Railway and road parallel isolation protection full life cycle intelligent management method and system based on end-cloud cooperation

Abstract

The application discloses a highway-railway parallel isolation protection full-life-cycle intelligent management method and system based on end-cloud cooperation, relates to the technical field of full-life-cycle management, and comprises the following steps: collecting continuous time sequence fluctuation data and communication delay data around the protection execution state of an edge node, and establishing a dynamic offset record on the same time sequence; based on the dynamic offset record, interval recombination is performed on a trigger threshold of a protection instruction, a time period with a strong drift trend is divided into a buffer section, the trigger frequency of a protection action is limited in the buffer section, and a safety threshold distribution with self-adjusting characteristics is generated. Through the construction of the dynamic offset record and the cooperation of threshold recombination and rhythm regulation, the application realizes the continuous evolution and boundary smooth transition of protection determination, and reduces the risk of misjudgment. Meanwhile, the end-cloud cooperation synchronization mechanism is introduced, so that the protection decision is consistent among multiple levels, the diffusion of isolation instructions is inhibited, and the operation continuity and management reliability are ensured.

Description

Technical Field

[0001] This invention relates to the field of full lifecycle management technology, specifically to a method and system for intelligent full lifecycle management of parallel isolation and protection of public and railway lines based on end-to-end cloud collaboration. Background Technology

[0002] Intelligent lifecycle management based on edge-cloud collaboration and parallel isolation protection refers to establishing a multi-layered collaborative architecture between devices, edge devices, and the cloud. This architecture integrates security protection strategies, data analysis, and risk response throughout the entire system lifecycle, maintaining dynamic linkage and intelligent optimization from design, deployment, operation, maintenance to decommissioning. It is particularly suitable for complex system scenarios such as public transportation and railways, which have high requirements for continuous operation and security stability. Specifically, real-time isolation and local security detection are implemented at the device level to ensure primary protection is completed before data is collected and transmitted. At the edge, high-frequency data aggregation and abnormal behavior identification are performed for rapid response and area isolation of potential intrusions. In the cloud, a global security policy engine and lifecycle knowledge graph are used to integrate multi-source operational data, achieving closed-loop management of policy push, risk prediction, and intelligent decision-making. The entire system achieves layered decoupling and collaborative reinforcement of security tasks through synchronous scheduling and parallel execution between the edge and cloud, ensuring both the system's self-healing capability under localized attacks and maintaining global situational awareness and continuous evolutionary protection capabilities, meeting the long-term reliable operation requirements of critical infrastructure such as public transportation and railways.

[0003] The existing technology has the following shortcomings: In the parallel isolation and protection process of edge-cloud collaboration, edge nodes are prone to unstable isolation boundary judgments due to timing drift, data jitter, or slight deviations in policy thresholds when performing dynamic protection and isolation. When the distinction boundary drifts, the system may mistakenly identify normal communication traffic as high-risk traffic, thereby falsely triggering cross-domain blocking commands. In high-density communication and high-concurrency scheduling environments such as railways and highways, such misjudgments often occur during high-concurrency or multi-task cross-scheduling phases, easily leading to the interruption of critical task links, instantaneous failure of communication channels, and disruption of the collaborative relationship between edge devices. Furthermore, with the automatic isolation mechanism continuously executing, falsely triggered blocking commands can be rapidly propagated to adjacent nodes, forming a chain-like disconnection phenomenon, ultimately causing the entire edge device cluster to lose connectivity, affecting the continuous operation and remote management of the system.

[0004] The information disclosed in the background section is only intended to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0005] The purpose of this invention is to provide a method and system for intelligent management of the entire lifecycle of parallel isolation and protection between public and railway systems based on end-to-end cloud collaboration, so as to solve the problems in the background art mentioned above.

[0006] To achieve the above objectives, this invention provides the following technical solution: a smart management method for the entire lifecycle of parallel isolation and protection between public and railway systems based on edge-cloud collaboration, comprising the following steps: Step 1: Collect continuous time-series fluctuation data and communication delay data around the protection execution status of edge nodes, establish dynamic offset records on the same time series, use them to identify the drift trend of the isolation judgment area, and use the dynamic offset records as the basis data for defining the stable interval. Step 2: Based on the dynamic offset record, the trigger threshold of the protection command is reorganized into intervals, the time period with strong drift trend is divided into buffer segments, and the trigger frequency of the protection action is limited within the buffer segments to generate a safety threshold distribution with self-adjusting characteristics. Step 3: Based on the distribution of security thresholds, the timing of the command issuance rhythm of edge nodes is rearranged. Transition segments are pre-inserted in the continuous distribution interval of normal communication traffic. The transition segments are used to balance the burst traffic, so that the timing of the protection command issuance and the distribution of communication traffic maintain a stable correspondence in the time series. Step 4: Based on the timing rearrangement results and combined with the operational feedback of the transition section, the update rhythm of the protection and isolation boundary is adjusted by sliding. The isolation judgment boundary is updated with the adjusted rhythm to ensure a smooth transition of isolation judgment during dynamic communication and reduce the probability of misjudgment caused by boundary drift. Step 5: Under the sliding adjustment protection rhythm, construct a synchronous control mechanism for edge-cloud collaboration. Based on the feedback cycle of edge nodes, update the cloud strategy mapping relationship in real time, generate a response sequence that continuously matches time and space, realize a collaborative protection closed loop between the edge and cloud, and reduce false blocking caused by isolation zone drift from the source.

[0007] Preferably, the step of establishing dynamic offset records on the same time series includes: Around the protection execution status of edge nodes, collect time-series fluctuation data and communication latency data generated during the protection execution process, and update the data records with millisecond-level time granularity within a fixed period; The collected time-series fluctuation data and communication delay data are uniformly mapped to the same time series, and continuous time-series records are formed through time alignment, sequence synchronization and data integration; By performing differential comparisons on continuous records in the time series, the synchronous change relationship between the protection status change value and the communication delay change value is extracted, and dynamic offset records are generated. By analyzing continuous time periods based on dynamic offset records, the stable and fluctuating intervals of the protection execution status are identified, and the stable interval is defined based on the time axis distribution, serving as the basic reference data for subsequent protection threshold adjustment and timing control.

[0008] Preferably, the stable interval delineation includes comparing the protection status change value and communication delay change value of continuous time periods in the dynamic offset record, and determining the candidate range of the stable interval of the protection execution status based on the correspondence between the change direction and the change amplitude. The continuous and stable time period is defined as the stable interval, the time period with frequent fluctuations is defined as the unstable interval, and the final stable interval is determined based on the distribution of the stable interval on the time axis, which is used as the time reference for protection threshold adjustment and time sequence rearrangement.

[0009] Preferably, the step of reorganizing the trigger threshold of the protection command based on the dynamic offset record includes: Based on the established dynamic offset records, the time series during the protection execution process is continuously read, the drift change amplitude and direction information of each time period is extracted, and characteristic parameters such as offset amplitude, offset direction and offset duration are recorded. Based on the extracted drift feature distribution, the drift trend concentration time period is divided according to the time series, and the start time, end time, duration and drift intensity information of the buffer segment are determined. The offset direction of adjacent time periods is used as an auxiliary condition to merge adjacent segments. Based on the drift characteristics of each buffer segment, the trigger threshold of the protection command is reorganized into intervals. The upper and lower limits of the trigger threshold are adjusted according to the drift amplitude of the buffer segment, and the boundaries are connected between each segment to form a continuous threshold distribution structure. Based on the completion of threshold interval reorganization, the trigger frequency of protection actions is limited within the buffer segment, and the protection trigger interval is adjusted according to the drift trend to generate a safety threshold distribution with self-adjusting characteristics.

[0010] Preferably, in the step of limiting the triggering frequency of protective actions within the buffer segment, the triggering interval of the protective actions is determined based on the drift amplitude and drift duration of the buffer segment, and a uniform triggering rhythm is maintained within the time interval of continuous drift trend. Within the drift change interval, the number of consecutive triggers is reduced by extending the triggering interval, thereby maintaining the time correspondence between the protective command and the communication status during the protection execution process, and maintaining the time continuity of the safety threshold distribution.

[0011] Preferably, the step of reordering the command issuance rhythm of edge nodes according to the security threshold distribution includes: Based on the established security threshold distribution, time series analysis is performed on the protection command issuance status of edge nodes in different time periods. By recording the issuance time, duration, interval length and trigger number of protection commands, the original time series trajectory of protection commands is obtained. The original timeline of the protection commands is aligned with the time distribution curve of the communication traffic. The differences between the protection commands and the communication traffic in terms of time distribution are compared and analyzed to determine the key time period for timeline rearrangement. Based on the rhythmic characteristics of the safety threshold distribution, the rhythm of issuing protection commands is adjusted in segments, and the issuance time of each protection command is matched with the trigger interval boundary of the safety threshold distribution to form a issuance rhythm that is coordinated with the threshold distribution. Transition segments are pre-inserted within the continuous distribution range of normal communication traffic. The length of the transition segment is determined according to the range span of the security threshold distribution. A delay buffer strategy is set to balance burst traffic and achieve a stable correspondence between the rhythm of protection command issuance and the distribution of communication traffic in the time series.

[0012] Preferably, the delay buffering strategy in the transition segment includes adjusting the issuance time of protection commands based on the instantaneous changes in communication traffic. When communication traffic increases, the issuance time of some protection commands is delayed, and when communication traffic decreases, the issuance time of some protection commands is advanced, so as to maintain the continuous correspondence between the execution frequency of protection commands and the rhythm of communication traffic changes in the time series.

[0013] Preferably, the steps for implementing a sliding adjustment of the update rhythm of the protective isolation boundary based on the time-series rearrangement results and the operational feedback of the transition phase include: Based on the result of the timing reordering of protection commands, a time correlation analysis is performed on the execution status of protection commands and the response status of communication traffic in each time segment. Operational feedback information of the transition segment is extracted, and the frequency of protection command triggering, response delay, traffic recovery time and traffic balance status are recorded. Based on the feedback results of the transition phase operation, the update cycle of the protection isolation boundary is determined according to the communication recovery time and the protection response delay, forming the boundary update rhythm parameters corresponding to each time interval, and maintaining time consistency with the result of the protection command timing rearrangement. Based on the results of the timing rearrangement of protection instructions and the feedback data of the transition period, the update frequency of the protection and isolation boundary is adjusted by sliding within a continuous time window. The length of the time window is determined and the rhythm is switched between adjacent windows so that the boundary update rhythm remains continuous and gradual in the time series. The boundary update rhythm adjusted by sliding is applied to the protection and isolation determination process. The time threshold and determination range of isolation determination are adjusted in real time according to the sliding rhythm, so that the protection and isolation boundary can maintain a smooth transition in the dynamic communication process and maintain the coordination and consistency between protection determination and communication behavior.

[0014] Preferably, the steps for constructing a synchronous control mechanism for edge-cloud collaboration under a sliding adjustment protection rhythm include: Based on the adjusted protection rhythm, a time synchronization relationship is established between the end-side devices, edge nodes, and cloud policy engine. The protection execution time, policy trigger time, and policy update cycle are uniformly mapped onto the same time axis, and a time window is defined based on the protection rhythm results to maintain rhythm consistency. Based on the protection execution status and feedback cycle of edge nodes, a dynamic relationship between edge node feedback and cloud policy mapping is constructed. Feedback data is summarized in order of timestamp and grouped by sliding window to form a continuous feedback sequence to update cloud policy parameters. Based on the time synchronization relationship and feedback correlation characteristics, a response sequence that continuously matches time and space is generated. The cloud generates strategy response content based on the feedback information from edge nodes and distributes it to the corresponding nodes according to spatial location and time order. The generated response sequence is applied to the edge-cloud collaborative protection process. Based on the feedback results, the strategy mapping relationship is periodically updated and pushed to each node, forming a collaborative protection closed loop that is dynamically coordinated in both time and space dimensions.

[0015] The intelligent management system for the entire lifecycle of parallel isolation and protection between public and railway systems based on edge-cloud collaboration includes a dynamic offset recording module, a safety threshold reorganization module, a time sequence rhythm rearrangement module, a boundary sliding adjustment module, and an edge-cloud synchronous control module. The dynamic offset recording module collects continuous time-series fluctuation data and communication delay data around the protection execution status of edge nodes, and establishes dynamic offset records on the same time series. The safety threshold reorganization module, based on dynamic offset records, reorganizes the trigger thresholds of protection commands into intervals, divides the time periods with strong drift trends into buffer segments, and limits the trigger frequency of protection actions within the buffer segments, generating a safety threshold distribution with self-adjusting characteristics. The timing rhythm reordering module reorders the instruction issuance rhythm of edge nodes according to the distribution of safety thresholds, and pre-inserts transition segments in the continuous distribution range of normal communication traffic. The boundary sliding adjustment module, based on the timing rearrangement results and combined with the operation feedback of the transition section, implements sliding adjustment on the update rhythm of the protection and isolation boundary, and updates the isolation judgment boundary with the adjusted rhythm, so that the isolation judgment maintains a smooth transition during dynamic communication. The edge-cloud synchronous control module constructs an edge-cloud collaborative synchronous control mechanism under the sliding adjustment protection rhythm. It updates the cloud strategy mapping relationship in real time according to the feedback cycle of edge nodes, and generates a response sequence that continuously matches time and space.

[0016] The technical effects and advantages provided by the present invention in the above technical solution are as follows: This invention transforms the originally discrete and easily fluctuating protection decision-making process into a traceable dynamic offset evolution process by continuously modeling the changes in the protection execution status and communication latency of edge nodes over time. Protection decisions no longer rely on instantaneous state judgments but are adjusted based on the overall trend of temporal continuity, thereby maintaining the stability of the judgment boundary in high-concurrency and multi-task interaction scenarios. With the introduction of dynamic offset recording and security threshold distribution, the protection rhythm can naturally adjust with the communication status, preventing normal traffic from being amplified and misinterpreted during short-term fluctuations, effectively reducing the probability of false triggers, and ensuring the continuous connectivity and operational reliability of critical business links in complex operating environments.

[0017] This invention introduces a timing rearrangement and sliding adjustment mechanism during the protection execution process, combined with a synchronous control structure for edge-cloud collaboration, enabling the protection strategy to form a continuous and consistent response relationship at both the temporal and spatial levels. Protection boundary updates no longer exhibit abrupt changes but evolve gradually with changes in communication load, thereby reducing the impact of isolation actions on the overall system coordination. By constructing a feedback loop between the edge and cloud, the protection strategy can be continuously optimized according to the operational status, preventing local anomalies from spreading and amplifying, ultimately maintaining the overall stability and remote management capabilities of the device cluster, and ensuring the consistency and predictability of protection decisions under long-term operating conditions. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this invention. For those skilled in the art, other drawings can be obtained based on these drawings.

[0019] Figure 1 This is a flowchart of the intelligent management method for the entire lifecycle of parallel isolation and protection of public and railway lines based on end-to-end cloud collaboration, as described in this invention.

[0020] Figure 2 This is a schematic diagram of the modules of the intelligent management system for the entire lifecycle of parallel isolation and protection between public and railway lines based on end-to-end cloud collaboration, as described in this invention. Detailed Implementation

[0021] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, they are provided so that the description of this disclosure will be more complete and fully convey the concept of the exemplary embodiments to those skilled in the art.

[0022] This invention provides, for example Figure 1The illustrated intelligent management method for the entire lifecycle of parallel isolation and protection between public and railway systems based on edge-cloud collaboration includes the following steps: Step 1: Collect continuous time-series fluctuation data and communication delay data around the protection execution status of edge nodes, establish dynamic offset records on the same time series, use them to identify the drift trend of the isolation judgment area, and use the dynamic offset records as the basis data for defining the stable interval. The specific implementation method for this step is as follows: The system continuously collects time-series fluctuation data and communication latency data generated during the protection execution process, focusing on the protection execution status of edge nodes. The collected time-series fluctuation data includes the state transition time when edge nodes execute protection commands, the execution interval of each protection task, the start time and end time of command triggering, the interval period, and the time difference between commands. Communication latency data includes round-trip latency, one-way latency, response waiting time, data packet transmission time, and acknowledgment response time incurred in each stage of protection command transmission, confirmation, response, and feedback. All collected data is appended with a uniform timestamp and recorded in a continuous time-series record table, ensuring that data from different sources are aligned on the same timeline. The collection process is executed within a fixed period, updating data records with millisecond-level time granularity, thereby ensuring the continuity and comparability of time-series fluctuation data and communication latency data in continuous protection tasks. In this way, the system can fully reflect the time-series behavioral characteristics and communication response change patterns of edge nodes during continuous protection task execution, providing a directly correlated time basis for subsequent data mapping.

[0023] After continuous data acquisition of the protection execution status, the obtained time-series fluctuation data and communication delay data are mapped to the same time series. The mapping process includes three stages: time alignment, sequence synchronization, and data integration. Time alignment arranges the timestamps recorded from different data sources in chronological order, ensuring a correspondence between changes in protection status and changes in communication delay over time. Sequence synchronization matches each time-series node of the protection execution status with its corresponding communication delay node, forming synchronization points on the same time axis, thus establishing a temporal correlation between the time-series fluctuation data and the communication delay data. Data integration aggregates the synchronized data into a continuous time series in chronological order, using milliseconds as the minimum interval. Changes in protection status and communication delay are recorded side-by-side, forming a continuous record set that can be directly used for offset calculation. This mapping method allows for a complete display of the dynamic response of edge nodes during protection execution on a unified time axis, providing comprehensive temporal data support for the establishment of offset records.

[0024] After establishing a unified time series, dynamic offset records are generated by comparing the differences between continuous records in the time series. These offset records reflect the synchronous changes in protection status and communication latency of edge nodes over continuous time periods. To construct dynamic offset records, the time intervals of continuous data in the time series are first determined. The changes in protection status and communication latency at adjacent record points are compared to extract the direction and magnitude of change for each time period. Subsequently, the trends within continuous time periods are integrated to generate an offset curve representing changes in protection execution status. Each data point on this curve corresponds to a combination of protection execution and communication response at a given time node. This recording method identifies latency fluctuations, response offsets, and periods of unstable protection rhythm that occur during the continuous execution of protection tasks at edge nodes. In generating dynamic offset records, the execution status of each protection instruction in the time series is also retained, ensuring that the offset records reflect not only the overall trend but also local features of protection status changes, thus enabling continuous tracking of the entire protection process.

[0025] It should be noted that: Dynamic offset logs can be understood as a time-varying record reflecting the stability of the rhythm of edge nodes during protection execution. It is generated by comparing the time changes during protection command execution with the latency changes during communication on the same timeline, observing whether asynchrony or offset occurs between the two during continuous operation. This record provides a clear view of which time periods show stable protection actions and communication responses, and which periods exhibit increased fluctuations or rhythmic irregularities. This provides a direct basis for subsequently determining whether the isolation boundary has drifted and how to adjust the protection strategy.

[0026] After the dynamic offset records are generated, the drift trend of the isolation judgment area is identified by analyzing the continuous time periods in the offset records, and the dynamic offset records are used as the basis for defining the stable interval. Specifically, firstly, each time period in the dynamic offset records is compared. By analyzing the relative differences in the changes in protection status and communication delay within continuous time periods, the time periods where the protection execution status remains stable and the time periods where fluctuations occur are identified. After identifying these time periods, the continuous stable time periods are defined as the candidate range of the stable interval, and the time periods with more frequent fluctuations are defined as the candidate range of the unstable interval. Subsequently, based on the distribution of the candidate ranges of the stable interval on the time axis, a time period with a moderate time span is selected as the final stable interval to ensure that the protection task can be executed at a balanced pace within this interval. In this way, the stable interval definition process is based on the temporal continuity and change characteristics of the dynamic offset records, making the temporal characteristics of protection execution continuously controllable in the time dimension. Finally, the stable interval definition results are recorded as the basic reference data for subsequent protection threshold adjustment, temporal rearrangement, and synchronization control, ensuring that protection commands remain consistent in temporal distribution and reducing misjudgments caused by protection boundary drift.

[0027] By implementing the above steps, a complete process can be formed in the time dimension, from collecting protection execution status data to establishing dynamic offset records and defining stable intervals. The entire process is based on a unified time series, precisely mapping the protection execution status of edge nodes to changes in communication latency, enabling continuous tracking of protection execution status and identification of dynamic offset trends. Throughout this process, the protection execution status, time-series fluctuation data, and communication latency data maintain a one-to-one correspondence, allowing the drift trend of the isolation judgment area to be quantified, recorded, and controlled. This provides a reliable foundation for subsequent threshold adjustments, timing optimization, and synchronization strategies in the edge-cloud collaborative protection system, enabling the system to maintain the stability and consistency of protection judgments during continuous operation, reducing isolation misjudgments and communication interruptions caused by time drift and data jitter, and achieving temporal consistency and spatial continuity between protection and communication.

[0028] Step 2: Based on the dynamic offset record, the trigger threshold of the protection command is reorganized into intervals, the time period with strong drift trend is divided into buffer segments, and the trigger frequency of the protection action is limited within the buffer segments to generate a safety threshold distribution with self-adjusting characteristics. The specific implementation method for this step is as follows: Based on the established dynamic offset records, the time series during the protection execution process is continuously read to extract the drift amplitude and direction information for different time periods. Each set of data in the dynamic offset records corresponds to a combination of protection status change values ​​and communication delay change values. By analyzing the relationship between these two changes over continuous time periods, time segments with concentrated drift changes can be identified. The extraction process is performed chronologically, dividing the continuous data into intervals and recording characteristic information such as offset amplitude, offset direction, and offset duration within each interval. In this way, the drift distribution of the protection execution status can be obtained on a continuous time axis, clarifying the drift change patterns and duration within different time periods. This step provides a foundation for subsequent buffer segment division, enabling subsequent threshold reassessment to be adjusted specifically based on time distribution characteristics.

[0029] After obtaining the drift characteristic distribution in the time series, time periods with strong drift trends are divided into buffer segments based on the combined performance of drift amplitude and duration. The segmentation process uses the time series as the main thread, first determining the start and end points of time periods with large drift amplitudes in the dynamic offset record, and then using these time periods as the boundary range of the buffer segments. During segmentation, the offset direction of adjacent time periods is also used as an auxiliary condition; if the offset directions of adjacent time periods are continuous, they are considered extensions of the same buffer segment, thus avoiding over-segmentation. After segmentation, each buffer segment has a defined start time, end time, duration, and drift intensity information. In this way, the timeline of the entire protection execution process is divided into multiple regions, including ordinary segments with stable drift and buffer segments with strong drift changes. The establishment of buffer segments provides a range of action for subsequent threshold adjustments, enabling protection actions to adopt different triggering strategies in different time intervals to achieve adaptive adjustment to dynamic drift.

[0030] After dividing the buffer zones, the trigger thresholds for protection commands are reorganized based on the drift characteristics of each buffer zone. This reorganization process adjusts the original fixed threshold structure into a distributed structure with time-interval correlation. First, based on the drift amplitude of a buffer zone, the adjustment range of the upper and lower limits of the protection command trigger threshold within that zone is determined. If the drift amplitude within a buffer zone is large, the corresponding upper and lower limits of the trigger threshold are redistributed to expand the tolerance range for protection triggering; if the drift change within a buffer zone is relatively stable, the original threshold range remains unchanged. After adjusting the interval range, the boundaries between each buffer zone are connected to ensure that the threshold distribution remains continuous on the time axis without abrupt changes. Through this reorganization, the originally uniform protection trigger thresholds are reconstructed into a dynamically changing distribution structure based on time-drift characteristics, enabling protection decisions to automatically adapt to threshold boundaries according to drift trends in different time periods. This process directly utilizes the drift trend information in the dynamic offset record, making the adjustment of the protection threshold time-related and protection status-related.

[0031] After reorganizing the protection command trigger threshold intervals, the trigger frequency of protection actions is limited within designated buffer segments, thereby generating a self-adjusting safety threshold distribution. The trigger frequency limitation process uses the reorganized threshold intervals as a basis to control the trigger interval of protection actions within each buffer segment. Specifically, in buffer segments with significant drift changes, the trigger interval of protection actions is extended to reduce the number of consecutive triggers in a short period, maintaining a stable time correspondence between the execution of protection commands and the communication status. In ordinary segments with smaller drift changes, the original trigger frequency is maintained to ensure the real-time performance of protection actions under normal conditions. Through this differentiated frequency control, protection commands gain dynamic adjustment capabilities in the time dimension, automatically adjusting the trigger rhythm according to changes in drift trends, thus maintaining the continuity and stability of actions during protection execution. Finally, through the combination of the above threshold interval reorganization and trigger frequency limitation, a self-adjusting safety threshold distribution is formed. This distribution is continuous on the time axis, has independent adjustment capabilities in different segments, and achieves adaptive updates based on dynamic offset records. This security threshold distribution not only improves the timing stability of protection actions, but also enables edge nodes to maintain consistency in protection decisions when facing communication fluctuations, avoiding false triggering or omission of protection commands due to timing drift.

[0032] Through the aforementioned sequential steps, the entire process achieves full-process coordination in the time dimension, from reading dynamic offset records to dividing buffer segments, and then to threshold interval reorganization and trigger frequency limitation. The protection threshold is no longer fixed but is continuously adjusted based on the dynamic characteristics of the time series, thus forming a safety threshold distribution that automatically adapts to drift trends. This implementation introduces buffer segments and interval reorganization mechanisms in the time dimension, enabling the triggering of protection actions to have dynamic balancing capabilities. It also ensures the continuity and stability of the protection strategy in a multi-layered architecture of end-cloud collaboration, fundamentally reducing the risk of false triggering of protection due to isolation judgment drift. This allows the entire protection system to maintain temporal coordination and logical consistency between protection and response in a continuously changing communication environment.

[0033] Step 3: Based on the distribution of security thresholds, the timing of the command issuance rhythm of edge nodes is rearranged. Transition segments are pre-inserted in the continuous distribution interval of normal communication traffic. The transition segments are used to balance the burst traffic, so that the timing of the protection command issuance and the distribution of communication traffic maintain a stable correspondence in the time series. The specific implementation method for this step is as follows: Based on the established security threshold distribution, a time-series analysis was performed on the protection command issuance status of edge nodes in different time periods. The security threshold distribution includes the trigger range of protection actions and the corresponding threshold changes for each time segment. By reading the security threshold distribution, the issuance frequency and trigger interval of protection commands in different time intervals can be determined. During the analysis, the issuance time, duration, interval between adjacent commands, and number of triggers within the same time segment of the protection commands were recorded, thus forming the original time-series trajectory of the protection commands. This time-series trajectory was time-aligned with the time distribution curve of communication traffic. By comparing the time correspondence between the two, segments where protection commands and communication traffic are out of sync in time distribution can be identified. These out-of-sync segments are usually areas where protection actions are too frequent or where communication traffic surges. By analyzing the differences between the security threshold distribution and the protection command time trajectory, the key time periods that need to be adjusted for time-series reordering can be identified, providing a basis for subsequent time-series optimization.

[0034] After completing the time analysis of the protection command issuance status, the issuance rhythm of the protection commands is initially rearranged based on the rhythmic characteristics of the security threshold distribution. During this process, the original protection command time sequence is segmented and adjusted to match the issuance time of each protection command with the trigger interval boundaries in the security threshold distribution. Specifically, for time intervals where protection commands are concentrated, the time intervals between commands are expanded according to the threshold range in the security threshold distribution to make the command issuance more balanced; for intervals where protection commands are sparsely distributed, the intervals are appropriately shortened without changing the original triggering logic to improve the continuity of protection action responses. Through this adjustment, the protection commands are rearranged on the time axis to form a issuance rhythm coordinated with the security threshold distribution. At this point, the issuance order and time intervals of the protection commands are no longer fixed but dynamically adjusted according to the interval characteristics of the security threshold distribution, establishing a rhythmic basis for the insertion of subsequent transition segments. This step ensures a dynamic correlation between the execution rhythm of the protection commands and the protection thresholds, giving the rearranged command distribution time-adaptive characteristics.

[0035] After the protection command issuance rhythm is rearranged, transition segments are pre-inserted within the continuous distribution range of normal communication traffic to balance sudden traffic surges. The transition segments are set with reference to the rearranged protection command sequence. First, the time intervals where the data packet transmission rate is continuous and stable in the communication traffic curve are determined, and these intervals are defined as the continuous distribution range of normal communication traffic. Transition segments are inserted at the beginning and end boundaries of each continuous distribution range. The length of the transition segment is determined according to the range span of the security threshold distribution. Its function is to absorb the time impact caused by traffic fluctuations when protection commands are concentrated or communication traffic surges. A delay buffering strategy is implemented within the transition segments. When communication traffic increases in a short period, the issuance of some protection commands is delayed to buffer the concentrated impact of sudden data surges; when communication traffic decreases, the issuance of some protection commands is advanced to maintain the continuity of protection actions. In this way, the issuance of protection commands and changes in communication traffic form a flexible connection on the time axis, ensuring that the execution frequency of protection commands is consistent with the rhythm of traffic changes, avoiding false triggering of protection or communication interruption due to sudden traffic surges.

[0036] After completing the pre-insertion and traffic balancing of the transition phase, the overall timing rearrangement results are integrated to maintain a stable correspondence between the protection command issuance rhythm and the communication traffic distribution in the time series. During the integration process, the rearranged protection command sequence is overlaid with the time distribution curve of communication traffic, and the degree of time overlap between the two is compared. By analyzing the time relationship between the protection command issuance point and the peak point of communication traffic, local time intervals are adjusted to dynamically match the issuance rhythm of protection commands with the fluctuation rhythm of communication traffic. For sections with slight deviations, the time interval or sequence of protection commands is fine-tuned to maintain balance with the continuous changes in communication traffic. After integration, a stable correspondence between protection commands and communication traffic is formed, and the execution cycle of protection commands and the transmission cycle of communication traffic are synchronized in the time series, ensuring that the entire protection process maintains consistent time response characteristics under different communication loads. Through this process, the rhythm of protection command issuance is no longer limited by a single threshold, but is adjusted synchronously with changes in communication traffic, forming a time-adaptive dynamic correspondence mechanism. Ultimately, after the entire timing reordering is completed, the correspondence between protection commands and communication traffic remains stable, enabling the protection execution process to have long-term time coordination. The protection actions and communication rhythm are more closely matched, thereby achieving synchronous and stable operation of protection and communication in an end-to-cloud collaborative environment, reducing the risk of false triggering of protection and link blockage caused by traffic mutations.

[0037] Through the above steps, the timing of protection command issuance changes from a fixed pattern to an adaptive rhythm based on security threshold distribution and dynamic characteristics of communication traffic. This implementation achieves a flexible balance between protection and communication in the time dimension by setting transition segments within the continuous distribution range of normal communication traffic. This allows the rhythm of protection command issuance to automatically adjust with changes in communication load, ensuring that protection behavior has a stable rhythm and predictable response cycle in a continuous time series. This improves the time coordination of edge nodes and the continuity of protection decisions in the edge-cloud collaborative protection system.

[0038] Step 4: Based on the timing rearrangement results and combined with the operational feedback of the transition section, the update rhythm of the protection and isolation boundary is adjusted by sliding. The isolation judgment boundary is updated with the adjusted rhythm to ensure a smooth transition of isolation judgment during dynamic communication and reduce the probability of misjudgment caused by boundary drift. The specific implementation method for this step is as follows: Based on the timing reordering results of protection commands obtained in the previous stage, a time correlation analysis was performed on the execution status of protection commands and the response status of communication traffic in each time segment to extract operational feedback information of the transition segment. The timing reordering results of protection commands recorded the execution time, duration, and correspondence with communication traffic peaks of protection commands, while the operational feedback of the transition segment included the triggering frequency, response delay, traffic recovery time, and traffic balance status of protection commands within the transition interval. By correlating the two in chronological order, the linkage pattern between protection commands and communication traffic changes in each time segment can be obtained. This analysis process can reveal the degree to which the rhythm of protection commands adapts to traffic fluctuations under different communication load conditions, as well as the impact of protection actions on communication stability, thus providing basic data support for determining the rhythm of subsequent sliding adjustments. In this process, it was ensured that all feedback data were recorded with continuous time identifiers, so that the execution status of protection commands and communication traffic feedback were accurately aligned on the time axis, providing a complete time reference for the subsequent adjustment of the boundary update rhythm.

[0039] After completing the time correlation analysis of protection execution and communication feedback, and combining the operational feedback results of the transition phase, the update rhythm of the protection isolation boundary was initially defined. The definition process used the communication recovery time and protection response delay recorded in the transition phase operational feedback as reference parameters to adjust the update cycle of the protection isolation boundary in segments. For intervals in the transition phase where communication traffic recovers quickly and latency fluctuations are small, the update cycle of the isolation boundary was shortened, enabling protection decisions to respond more quickly to changes in communication status. For intervals in the transition phase where communication recovery is slow or latency changes are frequent, the update cycle of the isolation boundary was extended to avoid excessively frequent boundary switching that could cause fluctuations in protection status. In this way, the update rhythm of the isolation boundary is no longer fixed-periodic but dynamically allocated based on the real-time characteristics of the transition phase operational feedback. After the definition is completed, each time interval corresponds to an independent boundary update rhythm parameter. This parameter maintains temporal consistency with the timing rearrangement results of protection commands, enabling the protection isolation boundary to dynamically match the protection rhythm in the time series, providing a basic framework for subsequent sliding adjustments.

[0040] After determining the initial boundary update rhythm, a sliding adjustment is implemented based on the timing rearrangement results of protection commands and feedback data from the transition segment. The sliding adjustment process smooths the update frequency of the protection isolation boundary within a continuous time window. First, the length of the sliding adjustment time window is determined based on the communication fluctuation trends in the transition segment feedback. The execution status of protection commands, changes in communication latency, and traffic recovery rate within the window collectively constitute the reference basis for rhythm adjustment. By sliding the boundary update rhythm between adjacent time windows, excessive contraction or expansion of the protection judgment boundary in a short period is prevented. The boundary update rhythm after sliding adjustment exhibits a continuous and gradual change characteristic in the time series, ensuring that changes in boundary position no longer present abrupt changes but rather smoothly transition with changes in protection rhythm and communication traffic. Through this sliding adjustment, the protection isolation boundary can continuously follow the changing trend of traffic distribution in a dynamic communication environment, ensuring that protection judgment and communication behavior remain consistent in the time dimension, reducing the possibility of misjudgment caused by boundary drift.

[0041] After the sliding adjustment is completed, the adjusted boundary update rhythm is applied to the protection and isolation determination process. The isolation determination boundary is updated according to the adjusted rhythm, ensuring stable temporal continuity of protection and isolation during dynamic communication. Specifically, during the protection execution process at the edge nodes, the adjusted boundary update rhythm serves as the time benchmark for triggering protection determination, adjusting the time threshold and determination range of isolation determination in real time. When communication traffic experiences periodic fluctuations, the edge nodes gradually update the determination range of the isolation boundary according to the sliding adjustment rhythm, smoothly transitioning the protection determination from the current state to the new boundary position, avoiding misjudgments caused by instantaneous changes in the determination boundary. Simultaneously, the adjusted rhythm is also fed back to the protection command issuance logic, ensuring that the protection command and boundary update actions remain synchronized, maintaining the continuity of protection actions during boundary changes. Through this sliding update method, the isolation determination boundary no longer switches at fixed time intervals but continuously adjusts with the actual changes in communication traffic, enabling the protection system to achieve a smooth transition in boundary determination under dynamic communication conditions. This approach not only effectively reduces false triggering of protection caused by boundary drift, but also ensures the stability of protection execution and the continuous connectivity of communication links, enabling the end-to-cloud collaborative protection system to maintain dynamic balance and continuous and reliable protection response during long-term operation.

[0042] Through the above steps, the update process of the protection and isolation boundary is transformed from a static switching to a dynamic sliding adjustment process based on the time-series rearrangement results and transition segment feedback. This implementation introduces a sliding adjustment rhythm on the time axis, ensuring a continuous and consistent time correspondence between the update action of the protection and isolation boundary and changes in communication traffic. The boundary changes in protection judgment exhibit a continuous transitional characteristic. The entire process achieves synchronous coordination of protection rhythm, communication status, and boundary updates in the time dimension, thereby forming a protection execution mechanism in a dynamic communication environment that ensures smooth transition of the protection boundary, reduces the probability of false judgments, and maintains continuous stability of judgment results. This provides an edge-cloud collaborative protection system with a dynamic boundary adjustment method that can adapt to temporal changes and continuously maintain judgment accuracy.

[0043] Step 5: Under the sliding adjustment protection rhythm, construct a synchronous control mechanism for end-to-cloud collaboration, update the cloud strategy mapping relationship in real time according to the feedback cycle of edge nodes, generate a response sequence that continuously matches time and space, realize a collaborative protection closed loop between the end side, edge end and cloud, and reduce false blocking caused by isolation zone drift from the source. The specific implementation method for this step is as follows: Based on the adjusted protection rhythm, the time synchronization relationship between edge devices, edge nodes, and the cloud policy engine is established and adjusted. The adjusted protection rhythm contains the time correspondence between protection actions and communication traffic, reflecting the dynamic balance between protection command execution, isolation boundary updates, and communication responses. During the synchronization establishment process, the time nodes recorded in the protection rhythm are used as a benchmark to map the protection execution time of edge devices, the policy triggering time of edge nodes, and the policy update cycle of the cloud onto the same time axis. This ensures a consistent rhythm across the edge, cloud, and device layers. To ensure the stability of the synchronization relationship, a time window is defined for each protection execution cycle on the time axis. The length of the time window is determined based on the sliding adjustment result of the protection rhythm, ensuring that each node completes policy interaction and response feedback within the same window. This time synchronization method based on the sliding protection rhythm ensures that the execution of protection commands and policy responses remain continuous and consistent in time, providing a basic time framework for subsequent collaborative control.

[0044] Based on the established time synchronization relationship, a dynamic correlation is constructed between edge node feedback and cloud policy mapping according to the protection execution status and feedback cycle of edge nodes. During the execution of protection tasks, edge nodes periodically generate protection execution feedback data according to a sliding adjustment protection rhythm. This feedback data includes isolation judgment status, protection action execution delay, communication traffic changes, and local risk indicators. By aggregating this feedback data in timestamp order and grouping it by the sliding window of the protection rhythm, a continuous feedback sequence can be formed on the timeline. The cloud updates the policy mapping relationship based on this feedback sequence, matching the data characteristics in the feedback cycle with cloud policy parameters, thus forming a one-to-one correspondence between the edge node protection status and the cloud policy logic. This mapping relationship enables the cloud to adjust policy allocation weights and triggering conditions in real time based on the protection feedback of edge nodes in different time periods, achieving continuous tracking and adaptive correction of the edge node's operational status by the cloud policy.

[0045] After establishing the mapping relationship between edge feedback and the cloud, a response sequence with continuous temporal and spatial matching is generated based on the time synchronization relationship and feedback correlation characteristics. The generation process of the response sequence is based on the collaborative state among the end-device, edge nodes, and cloud, integrating the synchronization characteristics in the time dimension and the node distribution characteristics in the spatial dimension. Specifically, within each sliding protection cycle, the cloud generates policy response content based on the status information fed back by the edge nodes, and distributes this content to the corresponding edge nodes and end-devices according to spatial location and temporal order. Each response unit includes a timestamp identifier, protection instruction priority, execution duration, and policy matching identifier. Through the concatenation of continuous response units, a response sequence that is continuous in time and hierarchical in space is formed. During the response sequence generation process, the cloud dynamically adjusts the distribution rhythm of the response sequence according to the sliding rhythm adjustment results, so that the protection execution and policy response of each node remain synchronized in different time periods. In this way, the temporal protection rhythm and the spatial policy distribution form a correspondence, ensuring the continuous matching of protection actions and communication status, and enabling the system to form a continuous data flow and policy flow between the end, edge, and cloud layers.

[0046] After generating a response sequence that continuously matches time and space, this response sequence is applied to the edge-cloud collaborative protection process to achieve closed-loop updates of the protection strategy. Specifically, at the end of each protection cycle, the cloud updates the strategy mapping relationship based on the latest status information fed back by the edge nodes, and pushes the corrected strategy parameters back to the edge nodes and end-side devices, ensuring that the adjustment of the protection strategy is consistent with the sliding update of the protection rhythm. In the next cycle, the end-side devices and edge nodes execute protection tasks according to the new strategy parameters and again return the feedback results to the cloud, forming a continuous strategy feedback loop. In this way, the execution behavior of the end-side devices, the judgment logic of the edge nodes, and the strategy decisions of the cloud are always in a dynamic interactive state, forming a collaborative protection closed loop with adaptive adjustment characteristics. In this closed loop, the protection rhythm in the time dimension and the strategy mapping in the spatial dimension always maintain a corresponding relationship, enabling the entire edge-cloud collaborative protection system to continuously adjust itself based on real-time feedback during continuous operation, reducing false blocking caused by isolation zone drift from the source. Ultimately, the updating of protection strategies, the execution of edge nodes, and the decision-making in the cloud form an integrated dynamic coordination mechanism in time and space, ensuring the continuity and consistency of protection judgments across different nodes, so that the system can still maintain efficient collaboration and continuous stable operation when facing multi-source communication fluctuations and changes in protection status.

[0047] Through the above steps, the edge-cloud collaborative synchronization control mechanism achieves end-to-end coordination of time synchronization, feedback correlation, response generation, and policy closure under the sliding adjustment protection rhythm. This implementation establishes dynamic matching relationships simultaneously in the time and spatial dimensions, enabling the edge device, edge device, and cloud to form a continuously coordinated response mechanism during protection execution and policy adjustment, thereby achieving adaptive linkage between protection and communication at different levels. The entire process uses the sliding adjustment protection rhythm as the main timeline and edge node feedback as the spatial trigger point, constructing a sustainably evolving protection closed-loop structure, fundamentally reducing false blocking caused by isolation zone drift, and maintaining the temporal balance and policy consistency of edge-cloud collaborative protection during long-term system operation.

[0048] Beneficial effects This invention transforms the originally discrete and easily fluctuating protection decision-making process into a traceable dynamic offset evolution process by continuously modeling the changes in the protection execution status and communication latency of edge nodes over time. Protection decisions no longer rely on instantaneous state judgments but are adjusted based on the overall trend of temporal continuity, thereby maintaining the stability of the judgment boundary in high-concurrency and multi-task interaction scenarios. With the introduction of dynamic offset recording and security threshold distribution, the protection rhythm can naturally adjust with the communication status, preventing normal traffic from being amplified and misinterpreted during short-term fluctuations, effectively reducing the probability of false triggers, and ensuring the continuous connectivity and operational reliability of critical business links in complex operating environments.

[0049] Beneficial effects This invention introduces a timing rearrangement and sliding adjustment mechanism during the protection execution process, combined with a synchronous control structure for edge-cloud collaboration, enabling the protection strategy to form a continuous and consistent response relationship at both the temporal and spatial levels. Protection boundary updates no longer exhibit abrupt changes but evolve gradually with changes in communication load, thereby reducing the impact of isolation actions on the overall system coordination. By constructing a feedback loop between the edge and cloud, the protection strategy can be continuously optimized according to the operational status, preventing local anomalies from spreading and amplifying, ultimately maintaining the overall stability and remote management capabilities of the device cluster, and ensuring the consistency and predictability of protection decisions under long-term operating conditions.

[0050] This invention provides, for example Figure 2 The intelligent management system for the entire lifecycle of parallel isolation and protection of public and railway roads based on edge-cloud collaboration shown includes a dynamic offset recording module, a safety threshold reorganization module, a time sequence rhythm rearrangement module, a boundary sliding adjustment module, and an edge-cloud synchronous control module. The dynamic offset recording module collects continuous time-series fluctuation data and communication delay data around the protection execution status of edge nodes, and establishes dynamic offset records on the same time series. The safety threshold reorganization module, based on dynamic offset records, reorganizes the trigger thresholds of protection commands into intervals, divides the time periods with strong drift trends into buffer segments, and limits the trigger frequency of protection actions within the buffer segments, generating a safety threshold distribution with self-adjusting characteristics. The timing rhythm reordering module reorders the instruction issuance rhythm of edge nodes according to the distribution of safety thresholds, and pre-inserts transition segments in the continuous distribution range of normal communication traffic. The boundary sliding adjustment module, based on the timing rearrangement results and combined with the operation feedback of the transition section, implements sliding adjustment on the update rhythm of the protection and isolation boundary, and updates the isolation judgment boundary with the adjusted rhythm, so that the isolation judgment maintains a smooth transition during dynamic communication. The edge-cloud synchronous control module constructs an edge-cloud collaborative synchronous control mechanism under the sliding adjustment protection rhythm. It updates the cloud strategy mapping relationship in real time according to the feedback cycle of edge nodes, and generates a response sequence that continuously matches time and space.

[0051] The intelligent management method for the entire lifecycle of parallel isolation and protection of public transportation and railways based on end-to-cloud collaboration provided in this invention is implemented through the aforementioned intelligent management system for the entire lifecycle of parallel isolation and protection of public transportation and railways based on end-to-cloud collaboration. For details of the specific methods and processes of the intelligent management system for the entire lifecycle of parallel isolation and protection of public transportation and railways based on end-to-cloud collaboration, please refer to the embodiments of the aforementioned intelligent management method for the entire lifecycle of parallel isolation and protection of public transportation and railways based on end-to-cloud collaboration, which will not be repeated here.

[0052] The foregoing has only described certain exemplary embodiments of the present invention by way of illustration. Undoubtedly, those skilled in the art can modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the foregoing drawings and descriptions are illustrative in nature and should not be construed as limiting the scope of protection of the claims of the present invention.

Claims

1. A method for intelligent management of the entire lifecycle of parallel isolation and protection between public and railway lines based on edge-cloud collaboration, characterized in that: Includes the following steps: Step 1: Collect continuous time-series fluctuation data and communication delay data around the protection execution status of edge nodes, and establish dynamic offset records on the same time series. Step 2: Based on the dynamic offset record, the trigger threshold of the protection command is reorganized into intervals, the time period with strong drift trend is divided into buffer segments, and the trigger frequency of the protection action is limited within the buffer segments to generate a safety threshold distribution with self-adjusting characteristics. Step 3: Based on the distribution of security thresholds, the timing of instruction issuance to edge nodes is rearranged, and transition segments are pre-inserted in the continuous distribution interval of normal communication traffic. Step 4: Based on the timing rearrangement results and combined with the operational feedback of the transition segment, the update rhythm of the protection and isolation boundary is adjusted by sliding. The isolation decision boundary is updated with the adjusted rhythm to ensure a smooth transition of the isolation decision during dynamic communication. Step 5: Under the sliding adjustment protection rhythm, construct a synchronous control mechanism for edge-cloud collaboration, update the cloud strategy mapping relationship in real time according to the feedback cycle of edge nodes, and generate a response sequence that continuously matches time and space.

2. The intelligent management method for the entire lifecycle of parallel isolation and protection of public and railway lines based on end-to-cloud collaboration as described in claim 1, characterized in that, The steps for establishing dynamic offset records on the same time series include: Around the protection execution status of edge nodes, collect time-series fluctuation data and communication latency data generated during the protection execution process, and update the data records with millisecond-level time granularity within a fixed period; The collected time-series fluctuation data and communication delay data are uniformly mapped to the same time series, and continuous time-series records are formed through time alignment, sequence synchronization and data integration; By performing differential comparisons on continuous records in the time series, the synchronous change relationship between the protection status change value and the communication delay change value is extracted, and dynamic offset records are generated. By analyzing continuous time periods based on dynamic offset records, the stable and fluctuating intervals of the protection execution status are identified, and the stable interval is defined based on the time axis distribution.

3. The intelligent management method for the entire lifecycle of parallel isolation and protection of public transportation and railways based on end-to-cloud collaboration as described in claim 2, is characterized in that... The stable interval delineation involves comparing the protection status change value and communication delay change value in the dynamic offset record for a continuous time period, and determining the candidate range of the stable interval for the protection execution status based on the correspondence between the change direction and the change magnitude. The continuous and stable time period is defined as the stable interval, the time period with frequent fluctuations is defined as the unstable interval, and the final stable interval is determined based on the distribution of the stable interval on the time axis.

4. The intelligent management method for the entire lifecycle of parallel isolation and protection of public transportation and railways based on end-to-cloud collaboration as described in claim 2, characterized in that, The steps for reorganizing the trigger threshold of protection commands based on dynamic offset records include: Based on the established dynamic offset records, the time series during the protection execution process is continuously read to extract the drift change amplitude and direction information for each time period; Based on the extracted drift feature distribution, the drift trend concentration time period is divided according to the time series, and the start time, end time, duration and drift intensity information of the buffer segment are determined. The offset direction of adjacent time periods is used as an auxiliary condition to merge adjacent segments. Based on the drift characteristics of each buffer segment, the trigger threshold of the protection command is reorganized into intervals. The upper and lower limits of the trigger threshold are adjusted according to the drift amplitude of the buffer segment, and the boundaries are connected between each segment to form a continuous threshold distribution structure. Based on the completion of threshold interval reorganization, the trigger frequency of protection actions is limited within the buffer segment, and the protection trigger interval is adjusted according to the drift trend to generate a safety threshold distribution with self-adjusting characteristics.

5. The intelligent management method for the entire lifecycle of parallel isolation and protection of public transportation and railways based on end-to-cloud collaboration as described in claim 4, characterized in that, In the step of limiting the triggering frequency of protective actions within the buffer zone, the triggering interval of the protective actions is determined based on the drift amplitude and drift duration of the buffer zone, and a uniform triggering rhythm is maintained within the time interval of continuous drift trend, while the number of consecutive triggers is reduced by extending the triggering interval within the drift change interval.

6. The intelligent management method for the entire lifecycle of parallel isolation and protection of public transportation and railways based on end-to-cloud collaboration as described in claim 4, characterized in that, The steps for reordering the command delivery rhythm of edge nodes based on the distribution of security thresholds include: Based on the established security threshold distribution, time series analysis is performed on the protection command issuance status of edge nodes in different time periods. By recording the issuance time, duration, interval length and trigger number of protection commands, the original time series trajectory of protection commands is obtained. The original timeline of the protection commands is aligned with the time distribution curve of the communication traffic. The differences between the protection commands and the communication traffic in terms of time distribution are compared and analyzed to determine the key time period for timeline rearrangement. Based on the rhythmic characteristics of the safety threshold distribution, the rhythm of issuing protection commands is adjusted in segments, and the issuance time of each protection command is matched with the trigger interval boundary of the safety threshold distribution to form a issuance rhythm that is coordinated with the threshold distribution. Transition segments are pre-inserted within the continuous distribution range of normal communication traffic. The length of the transition segment is determined according to the range span of the safety threshold distribution, and a delay buffer strategy is set to balance burst traffic.

7. The intelligent management method for the entire lifecycle of parallel isolation and protection of public transportation and railways based on end-to-cloud collaboration as described in claim 6, characterized in that, The delay buffering strategy in the transition phase includes adjusting the issuance time of protection commands based on the instantaneous changes in communication traffic. When communication traffic increases, the issuance time of some protection commands is delayed, and when communication traffic decreases, the issuance time of some protection commands is advanced.

8. The intelligent management method for the entire lifecycle of parallel isolation and protection of public transportation and railways based on end-to-cloud collaboration as described in claim 6, characterized in that, The steps for implementing a sliding adjustment of the update rhythm of the protective isolation boundary based on the time-series rearrangement results and the operational feedback of the transition phase include: Based on the result of the timing reordering of protection commands, a time correlation analysis is performed on the execution status of protection commands and the response status of communication traffic in each time segment. Operational feedback information of the transition segment is extracted, and the frequency of protection command triggering, response delay, traffic recovery time and traffic balance status are recorded. Based on the feedback results of the transition phase operation, the update cycle of the protection isolation boundary is determined according to the communication recovery time and the protection response delay, forming the boundary update rhythm parameters corresponding to each time interval, and maintaining time consistency with the result of the protection command timing rearrangement. Based on the results of the timing rearrangement of protection instructions and the feedback data of the transition period, the update frequency of the protection and isolation boundary is adjusted by sliding within a continuous time window. The length of the time window is determined and the rhythm is switched between adjacent windows so that the boundary update rhythm remains continuous and gradual in the time series. The boundary update rhythm adjusted by sliding is applied to the protection and isolation determination process. The time threshold and determination range of isolation determination are adjusted in real time according to the sliding rhythm, so that the protection and isolation boundary maintains a smooth transition during dynamic communication.

9. The intelligent management method for the entire lifecycle of parallel isolation and protection of public transportation and railways based on end-to-cloud collaboration as described in claim 8, characterized in that, The steps for building a synchronous control mechanism for edge-cloud collaboration under the sliding adjustment protection rhythm include: Based on the adjusted protection rhythm, a time synchronization relationship is established between the end-side devices, edge nodes, and cloud policy engine, and the protection execution time, policy trigger time, and policy update cycle are uniformly mapped to the same time axis. Based on the protection execution status and feedback cycle of edge nodes, a dynamic relationship between edge node feedback and cloud policy mapping is constructed. Feedback data is summarized in order of timestamp and grouped by sliding window to form a continuous feedback sequence to update cloud policy parameters. Based on the time synchronization relationship and feedback correlation characteristics, a response sequence that continuously matches time and space is generated. The cloud generates strategy response content based on the feedback information from edge nodes and distributes it to the corresponding nodes according to spatial location and time order. The generated response sequence is applied to the edge-cloud collaborative protection process. Based on the feedback results, the strategy mapping relationship is periodically updated and pushed to each node, forming a collaborative protection closed loop that is dynamically coordinated in both time and space dimensions.

10. A smart management system for the entire lifecycle of parallel road-railway isolation and protection based on edge-cloud collaboration, used to implement the smart management method for the entire lifecycle of parallel road-railway isolation and protection based on edge-cloud collaboration as described in any one of claims 1-9, characterized in that, It includes a dynamic offset recording module, a safety threshold reconstruction module, a timing rhythm rearrangement module, a boundary sliding adjustment module, and an edge-cloud synchronous control module; The dynamic offset recording module collects continuous time-series fluctuation data and communication delay data around the protection execution status of edge nodes, and establishes dynamic offset records on the same time series. The safety threshold reorganization module, based on dynamic offset records, reorganizes the trigger thresholds of protection commands into intervals, divides the time periods with strong drift trends into buffer segments, and limits the trigger frequency of protection actions within the buffer segments, generating a safety threshold distribution with self-adjusting characteristics. The timing rhythm reordering module reorders the instruction issuance rhythm of edge nodes according to the distribution of safety thresholds, and pre-inserts transition segments in the continuous distribution range of normal communication traffic. The boundary sliding adjustment module, based on the timing rearrangement results and combined with the operation feedback of the transition section, implements sliding adjustment on the update rhythm of the protection and isolation boundary, and updates the isolation judgment boundary with the adjusted rhythm, so that the isolation judgment maintains a smooth transition during dynamic communication. The edge-cloud synchronous control module constructs an edge-cloud collaborative synchronous control mechanism under the sliding adjustment protection rhythm. It updates the cloud strategy mapping relationship in real time according to the feedback cycle of edge nodes, and generates a response sequence that continuously matches time and space.

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