A vehicle-mounted wireless data communication method for railway section construction protection

Abstract

The application discloses a vehicle-mounted wireless data communication method for railway section construction protection, and particularly relates to the technical field of vehicle-mounted wireless data communication, wherein a state mismatch abnormal coefficient is used to accurately identify the risk of inconsistent information between a vehicle and a worker end, and in a boundary ambiguity field caused by a decrease in positioning accuracy or static description of boundary coordinates, an area boundary ambiguity coefficient is used to quantify the proximity uncertainty of a train and a construction area, a misjudgment risk model is constructed, early warning of a high-risk scene, i.e., entering a construction area without triggering any early warning, is realized, and the time sequence fluctuation analysis of continuous high-risk signals is combined to dynamically compensate the fault-tolerant mechanism of a data communication link, the risk factor is quantified, the fault-tolerant strategy is dynamically adjusted in real time, the safety redundancy and robustness of train operation during construction are improved, the accurate perception and rapid response to implicit faults and coupled failures in a complex environment are realized, and the intelligent level and safety guarantee capability of railway section construction protection are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of vehicle-mounted wireless data communication, and more particularly to a vehicle-mounted wireless data communication method for railway section construction protection. BACKGROUND

[0002] In the scenario of parallel operation of railway sections and temporary construction, dynamic perception and communication linkage between trains and construction activities have become a key link in the safety control of railway transportation. Currently, the widely used vehicle-mounted wireless communication protection method mainly relies on the vehicle-mounted communication equipment to receive broadcast information from the construction protection end to determine whether the train is approaching the construction area and trigger the warning or control strategy. However, in actual application, this kind of system still faces some deep-seated challenges.

[0003] Specifically, on the one hand, the construction end equipment may cause the train end to fail to receive the construction broadcast signal in time due to disconnection, configuration error or state abnormality, resulting in misidentification of the state of the construction area. On the other hand, the superposition of train positioning error and the fuzziness of the boundary of the construction area amplifies the risk hidden danger. In complex terrains such as mountains, tunnels and bridges, the train positioning information has problems such as precision decline and data drift, while the current construction area is described statically by coordinate points, and its boundary lacks physical or dynamic constraints, making it difficult for the vehicle-mounted system to accurately determine whether to enter the protection area. When the above problems occur at the same time, the fault tolerance mechanism of the system will fail. Even if one end of the communication system is still running normally, the defects of the other end plus the positioning error will produce a mutual masking effect, causing the train to enter the construction area that is being operated without receiving any warning, which seriously threatens the safety of train operation and the safety of construction personnel.

[0004] Therefore, it is urgent to build a vehicle-mounted wireless data communication method with the capabilities of state redundancy judgment, dynamic boundary modeling and abnormal perception verification, to realize high-trust identification and real-time linkage response to the state of railway section construction, and fundamentally improve the intelligence and safety level of railway section construction protection. SUMMARY

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

[0006] To achieve the above-mentioned purpose, the present application provides the following technical solutions:

[0007] A vehicle-mounted wireless data communication method for railway section construction protection, comprising the following steps:

[0008] Step S1, the construction protection end broadcasts construction information to the vehicle-mounted system at the start of construction, and the vehicle-mounted communication unit periodically receives the broadcast information and checks the information integrity to determine whether to start the state mismatch monitoring mechanism;

[0009] Step S2, if the state mismatch monitoring mechanism is started, a vehicle-construction equipment state comparison matrix is established to calculate a state mismatch abnormality coefficient, and the state mismatch deterioration degree of the vehicle-mounted and construction end equipment is evaluated;

[0010] Step S3, the current train positioning data is obtained through the vehicle-mounted positioning module, and the region boundary fuzzy function is constructed by combining the start and end boundary coordinates of the construction area to calculate the region boundary fuzzy coefficient, and the construction area boundary fuzzy degree caused by the train positioning error is evaluated;

[0011] Step S4, a train early warning trigger false alarm model is constructed according to the state mismatch abnormality coefficient and the region boundary fuzzy coefficient, a train early warning trigger false alarm index is obtained, and the false alarm risk of the train entering the construction area being operated without triggering any early warning is evaluated;

[0012] Step S5, the fault-tolerant mechanism of the current data communication system is dynamically compensated according to the false alarm risk.

[0013] In a preferred embodiment, if the state mismatch monitoring mechanism is started, a vehicle-construction equipment state comparison matrix is established to calculate a state mismatch abnormality coefficient, which is as follows:

[0014] The vehicle end state data is recorded in N consecutive periods and the construction end state data The following state comparison matrix is constructed : ;

[0015] The time series of each state field is obtained from the state comparison matrix , and each time series represents the state mismatch trajectory of the state field m; the local mutation rate of each state field is calculated ;

[0016] The average value of the local mutation rate of the state field in N observation periods is calculated to obtain the period average mutation rate ;

[0017] For each observation period n, the dispersion degree of the state mismatch between different fields is analyzed and calculated to obtain the cross-field difference fluctuation value ;

[0018] The average value of the cross-field difference fluctuation value is calculated in N observation periods to obtain the cross-field difference fluctuation average value ;

[0019] Computing a state mismatch anomaly coefficient : .

[0020] In a preferred embodiment, the current train positioning data is obtained by the on-board positioning module and combined with the start and end boundary coordinates of the construction area to construct a regional boundary fuzzy function to calculate the regional boundary fuzzy coefficient, as follows:

[0021] Obtain the positioning coordinate value , error value of the train at time ;

[0022] Mark the start and end boundary coordinates of the construction area as , introduce the fuzzy bandwidth , and obtain the fuzzy boundary domain , where is the fuzzy boundary domain at time ; ;

[0023] Calculate the spatial residual offset according to the fuzzy boundary domain: , where is the minimum value obtaining function;

[0024] Construct a regional boundary fuzzy function according to the spatial residual offset to obtain the degree of perception of the train position to the boundary fuzziness and obtain the regional boundary fuzzy value , and the specific formula of the regional boundary fuzzy function is as follows: , where is the regional boundary fuzzy value at time , and is a very small constant to prevent division by zero;

[0025] Obtain the maximum time sequence disturbance slope of the fuzzy degree according to the regional boundary fuzzy function: , where is the actual operation period of the on-board positioning module;

[0026] Calculate the regional boundary fuzzy coefficient : .

[0027] In a preferred embodiment, a train early warning trigger misjudgment model is constructed according to the state mismatch anomaly coefficient and the regional boundary fuzzy coefficient to obtain a train early warning trigger misjudgment index, and the formula on which the train early warning trigger misjudgment model is based is as follows , where is the train early warning trigger misjudgment index, is the state mismatch anomaly coefficient, is the regional boundary fuzzy coefficient. These represent the preset scaling factors for the state mismatch anomaly coefficient and the region boundary ambiguity coefficient, respectively. All are greater than 0.

[0028] 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:

[0029] 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.

[0030] 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.

[0031] 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 collection period;

[0032] Calculate the volatility index of risk time series : ,in This represents the average value of the data points in the risk time series. .

[0033] 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:

[0034] 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.

[0035] If the volatility index is less than or equal to the volatility index threshold, the current fault tolerance mechanism configuration will be maintained.

[0036] The technical effects and advantages of this invention are as follows:

[0037] 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

[0038] To facilitate understanding by those skilled in the art, the present invention will be further described below with reference to the accompanying drawings;

[0039] Figure 1 This is a flowchart of a method according to an embodiment of the present invention. Detailed Implementation

[0040] 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.

[0041] Example: Figure 1 The present invention discloses a vehicle-mounted wireless data communication method for construction protection in railway sections, comprising the following steps:

[0042] 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.

[0043] 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.

[0044] 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.

[0045] Step S4, according to the state mismatch abnormal coefficient, the regional boundary fuzzy coefficient, a train early warning trigger misjudgment model is constructed, a train early warning trigger misjudgment index is obtained, and the misjudgment risk of the train entering the construction area being operated without triggering any early warning is evaluated;

[0046] Step S5, according to the misjudgment risk, a dynamic early warning compensation is made to the fault-tolerant mechanism of the current data communication system;

[0047] Step S1, the construction protection end broadcasts construction information to the vehicle-mounted system when the construction is started, and the construction information includes construction area identifier (ID), construction area start and end boundary coordinates (GPS / mileage position), construction level, broadcast timestamp and update period, protection terminal device state code (including running state, reporting interval, fault code);

[0048] The vehicle-mounted communication unit periodically receives the broadcast information and checks the information integrity, judges whether to start the state mismatch monitoring mechanism, and the judgment logic is as follows:

[0049] A. Check whether the broadcast field is complete;

[0050] B. Compare whether the broadcast timestamp is continuously increasing, if there is an abnormal packet loss interval (such as missing more than 3 times continuously);

[0051] C. Check whether the construction state and the vehicle-end cache state are consistent;

[0052] If there is a missing broadcast field, an abnormal packet loss interval, or an inconsistent construction state and vehicle-end cache state, the state mismatch monitoring mechanism is started;

[0053] Step S2, if the state mismatch monitoring mechanism is started, a vehicle-construction equipment state comparison matrix is established to calculate the state mismatch abnormal coefficient, which is as follows:

[0054] In the next N cycles, the vehicle-end state data and the construction-end state data are recorded respectively. : Wherein is the mth state parameter of the vehicle-end, is the mth state parameter of the construction-end, is the observation cycle number, is the state field number, indicates whether the mth state in the nth cycle is different, 0 indicates consistent, and 1 indicates mismatch;

[0055] The state parameters collected in each broadcast cycle are shown in Table 1, but are not limited thereto:

[0056] Table 1

[0057]

[0058] Obtain the time series of each state field from the state comparison matrix , each time series represents the state mismatch trajectory of state field m; calculate the local mutation rate of each time series of state field : ;

[0059] Obtain the period average mutation rate by averaging the local mutation rate of state field in N observation periods : ;

[0060] For each observation period n, analyze and calculate the dispersion degree of state mismatch between different fields to obtain the cross-field difference fluctuation value : , where is the proportion of the difference state k (0 or 1) in the nth observation period: , where is an indicator function used to count the number of state fields of and in the nth observation period;

[0061] Obtain the cross-field difference fluctuation average by averaging the cross-field difference fluctuation value in N observation periods : ;

[0062] Calculate the state mismatch anomaly coefficient : ;

[0063] It should be noted that the above formulas are all dimensionless values, and common dimensionless methods include Min-Max normalization, Z-Score standardization, etc., which will not be repeated here;

[0064] The state mismatch abnormality coefficient in the application is used for measuring the state mismatch deterioration degree caused by the problems such as inconsistency of state information, signal interruption or abnormal fault feedback between the vehicle-mounted system and the construction protection end device in the running process, and the essence is a quantitative evaluation index of the reliability of the railway section construction communication link. The coefficient not only reflects whether there is configuration error or initialization mismatch in the static construction stage of the system, but also can dynamically capture the system state fluctuation caused by hardware failure, channel interference, heartbeat loss, broadcast abnormality and the like in the running process, and has an important influence on the stability and timeliness of the early warning trigger mechanism. In the actual railway construction protection application, the train operation safety seriously depends on whether the vehicle-mounted system can stably, continuously and accurately obtain the construction area information broadcast to trigger the response mechanism such as speed limit, avoidance or parking. Once the state of one end of the communication link is abnormal and the other end is normal, the train may misjudge the regional safety state, so as to drive into the blocking area being worked without any early warning trigger, which brings great safety hazard. The introduction of the state mismatch abnormality coefficient is just to solve the problem that the double-end collaborative failure cannot be perceived. Specifically, when the state mismatch abnormality coefficient significantly increases, it indicates that there is serious inconsistent state between the vehicle-mounted end and the construction end, which may be caused by broadcast delay, link disconnection, device configuration error or temporary channel interruption. Such inconsistency will directly interfere with the normal start of the early warning mechanism, causing the system to be in the blind state of 'thinking safe but actually dangerous'. When the state mismatch abnormality coefficient is small, it indicates that the information synchronization between the vehicle and the worker is normal, the communication link is reliable, and the system has high confidence in the identification of the construction area. In this case, even if there is a certain error in positioning or boundary identification is fuzzy, it is not easy to trigger systematic misjudgment, thereby improving the overall early warning accuracy. The application realizes the accurate prediction and dynamic intervention of the missing risk of mis-triggering by taking the state mismatch abnormality coefficient as one of the core parameters in the misjudgment risk model and constructing the train early warning trigger misjudgment index together with the regional boundary fuzzy coefficient. This strategy not only enhances the perception ability of the communication system to the implicit failure and coupling abnormality, but also improves the fault tolerance and self-adaptive adjustment ability of the protection system in the abnormal state, effectively reduces the probability of the train entering the construction work area without triggering any early warning, and has significant safety guarantee value and engineering application prospect.

[0065] In step S3, the current train positioning data is obtained by the vehicle-mounted positioning module, and the regional boundary fuzzy function is constructed by combining the start and end boundary coordinates of the construction area to calculate the regional boundary fuzzy coefficient, as follows:

[0066] The positioning coordinate value of the train in time (two-dimensional / three-dimensional, and one-dimensional track coordinate is taken as an example in the application, which can be extended to two-dimensional / three-dimensional according to the actual situation in actual deployment), error value ; ​

[0067] Mark the start and end boundary coordinates of the construction area as , introduce the fuzzy bandwidth , get the fuzzy boundary domain , where is the time fuzzy boundary domain, ;

[0068] It should be noted that the fuzzy bandwidth is the buffer interval width allowed to identify fuzziness or error near the boundary of the construction area, which is a key parameter for describing the "fuzzy judgment ability" of the boundary. In actual engineering, its source can be as follows:

[0069] Construction boundary setting error: positioning deviation and limit division error when manually laying out the construction area;

[0070] GNSS positioning drift: spatial drift of vehicle-mounted positioning system caused by trackside electromagnetic interference, tunnel shielding, etc.;

[0071] Device configuration tolerance band: redundant bandwidth set for "boundary misidentification" in system fault tolerance design;

[0072] Dynamic fitting parameter adjustment: can be automatically generated by historical trajectory statistics + fuzzy recognition accuracy evaluation (such as output by a supervised learning model);

[0073] According to the fuzzy boundary domain, calculate the spatial residual offset : , where is the minimum value acquisition function, used to obtain the minimum value between and ;

[0074] According to the spatial residual offset, construct the regional boundary fuzzy function to obtain the perception degree of the train position to the boundary fuzziness and get the regional boundary fuzzy value , the specific formula of the regional boundary fuzzy function is as follows: , where is the time regional boundary fuzzy value, is a very small constant to prevent division by zero (usually );

[0075] According to the regional boundary fuzzy function, get the maximum time sequence disturbance slope of the fuzzy degree : , where is the actual operation period of the vehicle-mounted positioning module;

[0076] Calculate the regional boundary fuzzy coefficient : ;

[0077] It should be noted that the above formulas are all dimensionless values calculated, and common dimensionless methods include Min-Max normalization, Z-Score standardization, etc., which will not be repeated here.

[0078] The region boundary fuzzy coefficient in the application is used to measure the boundary uncertainty degree of train positioning error in construction region identification. The core purpose is to identify the reliability of the vehicle-mounted system in identifying the boundary of the construction region, especially in the situation of GNSS drift, inertial navigation error accumulation or positioning fusion algorithm failure, to accurately model the offset relationship between the actual position of the train and the "perceived boundary" of the construction region. The larger the region boundary fuzzy coefficient value is, the stronger the uncertainty of the train's current position near the edge of the construction region is, and the system cannot clearly determine whether it has entered the construction protection zone; on the contrary, the smaller the coefficient is, the more stable and clear the distance or trajectory position between the train and the boundary is, and the higher the boundary determinability is.

[0079] In actual scenarios, the train-mounted system usually receives GNSS, odometer and trackside beacon positioning information to infer whether it has entered the construction region. However, due to the floating nature of the construction region boundary in the actual layout process (for example, non-accurate coordinate configuration, non-linear boundary setting bandwidth, etc.), combined with positioning errors, it may cause the vehicle-mounted system to be in a fuzzy state near the critical value when judging "whether to enter the construction region", resulting in misjudgment. The region boundary fuzzy coefficient is exactly the modeling of this "boundary judgment uncertainty", thereby providing quantitative input basis for subsequent misjudgment risk assessment.

[0080] The application couples the region boundary fuzzy coefficient with the state mismatch abnormality coefficient to model and build a train warning trigger misjudgment index, thereby accurately assessing the potential risk of the train entering the construction region being worked on without triggering any warning (such as speed limit, prompt, parking). In actual operation, when the region boundary fuzzy coefficient is large, even if the state broadcast is normal, the system may not trigger a warning due to the fuzzy boundary; on the contrary, when the boundary fuzzy coefficient is small, even if there is a slight state abnormality, the actual risk of the train entering the boundary region can be reliably identified. Therefore, by introducing and dynamically adjusting the fuzzy coefficient, the robustness and misjudgment prevention and control ability of the system in complex positioning uncertain scenarios are significantly improved, providing a more reliable intelligent protection mechanism for the intrinsic safety of train operation during construction.

[0081] Step S4, constructing a train warning trigger misjudgment model according to the state mismatch abnormality coefficient and the region boundary fuzzy coefficient, obtaining a train warning trigger misjudgment index, and evaluating the misjudgment risk of the train entering the construction region being worked on without triggering any warning;

[0082] A train early warning trigger misjudgment model is constructed according to the state mismatch abnormality coefficient and the regional boundary fuzzy coefficient to obtain a train early warning trigger misjudgment index, and the formula of the train early warning trigger misjudgment model is as follows , wherein is the train early warning trigger misjudgment index, is the state mismatch abnormality coefficient, is the regional boundary fuzzy coefficient, respectively represent preset proportion coefficients of the state mismatch abnormality coefficient and the regional boundary fuzzy coefficient, and are both greater than 0;

[0083] It should be noted that the above formulas are all dimensionless numerical calculations, and common dimensionless methods include Min-Max normalization and Z-Score standardization, which are not described here; The actual situation is set, for example, an expert weighting method is used, that is, experts in the relevant field are invited to determine the preset proportion coefficients of each index through professional opinion surveys and comprehensive evaluations, for example, may be 0.5, 0.5;

[0084] As can be seen from the above calculation expression, the greater the state mismatch abnormality coefficient and the regional boundary fuzzy coefficient, the greater the train early warning trigger misjudgment index, indicating that the higher the risk of the train system incorrectly entering the construction area being operated without correctly triggering the early warning signal, that is, when the index is large, the train has the possibility of "perceiving a false sense of security", that is, the system fails to correctly receive or analyze the construction broadcast due to equipment state abnormalities, and is affected by positioning errors and boundary fuzziness in boundary identification, and fails to accurately determine whether to enter the construction area, so that the early warning mechanism is not triggered, and finally leads to the train entering the high-risk operation area without the driver's perception, on the contrary, the smaller the state mismatch abnormality coefficient and the regional boundary fuzzy coefficient, the smaller the train early warning trigger misjudgment index, indicating that the overall consistency and perception accuracy of the system under the current running state are high, and the early warning mechanism can operate reliably as expected, and the risk of the train misjudging to enter the construction area is extremely low;

[0085] The train early warning trigger misjudgment index is compared with a preset train early warning trigger misjudgment index threshold to evaluate the misjudgment risk of the train entering the construction area being operated without triggering any early warning, and the specific process is as follows:

[0086] If the train pre-alarm trigger misjudgment index is greater than the train pre-alarm trigger misjudgment index threshold value, it indicates that the current train is in a state of obvious system state mismatch risk and enhanced boundary identification ambiguity, the internal information consistency and perception accuracy of the system are reduced, which may lead to the failure of the pre-alarm mechanism at a critical moment. Especially when the vehicle-mounted device does not correctly perceive the signal in the construction area, the positioning system misjudges the current position without crossing the boundary, the train is likely to enter the construction site without any speed reduction, speed limit, or braking intervention. The misjudgment risk of the current train entering the construction area without triggering any pre-alarm is marked as high misjudgment risk;

[0087] If the train pre-alarm trigger misjudgment index is less than or equal to the train pre-alarm trigger misjudgment index threshold value, it indicates that the overall system operation state is good, the device perception is consistent, the boundary determination is clear, and the pre-alarm trigger mechanism is within a controllable range. At this time, even if the train approaches the construction area, it can be reminded and braked in time through the existing system, without obvious misjudgment risk. The misjudgment risk of the current train entering the construction area without triggering any pre-alarm is marked as low misjudgment risk, and the normal operation mode can be maintained.

[0088] It should be noted that the train pre-alarm trigger misjudgment index threshold value can be set according to different operating conditions, line levels, construction intensities, etc. Its setting methods include but are not limited to the following:

[0089] Expert experience method: set an experience threshold value (such as 0.6 or 0.7) suitable for the safety boundary in combination with the opinions of signal system engineers, dispatchers, and equipment suppliers;

[0090] Data-driven method: based on historical accident data and misjudgment samples, a reasonable threshold value is fitted through machine learning or regression analysis;

[0091] Operation grading method: set different threshold values for different operation modes (such as lower for automatic driving mode and moderately relaxed for manual driving mode);

[0092] Step S5, dynamically pre-alarm compensation for the fault tolerance mechanism of the current data communication system according to the misjudgment risk;

[0093] If three consecutive high misjudgment risk signals are generated (which can be dynamically adjusted according to actual operation requirements), the train pre-alarm trigger misjudgment index generated by the train pre-alarm trigger misjudgment model at subsequent different times is collected to construct a risk time sequence: , wherein is the train pre-alarm trigger misjudgment index collected at time t, is the train pre-alarm trigger misjudgment index collected at time t, is the collection period;

[0094] The fluctuation index of the risk time sequence is calculated as follows: , wherein ​an average value of data points in the risk timing, ;

[0095] The fluctuation index of the risk timing is compared with a preset fluctuation index threshold, and a dynamic early warning compensation is performed on the fault tolerance mechanism of the current data communication system, as follows:

[0096] If the fluctuation index is greater than the fluctuation index threshold, it indicates that the misjudgment risk is not only in a high position, but also in a state of severe fluctuation, which means that the system has poor robustness in construction perception, and there is a situation of “frequent alternating triggering and failure”; it is easy to cause multiple false alarms or missed alarms of the early warning mechanism, leading to driver system trust fatigue or decision lag, generating a risk instability signal, and immediately upgrading the data communication fault tolerance mechanism:

[0097] D1. enable dual-channel redundant broadcast verification mechanism;

[0098] D2. forcibly enable the reverse broadcast confirmation mechanism (construction end return confirmation);

[0099] D3. switch the positioning service to a high-precision GNSS + inertial navigation redundant positioning mode;

[0100] D4. prompt the dispatch center to perform manual intervention review;

[0101] If the fluctuation index is less than or equal to the fluctuation index threshold, it indicates that the current risk has risen once, but the overall fluctuation is within a controllable range, indicating that the system has certain self-stabilizing ability and maintains the current fault tolerance mechanism configuration.

[0102] The multi-level state redundancy judgment and dynamic boundary fuzzy modeling mechanism constructed by the application enables the vehicle-mounted system to accurately identify the inconsistency risk between the vehicle and the worker end information when the construction broadcast is missing or the construction end equipment is abnormal through the state mismatch abnormal coefficient, and in the boundary fuzzy field caused by the decrease in positioning accuracy or the static description of the boundary coordinates, the proximity uncertainty of the train and the construction area is quantified through the regional boundary fuzzy coefficient, and then a misjudgment risk model is constructed based on the two types of coefficients, to realize early warning for the high-risk scene of “entering the construction area without triggering any early warning”; at the same time, combined with the time series fluctuation analysis of continuous high-risk signals, the fault tolerance mechanism of the data communication link can be dynamically compensated; by quantifying the risk factor and dynamically adjusting the fault tolerance strategy, the safety redundancy and robustness of the train during construction are improved, the precise perception and rapid response to implicit faults and coupled failures in complex environments are realized, and the intelligent level and safety protection ability of railway section construction protection are improved.

[0103] The above formulas are dimensionless and the numerical values are calculated, the formula is obtained by software simulation of a large amount of data to obtain a formula of the latest real situation, and the preset parameters in the formula are set by a person skilled in the art according to the actual situation.

[0104] It should be understood that the magnitude of the sequence number of each process described above does not mean the order of execution, and the execution order of each process should be determined according to its function and inherent logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0105] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto, and any person skilled in the art can easily think of changes or replacements within the technical scope disclosed by the present application, which should be covered within the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A vehicle-mounted wireless data communication method for railway section construction protection, characterized in that: The method comprises the following steps: Step S1, the construction protection end broadcasts construction information to the vehicle-mounted system at the start of construction, and the vehicle-mounted communication unit periodically receives the broadcast information and checks the information integrity to determine whether to start the state mismatch monitoring mechanism; Step S2, if the state mismatch monitoring mechanism is started, a vehicle-construction equipment state comparison matrix is established to calculate the state mismatch abnormality coefficient and evaluate the state mismatch deterioration degree of the vehicle-mounted and construction end equipment; Step S3, the current train positioning data is obtained through the vehicle-mounted positioning module, and the regional boundary fuzzy function is constructed in combination with the start and end boundary coordinates of the construction area to calculate the regional boundary fuzzy coefficient and evaluate the construction area boundary fuzzy degree caused by the train positioning error; Step S4, a train early warning trigger misjudgment model is constructed according to the state mismatch abnormality coefficient and the regional boundary fuzzy coefficient, a train early warning trigger misjudgment index is obtained, and the misjudgment risk of the train entering the construction area being operated without triggering any early warning is evaluated; Step S5, the fault-tolerant mechanism of the current data communication system is dynamically compensated according to the misjudgment risk; If the state mismatch monitoring mechanism is started, a vehicle-construction equipment state comparison matrix is established to calculate the state mismatch abnormality coefficient, which is as follows: Record the vehicle end state data respectively in the continuous N cycles and the construction end state data Construct the following state comparison matrix : Wherein is the mth state parameter of the vehicle end, is the mth state parameter of the construction end, is the observation cycle number, is the state field number, indicates whether the mth state of the n th cycle is different, 0 indicates consistent, and 1 indicates mismatch. obtaining a time series for each state field from the state comparison matrix , each time series representing a state mismatch trajectory for state field m; computing a local mutation rate for each state field from its time series : ; The average of the local mutation rate of the state field over N observation periods is taken to obtain the period average mutation rate : ; For each observation period n, analyze and calculate the cross-field difference fluctuation value across the discrete degrees of state mismatch between different fields : wherein is the proportion of the difference state k in the n th observation period wherein is an indicator function for counting the number of state fields in the n th observation period and the number of state fields averaging the cross-field difference fluctuation values over N observation periods to obtain a cross-field difference fluctuation average : ; Computing a state mismatch anomaly coefficient : ; The current train positioning data is obtained through the vehicle-mounted positioning module, and the regional boundary fuzzy function is constructed in combination with the start and end boundary coordinates of the construction area to calculate the regional boundary fuzzy coefficient, which is as follows: Get the train in time Positioning coordinates Error value ; Mark the start and end boundary coordinates of the construction area as , introduce the fuzzy bandwidth , obtain the fuzzy boundary domain , wherein is the fuzzy boundary domain of time , ; Computing spatial residual offsets from fuzzy boundary regions : where is a min function that obtains the minimum value between and ; According to the spatial residual deviation offset, a region boundary ambiguity function is constructed, a perception degree of the train position to the boundary ambiguity is obtained, and a region boundary ambiguity value is obtained The specific formula of the region boundary ambiguity function is as follows: Wherein is a region boundary ambiguity value at time , and is a minimum constant for preventing zero division. A maximum time disturbance slope of a blur degree is acquired according to a regional boundary blur function : , Actual operation period of the vehicle positioning module Computing a region boundary blur coefficient : ; According to the state mismatch abnormal coefficient and the regional boundary fuzzy coefficient, a train early warning trigger misjudgment model is constructed to obtain a train early warning trigger misjudgment index, and the formula of the train early warning trigger misjudgment model is as follows , wherein is the train early warning trigger misjudgment index, is the state mismatch abnormal coefficient, is the regional boundary fuzzy coefficient, respectively represent preset proportional coefficients of the state mismatch abnormal coefficient and the regional boundary fuzzy coefficient, and are both greater than 0. The train early warning trigger misjudgment index is compared with the preset train early warning trigger misjudgment index threshold value to evaluate the misjudgment risk of the train entering the construction area being operated without triggering any early warning, which is as follows: If the train early warning trigger misjudgment index is greater than the train early warning trigger misjudgment index threshold value, the misjudgment risk of the current train entering the construction area being operated without triggering any early warning is marked as high misjudgment risk; If the train early warning trigger misjudgment index is less than or equal to the train early warning trigger misjudgment index threshold value, the misjudgment risk of the current train entering the construction area being operated without triggering any early warning is marked as low misjudgment risk.

2. The vehicle-mounted wireless data communication method for railway section construction protection according to claim 1, characterized in that: If three high-misjudgment risk signals are generated continuously, the train pre-alarm trigger misjudgment model generates a train pre-alarm trigger misjudgment index at a subsequent different time to build a risk time sequence: wherein is a train pre-alarm trigger misjudgment index collected at the time, is a collection period. Computing a volatility index for a risk chronology : where is the average of the data points in the risk chronology, .

3. The vehicle-mounted wireless data communication method for railway section construction protection according to claim 2, characterized in that: The fluctuation index of the risk time sequence is compared with the preset fluctuation index threshold value to dynamically compensate the fault-tolerant mechanism of the current data communication system, which is as follows: If the fluctuation index is greater than the fluctuation index threshold value, a risk instability signal is generated to immediately force the upgrade of the data communication fault-tolerant mechanism; If the fluctuation index is less than or equal to the fluctuation index threshold value, the current fault-tolerant mechanism configuration is maintained.

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