A system and method for determining redundancy guarantee of train-ground communication in complex operation scenarios of urban rail transit

Abstract

The application discloses a kind of urban rail transit complex operation scene under vehicle-ground communication determinacy redundancy guarantee system and method, its steps are: offline construction operation scene knowledge base and link recession mode library;Real-time acquisition train operation state and each communication link quality index;Operation scene matching and link recession trend evaluation are carried out;Redundancy level decision algorithm is operated, target redundancy level is calculated and determinacy configuration decision is generated;Redundancy configuration adjustment is executed, and parallel redundancy transmission is realized through multi-network cooperation.The application quantifies the communication environment difference of different sections by scene feature vector, combined with link recession trend prediction and scene-link coordination decision mechanism, realizes the determinacy guarantee leap of redundancy guarantee strategy from static passive response to scene change, trend prediction, level quantization, and can provide adaptive vehicle-ground communication guarantee under elevated section, tunnel section, station area and other complex operation scenes, taking into account train safety requirements and communication resource utilization efficiency.

Description

Technical Field

[0001] This invention relates to the field of urban rail transit communication technology, and in particular to a deterministic redundancy guarantee system and method for vehicle-to-ground communication in complex urban rail transit operation scenarios. Background Technology

[0002] With the acceleration of urbanization, the demand for operational efficiency and safety in urban rail transit systems is increasing. Wireless communication-based train control systems have become a core component of modern rail transit, enabling real-time monitoring and precise control of train operation through two-way wireless communication between the train and the ground. The quality of the train-to-ground communication link directly affects the safety and control accuracy of train operation; any communication interruption or excessive latency may lead to reduced train speed or even emergency braking, severely impacting operational efficiency and passenger experience.

[0003] Existing vehicle-to-ground communication systems generally employ redundancy designs to improve communication reliability. The main technical approaches can be summarized as follows:

[0004] The first type is a dual-network redundancy architecture, which typically deploys two independent wireless communication networks as backups for each other. When the primary network fails, it switches to the backup network. While this type of solution improves system availability to some extent, its redundancy level is static and fixed. It cannot dynamically adjust the redundancy configuration according to the actual needs of the operational scenario, resulting in wasted redundant resources during off-peak hours. In extremely complex operational scenarios, it is difficult to provide sufficient multiple guarantees.

[0005] The second type is based on multi-mode communication terminals, which integrates communication modules of multiple standards such as LTE-M, Wi-Fi, and 5G-R into the vehicle equipment to switch or transmit concurrently between multiple networks. This type of solution mainly focuses on the diversity of network standards, but lacks quantitative analysis of the complexity of specific operational scenarios. Network switching decisions are based solely on the signal quality of the current link, failing to predict the impact of the characteristics of the upcoming area on the communication link, and lacking the ability to proactively build redundancy.

[0006] The third type is based on expanding the number of redundant links, which establishes multiple vehicle-to-ground communication links by increasing the number of vehicle-mounted radio units. Although this type of solution increases the number of links, it often adopts simple concurrent redundancy or master-slave switching strategies, resulting in insufficient coordination between links. In complex mixed scenarios such as tunnels, overpasses, and platforms, it lacks targeted redundancy level adjustment mechanisms and link coordination optimization strategies.

[0007] In summary, the main shortcomings of existing technologies are reflected in the following three aspects: First, there is a lack of quantitative modeling methods for specific operational scenarios in urban rail transit, such as station entrance and exit areas, tunnel sections, elevated sections, and long ramps, making it impossible to structurally associate operational scenario characteristics with communication requirements; second, redundancy assurance strategies are usually static configurations with a "one-size-fits-all" approach, failing to adjust redundancy levels on demand based on scenario complexity and real-time business needs; and finally, there is a lack of systematic technical solutions for deterministic assurance, that is, the ability to make deterministic redundancy configuration decisions based on a comprehensive assessment of scenario knowledge and link status in specific scenarios.

[0008] This invention aims to solve the technical problems existing in the prior art. To this end, it proposes a deterministic redundancy guarantee system and method for vehicle-to-ground communication in complex operation scenarios of urban rail transit. Summary of the Invention

[0009] The purpose of this invention is to provide a deterministic redundancy guarantee system and method for vehicle-to-ground communication in complex operation scenarios of urban rail transit, so as to solve the technical problems existing in the prior art.

[0010] This invention provides a deterministic redundancy assurance system for vehicle-to-ground communication in complex urban rail transit operation scenarios. This system is an intelligent enhancement of the conventional vehicle-to-ground communication architecture, mainly comprising an offline knowledge base construction module deployed on the ground, a real-time link status awareness module, a deterministic redundancy assurance engine, and redundancy configuration execution and multi-network collaborative transmission modules deployed on both sides of the vehicle and ground. A data supply link exists between the offline knowledge base construction module and the deterministic redundancy assurance engine; the real-time link status awareness module establishes an information acquisition link with the deterministic redundancy assurance engine through various communication network devices; and the redundancy configuration execution and multi-network collaborative transmission module establishes a control and data link with the deterministic redundancy assurance engine through a standard communication interface.

[0011] The offline knowledge base construction module is used to build and maintain an operational scenario knowledge base and a link decay pattern library. The operational scenario knowledge base stores the mapping relationship between scenario feature vectors and recommended redundancy levels for various typical operational scenarios; the link decay pattern library stores the link indicator change feature sequences under various link decay modes and their corresponding pre-adaptive redundancy strategies.

[0012] The real-time link status sensing module is used to collect train operation status parameters and wireless link quality indicators in real time through multi-source sensors and communication equipment interfaces on the vehicle and along the track. The train operation status parameters include, but are not limited to, current train location information, operating speed information, acceleration information, and zone information. The wireless link quality indicators include, but are not limited to, received signal strength indication, signal-to-noise ratio, latency, and packet loss rate for each communication link.

[0013] The deterministic redundancy assurance engine is used to receive data collected by the real-time link status awareness module, call the scenario knowledge templates provided by the offline knowledge base construction module, perform real-time scenario matching and link status assessment, and run the redundancy level decision algorithm to generate deterministic redundancy configuration decision instructions.

[0014] The redundant configuration execution and multi-network collaborative transmission module is used to receive control commands from the deterministic redundancy assurance engine and dynamically configure the working mode, number of redundant links, and service data distribution strategy of vehicle-mounted and ground communication equipment.

[0015] Furthermore, the typical operating scenarios in the operating scenario knowledge base include: high-speed operation scenario on elevated sections, weak coverage scenario in tunnel sections, low-speed operation scenario when entering and exiting stations, variable speed operation scenario on long and steep slopes, dense communication scenario when lines intersect, and fault-degraded operation scenario.

[0016] Furthermore, the scene feature vector is a multi-dimensional vector composed of scene geometric parameters, environmental electromagnetic parameters, and operating mode parameters, and the recommended redundancy level is one of at least three discrete levels.

[0017] Furthermore, the deterministic redundancy assurance engine adopts a periodic decision-making mechanism based on time windows. Within each decision cycle, the entire process from scenario matching to decision instruction generation is completed once, and the length of the decision cycle is adaptively adjusted according to the current train speed section.

[0018] Furthermore, the multi-network collaborative transmission module supports a hybrid working mode of parallel redundancy protocol and master-slave switching protocol. When the number of redundant links is greater than a single link, the parallel redundancy protocol is used first for concurrent transmission. When only a single redundant link remains, it automatically degrades to the basic transmission mode.

[0019] On the other hand, the present invention provides a method for ensuring deterministic redundancy in vehicle-to-ground communication under complex operation scenarios of urban rail transit, comprising the following steps:

[0020] Step 1: Offline construction of an operational scenario knowledge base and a link decay pattern library. Historical data of the target lines is collected, scenario feature vectors for typical operational scenarios are extracted, and a mapping relationship is established between scenario feature vectors and recommended redundancy levels. The characteristic sequences of link indicator changes under various link decay patterns are statistically analyzed, and pre-adapted redundancy strategies are associated for each link decay pattern.

[0021] Step two: Real-time monitoring of train operation status and link quality. Current train operation status parameters are acquired through onboard sensors; link quality indicators are obtained through the wireless measurement interfaces of each communication link.

[0022] Step 3: Match operational scenarios and evaluate link status, including calculating link quality degradation trend indicators. The real-time collected operational status parameters are matched with scenario feature vectors in the knowledge base to determine the current operational scenario type and its corresponding recommended redundancy level; degradation trend analysis is performed on link quality indicators to determine whether each link exhibits a preset link degradation pattern.

[0023] Step four: Run the redundancy level decision algorithm to generate deterministic redundancy configuration decisions. Based on the combined scenario matching results and link status assessment results, calculate the target redundancy level, and determine the redundancy adjustment actions and link selection strategies according to the difference between the target redundancy level and the current actual redundancy level.

[0024] Step 5: Perform redundancy configuration adjustments and coordinate multi-network transmission. Based on the decision results, configure the operating modes of the vehicle-mounted and ground communication equipment, activate or deactivate redundant links, and allocate service data to the selected links for coordinated transmission.

[0025] Furthermore, the redundancy level decision algorithm in step four calculates the target redundancy level according to the following formula:

[0026]

[0027] in, For the target redundancy level, For scene-matching-based recommendation of redundancy levels, For link requirement redundancy levels based on link state assessment, This is the scenario-link coordination factor.

[0028] Furthermore, the scenario-link coordination factor The value of α is dynamically determined based on the type of communication service: when the current service being carried is mainly safety-critical train control data, α takes a larger value to make the target redundancy level biased towards scenario prediction requirements; when the current service being carried is mainly non-safety-critical passenger information service data, α takes a smaller value to make the target redundancy level biased towards link real-time status response.

[0029] Furthermore, the link decay trend analysis in step three is based on the following formula:

[0030]

[0031] in, This is an indicator of the link quality degradation trend. The time derivative of the signal-to-interference-plus-noise ratio (SINR). The time derivative of the delay. The time derivative of the packet loss rate. , , For the weighting coefficients, satisfying + + =1.

[0032] Furthermore, when When the preset decay threshold is exceeded, the corresponding link is determined to enter the decay mode. The matching pre-adapted redundancy strategy is retrieved from the link decay mode library first, and the redundant link activation or switching action is triggered in advance.

[0033] Furthermore, when constructing the operational scenario knowledge base offline in step one, the basis for establishing the mapping relationship between scenario feature vectors and recommended redundancy levels includes: signal coverage continuity index, reflection and interference intensity index, train speed change rate, and scenario switching frequency under the scenario.

[0034] Compared with the prior art, the beneficial effects of the present invention are:

[0035] This represents a leap from "static configuration" to "scenario adaptation" in redundancy assurance strategies. By constructing an operational scenario knowledge base that includes scenario geometric parameters, environmental electromagnetic parameters, and operating mode parameters, the differences in communication environments across different sections of the line are quantified into computable feature vectors and mapped to recommended redundancy levels. This enables the system to adaptively adjust redundancy configurations based on the current operational scenario, fundamentally overcoming the contradiction between resource waste and insufficient assurance caused by traditional solutions that adopt a uniform redundancy level across the entire line.

[0036] A link degradation trend prediction mechanism has been introduced, enhancing the initiative and certainty of redundancy adjustments. By constructing a link quality degradation trend index and performing a weighted comprehensive analysis of the time derivatives of key parameters such as signal-to-interference-plus-noise ratio, latency, and packet loss rate, typical degradation patterns can be identified before substantial deterioration of link quality occurs, triggering the activation or switching of redundant links in advance. This overcomes the lag problem of existing solutions that rely solely on instantaneous thresholds for passive response, significantly enhancing the deterministic guarantee capability of vehicle-to-ground communication under complex environmental changes.

[0037] A dynamic coordination decision-making mechanism that balances safety requirements and resource efficiency was established. The proposed target redundancy level calculation formula incorporates a scenario-link coordination factor. This factor dynamically adjusts based on the ratio of safety-critical data to non-safety-critical data in the current service being carried. This allows the system to prioritize scenario-preset safety redundancy requirements when processing critical data such as train control, while prioritizing real-time link status to release spectrum resources when carrying non-safety data such as passenger information services. This maximizes resource utilization efficiency while ensuring train operation safety.

[0038] It provides a complete deterministic redundancy execution and multi-network collaborative transmission scheme. With the support of the redundancy configuration execution and multi-network collaborative transmission modules, the system can flexibly combine parallel redundancy protocols and master-slave switching protocols among multiple independent communication links according to decision instructions. When there are enough redundant links, concurrent replication transmission is used to eliminate single points of failure. When the number of links decreases, it automatically degrades to the basic mode to maintain communication continuity. At the same time, data redundancy removal processing is completed at the receiving end to ensure the consistency and integrity of upper-layer business application data. Attached Figure Description

[0039] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0040] Figure 1 This is a diagram of the overall system architecture of the present invention.

[0041] Figure 2 This is a flowchart of the overall process of the method of the present invention.

[0042] Figure 3 This is a flowchart illustrating the construction process of the knowledge base for the operational scenarios of this invention.

[0043] Figure 4 This is a flowchart of the redundancy level decision algorithm of the present invention.

[0044] Figure 5 This is a schematic diagram of multi-network collaborative data transmission according to the present invention. Detailed Implementation

[0045] To make the technical problems, solutions, and beneficial effects of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0046] Example 1

[0047] This embodiment discloses a deterministic redundancy guarantee system for vehicle-to-ground communication in complex operation scenarios of urban rail transit. Its system architecture is shown in the attached figure. Figure 1 As shown, it mainly includes an offline knowledge base construction module, a real-time link status awareness module, a deterministic redundancy guarantee engine, and a redundancy configuration execution and multi-network collaborative transmission module.

[0048] The offline knowledge base construction module is the fundamental data support unit of the entire system, responsible for building and continuously maintaining two types of core knowledge bases. The first type is the operational scenario knowledge base, which stores the structured mapping relationship between scenario feature vectors and recommended redundancy levels for various typical operational scenarios in urban rail transit lines. Typical operational scenarios include, but are not limited to: high-speed operation on elevated sections, weak coverage in tunnel sections, low-speed operation at station entrances and exits, variable-speed operation on long and steep slopes, dense communication at line intersections, and fault-degraded operation scenarios. The scenario feature vector for each scenario is composed of scenario geometric parameter features, environmental electromagnetic parameter features, and operational mode parameter features, covering dimensions such as curve curvature, slope gradient, station spacing, multipath reflection intensity, occlusion degree, typical speed range, and acceleration characteristics. The recommended redundancy level is one of at least three discrete levels divided according to the scenario's communication guarantee requirements; a higher level indicates a greater number of independent communication links and a larger safety redundancy margin required for that scenario. The second category is a link decay mode library, which obtains the characteristic sequences of link indicator changes under various common link decay modes through statistical methods, including the gradual decay mode of signal-to-interference-plus-noise ratio, the sudden increase mode of latency, and the intermittent deterioration mode of packet loss rate. It also binds a set of pre-adaptive redundancy strategies to each link decay mode, including activating backup links in advance, increasing the number of redundant links, and adjusting the business data diversion ratio.

[0049] The real-time link status awareness module collects information through multi-source sensors and communication equipment interfaces on both the vehicle and trackside. On the vehicle side, it uses the train positioning unit to obtain the train's precise position and speed, the inertial measurement unit to obtain acceleration information, and the onboard signaling equipment to obtain the current zone information and traffic permit status. Real-time quality indicators for each communication link are obtained through the onboard wireless terminal measurement interfaces of each communication network, including received signal strength indication, signal-to-interference-plus-noise ratio, end-to-end latency, and packet loss rate. On the trackside side, the module obtains the operating status and link quality information of each access point through trackside wireless access equipment.

[0050] The deterministic redundancy assurance engine is the core control node of this invention. Running on the vehicle-mounted main control equipment or ground control center, it periodically receives data collected by the real-time link status perception module, calls scene knowledge templates from the offline knowledge base construction module, and executes three main functions: scene matching, link evaluation, and decision-making algorithms. The specific processing flow of this engine is shown in the appendix. Figure 4As shown: First, the real-time collected operating status parameters are matched with the feature vectors of each scenario in the operation scenario knowledge base to determine the current operation scenario type and obtain the corresponding recommended redundancy level. At the same time, the real-time quality indicators of each link are analyzed within a time window to determine whether there is a preset link decay mode. When a link is detected to enter a decay mode, the corresponding pre-adaptive redundancy strategy is retrieved from the link decay mode library and the required redundancy level of the link is obtained. Then, the redundancy level decision algorithm is run by combining the scenario matching results and the link status evaluation results to calculate the target redundancy level. A deterministic redundancy adjustment instruction is generated based on the difference between the target redundancy level and the current actual redundancy level. If there is no change in either, the current configuration is maintained.

[0051] The redundancy configuration execution and multi-network collaborative transmission module is responsible for executing decision-making instructions and dynamically configuring the operating modes, number of redundant links, and service data distribution strategies of vehicle-mounted and ground communication equipment. This module supports multiple redundancy protocols; when the number of redundant links exceeds a single link, it defaults to using a parallel redundancy protocol for concurrent copying and transmission of data packets, as shown in the attached diagram. Figure 5 As shown, the same service data packet is copied into multiple copies and transmitted simultaneously through wireless links of different standards; when only a single redundant link remains, it automatically degrades to the basic transmission mode. The multi-network collaborative transmission controller is also responsible for data aggregation and redundancy removal at the receiving end, that is, identifying and removing duplicate packets from multiple data packets arriving from multiple links, and delivering one and only one complete copy of the data to the upper-layer service application.

[0052] Example 2

[0053] This embodiment, based on the system described in Embodiment 1, details the implementation steps of a deterministic redundancy guarantee method for vehicle-to-ground communication in complex urban rail transit operation scenarios. The overall process of this method is attached. Figure 2 As shown, the specific steps include:

[0054] Step 1: Offline Construction of an Operational Scenario Knowledge Base and Link Fading Pattern Library. Based on historical test data from the line construction and operation preparation phases, as well as accumulated data from existing lines, and combined with wireless signal coverage data collected by onboard testing equipment, communication performance statistics for each operating section, and expert experience, typical scenarios are classified and labeled for all operating sections of the target line. Scenario classification is based on a comprehensive determination of the section's spatial geometric characteristics, electromagnetic environment characteristics, and train operation characteristics.

[0055] The specific construction process of the operation scenario knowledge base is shown in the attached figure. Figure 3As shown. First, the target line is segmented and scanned to identify and label segments with significant differences in characteristics. Second, for each identified scene segment, scene geometric parameters are extracted, including tunnel cross-sectional dimensions, curve radius of curvature, and gradient percentage; environmental electromagnetic parameters are extracted, including reflection intensity under multipath propagation conditions, additional path loss caused by tunnel walls or building obstructions, and the distribution density of co-frequency interference sources; and operating mode parameters are extracted, including the typical speed range of trains operating in the segment, the frequency of speed changes, and the headway level. After normalizing the above parameters, a scene feature vector is formed for the scene. Then, based on engineering experience and historical performance analysis, a recommended redundancy level is determined for each scene. The determination of this mapping relationship follows the following principles: the worse the signal coverage continuity, the higher the signal reflection and interference intensity, and the more drastic the train speed changes, the higher the recommended redundancy level; for segments with more frequent scene geographical transitions and higher scene switching frequencies, the recommended redundancy level is appropriately increased by one level to ensure communication continuity during switching. The link decay pattern library is built by statistical analysis of historical fault data, extracts typical link decay time series patterns, and presets corresponding redundancy response strategies for each pattern.

[0056] Step Two: Real-time Sensing of Train Operation Status and Link Quality. During train operation, the real-time link status sensing module continuously collects train operation status parameters and wireless link quality indicators. Train operation status parameters are collected and updated at a preset first cycle, and wireless link quality indicators are collected and updated at a preset second cycle. The second cycle is usually shorter than the first cycle to meet the need for rapid sensing of link status.

[0057] Step 3: Match operational scenarios and evaluate link status, including calculating link quality degradation trend indicators. The deterministic redundancy assurance engine executes a complete processing flow once in each decision cycle. In the scenario matching phase, the similarity between the currently collected operational status parameter vector and the feature vectors of each scenario in the operational scenario knowledge base is calculated. The currently collected operational status parameter vector mainly includes the current location coordinates, velocity, acceleration, and segment identifier. Similarity calculation can use standardized methods such as Euclidean distance or cosine similarity. The scenario with the highest similarity is selected as the current operational scenario, and its corresponding recommended redundancy level is read. During the link status assessment phase, trend analysis is performed on the quality indicator sequence of each communication link within a sliding time window.

[0058] Link degradation trend analysis uses a composite index method, and the link quality degradation trend index is calculated using the following formula. :

[0059]

[0060] in, The time derivative of the signal-to-interference-plus-noise ratio (SINR) within the sliding time window reflects the rate of signal quality decay. It is the time derivative of the end-to-end delay, reflecting the rate of degradation of the link transmission performance; This is the time derivative of the packet loss rate, reflecting the rate at which link reliability deteriorates. , , These are weighting coefficients, and the sum of the three is 1. Their values ​​can be determined based on the main contradiction of link degradation in the actual line: in lines where channel quality fluctuation is the dominant factor, The value is relatively large; in lines where increased latency due to congestion is the dominant factor, The value is relatively large. When When the preset fading threshold is exceeded, the corresponding link is determined to enter fading mode. A matching pre-adaptive redundancy strategy is first retrieved from the link fading mode library, and the required redundancy level of the link is determined based on the strategy type. .

[0061] Step 4: Run the redundancy level decision algorithm to generate deterministic redundancy configuration decisions. (Based on the overall scenario matching results) Link state assessment results The target redundancy level is calculated using the following formula. :

[0062] Here, α is the scenario-link coordination factor, with a value between 0 and 1, dynamically set according to the characteristics of different communication services. When the proportion of safety-critical train control data in the current service exceeds a preset threshold, α is set to a larger value, making the target redundancy level more based on the safety requirements predicted by the scenario; when the proportion of non-safety-critical video surveillance data, passenger information service, and onboard status monitoring data in the current service exceeds a preset threshold, α is set to a smaller value, making the target redundancy level more based on the response requirements of the link's real-time status. This design enables the system to achieve a dynamic balance between safety and resource efficiency.

[0063] Calculation Then, compared with the current actual redundancy level Compare. If > Then, a redundancy enhancement decision is generated, including activating each used link and increasing the number of parallel transmission links; if < If there is no security impact, a redundancy reduction decision is generated, including shutting down some redundant links to reduce power consumption and spectrum resource usage; if the two are equal, the current configuration is maintained. The calculation results and redundancy adjustment decisions are deterministic, meaning that each decision generates a unique set of executable control instructions.

[0064] Step 5: Perform redundancy configuration adjustments and coordinate multi-network transmission. After receiving the decision command, the redundancy configuration execution module sends configuration commands to the vehicle-mounted and ground communication equipment through the standard control interface to adjust the working status and parameters of each communication module. When it is necessary to add redundant links, the backup link with the best signal quality in the current environment is activated according to the preset link selection priority strategy; when it is necessary to reduce redundant links, some redundant links are shut down according to the principle of prioritizing the retention of the link with the strongest signal. After the link configuration adjustment is completed, the multi-network coordinated transmission controller replicates the service data packets into the corresponding number of data replicas according to the current redundancy level, and transmits them in parallel through each active wireless link. The service data packets carry a unified sequence number identifier. At the receiving end, the data aggregation and de-redundancy processing unit identifies and eliminates duplicate data packets through the sequence number, and reconstructs a complete and intact service data stream for the upper layer.

[0065] Example 3

[0066] This embodiment further illustrates the working process and technical advantages of the present invention through specific applications in several typical operational scenarios.

[0067] Scenario 1: The train is traveling at high speed from the elevated section into the tunnel section. While the train is running on the elevated section, wireless signal coverage is good, the communication link is in a high signal-to-interference-plus-noise ratio (SIR) state, and the system's current redundancy level remains at the basic configuration level. As the train approaches the tunnel entrance, the real-time link status awareness module detects that the train is about to enter the tunnel section's marked area. The deterministic redundancy assurance engine immediately triggers scenario matching, identifying the upcoming operational scenario as a "weak coverage tunnel section scenario." The recommended redundancy level for this scenario in the knowledge base is within the preset high-level range. Simultaneously, the engine detects that the SIR of the current link has begun to show a gradual decreasing trend, and calculates the redundancy using the decay trend analysis formula. The preset threshold has been exceeded, and the link is determined to have entered "tunnel entrance attenuation mode." The pre-adaptive redundancy strategy corresponding to this mode in the link attenuation mode library is "pre-activate all redundant links and switch to parallel redundant transmission mode." The engine comprehensively considers the scenario-recommended redundancy level and the required link redundancy level. Calculating that the target redundancy level falls within the preset high-level range, a redundancy enhancement decision is immediately generated. Before the train enters the tunnel, all independent redundant communication links are activated, and the system is switched to the highest redundancy level multi-network parallel redundant transmission mode. After the train enters the tunnel, although the wireless signal weakens due to tunnel wall obstruction, thanks to the pre-established multiple redundant transmission channels, train-to-ground communication is not interrupted or data packet loss occurs, ensuring highly reliable transmission of train control information.

[0068] Scenario 2: Train's state when entering and exiting the station area at low speed. The station area is a complex electromagnetic environment, with multipath reflections, passenger terminal signal interference, and signal fluctuations caused by frequent train starts and stops. When the train decelerates to enter the station area, real-time location information triggers the matching of the "low-speed operation scenario for station entry and exit". This scenario is characterized by low train speed, long dwell time, and frequent changes in the electromagnetic environment, with the corresponding recommended redundancy level in the knowledge base being medium. Due to the low train speed range, the frequency of scenario switching is low, and the main threat to link decay comes from interference rather than signal attenuation. Based on the current business characteristics of mixed train control data and platform passenger information services, the engine appropriately lowers the value of the scenario-link coordination factor α, making the target redundancy level more dependent on the real-time link status. At this time, the system maintains a medium redundancy level, avoiding unnecessary redundant resource occupation. When the train stops and the doors open, the amount of passenger information business data increases significantly, and the proportion of non-safety data in the business type increases. The engine further dynamically reduces the α value, allowing the system to adjust the redundancy level to a lower level while ensuring safe communication, releasing more spectrum resources for high-bandwidth data transmission of passenger information services.

[0069] Scenario 3: Train continuously accelerates or decelerates on a long gradient. The long gradient scenario is characterized by the dynamic evolution of altitude changes, continuous speed changes, and signal multipath effects. Because the train continuously accelerates or decelerates in this scenario, the continuous speed changes have a sustained impact on the wireless channel, exhibiting dynamic characteristics in both Doppler effect and multipath propagation. When the train enters the long gradient area, the engine identifies the "long gradient speed change scenario" with high matching confidence. In this scenario, the channel conditions are unstable due to continuous speed changes, and the knowledge base recommends a redundancy level in the medium-to-high range. Simultaneously, link status assessment reveals that the signal-to-interference-plus-noise ratio (SIR) of each link fluctuates significantly but does not show a continuous deterioration trend. The redundancy level fluctuates below the threshold. The engine's scenario-link coordination factor α takes the default median value, and after comprehensive calculation, the target redundancy level is near the balance point between knowledge base suggestions and link feedback. Based on this, the system activates some redundant links, bringing the redundancy level to a medium-high level, providing sufficient redundancy margin for continuous channel fluctuations, and ensuring the reliability of data transmission and safe operation of the train on long slopes. When the train leaves the long slope and enters a straight section, the scenario matching is updated to a section with good signal coverage, and the recommended redundancy level is restored to the basic level. Based on this, the engine gradually shuts down redundant links and restores the resource-saving basic communication mode.

[0070] As can be seen from the above embodiments, the core inventive point of this invention lies in the fact that, unlike traditional solutions that passively react based solely on the real-time state of the link ("static redundancy" or "blind switching"), this invention constructs a deterministic joint decision-making mechanism based on "offline scenario knowledge-driven + online link trend prediction." This mechanism maps the coarse communication environment to discrete operational scenario vectors, evolving the redundancy guarantee strategy from a "one-size-fits-all" approach to "scenario adaptation." Simultaneously, it introduces the ability to perceive link degradation trends, enabling "predictive reconstruction" before the link quality completely deteriorates, thus achieving determinism and automation in communication redundancy guarantee.

[0071] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0072] The above are merely preferred embodiments of the present invention and do not limit the scope of the patent. Any equivalent structural or procedural transformations made based on the description and drawings of the present invention, or direct or indirect applications in other related technical fields, are similarly included within the scope of patent protection of the present invention.

Claims

1. A deterministic redundancy guarantee system for vehicle-to-ground communication in complex operation scenarios of urban rail transit, characterized in that, include: The offline knowledge base building module is used to build an operational scenario knowledge base and a link decay pattern library; The real-time link status awareness module is used to collect train operation status parameters and wireless link quality indicators in real time. The deterministic redundancy assurance engine is used to perform scenario matching and link evaluation by calling the operation scenario knowledge base and link decay mode library, and to run the redundancy level decision algorithm to generate deterministic redundancy configuration decisions. The redundant configuration execution and multi-network collaborative transmission module is used to adjust the working state of the communication link and perform collaborative data transmission based on the decision.

2. The system according to claim 1, characterized in that, The operational scenario knowledge base contains a mapping between scenario feature vectors of typical operational scenarios and recommended redundancy levels; the typical operational scenarios include at least: elevated sections, tunnel sections, station entrance and exit areas, long slope sections, line intersection areas, and fault degradation areas; the scenario feature vectors are multi-dimensional vectors containing scenario geometric parameters, environmental electromagnetic parameters, and operation mode parameters.

3. The system according to claim 2, characterized in that, The deterministic redundancy assurance engine employs a redundancy level decision algorithm based on scenario-recommended redundancy levels and link decay assessment results, generating a target redundancy level through comprehensive weighted calculation; the link decay assessment results are obtained by the deterministic redundancy assurance engine after calling the link decay mode library to perform decay trend analysis on real-time link quality indicators. The weighting coefficients are dynamically adjusted based on the ratio of security-critical data to non-security-critical data in the current service being carried.

4. The system according to claim 1, characterized in that, The link decay mode inventory contains a correspondence between the link indicator change feature sequence and the pre-adaptive redundancy strategy; the pre-adaptive redundancy strategy includes link pre-activation and parallel transmission mode switching.

5. The system according to claim 1, characterized in that, The decision-making cycle of the deterministic redundancy protection engine is adaptively adjusted according to the current train speed section, with a shorter decision-making cycle used in high-speed sections.

6. The system according to claim 1, characterized in that, The multi-network collaborative transmission module can switch between parallel redundancy protocol and primary / backup switching protocol. When the number of active redundant links is higher than that of a single link, the parallel redundancy protocol is preferred.

7. A method for ensuring deterministic redundancy in vehicle-to-ground communication under complex operation scenarios of urban rail transit, characterized in that, Includes the following steps: Step 1: Build an offline knowledge base for operational scenarios and a library of link decay patterns; Step 2: Real-time monitoring of train operation status and link quality; Step 3: Match the operational scenario and assess the link status, including calculating the link quality degradation trend indicator; Step 4: Run the redundancy level decision algorithm to generate deterministic redundancy configuration decisions; Step 5: Perform redundant configuration adjustments and coordinate multi-network transmission.

8. The method according to claim 7, characterized in that, The redundancy level decision algorithm described in step four follows the following formula: in For the target redundancy level, For scene-matching-based recommendation of redundancy levels, For link requirement redundancy levels based on link state assessment, This is the scenario-link coordination factor.

9. The method according to claim 7 or 8, characterized in that, Step three, the assessment of link status, includes calculating link quality degradation trend indicators. when When the preset threshold is exceeded, the corresponding link is determined to have entered the decay mode, and a search of the link decay mode library is triggered.

10. The method according to claim 7, characterized in that, In the operational scenario knowledge base described in Step 1, the mapping relationship between scenario feature vectors and recommended redundancy levels is determined comprehensively based on the signal coverage continuity index, reflection and interference intensity index, train speed change rate, and scenario switching frequency under each scenario.

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