Vehicle part evaluation method and electronic device

Abstract

The application provides a vehicle part evaluation method and an electronic device. The method is applied to a vehicle part. The method comprises the following steps: obtaining an internal actual pressure value, an internal temperature value and a vibration value of the vehicle part at a current time; calculating an internal theoretical pressure value of the vehicle part at the current time according to an aging coefficient of the vehicle part at the current time, a contact stiffness of a sealing interface and the internal temperature value and the vibration value of the vehicle part at the current time; updating the aging coefficient of the vehicle part at the current time and the contact stiffness of the sealing interface according to the internal actual pressure value and the internal theoretical pressure value of the vehicle part at the current time; and evaluating a remaining service life of the vehicle part according to the updated aging coefficient and the contact stiffness at the current time. Thus, the problem that sealing detection can only be performed in a static test in a factory or a laboratory environment and the test result is inaccurate due to the essential difference between a test working condition and an actual working condition of a vehicle is solved.

Description

Technical Field

[0001] This application relates to the field of intelligent sensing, and more specifically, to vehicle component evaluation methods and electronic devices in the field of intelligent sensing. Background Technology

[0002] In related technologies, the sealing performance testing of automotive components (such as camera modules, radar antennas, lidar, etc.) mainly relies on static air pressure tests and immersion tests. However, these tests are only conducted at the factory stage or in a laboratory environment, which can only reflect the sealing performance of the components in the initial stage and cannot characterize the actual sealing state of the vehicle during actual driving. As a result, the test results are difficult to truly reflect the sealing reliability of the parts throughout their entire life cycle. Summary of the Invention

[0003] This application provides a vehicle component evaluation method and electronic device. This method calculates the theoretical internal pressure value of a vehicle component, reflecting its current degradation level and sealing condition, based on its internal temperature and vibration values, current aging coefficient, and sealing interface contact stiffness. This eliminates reliance on factory static calibration for the estimation of the theoretical internal pressure value, instead reflecting the multi-physics coupling effect under current service conditions. This effectively solves the problem that traditional static testing cannot characterize sealing fatigue in the dynamic environment of a real vehicle. The aging coefficient and contact stiffness are updated online based on the actual and theoretical internal pressure values ​​of the vehicle component, allowing them to dynamically adjust with the creep, wear, and thermal aging processes of the sealing material. This avoids the fixed deviations caused by environmental evolution and degradation accumulation during long-term service, as seen in offline calibration parameters. The remaining service life is assessed based on the updated aging coefficient and contact stiffness, ensuring both the timeliness and relevance of the remaining service life assessment and significantly improving the accuracy and robustness of life prediction through theoretical calculations and measured data.

[0004] Firstly, a method for evaluating vehicle parts is provided. This method includes: acquiring the actual internal pressure, internal temperature, and vibration values ​​of the vehicle parts at the current moment; calculating the theoretical internal pressure value of the vehicle parts at the current moment based on the aging coefficient and contact stiffness of the sealing interface of the vehicle parts at the current moment, as well as the internal temperature and vibration values ​​of the vehicle parts at the current moment; updating the aging coefficient and contact stiffness of the sealing interface of the vehicle parts at the current moment based on the actual internal pressure and theoretical internal pressure values ​​of the vehicle parts at the current moment; and evaluating the remaining service life of the vehicle parts based on the updated aging coefficient and contact stiffness at the current moment.

[0005] Through the above technical solution, the embodiments of this application can calculate the internal theoretical pressure value of vehicle parts that reflects the current degree of degradation and sealing condition by using the internal temperature and vibration values ​​of vehicle parts, the aging coefficient of vehicle parts at the current moment, and the contact stiffness of the sealing interface. This makes the deduction of the internal theoretical pressure value of vehicle parts no longer dependent on the factory static calibration, but reflects the multi-physics coupling effect under the current service conditions in real time. It effectively solves the problem that traditional static testing cannot characterize the sealing fatigue of the actual vehicle in the dynamic environment. The aging coefficient and contact stiffness are updated online according to the actual internal pressure value and the internal theoretical pressure value of the vehicle parts at the current moment. This allows the aging coefficient and contact stiffness to be dynamically adjusted according to the creep, wear and thermal aging process of the sealing material. It avoids the fixed deviation caused by the environmental evolution and degradation accumulation of offline calibration parameters during long-term service. The remaining service life is evaluated based on the updated aging coefficient and contact stiffness. This not only ensures the timeliness and pertinence of the remaining service life evaluation, but also significantly improves the accuracy and robustness of the life prediction through theoretical calculation and measured data.

[0006] In conjunction with the first aspect, in some possible implementations, the theoretical internal pressure value of the vehicle part at the current moment is calculated based on the aging coefficient and contact stiffness of the sealing interface of the vehicle part at the current moment, as well as the internal temperature and vibration values ​​of the vehicle part at the current moment. This includes: calculating the internal reference pressure value of the vehicle part at the current moment based on the internal temperature value of the vehicle part at the current moment; calculating the pressure leakage of the vehicle part at the current moment based on the vibration value, aging coefficient, and contact stiffness of the vehicle part at the current moment; and calculating the theoretical internal pressure value of the vehicle part at the current moment based on the internal reference pressure value and the pressure leakage.

[0007] Through the above technical solution, the embodiments of this application can calculate the internal reference pressure based on the internal temperature value of vehicle parts. Since temperature changes directly alter the internal medium volume and material elastic modulus, causing pressure fluctuations, the direct influence of thermodynamic state on pressure is quantified, thereby eliminating the interference of temperature drift on subsequent leakage calculations. In addition, vibration value, aging coefficient, and contact stiffness are introduced to jointly calculate the pressure leakage. Vibration characterizes the dynamic disturbance amplitude, the aging coefficient reflects the degree of material performance degradation over service time, and the contact stiffness reflects the integrity of the sealing interface. Together, they determine the dynamic response characteristics of the sealing interface under vibration excitation and the medium leakage rate, thus considering the coupled influence of mechanical degradation and dynamic operating conditions on sealing performance. Based on the reference pressure and leakage, the theoretical internal pressure value of the vehicle parts at the current moment is calculated, achieving a refined characterization of the sealing state under the coupled effects of temperature influence and vibration aging. This more realistically reflects the pressure evolution law of vehicle parts under complex operating conditions, improves the accuracy of theoretical pressure calculation, and provides a reliable basis for subsequent comparison with actual pressure, seal health assessment, fault warning, and prediction of remaining service life.

[0008] Combining the first aspect and the above implementation methods, in some possible implementation methods, the internal reference pressure value of the vehicle parts at the current moment is calculated based on the internal temperature value of the vehicle parts at the current moment, including: obtaining the external atmospheric pressure of the area where the vehicle is located at the current moment; obtaining the internal theoretical pressure value of the vehicle parts at the previous moment; and calculating the internal reference pressure value of the vehicle parts at the current moment based on the internal theoretical pressure value of the vehicle parts at the previous moment, the current external atmospheric pressure, and the internal temperature value of the vehicle parts at the current moment.

[0009] Through the above technical solution, the embodiments of this application can calculate in real time the internal pressure thermodynamic drift caused by ambient temperature fluctuations and changes in external air pressure using the theoretical pressure value of the previous moment, the current external atmospheric pressure, and the current internal temperature. Essentially, it couples the ideal gas law with the dynamic operating conditions of the actual vehicle, thereby continuously extracting the pressure component caused by non-sealing degradation factors in the time series. Under the influence of external operating conditions such as temperature changes and altitude air pressure fluctuations, it can accurately restore the internal pressure baseline that vehicle parts should have under conditions of no leakage and no abnormal vibration. Compared with the traditional solution that uses a fixed nominal initial value, it can automatically track the cumulative effect of regional changes and seasonal changes, thereby significantly improving the accuracy and stability of sealing condition assessment.

[0010] Combining the first aspect and the above implementation methods, in some possible implementation methods, the pressure leakage of the vehicle parts at the current moment is calculated based on the vibration value, aging coefficient, and contact stiffness of the vehicle parts at the current moment. This includes: obtaining the external atmospheric pressure of the area where the vehicle is located at the current moment; obtaining the internal theoretical pressure value of the vehicle parts at the previous moment; calculating the leakage constant of the vehicle parts based on the aging coefficient; calculating the vibration correction coefficient of the vehicle parts based on the contact stiffness; and calculating the pressure leakage of the vehicle parts at the current moment based on the vibration value of the vehicle parts at the current moment, the external atmospheric pressure of the area where the vehicle is located at the current moment, the internal theoretical pressure value of the vehicle parts at the previous moment, the leakage constant, and the vibration correction coefficient. The leakage constant represents the gas leakage rate of the vehicle parts.

[0011] Through the above technical solution, the embodiments of this application can obtain the external atmospheric pressure of the area where the vehicle is located at the current moment and the internal theoretical pressure value of the vehicle parts at the previous moment. Furthermore, based on the aging coefficient, the leakage constant is derived, and based on the contact stiffness, the vibration correction coefficient is derived. Then, the pressure leakage is calculated together with the current vibration value. This upgrades static leakage to real-time dynamic evaluation of multi-physics field coupling. By introducing the vibration correction coefficient and the aging correction coefficient, the leakage estimation can truly reflect the nonlinear degradation behavior of the seal under the conditions of vibration impact, material aging and changes in ambient air pressure during actual driving. This more accurately characterizes the leakage evolution process of the sealing interface under real working conditions, thereby significantly improving the sensitivity and accuracy of capturing and predicting the deterioration of sealing performance.

[0012] Combining the first aspect and the above implementation methods, in some possible implementation methods, assessing the remaining service life of vehicle parts based on the updated aging coefficient and contact stiffness at the current moment includes: recursively estimating the aging coefficient and contact stiffness of vehicle parts at future moments based on the updated aging coefficient and contact stiffness at the current moment; acquiring historical driving data of the vehicle, and calculating the sealing health value of the vehicle parts at future moments based on the historical driving data, the aging coefficient and contact stiffness of the vehicle parts at future moments, where the sealing health value represents the sealing status of the vehicle parts; and assessing the remaining service life of vehicle parts based on the sealing health value of vehicle parts at future moments.

[0013] Through the above technical solution, the embodiments of this application can use the most realistic aging coefficient and contact stiffness after online calibration as the initial boundary conditions to recursively predict the evolution of key parameters at future moments. By introducing historical driving data of the vehicle to statistically generate a future operating condition sequence that fits individual usage habits, the sealing health value at each future moment can be dynamically calculated. Based on the sealing health value of the vehicle parts at future moments, the remaining service life of the vehicle parts can be assessed. By integrating the user's unique historical driving style (such as mileage, speed, and climate / altitude of the usual residence area) into the calculation of the sealing health value of the vehicle parts, the inherent bias of the assessment under standard operating conditions can be effectively eliminated, making the remaining service life prediction highly personalized and accurate, minimizing the risk of sudden sealing failure, and significantly improving the safety and reliability of the vehicle throughout its entire life cycle.

[0014] Combining the first aspect and the above implementation methods, in some possible implementation methods, the aging coefficient and contact stiffness of the vehicle parts at future times are recursively calculated based on the updated aging coefficient and contact stiffness at the current time, including: recursively calculating the aging coefficient of the vehicle parts at future times based on the updated aging coefficient at the current time; and recursively calculating the contact stiffness of the vehicle parts at future times based on the updated aging coefficient and contact stiffness at the current time and the aging coefficient of the vehicle parts at future times.

[0015] Through the above technical solution, the embodiments of this application can recursively predict the aging coefficient at future times based on the aging coefficient updated at the current time, and further combine the aging coefficient updated at the current time and the contact stiffness with the aging coefficient at future times to recursively predict the contact stiffness at future times. This constructs a continuous transmission relationship between aging evolution and contact degradation, enabling the state of the part to be predicted forward and dynamically extended over time. This not only enhances the continuity and consistency of the temporal changes in the aging coefficient and contact stiffness, but also reflects the decay trend of sealing performance in advance, avoiding the superposition of deviations and uncertainties caused by prediction, and improving the engineering reliability of subsequent remaining life assessment.

[0016] Combining the first aspect and the above-mentioned implementation methods, in some possible implementation methods, the sealing health value of the vehicle parts at a future time is calculated based on historical driving data, the aging coefficient and contact stiffness of the vehicle parts at a future time, including: generating the internal temperature and vibration values ​​of the vehicle parts at a future time based on historical driving data; calculating the internal theoretical pressure value of the vehicle parts at a future time based on the aging coefficient and contact stiffness of the vehicle parts at a future time, as well as the internal temperature and vibration values ​​of the vehicle parts at a future time; calculating the operating condition compensation value based on the external temperature and vibration values ​​of the vehicle parts at a future time, the operating condition compensation value representing the compensation value for the sealing state of the vehicle parts; calculating the pressure attenuation trend value based on the internal theoretical pressure value of the vehicle parts at a future time; calculating the pressure ratio between the internal theoretical pressure value and the internal rated pressure value of the vehicle parts at a future time; and calculating the sealing health value of the vehicle parts at a future time based on the operating condition compensation value, the pressure attenuation trend value, and the pressure ratio.

[0017] Through the above technical solution, the embodiments of this application can generate a future temperature and vibration condition sequence that fits individual usage habits based on historical driving data statistics. The aging coefficient and contact stiffness obtained by recursion at future time are used to calculate the theoretical internal pressure value that the part should have under the current predicted working condition. The working condition compensation value is formed based on the external temperature and vibration values ​​at future time. At the same time, the pressure decay trend value is extracted based on the internal theoretical pressure value and the pressure ratio with the rated pressure is calculated. Finally, the sealing health value at future time is obtained by integrating the above multi-dimensional information. A complete derivation link from historical working conditions to future state and from temperature and vibration to pressure decay is formed. It can simultaneously reflect the coupling effect of environmental changes, structural degradation and pressure evolution on sealing performance, improve the foresight, continuity and accuracy of sealing health assessment, and improve the accuracy and reliability of subsequent life prediction.

[0018] In combination with the first aspect and the above implementation methods, in some possible implementation methods, assessing the remaining service life of vehicle parts based on the sealing health values ​​of vehicle parts at future times includes: generating a health value curve based on the sealing health values ​​of vehicle parts at multiple future times, wherein the health value curve is a curve showing the correspondence between the sealing health value of the vehicle parts and the corresponding time; identifying a target time corresponding to a pre-set health value threshold from the health value curve; and calculating the remaining service life of the vehicle parts based on the target time and the current time, wherein the health value threshold is a critical value for the sealing failure of the vehicle parts.

[0019] Through the above technical solution, the embodiments of this application can transform discrete future sealing health index prediction points with complex physical meanings into a health value curve based on sealing health values ​​at multiple future times. The target time corresponding to a pre-set health value threshold is identified on the curve, and the remaining service life of the vehicle parts is calculated by combining the target time with the current time. This transforms the discrete future health state into a continuous degradation trend judgment, thereby accurately locating the critical node of sealing failure. It can avoid the life estimation deviation caused by relying on a single point health value, more realistically reflect the decay process of the sealing performance of the parts over time, and improve the accuracy and reliability of life prediction.

[0020] In conjunction with the first aspect and the above implementation methods, in some possible implementation methods, before assessing the remaining service life of vehicle parts based on the updated aging coefficient and contact stiffness at the current moment, the method further includes: obtaining the external temperature value of the vehicle parts at the current moment; calculating the operating condition compensation value based on the external temperature value and vibration value of the vehicle parts at the current moment; calculating the pressure attenuation trend value based on the actual internal pressure value of the vehicle parts at the current moment; calculating the pressure ratio between the actual internal pressure value and the rated internal pressure value of the vehicle parts at the current moment; calculating the sealing health value of the vehicle parts at the current moment based on the operating condition compensation value, the pressure attenuation trend value, and the pressure ratio; and executing the target level warning action based on the sealing health value of the vehicle parts at the current moment.

[0021] Through the above technical solution, the embodiments of this application can effectively eliminate the interference of environmental fluctuations on pressure readings by integrating the current external temperature and vibration calculation conditions, so that the evaluation results have robust consistency across seasons and regions. At the same time, by combining the ratio of the current actual pressure to the rated pressure (reflecting static pressure holding capacity) and the pressure decay trend (reflecting dynamic deterioration rate), the sealing health index is comprehensively calculated from two dimensions: instantaneous performance and degradation inertia. This makes the evaluation not only accurately capture the current state, but also implies information about the future trend. The graded early warning action triggered by this health index can issue maintenance reminders or functional coordination instructions in a timely manner when the sealing performance has not yet reached the failure threshold but has already shown signs of deterioration. This transforms traditional passive fault repair into proactive preventive maintenance, significantly reducing the risk of component damage and vehicle function degradation caused by sudden water ingress.

[0022] Secondly, a vehicle part evaluation device is provided, comprising: an acquisition module for acquiring the actual internal pressure value, internal temperature value, and vibration value of the vehicle part at the current moment; a calculation module for calculating the theoretical internal pressure value of the vehicle part at the current moment based on the aging coefficient and contact stiffness of the sealing interface of the vehicle part at the current moment, as well as the internal temperature value and vibration value of the vehicle part at the current moment; and an evaluation module for updating the aging coefficient and contact stiffness of the sealing interface of the vehicle part at the current moment based on the actual internal pressure value and the theoretical internal pressure value of the vehicle part at the current moment, and evaluating the remaining service life of the vehicle part based on the updated aging coefficient and contact stiffness at the current moment.

[0023] Thirdly, an electronic device is provided, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the vehicle parts evaluation method as described in the above embodiments.

[0024] Fourthly, a computer program product is provided, comprising: computer program code, which, when run on a computer, causes the computer to execute the vehicle part evaluation method of the first aspect or any possible implementation thereof.

[0025] Fifthly, a computer-readable storage medium is provided that stores computer program code, which, when executed on a computer, causes the computer to perform the vehicle part evaluation method of the first aspect or any possible implementation thereof. Attached Figure Description

[0026] Figure 1 This is a flowchart of the vehicle parts evaluation method provided in the embodiments of this application; Figure 2 This is a block diagram of the vehicle parts evaluation device provided in the embodiments of this application; Figure 3 This is a framework architecture diagram of the vehicle parts evaluation system provided in the embodiments of this application. Detailed Implementation

[0027] The technical solutions in this application will be clearly and thoroughly described below with reference to the accompanying drawings. In the description of the embodiments of this application, unless otherwise stated, " / " means "or," for example, A / B can mean A or B. "And / or" in the text is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Furthermore, in the description of the embodiments of this application, "multiple" refers to two or more than two.

[0028] Hereinafter, the terms "first" and "second" are used for descriptive purposes only and should not be construed as implying or suggesting relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature.

[0029] Before explaining the vehicle parts evaluation method provided in the embodiments of this application, the vehicle structure involved in the embodiments of this application will be described first.

[0030] The vehicle in the embodiments of this application can be a gasoline-powered vehicle, an electric vehicle, or a hybrid vehicle, or it can be an autonomous vehicle, an intelligent connected vehicle, or other types of motor vehicles. This application does not make any specific limitations on this.

[0031] A vehicle typically comprises major components such as the body, powertrain, chassis, electrical and electronic systems, and intelligent driving systems. The body forms the vehicle's structural frame and external coverings. Various parts, especially doors, windows, sunroofs, and sensor mounting interfaces, are equipped with corresponding sealing structures to prevent external moisture, dust, and foreign objects from entering the interior / exterior interfaces or passenger compartment. The powertrain provides the vehicle with driving force and includes the engine or drive motor, transmission, and related intake and exhaust systems. The chassis system, including the suspension, braking, and steering systems, supports the vehicle and ensures handling stability. The vibration characteristics of the suspension system are closely related to road noise and vibration transmission during vehicle operation, directly affecting the vibration environment of components installed in different locations on the vehicle. The electrical and electronic systems include the vehicle's power supply, wiring harnesses, various electronic control units, and the vehicle network. The vehicle network typically uses a controller area network (Controller Area Network) bus or vehicle Ethernet to achieve data communication and information sharing between various electronic control units.

[0032] The intelligent driving system is the core subsystem for realizing the intelligent functions of a vehicle, including a perception module, a decision-making module, and an execution module. The perception module uses sensor modules installed at multiple key locations around the vehicle to perceive the surrounding environment and the vehicle's own state. These sensor modules include camera modules, radar antennas, lidar, high-precision positioning modules, and inertial measurement units. The camera module collects image information about the vehicle's surroundings to achieve functions such as lane line recognition, obstacle detection, and traffic sign recognition. The radar antenna transmits and receives electromagnetic waves to measure the distance and velocity of target objects. The lidar is used to construct a three-dimensional point cloud map of the vehicle's surrounding environment. The high-precision positioning module receives signals from the Global Navigation Satellite System to achieve centimeter-level positioning. The inertial measurement unit measures the vehicle's acceleration and angular velocity to achieve trajectory calculation and attitude estimation.

[0033] Sensor modules require a high degree of cleanliness and dryness in their operating environment. Their housings and vehicle body mounting interfaces are typically equipped with sealing rings, sealing strips, or sealant to prevent the intrusion of external moisture, water vapor, and dust, ensuring the normal operation of the internal optical and electronic components. The sealing performance of this structure directly affects the lifespan and measurement accuracy of the sensor module. If the seal fails and moisture enters, it can cause fogging of the optical components, affecting sensing performance, or even short circuits or corrosion of the circuit board, leading to complete sensor failure and endangering driving safety. To enable online monitoring of sealing performance, each sensor module integrates a miniature pressure sensor, a miniature temperature sensor, and a vibration sensor within its sealed cavity. The miniature pressure sensor collects real-time absolute pressure data within the sealed cavity, the miniature temperature sensor collects real-time temperature data, and the vibration sensor collects real-time vibration acceleration data experienced by the sensor module. These sensors are all electrically connected to the airtightness detection circuit within the sensor module. This circuit amplifies, filters, and performs analog-to-digital conversion on the analog signals output by the sensors, generating digital signals that can be read by the electronic control unit.

[0034] The sensor module interacts with the vehicle's electronic control unit (ECU) via the in-vehicle network, uploading real-time pressure, temperature, and vibration data from inside the sealed cavity. Simultaneously, the ECU also acquires the vehicle's own operational status data via the in-vehicle network, including vehicle speed, longitudinal and lateral acceleration signals, gradient signals, window open / close status signals, and mileage signals. The ECU connects to the in-vehicle communication unit, which uploads the received sealing status data and vehicle operational status data to a cloud server via a cellular network. The cloud server is a remote computing center deployed with a sealing health assessment platform, comprising a data receiving module, a data storage module, a model calculation module, and a warning push module. The data receiving module receives real-vehicle data uploaded from multiple vehicles; the data storage module stores the full lifecycle sealing health records for each vehicle component; the model calculation module runs a multi-physics digital twin model and performs sealing health index calculations and remaining life predictions; and the warning push module pushes assessment results and tiered warning information to user terminal devices and service station terminal devices.

[0035] It should be noted that the vehicle structure involved in the embodiments of this application is not limited to the components listed above. The vehicle may also include air conditioning systems, lighting systems, audio-visual entertainment systems, etc. The operating status of these systems may also be collected through the vehicle network and used as operating condition tags for subsequent analysis. This application will not elaborate on these aspects. Based on the above vehicle hardware architecture and data pathway, the embodiments of this application can realize online real-time monitoring and full life cycle health management of the sealing performance of key vehicle components, providing a hardware foundation and data support for the implementation of subsequent sealing performance evaluation methods.

[0036] Figure 1 This is a schematic flowchart of a vehicle parts evaluation method provided in an embodiment of this application.

[0037] For example, such as Figure 1 As shown, vehicle part evaluation methods include: Step S101: Obtain the actual internal pressure value, internal temperature value, and vibration value of the vehicle parts at the current moment.

[0038] It is understood that the embodiments of this application can obtain the internal pressure value, internal temperature value and internal vibration value of vehicle parts at the current moment, so as to facilitate the subsequent assessment of the sealing performance of vehicle parts.

[0039] It should be noted that this application pre-integrates a miniature pressure sensor, a miniature temperature sensor, and a vibration sensor inside the sealed cavity. The miniature pressure sensor provides real-time internal pressure data for the vehicle component, sensitively capturing minute pressure changes within the sealed cavity caused by temperature variations, external air pressure fluctuations, or gas leakage. The miniature temperature sensor collects real-time temperature data within the sealed cavity, accurately reflecting temperature fluctuations caused by changes in ambient temperature or the component's own heating. The vibration sensor is mounted on the surface of the component module housing (outer or inner surface). The inner surface arrangement offers higher durability due to housing protection, making it less prone to damage. On one hand, the real-time vibration data serves as a key input variable in the extended Kalman filter state equation, participating in system state recursion and effectively assisting in the online dynamic updating of the aging coefficient. On the other hand, it accurately detects and quantifies the periodic minute opening and closing of the sealing interface caused by vibration, providing direct physical evidence for the real-time calculation of the vibration correction coefficient Kvib. This ensures that the digital twin model's estimation of the dynamic leakage rate more closely reflects actual operating conditions, significantly improving the sensitivity and accuracy of the seal health assessment. The embodiments of this application can acquire multi-source data on pressure, temperature and vibration inside the sealed cavity in real time and continuously without affecting the normal function of vehicle parts.

[0040] Step S102: Calculate the theoretical internal pressure value of the vehicle parts at the current moment based on the aging coefficient of the vehicle parts, the contact stiffness of the sealing interface, and the internal temperature and vibration values ​​of the vehicle parts at the current moment.

[0041] It is understood that the embodiments of this application can calculate the theoretical internal pressure value at the current moment by jointly modeling the aging coefficient of the vehicle parts, the contact stiffness of the sealing interface, and the internal temperature and vibration values. This allows for the unified mapping of material degradation, changes in contact state, and operational disturbances to the pressure response, enabling a more accurate characterization of the actual load and leakage evolution trend of the sealing system under the current state, and improving the comprehensiveness and reliability of the internal pressure estimation.

[0042] It should be noted that the aging coefficient of a component characterizes the intrinsic performance degradation of sealing materials (such as rubber and silicone) during long-term service due to temperature cycling, chemical corrosion and stress relaxation. Specifically, it manifests as a decrease in elastic modulus, creep accumulation and an increase in permanent compression deformation. In the model, the aging coefficient directly affects the geometric leakage constant. The larger the value, the larger the equivalent leakage channel cross-sectional area. Even without vibration interference, the gas escape rate under static conditions will increase significantly.

[0043] A sealing interface refers to the physical contact area formed by compression deformation between two mating solid surfaces. Within this area, an elastomer (such as a rubber sealing ring) is compressed, filling the microscopic gaps between mating parts (such as a housing and an end cap), thereby preventing the exchange of gas or liquid between the inside and outside. In this embodiment, taking an automotive camera module as an example, the sealing interface refers to the contact area between the compressed rubber sealing ring (O-ring or rectangular gasket) and the mating surfaces on both sides between the rear cover (metal part) and the front shell (plastic or metal part). If the rubber molecular chains are compressed microscopically when the part is in its factory condition, completely blocking the micropores of the metal surface, the interface is in a static sealing state. However, after 3 years of service, high temperatures cause the rubber to creep and relax, reducing the compression from 20% to 12%. At this point, the sealing interface is no longer completely sealed, but rather micron-level gap channels appear, causing gas to leak inward through the aging sealing interface. This results in a continuous loss of gas mass inside the sealed cavity over time, directly manifested as an observable gradual decrease in the internal measured pressure relative to the theoretical pressure.

[0044] Contact stiffness refers to the ability of a sealing interface to resist separation or relative slippage when subjected to external dynamic loads (such as vibration or pressure fluctuations). In this embodiment, taking a vehicle-mounted camera module as an example, after the camera is installed in the vehicle, the sealing ring is compressed, resulting in a very high contact stiffness (e.g., a value close to 1.0). When the vehicle experiences a 0.3g vibration while driving on a rough road, due to the tight compression and high stiffness of the interface, the external force cannot shake the sealing surface and the housing apart, and the interface gap is almost zero. Therefore, the vibration correction coefficient is almost equal to 1, meaning that vibration will not cause additional leakage. After 80,000 kilometers, the rubber ages, hardens, and loses its elasticity (compression permanent deformation), and the contact stiffness decreases significantly (e.g., the value drops to 0.4). At this point, even if the vehicle is driving on the same rough road (still experiencing a 0.3g vibration), because the interface is looser and the stiffness is lower, the inertial force of the vibration can easily "shake open" the sealing interface for a very small instantaneous opening and closing (a micrometer-level breathing effect), leading to a sharp increase in the dynamic leakage rate.

[0045] The contact stiffness of the sealing interface characterizes the attenuation of the actual contact capability caused by assembly tolerances, vibration break-in, and stress relaxation. It reflects the degradation of the interfacial mechanical contact quality, rather than the intrinsic material properties. In the model, contact stiffness directly affects the vibration correction coefficient. When its value decreases, the periodic small opening and closing amplitude of the sealing interface caused by vibration increases significantly, leading to a sharp increase in the dynamic leakage rate. It is worth noting that the degradation of contact stiffness is not an independent process; it is driven by the aging coefficient and follows the stiffness degradation model, decaying synchronously with the aging process.

[0046] In summary, high temperatures cause rubber to age, which damages the microscopic contact state of the sealing interface. This is directly manifested as a decrease in contact stiffness (from 1 to 0). The decrease in contact stiffness means that the same bumpy road section (vibration input) can tear a larger instantaneous gap in this aged sealing interface, thus causing serious dynamic leakage.

[0047] The aging coefficient is the fundamental driving force of physical degradation, representing the loss of the material's inherent load-bearing capacity; contact stiffness is a direct manifestation of interface response, representing the deterioration of the assembly fit. Together, they form a complete chain of seal degradation from the material's intrinsic causes to the interface's external manifestations: material aging leads to a weakening of elastic recovery force, which in turn reduces interface contact pressure, ultimately causing the seal to lose its dynamic sealing capability under vibration. This coupled design allows the digital twin model to accurately distinguish the root causes of failure. When both factors increase simultaneously, it is determined to be intrinsic aging; when only contact stiffness degrades abnormally while the aging coefficient remains low, it can be located as assembly loosening or wear, providing a clear physical basis for fault root cause analysis and precise repair.

[0048] In one embodiment of this application, the aging coefficient of the vehicle parts and the contact stiffness of the sealing interface at the current moment are the iterative update results of a digital twin model constructed based on the multi-physics coupling mechanism, which is based on the input of the actual internal pressure value and the theoretical internal pressure value of the vehicle parts at the previous moment.

[0049] The multiphysics coupling model is generated offline. Typical sealing component samples are selected, and systematic accelerated aging tests at multiple temperature points, high and low temperature cycling tests, and vibration table tests are conducted. This covers the thermodynamic, fluid dynamics, and solid mechanics conditions that the component may encounter throughout its entire lifespan, and records the pressure-temperature-vibration response curves at different aging stages. Based on these measured data, a finite element simulation model integrating the ideal gas law, sealing interface contact mechanics, and aging dynamics is established. Through parameter fitting and joint verification, a complete set of parameters is calibrated, including the initial geometric leakage constant, vibration sensitivity coefficient, pre-exponential factor, activation energy, stiffness degradation amplification factor, and equivalent cavity volume. Once calibrated, this model remains fixed; all parameters represent only the general initial performance of this type of component and cannot be automatically adjusted according to individual differences or service progress. Manual recalibration is only required when the product design changes.

[0050] The digital twin model is generated online and continuously evolves adaptively during the actual vehicle operation phase. Based on the initial parameter set derived from the multiphysics coupling model, the initial state of the model is established in the cloud computing engine. Subsequently, during vehicle operation, multi-source time-series data such as internal pressure, temperature, vibration, and external atmospheric pressure uploaded by the sensing layer are accessed in real time. Using the current operating conditions as the external driving force, the theoretical pressure value is calculated step by step according to the recursive logic of internal reference pressure value compensation and dynamic leakage rate deduction. The residual between the theoretical pressure and the measured pressure is used as a correction signal. The aging coefficient and the contact stiffness of the sealing interface are estimated and updated online through an extended Kalman filter algorithm, so that the core state variables of the model continuously approximate the actual degradation trajectory of the individual part.

[0051] The model parameters of the digital twin model include the aging coefficient and the contact stiffness of the sealing interface. The aging coefficient controls the aging rate of the sealing material over time and temperature, evolving according to the aging kinetic equation with temperature and time. The contact stiffness of the sealing interface reflects the gradual relaxation behavior of the sealing interface under thermodynamic cycling. The coordinated updating of these two parameters allows the simulation model to dynamically adjust to follow the actual degradation process of the part, ensuring that the digital twin in the model always maintains consistency with the current health state of the physical entity. Therefore, the actual internal pressure value collected at the current moment is a true physical reflection of the gas state within the sealed cavity of the part, carrying comprehensive information on all thermal loads, vibration loads, and cumulative aging effects since the part was manufactured. The reference pressure value calculated based on the current internal temperature and vibration values ​​represents the theoretical pressure level that the part should have under the current operating conditions to maintain a healthy state. The pressure difference between the two is not simply measurement noise, but essentially represents the deviation between the current parameter settings of the model and the actual degradation state of the part. When time-varying parameters such as aging coefficient and contact stiffness do not match the actual degradation state of the part, the theoretical pressure value calculated by the model will inevitably deviate from the measured value, and the residual reflects the degree of this deviation.

[0052] In another embodiment of this application, the internal theoretical pressure value of the vehicle parts at the current moment can be calculated, and complex nonlinear relationships in the data can also be captured by a deep learning model (such as a multi-scale convolutional-attention fusion network that combines physical information constraints).

[0053] In another embodiment of this application, algorithms such as causal attention mechanisms are used to capture the directional causal relationship between various variables (such as temperature and vibration) and the internal theoretical pressure value in order to calculate the internal theoretical pressure value of the vehicle parts at the current moment.

[0054] In summary, this application is not limited to calculating the theoretical internal pressure value of vehicle parts at the current moment through the specific embodiments described above.

[0055] Step S103: Based on the actual internal pressure value and theoretical internal pressure value of the vehicle parts at the current moment, update the aging coefficient and contact stiffness of the sealing interface of the vehicle parts at the current moment, and evaluate the remaining service life of the vehicle parts based on the updated aging coefficient and contact stiffness at the current moment.

[0056] It is understood that the embodiments of this application can update the aging coefficient and contact stiffness online based on the actual internal pressure value and theoretical internal pressure value of the vehicle parts at the current moment. This ensures that the aging coefficient and sealing interface contact stiffness are highly consistent with the actual material degradation degree and interface adhesion state of the individual parts. The updated parameters are used as physically reliable initial boundary conditions. Historical driving data is loaded to generate a future working condition sequence that fits the user's habits. The degradation trajectory of the sealing health index is recursively deduced step by step over time and the moment when it first touches the failure threshold is accurately located. In this way, the remaining service life is quantified. This fundamentally solves the inherent defects of traditional fixed parameter models that cannot track individual differences and service processes. It ensures that the remaining service life prediction is always based on the physical foundation that is closest to the current real state of the part, and avoids the evaluation distortion caused by the model state lagging behind the actual degradation.

[0057] It should be noted that during vehicle service, the material properties and interface adhesion of sealing components continuously degrade due to factors such as temperature cycling, vibration, shock, and chemical corrosion. However, the initial physical model parameters (aging coefficient, contact stiffness) are fixed values ​​based on laboratory sample calibration and cannot automatically track the unique degradation trajectory of each individual component. If not updated, the theoretical pressure calculation benchmark will gradually deviate from reality, and all subsequent evaluations will be distorted. Therefore, it is necessary to use measured data at every moment to reverse-correct the parameters within the model, continuously calibrating the model to most closely reflect the current actual state of the component.

[0058] The deviation value is calculated by comparing the actual internal pressure value of the vehicle part with the theoretical pressure value at the current moment. The deviation value directly reflects the degree of mismatch between the current model parameters and the actual degradation degree of the part. If the residual is small, it means that the current model parameters (aging coefficient, contact stiffness) can still accurately describe the state of the part, and no major adjustment is needed. If the residual exceeds a preset threshold, it means that the model's perception has fallen behind the actual degradation. At this time, the system starts the extended Kalman filter algorithm, uses the deviation value to iteratively calculate in reverse, and integrates multi-source information such as current temperature and vibration to jointly solve for a new set of parameters that can rematch the theoretical and measured values, namely the updated aging coefficient and contact stiffness. Among them, the aging coefficient reflects the degree of degradation of the intrinsic properties of the sealing material (such as loss of elastic modulus, creep accumulation); the contact stiffness reflects the degree of attenuation of the sealing interface's adhesion ability (such as the decrease in contact pressure caused by vibration break-in).

[0059] Once the aging coefficient and contact stiffness are corrected to best match the current state, they are used as the initial boundary conditions to extrapolate the aging increment at each future moment, and the contact stiffness is updated synchronously. Then, the theoretical pressure value at each moment is calculated step by step according to the future working conditions, and then converted into a sealing health index. The sealing health index at each future moment is fitted into a degradation curve that decreases with time. The moment when the sealing health index first falls below the preset failure threshold (e.g., 60 or 50) is found, and the difference between that moment and the current moment is taken as the remaining service life.

[0060] In step S102, the theoretical internal pressure value of the vehicle part at the current moment is calculated based on the aging coefficient and contact stiffness of the sealing interface of the vehicle part at the current moment, as well as the internal temperature and vibration value of the vehicle part at the current moment. This includes: calculating the internal reference pressure value of the vehicle part at the current moment based on the internal temperature value of the vehicle part at the current moment; calculating the pressure leakage of the vehicle part at the current moment based on the vibration value, aging coefficient, and contact stiffness of the vehicle part at the current moment; and calculating the theoretical internal pressure value of the vehicle part at the current moment based on the internal reference pressure value and the pressure leakage.

[0061] It is understood that the embodiments of this application can calculate the internal reference pressure based on the internal temperature value of the vehicle parts. Since temperature changes directly alter the internal medium volume and material elastic modulus, causing pressure fluctuations, the direct influence of thermodynamic state on pressure is quantified to eliminate the interference of temperature drift on subsequent leakage calculations. In addition, vibration value, aging coefficient, and contact stiffness are introduced to jointly calculate the pressure leakage. Vibration characterizes the dynamic disturbance amplitude, the aging coefficient reflects the degree of material performance degradation over service time, and the contact stiffness reflects the integrity of the sealing interface. Together, they determine the dynamic response characteristics of the sealing interface under vibration excitation and the medium leakage rate, thus considering the coupled influence of mechanical degradation and dynamic operating conditions on sealing performance. Based on the reference pressure and leakage, the theoretical internal pressure value of the vehicle parts at the current moment is calculated, realizing a refined characterization of the sealing state under the coupled effects of temperature influence and vibration aging. This more realistically reflects the pressure evolution law of vehicle parts under complex operating conditions, improves the accuracy of theoretical pressure calculation, and provides a reliable basis for subsequent comparison with actual pressure, seal health assessment, fault warning, and prediction of remaining service life.

[0062] It should be noted that the internal reference pressure value is the pressure that the gas in the sealed cavity should present due to thermal expansion and contraction and changes in external air pressure under the current temperature conditions, assuming that the parts are completely leak-free and free from vibration interference. The pressure fluctuations caused by temperature and external air pressure changes are separated from the measured values. The reference pressure value represents the passive drift driven by the environment, rather than the pressure loss caused by seal failure.

[0063] Pressure leakage is the real-time gas escape rate caused by the aging of sealing materials and the degradation of interface contact stiffness under the current vibration excitation, and is converted into the pressure loss within the current sampling interval. The intrinsic aging of materials and the degradation of interface adhesion are coupled into the calculation of leakage rate, so that the pressure loss is no longer a fixed constant, but dynamically changes with the real-time vibration intensity and the degradation state of the seal, truly reflecting the nonlinear loss of sealing performance caused by vibration impact during driving.

[0064] The internal theoretical pressure value is obtained by subtracting the pressure loss caused by leakage from the thermodynamic reference pressure, resulting in the final internal pressure value that the model believes the part should exhibit under the current operating conditions and current aging state.

[0065] Specifically, the theoretical internal pressure value of the vehicle parts at the current moment incorporates the first law of thermodynamics and the gas law into the seal degradation assessment, achieving dynamic coupling of multiple physical quantities such as temperature, pressure, vibration, aging, and leakage within a unified mathematical framework. It is a model that integrates historical recursion, environmental compensation, and degradation quantification to determine the theoretical internal pressure value of the vehicle parts at the current moment. Specifically: ; in, This represents the theoretical internal pressure value of the vehicle parts at the current moment. Let R be the internal reference pressure at the current time t, and R be the universal gas constant. The internal temperature value at the current time t. This represents the pressure leakage of vehicle parts at the current moment. The sampling time interval, The equivalent volume of the cavity. is the average molar mass of air.

[0066] In step S102, the internal reference pressure value of the vehicle parts at the current moment is calculated based on the internal temperature value of the vehicle parts at the current moment, including: obtaining the external atmospheric pressure of the area where the vehicle is located at the current moment; obtaining the internal theoretical pressure value of the vehicle parts at the previous moment; and calculating the internal reference pressure value of the vehicle parts at the current moment based on the internal theoretical pressure value of the vehicle parts at the previous moment, the current external atmospheric pressure, and the internal temperature value of the vehicle parts at the current moment.

[0067] It is understood that the embodiments of this application can calculate in real time the internal pressure thermodynamic drift caused by ambient temperature fluctuations and changes in external air pressure using the theoretical pressure value of the previous moment, the current external atmospheric pressure, and the current internal temperature. Essentially, it couples the ideal gas law with the dynamic operating conditions of the actual vehicle, thereby continuously extracting the pressure component caused by non-sealing degradation factors in the time series. Under the influence of external operating conditions such as temperature changes and altitude air pressure fluctuations, it can accurately restore the internal pressure baseline (internal reference pressure value) that vehicle parts should have under conditions of no leakage and no abnormal vibration. Compared with the traditional scheme that uses a fixed nominal initial value, it can automatically track the cumulative effect of regional changes and seasonal changes, thereby significantly improving the accuracy and stability of sealing condition assessment.

[0068] Specifically, in order to eliminate the passive pressure drift caused by pure temperature changes and obtain the reference pressure that the cavity of the vehicle part should reach only due to temperature changes, assuming that the mold volume change is negligible and the gas follows the ideal gas law, the pressure drift caused only by temperature and external air pressure changes is calculated.

[0069] Therefore, the internal reference pressure value of the vehicle parts at the current moment is calculated as follows: ; in, This represents the internal reference pressure value of the vehicle parts at the current time t. This represents the internal reference pressure value of the vehicle parts at the previous time step t-1. This represents the internal temperature value of the vehicle component at the current time t. This represents the internal temperature value of the vehicle parts at the previous time t-1. Let be the external atmospheric pressure at the current time t. The external atmospheric pressure is the pressure at the previous time t-1.

[0070] It should be noted that, according to the ideal gas law For a closed rigid cavity (volume) (constant), if the air mass inside the cavity If it remains unchanged, then the pressure... With absolute temperature Proportional. Under normal automotive operating conditions, atmospheric pressure is approximately 100 kPa, therefore... It is not zero. As temperature rises, the thermal motion of gas molecules intensifies, the collision frequency increases, and the pressure rises proportionally; conversely, as temperature falls, the pressure decreases.

[0071] In step S102, the pressure leakage of the vehicle parts at the current moment is calculated based on the vibration value, aging coefficient, and contact stiffness of the vehicle parts at the current moment. This includes: obtaining the external atmospheric pressure of the area where the vehicle is located at the current moment; obtaining the internal theoretical pressure value of the vehicle parts at the previous moment; calculating the leakage constant of the vehicle parts based on the aging coefficient, where the leakage constant represents the gas leakage rate of the vehicle parts; calculating the vibration correction coefficient of the vehicle parts based on the contact stiffness; and calculating the pressure leakage of the vehicle parts at the current moment based on the vibration value of the vehicle parts at the current moment, the external atmospheric pressure of the area where the vehicle is located at the current moment, the internal theoretical pressure value of the vehicle parts at the previous moment, the leakage constant, and the vibration correction coefficient.

[0072] It is understood that the embodiments of this application can obtain the external atmospheric pressure of the area where the vehicle is located at the current moment and the internal theoretical pressure value of the vehicle parts at the previous moment, and further derive the leakage constant based on the aging coefficient and the vibration correction coefficient based on the contact stiffness, and then combine the current vibration value to calculate the pressure leakage amount. This upgrades static leakage to real-time dynamic evaluation of multi-physics field coupling. By introducing the vibration correction coefficient and the aging correction coefficient, the leakage amount can be estimated to truly reflect the nonlinear degradation behavior of the seal under the conditions of vibration impact, material aging and changes in ambient air pressure during actual driving. This more accurately characterizes the leakage evolution process of the sealing interface under real working conditions, thereby significantly improving the sensitivity and prediction accuracy of capturing the deterioration of sealing performance.

[0073] Specifically, considering the dynamic opening and closing of the sealing interface caused by vibration, the pressure leakage is no longer a static constant, so a vibration correction factor is introduced: ; in, The vibration sensitivity coefficient is obtained through laboratory vibration table testing and calibration. Let be the vibration value of the vehicle parts at the current time t. This is the vibration correction factor.

[0074] Therefore, the formula for calculating the pressure leakage of vehicle parts at the current moment is: ; in, The density of air at the current temperature; Leakage constant ( ); This is the vibration correction factor; For the previous moment The theoretical internal pressure value of vehicle parts; For the current moment The external atmospheric pressure; This represents the pressure leakage of the vehicle parts at the current time t.

[0075] Among them, aging coefficient It affects the equivalent value of the geometric leakage constant. Therefore, the leakage constant at the current time t : ,in, This is the initial geometric leakage constant (calibrated under brand new conditions). The aging-leakage amplification factor (laboratory calibration) is the above. Substituting the above To calculate the pressure leakage of vehicle parts at the current moment.

[0076] Among them, the sensitivity coefficient in the vibration correction factor is affected by the contact stiffness. Therefore, the vibration sensitivity coefficient at the current time t : ; in, The initial vibration sensitivity coefficient, This is the stiffness degradation amplification factor. Let t be the contact stiffness of the sealing interface of the vehicle parts at the current time t; The vibration sensitivity coefficient at the current time t Substitute the vibration correction factor : ; in, The vibration sensitivity coefficient at the current time t. Let be the vibration value of the vehicle component at the current time t. The above... Substitute the values ​​into the calculation to determine the pressure leakage of the vehicle parts at the current moment.

[0077] In step S103, the aging coefficient and contact stiffness of the sealing interface of the vehicle parts at the current moment are updated based on the actual internal pressure value and the theoretical internal pressure value of the vehicle parts at the current moment. This includes: calculating the pressure difference between the actual internal pressure value and the theoretical internal pressure value of the vehicle parts at the current moment; if the pressure difference is greater than the pressure threshold, the aging coefficient and contact stiffness of the sealing interface of the vehicle parts at the current moment are calculated based on the actual internal pressure value, internal temperature value and vibration value of the vehicle parts at the current moment.

[0078] The pressure threshold can be set according to actual needs, without specific limitations.

[0079] It is understood that the embodiments of this application can only perform joint inversion calculations by integrating the current actual pressure, temperature and vibration multi-source data when the residual between the measured pressure and the theoretical pressure exceeds the preset tolerance. This dynamically updates the aging coefficient and the contact stiffness of the sealing interface. Threshold gating effectively filters out the pseudo-bias caused by sensor noise and fluctuations in normal operating conditions, avoiding frequent miscorrections of the model due to minor disturbances. This significantly reduces the ineffective consumption of on-board edge computing resources. Through deep coupling of multi-physical field information such as temperature and vibration, it can accurately decouple the contributions of material creep (corresponding to the aging coefficient) and interface wear / loosening (corresponding to contact stiffness) to the seal degradation, generating a real service degradation trajectory that conforms to the individualization of each part. This significantly improves the accuracy, robustness and engineering practicality of the full life cycle seal condition assessment.

[0080] It should be noted that the digital twin model provides an initial theoretical pressure benchmark based on multi-physics coupling calculations, while the sealing health value is a comprehensive quantitative output after weighted fusion of the residual between the sensor measured values ​​and the theoretical values, as well as the update amplitude of the model's internal parameters (especially the aging coefficient). When the theoretical value matches the measured value without significant adjustment of the model parameters, it indicates that the current state of the part deviates very little from the factory health benchmark, and the sealing health value is close to 100. Conversely, if the aging coefficient must be significantly increased through extended Kalman filtering to fit the measured pressure data, it indicates that the material has undergone irreversible performance degradation, and the sealing health value decreases accordingly.

[0081] If the pressure difference is less than or equal to the pressure threshold, it indicates that the current aging coefficient and contact stiffness can still accurately describe the true degradation level of the part, and the model does not need to be adjusted. If the pressure difference is greater than the pressure threshold, it indicates that the model parameters have fallen behind the actual degradation process and must be corrected. Therefore, an extended Kalman filter or machine learning regression model can be activated. This application uses an extended Kalman filter to dynamically update the aging coefficient and the contact stiffness of the sealing interface. Specifically: In this embodiment, the Extended Kalman Filter (EKF) acts as a state observer. Utilizing the corrected state estimates (aging coefficient and contact stiffness) from the previous time step, combined with the current operating conditions (temperature, vibration) and the physical model, it performs a prediction step to deduce the prior estimate and covariance of the current state. Simultaneously, it calculates the theoretical pressure value under the current state perception based on a coupled thermodynamic and leakage mechanics model. Then, it enters an update step, introducing the residual (i.e., innovation) between the sensor-measured pressure and the theoretical pressure. The Kalman gain is calculated to determine the correction strength of the measured data to the state estimate. This gain is essentially a dynamic weighting factor between the uncertainty of the model prediction and the sensor measurement noise; a larger gain indicates a stronger contribution of the measured value to the model correction. Finally, the gain is multiplied by the residual to perform a reverse correction on the prior state estimate, yielding the posterior state estimate, i.e., the updated aging coefficient and contact stiffness. Simultaneously, the covariance matrix is ​​updated to quantify the uncertainty of the new estimate.

[0082] (1) Establishment of state-space model: 1) Define the state of the sealed system as a vector. The observed value is the actual internal pressure value. ; Wherein, the state vector , The aging factor is... The contact stiffness of the normalized sealing interface.

[0083] 2) System equations (state transitions): ; Where f is the physical model, describing the natural evolution of the state over time, u is the operating condition input (temperature, vibration), w is the process noise (uncertain in the simulation model), and k-1 is the previous discrete time step, i.e., the previous sampling time. It is the state vector (the first one) k The internal state that needs to be estimated at all times, including the aging factor. and contact stiffness ), This is the posterior state estimate of k-1 at the previous time step after Kalman filtering correction (i.e., the optimal estimate output from the previous update step, including the aging coefficient from the previous time step). (k-1) and the contact stiffness of the sealing interface at the previous moment .

[0084] 3) Observation equations: describe how to derive measurable pressure from the state. ; Among them, the theoretical internal pressure value of the part at the current time k is predicted and calculated by the digital twin model. For state vectors, Let k be the current operating condition input vector at the current time. For observing or measuring noise, h is a leakage formula based on thermodynamics and mechanics.

[0085] It should be noted that the current operating condition is a collection of all external measurable environmental and vehicle operating state parameters collected by the perception layer (end-side sensors and vehicle bus) to drive the digital twin model for state recursion and pressure prediction, including vehicle bus data such as vehicle speed, slope, window status, and mileage, without specific limitations.

[0086] (2) Iterative steps of extended Kalman filter: Using the corrected aging coefficient and the contact stiffness of the sealing interface (representing the system's best understanding of the past state of the parts) from the previous moment, combined with the actual internal temperature and vibration load (as the external driving force) at the previous sampling moment, the multiphysics recursive model constructed in this scheme is substituted into the model for deduction. The specific logic is: temperature drives the intrinsic aging process of the material (following the thermodynamic accelerated aging law), and the increment of aging further leads to the synchronous degradation of the contact capability of the sealing interface. The aging coefficient and contact stiffness at the current moment are thus deduced and are therefore called the prior estimate.

[0087] To ensure that subsequent corrections are based on sound reasoning, the covariance matrix of the current predicted value is calculated simultaneously. Its physical meaning is to quantify the uncertainty of the aforementioned prior estimates. This uncertainty stems from both the propagation of residual uncertainty from the previous correction to the current moment and from the inherent simplification bias of the physical model itself (such as complex operating condition disturbances not fully covered by the Arrhenius model).

[0088] Since the aging coefficient and contact stiffness are internal implicit states and cannot be directly measured, they must be converted into observable physical quantities for comparison with sensors. Therefore, the system takes the prior aging coefficient and contact stiffness derived in the previous step, combines them with the real-time operating conditions input at the current moment (i.e., the current internal temperature, vibration acceleration, and external atmospheric pressure), and substitutes them into a multiphysics coupled leakage model for forward calculation. The model first calculates the thermodynamic reference pressure under no-leakage conditions based on the current temperature and pressure environment, then calculates the dynamic leakage rate by combining the coupling effects of current vibration and aging, and finally synthesizes a theoretical internal pressure value under the current theoretical conditions.

[0089] 1) Prediction step: The state vector from the previous time step... and current working conditions Substitute into the observation function Output the theoretical internal pressure value of the part at the current moment. .

[0090] 2) Update step: Calculate the residuals: ,in, This is the difference between the actual internal pressure value of the vehicle part at the current moment and the theoretical internal pressure value of the vehicle part at the current moment. This represents the actual internal pressure value of the vehicle parts at the previous moment. This represents the theoretical internal pressure value of the vehicle parts at the current moment. Calculate Kalman gain We need to balance the confidence level of the model prediction with sensor noise.

[0091] The prior state vector is inversely corrected by multiplying the gain by the residual, where the state estimate is updated: ,in, Let k be the aging coefficient and sealing interface contact stiffness output after the extended Kalman filter update step correction at the current time. For the current time k, in the extended Kalman filter update step, the predicted value of the state vector is derived solely from the posterior state of the previous time step and the physical laws. The Kalman gain vector. This is the difference between the actual internal pressure value of the vehicle part at the current moment and the theoretical internal pressure value of the vehicle part at the current moment.

[0092] Update the covariance matrix: This indicates that the confidence level of the estimate of the state at that moment has increased after the introduction of updated measured data.

[0093] The extended Kalman filter, the sealing interface contact stiffness, and the covariance matrix, obtained after the current time k is updated and corrected by the extended Kalman filter update step, are used as the starting input for the prediction step at the next sampling time.

[0094] In step S103, the remaining service life of the vehicle parts is assessed based on the updated aging coefficient and contact stiffness at the current moment, including: recursively estimating the aging coefficient and contact stiffness of the vehicle parts at future moments based on the updated aging coefficient and contact stiffness at the current moment; acquiring historical driving data of the vehicle, and calculating the sealing health value of the vehicle parts at future moments based on the historical driving data, the aging coefficient and contact stiffness of the vehicle parts at future moments, where the sealing health value represents the sealing status of the vehicle parts; and assessing the remaining service life of the vehicle parts based on the sealing health value of the vehicle parts at future moments.

[0095] It is understood that the embodiments of this application can use the most realistic aging coefficient and contact stiffness after online calibration as the initial boundary conditions to recursively deduce the evolution of key parameters at future moments, and introduce historical vehicle driving data to statistically generate a future operating condition sequence that fits individual usage habits. Then, the sealing health value at each future moment is dynamically calculated, and the remaining service life of vehicle parts is assessed based on the sealing health value of vehicle parts at future moments. By integrating the user's unique historical driving style (such as mileage, speed, and climate / altitude of the usual residence area) into the calculation of the sealing health value of vehicle parts, the inherent bias of the assessment under standard operating conditions can be effectively eliminated, making the remaining service life prediction highly personalized and accurate, minimizing the risk of sudden sealing failure, and significantly improving the safety and reliability of the vehicle throughout its entire life cycle.

[0096] It should be noted that historical driving data can be user's historical driving habits data (e.g., average daily driving of 20km, average speed of 40km / h, and the usual driving area is a plain) or standard test cycle, without specific limitations.

[0097] This application generates a driving condition distribution sequence {u} for the next N days (or X 10,000 kilometers) based on user historical driving habit data (e.g., average daily mileage of 20km, average speed of 40km / h, and frequent driving area being plains) or standard test cycles. t+1 ,u t+2 ,...,u t+T The process involves analyzing historical vehicle data to determine driving characteristics (average daily mileage, speed distribution, altitude and temperature of frequently driven areas, etc.). Based on this, a series of operating conditions for the future is generated. Specifically: when sufficient historical data is available, the system generates conditions based on historical statistical patterns, ensuring consistency with the driver's actual driving style; when historical data is insufficient, industry-standard test conditions are used as input. Multiple possible operating condition sequences are generated through repeated sampling (e.g., 1000 times), and the degradation trajectory of sealed health values ​​is recursively calculated. Finally, a confidence interval for the remaining lifespan (e.g., 90% probability between 6 and 8 months) is determined, and the median is used as the predicted value.

[0098] The state estimate at current time t (updated aging coefficient) and contact stiffness As initial conditions, we substitute them into the digital twin model to determine the operating conditions at each time step. Calculate the theoretical internal pressure values ​​of the vehicle parts at the current moment.

[0099] Meanwhile, based on the material aging kinetics model (such as the Arrhenius equation, which considers the effect of temperature on the aging rate), the aging state at the next moment is deduced.

[0100] In step S103, the aging coefficient and contact stiffness of the vehicle parts at future times are recursively calculated based on the updated aging coefficient and contact stiffness at the current time, including: recursively calculating the aging coefficient of the vehicle parts at future times based on the updated aging coefficient at the current time; and recursively calculating the contact stiffness of the vehicle parts at future times based on the updated aging coefficient and contact stiffness at the current time and the aging coefficient of the vehicle parts at future times.

[0101] It is understood that the embodiments of this application can recursively deduce the aging coefficient at future times based on the aging coefficient updated at the current time, and further combine the aging coefficient updated at the current time and the contact stiffness with the aging coefficient at future times to recursively deduce the contact stiffness at future times, thus constructing a continuous transmission relationship between aging evolution and contact degradation. This enables the state of the part to be predicted forward and dynamically extended over time, which not only enhances the continuity and consistency of the temporal changes in the aging coefficient and contact stiffness, but also reflects the degradation trend of sealing performance in advance, avoiding the superposition of deviations and uncertainties caused by double blind prediction, and improving the engineering reliability of subsequent remaining life assessment.

[0102] Specifically, based on the updated aging coefficient and contact stiffness at the current moment, the aging coefficient and contact stiffness of vehicle parts at future moments are recursively calculated, specifically: The state estimate at current time t (updated aging coefficient) and contact stiffness As initial conditions, we substitute them into the digital twin model to determine the operating conditions at each time step. Calculate the theoretical internal pressure values ​​of the vehicle parts at the current moment.

[0103] Simultaneously, based on the material aging kinetics model (such as the Arrhenius equation, considering the influence of temperature on the aging rate), the aging state at the next moment is deduced. The aging coefficient is derived based on the Arrhenius accelerated aging model as follows: ; in, The aging coefficient of the vehicle parts at the current moment (0 = brand new, 1 = completely failed), and A is the pre-exponential factor, which reflects the intrinsic aging rate of the material and is obtained by fitting through laboratory accelerated aging tests. The activation energy reflects the material's sensitivity to temperature and is also determined experimentally. This is the universal gas constant; For the future Internal temperature values ​​of vehicle parts; For time step.

[0104] It should be noted that the calibration method involves conducting accelerated aging tests at multiple temperature points (85℃ / 105℃ / 125℃) to obtain raw data on the performance degradation of the sealing material over time under different temperature conditions, and establishing a failure rate benchmark at each temperature. Based on this, the Arrhenius equation is used to perform linear regression fitting on multiple sets of data to deduce the pre-exponential factor A, which characterizes the intrinsic aging rate of the material, and the activation energy Ea, which characterizes the temperature sensitivity. This constructs a mathematical mapping rule for extrapolating from high-temperature accelerated conditions to room-temperature service conditions. Finally, by verifying the prediction accuracy of the fitting model at each test temperature, the effectiveness of this mapping relationship within the target temperature range is confirmed, ensuring the reliability of the extrapolation of the calibration results under extremely slow aging scenarios at room temperature.

[0105] The recursive formula for the contact stiffness of the sealed interface is: ; in, For the contact stiffness of the sealing interface of vehicle parts. The normalized contact stiffness at the current moment. This is the stiffness degradation amplification factor. This represents the cumulative increase in aging since the current moment.

[0106] In step S103, the sealing health value of the vehicle parts at future times is calculated based on historical driving data, the aging coefficient and contact stiffness of the vehicle parts at future times, including: generating the internal temperature and vibration values ​​of the vehicle parts at future times based on historical driving data; calculating the internal theoretical pressure value of the vehicle parts at future times based on the aging coefficient and contact stiffness of the vehicle parts at future times, as well as the internal temperature and vibration values ​​of the vehicle parts at future times; calculating the operating condition compensation value based on the external temperature and vibration values ​​of the vehicle parts at future times; calculating the pressure attenuation trend value based on the internal theoretical pressure value of the vehicle parts at future times; calculating the pressure ratio between the internal theoretical pressure value and the internal rated pressure value of the vehicle parts at future times; the operating condition compensation value represents the compensation value for the sealing state of the vehicle components; and calculating the sealing health value of the vehicle parts at future times based on the operating condition compensation value, the pressure attenuation trend value, and the pressure ratio.

[0107] It is understood that the embodiments of this application can generate a future temperature and vibration condition sequence that fits individual usage habits based on historical driving data statistics. The aging coefficient and contact stiffness obtained by recursion at future time are used to calculate the theoretical internal pressure value that the part should have under the current predicted working condition. The working condition compensation value is formed based on the external temperature and vibration values ​​at future time. The pressure decay trend value is extracted based on the internal theoretical pressure value and the pressure ratio with the rated pressure is calculated. Finally, the sealing health value at future time is obtained by combining the above multi-dimensional information. This forms a complete derivation link from historical working conditions to future state and from temperature and vibration to pressure decay. It can simultaneously reflect the coupling effect of environmental changes, structural degradation and pressure evolution on sealing performance, improve the foresight, continuity and accuracy of sealing health assessment, and improve the accuracy and reliability of subsequent life prediction.

[0108] Specifically, this application calculates the theoretical internal pressure value of the vehicle parts in the future based on the aging coefficient and contact stiffness of the vehicle parts in the future, as well as the internal temperature and vibration values ​​of the vehicle parts in the future. The calculation formula is as follows: ; in, For the future The theoretical internal pressure value of vehicle parts For the future The internal reference pressure value of the vehicle parts, where R is the universal gas constant. For the future Internal temperature values ​​of vehicle parts For the future Pressure leakage of vehicle parts. The sampling time interval, The equivalent volume of the cavity. is the average molar mass of air.

[0109] Therefore, the internal reference pressure values ​​of vehicle parts at future moments: ; in, For the future Internal reference pressure values ​​for vehicle parts For the future Internal reference pressure values ​​for vehicle parts For the future Internal temperature values ​​of vehicle parts For the future Internal temperature values ​​of vehicle parts For the future external atmospheric pressure, For the future The external atmospheric pressure.

[0110] Therefore, the formula for calculating the pressure leakage of vehicle parts in the future is: ; in, The density of air at the current temperature; The leakage constant; This is the vibration correction factor; For the future The theoretical internal pressure value of vehicle parts; For the current moment The external atmospheric pressure; For the future Pressure leakage of vehicle parts.

[0111] It should be noted that the aging coefficient and contact stiffness at the future time t+k are obtained recursively from the updated aging coefficient and contact stiffness at the current time. Specifically: Future Moments Aging coefficient: ; in, For the future The aging coefficient of vehicle parts, where A is the pre-exponential factor, reflects the intrinsic aging rate of the material and is obtained by fitting through laboratory accelerated aging tests. The activation energy reflects the material's sensitivity to temperature and is also determined experimentally. This is the universal gas constant; For the future Internal temperature values ​​of vehicle parts; For time step.

[0112] Future Moments Sealing interface contact stiffness: ; in, For the future Contact stiffness of the sealing interface of vehicle parts For the future Contact stiffness of the sealing interface of vehicle parts This is the stiffness degradation amplification factor. For self The cumulative increase in aging since the beginning of time.

[0113] Therefore, the future moment Leakage constant : ; in, This is the initial geometric leakage constant (calibrated under brand new conditions). The aging-leakage amplification factor (laboratory calibration) is the above. Substituting the above To calculate the pressure leakage of vehicle parts at future moments.

[0114] Among them, the sensitivity coefficient in the vibration correction factor is affected by the contact stiffness. Therefore, the vibration sensitivity coefficient at the current time t :

[0115] in, The initial vibration sensitivity coefficient, This is the stiffness degradation amplification factor. For sealing interface contact stiffness; Future moments Vibration sensitivity coefficient Substitute the vibration correction factor : ; in, For the future The vibration sensitivity coefficient, For the future Vibration values ​​of vehicle parts. The above... Substitute to calculate future moments Pressure leakage of vehicle parts.

[0116] Secondly, based on the operating condition compensation value, pressure attenuation trend value, and pressure ratio, the specific process for calculating the sealing health value of vehicle parts at future times is as follows: Future Moments Sealing health values ​​of vehicle parts:

[0117] in, This is the pressure ratio. This represents the pressure attenuation trend value. This is the working condition compensation value. As the pressure ratio weight, Weighted by the pressure decay trend value. The weights of the working condition compensation values ​​are denoted as , where + + =1.

[0118] in, For the future The theoretical internal pressure value of vehicle parts The standard pressure for vehicle parts (the initial pressure value set at the factory or determined during the design phase when the seal is in brand new, perfect condition). For the future Pressure decay value of vehicle parts This is the maximum limit for pressure attenuation. This is the working condition compensation value. This represents the effective value of vehicle vibration acceleration. The temperature of the sealing surface or the ambient temperature.

[0119] The operating condition compensation value is: ; in, The vibration weighting coefficient is 0~1. Temperature weighting coefficient (0~1). + ≤1, For the future The root mean square value of vibration acceleration, Vibration threshold For the future Internal temperature values ​​of vehicle parts For reference temperature, 25℃ (normal temperature standard conditions) is usually taken. The maximum temperature deviation tolerance is typically set to 80℃ (i.e., the allowable temperature deviation from the reference temperature is ±80℃).

[0120] Among them, the future moment The pressure attenuation value is: = ; in, For the future The theoretical internal pressure value of vehicle parts for The theoretical internal pressure value of vehicle parts at any given time. For time step.

[0121] It should be noted that by applying different levels of vibration excitation and temperature environment to brand-new sealing components under laboratory conditions, and simultaneously recording their internal pressure fluctuation response data, multiple sets of correspondences between external operating conditions (vibration intensity / temperature deviation) and internal pressure fluctuation amplitude were established. Based on this, statistical analysis was performed on the collected multi-condition experimental data to quantify the contribution and sensitivity differences of vibration and temperature to pressure fluctuations, thereby extracting the average interference intensity reflecting the two types of environmental factors on pressure readings. Finally, with the constraint that the operating condition compensation factor f of the brand-new part should be close to 1 under all test conditions, the least squares method was used to... (Vibration weight) and (Temperature weight) is used for joint fitting to minimize the sum of squared fitting deviations after weighting the three factors under all extreme operating conditions. This results in a set of weighting coefficients that enable the operating condition compensation factor to accurately offset environmental interference and ensure that the SHI (Sealing Health Index) of the new part remains stable at a high level of health under both normal and extreme operating conditions.

[0122] In step S103, the remaining service life of the vehicle parts is assessed based on the sealing health values ​​of the vehicle parts at future times, including: generating a health value curve based on the sealing health values ​​of the vehicle parts at multiple future times, wherein the health value curve is a curve showing the correspondence between the sealing health values ​​of the vehicle parts and the corresponding times; identifying the target time corresponding to a pre-set health value threshold from the health value curve; and calculating the remaining service life of the vehicle parts based on the target time and the current time, wherein the health value threshold is a critical value for the sealing failure of the vehicle parts.

[0123] The preset health threshold can be set according to actual needs and experience, such as 60 or 70, without specific limitations.

[0124] It is understood that the embodiments of this application can construct a health value curve based on the seal health values ​​at multiple future times by predicting discrete future sealing health index points with complex physical meanings, and identify the target time corresponding to the pre-set health value threshold on the curve. Then, the remaining service life of the vehicle parts is calculated by combining the target time with the current time. This transforms the discrete future health state into a continuous degradation trend judgment, thereby accurately locating the critical node of sealing failure. This avoids the life estimation deviation caused by relying on a single health value, more realistically reflects the decay process of the sealing performance of the parts over time, and improves the accuracy and reliability of life prediction.

[0125] It should be noted that this application transforms the future discrete seal health value sequence generated by the digital twin model step by step into a continuous and smooth seal health value degradation trajectory curve over time through interpolation and curve fitting. This connects the originally isolated prediction points into a traceable health evolution trend. By scanning the curve forward along the time axis, the moment when the seal health value first falls below the preset seal failure threshold is accurately located. This moment is the predicted failure threshold moment. Finally, the difference between this moment and the current moment is taken as the remaining service life.

[0126] Before step S103, and before assessing the remaining service life of the vehicle parts based on the updated aging coefficient and contact stiffness at the current moment, the method further includes: obtaining the external temperature value of the vehicle parts at the current moment; calculating the operating condition compensation value based on the external temperature value and vibration value of the vehicle parts at the current moment; calculating the pressure attenuation trend value based on the actual internal pressure value of the vehicle parts at the current moment; calculating the pressure ratio between the actual internal pressure value and the rated internal pressure value of the vehicle parts at the current moment; calculating the sealing health value of the vehicle parts at the current moment based on the operating condition compensation value, the pressure attenuation trend value, and the pressure ratio; and executing a warning action of the target level based on the sealing health value of the vehicle parts at the current moment.

[0127] It is understood that the embodiments of this application can effectively eliminate the interference of environmental fluctuations on pressure readings by integrating the current external temperature and vibration to calculate the compensation value of the operating conditions, so that the evaluation results have robust consistency across seasons and regions. At the same time, by combining the ratio of the current actual pressure to the rated pressure (reflecting the static pressure holding capacity) and the pressure decay trend (reflecting the dynamic deterioration rate), the sealing health index is comprehensively calculated from two dimensions: instantaneous performance and degradation inertia. This makes the evaluation not only accurately capture the current state, but also implies information about the future trend. The graded early warning action triggered by this health index can issue maintenance reminders or functional coordination commands in a timely manner when the sealing performance has not yet reached the failure threshold but has already shown signs of deterioration. This transforms traditional passive fault repair into proactive preventive maintenance, significantly reducing the risk of component damage and vehicle function degradation caused by sudden water ingress.

[0128] It should be noted that the target level can be set according to actual needs, and can be level three or four, without specific limitations.

[0129] For example, if the sealing health value of a vehicle part is higher than 80 at the current moment, triggering the green level, the system will only maintain regular data recording and cloud storage, without performing any active intervention, in order to minimize computational overhead and ensure the continuous operation of the core assessment process, while accumulating baseline data for subsequent trend analysis; If the sealing health value of a vehicle part is between 60 and 80 at the current moment, triggering the yellow level, the system determines that the sealing performance has shown observable degradation but has not yet approached the failure boundary. In addition to recording detailed diagnostic data and generating a comprehensive report including pressure decay trend, aging coefficient evolution and operating condition characteristics, and embedding it into the next maintenance reminder, if the part is associated with intelligent driving sensors (such as cameras and radar), the system will also slightly lower the confidence weight of the sensor in the perception fusion layer, so that the vehicle decision-making logic still maintains sufficient redundancy safety margin when there are potential performance fluctuations. At the same time, continuous observation during the maintenance cycle provides more sufficient time sequence basis for subsequent trend judgment. If the sealing health value of a vehicle part drops to 60 or below at the current moment, triggering the red level, the system determines that the sealing performance has been severely degraded and there is a risk of immediate failure. At this time, the system will proactively send a parameter package to adjust the fault tolerance logic of the sensor via an over-the-air update (such as reducing the confidence weight of GPS positioning in fusion navigation or switching to a pure vision / inertial navigation backup strategy). At the same time, a clear repair work order will be generated and an appointment will be made for the nearest service station to perform physical replacement. Before the physical replacement is completed, the system will provide maximum robust protection for the vehicle's functions.

[0130] Specifically, based on the operating condition compensation value, pressure attenuation trend value, and pressure ratio, the sealing health value of the vehicle parts at the current moment is calculated.

[0131] in, For the current moment The theoretical internal pressure value of vehicle parts The standard pressure for vehicle parts (the initial pressure value set at the factory or determined during the design phase when the seal is in brand new, perfect condition). This represents the current pressure decay value. This is the maximum limit for pressure attenuation. This is the working condition compensation value. This represents the effective value of vehicle vibration acceleration. The temperature of the sealing surface or the ambient temperature.

[0132] The operating condition compensation value is: ; in, The vibration weighting coefficient is 0~1. Temperature weighting coefficient (0~1). + ≤1, For the current moment The root mean square value of vibration acceleration, Vibration threshold For the current moment Internal temperature values ​​of vehicle parts For reference temperature, 25℃ (normal temperature standard conditions) is usually taken. The maximum temperature deviation tolerance is typically set to 80℃ (i.e., the allowable temperature deviation from the reference temperature is ±80℃).

[0133] The tiered early warning mechanism divides the health status of the sealed area into three progressive levels: green, yellow, and red. Specifically: In the green phase (SHI>80), the component is deemed to be in a healthy state. Only routine data recording and cloud storage are performed to accumulate baseline information for subsequent trend analysis without triggering any intervention actions, maintaining the continuous operation of the core evaluation process with minimal computational overhead. When SHI drops to the 60-80 range, the yellow concern phase begins. At this point, the system determines that the sealing performance has shown observable degradation but has not yet approached the failure boundary. It then activates the detailed data recording and diagnostic report generation mechanism and embeds the diagnostic results into the next maintenance reminder. Simultaneously, if intelligent driving sensors are involved, their confidence weights are slightly reduced to ensure that the vehicle's fusion perception maintains sufficient safety redundancy margin under potential performance fluctuations, and also provides more sufficient timing basis for subsequent trend judgment. When SHI further drops to 60 or below, the red danger phase is triggered. The system determines that the sealing performance has severely deteriorated and faces the risk of immediate failure. At this point, it proactively pushes a parameter package to adjust the fault tolerance logic of the sensor via OTA (Over-The-Air) (such as reducing the weight of the corresponding sensor in fusion positioning or switching to a backup strategy), while generating a repair work order and scheduling a physical replacement at the nearest service station.

[0134] According to the vehicle component evaluation method of this application, the internal theoretical pressure value of the vehicle component is calculated by using the internal temperature and vibration values ​​of the vehicle component, the aging coefficient of the vehicle component at the current moment, and the contact stiffness of the sealing interface. This allows the derivation of the internal theoretical pressure value of the vehicle component to no longer rely on the factory static calibration, but to reflect the multi-physics coupling effect under the current service conditions in real time. This effectively solves the problem that traditional static testing cannot characterize the sealing fatigue of the actual vehicle in the dynamic environment. The aging coefficient and contact stiffness are updated online based on the actual internal pressure value and the internal theoretical pressure value of the vehicle component at the current moment. This allows the aging coefficient and contact stiffness to be dynamically adjusted according to the creep, wear, and thermal aging process of the sealing material. This avoids the fixed deviation caused by the environmental evolution and degradation accumulation of offline calibration parameters during long-term service. The remaining service life is evaluated based on the updated aging coefficient and contact stiffness, which not only ensures the timeliness and pertinence of the remaining service life evaluation, but also significantly improves the accuracy and robustness of the life prediction through theoretical calculation and measured data.

[0135] Next, the vehicle parts sealing performance evaluation device according to the embodiments of this application is described with reference to the accompanying drawings.

[0136] Figure 2 This is a block diagram of a vehicle parts sealing performance evaluation device according to an embodiment of this application.

[0137] like Figure 2 As shown, the vehicle parts sealing performance evaluation device 10 includes: an acquisition module 100, a calculation module 200, and an evaluation module 300.

[0138] The acquisition module 100 is used to acquire the actual internal pressure value, internal temperature value, and vibration value of the vehicle parts at the current moment; the calculation module 200 is used to calculate the theoretical internal pressure value of the vehicle parts at the current moment based on the aging coefficient and contact stiffness of the sealing interface of the vehicle parts at the current moment, as well as the internal temperature value and vibration value of the vehicle parts at the current moment; the evaluation module 300 is used to update the aging coefficient and contact stiffness of the vehicle parts at the current moment based on the actual internal pressure value and the theoretical internal pressure value of the vehicle parts at the current moment, and evaluate the remaining service life of the vehicle parts based on the updated aging coefficient and contact stiffness at the current moment.

[0139] In this embodiment of the application, the calculation module 200 is further configured to: calculate the internal reference pressure value of the vehicle part at the current time based on the internal temperature value of the vehicle part at the current time; calculate the pressure leakage of the vehicle part at the current time based on the vibration value, aging coefficient and contact stiffness of the vehicle part at the current time; and calculate the internal theoretical pressure value of the vehicle part at the current time based on the internal reference pressure value and the pressure leakage.

[0140] In this embodiment of the application, the calculation module 200 is further configured to: obtain the external atmospheric pressure of the area where the vehicle is located at the current moment; obtain the internal theoretical pressure value of the vehicle parts at the previous moment; and calculate the internal reference pressure value of the vehicle parts at the current moment based on the internal theoretical pressure value of the vehicle parts at the previous moment, the current external atmospheric pressure, and the internal temperature value of the vehicle parts at the current moment.

[0141] In this embodiment of the application, the calculation module 200 is further configured to: obtain the external atmospheric pressure of the area where the vehicle is located at the current moment; obtain the internal theoretical pressure value of the vehicle parts at the previous moment; calculate the leakage constant of the vehicle parts based on the aging coefficient, wherein the leakage constant represents the gas leakage rate of the vehicle parts; calculate the vibration correction coefficient of the vehicle parts based on the contact stiffness; and calculate the pressure leakage amount of the vehicle parts at the current moment based on the vibration value of the vehicle parts at the current moment, the external atmospheric pressure of the area where the vehicle is located at the current moment, the internal theoretical pressure value of the vehicle parts at the previous moment, the leakage constant, and the vibration correction coefficient.

[0142] In this embodiment of the application, the evaluation module 300 is further used to: calculate the pressure difference between the actual internal pressure value and the theoretical internal pressure value of the vehicle part at the current moment; if the pressure difference is greater than the pressure threshold, calculate the aging coefficient and the contact stiffness of the sealing interface of the vehicle part at the current moment based on the actual internal pressure value, internal temperature value and vibration value of the vehicle part at the current moment.

[0143] In this embodiment of the application, the evaluation module 300 is further configured to: recursively calculate the aging coefficient and contact stiffness of the vehicle parts at future times based on the updated aging coefficient and contact stiffness at the current time; acquire historical driving data of the vehicle, and calculate the sealing health value of the vehicle parts at future times based on the historical driving data, the aging coefficient and contact stiffness of the vehicle parts at future times, wherein the sealing health value represents the sealing status of the vehicle parts; and evaluate the remaining service life of the vehicle parts based on the sealing health value of the vehicle parts at future times.

[0144] In this embodiment of the application, the evaluation module 300 is further configured to: recursively deduce the aging coefficient of the vehicle parts at future times based on the aging coefficient updated at the current time; and recursively deduce the contact stiffness of the vehicle parts at future times based on the aging coefficient updated at the current time, the contact stiffness, and the aging coefficient of the vehicle parts at future times.

[0145] In this embodiment, the evaluation module 300 is further configured to: generate internal temperature and vibration values ​​of vehicle parts at future times based on historical driving data; calculate the theoretical internal pressure value of vehicle parts at future times based on the aging coefficient and contact stiffness of vehicle parts at future times, as well as the internal temperature and vibration values ​​of vehicle parts at future times; calculate the operating condition compensation value based on the external temperature and vibration values ​​of vehicle parts at future times; calculate the pressure attenuation trend value based on the theoretical internal pressure value of vehicle parts at future times; calculate the pressure ratio between the theoretical internal pressure value and the rated internal pressure value of vehicle parts at future times; the operating condition compensation value represents the compensation value for the sealing state of vehicle components; and calculate the sealing health value of vehicle parts at future times based on the operating condition compensation value, the pressure attenuation trend value, and the pressure ratio.

[0146] In this embodiment of the application, the evaluation module 300 is further configured to: generate a health value curve based on the sealing health values ​​of vehicle parts at multiple future times, wherein the health value curve is a curve showing the correspondence between the sealing health value of the vehicle parts and the corresponding time; identify the target time corresponding to a pre-set health value threshold from the health value curve; and calculate the remaining service life of the vehicle parts based on the target time and the current time, wherein the health value threshold is a critical value for the sealing failure of the vehicle parts.

[0147] In this embodiment, the system further includes: an early warning module, configured to acquire the external temperature value of the vehicle parts at the current moment; calculate a working condition compensation value based on the external temperature value and vibration value of the vehicle parts at the current moment; calculate a pressure attenuation trend value based on the actual internal pressure value of the vehicle parts at the current moment; calculate the pressure ratio between the actual internal pressure value and the rated internal pressure value of the vehicle parts at the current moment; calculate the sealing health value of the vehicle parts at the current moment based on the working condition compensation value, the pressure attenuation trend value, and the pressure ratio; and execute an early warning action of the target level based on the sealing health value of the vehicle parts at the current moment.

[0148] According to the vehicle part sealing performance evaluation device proposed in this application, the internal theoretical pressure value of the vehicle part is calculated based on the internal temperature and vibration values ​​of the vehicle part, the aging coefficient of the vehicle part at the current moment, and the contact stiffness of the sealing interface. This allows the derivation of the internal theoretical pressure value of the vehicle part to no longer rely on the factory static calibration, but to reflect the multi-physics coupling effect under the current service conditions in real time. This effectively solves the problem that traditional static testing cannot characterize the sealing fatigue of the actual vehicle in the dynamic environment. The aging coefficient and contact stiffness are updated online based on the actual internal pressure value and the internal theoretical pressure value of the vehicle part at the current moment. This allows the aging coefficient and contact stiffness to be dynamically adjusted according to the creep, wear and thermal aging process of the sealing material. This avoids the fixed deviation caused by environmental evolution and degradation accumulation of offline calibration parameters during long-term service. The remaining service life is evaluated based on the updated aging coefficient and contact stiffness, which not only ensures the timeliness and pertinence of the remaining service life evaluation, but also significantly improves the accuracy and robustness of the life prediction through theoretical calculation and measured data.

[0149] Furthermore, this application also protects an electronic device, which may include a memory and a processor, wherein the memory stores executable program code, and the processor is used to call and execute the executable program code to perform the vehicle parts evaluation method provided in this application.

[0150] It should be noted that the electronic device in this application embodiment can be deployed locally in the vehicle or remotely in the cloud, or adopt a vehicle-cloud collaborative computing architecture, possessing flexible multi-terminal adaptation capabilities and deployment scalability. The following will combine... Figure 3 The overall architecture of this application is described in detail below: The overall architecture of this application is constructed from bottom to top into four layers: the perception layer, the transmission layer, the core algorithm layer, and the application layer, forming a complete closed-loop system from data acquisition, physical modeling, online correction, forward prediction to decision execution. Specifically: The perception layer, as the data source of the entire architecture, takes the vehicle-mounted intelligent component module as its carrier and integrates miniature pressure sensors, temperature sensors, and airtightness detection circuits. It collects the actual pressure and internal temperature values ​​inside the component in real time. At the same time, it obtains the external vibration spectrum through vibration sensors arranged on the surface of the module housing (inner or outer surface) and synchronously reads vehicle bus data (including vehicle speed, road slope, window status, mileage, etc.) through CAN bus or vehicle Ethernet, realizing the collaborative perception of multi-source heterogeneous physical quantities.

[0151] As an edge-cloud bridge, the transport layer transmits multi-source data collected by the perception layer through CAN bus or vehicle Ethernet to the T-box edge gateway after time synchronization and filtering preprocessing. Then, it is uploaded to the cloud digital twin platform via 4G / 5G cellular network with TLS (Transport Layer Security) encryption protocol to ensure the real-time performance and security of data transmission, and desensitizes sensitive information such as vehicle identification and location.

[0152] The core algorithm layer is the intelligent hub of the overall architecture, which consists of four closely cooperating sub-modules: (1) Multiphysics coupling model (S2), which establishes a physical simulation model that integrates thermodynamics (ideal gas law), structural mechanics (sealed interface contact mechanics) and aging dynamics (Arrhenius equation) based on laboratory accelerated aging test (high and low temperature cycle, vibration table test) and air tightness test data in the offline stage, and calibrates the initial material parameters (initial geometric leakage constant, initial vibration sensitivity coefficient, pre-exponential factor, activation energy, stiffness degradation amplification factor, etc.); (2) Digital twin engine and online correction module (S3), which loads the initial parameter set exported by S2 in the actual vehicle operation stage, receives real-time temperature, vibration and pressure data uploaded by the perception layer, and executes the state prediction step. (2) Prior deduction and theoretical pressure calculation, and based on the residual between measured pressure and theoretical pressure, the aging coefficient and sealing interface contact stiffness are dynamically updated online through extended Kalman filter (EKF); (3) Model training and parameter drift detection module (S3-4), when the parameters after EKF correction deviate from the initial physical constraint boundary for a long time, actively feed back to S2 to correct the physical boundary of the multiphysics model and realize the continuous evolution of the model; (4) Health assessment and data prediction module (S1), with the aging coefficient and contact stiffness after EKF correction as the initial conditions, load the future working condition sequence (temperature, vibration, external air pressure) generated by the statistics of historical driving data, and recursively deduce the future theoretical pressure and sealing health index SHI step by step, and generate the SHI degradation curve, locate the critical moment of failure and output the remaining service life.

[0153] The application layer, serving as the output port and execution interface of the architecture, converts the current SHI and RUL calculated by the core algorithm layer into visual indicators, and triggers a three-level progressive early warning strategy according to the SHI value: when the status is green (SHI>80), only logs are recorded; when the status is yellow (60<SHI≤80), a diagnosis report is generated with a maintenance reminder embedded, and the confidence weight of relevant sensors is slightly reduced as appropriate; when the status is red (SHI≤60), an OTA upgrade package is actively pushed to adjust the sensor fault tolerance logic and a physical replacement at the service station is scheduled, thereby realizing full-link closed-loop management from component health perception to vehicle-level functional safety strategies and after-sales service systems. The entire architecture realizes dynamic adaptation between the general physical model and the differentiated degradation trajectory of individual vehicles through the bidirectional feedback mechanism between S2 and S3, so that the evaluation and prediction results are not only physically interpretable, but also highly consistent with the actual service state of the individual part.

[0154] This embodiment further provides a computer-readable storage medium, in which computer program code is stored. When the computer program code runs on a computer, the computer is caused to execute the relevant method steps above to implement the vehicle part evaluation method provided in the above embodiment.

[0155] Wherein, the apparatus, computer-readable storage medium or chip provided in this embodiment are all configured to execute the corresponding methods provided above, so the beneficial effects they can achieve can be referred to the beneficial effects in the corresponding methods provided above, and will not be repeated herein.

[0156] Through the description of the above embodiments, those skilled in the art can understand that for the convenience and conciseness of description, the division of the functional modules is only illustrated as an example. In practical applications, the above functions can be allocated to be completed by different functional modules as required, that is, the internal structure of the apparatus is divided into different functional modules to complete all or part of the functions described above.

[0157] In the embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other manners. For example, the apparatus embodiments described above are merely illustrative. For example, the division of modules or units is only a logical function division, and there may be other division manners in actual implementation, for example, a plurality of units or components may be combined or integrated into another apparatus, or some features may be omitted or not executed. In addition, the displayed or discussed mutual coupling or direct coupling or communication connection may be through some interfaces, and the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical or in other forms.

[0158] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for evaluating vehicle parts, characterized in that, The method includes: Obtain the current internal pressure, internal temperature, and vibration values ​​of vehicle parts; Calculate the theoretical internal pressure value of the vehicle part at the current moment based on the aging coefficient and contact stiffness of the sealing interface, as well as the internal temperature and vibration values ​​of the vehicle part at the current moment. Based on the actual internal pressure value and theoretical internal pressure value of the vehicle part at the current moment, update the aging coefficient and contact stiffness of the sealing interface of the vehicle part at the current moment, and evaluate the remaining service life of the vehicle part based on the updated aging coefficient and contact stiffness at the current moment.

2. The vehicle parts evaluation method according to claim 1, characterized in that, The step of calculating the theoretical internal pressure value of the vehicle part at the current moment based on the aging coefficient and contact stiffness of the sealing interface of the vehicle part at the current moment, as well as the internal temperature and vibration values ​​of the vehicle part at the current moment, includes: Calculate the internal reference pressure value of the vehicle part at the current moment based on the internal temperature value of the vehicle part at the current moment; Calculate the pressure leakage of the vehicle part at the current moment based on the vibration value, aging coefficient, and contact stiffness of the vehicle part at the current moment; Calculate the theoretical internal pressure value of the vehicle component at the current moment based on the internal reference pressure value and the pressure leakage.

3. The vehicle parts evaluation method according to claim 2, characterized in that, The step of calculating the internal reference pressure value of the vehicle component at the current moment based on the internal temperature value of the vehicle component at the current moment includes: Obtain the external atmospheric pressure of the area where the vehicle is located at the current moment; Obtain the theoretical internal pressure value of the vehicle parts mentioned in the previous moment; Based on the theoretical internal pressure value of the vehicle part at the previous moment, the current external atmospheric pressure, and the internal temperature value of the vehicle part at the current moment, calculate the internal reference pressure value of the vehicle part at the current moment.

4. The vehicle parts evaluation method according to claim 2, characterized in that, The step of calculating the pressure leakage of the vehicle part at the current moment based on the vibration value, aging coefficient, and contact stiffness of the vehicle part at the current moment includes: Obtain the external atmospheric pressure of the area where the vehicle is located at the current moment; Obtain the theoretical internal pressure value of the vehicle parts mentioned in the previous moment; The leakage constant of the vehicle part is calculated based on the aging coefficient, and the leakage constant represents the gas leakage rate of the vehicle part; Calculate the vibration correction factor for the vehicle parts based on the contact stiffness; The pressure leakage of the vehicle component at the current moment is calculated based on the vibration value of the vehicle component at the current moment, the external atmospheric pressure of the area where the vehicle is located at the current moment, the internal theoretical pressure value of the vehicle component at the previous moment, the leakage constant, and the vibration correction coefficient.

5. The vehicle parts evaluation method according to claim 1, characterized in that, The process of assessing the remaining service life of the vehicle parts based on the updated aging coefficient and contact stiffness at the current moment includes: Based on the aging coefficient and contact stiffness updated at the current moment, the aging coefficient and contact stiffness of the vehicle parts at future moments are recursively calculated. The vehicle's historical driving data is acquired, and based on the historical driving data, the aging coefficient of the vehicle parts at future times, and the contact stiffness, the sealing health value of the vehicle parts at future times is calculated, whereby the sealing health value represents the sealing status of the vehicle parts. The remaining service life of the vehicle parts is assessed based on the sealing health value of the vehicle parts at a future time.

6. The vehicle parts evaluation method according to claim 5, characterized in that, The method of recursively calculating the aging coefficient and contact stiffness of the vehicle parts at future times based on the updated aging coefficient and contact stiffness at the current moment includes: The aging coefficient of the vehicle parts at future times is inferred from the aging coefficient updated at the current time. The contact stiffness of the vehicle parts at future times is recursively calculated based on the updated aging coefficient and contact stiffness at the current time and the aging coefficient of the vehicle parts at future times.

7. The vehicle parts evaluation method according to claim 5, characterized in that, The step of calculating the sealing health value of the vehicle parts at a future time based on the historical driving data, the aging coefficient of the vehicle parts at a future time, and the contact stiffness includes: Generate the internal temperature and vibration values ​​of the vehicle parts at future moments based on historical driving data; Calculate the theoretical internal pressure value of the vehicle parts at future time based on the aging coefficient and contact stiffness of the vehicle parts at future time, as well as the internal temperature and vibration values ​​of the vehicle parts at future time. The working condition compensation value is calculated based on the external temperature and vibration values ​​of the vehicle parts at the future time. The working condition compensation value represents the compensation value for the sealing state of the vehicle parts. The pressure attenuation trend value is calculated based on the internal theoretical pressure value of the vehicle parts at the future time. The pressure ratio between the internal theoretical pressure value and the internal rated pressure value of the vehicle parts at the future time is calculated. The sealing health value of the vehicle parts at future times is calculated based on the operating condition compensation value, the pressure attenuation trend value, and the pressure ratio.

8. The vehicle parts evaluation method according to claim 5, characterized in that, The method of assessing the remaining service life of the vehicle parts based on their sealing health values ​​at future time points includes: A health value curve is generated based on the sealing health values ​​of the vehicle parts at multiple future time points, wherein the health value curve is a curve showing the correspondence between the sealing health values ​​of the vehicle parts and the corresponding time points. Identify the target time corresponding to the pre-set health value threshold from the health value curve, and calculate the remaining service life of the vehicle part based on the target time and the current time, wherein the health value threshold is the critical value for the sealing failure of the vehicle part.

9. The vehicle parts evaluation method according to claim 1, characterized in that, Before assessing the remaining service life of the vehicle parts based on the updated aging coefficient and contact stiffness at the current moment, the following steps are also included: Obtain the external temperature value of the vehicle parts at the current moment; The working condition compensation value is calculated based on the external temperature and vibration values ​​of the vehicle parts at the current moment; the pressure attenuation trend value is calculated based on the actual internal pressure value of the vehicle parts at the current moment; and the pressure ratio between the actual internal pressure value and the rated internal pressure value of the vehicle parts at the current moment is calculated. Based on the operating condition compensation value, the pressure attenuation trend value, and the pressure ratio, the sealing health value of the vehicle parts at the current moment is calculated, and a warning action of the target level is executed based on the sealing health value of the vehicle parts at the current moment.

10. An electronic device, characterized in that, The electronic device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, the processor executing the program to implement the vehicle parts evaluation method as described in any one of claims 1 to 9.

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