulative mileage during normal operation of the vehicle and periodically accumulates the distance traveled through the mileage recording module, and when the cumulative mileage reaches the preset verification period threshold, such as every 1000 kilometers of travel or a specified tire position replacement period, the position verification process is triggered. At the trigger point, the electronic control unit requests the tire pressure monitoring system to upload the current three-axis acceleration data of each of the four tire pressure sensors, and continuously collects data at a certain sampling period. According to the collected three-axis acceleration values, the current radial acceleration sequence of each sensor is synthesized according to the radial acceleration calculation model, and the real-time root mean square of the radial acceleration sequence is calculated to extract its stability features and motion state features. Compare the real-time root mean square value with the reference root mean square value, which comes from the standard reference value recorded when the vehicle is shipped or the last time the position is calibrated. Using feature similarity algorithms, such as using normalized correlation, dynamic time warping, or Euclidean distance similarity coefficient, etc. Methods, quantify the degree of change between the current position state and the reference position state. By calculating the similarity between the current root mean square and the reference root mean square of the four positions respectively, it is identified whether the position has actually changed. When the feature similarity of any sensor is lower than the set similarity threshold, such as lower than 90% of the lower limit of the similarity, it is automatically determined that the position of the current sensor has actually shifted or exchanged, and a position relearning trigger signal is generated. The position relearning trigger signal is used as the starting condition of the position adaptive module, triggering the system to re-enter the position reasoning process, identifying front and rear wheels, turning and left and right position mapping updates by re-collecting acceleration dynamic features, and generating new position determination results. The position determination result is organized as a corresponding relationship between the new sensor ID number and the position, i.e. the second mapping relationship, which replaces the previous first mapping relationship, and is written to the position configuration storage area of the electronic control unit through the extended diagnostic service, realizing automatic position self-learning update based on travel features.

[0108] The electronic control unit extracts a vehicle speed signal from a CAN bus vehicle speed sensor message for integral calculation to obtain a cumulative mileage value, triggers a wheel position verification mode when the cumulative mileage value reaches a preset verification period mileage, broadcasts a data request frame to the four sensors at a preset frequency and continuously receives three-axis acceleration data reported by each sensor within a preset sampling time, squares and sums the radial acceleration data sequence of each sensor, takes the average value, and then takes the square root operation to obtain the current radial acceleration root mean square value of the four sensors; the reference radial acceleration root mean square value stored when the mapping relationship is established is read from the non-volatile memory of the electronic control unit, the absolute value of the difference between the current radial acceleration root mean square value of each sensor and the corresponding reference radial acceleration root mean square value is calculated, and the reference radial acceleration root mean square value is divided to obtain the deviation proportion, and the characteristic similarity value of each sensor is obtained by subtracting 1 from the deviation proportion; whether the characteristic similarity values of the four sensors are all greater than a preset similarity threshold value is judged, a wheel position relearning trigger signal is generated when the characteristic similarity value of the sensor is lower than the preset similarity threshold value, and a sensor position change prompt information is pushed to the diagnostic panel, based on the wheel position relearning trigger signal, the front and rear wheel group discrimination under the vehicle uniform speed running condition and the left and right wheel position discrimination under the vehicle turning condition are automatically re-executed, the updated sensor ID number and the mapping relationship of the four wheel positions are obtained, and written into the electronic control unit, and the reference radial acceleration root mean square value stored in the non-volatile memory is updated.

[0109] Referring to Figure 2 The embodiment provides a kind of vehicle-mounted tire pressure sensor self-calibration and dynamic matching system, comprising:

[0110] Sending module 1, for sending extension diagnostic service request frame to electronic control unit by VCI device to read reference pressure value and sensor ID list;

[0111] Wake-up module 2, for selecting target sensor ID based on sensor ID list, and transmitting wake-up data frame containing target sensor ID by VCI device to activate original sensor on target tire, and receiving working state data returned by original sensor;

[0112] Reading module 3, for determining network connection state according to vehicle identification code, obtaining calibration parameters from cloud server in online mode, and reading calibration parameters from local storage path in offline mode;

[0113] Updating module 4, for writing calibration parameters into new sensor, calculating calibration pressure value of new sensor based on reference pressure value and working state data and updating new sensor.

[0114] In the embodiment, the specific implementation of each unit in the above system embodiment is described in the above method embodiment, which will not be repeated here.

[0115] It has to be understood that, in the present document, the terms "comprising", "including", or any other variant thereof, are intended to mean non-exclusive inclusion, such that processes, systems, articles, or methods that comprise, include, or are otherwise including a list of elements, do not include only those elements recited, but can also include other elements not expressly listed or inherent to such processes, systems, articles, or methods. Without further limitation, an element preceded by "comprising" does not exclude the presence of additional identical elements in the process, system, article, or method that includes the element.

[0116] The preferred embodiments of the present application have been described above with the intent to enable those skilled in the art to make and use it. Various modifications to these embodiments will occur to those skilled in the art and are intended to be encompassed by the present application. The modifications are within the scope of the present application, and are intended to be encompassed by the description herein. Accordingly, the patent is not intended to limit the scope of the application to the embodiments described herein, but has been drafted to include any embodiments that are equivalent in substance to the methods and compositions described.

Claims

1. A method for self-calibration and dynamic matching of an on-board tire pressure sensor, characterized in that, include: The VCI device sends an Extended Diagnostic Service Request frame to the Electronic Control Unit to read the reference pressure value and sensor ID list. Based on the sensor ID list, a target sensor ID is selected, and a wake-up data frame containing the target sensor ID is transmitted through the VCI device to activate the original sensor on the target tire, and the working status data returned by the original sensor is received. The network connection status is determined based on the vehicle identification number. In online mode, calibration parameters are obtained from the cloud server, and in offline mode, calibration parameters are read from the local storage path. The calibration parameters are written to a new sensor. The calibration pressure value of the new sensor is calculated based on the reference pressure value and the operating status data, and the new sensor is updated. This includes: dividing the calibration parameters into multiple data blocks and adding a block number and target EEPROM address offset to each data block; sequentially sending data block write instructions to the new sensor via the VCI device; the new sensor calculating a cyclic redundancy check code for each data block and returning an acknowledgment frame; the new sensor calculating a temperature correction factor based on the raw temperature ADC value in the operating status data; and calculating the calibration pressure value based on the raw pressure ADC value in the operating status data, the temperature correction factor, and the reference pressure value. Specifically, this includes: multiplying the raw pressure ADC value in the operating status data by the reference pressure value and dividing by a preset ADC reference value to obtain a first pressure value, and performing nonlinear correction on the first pressure value to obtain a linear pressure value; calculating the voltage deviation between the raw battery voltage value in the operating status data and a preset standard operating voltage, and dividing the voltage deviation value by the preset standard operating voltage to obtain a voltage compensation coefficient; and based on the... The process involves calculating a second pressure value using the linear pressure value and the temperature correction factor, then multiplying the second pressure value by the voltage compensation coefficient to obtain a calibration pressure value. A gain deviation rate is calculated based on the reference pressure value and the calibration pressure value. When the gain deviation rate exceeds a preset deviation threshold, the gain register value of the new sensor is updated and written to the gain register address of the new sensor's EEPROM, resulting in a new sensor that has completed self-calibration. Specifically, this includes: subtracting the calibration pressure value from the reference pressure value to obtain a pressure deviation value; dividing the pressure deviation value by the reference pressure value to obtain a gain deviation rate; generating a gain update trigger signal when the gain deviation rate exceeds the preset deviation threshold; maintaining the original gain register value when the absolute value of the gain deviation rate is less than or equal to the preset deviation threshold; calculating a gain correction coefficient based on the gain update trigger signal; multiplying the sensor's factory gain coefficient by the gain correction coefficient to obtain an updated gain register value; and writing the updated gain register value to the gain register address of the new sensor's EEPROM, thus obtaining a new sensor that has completed self-calibration.

2. The self-calibration and dynamic matching method for vehicle tire pressure sensors according to claim 1, characterized in that, The step of sending an extended diagnostic service request frame to the electronic control unit via the VCI device to read the reference pressure value and sensor ID list includes: Establish a diagnostic communication link between the VCI device and the electronic control unit; Based on the diagnostic communication link, an extended diagnostic service request frame is sent to the electronic control unit, triggering the electronic control unit to read the reference pressure value, the sensor factory gain coefficient, and the sensor ID number corresponding to the four wheel positions, and obtain the diagnostic response frame returned by the electronic control unit. The diagnostic response frame is parsed to extract the baseline pressure value and a list of sensor ID numbers.

3. The self-calibration and dynamic matching method for vehicle tire pressure sensors according to claim 1, characterized in that, The process of selecting a target sensor ID based on the sensor ID list, activating the original sensor on the target tire by transmitting a wake-up data frame containing the target sensor ID through the VCI device, and receiving the working status data returned by the original sensor includes: Select a target sensor ID from the sensor ID list and construct a wake-up data frame. Then, transmit a low-frequency radio frequency signal to activate the original sensor through the VCI device based on the wake-up data frame. The original sensor on the target tire receives the low-frequency radio frequency signal and collects the original values ​​of the pressure ADC, temperature ADC, and battery voltage to obtain the working status data returned by the original sensor.

4. The self-calibration and dynamic matching method for vehicle tire pressure sensors according to claim 1, characterized in that, The process of determining network connection status based on vehicle identification number, obtaining calibration parameters from the cloud server in online mode, and reading calibration parameters from local storage path in offline mode includes: The vehicle identification code is obtained by sending a diagnostic request to the electronic control unit via the VCI device to read the vehicle identification code; The network connection status is detected based on the vehicle identification code. If the network connection status shows a valid wireless network connection or mobile network connection, it is determined to be in online mode. If the network connection status does not show a valid wireless network connection or mobile network connection, it is determined to be in offline mode. In online mode, a request containing the vehicle identification code and sensor model is sent to the cloud calibration data server, which then obtains the calibration parameters and returns them. In offline mode, a local storage file name is constructed based on the vehicle identification code, and calibration parameters are read from a preset local storage path.

5. The self-calibration and dynamic matching method for vehicle tire pressure sensors according to claim 1, characterized in that, The self-calibration and dynamic matching method for the vehicle-mounted tire pressure sensor also includes: While the vehicle is traveling at a constant speed, triaxial acceleration data from four sensors are collected to calculate radial acceleration and extract the peak moment. The theoretical time delay difference is calculated based on wheelbase and vehicle speed and compared with the peak time of each sensor to obtain the front wheel sensor group and the rear wheel sensor group; Radial acceleration data is collected and the time average is calculated when the vehicle is turning. The left and right wheel positions in the front wheel sensor group and the rear wheel sensor group are determined based on the time average and the turning direction to obtain the wheel position determination result. The wheel position determination results are organized into a first mapping relationship between sensor ID numbers and four wheel positions, and written into the electronic control unit through extended diagnostic services.

6. The self-calibration and dynamic matching method for vehicle tire pressure sensors according to claim 5, characterized in that, The self-calibration and dynamic matching method for the vehicle-mounted tire pressure sensor also includes: The electronic control unit monitors the cumulative mileage. When the cumulative mileage reaches the preset verification period, it collects the radial acceleration data from the sensor and calculates the real-time root mean square value. Calculate the feature similarity between the real-time root mean square value and the baseline root mean square value; Determine whether the feature similarity is lower than a preset similarity threshold. If it is lower than the similarity threshold, generate a wheel position relearning trigger signal and update the first mapping relationship to obtain a second mapping relationship.

7. A self-calibration and dynamic matching system for an on-board tire pressure sensor, characterized in that, The steps for implementing the self-calibration and dynamic matching method for vehicle tire pressure sensors according to any one of claims 1 to 6 include: The sending module is used to send an extended diagnostic service request frame to the electronic control unit via the VCI device to read the reference pressure value and sensor ID list; The wake-up module is used to select a target sensor ID based on the sensor ID list, activate the original sensor on the target tire by transmitting a wake-up data frame containing the target sensor ID through the VCI device, and receive the working status data returned by the original sensor. The reading module is used to determine the network connection status based on the vehicle identification code. In online mode, it obtains calibration parameters from the cloud server, and in offline mode, it reads calibration parameters from the local storage path. An update module is used to write the calibration parameters into a new sensor, calculate the calibration pressure value of the new sensor based on the reference pressure value and the operating status data, and update the new sensor.

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