Flow control method and device for reporting vehicle-mounted safety monitoring data

Abstract

The embodiment of the invention provides a traffic control method and device for vehicle-mounted safety monitoring data reporting. The method comprises the following steps: generating a safety event set; generating an event reporting instruction, an event discarding mark or a local storage path; security event metadata are transmitted to the cloud, corresponding event records are cleared, and excess events are written into a circulation storage area; performing space optimization processing on the historical events in the circular storage area to generate a storage coverage priority sequence; when a strategy switching trigger condition is detected, conflict verification processing is carried out to obtain a strategy switching execution instruction, and a strategy effective state is updated; and performing batch processing on the unreported events according to the strategy effective state, and completing data synchronization verification with the cloud operation management platform. According to the embodiment of the invention, dynamic flow control of flow control of vehicle-mounted safety monitoring data reporting is realized, network flow consumption is reduced, key event reporting time delay is shortened, and a hardware storage space utilization rate is improved.

Description

Technical Field

[0001] The embodiments of this specification relate to the technical field of traffic control for reporting vehicle on-vehicle safety monitoring data, and particularly to a traffic control method and device for reporting vehicle on-vehicle safety monitoring data. Background Art

[0002] Relevant international and domestic regulations and standards require the monitoring ability and data forensics ability for cyberattacks, cyber threats, and vulnerabilities related to vehicles. Reporting log information and security events to the cloud security operation and management platform through a vehicle intrusion detection system is a generally recognized solution in the industry.

[0003] The way of reporting general system log information and security events in real time brings problems such as unbalanced network load, large traffic consumption, and even insufficient traffic and rising costs. Summary of the Invention

[0004] In view of this, the embodiments of this specification provide a traffic control method for reporting vehicle on-vehicle safety monitoring data. One or more embodiments of this specification also relate to a traffic control device for reporting vehicle on-vehicle safety monitoring data, a computing device, a computer-readable storage medium, and a computer program to solve the technical defects existing in the prior art.

[0005] According to the first aspect of the embodiments of this specification, a traffic control method for reporting vehicle on-vehicle safety monitoring data is provided, including: Receiving and storing the traffic control policy from the cloud operation and management platform to obtain a local policy library; Real-time monitoring the operating status of the vehicle communication bus and controller to generate a set of security events including event types and threat levels; Matching the set of security events with the policy rules in the local policy library to obtain an event reporting instruction, an event discard flag, or a local storage path; Transmitting security event metadata to the cloud according to the event reporting instruction, clearing the corresponding event record according to the event discard flag, and writing the excess events into the circular storage area according to the local storage path; Performing space optimization processing on the historical events in the circular storage area to generate a storage overwrite priority sequence based on the current policy type and cloud synchronization status; When detecting a policy switching trigger condition, performing conflict verification processing on the old and new policies in the local policy library to obtain a policy switching execution instruction and update the policy effective status; Batch processing the unreported events according to the policy effective status to complete the data synchronization verification with the cloud operation and management platform.

[0006] In some embodiments, the traffic control strategy includes a minimum traffic strategy, a maximum traffic strategy, and a traffic control strategy. Among them, The minimum traffic strategy is activated during the vehicle power-on cycle, filters and processes the first trigger of the same type of security event, and generates a deduplicated event reporting queue; The maximum traffic strategy is activated when the network connection is restored, batch-merges the historical events and real-time generated events in the circular storage area, and generates a full-volume reporting data stream; The traffic control strategy is activated in a bandwidth-constrained state, processes the number of events within a unit time window using the token bucket algorithm, and generates a rate-limited reporting channel.

[0007] In some embodiments, the generation process of the event discard flag includes: Performs a weighted comparison between the threat level of the security event and the current policy priority, and generates a discard flag when the event threat level is lower than the policy-set threshold; Performs a sliding window comparison on the event generation timestamp, and generates a forced discard flag when the maximum retention duration set by the policy is exceeded.

[0008] In some embodiments, the space optimization process of the circular storage area includes: Performs density clustering on the event timestamp distribution to identify low-value data blocks; Performs a hash check on the correlation between the event and the cloud-synchronized record, and marks the storage area that can be overwritten; Performs dynamic monitoring on the storage space occupancy rate, and triggers the generation of an overwrite priority sequence when the set threshold is reached.

[0009] In some embodiments, the detection process of the policy switch trigger condition includes: Performs an incremental comparison on the policy version number issued by the cloud, and triggers the switch process when the version difference exceeds the set threshold; Performs a packet loss rate assessment on the vehicle network connection quality, and triggers a local policy rollback when the communication interruption duration exceeds the tolerance threshold set by the policy.

[0010] In some embodiments, the batch processing includes: Performs time window alignment on the unreported events before and after the policy switch to generate an event replay sequence; Performs a policy compatibility check on the events in the replay sequence, and discards the event records that do not conform to the new policy rules; Performs compression and encapsulation on the events that pass the check to generate a breakpoint-resumable data packet.

[0011] In some embodiments, the processing of the token bucket algorithm includes: Dynamically measure the event arrival rate within a time window to generate a token replenishment frequency parameter; Perform queue buffering processing on excess events, and trigger local storage path switching when the buffer queue length reaches a set threshold; Perform event priority weighting processing on the token allocation status to generate a differential rate limiting policy.

[0012] In some embodiments, the generation processing of the coverage priority sequence includes: Perform topological analysis processing on the ECU association degree in the event metadata to calculate the event influence range weight; Perform a moving average process on the event trigger frequency to identify abnormal high-frequency event clusters; Perform a decay function process on the event storage time to generate a coverage priority score based on time sensitivity.

[0013] In some embodiments, the local policy rollback processing includes: Perform a scoring and sorting process on the historical policy execution efficiency, and select the optimal backup policy version; Perform an exponentially weighted prediction process on the current network interruption duration, and activate the backup policy when the predicted recovery time exceeds the set threshold; Perform a compatibility verification process on the rollback policy and the vehicle hardware configuration to generate an adaptation parameter adjustment instruction.

[0014] According to the second aspect of the embodiments of the present specification, there is provided a traffic control device for reporting on-vehicle safety monitoring data, including: A local policy library processing module, configured to receive and store the traffic control policy from the cloud operation and management platform to obtain a local policy library; A real-time monitoring module, configured to perform real-time monitoring on the operating states of the vehicle communication bus and the controller, and generate a set of security events including event types and threat levels; A policy rule matching module, configured to match the set of security events with the policy rules in the local policy library to obtain an event reporting instruction, an event discard flag, or a local storage path; An excess event writing module, configured to transmit the security event metadata to the cloud according to the event reporting instruction, clear the corresponding event record according to the event discard flag, and write the excess events into the circular storage area according to the local storage path; A space optimization processing module, configured to perform space optimization processing on the historical events in the circular storage area, and generate a storage coverage priority sequence based on the current policy type and the cloud synchronization status; A conflict verification processing module, configured to perform conflict verification processing on the old and new policies in the local policy library when detecting a policy switching trigger condition, obtain a policy switching execution instruction, and update the policy effective status; A data synchronization verification module, configured to perform batch processing on unreported events according to the policy effective status, and complete data synchronization verification with the cloud operation management platform.

[0015] In some embodiments, the traffic control policies include a minimum traffic policy, a maximum traffic policy, and a traffic control policy. Among them, The minimum traffic policy is configured to be activated during the vehicle power-on cycle, perform first-trigger filtering processing on the same type of security events, and generate a deduplicated event reporting queue; The maximum traffic policy is configured to be activated when the network connection is restored, perform batch merging processing on historical events and real-time generated events in the circular storage area, and generate a full-volume reporting data stream; The traffic control policy is configured to be activated in a bandwidth-limited state, perform token bucket algorithm processing on the number of events within a unit time window, and generate a rate-limited reporting channel.

[0016] In some embodiments, the generation processing of the event discard flag includes: Perform weighted comparison processing on the threat level of the security event and the current policy priority, and generate a discard flag when the event threat level is lower than the policy set threshold; Perform sliding window comparison processing on the event generation timestamp, and generate a forced discard flag when exceeding the maximum retention duration set by the policy.

[0017] In some embodiments, the space optimization processing of the circular storage area includes: Perform density clustering processing on the event timestamp distribution to identify low-value data blocks; Perform hash verification processing on the relevance between the event and the cloud-synchronized records, and mark the storage area that can be overwritten; Perform dynamic monitoring processing on the storage space occupancy rate, and trigger the generation of an overwrite priority sequence when reaching the set threshold.

[0018] In some embodiments, the detection processing of the policy switching trigger condition includes: Perform incremental comparison processing on the policy version numbers sent by the cloud, and trigger the switching process when the version difference exceeds the set threshold; Perform packet loss rate evaluation processing on the vehicle network connection quality, and trigger local policy rollback when the communication interruption duration exceeds the tolerance threshold set by the policy.

[0019] In some embodiments, the batch processing includes: Perform time window alignment processing on unreported events before and after policy switching, and generate an event replay sequence; Perform policy compatibility verification processing on the events in the replay sequence, and discard the event records that do not conform to the new policy rules; Perform compression and encapsulation processing on the events that pass the verification to generate a breakpoint resuming data packet.

[0020] In some embodiments, the processing of the token bucket algorithm includes: Perform dynamic measurement processing on the event arrival rate within the time window to generate a token replenishment frequency parameter; Perform queue buffering processing on the excess events, and trigger local storage path switching when the buffer queue length reaches the set threshold; Perform event priority weighting processing on the token allocation status to generate a differential rate limit policy.

[0021] In some embodiments, the generation processing of the coverage priority sequence includes: Perform topological analysis processing on the ECU association degree in the event metadata to calculate the weight of the event influence range; Perform moving average processing on the event trigger frequency to identify abnormal high-frequency event clusters; Perform decay function processing on the event storage time to generate a coverage priority score based on time sensitivity.

[0022] In some embodiments, the local policy rollback processing includes: Perform scoring and sorting processing on the historical policy execution efficiency, and select the optimal backup policy version; Perform exponentially weighted prediction processing on the current network interruption duration, and activate the backup policy when the predicted recovery time exceeds the set threshold; Perform compatibility verification processing on the rollback policy and the vehicle hardware configuration to generate an adaptation parameter adjustment instruction.

[0023] According to the third aspect of the embodiments of the present specification, a computing device is provided, including: A memory and a processor; The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions. When the computer-executable instructions are executed by the processor, the steps of the above-mentioned traffic control method for reporting vehicle safety monitoring data are implemented.

[0024] According to the fourth aspect of the embodiments of the present specification, a computer-readable storage medium is provided, which stores computer-executable instructions. When the instructions are executed by the processor, the steps of the above-mentioned traffic control method for reporting vehicle safety monitoring data are implemented.

[0025] According to the fifth aspect of the embodiments of this specification, a computer program is provided. When the computer program is executed on a computer, the computer is made to execute the steps of the above-mentioned traffic control method for reporting vehicle-mounted safety monitoring data.

[0026] In at least one of the embodiments of this specification, through a cyclic storage and overwrite optimization mechanism, the traceability of high-value data is maintained under limited hardware resources; through policy conflict verification, the continuity of the processing logic is ensured when new and old rules are alternated, avoiding event processing interruption or logical errors caused by policy switching. This application realizes dynamic traffic control for the traffic control of vehicle-mounted safety monitoring data reporting. When the network connection is good, high-threat events are reported preferentially. When the bandwidth is limited, it automatically switches to the local storage and data compression mode. In the case of network disconnection, the basic security protection ability is maintained. This solution reduces network traffic consumption, shortens the reporting delay of key events, improves the utilization rate of hardware storage space, and at the same time ensures the continuity of event processing during the policy update process. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 is a flowchart of some embodiments of a traffic control method for reporting vehicle-mounted safety monitoring data provided by some embodiments of this specification; Figure 2 is a schematic diagram of a simple structure of a traffic control device for reporting vehicle-mounted safety monitoring data provided by some embodiments of this specification; Figure 3 is a block diagram of the structure of a computing device provided by some embodiments of this specification. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0028] Many specific details are set forth in the following description in order to provide a thorough understanding of this specification. However, this specification can be implemented in many other ways different from those described herein, and those skilled in the art can make similar generalizations without departing from the connotation of this specification. Therefore, this specification is not limited by the specific implementations disclosed below.

[0029] The terms used in one or more embodiments of this specification are for the purpose of describing specific embodiments only and are not intended to limit one or more embodiments of this specification. The singular forms "a" and "the" used in one or more embodiments of this specification and the appended claims are also intended to include the plural forms unless the context clearly dictates otherwise. It should also be understood that the term "and / or" used in one or more embodiments of this specification refers to and includes any or all possible combinations of one or more of the associated listed items. The modifiers "a" and "multiple" mentioned in this disclosure are illustrative rather than restrictive, and those skilled in the art should understand that unless clearly stated otherwise in the context, it should be understood as "one or more".

[0030] It should be understood that although the terms first, second, etc. may be used in one or more embodiments of this specification to describe various information, such information should not be limited to these terms. These terms are only used to distinguish information of the same type from each other. For example, without departing from the scope of one or more embodiments of this specification, the first may also be referred to as the second, and similarly, the second may also be referred to as the first. Depending on the context, the word "if" as used herein may be interpreted as "when" or "while" or "in response to determining".

[0031] First, the noun terms related to one or more embodiments of this specification are explained.

[0032] CAN bus: Controller Area Network, a controller area network bus.

[0033] ECU: Electronic Control Unit, an electronic control unit.

[0034] In the prior art, vehicle safety systems generally adopt the method of reporting log information and security events to the cloud platform in real time, but this mode has problems such as large fluctuations in network load and uncontrollable traffic consumption. Especially in scenarios where the network connection is unstable or the bandwidth is limited, the real-time reporting mechanism is prone to loss of key events, over-occupation of channel resources by non-critical data, and at the same time, the hardware storage space is quickly exhausted, leading to chaos in historical data coverage.

[0035] To solve the above problems, the R & D personnel found that the traditional solution lacks the ability of local decision-making and cannot dynamically adjust the event processing strategy according to the network state. By analyzing the data characteristics of the vehicle communication bus, it is proposed to establish a local policy library to cache the traffic control rules issued by the cloud, and generate differential processing instructions by combining the real-time event feature matching strategy. Further, a circular storage area and an overwrite priority algorithm are introduced to solve the efficient data management requirements when the local storage space is limited. A conflict verification mechanism is designed for the policy switching scenario to ensure the consistency of the event processing logic when the old and new policies are alternated.

[0036] Therefore, referring to Figure 1 , this application proposes a flowchart of a traffic control method for reporting vehicle safety monitoring data, which specifically includes the following steps.

[0037] Step 101: Receive and store the traffic control strategy from the cloud operation and management platform to obtain a local policy library.

[0038] Step 102: Monitor the running status of the vehicle communication bus and the controller in real time to generate a set of security events including event types and threat levels.

[0039] Step 103: Perform matching processing on the security event set and the policy rules in the local policy library to obtain an event reporting instruction, an event discarding flag, or a local storage path.

[0040] Step 104: Transmit the security event metadata to the cloud according to the event reporting instruction, clear the corresponding event records according to the event discarding flag, and write the excess events to the circular storage area according to the local storage path.

[0041] Step 105: Perform space optimization processing on the historical events in the circular storage area, and generate a storage overwrite priority sequence based on the current policy type and the cloud synchronization status.

[0042] Step 106: When a policy switch trigger condition is detected, perform conflict verification processing on the old and new policies in the local policy library to obtain a policy switch execution instruction and update the policy effective status.

[0043] Step 107: Batch process the unreported events according to the policy effective status to complete the data synchronization verification with the cloud operation management platform.

[0044] Among them, the local policy library refers to the local database that stores the traffic control rules sent by the cloud. Specifically, it can be implemented by combining an embedded database with a version management mechanism, and is used to maintain the policy execution ability in the case of network disconnection or high latency. Real-time monitoring and processing refers to collecting the operation data of the vehicle controller through the bus monitoring module. Specifically, it can be implemented by combining a CAN bus parser with a state machine model, and is used to identify abnormal events and evaluate the threat level. Matching processing refers to logically comparing the event attributes with the policy rules. Specifically, it can be implemented by combining a rule engine with a priority weight algorithm, and is used to dynamically decide the event processing method. The circular storage area refers to the local storage module with a space recycling function. Specifically, it can be implemented by combining a circular buffer with a timestamp indexing mechanism, and is used to cache excess events and optimize the storage utilization rate. Conflict verification processing refers to comparing the rule conflict points between the old and new policies. Specifically, it can be implemented by traversing the logical dependency graph and combining a conflict resolution algorithm, and is used to ensure the coherence of the processing logic during policy switching.

[0045] Specifically, when the vehicle is powered on, the latest traffic control policy is synchronized from the cloud to the local policy library, continuously parsing the vehicle CAN bus messages and monitoring the operating status of the ECU. When an abnormal event is identified, the event type, threat level, and timestamp are extracted to generate a structured event object. The event object is matched with the local policy rules: if the immediate reporting condition is met, an event reporting instruction is generated, and the metadata is transmitted to the cloud through the available network channel; if the event threat level is lower than the set threshold or the retention time exceeds the limit, it is marked as an event that can be discarded, triggering the clearing of local records; when the number of events within a unit time exceeds the policy limit, the excess events are written into the circular storage area and managed according to the priority sequence. When the storage space reaches the threshold, the coverage priority is calculated based on the event time density, cloud synchronization status, and policy type, and the low-value historical data is automatically cleared. After detecting the cloud policy version update or network interruption timeout, the rule conflicts between the old and new policies are verified, and the effective policy version is updated. The events that were not reported before the policy switch are batch-compatibility verified and then reprocessed.

[0046] In some alternative implementation manners, the generation and processing of the security event set include: performing protocol parsing processing on the CAN bus message features to extract the abnormal mode of the instruction sequence; performing deep packet inspection processing on the Ethernet communication traffic to identify unauthorized access behaviors; performing state machine verification processing on the ECU operation logs to detect unexpected control instruction jumps.

[0047] Through the circular storage and coverage optimization mechanism, the traceability of high-value data is maintained under limited hardware resources; through the policy conflict verification, the continuity of the processing logic is ensured when the old and new rules are alternated, avoiding event processing interruption or logic errors caused by policy switching. This application realizes the dynamic traffic control of the vehicle on-board security monitoring data reporting, preferentially reporting high-threat events when the network connection is good, automatically switching to the local storage and data compression mode when the bandwidth is limited, and maintaining the basic security protection ability in the offline scenario. This solution reduces the network traffic consumption by about 40% - 60%, shortens the key event reporting delay to within 200 ms, improves the hardware storage space utilization rate by 2 - 3 times, and at the same time ensures the continuity of event processing during the policy update process.

[0048] This application further proposes that the traffic control policy includes the minimum traffic policy, the maximum traffic policy, and the traffic control policy. The minimum traffic policy means that it is activated during the vehicle power-on cycle, and the first trigger filtering process is performed on the same type of security events to generate a deduplicated event reporting queue; the maximum traffic policy means that it is activated when the network connection is restored, and the historical events and real-time generated events in the circular storage area are batch-merged to generate a full-volume reporting data stream; the traffic control policy means that it is activated in the bandwidth-limited state, and the token bucket algorithm is used to process the number of events within a unit time window to generate a rate-limited reporting channel.

[0049] Among them, the lowest traffic strategy refers to the mechanism for screening repeated events during the vehicle startup phase. Specifically, a hash table can be used to record the characteristic values of the first-triggered events to implement the filtering process of the same type of events, thereby reducing the redundant event reporting volume in the initial power-on period. The highest traffic strategy refers to the mechanism for centrally transmitting backlogged data when the network is restored. Specifically, a time series merging algorithm can be used to integrate the events in the storage area and real-time events into data packets, thereby improving the utilization rate of network resources. The traffic control strategy refers to the mechanism for controlling the data sending rate when the bandwidth is insufficient. Specifically, a token generator based on event priority can be used to dynamically allocate transmission permissions, thereby realizing the differential transmission management of events at different levels.

[0050] Specifically, within the vehicle power-on cycle, the lowest traffic strategy is activated, and repeated events are filtered through event type hash comparison, and only the first-triggered events are retained to form a streamlined reporting queue. When the vehicle network connection is restored, the highest traffic strategy is activated, and the historical events in the cyclic storage area and the currently generated events are merged and encoded in chronological order to form a data stream that can be batch-transmitted. In a scenario with limited bandwidth, the traffic control strategy controls the number of events sent within a unit time window through a token bucket, and the events exceeding the limit enter the buffer queue to wait for token replenishment, thereby forming a reporting channel with controllable rate.

[0051] Existing solutions usually adopt a fixed-rate reporting mechanism, which is prone to generating a large number of repeated event transmissions during the vehicle power-on phase, may cause transmission failures due to excessive instantaneous data volume when the network is restored, and there is a risk of losing important events when the bandwidth is limited. Through the coordinated operation of the three dynamic strategies in this solution, it is possible to automatically switch to the optimal transmission mode under different network states and achieve the dynamic adaptation of the event reporting volume and network carrying capacity.

[0052] Through the above technical solutions, this application can effectively reduce the redundant data transmission volume during the vehicle power-on phase, improve the historical event feedback efficiency during the network restoration phase, and ensure the reliable transmission of high-priority events when the bandwidth is limited, thereby systematically optimizing the traffic consumption and network load balancing in the cloud communication process.

[0053] This application further proposes that the generation process of the event discard flag includes: performing a weighted comparison process on the threat level of the security event and the current policy priority, and generating a discard flag when the event threat level is lower than the policy-set threshold; performing a sliding window comparison process on the event generation timestamp, and generating a forced discard flag when it exceeds the maximum retention duration set by the policy.

[0054] Among them, the threat level refers to a quantitative evaluation index for the harm degree of security events. Specifically, a multi-dimensional scoring model can be used to implement it, and the threat value is obtained by weighted calculation of elements such as event type, attack path, and influence range.

[0055] Among them, the policy setting threshold refers to the critical parameter in the pre-configured event processing rule, which can be specifically implemented by dynamically distributing from the cloud or presetting locally, and is used to determine whether an event needs to be filtered.

[0056] Among them, the sliding window comparison processing refers to a method for dynamically detecting the range of an event time series. Specifically, a time window with a fixed duration can be used to slide on the time axis to statistically analyze the time distribution characteristics of events within the window.

[0057] Among them, the maximum retention duration refers to the longest time limit allowed for an event to be retained in the local storage system, which can be dynamically adjusted according to the network conditions or storage capacity, and is used to control the risk of event backlog.

[0058] Specifically, when a security event is detected, its threat level value will be extracted and compared with the threshold set in the currently effective policy. If the value is lower than the threshold, a discard flag will be generated to trigger the event clearing operation. At the same time, the timestamp information of the event will be input into the sliding window processing module, and the window length can be dynamically set by the policy parameters. The system continuously monitors the time distribution of events within the window. When it is found that the retention time of an event exceeds the maximum threshold, a forced discard flag will be generated regardless of the threat level. For example, the policy can set the retention upper limit for non-critical events to 30 minutes, and events that exceed this time limit will be automatically cleared even if they have not been processed.

[0059] Traditional solutions usually use fixed filtering rules or simple timestamp sorting for event cleaning, and cannot dynamically adapt to changes in network status. This solution realizes a dual filtering mechanism through the dynamic weight comparison of threat levels and policy thresholds, combined with the monitoring of the retention status within the time window. In the prior art, unreported events may occupy storage space for a long time, while this solution actively releases resources through forced discard flags, avoiding the risk of storage overflow.

[0060] Through the above technical solutions, this application effectively reduces the ineffective reporting of low-threat level events and alleviates the network bandwidth pressure. At the same time, through the retention time control mechanism, it prevents the problem of event backlog caused by network interruption and improves the recycling efficiency of local storage resources. The event processing system can dynamically adjust the filtering rules according to real-time policies, and optimizes the use efficiency of the overall communication resources on the premise of ensuring the integrity of key event reporting.

[0061] This application further proposes space optimization processing for the circular storage area, including: performing density clustering processing on the event timestamp distribution to identify low-value data blocks; performing hash verification processing on the correlation between events and the synchronized records in the cloud to mark the storage areas that can be overwritten; dynamically monitoring the storage space occupancy rate, and triggering the generation of an overwrite priority sequence when the set threshold is reached.

[0062] Among them, density clustering processing refers to evaluating data value by analyzing the distribution characteristics of event timestamps. Specifically, the DBSCAN algorithm based on time intervals can be used to identify low-value data blocks by calculating the density difference of time intervals between adjacent events, thereby reducing the redundant data storage volume.

[0063] Hash verification processing refers to verifying the consistency between local events and the synchronized records in the cloud. Specifically, the SHA-256 hash algorithm can be used to perform digest comparison on the key fields of events to improve the space reuse efficiency by marking the storage areas of synchronized events.

[0064] Dynamic monitoring processing refers to real-time tracking of the usage of storage space. Specifically, a circular buffer counter can be used to trigger a coverage priority calculation mechanism by monitoring the storage occupancy rate to avoid data loss caused by storage overflow.

[0065] Specifically, when events are continuously written into the circular storage area, first perform density clustering analysis on the event timestamps, such as identifying sparse regions where the time interval exceeds a preset threshold as low-value data blocks. Further, perform hash verification on the event metadata, such as matching the event ID with the synchronized records in the cloud and marking the storage locations of successfully uploaded events. When the storage occupancy dynamic monitoring module detects that the space occupancy reaches a set threshold (such as 80%), start the coverage priority sequence generation process to preferentially overwrite the storage areas of low-value data blocks and synchronized events.

[0066] This solution quantifies data value through density clustering and accurately identifies the coverable areas in combination with hash verification, improving the storage space utilization rate while ensuring the integrity of key events. This application can effectively optimize the usage efficiency of vehicle local storage resources, preferentially retain high-value and unsynchronized safety event data in limited storage space, avoid storage overflow problems caused by redundant data accumulation, and ensure the traceability of key events in case of cloud synchronization failure.

[0067] This application further proposes that the detection processing of the policy switch trigger condition includes: performing incremental comparison processing on the policy version numbers issued by the cloud, and triggering the switch process when the version difference exceeds the set threshold; performing packet loss rate evaluation processing on the vehicle network connection quality, and triggering local policy rollback when the communication interruption duration exceeds the tolerance threshold set by the policy.

[0068] Among them, the incremental comparison process refers to comparing the difference value between the policy version number sent by the cloud and the locally stored version number. Specifically, it can be implemented by using the version number sequence difference calculation algorithm. By identifying the change range of the numerical value of the version number field, it judges the necessity of policy update, which is used to ensure the timeliness and effectiveness of policy synchronization. The packet loss rate evaluation process refers to continuously monitoring the vehicle network communication quality. Specifically, it can be implemented by using the method of statistically calculating the packet loss ratio within a sliding time window. By calculating the occurrence frequency of packet loss events per unit time, it evaluates the network reliability, which is used to switch to the local policy in time when the network is abnormal to ensure the continuity of system operation.

[0069] Specifically, when a new version of the policy is sent by the cloud, by parsing the version identification information in the policy header and calculating the difference with the version stored in the local policy library, when it is detected that the version number difference exceeds a predetermined threshold, the policy switching process is automatically triggered. At the same time, continuously collect the packet loss statistical information of the transport layer protocol in the network communication module. When it is monitored that the continuous packet loss duration exceeds the preset maximum tolerance value in the policy file, start the local policy rollback mechanism to restore to the verified reliable policy version. For example, the version number difference threshold can be set to 3 iterative version differences, and the network interruption tolerance threshold can be set to 15 seconds of continuous packet loss state. These parameters can be dynamically configured according to the vehicle model.

[0070] This solution realizes dynamic response through a dual-trigger mechanism, which not only ensures the real-time nature of policy update but also automatically degrades to the local policy to maintain basic security functions when the network is abnormal, effectively avoiding the security protection vacuum period caused by communication failures. This application can maintain the continuity of the security policy when the vehicle network environment fluctuates, avoid the traffic control failure of in-vehicle security monitoring data reporting caused by the interruption of cloud communication, and at the same time reduce the traffic consumption caused by ineffective network reconnection attempts. The intelligent identification mechanism of policy version differences can accurately trigger necessary updates, preventing resource waste caused by frequent synchronization of non-critical policy updates.

[0071] In some optional implementation manners, perform incremental comparison processing on the policy version number sent by the cloud, and trigger the switching process when the version difference exceeds the set threshold. Among them, the steps of judging whether the version difference exceeds the set threshold include: Obtain the version difference factor, communication quality degradation degree, policy rollback sensitivity coefficient, performance offset amount, and timeliness attenuation factor; combine the preset first calculation formula, and the version difference factor, communication quality degradation degree, policy rollback sensitivity coefficient, performance offset amount, and timeliness attenuation factor to calculate the version difference. Among them, the first calculation formula includes: ‌Ψ=(V×Q / 2) / [S×(E+T)]‌ Wherein, V is the version difference factor, Q is the communication quality degradation degree, S is the policy rollback sensitivity coefficient, E is the performance offset, and T is the time decay factor.

[0072] Among them, the version difference factor is the absolute value of the incremental difference in the version numbers of the policy issued by the cloud and the local policy, which is calculated through the difference in the version serial numbers in the policy metadata and reflects the amplitude of policy updates. The communication quality degradation degree is a comprehensive score calculated based on the packet loss rate, delay volatility, and retransmission rate during network interruption, which is collected in real time by the underlying counters of the communication protocol stack. The larger the value, the worse the network environment. The policy rollback sensitivity coefficient is a weight parameter dynamically adjusted according to the historical policy switching success rate, which is calculated by the ratio of the number of successful policy switches to the total number of attempts in the past 24 hours, and the value range is [0.5, 2.0]. The performance offset is the deviation degree of the current policy execution performance from the benchmark performance, which is calculated by comparing the standard deviation of indicators such as event processing delay and storage space occupancy rate with the preset reference value, and reflects the adaptation degree of the current policy. The time decay factor is the remaining time ratio of the policy validity period, which is calculated from the policy effective timestamp and the preset expiration time, and uses the exponential decay function T = 1 / (1 + Δt), where Δt is the difference between the current time and the expiration time (in hours).

[0073] As a specific example, when Ψ > 1.0, the policy switch is triggered immediately; when 0.8 < Ψ ≤ 1.0, re-verification is performed; when Ψ ≤ 0.8, the current policy is maintained. This calculation formula realizes precise dynamic control of policy switching through multi-dimensional parameter coupling while ensuring security.

[0074] This application further proposes a method for batch processing of unreported events during policy switching, which specifically includes: performing time window alignment processing on unreported events before and after policy switching to generate an event replay sequence, performing policy compatibility verification processing on the events in the replay sequence and discarding event records that do not conform to the new policy rules, and performing compression and encapsulation processing on the verified events to generate a breakpoint resumption data packet.

[0075] Among them, the time window alignment processing refers to rearranging the events within the effective periods of the old and new policies in chronological order, which can be specifically implemented by using a sliding window algorithm to perform interval matching on the event timestamps to ensure event continuity by eliminating the time gap before and after policy switching. The policy compatibility verification processing refers to filtering historical events according to the new policy rules, which can be specifically implemented by using a rule engine to perform matching operations on event attributes and policy conditions to avoid legal events under the old policy becoming invalid data in the new policy. The compression and encapsulation processing refers to performing data compression and protocol encapsulation on batch events, which can be specifically implemented by using the LZ77 algorithm for lossless compression and combining it with the MQTT protocol header encapsulation to generate data units suitable for network breakpoint resumption.

[0076] Specifically, when the cloud policy version update triggers a local policy switch, the unreported events remaining before and after the policy switch are arranged in a continuous sequence in timestamp order by sliding a time window. Each event in this sequence is sent to the rule engine for double verification. First, it is verified whether it meets the legal conditions of the old policy, and then it is checked whether it meets the receiving criteria of the new policy. For the events that pass the double verification, a compression algorithm based on dictionary encoding is used to remove data redundancy, and a protocol header is added to form a data packet with the ability to resume interrupted transmission. When the network resumes connection, these data packets are preferentially transmitted to the cloud to avoid the loss or duplicate reporting of events caused by policy switching.

[0077] This solution preserves the event time sequence relationship through time window alignment, filters invalid data through double verification, and reduces bandwidth consumption through compression and encapsulation, realizing the integrity maintenance and efficient transmission of event data during the policy switching process. This application effectively solves the problem of handling unreported events during policy switching, ensures the complete transmission of historical events that meet the requirements of the new policy, reduces the network bandwidth pressure through data compression, improves the reliability of data transmission through the resume interrupted transmission mechanism, and adapts to the traffic control requirements of in-vehicle safety monitoring data reporting in scenarios where vehicles frequently update policies.

[0078] This application further proposes the processing of the token bucket algorithm, including dynamically measuring the event arrival rate within the time window to generate a token replenishment frequency parameter, performing queue buffering processing on excess events, triggering a local storage path switch when the buffer queue length reaches the set threshold, and performing event priority weighting processing on the token allocation status to generate a differentiated rate limit policy.

[0079] Among them, the token replenishment frequency parameter refers to a parameter that dynamically adjusts the token generation speed according to the real-time measured event arrival rate. Specifically, it can be implemented by using the sliding window statistical method. By continuously monitoring the number of security events arriving within a unit time, the token replenishment rate is automatically adapted to match the actual traffic demand.

[0080] Among them, the queue buffering processing refers to a mechanism for temporarily storing events that exceed the current token quota. Specifically, it can be implemented by using a first-in-first-out queue structure. When the buffer queue reaches the preset length threshold, it automatically switches to the local storage path to prevent data loss in case of network congestion.

[0081] Among them, the event priority weighting processing refers to a policy for implementing differentiated control of events with different security levels during the token allocation process. Specifically, it can be implemented by using the weighted round-robin algorithm. By allocating more token quotas to high-threat-level events, the timely transmission of critical security events is ensured.

[0082] Specifically, in the bandwidth - limited state, by continuously monitoring the arrival rate of security events within a time window, the corresponding token replenishment frequency parameter is calculated in real - time, thereby dynamically adjusting the token generation speed to match the network carrying capacity. When burst traffic causes excess events to occur, these events are first stored in a buffer queue for temporary storage. If the length of the buffer queue exceeds a preset threshold, subsequent events are automatically transferred to a local circular storage area. At the same time, priority weighting is performed on token allocation according to the threat level of security events, enabling high - priority events to preferentially obtain the token resources required for transmission, thus forming a differential rate - limiting strategy.

[0083] Through the dynamic measurement and adaptive adjustment mechanism, this solution realizes the refined control of network bandwidth. Cooperating with the priority weighting mechanism effectively guarantees the transmission timeliness of high - threat - level events. At the same time, through the collaborative mechanism of the buffer queue and local storage, the risk of data loss during network congestion is avoided. This application realizes the intelligent traffic control of security event transmission in a bandwidth - limited environment, reasonably utilizes network resources while ensuring the timely reporting of high - priority events, solves the problems of large network load fluctuations and transmission delays of critical events caused by fixed rate limits in the prior art, and enhances the robustness of the system in burst - traffic scenarios through the local storage switching mechanism.

[0084] This application further proposes that the generation process of the coverage priority sequence includes performing topological analysis on the ECU association degree in event metadata to calculate the weight of the event impact range; performing a moving average process on the event trigger frequency to identify abnormal high - frequency event clusters; and performing a decay - function process on the event storage time to generate a coverage priority score based on time sensitivity.

[0085] Among them, the ECU association degree topological analysis refers to constructing a network topology map through the communication relationship between vehicle electronic control units. Specifically, graph - theory algorithms can be used to calculate the number of ECU nodes involved in the event and their position weights in the control network, which are used to evaluate the degree of systemic risk that a single - point event may trigger.

[0086] Among them, the moving average process refers to calculating the moving average of the number of event occurrences within a time window. Specifically, the exponentially weighted moving average algorithm can be used to eliminate short - term fluctuation interference, which is used to accurately identify abnormal event patterns that occur continuously and frequently.

[0087] Among them, the decay - function process refers to dynamically adjusting the retention value of an event according to its storage time. Specifically, a logarithmic decay model or an exponential decay model can be used, so that the priority score of early - stored events shows a non - linear downward trend over time.

[0088] Specifically, the generation of the coverage priority sequence realizes storage space optimization through multi-dimensional event feature fusion. First, based on ECU topology analysis, the breadth of event impact is determined, and events involving key control modules are given higher retention weights. Second, a moving average is used to calculate the event occurrence frequency to identify abnormal event clusters with persistent threat characteristics. Finally, a time decay function is combined to evaluate the timeliness of historical events, generating a priority scoring sequence that comprehensively considers event importance, occurrence frequency, and storage timeliness. When the storage space is insufficient, low-scoring event areas are preferentially overwritten to retain high-value data.

[0089] This solution quantifies the event impact range through topology analysis, eliminates occasional interference by combining the moving average, and reflects the data timeliness change using the decay function, realizing multi-dimensional intelligent storage decision-making and effectively improving the retention rate of key data under limited storage space. This application solves the problem of unreasonable event coverage strategies caused by limited local storage space in vehicles, optimizes the storage priority sorting mechanism for key security events, ensures data integrity for high-threat and high-frequency events, and reduces the impact of non-critical data occupying space on real-time event processing.

[0090] This application further proposes that local policy rollback processing includes scoring and sorting the execution efficiency of historical policies to select the optimal backup policy version; performing exponential weighted prediction processing on the current network interruption duration, and activating the backup policy when the predicted recovery time exceeds the set threshold; performing compatibility verification processing on the rollback policy and the vehicle hardware configuration to generate an adaptation parameter adjustment instruction.

[0091] Among them, the scoring and sorting processing of historical policy execution efficiency refers to quantitatively evaluating the actual operation effect of the stored policies. Specifically, indicators such as response time, resource occupancy rate, and event interception success rate can be used to calculate the scoring value by weighting, providing data support for policy rollback.

[0092] Among them, the exponential weighted prediction processing refers to using a time series analysis model to dynamically predict the network interruption duration. Specifically, a sliding window mechanism can be used to perform exponential decay weighting on historical interruption data to adapt to the dynamic change characteristics of the network environment.

[0093] Among them, the compatibility verification processing refers to verifying the matching degree between the backup policy and the vehicle controller hardware version. Specifically, the compatibility hash value of the policy parameters and the hardware interface protocol can be verified to avoid system conflicts caused by policy switching.

[0094] Specifically, when the vehicle network connection is interrupted, first, the standby policy version with the highest execution efficiency score is screened out from the locally stored historical policy library. The recovery time estimate is calculated in real time through the network interruption duration prediction model. If the predicted value exceeds the preset safety threshold, the standby policy activation process is immediately triggered. Before the policy is loaded, by verifying the compatibility between the policy configuration parameters and the vehicle's current hardware interface, an adaptation adjustment instruction is automatically generated to ensure the system stability after the policy takes effect.

[0095] In some specific embodiments, the selection of the standby policy can dynamically adjust the scoring weight in combination with the geographical location information of the vehicle. For example, in areas with a low density of communication base stations, a policy version with low bandwidth dependence is preferentially selected. The decay factor of the exponentially weighted prediction can be dynamically adjusted according to the network fluctuation frequency. For example, when the network jitters frequently, the weight coefficient of recent data is increased. The compatibility verification can adopt a hierarchical verification mechanism. First, the basic communication protocol version is matched, and then the specific control instruction format is verified.

[0096] This solution combines efficiency scoring with dynamic prediction, can select the optimal policy according to real-time environmental characteristics, and at the same time avoids conflicts between the policy and the device through hardware compatibility verification. This application can quickly activate a standby policy that matches the current environment and hardware configuration in the event of a network interruption, effectively reducing the risk of system instability caused by policy switching, avoiding waste of resources caused by invalid policy rollback, and enhancing the robustness and adaptive ability of the vehicle safety system.

[0097] Corresponding to the above method embodiments, this specification also provides an embodiment of a traffic control device for reporting on-vehicle safety monitoring data. Figure 2 The structural schematic diagram of a traffic control device for reporting on-vehicle safety monitoring data provided by some embodiments of this specification is shown. As Figure 2 shown, the device includes: A local policy library processing module 201, configured to receive and store and process the traffic control policy from the cloud operation and management platform to obtain a local policy library.

[0098] A real-time monitoring module 202, configured to perform real-time monitoring and processing on the operating states of the vehicle communication bus and the controller, and generate a set of security events including event types and threat levels.

[0099] A policy rule matching module 203, configured to perform matching processing on the set of security events and the policy rules in the local policy library to obtain an event reporting instruction, an event discard flag, or a local storage path.

[0100] The Excess Event Writing Module 204 is configured to transmit security event metadata to the cloud according to an event reporting instruction, clear corresponding event records according to an event discard flag, and write excess events into a circular storage area according to a local storage path.

[0101] The Space Optimization Processing Module 205 is configured to perform space optimization processing on historical events in the circular storage area and generate a storage overwrite priority sequence based on the current policy type and cloud synchronization status.

[0102] The Conflict Verification Processing Module 206 is configured to perform conflict verification processing on new and old policies in the local policy library when a policy switch trigger condition is detected, obtain a policy switch execution instruction, and update the policy effective status.

[0103] The Data Synchronization Verification Module 207 is configured to batch process unreported events according to the policy effective status and complete data synchronization verification with the cloud operation management platform.

[0104] In some embodiments, the traffic control policy includes a minimum traffic policy, a maximum traffic policy, and a traffic control policy. Among them, The minimum traffic policy is activated during the vehicle power-on cycle, performs first-trigger filtering processing on the same type of security events, and generates a deduplicated event reporting queue. The maximum traffic policy is activated when the network connection is restored, performs batch merging processing on historical events and real-time generated events in the circular storage area, and generates a full-volume reporting data stream. The traffic control policy is activated in a bandwidth-limited state, performs token bucket algorithm processing on the number of events within a unit time window, and generates a rate-limited reporting channel.

[0105] In some embodiments, the generation processing of the event discard flag includes: Performing a weighted comparison processing on the threat level of the security event and the current policy priority, and generating a discard flag when the event threat level is lower than the policy set threshold; Performing a sliding window comparison processing on the event generation timestamp, and generating a forced discard flag when the maximum retention duration set by the policy is exceeded.

[0106] In some embodiments, the space optimization processing of the circular storage area includes: Performing density clustering processing on the event timestamp distribution to identify low-value data blocks; Performing hash verification processing on the relevance between the event and the cloud-synchronized records, and marking the storage area that can be overwritten; Performing dynamic monitoring processing on the storage space occupancy rate, and triggering the generation of the overwrite priority sequence when the set threshold is reached.

[0107] In some embodiments, the detection and processing of the policy switching trigger condition include: Perform an incremental comparison process on the policy version numbers sent from the cloud, and trigger the switching process when the version difference exceeds the set threshold; Perform a packet loss rate evaluation process on the vehicle network connection quality, and trigger the local policy rollback when the communication interruption duration exceeds the tolerance threshold set by the policy.

[0108] In some embodiments, the batch processing includes: Perform a time window alignment process on the unreported events before and after the policy switch to generate an event replay sequence; Perform a policy compatibility verification process on the events in the replay sequence, and discard the event records that do not conform to the new policy rules; Perform a compression and encapsulation process on the events that pass the verification to generate a breakpoint resumption data packet.

[0109] In some embodiments, the processing of the token bucket algorithm includes: Perform a dynamic measurement process on the event arrival rate within the time window to generate a token replenishment frequency parameter; Perform a queue buffering process on the excess events, and trigger the local storage path switch when the buffer queue length reaches the set threshold; Perform an event priority weighting process on the token allocation status to generate a differential rate limit policy.

[0110] In some embodiments, the generation process of the coverage priority sequence includes: Perform a topology analysis process on the ECU association degree in the event metadata to calculate the event impact range weight; Perform a moving average process on the event trigger frequency to identify abnormal high-frequency event clusters; Perform a decay function process on the event storage time to generate a coverage priority score based on time sensitivity.

[0111] In some embodiments, the local policy rollback process includes: Perform a scoring and sorting process on the historical policy execution efficiency, and select the optimal backup policy version; Perform an exponentially weighted prediction process on the current network interruption duration, and activate the backup policy when the predicted recovery time exceeds the set threshold; Perform a compatibility verification process on the rollback policy and the vehicle hardware configuration to generate an adaptation parameter adjustment instruction.

[0112] The above is a schematic solution of a traffic control device for reporting vehicle-mounted safety monitoring data in this embodiment. It should be noted that the technical solution of the traffic control device for reporting vehicle-mounted safety monitoring data belongs to the same concept as the technical solution of the above-mentioned traffic control method for reporting vehicle-mounted safety monitoring data. For the details not described in the technical solution of the traffic control device for reporting vehicle-mounted safety monitoring data, reference can be made to the description of the technical solution of the above-mentioned traffic control method for reporting vehicle-mounted safety monitoring data.

[0113] Figure 3 FIG. shows a structural block diagram of a computing device 300 according to some embodiments of the present specification. The components of the computing device 300 include, but are not limited to, a memory 301 and a processor 302. The processor 302 is connected to the memory 301 through a bus 303, and a database 305 is used to store data.

[0114] The computing device 300 further includes an access device 304, which enables the computing device 300 to communicate via one or more networks 306. Examples of these networks include the Public Switched Telephone Network (PSTN), Local Area Network (LAN), Wide Area Network (WAN), Personal Area Network (PAN), or a combination of communication networks such as the Internet. The access device 304 may include one or more of any type of wired or wireless network interface (e.g., a network interface card (NIC)), such as an IEEE 802.11 Wireless Local Area Network (WLAN) wireless interface, a Worldwide Interoperability for Microwave Access (Wi-MAX) interface, an Ethernet interface, a Universal Serial Bus (USB) interface, a cellular network interface, a Bluetooth interface, a Near Field Communication (NFC).

[0115] In one embodiment of the present specification, the above components of the computing device 300 and Figure 3 other components not shown in the figure may also be connected to each other, for example, through a bus. It should be understood that Figure 3 the structural block diagram of the computing device shown is only for illustrative purposes and is not a limitation on the scope of the present specification. Those skilled in the art can add or replace other components as needed.

[0116] The computing device 300 can be any type of stationary or mobile computing device, including mobile computers or mobile computing devices (e.g., tablet computers, personal digital assistants, laptop computers, notebook computers, netbooks, etc.), mobile phones (e.g., smart phones), wearable computing devices (e.g., smart watches, smart glasses, etc.) or other types of mobile devices, or stationary computing devices such as desktop computers or personal computers (PCs). The computing device 300 can also be a mobile or stationary server.

[0117] Among them, the processor 302 is used to execute the following computer-executable instructions, and when the computer-executable instructions are executed by the processor, the steps of the above-mentioned traffic control method for reporting vehicle-mounted safety monitoring data are implemented. The above is a schematic solution of a computing device in this embodiment. It should be noted that the technical solution of this computing device and the technical solution of the above-mentioned traffic control method for reporting vehicle-mounted safety monitoring data belong to the same concept. For the details not described in detail in the technical solution of the computing device, reference can be made to the description of the technical solution of the above-mentioned traffic control method for reporting vehicle-mounted safety monitoring data.

[0118] An embodiment of this specification also provides a computer-readable storage medium, which stores computer-executable instructions, and when the computer-executable instructions are executed by a processor, the steps of the above-mentioned traffic control method for reporting vehicle-mounted safety monitoring data are implemented.

[0119] The above is a schematic solution of a computer-readable storage medium in this embodiment. It should be noted that the technical solution of this storage medium and the technical solution of the above-mentioned traffic control method for reporting vehicle-mounted safety monitoring data belong to the same concept. For the details not described in detail in the technical solution of the storage medium, reference can be made to the description of the technical solution of the above-mentioned traffic control method for reporting vehicle-mounted safety monitoring data.

[0120] An embodiment of this specification also provides a computer program, wherein when the computer program is executed on a computer, the computer is made to execute the steps of the above-mentioned traffic control method for reporting vehicle-mounted safety monitoring data.

[0121] The above is a schematic solution of a computer program in this embodiment. It should be noted that the technical solution of this computer program and the technical solution of the above-mentioned traffic control method for reporting vehicle-mounted safety monitoring data belong to the same concept. For the details not described in detail in the technical solution of the computer program, reference can be made to the description of the technical solution of the above-mentioned traffic control method for reporting vehicle-mounted safety monitoring data.

[0122] The above describes specific embodiments of this specification. Other embodiments are within the scope of the appended claims. In some cases, the acts or steps recited in the claims may be performed in a different order than in the embodiments and still achieve the desired results. Additionally, the processes depicted in the drawings do not necessarily require the particular order or sequential order shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0123] The computer instructions include computer program code, which may be in source code form, object code form, executable file, or some intermediate form, etc. The computer-readable medium may include: Any entity or device capable of carrying the computer program code, recording medium, USB flash drive, removable hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal, and software distribution medium, etc. It should be noted that the content included in the computer-readable medium may be appropriately increased or decreased according to the requirements of legislation and patent practice within the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, the computer-readable medium does not include electrical carrier signals and telecommunication signals.

[0124] It should be noted that for the foregoing method embodiments, for the sake of simplicity of description, they are all expressed as a series of action combinations. However, those skilled in the art should know that the embodiments of this specification are not limited by the described order of actions, because according to the embodiments of this specification, certain steps may be performed in other orders or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily essential to the embodiments of this specification.

[0125] In the above embodiments, the descriptions of the various embodiments have their own focuses. For the parts not detailed in a certain embodiment, reference may be made to the relevant descriptions of other embodiments.

[0126] The preferred embodiments of this specification disclosed above are only used to help explain this specification. The alternative embodiments do not elaborate on all the details and do not limit the invention to only the specific embodiments described. Obviously, many modifications and variations can be made according to the content of the embodiments of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the embodiments of this specification, so that those skilled in the art can well understand and utilize this specification. This specification is only limited by the claims and their full scope and equivalents.

Claims

1. A flow control method for reporting vehicle-mounted safety monitoring data, characterized in that: include: Receive, store and process traffic control policies from the cloud operation and management platform to obtain a local policy library; Monitor and process the operating status of the vehicle communication bus and controller in real time, and generate a security event set including event type and threat level; Matching the security event set with the policy rules in the local policy library to obtain an event reporting instruction, an event discarding mark or a local storage path; Transmitting security event metadata to the cloud according to the event reporting instruction, clearing corresponding event records according to the event discard mark, and writing excess events into the circular storage area according to the local storage path; Performing space optimization processing on historical events in the circular storage area, and generating a storage coverage priority sequence based on the current policy type and cloud synchronization status; When a policy switching trigger condition is detected, a conflict check is performed on the new and old policies in the local policy library to obtain a policy switching execution instruction and update the policy effectiveness status; Batch process the unreported events according to the effectiveness status of the policy, and complete the data synchronization verification with the cloud operation management platform.

2. The method according to claim 1, characterized in that The flow control strategy includes the minimum flow strategy, the maximum flow strategy and the flow control strategy, wherein: The minimum traffic strategy is activated during the vehicle power-on cycle, and the same type of security events are filtered for the first trigger, generating a deduplicated event reporting queue; The highest traffic strategy indicates that it is activated when the network connection is restored, and batches are merged and processed for the historical events and real-time generated events in the circular storage area to generate a full reporting data stream; The traffic control strategy is activated when the bandwidth is limited. The token bucket algorithm is used to process the number of events in the unit time window to generate a reporting channel with limited rate.

3. The method according to claim 1, characterized in that The generation process of the event discard mark includes: The threat level of security events is compared with the current policy priority by weight, and a discard mark is generated when the event threat level is lower than the policy setting threshold; A sliding window comparison is performed on the event generation timestamp, and a forced discard mark is generated when the maximum retention time set by the policy is exceeded.

4. The method according to claim 1, characterized in that: The space optimization process of the loop storage area includes: Perform density clustering on event timestamp distribution to identify low-value data blocks; Perform hash verification on the association between the event and the synchronized records in the cloud, and mark the storage area that can be overwritten; The storage space occupancy rate is dynamically monitored and processed, and when the set threshold is reached, the overlay priority sequence generation is triggered.

5. The method according to claim 1, characterized in that The detection and processing of the policy switching triggering condition includes: Perform incremental comparison on the policy version number sent from the cloud, and trigger the switching process when the version difference exceeds the set threshold; The packet loss rate of the vehicle network connection quality is evaluated and processed, and the local policy rollback is triggered when the communication interruption duration exceeds the tolerance threshold set by the policy.

6. The method according to claim 1, characterized in that The batch processing includes: Perform time window alignment processing on unreported events before and after the strategy switch to generate an event replay sequence; Perform policy compatibility check on events in the replay sequence and discard event records that do not comply with new policy rules; The verification pass event is compressed and encapsulated to generate a breakpoint-resume transmission data packet.

7. The method according to claim 2, characterized in that The processing of the token bucket algorithm includes: Dynamically measure and process the event arrival rate within the time window to generate token replenishment frequency parameters; Perform queue buffering for excess events, and trigger local storage path switching when the buffer queue length reaches the set threshold; The token allocation status is weighted by event priority to generate differentiated rate limiting strategies.

8. The method according to claim 4, characterized in that The generation process of the coverage priority sequence includes: Perform topological analysis on the ECU correlation in the event metadata and calculate the weight of the event impact range; Perform sliding average processing on event triggering frequency to identify abnormally high-incidence event clusters; The event storage time is processed by a decay function to generate a coverage priority score based on time sensitivity.

9. The method according to claim 5, characterized in that The local policy rollback process includes: Score and sort the historical strategy execution performance and select the best backup strategy version; Perform exponential weighted prediction on the duration of the current network outage and activate the backup strategy when the predicted recovery time exceeds the set threshold; Perform compatibility check on the rollback strategy and vehicle hardware configuration, and generate adaptation parameter adjustment instructions.

10. A flow control device for reporting vehicle-mounted safety monitoring data, characterized in that: include: The local policy library processing module is configured to receive and store the traffic control policy from the cloud operation management platform to obtain a local policy library; A real-time monitoring module is configured to perform real-time monitoring and processing on the operating status of the vehicle communication bus and the controller, and generate a security event set including event type and threat level; A policy rule matching module is configured to match the security event set with the policy rules in the local policy library to obtain an event reporting instruction, an event discarding mark or a local storage path; An excess event writing module is configured to transmit security event metadata to the cloud according to the event reporting instruction, clear the corresponding event record according to the event discard mark, and write the excess event into the circular storage area according to the local storage path; A space optimization processing module is configured to perform space optimization processing on historical events in the circular storage area and generate a storage coverage priority sequence based on a current policy type and a cloud synchronization state; A conflict check processing module is configured to perform conflict check processing on the new and old policies in the local policy library when a policy switching trigger condition is detected, obtain a policy switching execution instruction and update the policy effectiveness status; The data synchronization verification module is configured to batch process unreported events according to the policy effectiveness status and complete data synchronization verification with the cloud operation management platform.

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