Vehicle networking-oriented sim card full life cycle operation and maintenance system and method

Abstract

The application relates to the technical field of life cycle operation and maintenance, and discloses a SIM card full life cycle operation and maintenance system and method for Internet of Vehicles. The application realizes partition incremental writing and dynamic updating according to digital twins by acquiring original data, greatly improves the fine degree of SIM card operation state sensing, dynamically matches a first working mode of the SIM card according to vehicle situation dimension data, simultaneously combines communication and physical comprehensive quantitative health degradation risks, dynamically fits the coverage boundary of an operator network based on vehicle position and navigation path and generates switching requirements, synchronously generates emergency detection signals, significantly improves communication continuity and abnormal emergency disposal capacity in a complex vehicle dynamic scene, dynamically generates expected targets that adapt to real-time working conditions, accurately checks operation execution effects, and makes the SIM card operation and maintenance strategy continuously adapt to dynamic working condition changes in the whole life cycle of the vehicle.

Description

Technical Field

[0001] This application relates to the field of lifecycle operation and maintenance technology, specifically to a SIM card lifecycle operation and maintenance system and method for the Internet of Vehicles. Background Technology

[0002] With the large-scale deployment of vehicle-to-everything (V2X) technology and the rapid popularization of intelligent connected vehicles, the vehicle SIM card, as the core hardware carrier for vehicle external communication, road network data interaction, remote operation and maintenance, and intelligent business support, determines the service quality of the overall V2X communication system through its operational stability, communication reliability, and lifecycle health status. Currently, the industry has gradually formed a basic technical system for status monitoring, network switching, and simple operation and maintenance management of vehicle SIM cards, which basically meets the basic communication needs of V2X under low-speed, steady-state, and simple road conditions, and has become the mainstream technical solution for the operation and maintenance of V2X terminal communication.

[0003] Current vehicle SIM card maintenance often employs a single-dimensional and static, extensive maintenance logic, which cannot adapt to complex and dynamic vehicle operation scenarios and the need for refined management and control of the entire SIM card lifecycle. Existing technologies only collect SIM card communication parameters or local environmental parameters separately, and cannot achieve time-series fusion, reliability calibration, and structured modeling of multi-source heterogeneous data. It is difficult to accurately restore the linkage between vehicle dynamic conditions and SIM card operating status. At the same time, most existing SIM cards are in a fixed mode and cannot be dynamically matched according to real-time operating conditions.

[0004] Existing technologies rely heavily on static maps for network coverage determination and handover triggering, failing to dynamically update network coverage boundaries based on vehicle trajectories and real-time road network conditions. Furthermore, when multiple tasks run concurrently, orderly, compliant, and efficient scheduling is impossible. Over long-term operation, policy adaptation errors accumulate, leading to a continuous decline in the accuracy of SIM card maintenance and management, making it difficult to adapt to the complex operating scenarios of high-speed mobility, high interference, and dynamic changes in the Internet of Vehicles (IoV). To address these technical shortcomings, this application aims to solve the technical problem of how to achieve collaborative maintenance of SIM cards throughout their entire lifecycle in the complex operating environment of the IoV. Summary of the Invention

[0005] To address the shortcomings of existing technologies, the purpose of this application is to provide a full lifecycle operation and maintenance system and method for SIM cards for the Internet of Vehicles, so as to effectively improve the operational stability, anti-interference capability and long-term service reliability of the vehicle communication system, and adapt to the large-scale application needs of intelligent connected vehicles in all scenarios.

[0006] To achieve the above objectives, this application adopts the following technical solution:

[0007] Firstly, this application provides a full lifecycle operation and maintenance method for SIM cards for the Internet of Vehicles, including:

[0008] The system acquires vehicle operation signals, SIM card communication status data, and physical environment data, corresponding to vehicle context dimension, communication health dimension, and physical health dimension, respectively. These data are then fused to construct a state vector and written into the digital twin bound to the SIM card.

[0009] The first working mode of the SIM card is determined based on the vehicle context dimension, and the health degradation risk of the SIM card is assessed based on the communication health dimension and the physical health dimension.

[0010] Based on vehicle location and navigation path, predict the coverage boundary of operator network and generate handover requirements; detect whether a preset type of emergency event occurs and generate an emergency detection signal.

[0011] The system takes the first working mode, health degradation risk, switching requirements and emergency detection signals as inputs, and outputs a sequence of operation and maintenance operations to be executed according to the preset priority logic and conflict resolution logic.

[0012] Execute the operation and maintenance operation sequence and update the operation execution results to the digital twin. Verify whether the operation execution results have achieved the expected goals. If the verification fails, generate an adjusted operation and maintenance operation sequence based on the execution deviation and trigger re-execution.

[0013] Furthermore, the process of fusing and constructing the state vector and writing it into the digital twin bound to the SIM card includes:

[0014] Perform time-series alignment and anomaly filtering on vehicle operation signals, SIM card communication status data, and physical environment data, and extract corresponding local feature vectors in different dimensions;

[0015] Dimension confidence is calculated based on data integrity and data fluctuation amplitude in different dimensions. Local feature vectors are assigned fusion weights based on the dimension confidence. After weighted fusion, cross-related features are concatenated to generate a state vector.

[0016] The state vector is mapped to the partition of the digital twin bound to the SIM card according to the feature type. Data writing is completed through incremental synchronization, and the writing frequency is switched according to the drop in dimensional confidence.

[0017] Furthermore, the extraction of the corresponding local feature vectors by dimension is achieved by dynamically setting the encoding bit width of the local features based on the distribution properties of data in different dimensions;

[0018] The digital twin is pre-divided into static attribute partitions, real-time status partitions, historical traceability partitions, and associated coupling partitions, and the cross-related features are mapped to the associated coupling partitions.

[0019] Furthermore, determining the first operating mode of the SIM card includes:

[0020] Retrieve the local feature vector of the vehicle context dimension within the real-time state partition of the digital twin, and perform weighted correction on the local feature vector of the vehicle context dimension in combination with the dimension confidence.

[0021] Vehicle speed, driving condition type, vehicle communication load and road scene features are extracted from the weighted and corrected local feature vectors and combined to form a condition determination factor.

[0022] The system retrieves local feature vector sequences of vehicle context dimensions within a preset time range from the historical traceability partition, performs time-series smoothing filtering on the working condition determination factor, and compares them with the determination interval of the preset working mode to obtain the first working mode of the SIM card that matches the current vehicle working condition.

[0023] Furthermore, the assessment of the SIM card's health degradation risk includes:

[0024] Retrieve the local feature vectors corresponding to the communication health dimension and physical health dimension within the real-time status partition of the digital twin, and perform weighted correction by combining the corresponding dimension confidence.

[0025] From the weighted and corrected local feature vectors, communication link loss, signal stability, physical temperature change features and electromagnetic interference features are extracted respectively, and integrated to form a health characterization factor;

[0026] The system retrieves local feature vector sequences of communication health and physical health dimensions within a preset time range from the historical traceability partition, and fits them to obtain the hardware aging trend and communication degradation trend of the SIM card.

[0027] The coupling impact coefficient of physical environment fluctuations on communication health is calculated. Combined with health characterization factors, hardware aging trends and communication attenuation trends, the degradation quantification value of SIM cards is calculated and compared with the preset degradation risk range. The corresponding health degradation risk of SIM cards is then output in a graded manner.

[0028] Furthermore, the generation of the emergency detection signal includes:

[0029] The system acquires vehicle location and navigation path in real time, extracts road segment types and road network density from the navigation path, and fits and generates the coverage boundary of the operator network that dynamically changes with the vehicle's driving trajectory.

[0030] By comparing the degree of offset between the vehicle's location and the coverage boundary, and combining this with the SIM card's health degradation risk classification, a handover requirement is generated.

[0031] It monitors vehicle driving status, network communication status, and vehicle operating status in real time, matches the judgment conditions of preset types of emergency events, and generates an emergency detection signal when a preset type of emergency event is detected.

[0032] Furthermore, the output sequence of operation and maintenance operations to be executed includes:

[0033] The mode requirements corresponding to the first working mode, the operation and maintenance requirements corresponding to the health degradation risk, the network scheduling requirements corresponding to the switching requirements, and the handling requirements corresponding to the emergency detection signals are collected into a set of tasks to be scheduled.

[0034] Configure priorities for different tasks to be scheduled, identify the conflict types between tasks to be scheduled and execute the corresponding conflict resolution logic, generate an initial operation sequence based on the priority and conflict resolution results, and output the operation and maintenance operation sequence to be executed after data verification of the real-time status partition within the digital twin.

[0035] Furthermore, the generation of the adjusted operation and maintenance sequence and triggering re-execution includes:

[0036] Execute the operation and maintenance sequence and update the operation results to the digital twin. Combine the vehicle driving status, vehicle operating status, health degradation risk and dimensional confidence to determine the expected goal of this operation.

[0037] Verify whether the operation results meet the expected goals. If the verification is successful, then proceed with the operation process.

[0038] If the verification fails to meet the standard, the deviation type and magnitude of the execution deviation are quantified, and historical execution data in the historical traceability partition are retrieved to complete the deviation traceability.

[0039] Based on the deviation type, deviation magnitude, and network communication status, combined with the vehicle driving status and on-board operating status, an adjusted maintenance operation sequence is generated. Retry count constraints are set according to health degradation risk, triggering the re-execution of the adjusted maintenance operation sequence.

[0040] Furthermore, the deviation types include explicit deviations and implicit deviations; generating the adjusted operation and maintenance sequence includes modifying at least one of the operation execution parameters, task execution order, and operation execution frequency of the original operation and maintenance sequence.

[0041] Secondly, this application provides a full lifecycle operation and maintenance system for SIM cards for the Internet of Vehicles, comprising a perception module, an evaluation module, an output module, and an execution module;

[0042] The perception module is used to acquire vehicle operation signals, SIM card communication status data, and physical environment data, corresponding to vehicle context dimension, communication health dimension, and physical health dimension, respectively. These data are fused to construct a state vector and written into the digital twin bound to the SIM card.

[0043] The evaluation module determines the first working mode of the SIM card based on the vehicle context dimension, and assesses the health degradation risk of the SIM card based on the communication health dimension and the physical health dimension.

[0044] The output module predicts the coverage boundaries of the operator's network based on the vehicle's location and navigation path and generates handover requirements, detects whether a preset type of emergency event occurs and generates an emergency detection signal.

[0045] The system takes the first working mode, health degradation risk, switching requirements and emergency detection signals as inputs, and outputs a sequence of operation and maintenance operations to be executed according to the preset priority logic and conflict resolution logic.

[0046] The execution module is used to execute the operation and maintenance operation sequence and update the operation execution results to the digital twin. It verifies whether the operation execution results have achieved the expected goals. If the verification fails, it generates an adjusted operation and maintenance operation sequence based on the execution deviation and triggers re-execution.

[0047] Compared with the prior art, the beneficial effects achieved by this application are as follows:

[0048] By acquiring raw data and implementing incremental partitioning and dynamic updates based on digital twins, a comprehensive, highly accurate, and traceable dynamic status perception system for SIM cards is constructed. This system achieves standardized fusion of multi-source heterogeneous data and cross-dimensional coupled feature mining, significantly improving the precision of SIM card operational status perception. It dynamically matches the SIM card's first operating mode based on vehicle contextual data, and combines communication and physical data to comprehensively quantify health degradation risks, achieving a dual intelligent upgrade of operational condition adaptation and health assessment. This enables precise control of SIM card health status and proactively avoids the risks of equipment aging failure and communication performance degradation.

[0049] Based on the dynamic fitting of the operator's network coverage boundary and the generation of handover requirements by vehicle location and navigation path, and the simultaneous detection and generation of emergency detection signals, the scenario adaptability of network scheduling and emergency management is greatly expanded. It realizes the advance prediction and differentiated scheduling of network handover, and significantly improves the communication continuity and emergency response capabilities in complex vehicle dynamic scenarios. Through multi-source task aggregation, priority configuration and conflict resolution mechanism, it realizes the orderly scheduling and compliant execution of various maintenance tasks, and improves the orderliness, stability and effectiveness of vehicle SIM card maintenance scheduling.

[0050] By dynamically generating expected targets that adapt to real-time operating conditions and accurately verifying the effectiveness of operation execution, the SIM card operation and maintenance strategy can continuously adapt to the dynamic changes in operating conditions throughout the entire vehicle lifecycle, significantly improving the adaptability, fault tolerance, and long-term operational stability of lifecycle operation and maintenance; and realizing the precision, intelligence, closed-loop, and adaptive upgrade of SIM card operation and maintenance in complex dynamic scenarios of vehicle networking. Attached Figure Description

[0051] Figure 1 A flowchart illustrating the full lifecycle operation and maintenance methodology for SIM cards in the context of the Internet of Vehicles (IoV).

[0052] Figure 2 A logic flowchart for assessing the health degradation risk of a SIM card;

[0053] Figure 3 This is a system diagram of a SIM card full lifecycle operation and maintenance system for the Internet of Vehicles. Detailed Implementation

[0054] The technical solution of this application will be described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the embodiments and specific features in the embodiments are detailed descriptions of the technical solution of this application, rather than limitations thereof.

[0055] Example 1

[0056] like Figure 1 As shown, this embodiment provides a SIM card full lifecycle operation and maintenance method for vehicle-to-everything (V2X) communication, including:

[0057] S1. Obtain vehicle operation signals and SIM card communication status data and physical environment data, corresponding to vehicle context dimension, communication health dimension and physical health dimension respectively, fuse them to construct a state vector and write it into the digital twin bound to the SIM card.

[0058] Specifically, the process of fusing and constructing the state vector and writing it into the digital twin bound to the SIM card includes:

[0059] Perform time-series alignment and anomaly filtering on vehicle operation signals, SIM card communication status data, and physical environment data, and extract corresponding local feature vectors in different dimensions;

[0060] Dimension confidence is calculated based on data integrity and data fluctuation amplitude in different dimensions. Local feature vectors are assigned fusion weights based on the dimension confidence. After weighted fusion, cross-related features are concatenated to generate a state vector.

[0061] The state vector is mapped to the partition of the digital twin bound to the SIM card according to the feature type. Data writing is completed through incremental synchronization, and the writing frequency is switched according to the drop in dimensional confidence.

[0062] Among them, the extraction of corresponding local feature vectors by dimension is achieved by dynamically setting the encoding bit width of local features according to the distribution properties of data in different dimensions;

[0063] The digital twin is pre-divided into static attribute partitions, real-time status partitions, historical traceability partitions, and related coupling partitions, with cross-related features mapped to the related coupling partitions.

[0064] Vehicle operation signals, SIM card communication status data, and physical environment data are acquired by different sampling devices. These devices use independent clocks and sampling periods, which leads to global timing misalignment in the raw data. Meanwhile, common problems in vehicle scenarios, such as high-speed vehicle movement, road electromagnetic interference, and momentary disconnection of the acquisition link, can cause abnormal samples such as null values, out-of-limit values, and transient noise in the raw data.

[0065] Timing alignment uses the global system clock output by the vehicle's main controller as the time reference. This clock is synchronized to all front-end sampling devices. Specifically, 1 second is set as the standard time slice, serving as the basic unit for data alignment. The timestamps of all raw data are traversed, and each data item is classified into its corresponding time slice. For data whose timestamps deviate from the slice boundaries, linear interpolation is used for time axis correction, mapping the offset data to adjacent valid slices. This ensures that the vehicle operation data, communication status data, and physical environment data within the same time slice represent the status data of the vehicle and SIM card operating at the same time.

[0066] The above data undergoes anomaly filtering. For null data caused by data disconnection, two sets of valid data within the same slice are selected for linear completion to avoid data link interruptions. For data exceeding the device's rated parameters, the legal value ranges for each dimension are pre-defined according to the SIM card hardware specifications and vehicle communication industry standards. Samples exceeding the range are discarded. For example, the legal range for SIM card communication signal strength is set to -110dBm to -50dBm. When the collected value is -20dBm, it is judged as out-of-range abnormal data and filtered out. For sudden noise data caused by transient electromagnetic interference, the variance is calculated using a sliding window consisting of five consecutive time slices. If the variance value exceeds the preset threshold, it is judged as noise sample and discarded.

[0067] After preprocessing, the normalized dataset is split into three dimensional datasets according to the preset dimensional division rules. First, the distribution characteristics of the data in each dimension are quantitatively determined. The range of values, sample dispersion, and time series change rate are selected to comprehensively represent the data distribution characteristics. Then, the encoding bit width is matched according to the determination results. The vehicle context dimension includes vehicle speed, driving trajectory, and vehicle communication load. It has a large range of values, high sample dispersion, and fast time series change rate. After quantitative determination, a 16-bit encoding bit width is configured.

[0068] The communication health dimension includes signal strength, link delay, and bit error rate, with a moderate range, moderate dispersion, and relatively stable temporal changes, configured with a 12-bit encoding width. The physical health dimension includes operating temperature and ambient electromagnetic intensity, with a narrow range, low dispersion, and slow temporal changes, configured with an 8-bit encoding width. After determining the encoding width, feature quantization and encoding mapping are performed on each dataset. Feature dimension compression and format regularization are completed according to the predetermined encoding width to generate local feature vectors for vehicle context, communication health, and physical health.

[0069] This enables the unification of the correlation between multiple data sources over time, allowing the information carrying capacity and storage volume of the feature vectors to be adapted to the characteristics of data in each dimension; it also provides basic computational samples for subsequent dimension confidence calculation and fusion weight allocation, so that the fused state vector accurately reflects the actual operating status of the equipment.

[0070] The local feature vectors of the three dimensions are generated based on different front-end acquisition links. Affected by factors such as occlusion, interference, and link failure, the data quality of each dimension is in a state of dynamic change. The reference value of the feature vectors of dimensions with insufficient data integrity or excessive fluctuation will be significantly reduced. Using equal weights to complete the fusion operation will reduce the overall state representation accuracy. At the same time, there is an objective coupling and linkage relationship between vehicle operation status, SIM card communication status, and surrounding physical environment. A single-dimensional feature can only describe the local operation status, and the lack of cross-dimensional correlation information will result in an incomplete state representation system.

[0071] Dimensional confidence is the core indicator for quantifying the reliability of single-dimensional data. It is calculated jointly by two basic indicators: data integrity and data volatility. Data integrity is the ratio of the number of valid data samples to the total number of samples within a single statistical period. This scheme selects 10 consecutive time slices as a fixed statistical period, with a total of 10 samples within this period. For example, the vehicle scenario dimension has 9 valid samples, with a data integrity of 0.9; the communication health dimension has 9 valid samples, with a data integrity of 0.9; and the physical health dimension has 7 valid samples due to short-term data collection interruption, with a data integrity of 0.7.

[0072] The data fluctuation amplitude is quantified using the standard deviation of the samples within the statistical period. The larger the standard deviation, the more drastic the data fluctuation. Here, the standard deviation of the vehicle scenario dimension is 0.08, the standard deviation of the communication health dimension is 0.06, and the standard deviation of the physical health dimension is 0.15. The dimension confidence score is calculated as 0.6 × data integrity + 0.4 × (1 - data fluctuation amplitude), where 0.6 and 0.4 are preset weighting coefficients. Substituting these values, we get the confidence scores for the vehicle scenario dimension as 0.6 × 0.9 + 0.4 × (1 - 0.08) = 0.908, the communication health dimension as 0.6 × 0.9 + 0.4 × (1 - 0.06) = 0.916, and the physical health dimension as 0.6 × 0.7 + 0.4 × (1 - 0.15) = 0.76.

[0073] After completing the confidence level calculation, the fusion weight allocation is carried out. First, the confidence levels of the three dimensions are normalized. The normalization formula is that the weight of a single dimension is equal to the confidence level of that dimension divided by the sum of the confidence levels of all dimensions. The sum of the three confidence levels is 0.908 + 0.916 + 0.76 = 2.584. The weights of the vehicle context dimension, communication health dimension, and physical health dimension are calculated to be approximately 0.351, 0.355, and 0.294 respectively. After normalization, the sum of the weights is 1, which meets the requirements of vector fusion.

[0074] Subsequently, a weighted fusion of local feature vectors is performed bit by bit. For features at the same position in the three sets of vectors, the corresponding weights are multiplied and then summed to generate a fused feature vector. This bit-by-bit operation is the core intermediate process of vector fusion, ensuring that features of each dimension are superimposed according to their weight ratios. Cross-correlation features are used to represent the coupling relationship between different dimensions. The extraction process involves sequentially calculating the correlation operation parameters of the three sets of dimension pairs: vehicle context and communication health, vehicle context and physical health, and communication health and physical health, forming a cross-correlation feature sequence. This sequence is then concatenated to the end of the fused feature vector to integrate and generate a state vector. This objectively quantifies the overall data quality of each dimension, allowing high-confidence data to dominate the fusion process and effectively suppressing interference from low-quality data. Cross-correlation features supplement the coupling and linkage information between dimensions, enabling the global state vector to simultaneously possess the ability to describe single-dimensional states and cross-dimensional correlations, thus improving the comprehensiveness of the overall data representation.

[0075] State vectors include various types of data such as static attributes, real-time operational features, cross-dimensional coupling features, and historical time-series samples. The storage cycle, read / write frequency, and business call scenarios of different types of data are significantly different. Hybrid storage will greatly reduce the efficiency of data retrieval and calling. Vehicle-to-everything (V2X) devices continuously generate state vectors. Using a full-volume repetitive write mode will occupy transmission bandwidth and local storage resources for a long time. Incremental synchronization only updates the data content that has changed, effectively reducing system resource overhead.

[0076] The state vector partitioning and mapping follows a preset partitioning rule. First, the state vector is decomposed and classified field by field. Permanent fixed parameters such as SIM card number, ICCID, hardware rated parameters, and bound vehicle identifier within the state vector are classified as static features and written to the static attribute partition. The basic features of each dimension within the state vector and the real-time operation features after weighted fusion are classified as real-time operation features and written to the real-time state partition. The current state vector is written to the historical traceability partition as a time-series sample in an append manner. This partition retains historical state data for the entire lifecycle of the device. The cross-correlation feature sequences within the state vector are separately split and written to the correlation coupling partition to realize the storage and maintenance of coupled features. The partitioning and mapping is split and transmitted segment by segment according to field type to complete the data landing of all partitions.

[0077] The core intermediate process of incremental synchronous writing is to compare the data of the old and new cycles field by field. It retrieves the state data stored in the previous cycle in the corresponding partition of the digital twin, compares the current state vector to be written with the historical stored data bit by bit and field by field, marks the feature positions where the values ​​have changed, and keeps the original stored content in the positions where the values ​​have not changed. Only the marked incremental data is written through the transmission link. This comparison process accurately filters the valid update data and avoids invalid duplicate writing from the mechanism.

[0078] The drop in dimensional confidence is calculated by subtracting the dimensional confidence from the previous period's dimensional confidence. A positive result indicates a decline in data quality, and a larger difference indicates a greater degree of drop. This solution sets three drop thresholds and corresponding write frequencies. The baseline write frequency is set to once every 1 second. When the drop is greater than 0 and less than or equal to 0.1, the baseline frequency is maintained. When the drop is greater than 0.1 and less than or equal to 0.3, the write frequency is adjusted to once every 0.5 seconds. When the drop is greater than 0.3, the write frequency is adjusted to once every 0.2 seconds.

[0079] Taking the physical health dimension as an example, the confidence level of the dimension in the previous period was 0.7, and the confidence level of the dimension in the current period was 0.55. The calculated drop amplitude was 0.15, which fell into the second threshold range. The write frequency was automatically switched to 0.5 seconds per time. The confidence level change values ​​of all dimensions were polled in real time, and the maximum drop amplitude of all dimensions was selected as the unified judgment basis for frequency switching, so as to realize the dynamic adaptive adjustment of the write frequency.

[0080] This application matches the data retrieval needs of different business modules, optimizes the efficiency of data reading and writing throughout the entire process, significantly reduces the amount of data transmission between the vehicle terminal and the digital twin, reduces the read and write pressure on the storage medium, and adapts to the resource-constrained operating environment of the vehicle terminal; it controls resource consumption during the normal operation of the equipment, and increases monitoring density during the stages of data quality decline and equipment anomaly risks, achieving a balance between system resource utilization and equipment operation monitoring reliability; all status data is stored in the four major partitions of the digital twin, which is the direct data source for subsequently determining the SIM card's primary working mode and assessing the risk of health degradation.

[0081] S2. Determine the first working mode of the SIM card based on the vehicle context dimension, and assess the health degradation risk of the SIM card based on the communication health dimension and the physical health dimension.

[0082] Furthermore, determining the first operating mode of the SIM card includes:

[0083] Retrieve the local feature vector of the vehicle context dimension within the real-time state partition of the digital twin, and perform weighted correction on the local feature vector of the vehicle context dimension in combination with the dimension confidence.

[0084] Vehicle speed, driving condition type, vehicle communication load and road scene features are extracted from the weighted and corrected local feature vectors and combined to form a condition determination factor.

[0085] The system retrieves local feature vector sequences of vehicle context dimensions within a preset time range from the historical traceability partition, performs time-series smoothing filtering on the working condition determination factor, and compares them with the determination interval of the preset working mode to obtain the first working mode of the SIM card that matches the current vehicle working condition.

[0086] The real-time state partition storage of the digital twin stores local feature vectors of the vehicle context dimension, which are generated by encoding multi-source vehicle-mounted data. Dynamic interference in the vehicle driving scenario, instantaneous fluctuations in the acquisition link, and differences in data integrity can lead to uneven reliability of the original feature vectors. The original feature components are mixed with low-quality invalid data. If used for working condition determination, it will cause distortion of working condition feature representation and cause working mode matching deviation.

[0087] The local feature vectors of the vehicle context dimension are solidified in the real-time state partition of the digital twin after pre-temporal alignment, anomaly filtering and dynamic encoding. This is used to structurally represent the feature dataset of the vehicle's real-time operating conditions. Dimension confidence is an evaluation index that quantifies the integrity and stability of the corresponding dimension's data. The value range is from 0 to 1. The higher the value, the higher the validity and credibility of the data collected in that dimension.

[0088] During execution, the local feature vector of the vehicle context dimension in the real-time state partition storage of the digital twin under the current operation cycle is retrieved at a fixed point. The dimension confidence score matched by this dimension is retrieved simultaneously. The dimension confidence score is used as the correction weight to perform a bit-by-bit weighted correction operation on all working condition feature components within the local feature vector. The complete feature weight is retained for high confidence feature components, and the weight is reduced for low confidence feature components to achieve full vector confidence balance calibration.

[0089] For example, if the confidence score of the vehicle context dimension in the current period is 0.93, and 0.93 is used as a correction coefficient, a bitwise weighted operation is performed on all feature dimensions of the original local feature vector to weaken the feature distortion caused by the acquisition fluctuation and output the calibrated vehicle context local feature vector.

[0090] This application achieves adaptive reliability calibration of vehicle operating condition features, shielding the feature distortion problems caused by instantaneous data anomalies, data loss and data fluctuations in the vehicle dynamic acquisition environment, and ensuring that the basic feature data on which subsequent operating condition determination is based has authenticity, validity and uniformity.

[0091] The adaptation working mode of the vehicle SIM card is jointly determined by four core elements: vehicle motion state, operating conditions, communication load, and road operating environment. A single feature cannot cover the complex operating scenarios of vehicles, and discrete operating condition parameters cannot achieve standardized quantitative comparison. Vehicle speed is used to quantify the real-time motion rate of the vehicle and distinguish between stationary, low-speed, medium-speed, and high-speed operating states. Driving condition type is used to define the overall operating scenario of the vehicle, including idling, urban congestion, urban cruising, highway driving, and mountain road driving. Vehicle communication load is used to characterize the current service load of the SIM card, including load parameters such as data transmission rate, number of online terminals, and service concurrency. Road scenario characteristics are used to define the external road network operating environment and distinguish between scenario types such as closed parks, urban main roads, highway networks, and rural roads.

[0092] During execution, the corresponding feature dimensions of the four types of features are accurately located from the calibrated local feature vectors. Feature data is extracted and decomposed. Normalization preprocessing is uniformly performed to eliminate numerical scale deviations of different features, taking into account the differences in dimensionality and value range of the four types of features. Then, according to the preset feature fusion weights and splicing rules, the four types of preprocessed core features are structurally combined to generate working condition judgment factors with fixed dimensions, quantified values, and uniform calculation, thereby realizing the quantitative representation of the comprehensive working condition of the vehicle.

[0093] This application transforms discrete, multi-dimensional, and heterogeneous vehicle operating condition parameters into standardized, comparable, and time-series-operable quantification factors, thereby achieving a precise quantitative description of complex vehicle operating conditions.

[0094] The operating condition judgment factors generated in a single operation cycle have instantaneous fluctuations. Instantaneous vehicle speed changes, brief road switching, and sudden communication load fluctuations can all cause sudden changes in single-cycle factors. If the instantaneous factor judgment working mode is directly adopted, it will lead to frequent changes in the SIM card working mode and the failure of the adaptation strategy due to jitter, which cannot adapt to the continuous and stable operating conditions required by the vehicle. The preset duration is a customizable time series statistics window. In this embodiment, the preset duration is fixed at 10 seconds. The local feature vector sequence of the vehicle context dimension of the past 10 seconds of continuous time series in the historical traceability partition of the digital twin is retrieved in a targeted manner. Based on the continuous time series feature sequence, the historical operating condition judgment factor sequence within the complete duration is reconstructed. The operating condition judgment factors generated in real time in the current cycle are included at the end of the time series sequence to form a time series dataset covering historical steady-state operating conditions and real-time operating conditions.

[0095] A sliding window temporal smoothing filtering algorithm is used to process the temporal factor dataset across the entire domain. Through the operation logic of weighted averaging of multi-frame temporal data, the instantaneous change characteristics of a single frame are suppressed, the continuity and regularity of the operating condition evolution over time are preserved, and random disturbances are filtered out. Finally, the operating condition judgment factor with steady-state characteristics is output. This eliminates the judgment jitter problem caused by instantaneous operating condition disturbances in the vehicle, enhances the temporal stability of the operating condition judgment, ensures that the working mode matching result fits the continuous operating state of the vehicle, and avoids SIM card communication disorder and resource waste caused by frequent mode switching.

[0096] Different steady-state operating conditions of different vehicles correspond to different communication scheduling, power consumption control, resource allocation and fault adaptation requirements of SIM cards; the judgment interval of the preset working mode is a quantitative interval pre-divided and solidified based on massive vehicle operating condition samples. The boundaries of each judgment interval are clear, non-overlapping and gapless, fully covering all vehicle network operation scenarios, and each interval is bound to the first working mode of a SIM card; the judgment interval of the preset working mode is the working condition judgment factor Q value interval [0,0.25) corresponding to low speed and low power consumption mode, [0.25,0.5) corresponding to urban conventional communication mode, [0.5,0.75) corresponding to high speed and high load communication mode, and [0.75,1.0] corresponding to emergency priority operation and maintenance mode; the judgment interval threshold is pre-divided based on massive vehicle operating condition samples.

[0097] The system iterates through the judgment intervals of all preset working modes, compares the quantified values ​​of the steady-state working condition judgment factors with the threshold values ​​of each interval, accurately locates the judgment interval to which the working condition factor belongs, and determines the working mode bound to that interval as the first working mode of the SIM card that is suitable for the current steady-state working condition of the vehicle. For example, preset working modes such as low-speed low-power mode, urban normal communication mode, high-speed high-load communication mode and emergency priority operation and maintenance mode and their corresponding judgment intervals are used to achieve accurate matching between working conditions and working modes through full-domain traversal and comparison.

[0098] This application establishes a matching mechanism between quantitative operating conditions and SIM card operating modes, enabling adaptive and precise switching of SIM card operating modes under complex and dynamic vehicle operating conditions. This ensures that the SIM card operating strategy is highly compatible with the vehicle's overall operating conditions and determines the adaptation logic of subsequent all-dimensional operation and maintenance scheduling and network scheduling strategies.

[0099] Specifically, such as Figure 2 As shown, assessing the health degradation risk of a SIM card includes:

[0100] Retrieve the local feature vectors corresponding to the communication health dimension and physical health dimension within the real-time status partition of the digital twin, and perform weighted correction by combining the corresponding dimension confidence.

[0101] From the weighted and corrected local feature vectors, communication link loss, signal stability, physical temperature change features and electromagnetic interference features are extracted respectively, and integrated to form a health characterization factor;

[0102] The system retrieves local feature vector sequences of communication health and physical health dimensions within a preset time range from the historical traceability partition, and fits them to obtain the hardware aging trend and communication degradation trend of the SIM card.

[0103] The coupling impact coefficient of physical environment fluctuations on communication health is calculated. Combined with health characterization factors, hardware aging trends and communication attenuation trends, the degradation quantification value of SIM cards is calculated and compared with the preset degradation risk range. The corresponding health degradation risk of SIM cards is then output in a graded manner.

[0104] The health status of a SIM card is determined by both communication health and physical health. The acquisition links, data characteristics, and interference environments of the two dimensions are independent of each other, and the data quality of each dimension fluctuates differently. The original uncorrected feature vector has a problem of unbalanced credibility weights, which cannot truly reflect the real-time communication performance and physical hardware status of the SIM card. The local feature vector of the communication health dimension is used to structurally characterize the real-time operating performance of the SIM card communication link, and the local feature vector of the physical health dimension is used to structurally characterize the physical operating environment and hardware status of the SIM card hardware. The two dimensions are configured with dimension confidence, and the data credibility of each dimension is independently quantified.

[0105] During execution, local feature vectors for the communication health dimension and physical health dimension of the current period are retrieved from the real-time status partition of the digital twin. Simultaneously, the matching confidence scores for each of the two dimensions are retrieved. A bit-by-bit weighted correction operation is performed on both sets of feature vectors, adjusting the feature weights according to the confidence scores of the corresponding dimensions. This weakens low-confidence feature components within each dimension and retains high-confidence effective features, completing the standardized calibration of the two-dimensional features. For example, if the confidence score for the communication health dimension in the current period is 0.94 and the confidence score for the physical health dimension is 0.87, corresponding coefficients are used to perform weighted correction on the two sets of feature vectors, outputting two sets of interference-free, highly adaptable health feature vectors. This achieves accurate calibration of the communication and physical health features, constructing an accurate and reliable basic dataset for health assessment.

[0106] SIM card health degradation is divided into communication performance degradation and physical hardware degradation. Communication link loss and signal stability reflect the degree of communication health degradation, while physical temperature change characteristics and electromagnetic interference characteristics reflect the degree of aging stress on the physical hardware. A single dimension or single characteristic cannot cover all forms of SIM card health degradation. Communication link loss characterizes the degree of signal attenuation during SIM card data transmission and is the core indicator of communication performance degradation. Signal stability characterizes the anti-interference capability and steady-state performance of the communication link during continuous operation. Physical temperature change characteristics characterize the fluctuation range of the SIM card hardware operating temperature, abnormal temperature rise, and long-term temperature change accumulation. Electromagnetic interference characteristics characterize the hardware stress intensity of the complex electromagnetic environment in the vehicle on the SIM card chip and communication module.

[0107] During execution, four core features are extracted from the two sets of calibrated local feature vectors. Normalization preprocessing is performed on the four types of heterogeneous features to eliminate numerical scale differences. Then, based on the preset health feature fusion rules, instantaneous communication performance parameters and physical environment stress parameters are integrated to generate a health characterization factor that can fully cover the real-time communication health and physical hardware health of the SIM card. The health characterization factor H = w1×CL + w2×SS + w3×PT + w4×EI, where CL is the normalized communication link loss, SS is the normalized signal stability, PT is the normalized physical temperature change feature, and EI is the normalized electromagnetic interference feature. w1, w2, w3, and w4 are preset health feature fusion weight coefficients that satisfy w1 + w2 + w3 + w4 = 1.

[0108] For example, if we set w1=0.3, w2=0.3, w3=0.2, and w4=0.2, and the normalized communication link loss CL=0.45, signal stability SS=0.35, physical temperature variation characteristic PT=0.5, and electromagnetic interference characteristic EI=0.3, then H=0.3×0.45+0.3×0.35+0.2×0.5+0.2×0.3=0.135+0.105+0.1+0.06=0.4.

[0109] This application enables a comprehensive quantitative representation of the real-time health status of SIM cards, avoiding the omissions and misjudgments of health status caused by single-dimensional assessment.

[0110] The health degradation of a SIM card is a gradual process that accumulates over a long period of time. Instantaneous health characteristics can only reflect the health status at a single moment and cannot reflect the long-term evolution of hardware aging and communication performance degradation. Relying solely on instantaneous status assessment will ignore the risk of cumulative degradation. In this embodiment, a preset statistical duration of 30 seconds is set, and the local feature vector sequence corresponding to the communication health and physical health dimensions within the historical traceability partition of the digital twin is retrieved in a targeted manner.

[0111] For the time-series feature sequence of physical health dimension, the trend line Y=a×t+b is fitted using the least squares linear regression fitting algorithm to eliminate instantaneous environmental fluctuation interference, extract persistent and regular hardware parameter degradation features, and generate SIM card hardware aging trend parameters to characterize the long-term aging evolution law of hardware, where t is the time sequence number and a is the trend slope, the larger the slope a, the faster the hardware aging rate; the slope a is normalized to the interval of 0 to 1 as the hardware aging trend parameter A. g Similarly, the same fitting is performed on the time-series feature sequences of the communication health dimension to obtain the communication attenuation trend parameter C. d b is a constant; for the time-series feature sequence of the communication health dimension, instantaneous communication interference noise is filtered out by using the same time-series fitting method, and continuous communication performance degradation features are extracted. The SIM card communication degradation trend parameters are fitted to characterize the long-term degradation law of communication performance. Both types of trend fitting retain the time-series gradual change features and eliminate irregular instantaneous disturbances to ensure that the trend parameters can truly reflect the long-term degradation characteristics of the SIM card.

[0112] The above steps overcome the limitations of instantaneous status assessment, construct a time-series health degradation assessment system, accurately depict the long-term evolution of SIM card hardware aging and communication performance degradation, achieve two-way coverage of instantaneous health status and long-term degradation trend, and determine the long-term assessment accuracy of overall degradation risk.

[0113] Temperature fluctuations and electromagnetic interference in the vehicle's physical environment can directly exacerbate the performance degradation of the SIM card communication link. Physical health and communication health are strongly coupled, and ignoring the cross-dimensional coupling effect will lead to one-sided and distorted health assessment results. The coupling effect coefficient is a parameter that quantifies the degree of correlation between physical environment fluctuations and communication performance degradation. It is obtained by calculating the temporal correlation between the amount of physical characteristic fluctuations and the amount of communication characteristic degradation within a preset time period. The larger the coefficient value, the stronger the stress effect of the physical environment on communication health.

[0114] Coupling influence coefficient K c =Cov(ΔP,ΔC) / (σ(ΔP)×σ(ΔC)), where K c K is the coupling effect coefficient, ranging from -1 to 1. c A value greater than 0 indicates a positive correlation between fluctuations in the physical environment and the degradation of communication performance; that is, a deterioration in the physical environment will exacerbate communication degradation. c =1 indicates a perfect positive correlation, where changes in the physical environment and communication attenuation are completely synchronized. K c =0 indicates no correlation; changes in the physical environment are unrelated to communication attenuation. K c =-1 indicates a completely negative correlation (which rarely occurs in real-world scenarios); ΔP is the sequence of changes in physical health dimension feature values ​​within a preset statistical period, and ΔC is the sequence of changes in communication health dimension feature values ​​within the same period.

[0115] Cov(ΔP,ΔC) is the covariance of two sets of change sequences, where Cov(ΔP,ΔC) = [(ΔP1 - ΔP2)] avg )(ΔC1-ΔC avg )+…+(ΔP i -ΔP avg )(ΔC i -ΔC avg )+…+(ΔP n -ΔP avg )(ΔC n -ΔC avg )] / n,(ΔP i -ΔP avg ) represents the centering bias of ΔP, i.e., the degree to which the physical temperature change at the i-th sampling time deviates from the mean, reflecting the direction and magnitude of the offset relative to the average level at that sampling time, (ΔC) i -ΔC avg ) represents the centering bias of ΔC, which is the degree to which the change in communication link loss at the i-th sampling time deviates from the mean, n represents the total number of sampling times, and σ(ΔP) and σ(ΔC) are the standard deviations of the two sets of sequences.

[0116] For example, using a preset statistical duration of 30 seconds as a window, the sequence of physical temperature change characteristics ΔP=(0.2,0.3,0.5,0.4,0.6) and the sequence of communication link loss change ΔC=(0.1,0.25,0.4,0.35,0.5) are extracted, and the mean value ΔP is calculated. avg =0.4, the mean of ΔC ΔC avg =0.32, covariance Cov(ΔP,ΔC)=[(ΔP1-ΔP avg )(ΔC1-ΔC avg )+(ΔP2-ΔP avg )(ΔC2-ΔC avg )+(ΔP3-ΔP avg )(ΔC3-ΔC avg )+(ΔP4-ΔP avg )(ΔC4-ΔC avg )+(ΔP5-ΔP avg )(ΔC5-ΔC avg )] / 5=[(-0.2)×(-0.22)+(-0.1)×(-0.07)+0.1×0.08+0×0.03+0.2×0.18] / 5=0.019,σ(ΔP)≈0.141,σ(ΔC)≈0.136,K c =0.019 / (0.141×0.136), indicating that physical temperature change has a significant coupled stress effect on communication health during this period.

[0117] After solving for the coupling effect coefficient, the health characterization factor, hardware aging trend, communication attenuation trend, and cross-dimensional coupling effect coefficient are fused together in a multi-dimensional operation. Taking into account the three factors of instantaneous health status, long-term aging pattern, and environmental coupling interference, a degradation quantification value that can accurately characterize the degree of SIM card degradation is obtained. Multi-level degradation risk intervals are preset, with each interval corresponding to a different level of health degradation risk. The interval thresholds are fixed based on the SIM card hardware rated parameters, aging critical threshold, and communication attenuation failure threshold. The interval divisions are non-overlapping and have no blind spots. The degradation quantification value is compared with all preset risk intervals one by one to determine the target interval to which the quantification value belongs, and the corresponding SIM card health degradation risk level is output in a graded manner.

[0118] Degradation quantification value D = α × H + β × A g +γ×C d +δ×K c Where D is the degradation quantification value, normalized to 0 to 1, with a larger value indicating a more severe degree of degradation; H is the health characterization factor, and A... g C is a parameter representing the hardware aging trend. d K is the communication attenuation trend parameter. cα is the coupling influence coefficient; α, β, γ, and δ are preset fusion weight coefficients, satisfying α+β+γ+δ=1.

[0119] For example, setting α=0.35, β=0.25, γ=0.25, and δ=0.15, when the health characterization factor H=0.4 and the hardware aging trend A... g =0.3, Communication Attenuation Trend C d =0.25, Coupling Influence Coefficient K c When the value is 0.7, D ​​= 0.35 × 0.4 + 0.25 × 0.3 + 0.25 × 0.25 + 0.15 × 0.7 = 0.3825.

[0120] The preset degradation risk range is [0, 0.25) corresponding to low risk, [0.25, 0.5) corresponding to medium risk, [0.5, 0.75) corresponding to high risk, and [0.75, 1] ​​corresponding to severe risk; the above D=0.3825 falls into the range [0.25, 0.5) and is judged as medium risk.

[0121] This application overcomes the technical shortcomings of single instantaneous assessment and single-dimensional assessment, and realizes the accurate quantification and graded output of SIM card health degradation risk, accurately distinguishes between mild, moderate and severe degradation risks, adapts to differentiated operation and maintenance management needs, and supports subsequent refined operation and maintenance scheduling throughout the entire life cycle.

[0122] S3. Based on the vehicle location and navigation path, predict the coverage boundary of the operator's network and generate handover requirements; detect whether a preset type of emergency event occurs and generate an emergency detection signal.

[0123] The system takes the first working mode, health degradation risk, switching requirements and emergency detection signals as inputs, and outputs a sequence of maintenance operations to be executed according to the preset priority logic and conflict resolution logic.

[0124] Furthermore, generating emergency detection signals includes:

[0125] The system acquires vehicle location and navigation path in real time, extracts road segment types and road network density from the navigation path, and fits and generates the coverage boundary of the operator network that dynamically changes with the vehicle's driving trajectory.

[0126] By comparing the degree of offset between the vehicle's location and the coverage boundary, and combining this with the SIM card's health degradation risk classification, a handover requirement is generated.

[0127] It monitors vehicle driving status, network communication status, and vehicle operating status in real time, matches the judgment conditions of preset types of emergency events, and generates an emergency detection signal when a preset type of emergency event is detected.

[0128] Traditional vehicle network coverage determination is based on fixed static base station maps, which cannot adapt to the dynamic driving scenarios of vehicles moving at high speeds. There is a significant deviation between static coverage boundaries and real-time road networks and driving trajectories. Different road types and road network densities correspond to inherent differences in operator base station deployment density and signal coverage capabilities. Fixed boundaries cannot accurately reflect the true communication coverage status of the vehicle's current location, which will lead to delayed network handover prediction and failure to identify coverage blind spots.

[0129] The vehicle location is the real-time positioning coordinate data output by the vehicle positioning module; the navigation path is the continuous driving path trajectory data pre-planned by the vehicle navigation system; the road segment type is used to distinguish the road attributes that the vehicle is currently entering and will soon enter, including highways, urban arterial roads, suburban roads, tunnel roads and congested urban roads; the road network density is used to characterize the road density and base station deployment density of the target road segment; and the operator network coverage boundary is the critical threshold boundary that defines the effective communication coverage area and the weak coverage and no coverage areas.

[0130] The system reads the vehicle's current location coordinates and incomplete navigation path in real time, breaks down and analyzes the navigation path segment by segment, identifies the road segment type and matching road network density corresponding to each segment, and combines the operator's pre-set base station geographical location, signal radiation range and deployment density database. Using a trajectory synchronization fitting algorithm, the static base station coverage parameters are fused with dynamic driving trajectory and road network environment parameters to correct the range and shape of the coverage boundary in real time, and finally generate a network coverage boundary that is dynamically updated according to the vehicle's driving trajectory.

[0131] For example, when a vehicle enters a tunnel section, where the road network density is low, base station deployment is sparse, and signal obstruction is severe, the effective network coverage boundary is adjusted and shrunken in real time; when a vehicle enters a core urban arterial road, where the road network is dense and base station deployment is sufficient, the network coverage boundary is automatically widened to ensure that the boundary is highly matched with the communication environment.

[0132] This application enables dynamic adaptive updating of the operator's network coverage boundary, eliminates the adaptation deviation between static network maps and actual driving scenarios, accurately matches the real-time communication coverage status during vehicle movement, and provides an accurate benchmark for subsequent location offset determination and handover requirement generation.

[0133] Relying solely on spatial determination of vehicle location offset can only identify physical network coverage offsets, without considering the differentiated adaptation requirements of the SIM card's own health status. SIM cards at high risk of health degradation experience communication performance degradation and weakened anti-interference capabilities, requiring higher sensitivity and timeliness in network switching. Conventional location offset determination thresholds cannot adapt to the operational needs of degraded equipment, easily leading to problems such as untimely switching and communication interruptions. Offset degree refers to the distance deviation and regional deviation between the vehicle's real-time positioning coordinates and the critical position of the dynamic network coverage boundary, used to quantify the trend and extent of the vehicle leaving effective communication coverage. The health degradation risk of the SIM card is used to characterize the degree of degradation of the device's communication and hardware. The switching requirement is a standardized task requirement for adapting to changes in network coverage and triggering SIM card network scheduling switching.

[0134] During execution, the system uses the dynamically fitted network coverage boundary as a benchmark to calculate the offset direction and magnitude of the vehicle's current position relative to the coverage boundary in real time, determining whether the vehicle is in a stable coverage area, a critical offset area, or out of coverage. Simultaneously, it retrieves the current SIM card's health degradation risk, configuring differentiated offset judgment thresholds and handover triggering strategies for different risk levels. For SIM cards with high degradation risk, it identifies small offset trends in advance and triggers network pre-handover requirements. For normal SIM cards with medium to low risk, it uses standard offset thresholds to determine the handover timing. For stable operating conditions with no offset, it determines that no network handover is required.

[0135] Combining the results of both the degree of offset and the risk level, network handover requests of corresponding intensities are generated in a tiered manner, distinguishing between three types of requests: pre-handover requests, emergency handover requests, and no handover requests. For example, when the SIM card is at a severe health degradation risk level, an emergency network handover request is generated if the vehicle only slightly deviates from the coverage boundary and does not fully enter the weak coverage area, thus avoiding the risk of communication interruption in advance. When the SIM card is in good health, a regular pre-handover request is generated only when the vehicle deviates significantly and is close to leaving the effective coverage area.

[0136] This application enables the linkage determination of network space status and device health status, breaking the limitations of single space determination. It adapts differentiated switching strategies for SIM cards with different degrees of aging and degradation, taking into account both the operational stability under normal working conditions and the risk avoidance capability under abnormal working conditions.

[0137] Sudden abnormalities in vehicle operation, SIM card communication link failures, and overload of vehicle-mounted terminals are emergency scenarios that cannot be addressed by conventional operation and maintenance scheduling strategies. These scenarios are characterized by their suddenness, high risk, and need for priority handling, and cannot be identified by conventional status monitoring alone. Vehicle operation status refers to abnormal motion parameters during real-time vehicle operation, including driving characteristics such as emergency braking, sudden speed changes, deviation from the planned path, and abnormal idling. Network communication status refers to abnormal real-time operating parameters of the SIM card communication link, including communication anomalies such as sudden drop in signal strength, excessive link latency, excessive bit error rate, instantaneous disconnection, and continuous packet loss. Vehicle-mounted terminal status refers to the operating load status of the vehicle-mounted communication terminal, including abnormal operating characteristics such as service concurrency overload, excessive hardware operating temperature, and excessive terminal resource utilization.

[0138] The preset emergency events are a predefined set of events based on vehicle communication failure scenarios, vehicle driving emergency scenarios, and terminal abnormality scenarios. Each type of emergency event has a corresponding judgment threshold and triggering conditions. During execution, real-time data is continuously and synchronously acquired, and the real-time data is matched against the judgment conditions of all preset emergency events. When any monitoring data meets the triggering conditions of the corresponding emergency event, the corresponding emergency scenario is immediately identified and an emergency detection signal is generated. The type and urgency level of the emergency event are marked simultaneously to provide a basis for subsequent priority scheduling.

[0139] For example, when a momentary disconnection of the SIM card communication link is detected and the duration exceeds the normal fault tolerance threshold, the emergency event judgment conditions for communication link failure are matched, and an emergency detection signal for communication failure is generated; when a sudden emergency braking or significant deviation from the navigation path is detected during high-speed driving, the emergency driving conditions of the vehicle are matched, and an emergency detection signal for driving conditions is generated, thereby achieving accurate identification and immediate triggering of various sudden abnormal conditions, providing a reliable triggering mechanism for high-priority emergency response.

[0140] Specifically, the output sequence of operation and maintenance operations to be executed includes:

[0141] The mode requirements corresponding to the first working mode, the operation and maintenance requirements corresponding to the health degradation risk, the network scheduling requirements corresponding to the switching requirements, and the handling requirements corresponding to the emergency detection signals are collected into a set of tasks to be scheduled.

[0142] Configure priorities for different tasks to be scheduled, identify the conflict types between tasks to be scheduled and execute the corresponding conflict resolution logic, generate an initial operation sequence based on the priority and conflict resolution results, and output the operation and maintenance operation sequence to be executed after data verification of the real-time status partition within the digital twin.

[0143] The upstream generates differentiated business requirements from four dimensions: working condition adaptation, health maintenance, network scheduling, and emergency response. These requirements differ in their dimensions, execution objectives, and data formats, exhibiting a decentralized and fragmented nature. Independent execution can easily lead to issues such as task omissions, duplicate scheduling, and policy conflicts. The mode requirement corresponding to the first working mode is the parameter adaptation and policy adjustment requirement to match the vehicle's steady-state driving conditions and adapt to the optimal communication working mode of the SIM card. The maintenance requirement corresponding to health degradation risk is the maintenance and management requirement generated based on the different degradation risk levels of the SIM card, including equipment monitoring, parameter optimization, status inspection, and risk warning. The network scheduling requirement corresponding to the switching requirement is the network switching and signal scheduling requirement to adapt to changes in network coverage boundaries, avoid communication blind spots, and ensure link stability. The emergency detection signal response requirement is the emergency priority handling, fault repair, and status fallback management requirement generated for various sudden abnormal working conditions.

[0144] During execution, valid business requirements from four upstream dimensions are received in real time. Requirements of different types and dimensions are standardized and encapsulated, and task data formats, task attributes, execution requirements and task identifiers are unified. Invalid, expired and duplicate redundant requirements are eliminated, and valid business requirements are integrated and collected into a structured and orderly set of tasks to be scheduled, realizing unified collection and centralized operation and maintenance of multi-source tasks. For example, the high-speed cruise condition of vehicles corresponds to the high-speed high-load mode adaptation requirement, the moderate degradation risk of SIM cards corresponds to the normalized status monitoring and operation and maintenance requirement, the road network switching corresponds to the pre-switching network scheduling requirement, and the instantaneous communication fluctuation corresponds to the mild emergency response requirement. After standardized encapsulation, these requirements are uniformly included in the set of tasks to be scheduled, completing the collection of multi-source tasks.

[0145] This application integrates and unifies multi-dimensional, heterogeneous, and fragmented business needs, constructs a fully covered and comprehensive task pool, completely solves the scheduling chaos caused by the scattered processing of multiple tasks, and provides a complete and standardized task foundation for subsequent priority ranking and conflict resolution.

[0146] Parallel execution of multiple tasks presents objective problems such as system resource contention and execution timing conflicts. Different tasks have significant differences in risk level, urgency, and business weight. Random execution without priority distinction will cause high-risk and high-urgency tasks to be delayed, leading to problems such as unstable communication, equipment risk out of control, and untimely emergency response. Priority configuration establishes fixed-level rules based on the business attributes, risk level, emergency weight, and urgency of the task in relation to the working conditions.

[0147] During execution, all tasks to be scheduled within the task set are individually identified and their levels are determined. Emergency response tasks corresponding to emergency detection signals have the highest priority, ensuring immediate handling of sudden abnormal operating conditions. Operation and maintenance management tasks corresponding to health degradation risks are prioritized to avoid hardware and communication risks caused by long-term equipment aging. Network scheduling tasks corresponding to switching requirements ensure the continuous stability of network links. Finally, routine mode adaptation tasks corresponding to the first working mode are completed to achieve fine-grained adaptation to steady-state operating conditions.

[0148] For the same type of task, the sub-priorities are further refined according to the risk level and urgency. High-risk and high-urgency sub-tasks have higher priority than low-risk and routine sub-tasks, forming a multi-level and comprehensive priority ranking system. For example, severe degradation risk operation and maintenance tasks have higher priority than mild degradation operation and maintenance tasks, emergency network switching tasks have higher priority than routine pre-switching tasks, and severe emergency response tasks have higher priority than mild emergency optimization tasks. This ensures that core high-risk tasks and emergency tasks occupy system execution resources first, while secondary routine tasks are scheduled to be postponed in an orderly manner, thus avoiding the operational risks caused by the lag of critical tasks from the scheduling mechanism.

[0149] In parallel scheduling scenarios involving multiple types of tasks, the execution parameters, scheduling instructions, resource occupancy methods, and mode adaptation strategies of different tasks are mutually exclusive and overlapping. When some tasks are executed simultaneously, problems such as parameter conflicts, resource preemption, mode conflicts, and timing contradictions may occur, leading to scheduling strategy failure and system malfunction. The conflict types of scheduling tasks include four types of conflict forms: parameter mutual exclusion conflict, system resource preemption conflict, working mode adaptation conflict, and timing execution logic conflict.

[0150] Parameter mutual exclusion conflict is when different tasks conflict with each other in their instructions to adjust SIM card communication parameters and working mode parameters; resource preemption conflict is when multiple tasks simultaneously occupy communication bandwidth, system computing power, and storage resources, resulting in insufficient resources; working mode adaptation conflict is when the working mode definitions of the normal mode adaptation task and the emergency response task are inconsistent; and timing execution conflict is when the execution timing and execution cycle of different tasks contradict each other.

[0151] During execution, all pairwise task combinations within the task set are iterated and compared to accurately determine the types of conflicts. For different conflict types, a pre-defined conflict resolution logic is invoked. The overall resolution follows the core rule of prioritizing the execution of high-priority tasks and adapting and adjusting low-priority tasks. For tasks with mutual exclusion conflicts, the execution rights of high-priority tasks are retained, while low-priority tasks are adapted by adjusting parameters, delaying timing, or temporarily suspending. For tasks without mutual exclusion conflicts, parallel execution rights are retained to maximize scheduling efficiency. For example, if there is a mutual exclusion conflict between the emergency communication failure handling task and the normal working mode adaptation task, after identifying the conflict type, a priority resolution is performed, the normal mode adaptation task is suspended, and the emergency handling task is executed first. Normal scheduling is resumed after the emergency situation is resolved.

[0152] This application enables the orderly, compliant, and conflict-free execution of multiple tasks, avoiding scheduling disorder and strategy failure caused by the superposition of multiple tasks, and is the core foundation for constructing a legitimate and effective initial operation sequence.

[0153] The initial operation sequence generated based on priority and conflict resolution is a theoretical scheduling logic sequence that does not match the real-time operating conditions of vehicles and SIM cards. It has problems such as mismatch between theoretical scheduling strategies and actual operating states, and some execution steps are not suitable for real-time operating conditions. Direct execution is prone to scheduling failures and parameter mismatches. The initial operation sequence is a theoretical execution sequence generated based on task priority sorting and conflict resolution results, and integrated according to reasonable timing and resource allocation rules. The real-time status partition of the digital twin stores all real-time accurate data on the current period's vehicle operating conditions, SIM card communication status, physical health status, and data quality status, and is the data carrier of the actual operating status of the equipment.

[0154] During execution, the priority ranking results and conflict resolution results are first integrated. According to the task execution sequence, logical progression, and resource adaptation rules, a complete and sequential initial operation sequence is generated. Then, real-time data from the real-time status partition of the digital twin is retrieved, and each execution step, parameter configuration, and scheduling logic of the initial operation sequence is checked line by line for feasibility. It is determined whether the sequence content is suitable for the current real-time operating conditions of the equipment, whether there are invalid steps that contradict the real-time status, and whether there are parameter adaptation deviations. Invalid execution logic is eliminated, and scheduling parameters with insufficient adaptability are corrected. Finally, an operation and maintenance operation sequence that fully fits the real-time operating conditions of the equipment and can be directly implemented is generated.

[0155] This application corrects theoretical scheduling deviations through a twin-based real-time data verification mechanism, ensuring that the final output operation and maintenance sequence has high adaptability to working conditions and feasibility for implementation. It serves as the execution object for iterative control, result verification, and adaptive retries, and determines the overall execution basis of the closed-loop control process throughout the entire lifecycle.

[0156] S4. Execute the operation and maintenance operation sequence and update the operation execution results to the digital twin. Verify whether the operation execution results have met the expected goals. If the verification fails, generate an adjusted operation and maintenance operation sequence based on the execution deviation and trigger re-execution.

[0157] Specifically, generating the adjusted operation and maintenance sequence and triggering its re-execution includes:

[0158] Execute the operation and maintenance sequence and update the operation results to the digital twin. Combine the vehicle driving status, vehicle operating status, health degradation risk and dimensional confidence to determine the expected goal of this operation.

[0159] Verify whether the operation results meet the expected goals. If the verification is successful, then proceed with the operation process.

[0160] If the verification fails to meet the standard, the deviation type and magnitude of the execution deviation are quantified, and historical execution data in the historical traceability partition are retrieved to complete the deviation traceability.

[0161] Based on the deviation type, deviation magnitude, and network communication status, combined with the vehicle driving status and on-board operating status, an adjusted maintenance operation sequence is generated. Retry count constraints are set according to health degradation risk, triggering the re-execution of the adjusted maintenance operation sequence.

[0162] The deviation types include explicit deviations and implicit deviations; the adjusted operation and maintenance sequence is generated, including modifying at least one of the operation execution parameters, task execution order and operation execution frequency of the original operation and maintenance sequence.

[0163] Traditional fixed-mode operation and maintenance uses a uniform preset execution target, which is not adapted to the real-time operating environment of the vehicle and the real-time health status of the SIM card. The reasonable execution standards for the same operation and maintenance are significantly different under different driving conditions, different equipment degradation levels, and different data quality conditions. Static uniform expected targets can lead to the problem of distorted judgments, with standards being too strict under normal operating conditions and too lenient under abnormal operating conditions, making it impossible to achieve refined and adaptable judgment of execution effect.

[0164] Vehicle driving status characterizes the real-time motion attributes of the vehicle, including dynamic motion parameters such as vehicle speed, driving trajectory, and driving stability; vehicle operating status characterizes the real-time workload attributes of the vehicle communication terminal, including equipment operating parameters such as communication service load, terminal power consumption, and hardware operating status; health degradation risk is the level of degradation of SIM card hardware and communication performance obtained from the previous assessment, characterizing the device's fault tolerance and operational reliability; dimensional confidence level characterizes the credibility and validity of various types of data collected in this operation.

[0165] During execution, the output operation and maintenance sequence is first implemented to complete the corresponding operation actions such as network scheduling, operation and maintenance management, mode adaptation and emergency response. After the operation is completed, all execution behaviors, execution status and execution results of this operation are used as incremental data and synchronously updated and written to the corresponding partition of the digital twin to ensure that the data stored in the twin is consistent with the execution status of the actual device in real time.

[0166] After data synchronization and update are completed, the vehicle driving status, vehicle operating status, SIM card health degradation risk, and dimensional confidence level of the current period are retrieved. Considering four types of constraints—external vehicle driving scenario, internal load of the vehicle equipment, SIM card's own health tolerance capability, and data collection reliability—the expected targets are quantified and generated to adapt to the current operation scenario and device status, thus achieving scenario configuration for the operation target. The expected targets consist of the following core indicators: communication link latency ≤ preset latency threshold (e.g., 50ms), data packet loss rate ≤ preset packet loss threshold (e.g., 1%), signal strength ≥ preset signal threshold (e.g., -85dBm), and mode switching effective time ≤ preset switching threshold (e.g., 2 seconds). The thresholds for each indicator are dynamically configured based on the current vehicle driving status, vehicle operating status, health degradation risk, and dimensional confidence level. For example, when the SIM card is at moderate degradation risk, the latency threshold is relaxed to 80ms and the packet loss threshold is relaxed to 3%.

[0167] For example, under conditions of high-speed vehicle operation, high communication load, and slight risk of SIM card degradation, stringent expected targets of high communication stability and low latency fluctuation are generated. Under conditions of low-speed vehicle idling, light load, and good SIM card health, basic expected targets of normal stability are generated. This ensures that the judgment criteria for operational effects are aligned with the actual execution scenario and equipment status of each operation, eliminating misjudgments caused by incompatible standards from the source of judgment and determining the accuracy of the overall verification process.

[0168] The actual execution effect of operation and maintenance is affected by multiple uncertain factors such as real-time network fluctuations, sudden changes in operating conditions, and instantaneous disturbances in equipment status, resulting in either meeting or failing to meet the standards. The verification operation is a standardized judgment process that compares the actual operation execution results with the dynamically generated expected goals item by item, matches the status, and verifies the effect. It goes through all the evaluation indicators and status requirements corresponding to this operation and maintenance operation, and matches and verifies the actual implementation effect with the expected goals.

[0169] If the verification result meets the standard, it means that the operation and maintenance is adapted to the current working conditions and equipment status, and the execution effect of operation and maintenance scheduling, network adaptation, and emergency response meets the preset control requirements. The single full life cycle control process is terminated, the process is closed-loop archived, and the current operation scheduling ends. If the verification result does not meet the standard, it means that the execution effect of the operation cannot adapt to the real-time dynamic scenario, and there are problems such as policy adaptation deviation, unreasonable parameters, or improper execution timing. The deviation tracing and policy correction branch is automatically switched to, and the optimization process is started.

[0170] This application achieves efficient closed-loop operation for effective operations and iterative correction for ineffective operations, balancing execution efficiency under normal operating conditions with fault-tolerant optimization capabilities under abnormal operating conditions.

[0171] Substandard execution is merely a symptom at the result level and cannot directly pinpoint the core cause of the deviation. Instantaneous execution deviations may originate from various reasons such as temporary network interference, improper parameter configuration, unreasonable task timing, and accumulated deviations from historical strategies. Blindly adjusting the operation strategy directly cannot address the core issues and is prone to repeated deviations and ineffective iterations. Deviation type refers to the category of faults and anomalies presented by the current non-compliance, while deviation magnitude refers to the quantitative degree to which the execution result deviates from the dynamic expected target. First, the difference between the actual execution result and the expected target is decomposed comprehensively, and deviation characteristics are identified from the functional implementation level, performance indicator level, and state adaptation level. The deviation category is accurately classified, and the degree of deviation from the standard is quantitatively determined, thus completing the structured quantification of deviation characteristics.

[0172] After quantification, historical execution data of similar operations, execution records under the same working conditions, and parameter configuration records under the same state are retrieved from the historical traceability partition for a preset duration. By comparing the parameter differences, timing differences, working condition differences, and network state differences between historical normal execution samples and current abnormal execution samples, the core causes of deviations are traced layer by layer. This distinguishes between occasional deviations caused by instantaneous environmental disturbances, inherent deviations caused by fixed parameter configurations, and structural deviations caused by improper task execution logic, thus completing the root cause location of deviations. For example, for deviation scenarios where communication stability is not up to standard, by comparing historical traceability data, it can be distinguished whether the current deviation is a temporary fluctuation deviation caused by instantaneous electromagnetic interference or a continuous performance deviation caused by long-term unreasonable parameter configuration, providing a basis for subsequent targeted adjustment strategies.

[0173] There are two completely different abnormal forms in the execution of SIM card maintenance operations: one is the directly observable functional failure, and the other is the implicit abnormality of normal function but degraded performance. The causes, scope of impact and correction logic of the two types of deviations are completely different, and they need to be classified and defined to support the adjustment of differentiated strategies. The deviation types are specifically divided into explicit deviations and implicit deviations.

[0174] Explicit deviations are visual deviations where the basic functions of the SIM card completely fail after the operation is executed, and the control objectives are not achieved. They are functional-level hard anomalies and can be directly determined through basic status detection. The core characteristics of explicit deviations are clear failure states such as unavailable service functions, failure of scheduling actions to be implemented, and interruption of communication links. For example, deviations such as failure to complete base station link switching after network switching operation, failure to trigger corresponding protection strategies after emergency response operation, and failure to take effect after mode adaptation operation are all explicit deviations.

[0175] Latent deviations are performance-level deviations where basic functions are implemented normally after operation, with no obvious functional failures, but core performance indicators and steady-state effects fail to meet dynamic expectations. These are non-visible, continuous state degradation issues. The core characteristics of latent deviations include functional availability but substandard performance, insufficient stability, large latency fluctuations, and unreasonable resource utilization. For example, high link latency after network switching or insufficient communication load balancing after mode adaptation are both examples of latent deviations. These two types of deviations have no overlap or blind spots, covering all scenarios where performance fails to meet targets.

[0176] This application achieves a complete closed loop from phenomenon identification to root cause location of execution deviations, accurately distinguishes between occasional anomalies and structural defects, avoids policy redundancy or ineffective correction caused by indiscriminate correction, and ensures the pertinence and effectiveness of subsequent policy adjustments; it distinguishes between functional failure hard faults and performance degradation soft faults, with explicit deviations focusing on correcting operational logic and execution processes, and implicit deviations focusing on optimizing operational parameters and execution frequency.

[0177] The adaptation and correction of operational deviations need to match multi-dimensional real-time scenarios. A single strategy based on deviation characteristics cannot adapt to complex vehicle networking conditions. The real-time communication quality of the network, the dynamic driving status of the vehicle, and the load conditions of the on-board terminal will all affect the adaptation effect of the strategy. At the same time, unlimited retry correction will continuously consume on-board computing power and communication resources. Frequent retries of high-risk degraded equipment will easily aggravate hardware wear and tear, while excessive constraints on retries of low-risk equipment will reduce fault tolerance and optimization capabilities.

[0178] Network communication status refers to dynamic communication parameters such as real-time link quality, signal coverage, and transmission stability, used to correct deviations in network adaptation operations; vehicle driving status and on-board operating status are used to match the current equipment operating scenario to ensure that the adjusted strategy adapts to the real-time operating environment; health degradation risk is used to quantify the equipment's fault tolerance and hardware endurance, and serves as the basis for determining the retry count constraint; based on the root causes and characteristics of deviations obtained from deviation tracing, and combined with the carrying capacity of real-time network communication, vehicle dynamic driving conditions, and real-time load status of on-board terminals, the unreasonable logic and incompatible parameters of the original operation and maintenance operations are comprehensively corrected and reconstructed to generate an operation and maintenance operation sequence adapted to the current abnormal scenario.

[0179] Simultaneously, based on the current health degradation risk of the SIM card, a differentiated retry count constraint threshold is dynamically configured. A lower retry count limit is set for devices with severe degradation risk to avoid frequent iterative operations exacerbating hardware wear and communication burden. For devices with mild degradation or good health, the retry count constraint is appropriately relaxed to ensure sufficient fault tolerance and correction space. Specifically, the maximum retry count is 5 for low-risk devices, 3 for medium-risk, 2 for severe-risk, and 1 for critical-risk. The retry interval increases with the number of retries, with a base retry interval of 2 seconds. Each retry interval is multiplied by the base interval and the retry sequence number, e.g., the second retry interval is 4 seconds. After the policy adjustment and count constraint configuration are completed, the adjusted new operation and maintenance sequence is automatically triggered and re-executed to complete closed-loop iterative optimization.

[0180] The adaptation deviations in the operation and maintenance sequence all stem from three core issues: unreasonable parameter configuration, execution timing logic conflicts, and mismatched execution density. Operation execution parameters refer to the core configuration parameters required for the implementation of operation and maintenance operations, including quantitative parameters such as communication thresholds, switching trigger thresholds, load balancing coefficients, mode adaptation parameters, and risk warning thresholds. These parameters are adjustment methods to address implicit performance deviations and are applicable to performance failures caused by improper parameter adaptation. For example, for implicit deviations such as high communication latency, strategy corrections can be achieved by fine-tuning network scheduling latency parameters and link balancing parameters.

[0181] Task execution order refers to the sequential execution order and linkage logic of various operation and maintenance tasks, network tasks, and emergency tasks during multi-task scheduling. It is a core means to resolve explicit deviations caused by timing conflicts and resource contention, and is applicable to operational failures caused by task timing disorder. For example, to address the explicit deviation of handling failures caused by delayed execution of emergency tasks, the priority timing of tasks can be adjusted to advance the execution order of emergency handling tasks.

[0182] Operation execution frequency refers to the execution cycle, polling density, and monitoring update frequency of operation and maintenance operations. It is a means to solve the adaptation deviation caused by insufficient execution density or excessive execution, and is suitable for scenarios where the dynamic changes of the working conditions are too rapid or resources are excessively occupied. For example, to address the deviation of untimely status updates under high-speed operating conditions, the execution frequency of status inspection and parameter adaptation can be increased. This embodiment supports any single adjustment or multiple combinations of the three adjustment methods, and adaptively selects the optimal correction method according to the deviation type and deviation magnitude to ensure that the correction effect accurately adapts to the deviation scenario.

[0183] This application accurately adapts to the root causes of deviations and real-time dynamic operating conditions. By using graded retry constraints, it balances the relationship between iterative optimization needs and equipment operation safety and system resource consumption, thereby improving the accuracy and execution efficiency of adaptive iteration. The adjusted operation and maintenance sequence becomes the new execution object, re-entering the full life cycle execution, verification and iteration closed loop, realizing continuous adaptive optimization of SIM card operation and maintenance strategies.

[0184] Example 2

[0185] like Figure 3 As shown, this embodiment provides a SIM card full lifecycle operation and maintenance system for the Internet of Vehicles, including a perception module, an evaluation module, an output module, and an execution module.

[0186] The perception module is used to acquire vehicle operation signals, SIM card communication status data, and physical environment data, corresponding to vehicle context dimension, communication health dimension, and physical health dimension, respectively. These data are fused to construct a state vector and written into the digital twin bound to the SIM card.

[0187] The evaluation module determines the first working mode of the SIM card based on the vehicle context dimension, and assesses the health degradation risk of the SIM card based on the communication health dimension and the physical health dimension.

[0188] The output module predicts the coverage boundaries of the operator's network based on the vehicle's location and navigation path and generates handover requirements, detects whether a preset type of emergency event occurs and generates an emergency detection signal.

[0189] The system takes the first working mode, health degradation risk, switching requirements and emergency detection signals as inputs, and outputs a sequence of operation and maintenance operations to be executed according to the preset priority logic and conflict resolution logic.

[0190] The execution module is used to execute the operation and maintenance operation sequence and update the operation execution results to the digital twin. It verifies whether the operation execution results have achieved the expected goals. If the verification fails, it generates an adjusted operation and maintenance operation sequence based on the execution deviation and triggers re-execution.

[0191] The above description is merely a preferred embodiment of this application. The scope of protection of this application is not limited to the above embodiments. All technical solutions falling within the scope of this application's concept are within the scope of protection of this application. It should be noted that for those skilled in the art, any improvements and modifications made without departing from the principles of this application should also be considered within the scope of protection of this application.

Claims

1. A SIM card full life cycle operation and maintenance method for Internet of Vehicles, characterized in that, include: The system acquires vehicle operation signals, SIM card communication status data, and physical environment data, corresponding to vehicle context dimension, communication health dimension, and physical health dimension, respectively. These data are then fused to construct a state vector and written into the digital twin bound to the SIM card. The first working mode of the SIM card is determined based on the vehicle context dimension, and the health degradation risk of the SIM card is assessed based on the communication health dimension and the physical health dimension. The determination of the first operating mode of the SIM card includes: Retrieve the local feature vector of the vehicle context dimension within the real-time state partition of the digital twin, and perform weighted correction on the local feature vector of the vehicle context dimension in combination with the dimension confidence. Vehicle speed, driving condition type, vehicle communication load and road scene features are extracted from the weighted and corrected local feature vectors and combined to form a condition determination factor. The local feature vector sequence of the vehicle context dimension within a preset time range is retrieved from the historical traceability partition, the working condition judgment factor is subjected to time-series smoothing filtering, and it is compared with the judgment interval of the preset working mode to obtain the first working mode of the SIM card that matches the current vehicle working condition. The assessment of the SIM card's health degradation risk includes: Retrieve the local feature vectors corresponding to the communication health dimension and physical health dimension within the real-time status partition of the digital twin, and perform weighted correction by combining the corresponding dimension confidence. From the weighted and corrected local feature vectors, communication link loss, signal stability, physical temperature change features and electromagnetic interference features are extracted respectively, and integrated to form a health characterization factor; The system retrieves local feature vector sequences of communication health and physical health dimensions within a preset time range from the historical traceability partition, and fits them to obtain the hardware aging trend and communication degradation trend of the SIM card. The coupling impact coefficient of physical environment fluctuations on communication health is calculated. Combined with health characterization factors, hardware aging trends and communication attenuation trends, the degradation quantification value of SIM card is calculated and compared with the preset degradation risk range. The corresponding health degradation risk of SIM card is output in a graded manner. Based on vehicle location and navigation path, predict the coverage boundary of operator network and generate handover requirements; detect whether a preset type of emergency event occurs and generate an emergency detection signal. The system takes the first working mode, health degradation risk, switching requirements and emergency detection signals as inputs, and outputs a sequence of operation and maintenance operations to be executed according to the preset priority logic and conflict resolution logic. Execute the operation and maintenance operation sequence and update the operation execution results to the digital twin. Verify whether the operation execution results have achieved the expected goals. If the verification fails, generate an adjusted operation and maintenance operation sequence based on the execution deviation and trigger re-execution.

2. The SIM card full life cycle operation and maintenance method for the Internet of Vehicles according to claim 1, characterized in that, The process of fusing and constructing the state vector and writing it into the digital twin bound to the SIM card includes: Perform time-series alignment and anomaly filtering on vehicle operation signals, SIM card communication status data, and physical environment data, and extract corresponding local feature vectors in different dimensions; Dimension confidence is calculated based on data integrity and data fluctuation amplitude in different dimensions. Local feature vector fusion weights are assigned based on the dimension confidence. After weighted fusion, cross-related features are concatenated to generate a state vector. The state vector is mapped to the partition of the digital twin bound to the SIM card according to the feature type. Data writing is completed through incremental synchronization, and the writing frequency is switched according to the drop in dimensional confidence.

3. The SIM card full life cycle operation and maintenance method for the Internet of Vehicles, according to claim 2, characterized in that, The extraction of corresponding local feature vectors by dimension is achieved by dynamically setting the encoding bit width of local features based on the distribution properties of data in different dimensions; The digital twin is pre-divided into static attribute partitions, real-time status partitions, historical traceability partitions, and associated coupling partitions, and the cross-related features are mapped to the associated coupling partitions.

4. The SIM card full lifecycle operation and maintenance method for vehicle-to-everything (V2X) communication as described in claim 3, characterized in that, The generation of the emergency detection signal includes: The system acquires vehicle location and navigation path in real time, extracts road segment types and road network density from the navigation path, and fits and generates the coverage boundary of the operator network that dynamically changes with the vehicle's driving trajectory. By comparing the degree of offset between the vehicle's location and the coverage boundary, and combining this with the SIM card's health degradation risk classification, a handover requirement is generated. It monitors vehicle driving status, network communication status, and vehicle operating status in real time, matches the judgment conditions of preset types of emergency events, and generates an emergency detection signal when a preset type of emergency event is detected.

5. The SIM card full lifecycle operation and maintenance method for vehicle-to-everything (V2X) communication as described in claim 4, characterized in that, The output sequence of maintenance operations to be executed includes: The mode requirements corresponding to the first working mode, the operation and maintenance requirements corresponding to the health degradation risk, the network scheduling requirements corresponding to the switching requirements, and the handling requirements corresponding to the emergency detection signals are collected into a set of tasks to be scheduled. Configure priorities for different tasks to be scheduled, identify the conflict types between tasks to be scheduled and execute the corresponding conflict resolution logic, generate an initial operation sequence based on the priority and conflict resolution results, and output the operation and maintenance operation sequence to be executed after data verification of the real-time status partition within the digital twin.

6. The SIM card full lifecycle operation and maintenance method for vehicle-to-everything (V2X) communication as described in claim 5, characterized in that, The process of generating the adjusted operation and maintenance sequence and triggering re-execution includes: Execute the operation and maintenance sequence and update the operation results to the digital twin. Combine the vehicle driving status, vehicle operating status, health degradation risk and dimensional confidence to determine the expected goal of this operation. Verify whether the operation results meet the expected goals. If the verification is successful, then proceed with the operation process. If the verification fails to meet the standard, the deviation type and deviation magnitude of the execution deviation are quantified, and historical execution data in the historical traceability partition are retrieved to complete the deviation traceability. Based on the deviation type, deviation magnitude, and network communication status, combined with the vehicle driving status and on-board operating status, an adjusted maintenance operation sequence is generated. Retry constraints are set according to the health degradation risk, triggering the re-execution of the adjusted maintenance operation sequence.

7. The SIM card full lifecycle operation and maintenance method for vehicle-to-everything (V2X) communication as described in claim 6, characterized in that, The deviation types include explicit deviations and implicit deviations; the generation of the adjusted operation and maintenance sequence includes modifying at least one of the operation execution parameters, task execution order and operation execution frequency of the original operation and maintenance sequence.

8. A SIM card lifecycle maintenance system for vehicle-to-everything (V2X) communication, used to implement the SIM card lifecycle maintenance method for V2X communication as described in any one of claims 1-7, characterized in that, include: The module consists of a perception module, an evaluation module, an output module, and an execution module. The perception module is used to acquire vehicle operation signals, SIM card communication status data, and physical environment data, corresponding to vehicle context dimension, communication health dimension, and physical health dimension, respectively. These data are fused to construct a state vector and written into the digital twin bound to the SIM card. The evaluation module determines the first working mode of the SIM card based on the vehicle context dimension, and assesses the health degradation risk of the SIM card based on the communication health dimension and the physical health dimension. The output module predicts the coverage boundaries of the operator's network based on the vehicle's location and navigation path and generates handover requirements, detects whether a preset type of emergency event occurs and generates an emergency detection signal. The system takes the first working mode, health degradation risk, switching requirements and emergency detection signals as inputs, and outputs a sequence of operation and maintenance operations to be executed according to the preset priority logic and conflict resolution logic. The execution module is used to execute the operation and maintenance operation sequence and update the operation execution results to the digital twin. It verifies whether the operation execution results have achieved the expected goals. If the verification fails, it generates an adjusted operation and maintenance operation sequence based on the execution deviation and triggers re-execution.

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