Remote upgrade method and system for tire pressure sensor

Abstract

The present invention relates to the field of sensor upgrade technology, and in particular to a remote upgrade method and system for a tire pressure sensor. The method comprises the following steps: obtaining real-time status data of the tire pressure sensor; removing outlier parameters from the real-time status data of the tire pressure sensor, and performing status diagnosis mining to generate a tire pressure sensor status diagnosis report; predicting upgrade requirements based on the tire pressure sensor status diagnosis report, and performing priority upgrade analysis to extract multiple upgradeable sensors; identifying byte-by-byte differences of multiple upgradeable sensors based on preset latest version information, and performing incremental data encapsulation to generate multiple incremental upgrade data blocks; obtaining a vehicle historical operation log; performing dynamic vehicle use behavior analysis and vehicle idle time period prediction on the vehicle historical operation log to generate multiple idle prediction time periods. The present invention improves the upgrade efficiency and safety performance of the tire pressure sensor.

Description

Technical Field

[0001] The present invention relates to the field of sensor upgrade technology, and in particular to a remote upgrade method and system for a tire pressure sensor. Background Art

[0002] With the continuous development of intelligent and automated technologies, modern automobiles have made significant progress in improving driving experience, enhancing safety and reducing energy consumption. As one of the important safety technologies, the tire pressure monitoring system (TPMS) has become a standard configuration for automobiles. The tire pressure sensor can monitor tire pressure and temperature in real time and feed the data back to the vehicle control system, helping the driver to understand the tire status in time and prevent safety hazards caused by insufficient or excessive tire pressure, such as tire blowouts and unstable handling.

[0003] However, with the increasing level of automobile intelligence, traditional tire pressure sensors rely on static hardware and solidified firmware, and cannot adapt to the ever-changing usage needs and environmental conditions in real time, resulting in the gradual lag of tire pressure sensor functions and even performance degradation. Especially in some high-end models and autonomous driving systems, tire pressure sensors not only need to provide accurate real-time data, but also need to have higher intelligence, adaptability and self-diagnosis capabilities. These demands put higher requirements on tire pressure sensor technology.

[0004] Traditional tire pressure sensor update and maintenance methods usually rely on manual inspection, disassembly or connecting to the vehicle through dedicated equipment for diagnosis and update. This method is not only cumbersome and time-consuming, but also cannot meet the needs of real-time and remote management. In addition, with the rapid iteration of automotive technology, car owners and maintenance personnel often cannot obtain the latest sensor updates in a timely manner, causing functional lags or failures during vehicle use. Therefore, how to remotely upgrade the firmware or software of tire pressure sensors without affecting the normal operation of the vehicle has become the key to solving this problem. Summary of the invention

[0005] In order to solve the above technical problems, the present invention proposes a remote upgrade method and system for a tire pressure sensor to solve at least one of the above technical problems.

[0006] To achieve the above object, the present invention provides a remote upgrade method for a tire pressure sensor, comprising the following steps:

[0007] Step S1: acquiring real-time status data of the tire pressure sensor; removing outlier parameters from the real-time status data of the tire pressure sensor, and performing status diagnosis mining to generate a tire pressure sensor status diagnosis report;

[0008] Step S2: performing upgrade demand prediction based on the tire pressure sensor status diagnosis report and performing priority upgrade analysis to extract multiple upgradeable sensors;

[0009] Step S3: Based on the preset latest version information, byte-by-byte difference identification is performed on multiple upgradeable sensors, and incremental data encapsulation is performed to generate multiple incremental upgrade data blocks;

[0010] Step S4: obtaining a vehicle historical operation log; performing dynamic vehicle usage behavior analysis and vehicle idle time period prediction on the vehicle historical operation log to generate multiple idle prediction time periods;

[0011] Step S5: calculating the upgrade requirement time for the multiple incremental upgrade data blocks according to the multiple idle prediction time periods, and matching the optimal upgrade time period, thereby extracting the optimal upgrade time period;

[0012] Step S6: Perform intelligent upgrade planning for the tire pressure sensor based on the optimal upgrade time period, optimize the cloud migration of transmission delay, and build an intelligent cloud optimization upgrade model.

[0013] The present invention monitors the status of tire pressure sensors in real time, promptly discovers and removes abnormal data, improves data accuracy and reliability, generates tire pressure sensor status diagnosis reports through status diagnosis mining, helps identify problems and take corresponding measures, improves vehicle safety and performance, predicts upgrade needs, helps prepare in advance, and improves upgrade efficiency. Through priority upgrade analysis, it determines upgradeable sensors, optimizes upgrade plans, and improves work efficiency and resource utilization. Byte-by-byte difference recognition and incremental data encapsulation help reduce the amount of transmitted data, improve transmission efficiency and save bandwidth. Generating incremental upgrade data blocks can speed up upgrades, reduce the impact on vehicle systems during the upgrade process, and improve user experience Dynamic vehicle behavior analysis and idle time period prediction can help optimize upgrade arrangements and reduce interference with normal vehicle operation. Generating idle prediction time periods can help select the best time for upgrades, improve upgrade efficiency and user satisfaction, calculate the upgrade requirement time based on the idle prediction time period, match the optimal upgrade time period, minimize interference with vehicle operation, and improve upgrade efficiency. Extracting the optimal upgrade time period can help avoid upgrades during peak hours, reduce system risks, and ensure upgrade success rates. Intelligent upgrade planning and transmission delay cloud migration optimization can help improve upgrade efficiency and success rates. Building an intelligent cloud-based optimized upgrade model can make the upgrade process more intelligent and optimized, and improve system stability and user experience.

[0014] Preferably, step S1 comprises the following steps:

[0015] Step S11: Acquire real-time status data of tire pressure sensor;

[0016] Step S12: performing outlier parameter elimination on the real-time state data of the tire pressure sensor to generate outlier elimination state data;

[0017] Step S13: Perform temperature fluctuation analysis on the outlier removal state data to extract the real-time temperature fluctuation characteristics of each sensor;

[0018] Step S14: performing voltage load calculation on the outlier rejection state data to generate sensor voltage load data;

[0019] Step S15: performing load time series change analysis on the sensor voltage load data to generate sensor load change data;

[0020] Step S16: Perform status diagnosis mining on the sensor load change data and the real-time temperature fluctuation characteristics of each sensor, thereby generating a tire pressure sensor status diagnosis report.

[0021] The present invention obtains the real-time status data of the tire pressure sensor to provide a data basis for subsequent analysis and diagnosis, thereby realizing real-time monitoring and remote management of the tire pressure sensor status. The real-time status data of the tire pressure sensor is subjected to outlier parameter elimination to generate outlier elimination status data, which helps to reduce the impact of abnormal data on subsequent analysis and improve data accuracy. The outlier elimination status data is subjected to temperature fluctuation analysis to extract the real-time temperature fluctuation characteristics of each sensor, which helps to understand the working status and stability of the sensor. The voltage load is calculated on the outlier elimination status data to generate the voltage load data of the sensor, which can help evaluate the voltage working status of the sensor and prevent voltage overload. The load timing change analysis is performed on the sensor voltage load data to generate the sensor load change data, which helps to understand the dynamic changes of the sensor load and timely discover abnormal situations. The sensor load change data and the real-time temperature fluctuation characteristics of each sensor are subjected to status diagnosis mining to generate a tire pressure sensor status diagnosis report to help diagnose the working status and performance problems of the sensor.

[0022] Preferably, the specific steps of step S16 are:

[0023] Calculate the communication signal strength of the tire pressure sensor to obtain the communication signal strength value of each sensor;

[0024] Performing communication link stability evaluation on the communication signal strength value of each sensor, thereby generating communication stability data of each sensor;

[0025] Perform time series temperature fluctuation fitting on the real-time temperature fluctuation characteristics of each sensor to generate a temperature fluctuation curve for each sensor;

[0026] Perform fluctuation frequency statistics on the temperature fluctuation curve of each sensor to obtain the temperature fluctuation frequency;

[0027] Performing temperature fluctuation trend analysis based on the temperature fluctuation frequency to generate temperature fluctuation trend characteristics;

[0028] Perform temperature safety fluctuation diagnosis on the temperature fluctuation trend characteristics based on the preset sensor temperature change range threshold to generate temperature change diagnosis data for each sensor;

[0029] Perform load peak identification on the load change data of the sensor to obtain multiple load change peaks;

[0030] Perform safety assessment on multiple load change peaks according to preset sensor load peaks, thereby obtaining load peak safety assessment data;

[0031] Comprehensive status diagnosis mining is performed on the communication stability data of each sensor, the temperature change diagnosis data of each sensor, and the load peak safety assessment data to generate a tire pressure sensor status diagnosis report.

[0032] The present invention obtains the communication quality data of each sensor by calculating the communication signal strength, which is helpful to evaluate the communication stability and reliability between the sensor and the central system, evaluate the communication link stability of each sensor, and generate communication stability data, which can be used to discover potential communication problems and take corresponding measures to ensure the reliability of data transmission. By fitting the real-time temperature fluctuation characteristics and generating a temperature fluctuation curve, it is helpful to better understand the temperature change trend and periodic change of the sensor, count the temperature fluctuation frequency, help determine the frequency characteristics of the sensor temperature change, provide data support for the subsequent temperature fluctuation trend analysis, analyze the temperature fluctuation trend, reveal the development direction of temperature change, and help identify temperature fluctuations. The system can detect the regularity of temperature movements and potential temperature anomalies in advance. According to the preset temperature change range threshold, it can diagnose the temperature fluctuation trend and generate temperature change diagnostic data to help determine whether the sensor temperature is within a safe range. It can also identify the peak value of sensor load changes, which helps to understand the extreme conditions of load changes and provide basic data for load safety assessment. Based on the set load peak value, it can conduct a safety assessment of the load change peak value to help confirm whether the load condition is within a safe range and prevent potential problems in a timely manner. It can also combine communication stability data, temperature change diagnostic data and load peak safety assessment data to conduct comprehensive status diagnosis and generate a tire pressure sensor status diagnostic report to provide comprehensive monitoring and evaluation for remote upgrades.

[0033] Preferably, step S2 specifically comprises the following steps:

[0034] Step S21: marking a plurality of safety upgrade sensors based on the tire pressure sensor status diagnosis report;

[0035] Step S22: extracting current version information of multiple security upgrade sensors, thereby obtaining version information of the sensors;

[0036] Step S23: Analyze historical operation data based on the version information of the sensor to generate operation efficiency characteristics of the current version;

[0037] Step S24: performing a computing power performance evaluation on the operating efficiency characteristics of the current version, thereby generating a computing power performance evaluation value for each sensor;

[0038] Step S25: performing upgrade demand prediction on the computing power performance evaluation value of each sensor to obtain sensor upgrade demand prediction data;

[0039] Step S26: performing a priority upgrade analysis based on the sensor upgrade demand prediction data to extract a plurality of upgradeable sensors.

[0040] The present invention marks multiple security upgrade sensors, performs targeted upgrade operations on these sensors, improves upgrade efficiency and accuracy, extracts current version information of multiple security upgrade sensors, helps to understand the current software version status of each sensor, and provides basic data for subsequent upgrades. Through historical operation data analysis, it can evaluate the operating efficiency characteristics of the current version, help understand the performance and stability of the sensor under different versions, perform computing power performance evaluation to quantify the computing performance of the current version of the sensor, and evaluate the operating efficiency and performance level of the sensor on this basis. Through upgrade demand prediction, the necessity of the upgrade is predicted according to the computing power performance evaluation value of the sensor, which helps to plan and arrange the upgrade work. Through priority upgrade analysis, it is identified which sensors have more urgent upgrade needs, so that upgrade operations are given priority, thereby improving the operating efficiency and security of the overall system.

[0041] Preferably, step S3 specifically comprises the following steps:

[0042] Step S31: extracting the version byte of each sensor based on multiple upgradeable sensors;

[0043] Step S32: Based on the preset latest version information, the version bytes of each sensor are identified byte by byte, and the version difference area is extracted;

[0044] Step S33: encapsulating the incremental data in the version difference area, thereby obtaining a version incremental upgrade encapsulation package;

[0045] Step S34: performing data segmentation processing on the version incremental upgrade package to generate multiple incremental upgrade data blocks.

[0046] The present invention helps to accurately record the current software version information of each sensor by extracting the version bytes of each sensor based on multiple upgradeable sensors, and extracting the version bytes of the sensor to provide basic data for subsequent version comparison and upgrade, thereby ensuring the accuracy and integrity of the upgrade process. The version bytes of each sensor are identified byte by byte based on the preset latest version information, and the version difference area is extracted, thereby helping to determine the specific differences between the sensor software versions. The extraction of the version difference area effectively reduces the size of the upgrade data, improves the upgrade efficiency, and reduces the resource consumption in the data transmission process. The version difference area is incrementally encapsulated to obtain a version incremental upgrade encapsulation package, thereby realizing the incremental upgrade of the sensor software version, reducing the amount of transmission data and time cost required for the upgrade. The generation of the version incremental upgrade encapsulation package reduces the network traffic in the sensor upgrade process, improves the upgrade efficiency, and reduces the risk of upgrade failure. The version incremental upgrade encapsulation package is subjected to data equal-to-large segmentation processing to generate multiple incremental upgrade data blocks, thereby helping to improve the stability and reliability of data transmission. The generation of the incremental upgrade data blocks makes the upgrade process more flexible and controllable, and is convenient for transmission and reception in different network environments, thereby ensuring the successful completion of the remote upgrade.

[0047] Preferably, the specific steps of step S4 are:

[0048] Step S41: Obtain vehicle historical operation log;

[0049] Step S42: Perform dynamic vehicle usage behavior analysis on the vehicle's historical operation log to generate vehicle owner's vehicle usage behavior feature data;

[0050] Step S43: performing time-series idle frequency statistics on the vehicle owner's vehicle use behavior characteristic data to obtain time-series idle frequency data;

[0051] Step S44: calculating the regular driving duration of the vehicle owner's vehicle use behavior characteristic data, thereby generating regular driving duration data;

[0052] Step S45: Evolving the vehicle idle pattern on the regular driving duration data and the time-series idle frequency data, thereby generating a vehicle idle cycle pattern;

[0053] Step S46: predicting the idle time period of the vehicle based on the idle cycle rule of the vehicle to generate a plurality of idle prediction time periods.

[0054] The present invention can provide a valuable data source by obtaining the vehicle historical operation log, record the vehicle's past driving conditions and operating status, provide basic data for subsequent behavior analysis and prediction, perform dynamic vehicle use behavior analysis on the vehicle historical operation log, generate vehicle owner's vehicle use behavior characteristic data, help understand the vehicle owner's driving habits and vehicle use mode, and provide a basis for the prediction of the vehicle's idle time period, perform time-series idle frequency statistics on the vehicle owner's vehicle use behavior characteristic data, obtain time-series idle frequency data, reveal the vehicle's idle situation and frequency, and provide an important reference for the prediction of the idle time period, perform regular driving time calculation on the vehicle owner's vehicle use behavior characteristic data, generate regular driving time data, help understand the regular use of the vehicle, and provide data support for the prediction of the idle time period, perform vehicle idle law evolution based on the regular driving time data and the time-series idle frequency data, generate vehicle idle cycle law, help understand the vehicle's idle mode and regularity, and provide a more accurate basis for the prediction of the idle time period, perform vehicle idle time period prediction based on the vehicle idle cycle law, and generate multiple idle prediction time periods, which helps the vehicle owner to reasonably arrange the use and management of the vehicle, improve the use efficiency of the vehicle and save resources.

[0055] Preferably, the specific steps of step S5 are:

[0056] Step S51: performing data block size calculation on a plurality of incremental upgrade data blocks to generate a data block size value;

[0057] Step S52: analyzing the data transmission bandwidth requirement of the data block size value, thereby obtaining the incremental upgrade bandwidth requirement;

[0058] Step S53: performing vehicle transmission quality prediction based on multiple idle prediction time periods to generate idle time period transmission quality prediction values;

[0059] Step S54: Calculate the upgrade requirement time for the incremental upgrade bandwidth requirement according to the transmission quality prediction value in the idle time period, thereby obtaining the time length required for sensor upgrade;

[0060] Step S55: Matching the optimal upgrade time period to the multiple idle prediction time periods according to the time length required for sensor upgrade, thereby extracting the optimal upgrade time period.

[0061] The present invention calculates the data block size of multiple incremental upgrade data blocks to generate a data block size value, which helps to accurately evaluate the size of each data block and provide basic guidance for data transmission and processing. The calculation of the data block size value helps to optimize the efficiency of data transmission, ensure that the size of the data block is moderate, and avoid resource waste and transmission delay in the data transmission process. The data transmission bandwidth demand analysis of the data block size value is performed to obtain the incremental upgrade bandwidth demand, which helps to evaluate the network bandwidth resources required in the sensor upgrade process. The analysis of the incremental upgrade bandwidth demand helps to adjust the network transmission parameters, ensure the stability and speed of data transmission, and improve the efficiency and success rate of the upgrade process. The vehicle transmission quality is predicted based on multiple idle prediction time periods, and the idle time period transmission quality prediction value is generated, which helps to evaluate the sensor upgrade in different time periods. The transmission quality of the sensor is determined by the transmission quality prediction value. The generation of the transmission quality prediction value helps to select the network idle time period for data transmission, improve the stability and success rate of data transmission, and calculate the upgrade demand time for the incremental upgrade bandwidth demand according to the transmission quality prediction value of the idle time period. The time required for sensor upgrade is obtained, which helps to formulate a reasonable upgrade time plan. The upgrade demand time calculation helps to determine the time schedule for sensor upgrades, avoid network congestion and transmission failure, and ensure the smooth progress of the upgrade process. According to the time required for sensor upgrade, multiple idle prediction time periods are matched with the optimal upgrade time period, and the optimal upgrade time period is extracted, which helps to upgrade the sensor in the most appropriate time period. The optimal upgrade time period matching makes the most of the network idle time period, improves the success rate and speed of data transmission, and ensures the efficient completion of sensor upgrades.

[0062] Preferably, the specific steps of step S6 are:

[0063] Step S61: Perform intelligent upgrade planning for the tire pressure sensor based on the optimal upgrade time period and construct a sensor intelligent upgrade planning strategy;

[0064] Step S62: performing real-time upgrade monitoring of the tire pressure sensor based on the sensor intelligent upgrade planning strategy, and collecting sensor upgrade monitoring response data;

[0065] Step S63: performing data packet transmission delay analysis on the sensor upgrade monitoring response data to generate data packet transmission delay data;

[0066] Step S64: Optimize the cloud migration of transmission delay based on the data packet transmission delay data, and build an intelligent cloud optimization and upgrade model.

[0067] The present invention performs intelligent upgrade planning for tire pressure sensors based on an optimal upgrade time period and constructs a sensor intelligent upgrade planning strategy, which helps to arrange sensor upgrade tasks according to the optimal time period. The formulation of the sensor intelligent upgrade planning strategy improves the efficiency and success rate of the upgrade process, ensures that the sensor upgrade is completed at the most appropriate time, and reduces interference with vehicle use. Based on the sensor intelligent upgrade planning strategy, the tire pressure sensor is upgraded in real time and monitored, and sensor upgrade monitoring response data is collected, which helps to understand the status and feedback information of the upgrade process in real time. The real-time upgrade monitoring helps to timely discover and handle problems in the upgrade process, ensure the smooth progress of the sensor upgrade, and improve user experience and system stability. The sensor upgrade monitoring response data is analyzed for data packet transmission delay, and data packet transmission delay data is generated, which helps to evaluate the delay in the data transmission process. The data packet transmission delay analysis helps to optimize the speed and stability of data transmission, improve the efficiency and success rate of sensor upgrades, and reduce the impact of transmission delay on the upgrade process. Based on the data packet transmission delay data, transmission delay cloud migration optimization is performed, and an intelligent cloud optimization upgrade model is constructed, which helps to optimize the transmission and processing of upgrade data in the cloud. The transmission delay cloud migration optimization improves the efficiency and stability of data transmission, reduces the delay and data loss in the sensor upgrade process, and improves the smooth progress of the upgrade process.

[0068] In this specification, a remote upgrade system of a tire pressure sensor is provided, which is used to execute the remote upgrade method of the tire pressure sensor as described above, including:

[0069] The status diagnosis module is used to obtain the real-time status data of the tire pressure sensor; remove outlier parameters from the real-time status data of the tire pressure sensor, and perform status diagnosis mining to generate a tire pressure sensor status diagnosis report;

[0070] An upgrade demand prediction module is used to predict upgrade demand based on tire pressure sensor status diagnosis reports and perform priority upgrade analysis to extract multiple upgradeable sensors;

[0071] An incremental upgrade module is used to identify byte-by-byte differences of multiple upgradeable sensors based on the preset latest version information, and to perform incremental data encapsulation to generate multiple incremental upgrade data blocks;

[0072] The idle prediction module is used to obtain the vehicle's historical operation log; perform dynamic vehicle usage behavior analysis and vehicle idle time period prediction on the vehicle's historical operation log to generate multiple idle prediction time periods;

[0073] An optimal matching module is used to calculate the upgrade requirement time for multiple incremental upgrade data blocks according to multiple idle prediction time periods, and to match the optimal upgrade time period, thereby extracting the optimal upgrade time period;

[0074] The upgrade planning optimization module is used to perform intelligent upgrade planning for tire pressure sensors based on the optimal upgrade time period, optimize cloud migration of transmission delays, and build an intelligent cloud-based optimization upgrade model.

[0075] The present invention obtains the real-time status data of the tire pressure sensor, and generates a tire pressure sensor status diagnosis report through outlier parameter elimination and status diagnosis mining, which is helpful to monitor the sensor status in real time, identify abnormal conditions and generate reports, improve the reliability and stability of the tire pressure sensor system, find problems in time and take measures, predict upgrade needs and prioritize upgrades based on the tire pressure sensor status diagnosis report, extract multiple upgradeable sensors, optimize upgrade plans through intelligent prediction of upgrade needs, improve the update efficiency of the sensor system, ensure timely upgrades of key sensors, reduce system risks, perform byte-by-byte difference identification and incremental data encapsulation for multiple upgradeable sensors, generate incremental upgrade data blocks, reduce data transmission volume, improve data transmission efficiency, save bandwidth and transmission time, ensure efficient and economical upgrade process, and obtain vehicle historical operation data. Based on the driving log, dynamic vehicle usage behavior analysis and vehicle idle time prediction are carried out, and multiple idle prediction time periods are generated. By predicting the idle time periods, the upgrade plan is optimized to avoid upgrading during high-load periods, improve the upgrade success rate and system stability, and the upgrade requirement time and optimal upgrade time period are calculated for the incremental upgrade data blocks according to multiple idle prediction time periods. The optimal upgrade time period is extracted to ensure that the sensor is upgraded in the most suitable time period, maximize the use of network idle time, improve upgrade efficiency, and reduce transmission costs. Based on the optimal upgrade time period, the tire pressure sensor is intelligently upgraded and planned, and the transmission delay is optimized in the cloud migration. An intelligent cloud optimization upgrade model is built to improve the intelligence and efficiency of the upgrade process, optimize the transmission delay, ensure the secure transmission and high-quality processing of the upgrade data, and improve the user experience and system performance. BRIEF DESCRIPTION OF THE DRAWINGS

[0076] Figure 1 A schematic flow chart of the steps of a remote upgrade method for a tire pressure sensor according to the present invention;

[0077] Figure 2 Detailed implementation flow chart of step S1;

[0078] Figure 3 Detailed implementation flow chart of step S2;

[0079] Figure 4 Detailed implementation flow chart of step S3. DETAILED DESCRIPTION

[0080] It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

[0081] The present application example provides a remote upgrade method and system for a tire pressure sensor. The execution subject of the remote upgrade method and system for the tire pressure sensor includes but is not limited to: mechanical equipment, data processing platform, cloud server node, network upload device, etc. equipped with the system can be regarded as the general computing node of the present application, and the data processing platform includes but is not limited to: at least one of an audio and image management system, an information management system, and a cloud data management system.

[0082] See also Figures 1 to 4 The present invention provides a remote upgrade method for a tire pressure sensor, and the remote upgrade method for the tire pressure sensor comprises the following steps:

[0083] Step S1: acquiring real-time status data of the tire pressure sensor; removing outlier parameters from the real-time status data of the tire pressure sensor, and performing status diagnosis mining to generate a tire pressure sensor status diagnosis report;

[0084] Step S2: performing upgrade demand prediction based on the tire pressure sensor status diagnosis report and performing priority upgrade analysis to extract multiple upgradeable sensors;

[0085] Step S3: Based on the preset latest version information, byte-by-byte difference identification is performed on multiple upgradeable sensors, and incremental data encapsulation is performed to generate multiple incremental upgrade data blocks;

[0086] Step S4: obtaining a vehicle historical operation log; performing dynamic vehicle usage behavior analysis and vehicle idle time period prediction on the vehicle historical operation log to generate multiple idle prediction time periods;

[0087] Step S5: calculating the upgrade requirement time for the multiple incremental upgrade data blocks according to the multiple idle prediction time periods, and matching the optimal upgrade time period, thereby extracting the optimal upgrade time period;

[0088] Step S6: Perform intelligent upgrade planning for the tire pressure sensor based on the optimal upgrade time period, optimize the cloud migration of transmission delay, and build an intelligent cloud optimization upgrade model.

[0089] The present invention monitors the status of tire pressure sensors in real time, promptly discovers and removes abnormal data, improves data accuracy and reliability, generates tire pressure sensor status diagnosis reports through status diagnosis mining, helps identify problems and take corresponding measures, improves vehicle safety and performance, predicts upgrade needs, helps prepare in advance, and improves upgrade efficiency. Through priority upgrade analysis, it determines upgradeable sensors, optimizes upgrade plans, and improves work efficiency and resource utilization. Byte-by-byte difference recognition and incremental data encapsulation help reduce the amount of transmitted data, improve transmission efficiency and save bandwidth. Generating incremental upgrade data blocks can speed up upgrades, reduce the impact on vehicle systems during the upgrade process, and improve user experience Dynamic vehicle behavior analysis and idle time period prediction can help optimize upgrade arrangements and reduce interference with normal vehicle operation. Generating idle prediction time periods can help select the best time for upgrades, improve upgrade efficiency and user satisfaction, calculate the upgrade requirement time based on the idle prediction time period, match the optimal upgrade time period, minimize interference with vehicle operation, and improve upgrade efficiency. Extracting the optimal upgrade time period can help avoid upgrades during peak hours, reduce system risks, and ensure upgrade success rates. Intelligent upgrade planning and transmission delay cloud migration optimization can help improve upgrade efficiency and success rates. Building an intelligent cloud-based optimized upgrade model can make the upgrade process more intelligent and optimized, and improve system stability and user experience.

[0090] In the embodiment of the present invention, refer to Figure 1 , is a schematic flow chart of the steps of a remote upgrade method of a tire pressure sensor of the present invention. In this example, the steps of the method include:

[0091] Step S1: acquiring real-time status data of the tire pressure sensor; removing outlier parameters from the real-time status data of the tire pressure sensor, and performing status diagnosis mining to generate a tire pressure sensor status diagnosis report;

[0092] In this embodiment, ensure that the tire pressure sensor is correctly installed and connected to the vehicle's OBD (on-board diagnostic) system or data acquisition system, confirm that the power supply and communication connection of the sensor are normal, use an appropriate communication protocol (such as CAN bus, RS-485, etc.), configure the parameters of the data acquisition system, including the acquisition frequency (such as once per second), data format (such as JSON, XML, etc.) and storage location (such as local database or cloud storage), start the data acquisition system, obtain the status data of the tire pressure sensor in real time, including parameters such as tire pressure, temperature, humidity, voltage, etc., record the data timestamp for subsequent time series analysis, store the acquired real-time status data in a database in a structured format for subsequent processing, select a suitable outlier detection algorithm, such as the Z-score method, IQR (interquartile range) or anomaly detection method based on machine learning (such as isolation forest), determine the judgment criteria for outliers, such as Z-score greater than 3 or less than -3, perform outlier detection on the real-time status data, and count the Z-score or IQR of each parameter value, identify abnormal data points, record the index of the outlier value and the corresponding original value for subsequent analysis, remove outlier parameters according to the detection results, generate a state data set after outlier removal, ensure that the structure of the data set after outlier removal is consistent with the original data for subsequent processing, select an appropriate state diagnosis method, such as rule-based diagnosis, decision tree, support vector machine (SVM) or other machine learning algorithms, determine the input features of the diagnosis model, such as tire pressure, temperature, voltage and other parameters, standardize or normalize the state data after outlier removal to ensure the equality of features of different dimensions in model training. If a machine learning method is used, the model must be trained using historical data; otherwise, apply predefined rules to make state judgments, diagnose the state of each sensor, identify normal, abnormal or faulty states, organize the diagnosis results, and generate a tire pressure sensor state diagnosis report. The report should include the state of each sensor, the cause of the fault and the recommended maintenance measures. The report format can be PDF or HTML for easy viewing and sharing.

[0093] Step S2: performing upgrade demand prediction based on the tire pressure sensor status diagnosis report and performing priority upgrade analysis to extract multiple upgradeable sensors;

[0094] In this embodiment, the latest tire pressure sensor status diagnosis report is extracted from the database to ensure that the status information of each sensor is obtained, including normal, abnormal and fault status, and parameters related to the upgrade are extracted, such as communication stability, temperature change, load peak, etc. According to historical data and status reports, a suitable upgrade demand prediction model is selected. Common methods include: regression analysis (linear regression, logistic regression, etc.), machine learning-based methods (such as random forest, support vector machine, etc.), determine the required input features, such as sensor status, historical fault records, operating efficiency, etc., pre-process the extracted status data, including missing value filling, normalization and feature selection to improve the accuracy of the prediction model, select appropriate training and test data sets, usually 70% of the data is used for training and 30% of the data is used for testing, use historical data to train the selected prediction model, adjust hyperparameters to optimize model performance, verify the accuracy of the model, use cross-validation and other methods to evaluate the performance of the model on different data sets, and apply the trained model to each sensor. Upgrade demand prediction: predict which sensor needs to be upgraded and its priority based on the status report; record the upgrade demand prediction value of each sensor, including the recommended upgrade time and necessity; determine the criteria for evaluating sensor upgrade priorities, such as: fault risk level (high, medium, low), communication stability (strong, weak), frequency and amplitude of temperature changes, historical fault records; for each sensor, perform a comprehensive score based on the upgrade demand prediction value and priority criteria; use weighted scoring method or multi-criteria decision analysis method (such as TOPSIS) to calculate the total score of each sensor and derive the priority upgrade order; extract multiple upgradeable sensors based on the comprehensive score; set a threshold and only select sensors with scores higher than the threshold for upgrade; record detailed information of the upgradeable sensors, including sensor ID, current status, upgrade demand and priority; store the extracted upgrade sensor information in the database to facilitate subsequent upgrade planning and execution; generate a report that summarizes the results of upgrade demand prediction and priority upgrade analysis, including detailed information of each upgradeable sensor.

[0095] Step S3: Based on the preset latest version information, byte-by-byte difference identification is performed on multiple upgradeable sensors, and incremental data encapsulation is performed to generate multiple incremental upgrade data blocks;

[0096] In this embodiment, the current version byte data of all upgradeable sensors are extracted from the database. These data usually include information such as the sensor's firmware, software, and configuration files. At the same time, the preset latest version information is obtained, which is usually provided by the R&D team and includes the expected update content and version bytes. A byte-by-byte comparison method is adopted. Common implementation methods include direct comparison method or hash comparison method (such as MD5, SHA-1). A difference threshold is set to facilitate identification of significant differences between versions. The current version byte of each sensor is compared byte by byte with the preset latest version byte, and the starting position and difference value of the difference byte are recorded. The difference bytes are stored in a data structure (such as a list or a dictionary), which includes the following information: the starting position of the difference, the length of the difference byte, and the difference content (the comparison result of the current version byte with the latest version byte). The encapsulation format of the incremental data is determined, including the following parts: an encapsulation header (including metadata, such as version number, sensor ID, payload length, etc.),

[0097] Difference area (containing byte-by-byte difference content), checksum (used to verify data integrity), build an incremental data encapsulation package based on the difference information recorded in step 2, the specific steps include: create an encapsulation header, fill in version number, sensor ID and other information, add the difference area data to the encapsulation package in sequence, calculate and add checksum to ensure data integrity, store the generated incremental upgrade data blocks in a temporary storage area or directly store them in a database for subsequent transmission, set the size of each incremental upgrade data block (such as 512 bytes, 1KB, etc.) according to actual transmission requirements and network bandwidth for efficient transmission, if the size of the incremental data encapsulation package exceeds the set block size, it needs to be split into multiple small data blocks, the specific steps include: calculate the start and end positions of each data block, cut the incremental data encapsulation package into multiple data blocks according to the set size, generate a unique identifier for each data block to facilitate subsequent tracking and transmission, store the generated multiple incremental upgrade data blocks in the database, and record the relevant information of each data block, including data block size, content, sensor ID, etc.

[0098] Step S4: obtaining a vehicle historical operation log; performing dynamic vehicle usage behavior analysis and vehicle idle time period prediction on the vehicle historical operation log to generate multiple idle prediction time periods;

[0099] In this embodiment, historical operation logs are extracted from the vehicle's OBD (on-board diagnostic) system, GPS system or vehicle management platform to ensure that the acquired data includes relevant information such as timestamp, driving status (such as driving, parking), speed, mileage, fuel consumption, engine status, etc. The acquired historical operation logs are formatted and converted into a data structure suitable for analysis (such as CSV, JSON or database table). The formatted historical operation logs are stored in a database to facilitate subsequent data analysis and processing, and the characteristics of vehicle use behavior are determined and analyzed, such as: average daily mileage, driving duration of each trip, driving frequency (such as the number of trips per day), parking duration and frequency. Data analysis tools (such as Pandas library in Python, R language, etc.) are used to clean and analyze the data, handle missing values ​​and outliers, ensure the accuracy and consistency of the data, perform statistical analysis on the stored historical operation logs, calculate various vehicle use behavior characteristics, and generate vehicle use behavior characteristics. Collect data reports, record indicators such as average daily mileage, driving frequency, and parking time, determine the definition of the vehicle's idle state, for example: a vehicle is considered idle if it has been parked for more than a certain time (such as 10 minutes), select appropriate prediction models, such as time series analysis, machine learning models (such as LSTM, random forest), etc., analyze the idle patterns and cycles of vehicles in historical data based on vehicle usage behavior characteristics, and identify high-frequency idle time periods by analyzing the usage of vehicles in different time periods. If machine learning methods are used, the model needs to be trained with historical data, and the model parameters are adjusted to optimize the prediction results. The vehicle's historical operating data is input, and the prediction model is run to generate future idle prediction time periods. The generated multiple idle prediction time periods (including start time and end time) are stored in the database for subsequent scheduling and use. A report is generated to summarize the results of dynamic vehicle usage behavior analysis and idle time period prediction, including vehicle usage behavior characteristics, idle time period list, etc.

[0100] Step S5: calculating the upgrade requirement time for the multiple incremental upgrade data blocks according to the multiple idle prediction time periods, and matching the optimal upgrade time period, thereby extracting the optimal upgrade time period;

[0101] In this embodiment, the size and expected transmission rate of each incremental upgrade data block are obtained from the previous step. This information usually includes: data block size (in bytes), expected transmission rate (in bps), and information of multiple idle prediction time periods is obtained. These time periods are generated based on historical data and real-time monitoring of the vehicle, and usually include: the start time and end time of each time period, and the duration of the time period. For each incremental upgrade data block, the required upgrade time is calculated to ensure that the data block size is converted into bits (1 byte = 8 bits), a greedy algorithm or a dynamic programming algorithm is used to match the required time of the incremental upgrade data block with the idle prediction time period, and the time periods are sorted according to the start time of the idle time period. For each data block, the idle time period is traversed. If the duration of the current time period is greater than or equal to the required upgrade time, a match is performed, and an empty matching result list is initialized to check whether the required time of the current incremental data block matches the duration of the available idle time period. If the match is successful, the data block and the corresponding time period are recorded in the matching result list, and the matched time period is removed from the available time period (if reuse is not allowed). The matched optimal upgrade time period is extracted from the matching result list, and the data block information corresponding to each time period is recorded, ensuring that the start time, end time, and corresponding data block ID of each time period are recorded. The information of the optimal upgrade time period is stored in the database to facilitate the subsequent actual upgrade scheduling and execution, and a report is generated to summarize the extracted optimal time period and its corresponding data block for convenient viewing and use by relevant personnel.

[0102] Step S6: Perform intelligent upgrade planning for the tire pressure sensor based on the optimal upgrade time period, optimize the cloud migration of transmission delay, and build an intelligent cloud optimization upgrade model.

[0103] In this embodiment, according to the extracted optimal upgrade time period, an upgrade strategy for the tire pressure sensor is formulated, which includes: determining the upgrade order of each sensor, determining the upgrade time window of each sensor, ensuring that the upgrade is completed in the idle time period, considering factors including the current status of the sensor, the importance of the upgrade and the time required, creating a detailed upgrade plan document, including the following information: the ID and version information of each sensor, the upgrade time period assigned to each sensor, the expected transmission rate and the time required, using project management tools (such as JIRA or Trello) to track and manage upgrade tasks, obtaining transmission delay data from the previous steps, analyzing the transmission performance of each sensor in different time periods, evaluating the main factors affecting the delay, such as network bandwidth, data packet size and network congestion, using machine learning algorithms (such as regression analysis or cluster analysis) to establish a transmission delay optimization model, selecting a suitable algorithm depends on the characteristics and goals of the data, establishing a delay prediction model through linear regression or polynomial regression, evaluating the key factors affecting the delay, input features including network status, data packet size, transmission distance, etc., dividing the collected delay data into a training set and a test set, using the training set to train the optimization model, using the test set to verify the accuracy of the model, using the mean square error (MSE) Or root mean square error (RMSE) and other indicators to evaluate model performance to ensure that the model has good predictive ability. According to the prediction results of the model, optimization suggestions are made, such as adjusting the location of the cloud server, reducing the transmission distance, optimizing network routing, selecting a low-latency transmission path, increasing bandwidth, and reducing congestion. The optimization strategy is integrated into the system to realize automatic network configuration and resource allocation. The optimized transmission delay data and related strategies are recorded in the database for subsequent analysis and auditing. A feedback mechanism is established to monitor the actual transmission delay during the upgrade process, evaluate the effect of the optimization strategy, and make adjustments based on the feedback.

[0104] In this embodiment, refer to Figure 2 , is a flowchart of detailed implementation steps of step S1. In this embodiment, the detailed implementation steps of step S1 include:

[0105] Step S11: Acquire real-time status data of tire pressure sensor;

[0106] Step S12: performing outlier parameter elimination on the real-time state data of the tire pressure sensor to generate outlier elimination state data;

[0107] Step S13: Perform temperature fluctuation analysis on the outlier removal state data to extract the real-time temperature fluctuation characteristics of each sensor;

[0108] Step S14: performing voltage load calculation on the outlier rejection state data to generate sensor voltage load data;

[0109] Step S15: performing load time series change analysis on the sensor voltage load data to generate sensor load change data;

[0110] Step S16: Perform status diagnosis mining on the sensor load change data and the real-time temperature fluctuation characteristics of each sensor, thereby generating a tire pressure sensor status diagnosis report.

[0111] In this embodiment, ensure that the tire pressure sensor is correctly installed and connected to the data acquisition system, configure relevant communication protocols (such as CAN bus, RS-485, etc.), confirm that the working status and power supply of the sensor are normal, regularly obtain real-time status data of the tire pressure sensor through the data acquisition system, including parameters such as tire pressure, temperature, humidity and voltage, set the data acquisition frequency (such as once per second) to ensure that real-time changing information is captured, store the acquired real-time status data in the database, organize it in a suitable format for subsequent processing and analysis, select a suitable outlier detection method, such as Z-score, IQR (interquartile range) or anomaly detection algorithm based on machine learning, and determine the judgment criteria for outliers, such as Z-score greater than 3 or less than -3, perform outlier detection on the real-time status data, identify abnormal data points, remove outlier parameters based on the detection results, generate outlier removal status data, ensure the quality of the data set, store the status data after outlier removal in the database for subsequent analysis and processing, extract the real-time temperature data of each sensor from the outlier removal status data for subsequent analysis, select a suitable temperature fluctuation analysis method, such as standard deviation, moving average method or other statistical analysis methods to evaluate the temperature fluctuation characteristics, calculate the temperature fluctuation characteristics of each sensor, including: the average and standard deviation of the temperature, the temperature change rate, the maximum and minimum temperature values, store the extracted temperature fluctuation characteristic data in the database, and provide a basis for subsequent status diagnosis, extract the voltage data of the sensor from the outlier removal status data for load calculation, calculate the voltage load of each sensor based on the extracted voltage data and the corresponding current data, record the calculation results, generate sensor voltage load data, and extract the voltage from the voltage Extract time series information from the load data to analyze the load time series changes. Select appropriate time series analysis methods, such as time series decomposition, trend analysis, or autoregressive model (ARIMA). Perform time series change analysis on the voltage load data of the sensor, identify the trend and periodicity of the load change, calculate the rate, peak, and valley values ​​of the load change, record the analysis results, store the load change data in the database for subsequent use, integrate the voltage load change data of the sensor and the real-time temperature fluctuation characteristics to form a complete data set, select appropriate status diagnosis methods, such as decision trees, support vector machines (SVM), or other machine learning algorithms for status diagnosis, use the selected diagnosis method to analyze the integrated data, identify the working status of the sensor, generate a diagnosis report, point out the faults, anomalies, and maintenance requirements, generate a document of the status diagnosis report, record the status, faultiness, and recommended maintenance measures of each sensor, and store the diagnosis report in the database for subsequent query and analysis.

[0112] In this embodiment, the specific steps of step S16 are:

[0113] Calculate the communication signal strength of the tire pressure sensor to obtain the communication signal strength value of each sensor;

[0114] Performing communication link stability evaluation on the communication signal strength value of each sensor, thereby generating communication stability data of each sensor;

[0115] Perform time series temperature fluctuation fitting on the real-time temperature fluctuation characteristics of each sensor to generate a temperature fluctuation curve for each sensor;

[0116] Perform fluctuation frequency statistics on the temperature fluctuation curve of each sensor to obtain the temperature fluctuation frequency;

[0117] Performing temperature fluctuation trend analysis based on the temperature fluctuation frequency to generate temperature fluctuation trend characteristics;

[0118] Perform temperature safety fluctuation diagnosis on the temperature fluctuation trend characteristics based on the preset sensor temperature change range threshold to generate temperature change diagnosis data for each sensor;

[0119] Perform load peak identification on the load change data of the sensor to obtain multiple load change peaks;

[0120] Perform safety assessment on multiple load change peaks according to preset sensor load peaks, thereby obtaining load peak safety assessment data;

[0121] Comprehensive status diagnosis mining is performed on the communication stability data of each sensor, the temperature change diagnosis data of each sensor, and the load peak safety assessment data to generate a tire pressure sensor status diagnosis report.

[0122] In this embodiment, real-time communication signal data is obtained from the tire pressure sensor, which is usually transmitted via a wireless signal (such as RF or Bluetooth) using a signal strength indicator (RSSI). Methods Calculate the communication signal strength value of each sensor, record the signal strength value, generate a signal strength data set for each sensor, determine indicators for evaluating the stability of the communication link, such as the standard deviation of the signal strength, packet loss rate, delay, etc., perform statistical analysis on the signal strength value of each sensor, calculate the standard deviation and mean, evaluate the stability of the link, for example, a signal link with a small standard deviation and a low packet loss rate is considered stable, generate communication stability data for each sensor, record the evaluation results, extract temperature data from the real-time sensor data, ensure data integrity and accuracy, select a suitable fitting method, such as linear regression, polynomial fitting or exponential smoothing, perform time series fitting on the temperature data of each sensor, generate a temperature fluctuation curve, and record the fitting parameters (such as the slope and intercept of the fitting curve), store the fitting results in a database for subsequent analysis, use Fourier transform or other frequency domain analysis methods to calculate the temperature fluctuation frequency, perform frequency statistics on the temperature fluctuation curve of each sensor, determine the main fluctuation frequency, record the temperature fluctuation frequency information of each sensor, and store it in a database, select a suitable trend analysis method, such as moving average method, linear regression or time series The analysis is based on the temperature fluctuation frequency. The temperature fluctuation trend of each sensor is analyzed, the trend characteristics are extracted, and the generated temperature fluctuation trend characteristic data is recorded to facilitate subsequent safety fluctuation diagnosis. According to the preset temperature change range threshold (for example, the normal operating temperature range), it is judged whether the temperature fluctuation trend characteristics exceed the threshold. If exceeded, it is marked as abnormal, and the temperature change diagnostic data of each sensor is generated. The abnormal and normal states are recorded. The load change data is analyzed using a peak detection algorithm (such as the threshold method and the local maximum method). Multiple load peaks in the load change curve are identified, and the peaks and their corresponding time points are recorded. The identified load peaks are evaluated according to the preset load safety threshold (for example, the maximum withstand current). Risk assessment is performed to determine which peaks are within the safety range and which are risky. Load peak safety assessment data is generated and the assessment results are recorded. The communication stability data, temperature change diagnostic data and load peak safety assessment data are integrated. Multivariate analysis methods (such as decision trees and random forests) are used for comprehensive status diagnosis. A tire pressure sensor status diagnosis report is generated, which includes the status assessment, potential risks and recommended measures for each sensor.

[0123] In this embodiment, refer to Figure 3 , is a flowchart of detailed implementation steps of step S2. In this embodiment, the detailed implementation steps of step S2 include:

[0124] Step S21: marking a plurality of safety upgrade sensors based on the tire pressure sensor status diagnosis report;

[0125] Step S22: extracting current version information of multiple security upgrade sensors, thereby obtaining version information of the sensors;

[0126] Step S23: Analyze historical operation data based on the version information of the sensor to generate operation efficiency characteristics of the current version;

[0127] Step S24: performing a computing power performance evaluation on the operating efficiency characteristics of the current version, thereby generating a computing power performance evaluation value for each sensor;

[0128] Step S25: performing upgrade demand prediction on the computing power performance evaluation value of each sensor to obtain sensor upgrade demand prediction data;

[0129] Step S26: performing a priority upgrade analysis based on the sensor upgrade demand prediction data to extract a plurality of upgradeable sensors.

[0130] In this embodiment, the status diagnosis report of the tire pressure sensor is extracted from the previous step, focusing on safety and performance related information, identifying sensors that need to be upgraded, usually including sensors with failure risks or insufficient performance, setting standards for safety upgrades according to industry standards and company policies, clarifying which states are marked as requiring upgrades, marking sensors that meet the safety upgrade standards, generating a list of safety upgrade sensors for subsequent processing, accessing information of each sensor marked for safety upgrade through a data acquisition system or a sensor management platform, extracting the current version information of each sensor, usually including firmware version, software version, and hardware version, etc., storing the extracted sensor version information in a database for subsequent analysis and comparison, extracting historical operating data related to the current version from the database, including performance indicators, failure records, and maintenance records, analyzing the historical operating data using statistical analysis methods (such as mean, standard deviation, regression analysis, etc.), generating operating efficiency characteristics of the current version, identifying key performance indicators (KPIs), such as failure rate, response time, and energy consumption, etc., storing the generated operating efficiency characteristic data in a database for subsequent use, and determining the algorithm. The indicators of power performance evaluation, such as processing speed, data throughput and response time, etc., select a suitable evaluation method, use performance benchmarking (Benchmarking) or other evaluation tools to evaluate the operating efficiency characteristics of each sensor, generate a computing power performance evaluation value, compare the performance difference between the current version and the previous version, select a suitable upgrade demand prediction model, use time series analysis, regression model or machine learning algorithm, based on the computing power performance evaluation value and historical operating efficiency characteristics, run the prediction model, generate upgrade demand prediction data for each sensor, identify which sensors need to be upgraded in the future, store the upgrade demand prediction data in the database for subsequent analysis, determine the priority upgrade criteria, such as performance degradation, failure risk and economy, etc., prioritize based on the sensor upgrade demand prediction data, extract multiple upgradeable sensors, use weighted scoring method or multi-criteria decision analysis (such as AHP, TOPSIS) and other methods for quantitative analysis, generate a priority upgrade list of upgradeable sensors, record the upgrade suggestions and priorities of each sensor, and store the results of the priority upgrade analysis in the database for subsequent execution and tracking.

[0131] In this embodiment, refer to Figure 4 , is a flowchart of detailed implementation steps of step S3. In this embodiment, the detailed implementation steps of step S3 include:

[0132] Step S31: extracting the version byte of each sensor based on multiple upgradeable sensors;

[0133] Step S32: Based on the preset latest version information, the version bytes of each sensor are identified byte by byte, and the version difference area is extracted;

[0134] Step S33: encapsulating the incremental data in the version difference area, thereby obtaining a version incremental upgrade encapsulation package;

[0135] Step S34: performing data segmentation processing on the version incremental upgrade package to generate multiple incremental upgrade data blocks.

[0136] In this embodiment, the version information of each upgradeable sensor is accessed from a database or a sensor management platform, the connection status of the sensor is confirmed, and its version data is obtained. An appropriate communication protocol (such as CAN, UART, etc.) is used to communicate with the sensor, and the byte data of its current version is extracted. The extracted version bytes are stored in a temporary variable or data structure for subsequent processing. The preset latest version byte data is obtained, usually from the development team or version management system. The version bytes of each sensor are compared with the preset latest version bytes, and the differences are identified byte by byte. A simple loop structure or bit operation method is used to compare byte by byte, and the difference position is recorded. The identified difference position and its corresponding byte value are extracted to generate a version difference area, and the start and end byte positions of the difference are recorded for subsequent processing. The format of the incremental data encapsulation is determined, including the encapsulation header, the difference area domain and checksum and other information, design an appropriate data structure to support the encapsulation process, encapsulate the difference data into the specified format according to the extracted version difference area, generate an incremental upgrade package, ensure that it contains all the required information (such as version number, difference data, etc.), store the generated version incremental upgrade package in a database or file system to facilitate subsequent upgrade operations, set the size of each incremental upgrade data block, usually set it according to network transmission efficiency and storage limitations, determine how to handle the last data block (such as whether it is filled) to ensure data integrity, divide the version incremental upgrade package into equal parts, use a loop or block algorithm to split the data according to the set size, record the starting position and size information of each data block, ensure that it can be reassembled during subsequent processing, and store the generated multiple incremental upgrade data blocks in a database or file system for subsequent sending and execution of upgrade operations.

[0137] In this embodiment, step S4 includes the following steps:

[0138] Step S41: Obtaining the vehicle's historical operation log;

[0139] Step S42: Perform dynamic vehicle usage behavior analysis on the vehicle's historical operation log to generate vehicle owner's vehicle usage behavior feature data;

[0140] Step S43: performing time-series idle frequency statistics on the vehicle owner's vehicle use behavior characteristic data to obtain time-series idle frequency data;

[0141] Step S44: calculating the regular driving duration of the vehicle owner's vehicle use behavior characteristic data, thereby generating regular driving duration data;

[0142] Step S45: Evolving the vehicle idle pattern on the regular driving duration data and the time-series idle frequency data, thereby generating a vehicle idle cycle pattern;

[0143] Step S46: predicting the idle time period of the vehicle based on the idle cycle rule of the vehicle to generate a plurality of idle prediction time periods.

[0144] In this embodiment, historical operation logs are extracted from the vehicle's OBD (on-board diagnostic) system, GPS system or vehicle management system to ensure that complete log data can be accessed, including driving time, speed, mileage, parking time and other information.

[0145] The acquired vehicle historical operation log is stored in the database to ensure the integrity and traceability of the data. The log data is cleaned to remove invalid or duplicate records to ensure data quality. The characteristics of vehicle use behavior, such as driving frequency, driving time, driving distance, parking time, etc., are determined and analyzed. Statistical analysis methods (such as descriptive statistics) are used to analyze the historical operation logs to identify the vehicle owner's vehicle use behavior characteristics and generate vehicle owner's vehicle use behavior characteristic data, including daily vehicle use patterns, common driving routes, etc. The generated vehicle use behavior characteristic data is stored in the database for subsequent analysis and use. The idle state definition of the vehicle is determined. For example, if the parking state exceeds a certain time (such as more than 10 minutes), it is considered idle. The vehicle owner's vehicle use behavior characteristic data is analyzed to count the idle frequency of the vehicle in different time periods, calculate the number of idle times in each time period, and record the idle frequency data for each time period. The time series idle frequency data is stored in the database for subsequent analysis and use. Analyze and determine the calculation standard of regular driving time, for example, a time period of continuous driving for more than 5 minutes is regarded as effective driving time, use historical operation log data to calculate the duration of each trip, accumulate regular driving time data, analyze the regular driving time in different time periods (such as weekdays and weekends), select a model for analyzing vehicle idle patterns, such as time series analysis, cluster analysis or pattern recognition, analyze the idle pattern of vehicles based on regular driving time data and time series idle frequency data, identify the pattern and periodicity of idle time, generate vehicle idle cycle patterns, store the generated vehicle idle cycle pattern data in a database for subsequent analysis, select a suitable prediction model, such as ARIMA (autoregressive integrated moving average), LSTM (long short-term memory network) or other time series prediction methods, run the prediction model based on the vehicle idle cycle pattern data, generate multiple idle prediction time periods, and record the prediction results, including the predicted idle start and end times.

[0146] In this embodiment, the specific steps of step S5 are:

[0147] Step S51: performing data block size calculation on a plurality of incremental upgrade data blocks to generate a data block size value;

[0148] Step S52: analyzing the data transmission bandwidth requirement of the data block size value, thereby obtaining the incremental upgrade bandwidth requirement;

[0149] Step S53: performing vehicle transmission quality prediction based on multiple idle prediction time periods to generate idle time period transmission quality prediction values;

[0150] Step S54: Calculate the upgrade requirement time for the incremental upgrade bandwidth requirement according to the transmission quality prediction value in the idle time period, thereby obtaining the time length required for sensor upgrade;

[0151] Step S55: Matching the optimal upgrade time period to the multiple idle prediction time periods according to the time length required for sensor upgrade, thereby extracting the optimal upgrade time period.

[0152] In this embodiment, all incremental upgrade data blocks are extracted from the storage, and the built-in function of the programming language (such as Python or C++) is used to obtain the byte size of each data block. The calculation process uses len(data_block) (in Python) or sizeof(data_block) (in C++) to obtain the size of each data block. The size value of each data block is stored in a list or array for subsequent analysis. The required bandwidth is calculated based on the size of each data block and the expected transmission time. Bandwidth requirement (bps) = data block size (bits) / Transmission time (seconds), set a reasonable transmission time (for example, the transmission time of a data block is estimated based on historical data or system performance), generate bandwidth requirement data for each data block, and store it in a database or table for subsequent use. Obtain the expected idle time period through the vehicle's real-time monitoring system or historical data analysis, use a machine learning model (such as linear regression or random forest) to train historical transmission quality data, and establish a transmission quality prediction model. The input of the prediction model is the characteristics of the idle time period (such as time period length, vehicle status, environmental factors, etc.), and the output is the transmission quality prediction value (such as transmission rate, packet loss rate, etc.). Combined with the transmission quality prediction value of the previous step and the incremental upgrade bandwidth requirement, calculate the time required to complete the upgrade. The required time (seconds) = Data block size (bits) / predicted transmission rate (bps). For each incremental upgrade data block, calculate the required time based on the expected transmission quality and add up the total upgrade time. Record the upgrade time and total time length of each data block for subsequent use. Use a greedy algorithm or dynamic programming algorithm to match the time required for each incremental upgrade with the idle time period. Traverse the idle time period to find a suitable time period to adapt to the required time length of each upgrade data block. Based on the matching results, extract the optimal upgrade time period to ensure that the upgrade is completed without affecting the normal operation of the vehicle.

[0153] In this embodiment, the specific steps of step S6 are:

[0154] Step S61: Perform intelligent upgrade planning for the tire pressure sensor based on the optimal upgrade time period and construct a sensor intelligent upgrade planning strategy;

[0155] Step S62: performing real-time upgrade monitoring of the tire pressure sensor based on the sensor intelligent upgrade planning strategy, and collecting sensor upgrade monitoring response data;

[0156] Step S63: performing data packet transmission delay analysis on the sensor upgrade monitoring response data to generate data packet transmission delay data;

[0157] Step S64: Optimize the cloud migration of transmission delay based on the data packet transmission delay data, and build an intelligent cloud optimization and upgrade model.

[0158] In this embodiment, according to the optimal upgrade time period, a smart upgrade planning strategy for sensors is formulated, which includes: selecting appropriate sensors, determining the upgrade order, setting upgrade priorities, etc. Factors to be considered include the sensor's status, current version, communication signal quality, and expected transmission quality. The smart upgrade planning strategy is recorded in a document, and the upgrade time, required time, priority, and other information of each sensor are clearly specified. The planning strategy is integrated into the upgrade management system to ensure that the system can automatically perform upgrade operations, build a real-time monitoring system, and use tools such as Grafana or Prometheus. Realize the monitoring of the sensor upgrade process, configure monitoring indicators, including upgrade progress, data transmission rate, error rate, etc. When the sensor is upgraded, collect monitoring response data in real time, including upgrade status (success, failure), transmission speed, delay, etc. Use API or data flow technology (such as MQTT) to send monitoring data to the central server for storage and analysis, analyze the collected monitoring response data, calculate the delay time of data packet transmission, collect the sending and receiving timestamps of each data packet to ensure the accuracy of the calculation, count the delays in each transmission process, generate data packet transmission delay data, including average delay, maximum delay, minimum delay, etc. Use data visualization tools (such as Matplotlib or Tableau) to facilitate the identification of potential problems, analyze transmission delay data, identify links with higher delays, and use machine learning algorithms (such as cluster analysis or regression analysis) to establish a transmission delay optimization model to predict data transmission delays under different conditions. Based on the results of the optimization model, make cloud migration optimization suggestions, such as adjusting the location of the data center, optimizing network routing, increasing bandwidth, etc., build an intelligent cloud optimization and upgrade model, integrate the optimization strategy into the system to achieve automated adjustment, and after implementing the optimization strategy, continuously monitor the transmission delay, evaluate the optimization effect, and make adjustments based on actual conditions.

[0159] In this embodiment, a remote upgrade system for a tire pressure sensor is provided, which is used to execute the remote upgrade method for the tire pressure sensor as described above, including:

[0160] The status diagnosis module is used to obtain the real-time status data of the tire pressure sensor; remove outlier parameters from the real-time status data of the tire pressure sensor, and perform status diagnosis mining to generate a tire pressure sensor status diagnosis report;

[0161] An upgrade demand prediction module is used to predict upgrade demand based on tire pressure sensor status diagnosis reports and perform priority upgrade analysis to extract multiple upgradeable sensors;

[0162] An incremental upgrade module is used to identify byte-by-byte differences of multiple upgradeable sensors based on the preset latest version information, and to perform incremental data encapsulation to generate multiple incremental upgrade data blocks;

[0163] The idle prediction module is used to obtain the vehicle's historical operation log; perform dynamic vehicle usage behavior analysis and vehicle idle time period prediction on the vehicle's historical operation log to generate multiple idle prediction time periods;

[0164] An optimal matching module is used to calculate the upgrade requirement time for multiple incremental upgrade data blocks according to multiple idle prediction time periods, and to match the optimal upgrade time period, thereby extracting the optimal upgrade time period;

[0165] The upgrade planning optimization module is used to perform intelligent upgrade planning for tire pressure sensors based on the optimal upgrade time period, optimize cloud migration of transmission delays, and build an intelligent cloud-based optimization upgrade model.

[0166] The present invention obtains the real-time status data of the tire pressure sensor, and generates a tire pressure sensor status diagnosis report through outlier parameter elimination and status diagnosis mining, which is helpful to monitor the sensor status in real time, identify abnormal conditions and generate reports, improve the reliability and stability of the tire pressure sensor system, find problems in time and take measures, predict upgrade needs and prioritize upgrades based on the tire pressure sensor status diagnosis report, extract multiple upgradeable sensors, optimize upgrade plans through intelligent prediction of upgrade needs, improve the update efficiency of the sensor system, ensure timely upgrades of key sensors, reduce system risks, perform byte-by-byte difference identification and incremental data encapsulation for multiple upgradeable sensors, generate incremental upgrade data blocks, reduce data transmission volume, improve data transmission efficiency, save bandwidth and transmission time, ensure efficient and economical upgrade process, and obtain vehicle historical operation data. Based on the driving log, dynamic vehicle usage behavior analysis and vehicle idle time prediction are carried out, and multiple idle prediction time periods are generated. By predicting the idle time periods, the upgrade plan is optimized to avoid upgrading during high-load periods, improve the upgrade success rate and system stability, and the upgrade requirement time and optimal upgrade time period are calculated for the incremental upgrade data blocks according to multiple idle prediction time periods. The optimal upgrade time period is extracted to ensure that the sensor is upgraded in the most suitable time period, maximize the use of network idle time, improve upgrade efficiency, and reduce transmission costs. Based on the optimal upgrade time period, the tire pressure sensor is intelligently upgraded and planned, and the transmission delay is optimized in the cloud migration. An intelligent cloud optimization upgrade model is built to improve the intelligence and efficiency of the upgrade process, optimize the transmission delay, ensure the secure transmission and high-quality processing of the upgrade data, and improve the user experience and system performance.

[0167] Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0168] The above is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A remote upgrade method for a tire pressure sensor, characterized in that: The following steps are involved: Step S1: acquiring real-time status data of the tire pressure sensor; removing outlier parameters from the real-time status data of the tire pressure sensor, and performing status diagnosis mining to generate a tire pressure sensor status diagnosis report; Step S2: performing upgrade demand prediction based on the tire pressure sensor status diagnosis report and performing priority upgrade analysis to extract multiple upgradeable sensors; Step S3: Based on the preset latest version information, byte-by-byte difference identification is performed on multiple upgradeable sensors, and incremental data encapsulation is performed to generate multiple incremental upgrade data blocks; Step S4: obtaining a vehicle historical operation log; performing dynamic vehicle usage behavior analysis and vehicle idle time period prediction on the vehicle historical operation log to generate multiple idle prediction time periods; Step S5: calculating the upgrade requirement time for the multiple incremental upgrade data blocks according to the multiple idle prediction time periods, and matching the optimal upgrade time period, thereby extracting the optimal upgrade time period; Step S6: Perform intelligent upgrade planning for the tire pressure sensor based on the optimal upgrade time period, optimize the cloud migration of transmission delay, and build an intelligent cloud optimization upgrade model; Among them, the specific steps of step S1 are: Step S11: Acquire real-time status data of tire pressure sensor; Step S12: performing outlier parameter elimination on the real-time state data of the tire pressure sensor to generate outlier elimination state data; Step S13: Perform temperature fluctuation analysis on the outlier removal state data to extract the real-time temperature fluctuation characteristics of each sensor; Step S14: performing voltage load calculation on the outlier rejection state data to generate sensor voltage load data; Step S15: performing load time series change analysis on the sensor voltage load data to generate sensor load change data; Step S16: Perform status diagnosis mining on the sensor load change data and the real-time temperature fluctuation characteristics of each sensor, thereby generating a tire pressure sensor status diagnosis report; the specific steps of step S16 are: Calculate the communication signal strength of the tire pressure sensor to obtain the communication signal strength value of each sensor; Performing communication link stability evaluation on the communication signal strength value of each sensor, thereby generating communication stability data of each sensor; Perform time series temperature fluctuation fitting on the real-time temperature fluctuation characteristics of each sensor to generate a temperature fluctuation curve for each sensor; Perform fluctuation frequency statistics on the temperature fluctuation curve of each sensor to obtain the temperature fluctuation frequency; Performing temperature fluctuation trend analysis based on the temperature fluctuation frequency to generate temperature fluctuation trend characteristics; Perform temperature safety fluctuation diagnosis on the temperature fluctuation trend characteristics based on the preset sensor temperature change range threshold to generate temperature change diagnosis data for each sensor; Perform load peak identification on the load change data of the sensor to obtain multiple load change peaks; Perform safety assessment on multiple load change peaks according to preset sensor load peaks, thereby obtaining load peak safety assessment data; Comprehensive status diagnosis mining is performed on the communication stability data of each sensor, the temperature change diagnosis data of each sensor, and the load peak safety assessment data to generate a tire pressure sensor status diagnosis report.

2. The remote upgrade method of the tire pressure sensor according to claim 1, characterized in that: The specific steps of step S2 are: Step S21: marking a plurality of safety upgrade sensors based on the tire pressure sensor status diagnosis report; Step S22: extracting current version information of multiple security upgrade sensors, thereby obtaining version information of the sensors; Step S23: Analyze historical operation data based on the version information of the sensor to generate operation efficiency characteristics of the current version; Step S24: performing a computing power performance evaluation on the operating efficiency characteristics of the current version, thereby generating a computing power performance evaluation value for each sensor; Step S25: performing upgrade demand prediction on the computing power performance evaluation value of each sensor to obtain sensor upgrade demand prediction data; Step S26: performing a priority upgrade analysis based on the sensor upgrade demand prediction data to extract a plurality of upgradeable sensors.

3. The remote upgrade method of the tire pressure sensor according to claim 1, characterized in that: The specific steps of step S3 are: Step S31: extracting the version byte of each sensor based on multiple upgradeable sensors; Step S32: Based on the preset latest version information, the version bytes of each sensor are identified byte by byte, and the version difference area is extracted; Step S33: encapsulating the incremental data in the version difference area, thereby obtaining a version incremental upgrade encapsulation package; Step S34: performing data segmentation processing on the version incremental upgrade package to generate multiple incremental upgrade data blocks.

4. The remote upgrade method of the tire pressure sensor according to claim 1, characterized in that: The specific steps of step S4 are: Step S41: Obtaining the vehicle's historical operation log; Step S42: Perform dynamic vehicle usage behavior analysis on the vehicle's historical operation log to generate vehicle owner's vehicle usage behavior feature data; Step S43: performing time-series idle frequency statistics on the vehicle owner's vehicle use behavior characteristic data to obtain time-series idle frequency data; Step S44: calculating the regular driving duration of the vehicle owner's vehicle use behavior characteristic data, thereby generating regular driving duration data; Step S45: Evolving the vehicle idle pattern on the regular driving duration data and the time-series idle frequency data, thereby generating a vehicle idle cycle pattern; Step S46: predicting the idle time period of the vehicle based on the idle cycle rule of the vehicle to generate a plurality of idle prediction time periods.

5. The remote upgrade method of the tire pressure sensor according to claim 1, characterized in that: The specific steps of step S5 are: Step S51: performing data block size calculation on a plurality of incremental upgrade data blocks to generate a data block size value; Step S52: analyzing the data transmission bandwidth requirement of the data block size value, thereby obtaining the incremental upgrade bandwidth requirement; Step S53: performing vehicle transmission quality prediction based on multiple idle prediction time periods to generate idle time period transmission quality prediction values; Step S54: Calculate the upgrade requirement time for the incremental upgrade bandwidth requirement according to the transmission quality prediction value in the idle time period, thereby obtaining the time length required for sensor upgrade; Step S55: Matching the optimal upgrade time period to the multiple idle prediction time periods according to the time length required for sensor upgrade, thereby extracting the optimal upgrade time period.

6. The remote upgrade method of the tire pressure sensor according to claim 1, characterized in that: The specific steps of step S6 are: Step S61: Perform intelligent upgrade planning for the tire pressure sensor based on the optimal upgrade time period and construct a sensor intelligent upgrade planning strategy; Step S62: performing real-time upgrade monitoring of the tire pressure sensor based on the sensor intelligent upgrade planning strategy, and collecting sensor upgrade monitoring response data; Step S63: performing data packet transmission delay analysis on the sensor upgrade monitoring response data to generate data packet transmission delay data; Step S64: Optimize the cloud migration of transmission delay based on the data packet transmission delay data, and build an intelligent cloud optimization and upgrade model.

7. A remote upgrade system for a tire pressure sensor, characterized in that: The method for performing the remote upgrade of the tire pressure sensor according to claim 1 comprises: The status diagnosis module is used to obtain the real-time status data of the tire pressure sensor; remove outlier parameters from the real-time status data of the tire pressure sensor, and perform status diagnosis mining to generate a tire pressure sensor status diagnosis report; An upgrade demand prediction module is used to predict upgrade demand based on tire pressure sensor status diagnosis reports and perform priority upgrade analysis to extract multiple upgradeable sensors; An incremental upgrade module is used to identify byte-by-byte differences of multiple upgradeable sensors based on the preset latest version information, and to perform incremental data encapsulation to generate multiple incremental upgrade data blocks; The idle prediction module is used to obtain the vehicle's historical operation log; perform dynamic vehicle usage behavior analysis and vehicle idle time period prediction on the vehicle's historical operation log to generate multiple idle prediction time periods; An optimal matching module is used to calculate the upgrade requirement time for multiple incremental upgrade data blocks according to multiple idle prediction time periods, and to match the optimal upgrade time period, thereby extracting the optimal upgrade time period; The upgrade planning optimization module is used to perform intelligent upgrade planning for tire pressure sensors based on the optimal upgrade time period, optimize cloud migration of transmission delays, and build an intelligent cloud-based optimization upgrade model.

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