Vehicle-mounted wireless data communication method for railway section construction protection

Abstract

The invention discloses a vehicle-mounted wireless data communication method for railway section construction protection, particularly relates to the technical field of vehicle-mounted wireless data communication, and aims to accurately identify the risk of vehicle-work end information inconsistency through a state mismatch abnormal coefficient, and accurately identify the risk of vehicle-work end information inconsistency in a boundary fuzzy field caused by positioning precision reduction or boundary coordinate static description. The approaching uncertainty of a train and a construction area is quantified through a region boundary fuzzy coefficient, a misjudgment risk model is constructed, and early warning on a high-risk scene that the train runs into the construction area without triggering any early warning is achieved; dynamic compensation is carried out on a fault-tolerant mechanism of a data communication link in combination with sequential fluctuation analysis of continuous high-risk signals; by quantifying risk factors and dynamically adjusting a fault-tolerant strategy in real time, the safety redundancy and robustness of train operation during construction are improved, accurate sensing and quick response to hidden faults and coupling failures in a complex environment are achieved, and the intelligent level and the safety guarantee capacity of railway section construction protection are improved.

Description

Technical Field

[0001] This invention relates to the field of vehicle-mounted wireless data communication technology, and more specifically, to a vehicle-mounted wireless data communication method for protection during railway construction. Background Technology

[0002] In scenarios where railway operations and temporary construction coexist, ensuring dynamic perception and communication between trains and construction activities has become a crucial aspect of railway transportation safety control. Currently, widely adopted onboard wireless communication protection methods primarily rely on onboard communication equipment receiving broadcast information from the construction protection end to determine whether a train is approaching the construction area and trigger warnings or control strategies. However, in practical applications, such systems still face some deep-seated challenges.

[0003] Specifically, on the one hand, construction equipment may fail to receive construction broadcast signals in a timely manner due to disconnection, configuration errors, or abnormal status, leading to misidentification of the construction area's status. On the other hand, the superposition of train positioning errors and the ambiguity of construction area boundaries amplifies potential risks. In complex terrains such as mountainous areas, tunnels, and bridges, train positioning information suffers from decreased accuracy and data drift. Currently, construction areas are mostly statically described using coordinate points, lacking physical or dynamic constraints on their boundaries, making it difficult for the onboard system to accurately determine whether it has entered a protected area. When these problems occur simultaneously, the system's fault tolerance mechanism will fail. Even if one end of the communication system is still operating normally, the defects at the other end, combined with positioning errors, will create a masking effect, causing the train to enter the construction area without receiving any warning, seriously threatening train safety and the lives of construction workers.

[0004] Therefore, it is urgent to construct an onboard wireless data communication method with capabilities such as state redundancy judgment, dynamic boundary modeling, and anomaly perception verification, so as to achieve highly reliable identification and real-time linkage response of the construction status of railway sections, and fundamentally improve the intelligence and safety level of railway section construction protection. Summary of the Invention

[0005] In order to overcome the above-mentioned defects of the prior art, embodiments of the present invention provide a vehicle-mounted wireless data communication method for railway section construction protection, so as to solve the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides the following technical solution: A vehicle-mounted wireless data communication method for protection during railway construction includes the following steps: Step S1: When construction starts, the construction protection terminal broadcasts construction information to the vehicle system. The vehicle communication unit periodically receives the broadcast information and verifies the integrity of the information to determine whether to activate the status mismatch monitoring mechanism. Step S2: If the state mismatch monitoring mechanism is activated, a vehicle-to-construction equipment state comparison matrix is ​​established to calculate the state mismatch anomaly coefficient and assess the degree of state mismatch deterioration between the vehicle-mounted and construction-end equipment. Step S3: Obtain the current train positioning data through the on-board positioning module and combine it with the start and end boundary coordinates of the construction area to construct the area boundary ambiguity function, calculate the area boundary ambiguity coefficient, and evaluate the degree of ambiguity of the construction area boundary caused by the train positioning error. Step S4: Construct a train warning trigger misjudgment model based on the state mismatch anomaly coefficient and the regional boundary fuzziness coefficient, obtain the train warning trigger misjudgment index, and assess the risk of misjudgment when the train enters the construction area without triggering any warning. Step S5: Perform dynamic early warning compensation on the fault tolerance mechanism of the current data communication system based on the risk of misjudgment.

[0007] In a preferred embodiment, if the state mismatch monitoring mechanism is activated, a vehicle-machine equipment state comparison matrix is ​​established to calculate the state mismatch anomaly coefficient, as follows: Record vehicle status data in N consecutive cycles. Construction end status data Construct the following state alignment matrix : ; Obtain the time series of each state field from the state alignment matrix. Represent the state mismatch trajectory of state field m; calculate the local mutation rate for the time series of each state field. ; The periodic average mutation rate is obtained by averaging the local mutation rates of the state field over N observation periods. ; For each observation period n, the dispersion of state mismatch across different fields is analyzed and the cross-field difference fluctuation value is calculated. ; The mean cross-field difference fluctuation value is obtained by averaging the cross-field difference fluctuation values ​​over N observation periods. ; Calculate the state mismatch anomaly coefficient : .

[0008] In a preferred embodiment, the current train positioning data is obtained through the on-board positioning module, and the region boundary ambiguity coefficient is calculated by constructing a region boundary ambiguity function in combination with the start and end boundary coordinates of the construction area, as follows: Get the train in time Positioning coordinates Error value ; The start and end boundary coordinates of the construction area are marked as follows: Introducing fuzzy bandwidth Obtaining the fuzzy boundary domain ,in For time fuzzy boundary domain, ; Calculate the spatial residual offset based on the fuzzy boundary domain. : ,in A function to find the minimum value; A region boundary ambiguity function is constructed based on the spatial residual offset, and the degree of perception of boundary ambiguity by the train position is obtained to obtain the region boundary ambiguity value. The specific formula for the region boundary fuzzy function is as follows: ,in For time The region boundary ambiguity value, This is a very small constant to prevent division by zero; The maximum temporal perturbation slope of the ambiguity level is obtained from the ambiguity function of the region boundary. : ,in The actual operating cycle of the vehicle positioning module; Calculate the ambiguity coefficient of the region boundary : .

[0009] In a preferred embodiment, a train warning triggering misjudgment model is constructed based on the state mismatch anomaly coefficient and the region boundary ambiguity coefficient to obtain the train warning triggering misjudgment index. The train warning triggering misjudgment model is based on the following formula: In the formula The train warning trigger misjudgment index, The state mismatch anomaly coefficient is... For the ambiguity coefficient of the region boundary, These represent the preset scaling factors for the state mismatch anomaly coefficient and the region boundary ambiguity coefficient, respectively. All are greater than 0.

[0010] In a preferred embodiment, the train warning triggering misjudgment index is compared with a preset train warning triggering misjudgment index threshold to assess the risk of misjudgment when the train enters the construction area without triggering any warning, as follows: If the train warning trigger misjudgment index is greater than the train warning trigger misjudgment index threshold, then the misjudgment risk of the current train entering the construction area without triggering any warning is marked as high misjudgment risk. If the train warning trigger misjudgment index is less than or equal to the train warning trigger misjudgment index threshold, then the misjudgment risk of the current train entering the construction area without triggering any warning is marked as low misjudgment risk.

[0011] In a preferred embodiment, if three high-risk false alarm signals are generated consecutively, the risk time series is constructed by collecting the train warning triggering false alarm index generated by the train warning triggering false alarm model at different subsequent times: ,in for The train warning trigger misjudgment index collected in real time. The data collection period; Calculate the volatility index of risk time series : ,in This represents the average value of the data points in the risk time series. .

[0012] In a preferred embodiment, the volatility index of the risk time series is compared with a preset volatility index threshold to dynamically provide early warning compensation for the fault tolerance mechanism of the current data communication system, as follows: If the volatility index exceeds the volatility index threshold, a risk instability signal is generated, and the data communication fault tolerance mechanism is immediately and forcibly upgraded. If the volatility index is less than or equal to the volatility index threshold, the current fault tolerance mechanism configuration will be maintained.

[0013] The technical effects and advantages of this invention are as follows: 1. The multi-level state redundancy judgment and dynamic boundary fuzzy modeling mechanism constructed in this invention enables the on-board system to accurately identify the risk of inconsistency between train and construction end information through state mismatch anomaly coefficients when construction broadcasts are missing or construction end equipment malfunctions. In the boundary fuzzy field caused by decreased positioning accuracy or static description of boundary coordinates, the uncertainty of train approaching the construction area is quantified through regional boundary fuzzy coefficients. Based on the two types of coefficients, a misjudgment risk model is constructed to achieve early warning of high-risk scenarios such as "entering the construction area without triggering any warning". At the same time, combined with the temporal fluctuation analysis of continuous high-risk signals, the fault tolerance mechanism of the data communication link can be dynamically compensated. By quantifying risk factors and adjusting the fault tolerance strategy in real time, the safety redundancy and robustness of train operation during construction are improved, and the accurate perception and rapid response to hidden faults and coupling failures in complex environments are achieved, thereby improving the intelligence level and safety assurance capability of railway section construction protection. Attached Figure Description

[0014] To facilitate understanding by those skilled in the art, the present invention will be further described below with reference to the accompanying drawings; Figure 1 This is a flowchart of a method according to an embodiment of the present invention. Detailed Implementation

[0015] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0016] Example: Figure 1 The present invention discloses a vehicle-mounted wireless data communication method for construction protection in railway sections, comprising the following steps: Step S1: When construction starts, the construction protection terminal broadcasts construction information to the vehicle system. The vehicle communication unit periodically receives the broadcast information and verifies the integrity of the information to determine whether to activate the status mismatch monitoring mechanism. Step S2: If the state mismatch monitoring mechanism is activated, a vehicle-to-construction equipment state comparison matrix is ​​established to calculate the state mismatch anomaly coefficient and assess the degree of state mismatch deterioration between the vehicle-mounted and construction-end equipment. Step S3: Obtain the current train positioning data through the on-board positioning module and combine it with the start and end boundary coordinates of the construction area to construct the area boundary ambiguity function, calculate the area boundary ambiguity coefficient, and evaluate the degree of ambiguity of the construction area boundary caused by the train positioning error. Step S4: Construct a train warning trigger misjudgment model based on the state mismatch anomaly coefficient and the regional boundary fuzziness coefficient, obtain the train warning trigger misjudgment index, and assess the risk of misjudgment when the train enters the construction area without triggering any warning. Step S5: Dynamically warn and compensate for the fault tolerance mechanism of the current data communication system based on the risk of misjudgment; Step S1: When construction starts, the construction protection terminal broadcasts construction information to the vehicle system. The construction information includes the construction area identifier (ID), the coordinates of the start and end boundaries of the construction area (GPS / mileage location), the construction level, the broadcast timestamp and update cycle, and the status code of the protection terminal equipment (including operating status, reporting interval, and fault code). The vehicle-mounted communication unit periodically receives broadcast information and verifies its integrity to determine whether to activate the state mismatch monitoring mechanism. The decision logic is as follows: A. Verify that all broadcast fields are complete; B. Compare whether the broadcast timestamp increases continuously. If there is an abnormal packet loss interval (such as more than 3 consecutive losses). C. Verify whether the construction status is consistent with the vehicle-side cache status; If there are issues such as missing broadcast fields, abnormal packet loss intervals, or inconsistencies between the construction status and the vehicle-side cache status, the status mismatch monitoring mechanism will be activated. Step S2: If the state mismatch monitoring mechanism is activated, a vehicle-equipment state comparison matrix is ​​established to calculate the state mismatch anomaly coefficient, as follows: Record vehicle status data in N consecutive cycles. Construction end status data Construct the following state alignment matrix : ,in This is the m-th state parameter on the vehicle side. This is the m-th state parameter at the construction end. Number the observation period. Number the status field. This indicates whether there is a difference in the state of the m-th item in the n-th period, with 0 indicating consistency and 1 indicating mismatch; The status parameters collected during each broadcast cycle are shown in Table 1, but are not limited to these: Table 1 Obtain the time series of each state field from the state alignment matrix. Represent the state mismatch trajectory of state field m; calculate the local mutation rate for the time series of each state field. : ; The periodic average mutation rate is obtained by averaging the local mutation rates of the state field over N observation periods. : ; For each observation period n, the dispersion of state mismatch across different fields is analyzed and the cross-field difference fluctuation value is calculated. : ,in The percentage of states with difference k (0 or 1) in the nth observation period: ,in The indicator function is used to statistically analyze all state fields within the nth observation period. and Number of status fields; The mean cross-field difference fluctuation value is obtained by averaging the cross-field difference fluctuation values ​​over N observation periods. : ; Calculate the state mismatch anomaly coefficient : ; It should be noted that the above formulas are all dimensionless calculations. Commonly used methods for removing dimensions include Min-Max normalization and Z-Score standardization, which will not be elaborated here. In this invention, the state mismatch anomaly coefficient is used to measure the degree of state mismatch deterioration caused by inconsistencies in state information, signal interruptions, or abnormal fault feedback between the onboard system and construction protection equipment during operation. Essentially, it is a quantitative assessment indicator of the reliability of the communication link during railway construction. This coefficient not only reflects whether there are configuration errors or initialization mismatches in the static construction phase of the system, but also dynamically captures system state fluctuations caused by hardware failures, channel interference, lost heartbeats, and broadcast anomalies during operation, significantly impacting the stability and timeliness of the early warning triggering mechanism. In practical railway construction protection applications, train operation safety heavily relies on the onboard system's ability to stably, continuously, and accurately acquire information broadcasts from the construction area to trigger response mechanisms such as speed limits, avoidance, or stopping. If one end of the communication link experiences a state anomaly while the other remains normal, it may cause the train to misjudge the safety status of the area, thus entering the blocked area under construction without any early warning trigger, posing a significant safety hazard. The introduction of the state mismatch anomaly coefficient is precisely to solve this problem of imperceptible failures at both ends. Specifically, a significant increase in the state mismatch anomaly coefficient indicates a severe inconsistency between the vehicle-mounted and construction-side systems. This inconsistency may be caused by broadcast delays, link drops, equipment configuration errors, or temporary channel interruptions. This inconsistency directly interferes with the normal activation of the early warning mechanism, leading to a blind zone where the system is perceived as safe but is actually dangerous. Conversely, a smaller state mismatch anomaly coefficient indicates normal synchronization between the vehicle and construction sides, reliable communication links, and high confidence in the system's identification of the construction area. In this case, even with some positioning errors or blurred boundary identification, systemic misjudgments are less likely to be triggered, thus improving overall early warning accuracy. This invention uses the state mismatch anomaly coefficient as a core parameter in the misjudgment risk model, combining it with the regional boundary ambiguity coefficient to construct a train early warning trigger misjudgment index, achieving accurate prediction and dynamic intervention for the risk of missed triggers. This strategy not only enhances the communication system's ability to perceive latent faults and coupling anomalies but also improves the fault tolerance and adaptive adjustment capabilities of the protection system under abnormal conditions, effectively reducing the probability of trains mistakenly entering construction areas without triggering any early warnings. It has significant safety assurance value and promising engineering application prospects.

[0017] Step S3: Obtain the current train positioning data through the on-board positioning module and combine it with the start and end boundary coordinates of the construction area to construct a region boundary ambiguity function and calculate the region boundary ambiguity coefficient, as detailed below: Get the train in time Positioning coordinates (Can be one-dimensional / two-dimensional / three-dimensional; this invention uses one-dimensional orbital coordinates as an example, but in actual deployment, it can be extended to two-dimensional / three-dimensional according to the actual situation), error value. ; The start and end boundary coordinates of the construction area are marked as follows: Introducing fuzzy bandwidth Obtaining the fuzzy boundary domain ,in For time fuzzy boundary domain, ; It should be noted that fuzzy bandwidth The width of the buffer zone near the boundary of the construction area, which allows for the identification of ambiguity or error, is a key parameter used to characterize the "ambiguity judgment capability" of the boundary. In actual engineering, its sources can be the following: Construction boundary setting error: positioning deviation and boundary demarcation error when manually setting up the construction area; GNSS and other positioning drift: Spatial drift of vehicle positioning systems caused by electromagnetic interference around the track, tunnel shielding, etc. Equipment configuration tolerance band: The redundant bandwidth set in the system fault-tolerant design to prevent "boundary misidentification"; Dynamic fitting parameter tuning: can be automatically generated from historical trajectory statistics + fuzzy recognition accuracy evaluation (e.g., through supervised learning model output); Calculate the spatial residual offset based on the fuzzy boundary domain. : ,in This is a function for finding the minimum value, used to obtain... and The minimum value between; A region boundary ambiguity function is constructed based on the spatial residual offset, and the degree of perception of boundary ambiguity by the train position is obtained to obtain the region boundary ambiguity value. The specific formula for the region boundary fuzzy function is as follows: ,in For time The region boundary ambiguity value, This is to prevent division by zero by a very small constant (generally taken as...). ); The maximum temporal perturbation slope of the ambiguity level is obtained from the ambiguity function of the region boundary. : ,in The actual operating cycle of the vehicle positioning module; Calculate the ambiguity coefficient of the region boundary : ; It should be noted that the above formulas are all dimensionless calculations. Commonly used methods for removing dimensions include Min-Max normalization and Z-Score standardization, which will not be elaborated here. In this invention, the regional boundary ambiguity coefficient is used to measure the degree of boundary uncertainty in train positioning error during construction area identification. Its core purpose is to assess the reliability of the onboard system's identification of the construction area boundary, especially in scenarios such as GNSS drift, accumulated inertial navigation errors, or failure of positioning fusion algorithms, to accurately model the offset relationship between the train's actual position and the "perceived boundary" of the construction area. A larger regional boundary ambiguity coefficient indicates greater uncertainty regarding the train's current position near the edge of the construction area, making it difficult for the system to definitively determine whether the train has entered the construction protection zone. Conversely, a smaller coefficient indicates a more stable and clear distance or trajectory position between the train and the boundary, resulting in higher boundary identifiability.

[0018] In real-world scenarios, train onboard systems typically determine whether a vehicle has entered a construction zone by receiving positioning information from GNSS, odometers, and trackside beacons, and then using this information for reasoning. However, due to the inherent variability in the actual deployment of construction zone boundaries (e.g., inaccurate coordinate configuration, nonlinear boundary bandwidth), coupled with positioning errors, the onboard system may be in an ambiguous state near a critical threshold when determining whether it has entered a construction zone, leading to misjudgments. The fuzzy coefficient of the area boundary models this "boundary judgment uncertainty," providing a quantitative input for subsequent misjudgment risk assessment.

[0019] This invention couples the ambiguity coefficient of the regional boundary with the anomaly coefficient of state mismatch to construct a train warning triggering misjudgment index, thereby accurately assessing the potential risk of a train entering a construction area without triggering any warnings (such as speed limits, alerts, or stops). In actual operation, when the ambiguity coefficient of the regional boundary is large, even if the state broadcast is normal, the system may not trigger a warning due to the ambiguity of the boundary; conversely, when the boundary ambiguity coefficient is small, even with slight state anomalies, the actual risk of a train entering the boundary area can be reliably identified. Therefore, by introducing and dynamically adjusting this ambiguity coefficient, this invention significantly improves the robustness and misjudgment prevention capability of the system in complex positioning uncertainty scenarios, providing a more reliable intelligent guarantee mechanism for the inherent safety of train operation during construction.

[0020] Step S4: Construct a train warning trigger misjudgment model based on the state mismatch anomaly coefficient and the regional boundary fuzziness coefficient, obtain the train warning trigger misjudgment index, and assess the risk of misjudgment when the train enters the construction area without triggering any warning. A train warning triggering misjudgment model is constructed based on the state mismatch anomaly coefficient and the regional boundary ambiguity coefficient, and the train warning triggering misjudgment index is obtained. The formula used for the train warning triggering misjudgment model is as follows: In the formula The train warning trigger misjudgment index, The state mismatch anomaly coefficient is... For the ambiguity coefficient of the region boundary, These represent the preset scaling factors for the state mismatch anomaly coefficient and the region boundary ambiguity coefficient, respectively. All are greater than 0; It should be noted that the above formulas are all dimensionless calculations. Commonly used methods for removing dimensions include Min-Max normalization and Z-Score standardization, which will not be elaborated here. The settings should be tailored to the specific circumstances. For example, an expert-empowered approach could be adopted, where experts in relevant fields are invited to determine the pre-defined proportions for each indicator through professional opinion surveys and comprehensive evaluations. It can be 0.5 or 0.5; As can be seen from the above calculation expression, the larger the state mismatch anomaly coefficient and the larger the area boundary ambiguity coefficient, the larger the train warning trigger misjudgment index. This indicates that the risk of the train system mistakenly entering the construction area when it fails to correctly trigger the warning signal is higher. That is, when the index is large, there is a possibility of "perceived safety illusion" for the train. This means that the system fails to receive or interpret the construction broadcast correctly due to equipment malfunction, and is also affected by positioning errors and boundary ambiguity in boundary recognition, failing to accurately determine whether it has entered the construction area. As a result, the warning mechanism is not triggered, and the train enters the high-risk work area without the driver's awareness. Conversely, the smaller the state mismatch anomaly coefficient and the smaller the area boundary ambiguity coefficient, the smaller the train warning trigger misjudgment index. This indicates that the overall consistency and perception accuracy of the system under the current operating state are high, the warning mechanism can operate reliably as expected, and the risk of the train mistakenly entering the construction area is extremely low. The train warning triggering misjudgment index is compared with the preset train warning triggering misjudgment index threshold to assess the risk of misjudgment when a train enters a construction area without triggering any warning, as detailed below: If the train warning trigger misjudgment index is greater than the train warning trigger misjudgment index threshold, it indicates that the current train has obvious system state mismatch risk and enhanced boundary recognition ambiguity. The consistency of information and perception accuracy within the system are reduced, which may cause the warning mechanism to fail at critical moments. Especially when the on-board equipment fails to correctly perceive the construction area signal and the positioning system misjudges that the current position has not crossed the boundary, the train is very likely to mistakenly enter the work site without any deceleration, speed limit, or braking intervention. The misjudgment risk of the current train entering the construction area without triggering any warning is marked as high misjudgment risk. If the train warning triggering misjudgment index is less than or equal to the train warning triggering misjudgment index threshold, it indicates that the overall system operation is good, the equipment perception is consistent, the boundary judgment is clear, and the warning triggering mechanism is within a controllable range. At this time, even if the train approaches the construction area, it can be promptly reminded and braked through the existing system, with no obvious risk of misjudgment. The risk of the current train entering the construction area without triggering any warning is marked as low risk of misjudgment, and normal operation mode can be maintained. It should be noted that the threshold for train warning triggering misjudgment can be set according to different operating conditions, line grade, construction intensity, and other factors. The setting methods include, but are not limited to, the following: Expert experience method: Combine the opinions of signal system engineers, dispatchers, and equipment manufacturers to set an experience threshold for the adaptive safety boundary (such as 0.6 or 0.7). Data-driven approach: Based on historical accident data and misjudged samples, a reasonable threshold is fitted through machine learning or regression analysis; Operational grading method: Set different thresholds for different operating modes (e.g., lower thresholds for autonomous driving mode and more lenient thresholds for manual driving mode). Step S5: Dynamically warn and compensate for the fault tolerance mechanism of the current data communication system based on the risk of misjudgment; If three high-risk misjudgment signals are generated consecutively (which can be dynamically adjusted according to actual operational requirements), the risk time series is constructed by collecting the train warning trigger misjudgment index generated by the train warning trigger misjudgment model at different subsequent times: ,in for The train warning trigger misjudgment index collected in real time. The data collection period; Calculate the volatility index of risk time series : ,in This represents the average value of the data points in the risk time series. ; The volatility index of the risk time series is compared with a preset volatility index threshold to dynamically warn and compensate for the fault tolerance mechanism of the current data communication system, as follows: If the volatility index is greater than the volatility index threshold, it indicates that the risk of misjudgment is not only high but also fluctuating wildly. This means that the system has poor robustness in sensing construction and is prone to "frequent alternating triggering and failure." This can easily lead to multiple false triggers and missed triggers in the early warning mechanism, causing trust fatigue or decision lag in the driver system, generating risk instability signals, and immediately forcibly upgrading the data communication fault tolerance mechanism. D1. Enable dual-channel redundant broadcast verification mechanism; D2. Force the activation of the reverse broadcast confirmation mechanism (confirmation sent back from the construction end); D3. Switch the positioning service to high-precision GNSS + inertial navigation redundancy positioning mode; D4. Prompt the dispatch center to conduct a manual review; If the volatility index is less than or equal to the volatility index threshold, it indicates that although the current risk has increased, the overall volatility is within a controllable range, indicating that the system has a certain self-stabilizing ability and can maintain the current fault tolerance mechanism configuration. This invention constructs a multi-level state redundancy judgment and dynamic boundary fuzzy modeling mechanism, enabling the onboard system to accurately identify the risk of inconsistency between train and construction end information through state mismatch anomaly coefficients when construction broadcasts are missing or construction end equipment malfunctions. Furthermore, in boundary fuzzy fields caused by decreased positioning accuracy or static boundary coordinate descriptions, the mechanism quantifies the uncertainty of train proximity to the construction area through regional boundary fuzziness coefficients. Based on these two types of coefficients, a misjudgment risk model is constructed, enabling early warning of high-risk scenarios such as "entering the construction area without triggering any warning." Simultaneously, by combining the temporal fluctuation analysis of continuous high-risk signals, the fault tolerance mechanism of the data communication link can be dynamically compensated. Through quantifying risk factors and dynamically adjusting fault tolerance strategies in real time, the safety redundancy and robustness of train operation during construction are improved, enabling accurate perception and rapid response to latent faults and coupling failures in complex environments, thereby enhancing the intelligence level and safety assurance capabilities of railway section construction protection.

[0021] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0022] It should be understood that in the various embodiments of this application, the order of the above-mentioned processes does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0023] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A vehicle-mounted wireless data communication method for protection during railway construction, characterized in that: Includes the following steps: Step S1: When construction starts, the construction protection terminal broadcasts construction information to the vehicle system. The vehicle communication unit periodically receives the broadcast information and verifies the integrity of the information to determine whether to activate the status mismatch monitoring mechanism. Step S2: If the state mismatch monitoring mechanism is activated, a vehicle-to-construction equipment state comparison matrix is ​​established to calculate the state mismatch anomaly coefficient and assess the degree of state mismatch deterioration between the vehicle-mounted and construction-end equipment. Step S3: Obtain the current train positioning data through the on-board positioning module and combine it with the start and end boundary coordinates of the construction area to construct the area boundary ambiguity function, calculate the area boundary ambiguity coefficient, and evaluate the degree of ambiguity of the construction area boundary caused by the train positioning error. Step S4: Construct a train warning trigger misjudgment model based on the state mismatch anomaly coefficient and the regional boundary fuzziness coefficient, obtain the train warning trigger misjudgment index, and assess the risk of misjudgment when the train enters the construction area without triggering any warning. Step S5: Perform dynamic early warning compensation on the fault tolerance mechanism of the current data communication system based on the risk of misjudgment.

2. The vehicle-mounted wireless data communication method for construction protection in railway sections according to claim 1, characterized in that: If the state mismatch monitoring mechanism is activated, a vehicle-equipment state comparison matrix is ​​established to calculate the state mismatch anomaly coefficient, as follows: Record vehicle status data in N consecutive cycles. Construction end status data Construct the following state alignment matrix : ; Obtain the time series of each state field from the state alignment matrix. Represent the state mismatch trajectory of state field m; calculate the local mutation rate for the time series of each state field. ; The periodic average mutation rate is obtained by averaging the local mutation rates of the state field over N observation periods. ; For each observation period n, the dispersion of state mismatch across different fields is analyzed and the cross-field difference fluctuation value is calculated. ; The mean cross-field difference fluctuation value is obtained by averaging the cross-field difference fluctuation values ​​over N observation periods. ; Calculate the state mismatch anomaly coefficient : .

3. The vehicle-mounted wireless data communication method for construction protection in railway sections according to claim 1, characterized in that: The current train positioning data is obtained through the on-board positioning module, and the fuzzy coefficient of the area boundary is calculated by constructing an area boundary fuzzy function and combining it with the start and end boundary coordinates of the construction area, as detailed below: Get the train in time Positioning coordinates Error value ; The start and end boundary coordinates of the construction area are marked as follows: Introducing fuzzy bandwidth Obtaining the fuzzy boundary domain ,in For time fuzzy boundary domain ; Calculate the spatial residual offset based on the fuzzy boundary domain. : ,in A function to find the minimum value; A region boundary ambiguity function is constructed based on the spatial residual offset, and the degree of perception of boundary ambiguity by the train position is obtained to obtain the region boundary ambiguity value. The specific formula for the region boundary fuzzy function is as follows: ,in For time The region boundary ambiguity value, This is a very small constant to prevent division by zero; The maximum temporal perturbation slope of the ambiguity level is obtained from the ambiguity function of the region boundary. : ,in The actual operating cycle of the vehicle positioning module; Calculate the ambiguity coefficient of the region boundary : .

4. The vehicle-mounted wireless data communication method for construction protection in railway sections according to claim 1, characterized in that: A train warning triggering misjudgment model is constructed based on the state mismatch anomaly coefficient and the regional boundary ambiguity coefficient, and the train warning triggering misjudgment index is obtained. The formula used for the train warning triggering misjudgment model is as follows: In the formula The train warning trigger misjudgment index, The state mismatch anomaly coefficient is... For the ambiguity coefficient of the region boundary, These represent the preset scaling factors for the state mismatch anomaly coefficient and the region boundary ambiguity coefficient, respectively. All are greater than 0.

5. A vehicle-mounted wireless data communication method for construction protection in railway sections according to claim 4, characterized in that: The train warning triggering misjudgment index is compared with the preset train warning triggering misjudgment index threshold to assess the risk of misjudgment when a train enters a construction area without triggering any warning, as detailed below: If the train warning trigger misjudgment index is greater than the train warning trigger misjudgment index threshold, then the misjudgment risk of the current train entering the construction area without triggering any warning is marked as high misjudgment risk. If the train warning trigger misjudgment index is less than or equal to the train warning trigger misjudgment index threshold, then the misjudgment risk of the current train entering the construction area without triggering any warning is marked as low misjudgment risk.

6. The vehicle-mounted wireless data communication method for construction protection in railway sections according to claim 5, characterized in that: If three high-risk false alarm signals are generated consecutively, the risk time series is constructed by collecting the train warning triggering false alarm index generated by the train warning triggering false alarm model at different subsequent times: ,in for The train warning trigger misjudgment index collected in real time. The collection period; Calculate the volatility index of risk time series : ,in This represents the average value of the data points in the risk time series. .

7. A vehicle-mounted wireless data communication method for construction protection in railway sections according to claim 6, characterized in that: The volatility index of the risk time series is compared with a preset volatility index threshold to dynamically warn and compensate for the fault tolerance mechanism of the current data communication system, as follows: If the volatility index exceeds the volatility index threshold, a risk instability signal is generated, and the data communication fault tolerance mechanism is immediately and forcibly upgraded. If the volatility index is less than or equal to the volatility index threshold, the current fault tolerance mechanism configuration will be maintained.

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