Data security enhancement method and system for vehicle-mounted log collection

Abstract

The application provides a kind of vehicle-mounted log acquisition data security enhancement method and system, belong to data security technical field, specifically include: obtaining target vehicle data, and in combination with the distribution data of target vehicle reading success period, determine the reading data of vehicle-mounted log data of target vehicle needs to be considered, root target vehicle reading data, determine in the nearest preset period in target vehicle in reading failure vehicle, to the reading data of reading failure vehicle vehicle-mounted log data as a basis, determine that the reading processing of other vehicle-mounted log data can be carried out, according to the reading data of target vehicle-mounted log data in different period, determine the vehicle that carries out the reading processing of vehicle-mounted log data in period, improve the security of vehicle-mounted log data of vehicle with higher vehicle-mounted log data reading failure probability.

Description

Technical Field

[0001] This invention belongs to the field of data security technology, and in particular relates to a method and system for enhancing data security in vehicle log collection. Background Technology

[0002] With the rapid development of intelligent vehicles, collecting vehicle log data via the cloud can enable the assessment of vehicle operating status and generate targeted warning signals. Specifically, in invention patent application CN202510850715.4, "A Communication Method and System for Real-time Download of Vehicle Data," dynamic encryption of data during wireless transmission and prioritization based on data type are used to quickly acquire vehicle data while ensuring data transmission security. This allows for rapid fault diagnosis, analysis, and repair. However, the following technical problems exist:

[0003] To improve the reliability of vehicle log reading, it is necessary to periodically read and process vehicle log data from the vehicle on the cloud platform according to user settings. This can improve the reliability of vehicle problem identification and handling. However, due to differences in driving habits, some vehicles may have low success rates in obtaining vehicle log data due to idleness or parking location. Therefore, it is crucial to determine the vehicle log acquisition and processing strategy on the cloud platform based on the failure rate of vehicle log acquisition for vehicles with low success rates. This will ensure that vehicle log data from vehicles with acquisition failures can be obtained in a timely manner, avoiding the technical problems of low data security caused by long-term storage on the vehicle.

[0004] Therefore, there is an urgent need for a method and system to enhance the data security of vehicle log collection. Summary of the Invention

[0005] To achieve the objectives of this invention, the following technical solution is adopted:

[0006] Specifically, this application provides a method for enhancing data security in vehicle log collection, which includes:

[0007] S1 uses the vehicle log data to determine the failure to read the vehicle date data in different time periods, and based on the failure to read the data, determines the vehicle with the target reading strategy and uses it as the target vehicle.

[0008] S2 acquires the target vehicle data and, in conjunction with the distribution data of the target vehicle's successful reading time periods, determines when it is necessary to consider the reading data of the target vehicle's onboard log data, and then proceeds to the next step.

[0009] S3 determines, based on the reading data of the target vehicle, the vehicles that failed to read the data in the most recent preset time period. Based on the reading data of the vehicle log data of the vehicles that failed to read the data, it determines when it is possible to read the vehicle log data of other vehicles. Based on the reading data of the vehicle log data of the target vehicle in different time periods, it determines the vehicles that will be read and processed in the time period.

[0010] The beneficial effects of this invention are as follows:

[0011] Based on the vehicle log data read from vehicles that failed to read, it is determined whether vehicle log data from other vehicles can be read and processed. This avoids the technical problem of high data security risks for target vehicles (i.e., vehicles with low success rates in reading and processing vehicle log data) caused by a large number of vehicles failing to read data and a large amount of vehicle log data not being successfully backed up and read in the cloud. By specifically restricting the reading and processing of vehicle log data from other vehicles, the security and reliability of the data are ensured.

[0012] Based on the reading data of the target vehicle's onboard log data in different time periods, the vehicles that need to read and process the onboard log data in those time periods are determined. This not only takes into account the need to read and process the onboard log data of the target vehicle in those time periods, but also the need to process the onboard log data of vehicles that failed to read the data. It also takes into account the differences in the reading and processing needs of the vehicles in those time periods due to differences in historical reading success rates. This achieves the determination of vehicles that need to read and process the onboard log data based on the reading and processing needs of various types of vehicles in those time periods, thereby further improving the security and timeliness of the onboard log data reading and processing.

[0013] Furthermore, the vehicle log data includes monitoring data from sensor devices and vehicle driving data during vehicle operation.

[0014] Furthermore, the data read includes the time period for reading the vehicle's onboard date data and the reading failure status for different time periods.

[0015] Furthermore, the failure to read the vehicle's onboard date data in different time periods is determined based on the number of times the onboard date data in the time period on different dates.

[0016] Furthermore, the time period is divided based on an hour.

[0017] Furthermore, the method for determining the target vehicle is as follows:

[0018] Based on the read failure scenarios, determine the number of read failures for the vehicle in different time periods;

[0019] Based on the number of read failures of vehicle date data in different time periods, and in combination with the number of read failures of vehicle log data in the same time period, the read failure probability in the specified time period is determined.

[0020] Based on the read failure probability at different time periods, determine whether the vehicle is the target vehicle.

[0021] Furthermore, the method for determining the vehicles that perform vehicle log data reading and processing during the aforementioned time period is as follows:

[0022] Based on the reading data of the vehicle logs of the target vehicle in different time periods, the target vehicle that belongs to the time period of successful reading is identified and used as the matching target vehicle.

[0023] Based on the composition data of the vehicles that failed to be read in the target vehicles, determine the proportion of the number of vehicles that failed to be read in the target vehicles;

[0024] Based on the number of target vehicles in the time period and the proportion of vehicles that failed to read the target vehicles, the vehicles that perform vehicle log data reading and processing in the time period are determined.

[0025] Secondly, the present invention provides a computer system comprising: a memory and a processor connected in communication, and a computer program stored in the memory and capable of running on the processor, wherein the processor executes the above-described method for enhancing data security of vehicle log collection when running the computer program.

[0026] Other features and advantages will be set forth in the following description, and the objects and other advantages of the invention are realized and obtained through the structures particularly pointed out in the description and the drawings.

[0027] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0028] The above and other features and advantages of the present invention will become more apparent from a detailed description of exemplary embodiments thereof with reference to the accompanying drawings.

[0029] Figure 1 This is a flowchart of a method for enhancing data security in vehicle log collection;

[0030] Figure 2 This is a flowchart of the method for determining the target vehicle;

[0031] Figure 3This is a flowchart for determining the data to be read from the vehicle's onboard logs, which needs to be considered for the target vehicle.

[0032] Figure 4 This is a flowchart that determines whether it is possible to read and process the onboard log data of other vehicles. Detailed Implementation

[0033] To enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this specification, and not all embodiments. Based on the embodiments of this specification, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of this specification.

[0034] Example 1

[0035] like Figure 1 As shown, this application provides a method for enhancing data security in vehicle log collection, specifically including:

[0036] S1 uses the vehicle log data to determine the failure to read the vehicle date data in different time periods, and based on the failure to read the data, determines the vehicle with the target reading strategy and uses it as the target vehicle.

[0037] Furthermore, the vehicle log data includes monitoring data from sensor devices and vehicle driving data during vehicle operation.

[0038] Furthermore, the data read includes the time period for reading the vehicle's onboard date data and the reading failure status for different time periods.

[0039] Furthermore, the failure to read the vehicle's onboard date data in different time periods is determined based on the number of times the onboard date data in the time period on different dates.

[0040] Furthermore, the time period is divided based on an hour.

[0041] The core objective of the method in this invention patent is to accurately select from the vehicle portfolio (i.e., the vehicle portfolio for vehicle log reading management on the cloud platform) those vehicles with the most unstable vehicle log data reading service and the most concentrated problems within a day, namely the "target vehicles", so as to prioritize maintenance and fault diagnosis.

[0042] It should be noted that the vehicle user's authorization is required to read and process vehicle log data.

[0043] The "target vehicle" is defined as a vehicle that requires data reading at different times throughout the day, implying a high success rate in reading its onboard log data. This method comprehensively assesses the health of the data link by analyzing the probability of data reading failure for each vehicle at different times throughout the 24 hours of the day. It is not a simple "yes / no" fault judgment, but a multi-dimensional evaluation system based on the failure probability at different times, the overall failure level, and the effective service window.

[0044] Specifically, such as Figure 2 As shown, the method for determining the target vehicle is as follows:

[0045] S11 determines the number of read failures of the vehicle in different time periods based on the read failure conditions;

[0046] In the above steps, data preparation is performed—the number of read failures is counted. The number of read failures is the cumulative number of times the vehicle's onboard date data is read but ultimately fails within the same time period (e.g., "09:00-10:00").

[0047] This is the most basic quantitative metric. It categorizes and counts "read failure" events according to different hourly times of the day, preparing for subsequent probability calculations. Time-series analysis helps to identify whether failures are concentrated at specific times (such as peak commuting hours, nighttime, etc.).

[0048] S12 determines the read failure probability in the time period based on the number of read failures of vehicle date data in different time periods and in combination with the number of read failures of vehicle log data in the time period;

[0049] Specifically, this step calculates the core metric—the probability of read failure. The probability of read failure is calculated as: the number of read failures / (number of read failures + number of read successes) within a specific time period, which is the probability of a single read attempt failing within that time period.

[0050] Simply looking at the number of failures is influenced by the total number of reads, which is unfair. The failure probability standardizes this metric, allowing for fair comparisons between vehicles with different activity levels (different read counts) and different time periods. It reflects the reliability of the vehicle's data service within a specific timeframe.

[0051] S13 determines whether the vehicle is the target vehicle based on the read failure probability in different time periods.

[0052] It is understood that the read failure probability is determined based on the proportion of read failures in the number of reads of vehicle date data during the time period.

[0053] It is understandable that determining whether a vehicle is the target vehicle based on the read failure probability at different time periods specifically includes:

[0054] S131 determines whether there is a time period in which the probability of reading failure of the vehicle is less than a preset failure probability threshold. If not, proceed to the next step. If yes, determine that the vehicle does not belong to the target vehicle.

[0055] Specifically, in the above steps, check whether there is an absolutely stable period of time. Determine: check whether the probability of reading failure of the vehicle in any period of time (even if there is only one) is less than an extremely strict preset failure probability threshold (e.g., 1%).

[0056] If so, it means the vehicle is highly reliable at least for some time of day. It does not fall under the category of "unreliable throughout the day" and is therefore excluded from the "target vehicle" list. This is a "veto" safety clause. If not, it means the vehicle has significant read problems at all times and requires further evaluation.

[0057] S132 determines whether the average reading failure probability of the vehicle in different time periods is greater than the preset recognition probability value. If yes, the vehicle is determined to be the target vehicle; otherwise, proceed to the next step.

[0058] Specifically, assess the overall failure level by calculating the average read failure probability for the vehicle over all 24 time periods and determining if it exceeds a preset recognition probability value (e.g., 15%). If so, it indicates that the overall data read service quality for the vehicle is very poor, and failures are the norm. Such vehicles undoubtedly belong to the "target vehicles" that require priority processing. If not, it suggests that the overall problem with the vehicle may not be so severe, and a final step of fine-tuning is needed.

[0059] S133 takes the time period when the probability of vehicle reading failure is not greater than the preset value of recognition probability as the reading success time period, and determines whether the number of reading success time periods of the vehicle is greater than the preset success time period number threshold. If not, the vehicle is determined to be the target vehicle; if so, the vehicle is determined not to be the target vehicle.

[0060] Specifically, in the above steps, the effective service window is evaluated, and the successful reading period refers to the period during which the probability of reading failure for the vehicle is no greater than the preset recognition probability value (i.e., 15% in S132). This can be understood as a period of "acceptable or good performance".

[0061] The system determines whether the number of such "successful read periods" exceeds a preset threshold (e.g., 18, representing 75% of the day). If yes, it indicates that the vehicle is basically usable for most of the day; although not perfect, it does not reach the severity level of a "target vehicle." Therefore, it is not considered a target vehicle. If no, it indicates that the vehicle not only performs poorly overall (as determined in S132), but also has very few stable and usable time windows, making the problem more concentrated and severe. Therefore, it is ultimately determined to be a target vehicle.

[0062] Comprehensive Implementation Examples:

[0063] Suppose we are monitoring a group of vehicles (including vehicle A, vehicle B, and vehicle C), and analyzing the data in one-hour intervals. Analysis process:

[0064] Vehicle A: The failure rate is 0.5% during the "02:00-03:00" time period (because it always successfully uploads late at night).

[0065] S131 Decision: Is there a time period with a failure probability of < 1%? Yes, conclusion: Not belonging to the target vehicle.

[0066] Vehicle B: The average failure probability over 24 time periods is 40%. S131 judgment: Failure probability in all time periods > 1%? Yes. S132 judgment: Average 40% > 15%? Yes. Conclusion: Belongs to the target vehicle due to its extremely high overall failure rate.

[0067] Vehicle C: The average failure probability across 24 time periods is 5%, with failures mainly concentrated in the "08:00-10:00" and "17:00-19:00" time periods (failure rate > 5%). The failure rates in the remaining 19 time periods are very low (< 5%). S131 Judgment: All time periods have a failure probability > 1%? Yes (assuming the lowest failure rate is 2%). S132 Judgment: Average 5% > 3%? Yes. S133 Judgment: There are 19 "successful read time periods" (failure rate ≤ 3%), 19 > 18 (preset threshold for the number of successful read time periods)? Yes. Conclusion: Not belonging to the target vehicle. Because although it has severe problems during peak hours, the service is available for most of the day, and the problem has a clear time-specific characteristic, rather than a global outage.

[0068] This implementation successfully identified "Vehicle B," a vehicle with a globally high failure rate, as the "target vehicle" requiring priority handling, while excluding "Vehicle C," which only has problems during specific time periods, and "Vehicle A," which has a stable time window. This ensures that the operations team can allocate limited resources to the most pressing and far-reaching issues.

[0069] It should be noted that the target vehicle is a vehicle whose vehicle log data is read and processed at different times throughout the day.

[0070] S2 acquires the target vehicle data and, in conjunction with the distribution data of the target vehicle's successful reading time periods, determines when it is necessary to consider the reading data of the target vehicle's onboard log data, and then proceeds to the next step.

[0071] Specifically, such as Figure 3 As shown, the data that needs to be considered for reading the target vehicle's onboard log data includes:

[0072] S21 determines the number of target vehicles based on the target vehicle data;

[0073] Specifically, assess the problem size—determine the number of target vehicles. The number of target vehicles is the total number of vehicles identified as "target vehicles" in the previous process.

[0074] This is the most intuitive indicator. If only a very small number of vehicles have the problem, it may be an isolated case, and there is no need to initiate a system-level optimization plan.

[0075] S22 determines the number of successful reading periods of the target vehicle based on the distribution data of the successful reading periods of the target vehicle;

[0076] Specifically, assess overall availability—determine the number of successful read sessions;

[0077] Number of successful read time periods: For all target vehicles, their respective "successful read time periods" are deduplicated and merged to obtain the total number of unique time periods. For example, if vehicle A's successful time periods are [1,2,3] and vehicle B's are [3,4,5], then the merged successful read time periods are [1,2,3,4,5], a total of 5.

[0078] Why do this? This metric answers the question: "Are there any time windows throughout the 24 hours in a day where we can successfully read at least a portion of the data from the target vehicles?" It measures the overall data readability that the entire group of problematic vehicles can provide.

[0079] S23 determines whether it is necessary to consider the reading data of the vehicle log data of the target vehicles based on the number of target vehicles, the number of successful reading time periods, and the number of successful reading time periods for different target vehicles.

[0080] It is understood that the target vehicle for matching and reading during the time period is the target vehicle that is a successful reading method during the time period.

[0081] Specifically, based on the number of target vehicles, the number of successful read times, and the number of successful read times for different target vehicles, it is determined whether the read data of the target vehicles' onboard logs needs to be considered. This includes:

[0082] S231 obtains the number of target vehicles and determines whether the number of target vehicles is greater than a preset target vehicle number threshold. If yes, proceed to the next step; if no, determine that the number of target vehicles is small, and therefore determine that it is not necessary to consider reading the vehicle log data of the target vehicles.

[0083] Specifically, in the above steps, a preliminary scale screening is performed to determine whether the number of target vehicles is greater than the preset target vehicle number threshold (e.g., accounting for 5% of the total fleet).

[0084] Decision-making logic: If no, it means the problem is an isolated incident with a small impact. Therefore, system-wide optimization is not necessary; only individual vehicles need to be inspected. If yes, it means the problem has a certain degree of universality, requiring further in-depth analysis.

[0085] S232 takes the successful reading time period belonging to any target vehicle as the successful identification time period, and determines whether the number of successful identification time periods is less than a preset number threshold. If so, it is determined that the number of successful identification time periods is small, so the vehicle date data of the target vehicle may not be effectively read in other time periods, making it difficult to effectively read the vehicle date data of other vehicles. Therefore, it is determined that the reading data of the vehicle log data of the target vehicle needs to be considered. If not, proceed to the next step.

[0086] In the above steps, assess the overall data readability and determine: Is the number of successfully identified time periods less than a preset threshold (e.g., 6 out of 24 time periods in a day)?

[0087] If so, this is an extremely dangerous signal. It means that for most of the day, data from almost all target vehicles is unreadable. This indicates a potential large-scale, homogeneous system failure (such as a network operator's service interruption in a specific area), making it difficult to analyze and infer potential problems for other normal vehicles using data from these faulty vehicles. Therefore, when processing vehicle log data from other vehicles, the data access issue for the target vehicle must be considered immediately to ensure the reliability of the target vehicle's log data processing. If not, proceed to the next step.

[0088] S233 determines whether there are target vehicles whose number of successful reading time periods is less than the preset threshold based on the number of successful reading time periods for different target vehicles. If yes, proceed to the next step. If no, it is determined that different target vehicles have a large number of time periods in which log data can be successfully read, so the reading reliability of different target vehicles is high. Therefore, it is determined that the reading data of the vehicle log data of the target vehicles does not need to be considered.

[0089] The specific check involves identifying any extremely difficult-to-access vehicles. The question is whether any target vehicle has a number of successful read sessions less than a preset threshold (e.g., 4). If no, it means that while all target vehicles are performing poorly, they all maintain a certain number of readable time windows (e.g., each vehicle can stably upload data for at least 4 hours per day). This implies that while the problem is widespread, there haven't been any extreme cases of complete data loss, and data acquisition is still guaranteed. Therefore, no specific optimization is needed to ensure the reliability of the target vehicle's read processing. If so, it indicates that some vehicles in the fleet have experienced near-total data link failures, requiring a final step to assess the prevalence of such vehicles.

[0090] S234 identifies target vehicles whose number of successful reading periods is less than a preset threshold for the number of successful reading periods as vehicles with difficulty in reading logs, and determines whether to consider reading the vehicle log data of the target vehicles based on the composition data of the vehicles with difficulty in reading logs.

[0091] It is understood that when the proportion of vehicles with log reading difficulties in the target vehicles is greater than a preset proportion threshold, it is determined that the reading of the vehicle log data of the target vehicles needs to be considered. When the proportion of vehicles with log reading difficulties in the target vehicles is not greater than the preset proportion threshold, it is determined that the reading of the vehicle log data of the target vehicles does not need to be considered.

[0092] Specifically, in the above steps, the proportion of "difficult users" is assessed, and defined as: vehicles with log reading difficulties: those target vehicles whose number of successful reading periods is less than the "preset threshold for the number of successful reading periods".

[0093] Judgment: If the ratio of the number of vehicles with log reading difficulties to the total number of target vehicles exceeds the preset threshold (e.g., 40%), then: This indicates that the "almost unreachable" vehicles are not isolated cases, but constitute a significant proportion of the problematic vehicles. This suggests a very serious and profound problem, requiring consideration of the target vehicles when reading their logs from other vehicles to meet their log reading needs. If not: This indicates that the "almost unreachable" vehicles are only a minority, and most target vehicles still have basic data readability. Therefore, specific optimization can be temporarily suspended, as the log reading needs of the target vehicles are not high at this time.

[0094] In a possible specific implementation, for example, scenario 1: a large-scale regional network failure, the number of target vehicles = 60 vehicles (greater than 50, S231 passes), all target vehicles can only successfully upload data in two time windows: "02:00-04:00" and "14:00-16:00", the number of successfully identified time periods = after merging and deduplication, there are only 4 time periods (less than 6, S232 is triggered);

[0095] Decision-making process: S231: 60 > 50? Yes, S232: 4 < 6? Yes.

[0096] Conclusion: It is necessary to consider the reading of vehicle log data from the target vehicle in order to ensure the reliability of reading and processing of vehicle log data from target vehicles with a high probability of reading failure.

[0097] S3 determines, based on the reading data of the target vehicle, the vehicles that failed to read the data in the most recent preset time period. Based on the reading data of the vehicle log data of the vehicles that failed to read the data, it determines when it is possible to read the vehicle log data of other vehicles. Based on the reading data of the vehicle log data of the target vehicle in different time periods, it determines the vehicles that will be read and processed in the time period.

[0098] Furthermore, the vehicles that failed to read are those that failed to successfully read vehicle log data within the most recent preset time period.

[0099] The core objective of this step is to determine whether, when there are "failed read vehicles" in the system, data reading of other normal vehicles should be suspended or restricted in order to prioritize the data recovery of these failed vehicles, so as to ensure the integrity of the overall system data.

[0100] When resources (such as network bandwidth and server processing power) are limited, data reading tasks may compete for space. The decision in this process involves balancing the "fairness" and "completeness" of data reading. Should all vehicles be polled equally, or should resources be allocated to vehicles with data gaps to ensure their log data can be successfully supplemented? This solution prioritizes data integrity to prevent technical problems arising from excessive data security risks caused by vehicles being unable to upload data for extended periods and storing it on the vehicle itself.

[0101] Specifically, such as Figure 4 As shown, it has been confirmed that the reading and processing of vehicle log data from other vehicles can be performed, specifically including:

[0102] S31 Based on the read data of the vehicle log data of the vehicle that failed to read, determine the date on which the log data of the vehicle that failed to read was not successfully obtained, and take it as the unread date;

[0103] In the above steps, locate missing data - determine the unread date. Unread date: refers to the specific date within the most recent preset time period (such as the past 7 days) when the system planned to read data from a certain "failed vehicle", but ultimately all attempts failed.

[0104] This step refines the problem from "some vehicles failed" to "which vehicles had missing data on which days." This forms the basis for assessing the severity of the missing data.

[0105] S32 determines the number of unread dates for different vehicles that failed to read data;

[0106] In the steps above, quantifying the degree of data loss involves determining the number of unread dates. The number of unread dates is the total number of unread dates for a single "failed read vehicle," which measures the duration of data loss for that vehicle. A larger number indicates a larger data gap for that vehicle and a higher urgency to complete it.

[0107] Based on the number of unread dates of different vehicles that failed to read, S33 determines whether it is possible to read and process the vehicle log data of other vehicles.

[0108] It is understood that the unread date refers to the date on which the vehicle that failed to read the data did not perform the on-board log data reading process.

[0109] Specifically, based on the number of unread dates for different vehicles that failed to read data, it is determined whether onboard log data from other vehicles can be read and processed. This includes:

[0110] S331 determines whether there are vehicles with more than a preset date number than the number of unread dates based on the number of unread dates of different vehicles that failed to read. If so, proceed to the next step. If not, the number of unread dates of different vehicles that failed to read is small, so there is no need to restrict the reading of vehicle log data of other vehicles. That is, the reading of vehicle log data is performed during the reading period of other vehicles that succeeded.

[0111] In the above steps, check if there are vehicles with serious data gaps and determine: Is there any vehicle that failed to read data and whose number of unread dates is greater than the preset date number threshold (e.g., 3 days)?

[0112] Decision logic: If no, it means that the data loss of all failed vehicles is only temporary and short-lived (e.g., only failed in the last 1-2 days). In this case, the data gap is small, and there is no need to disrupt the normal reading rhythm of the entire system to fill this small gap. Therefore, the reading and processing of vehicle log data for other vehicles can proceed normally. If yes, it means that at least one vehicle has developed a large data gap, requiring further assessment of the prevalence of this problem.

[0113] S332 obtains the number of vehicles that failed to read, and determines the reading failure factor based on the proportion of the number of vehicles that failed to read to the number of target vehicles. It then determines whether the reading failure factor is greater than a preset failure factor threshold. If not, it is determined that the reading of vehicle log data of other vehicles can be performed. If so, the reading of vehicle log data of the target vehicle and the vehicles that failed to read is performed only.

[0114] Specifically, assessing the prevalence of failed vehicles involves calculating the read failure factor. The read failure factor is a comprehensive indicator used to measure the severity of the failure problem. Its formula is: Read Failure Factor = Number of vehicles with read failures / Number of target vehicles.

[0115] The denominator is the "target vehicles" (i.e., all vehicles with problems), and the numerator is the "failed read vehicles" (i.e., the most severely problematic ones). This ratio reflects the proportion of vehicles whose situation has deteriorated drastically within the known group of problematic vehicles.

[0116] Judgment: Is the failure rate greater than the preset failure rate threshold (e.g., 50%)? If no: This means that although some vehicles have significant gaps, the proportion of failed vehicles among the target vehicles is not high, and the problem has not yet spread widely. The system still has breathing room and can handle both supplementary data collection and normal reading, meaning it can continue processing the onboard log data of other vehicles. If yes: This is a serious alarm. It means that in the already unstable "target vehicle" group, more than half of the vehicles have been unable to read data for several consecutive days. This indicates that the system may be facing serious network problems or batch hardware failures. At this time, all resources must be concentrated on emergency reading, prioritizing the completion of missing historical data for these vehicles to prevent permanent data loss. Therefore, the decision is to only process the onboard log data of the target vehicles and vehicles that have failed to read data, temporarily restricting or suspending reading from completely healthy vehicles.

[0117] In a possible specific embodiment, the background is as follows: Assume there are a total of 1000 vehicles, of which 60 "target vehicles" have been identified. System presets: Preset date threshold: 3 days; Preset failure factor threshold: 50%.

[0118] Scenario 1: Some vehicles have serious malfunctions. Data: There are 5 vehicles with "failed read". Among these 5 vehicles, 1 vehicle has 4 days of "unread date count" (greater than the 3-day threshold), and the other 4 vehicles have 1 day. The read failure factor = 5 / 60 ≈ 8.3%.

[0119] Decision-making process:

[0120] S331: Are there any vehicles with unread dates > 3? Yes (1 vehicle) -> Proceed to S332.

[0121] S332: 8.3% < 50%? Yes.

[0122] Conclusion: The system can read and process vehicle log data from other vehicles. While attempting to recollect data from the five failed vehicles, the system successfully read data from other vehicles.

[0123] This decision-making mechanism ensures that the data collection strategy can be dynamically adjusted according to the actual health status of the system, ensuring efficiency in normal times and prioritizing the integrity of core assets (data) in times of crisis, reflecting the intelligence and flexibility of operation and maintenance management.

[0124] Furthermore, the method for determining the vehicles that perform vehicle log data reading and processing during the aforementioned time period is as follows:

[0125] The core objective of this strategy is to make intelligent decisions on which vehicles to read data from within a single time period, in order to achieve the most efficient data completion and system resource utilization.

[0126] This process is a dynamic resource allocator. It no longer treats the "successful read period" as a fixed, equal read window for all vehicles. Instead, it determines whether to "cast a wide net" or "precision catch" within a specific timeframe based on the overall resource scarcity and the urgency of individual vehicles. Its core idea is: when resources are plentiful, read as many vehicles as possible; when resources are scarce, prioritize opportunities for vehicles that need them most and are least likely to waste resources.

[0127] S41 determines the target vehicle that belongs to the successful reading period in the target vehicle's on-board log data in different time periods, and uses it as the matching target vehicle;

[0128] Specifically, in the above steps, the qualified vehicles within the time period are identified—the target vehicles are determined. The target vehicles are matched as follows: In a specific time period (such as "14:00-15:00"), for those vehicles marked as "target vehicles", if this time period happens to be its own "successful reading time period", then it is a "matching target vehicle" for this time period.

[0129] This is the foundation of scheduling. It identifies a list of vehicles that are likely to be successfully accessed at any given time and are of priority.

[0130] S42 uses the composition data of the vehicles that failed to read in the target vehicles to determine the proportion of the number of vehicles that failed to read in the target vehicles;

[0131] Specifically, assess the severity of the problem within the assessment period—determine the percentage of vehicles that failed to read data. The percentage of vehicles that failed to read data is calculated as follows: in the list of "matching target vehicles", calculate the proportion of vehicles that also "failed to read data".

[0132] This measures the concentration of "emergency vehicles" that urgently need to supplement data among the available vehicles during this period. A high proportion means that this period is the "golden window" for carrying out supplementary data collection tasks.

[0133] S43 determines the vehicles that will be processed for reading vehicle log data during the time period based on the number of target vehicles and the proportion of vehicles that failed to read the target vehicles during the time period.

[0134] It is understood that the vehicles for reading and processing vehicle log data during the specified time period are determined based on the target vehicles in the specified time period and the proportion of vehicles among the target vehicles whose reading failed. Specifically, this includes:

[0135] S431 obtains the number of matching target vehicles in the time period, and determines the sorting result of the time period based on the number of matching target vehicles in the time period. Based on the sorting result, it determines whether the number of time periods with more than the number of matching target vehicles in the time period is greater than a preset time period number threshold. If yes, proceed to the next step. If no, it determines that in the time period, only vehicles that belong to the successful reading time period can have their vehicle log data read.

[0136] Specifically, the above steps assess the overall resource stress and determine whether the 24 time periods of the day are sorted from highest to lowest based on the number of target vehicles they match. Then, determine if the current time period belongs to a time period with a relatively small number of target vehicles (i.e., the number of time periods with fewer vehicles than the current time period is greater than a preset time period threshold, such as 18).

[0137] Decision logic: If no, it indicates that the current period is a peak time of resource scarcity (e.g., a large number of target vehicles, requiring reliable log reading and processing). To ensure the reliability of reading and processing vehicle log data, it is necessary to strictly limit reading to vehicles that are in the "successful read period" within this time frame. If yes, it indicates that the current period is a low time of resource availability, and we can proceed to the next step to consider whether to relax the strategy to help more vehicles.

[0138] S432 determines whether there are any vehicles that failed to read among the target vehicles in the time period. If yes, proceed to the next step. If no, determine that in the time period, all vehicles that belong to the time period of successful reading can be processed to read the vehicle log data.

[0139] S433 Based on the proportion of vehicles that failed to read among the target vehicles in the time period, determine the proportion of vehicles that failed to read, and determine whether the proportion of vehicles that failed to read is greater than a preset failure proportion threshold. If yes, proceed to the next step; otherwise, determine that in the time period, all vehicles that belong to the time period of successful reading can be processed to read vehicle log data.

[0140] In the two steps above, the urgency of the task within the time period is checked. S432 determines whether there are any "failed read vehicles" among the target vehicles for matching in the current time period. S433 determines whether the proportion of failed reads of these vehicles is greater than the preset failure proportion threshold (e.g., 30%).

[0141] If any of the above judgments is negative, it means that although this period is idle, it is not urgent (there are few or no vehicles that urgently need to supplement data). Therefore, we can freely read all vehicles that can be read in this period to make full use of idle resources. If S433 is positive, it means that this idle period is precisely the concentrated reading window for "urgent vehicles", and we need to proceed to the final step to prevent non-urgent vehicles from occupying valuable resources.

[0142] S434 identifies vehicles that have successfully read data during the specified time period as potential reading vehicles. It then determines whether the number of successful reading time periods for the potential reading vehicles is greater than a preset value for the number of time periods. If so, it determines that the vehicle log data reading process will not be performed on the potential reading vehicles during the specified time period. If not, it determines that the vehicle log data reading process will be performed on the potential reading vehicles during the specified time period.

[0143] Specifically, in the above steps, individual vehicle priority is determined. Potential reading vehicles refer to all vehicles that belong to the "read success period" in the current time period (including only non-target vehicles).

[0144] Judgment: For a potential vehicle to be read, determine if the total number of "successful read time slots" owned by the vehicle exceeds a preset value (e.g., 8 slots). If yes: This indicates the vehicle has "many opportunities," meaning there are many other times of the day when it can be read. During the current urgent and idle time slot, it should not occupy resources; the opportunity should be given to vehicles that need it more. If no: This indicates the vehicle has "few opportunities," meaning there are very few time windows for reading. Therefore, it should be read during the current time slot.

[0145] Example 2

[0146] Secondly, the present invention provides a computer system comprising: a memory and a processor connected in communication, and a computer program stored in the memory and capable of running on the processor, wherein the processor executes the above-described method for enhancing data security of vehicle log collection when running the computer program.

[0147] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the embodiments of apparatus, devices, and non-volatile computer storage media are basically similar to the method embodiments, so the descriptions are relatively simple; relevant parts can be referred to the descriptions of the method embodiments.

[0148] The foregoing has described specific embodiments of this specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims may be performed in a different order than that shown in the embodiments and may still achieve the desired result. Furthermore, the processes depicted in the drawings do not necessarily require the specific or sequential order shown to achieve the desired result. In some embodiments, multitasking and parallel processing are possible or may be advantageous.

[0149] The above description is merely one or more embodiments of this specification and is not intended to limit this specification. Various modifications and variations can be made to the one or more embodiments of this specification by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principle of one or more embodiments of this specification should be included within the scope of the claims of this specification.

Claims

1. A method for enhancing data security in vehicle log collection, characterized in that, Specifically, it includes: Based on the reading data of the vehicle log, determine the reading failure of the vehicle date data in different time periods. Based on the reading failure, determine the vehicle with the target reading strategy and use it as the target vehicle. Once the target vehicle data is acquired and the distribution data of the successful reading time periods of the target vehicle is combined, and the reading data of the vehicle log data of the target vehicle needs to be considered, proceed to the next step. Based on the reading data of the target vehicle, identify the vehicles that failed to read the data in the most recent preset time period. Based on the reading data of the vehicle log data of the vehicles that failed to read the data, determine when it is possible to read the vehicle log data of other vehicles. Based on the reading data of the vehicle log data of the target vehicle in different time periods, identify the vehicles that will be read the vehicle log data in the specified time period. The method for determining the vehicles that will be reading and processing vehicle log data during the specified time period is as follows: Based on the reading data of the vehicle log data of the target vehicle in different time periods, the target vehicle that belongs to the time period of successful reading is identified and used as the matching target vehicle. Based on the composition data of the vehicles that failed to be read in the target vehicles, determine the proportion of the number of vehicles that failed to be read in the target vehicles; Based on the number of target vehicles in the time period and the proportion of vehicles that failed to read the target vehicles, the vehicles that perform vehicle log data reading and processing in the time period are determined. Based on the target vehicles in the specified time period and the proportion of vehicles that failed to read the target vehicles, the vehicles that perform vehicle log data reading and processing in the specified time period are determined, specifically including: The number of matching target vehicles in the time period is obtained. Based on the number of matching target vehicles in the time period, the sorting result of the time period is determined. Based on the sorting result, if the number of time periods that are greater than the number of matching target vehicles in the time period is not greater than a preset time period number threshold, then it is determined that in the time period, only vehicles that belong to the successfully read time period in the time period will be processed for reading vehicle log data.

2. The data security enhancement method for vehicle log collection as described in claim 1, characterized in that, The vehicle log data includes monitoring data from sensor devices and vehicle driving data during vehicle operation.

3. The data security enhancement method for vehicle log collection as described in claim 1, characterized in that, The data read includes the time period for reading the vehicle's onboard date data and the reading failure situations for different time periods.

4. The data security enhancement method for vehicle log collection as described in claim 1, characterized in that, The method for determining the target vehicle is as follows: Based on the read failure scenarios, determine the number of read failures for the vehicle in different time periods; Based on the number of read failures of vehicle date data in different time periods, and in combination with the number of read failures of vehicle log data in the same time period, the read failure probability in the specified time period is determined. Based on the read failure probability at different time periods, determine whether the vehicle is the target vehicle.

5. The data security enhancement method for vehicle log collection as described in claim 4, characterized in that, The read failure probability is determined based on the proportion of read failures in the number of reads of vehicle date data during the time period.

6. The data security enhancement method for vehicle log collection as described in claim 4, characterized in that, Determining whether the vehicle is the target vehicle based on the read failure probability at different time periods specifically includes: Determine whether the vehicle exists in a period where the probability of reading failure is less than a preset failure probability threshold. If not, proceed to the next step; if so, determine that the vehicle does not belong to the target vehicle. Determine whether the average reading failure probability of the vehicle in different time periods is greater than the preset recognition probability value. If yes, then determine that the vehicle is the target vehicle; otherwise, proceed to the next step. The time period during which the probability of vehicle reading failure is not greater than the preset recognition probability value is taken as the reading success time period. It is determined whether the number of reading success time periods of the vehicle is greater than the preset threshold for the number of success time periods. If not, the vehicle is determined to be the target vehicle; if so, the vehicle is determined not to be the target vehicle.

7. The data security enhancement method for vehicle log collection as described in claim 1, characterized in that, The target vehicle is a vehicle whose onboard log data is read and processed at different times throughout the day.

8. A computer system, comprising: A memory and processor connected in communication, and a computer program stored in the memory and capable of running on the processor, characterized in that, when the processor runs the computer program, it executes a data security enhancement method for vehicle log collection as described in any one of claims 1-7.

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