A tunnel portal intelligent safety management and control system and method based on multi-source perception and linkage control

Abstract

The embodiment of the application discloses a tunnel portal intelligent safety management and control system and method based on multi-source perception and linkage control, which comprises the following steps: acquiring traffic operation perception data and portal safety facility operation state data in a tunnel portal area; identifying the reachable cause-effect relationship that the traffic operation state change influences the safety facility operation state, and constructing a tunnel portal facility traffic coupling system; using an interpretative structural modeling algorithm to construct a multi-level coupling state parameter set with cause-effect hierarchical constraints; implementing sequential probability determination on the time sequence evolution process of the multi-level coupling state parameter set to determine whether the facility traffic coupling system deviates from the stable operation interval and enters the unstable evolution interval; and triggering the linkage control operation mode of the tunnel portal safety facility when it is determined that the facility traffic coupling system enters the unstable evolution interval. The application greatly improves the intelligence, accuracy and overall efficiency of the tunnel portal area traffic safety management and control.

Description

Technical Field

[0001] This application relates to the field of intelligent traffic safety management and control technology, and in particular to an intelligent safety management and control system and method for tunnel entrances based on multi-source perception and linkage control. Background Technology

[0002] Tunnel entrance areas are among the most accident-prone sections of highways, with a significantly higher accident rate than inside the tunnel or on ordinary road sections. The main reasons for this high accident rate include: difficulties adapting to light and dark conditions due to the "black hole effect" and "white hole effect"; drastic changes in vehicle speed when entering and exiting tunnels; sudden changes in traffic flow and delayed or mismatched responses from safety facilities; existing control measures often rely on single-parameter threshold triggers (such as triggering speed limits when vehicle speed falls below a certain value, or activating lighting when illuminance falls below a certain value), lacking a systematic analysis of the complex coupling relationship between traffic operation and safety facility operation; and fixed sequence of intervention measures without dynamic priority adjustment, resulting in low intervention efficiency, high false trigger rates, or insufficient intervention at critical moments.

[0003] While some existing technologies have achieved simple linkage between speed limits and lighting, they generally suffer from the following shortcomings: they have not established a causal transmission model for the impact of changes in traffic operation status on the status of safety facilities; they have not achieved prediction of the evolution trend of multi-level coupled states; and they lack differentiated and sequential intervention strategies based on the propagation path of system instability.

[0004] Therefore, there is an urgent need for an intelligent safety management and control method for tunnel entrances that can systematically identify the coupling relationship of traffic facilities, predict instability trends in advance, and implement hierarchical differentiated linkage control. Summary of the Invention

[0005] This application provides an intelligent safety management and control system and method for tunnel entrances based on multi-source perception and linkage control, which significantly improves the intelligence, accuracy and overall effectiveness of traffic safety management and control in tunnel entrance areas.

[0006] This application provides the following solution: According to the first aspect, a method for intelligent safety management and control of tunnel entrances based on multi-source perception and linkage control is provided. The method includes: acquiring traffic operation perception data and tunnel entrance safety facility operation status data within the tunnel entrance area; identifying the reachable causal relationship between changes in traffic operation status and the operation status data of the safety facilities, and constructing a tunnel entrance facility traffic coupling system based on the traffic operation perception data and the operation status data of the safety facilities; using an interpreted structural model algorithm, constructing a multi-level coupling state parameter set with causal hierarchical constraints based on the directed dependency relationship between data in the facility traffic coupling system, wherein the multi-level coupling state parameter set includes a bottom triggering factor layer, an intermediate propagation path layer, and a top system influence layer; performing sequential probability determination on the temporal evolution process of the multi-level coupling state parameter set based on a preset stable operating range of the coupling system, to determine whether the facility traffic coupling system deviates from the stable operating range and enters an unstable evolution range; when the facility traffic coupling system is determined to have entered an unstable evolution range, triggering a linkage control operation mode of the tunnel entrance safety facilities, wherein the linkage control operation mode includes adjusting the coordinated intervention sequence of speed limits, lighting, and information guidance facilities according to the importance of each level of status.

[0007] According to one of the embodiments of this application, the traffic operation perception data is statistical feature data formed by performing time statistics and spatial aggregation processing on traffic monitoring data of the tunnel entrance and its upstream and downstream road sections. It is used to characterize the composition of vehicle types, the distribution of vehicle speed changes, and the statistical characteristics of vehicle headway.

[0008] According to one achievable method in the embodiments of this application, the safety facility operation status data is used to characterize the current configuration status of speed limit, lighting and information guidance facilities; the safety facility operation status data includes: speed limit thresholds and their variation range reflecting the control level of speed limit facilities; brightness level distribution characteristics reflecting the operation status of lighting facilities; and information release frequency and information content switching status reflecting the guidance intensity of information guidance facilities.

[0009] According to one achievable method in this application embodiment, the step of identifying the reachable causal relationship between changes in traffic operation status and the operating status of safety facilities based on the traffic operation perception data and the operating status data of safety facilities, and constructing a traffic coupling system for tunnel entrance facilities, includes: time-series alignment of the traffic operation perception data and the operating status data of safety facilities within a preset time window; generating corresponding discrete switching events for the operating status of safety facilities based on changes in the operating parameters of each facility in the operating status data that cross preset state boundaries or state codes; using the discrete switching events for the operating status of safety facilities as a reference, retrospectively tracing the sequence of changes in traffic operation status parameters in the traffic operation perception data within the time advancement window; determining that there is a reachable causal relationship between the traffic operation status parameters and the operating status of safety facilities when changes in traffic operation status parameters occur multiple times before switching events of the operating status of safety facilities within the time window, and satisfy a preset time-first consistency constraint; and incorporating the traffic operation status parameters and the operating status parameters of safety facilities that satisfy the reachable causal relationship into the system state set of the traffic coupling system for tunnel entrance facilities.

[0010] According to one achievable method in the embodiments of this application, the sequential probability determination of the temporal evolution process of the multi-level coupled state parameter set includes: selecting representative state parameters located at the bottom triggering factor layer, the intermediate propagation path layer, and the top system influence layer in the multi-level coupled state parameter set, and constructing a determination parameter sequence for the corresponding level; for each level's determination parameter sequence, based on the preset stable operating range of the coupled system, constructing stability assumptions and instability assumptions respectively, and performing sequential probability calculations that update the determination parameter sequence hourly; when the sequential probability calculation result of any level satisfies the corresponding instability determination condition, generating an instability determination result for that level; and comprehensively determining whether the tunnel entrance facility traffic coupling system has entered the instability evolution range based on the combination relationship of the instability determination results of each level.

[0011] According to one achievable method in this application embodiment, the step of comprehensively judging whether the tunnel entrance facility traffic coupling system has entered the instability evolution range based on the combination relationship of the instability judgment results at each level includes: obtaining the instability judgment results of the bottom triggering factor layer, the intermediate propagation path layer, and the top system influence layer respectively; judging whether the instability judgment result of the bottom triggering factor layer forms a continuous causal transmission path to the top system influence layer through the intermediate propagation path layer; when the continuous causal transmission path is determined to be valid, and the instability judgment result corresponding to the top system influence layer meets the preset confirmation conditions, the facility traffic coupling system is determined to have entered the instability evolution range; when the continuous causal transmission path is not formed, the facility traffic coupling system is judged to be in a stable or transitional operating state.

[0012] According to one achievable method in this application embodiment, the step of adjusting the coordinated intervention sequence of speed limits, lighting, and information guidance facilities based on the importance of each level of state includes: determining the dominant level in the current instability evolution process based on the instability determination results corresponding to each level in the multi-level coupled state parameter set; when the dominant level is determined to be the bottom triggering factor level, speed limit facilities used to directly regulate traffic operation behavior are triggered first; when the dominant level is determined to be the intermediate propagation path level, lighting facilities and information guidance facilities used to weaken the state transmission effect are triggered first; when the dominant level is determined to be the top system influence level, the operating state of speed limits, lighting, and information guidance facilities is adjusted synchronously according to preset coordinated control rules.

[0013] According to the second aspect, a tunnel portal intelligent safety management and control system based on multi-source perception and linkage control is provided. The system includes: a tunnel data acquisition unit configured to acquire traffic operation perception data and portal safety facility operation status data within the tunnel portal area; a facility-traffic coupling system construction unit configured to, based on the traffic operation perception data and the safety facility operation status data, identify the reachable causal relationships between changes in traffic operation status and the operation status of safety facilities, and construct a tunnel portal facility-traffic coupling system; and a coupling state parameter set construction unit configured to, using an interpreted structural model algorithm, construct a multi-layered system with causal hierarchical constraints based on the directed dependencies between data in the facility-traffic coupling system. The system comprises a multi-level coupling state parameter set, including a bottom-level triggering factor layer, an intermediate propagation path layer, and a top-level system influence layer; a sequential probability determination unit, configured to perform sequential probability determination on the temporal evolution process of the multi-level coupling state parameter set based on a preset stable operating range of the coupling system, to determine whether the facility-traffic coupling system deviates from the stable operating range and enters the unstable evolution range; and a linkage control triggering unit, configured to trigger the linkage control operation mode of the tunnel entrance safety facilities when the facility-traffic coupling system is determined to have entered the unstable evolution range, wherein the linkage control operation mode includes adjusting the coordinated intervention sequence of speed limits, lighting, and information guidance facilities according to the importance of each level of state.

[0014] According to a third aspect, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the steps of the method described in any one of the first aspects above.

[0015] According to a fourth aspect, an electronic device is provided, comprising: one or more processors; and a memory associated with the one or more processors, the memory being used to store program instructions that, when read and executed by the one or more processors, perform the steps of the method described in any one of the first aspects.

[0016] According to the specific embodiments provided in this application, the following technical effects are disclosed: This application constructs a coupled system of traffic operation and safety facilities in real time using multi-source sensing data. It employs an interpretive structural model to clearly delineate a three-tiered structure: bottom-level triggering, intermediate propagation, and top-level influence. Furthermore, it introduces sequential probability judgment to achieve early and gradual prediction of system instability trends, significantly outperforming traditional single-threshold triggering methods. When the system enters the instability evolution range, the coordinated intervention sequence and intensity of speed limits, lighting, and information guidance facilities are dynamically adjusted according to the importance of each level, forming a targeted and timely closed-loop control. This method effectively solves the problems of unclear causal relationships, delayed prediction, and rigid intervention strategies in existing technologies. It can provide early warning of potential accident risks, reduce false triggering rates, and significantly improve the intelligence, accuracy, and overall effectiveness of traffic safety management in tunnel entrance areas.

[0017] Of course, any product implementing this application does not necessarily need to achieve all of the advantages described above at the same time. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 This is a system architecture diagram applicable to the embodiments of this application; Figure 2 A flowchart illustrating the intelligent safety management method for tunnel entrances based on multi-source sensing and linkage control provided in this application embodiment; Figure 3 A structural block diagram of an intelligent safety management and control system for tunnel entrances based on multi-source sensing and linkage control, provided in an embodiment of this application; Figure 4 A schematic block diagram of an electronic device provided in an embodiment of this application. Detailed Implementation

[0020] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of this application are within the scope of protection of this application.

[0021] The terms used in the embodiments of the present invention are for the purpose of describing specific embodiments only and are not intended to limit the present invention. The singular forms "a", "said", and "the" used in the embodiments of the present invention and the appended claims are also intended to include the plural forms unless the context clearly dictates otherwise.

[0022] It should be understood that the term "and / or" used herein is merely an association relationship describing associated objects, indicating that three relationships may exist. For example, A and / or B may represent: A exists alone, A and B exist simultaneously, and B exists alone. In addition, the character " / " herein generally indicates that the associated objects before and after are in an "or" relationship.

[0023] Depending on the context, the word "if" as used herein may be interpreted as "when" or "while" or "in response to determining" or "in response to detecting". Similarly, depending on the context, the phrase "if determined" or "if detected (stated condition or event)" may be interpreted as "when determined" or "in response to determining" or "when detecting (stated condition or event)" or "in response to detecting (stated condition or event)".

[0024] For the convenience of understanding the present application, the system architecture on which the present application is based will be described first. Figure 1 An exemplary system architecture to which embodiments of the present application can be applied is shown, as Figure 1 shown in, the system architecture may include: a data acquisition device and a tunnel entrance intelligent safety control system based on multi-source perception and linkage control located at the server side.

[0025] Traffic operation perception data and the operation status data of the entrance safety facilities in the tunnel entrance area are obtained through the data acquisition device, and the data acquisition device sends them to the tunnel entrance intelligent safety control system based on multi-source perception and linkage control at the server side. The tunnel entrance intelligent safety control system based on multi-source perception and linkage control can adopt the method provided in the embodiments of the present application for data analysis and safety control, generate a linkage control operation mode instruction, and thus perform linkage control.

[0026] Among them, the data acquisition device may include a variety of data acquisition devices deployed at the tunnel entrance and its upstream and downstream sections. Including but not limited to, video vehicle detectors (VIDs) or high-definition cameras, light intensity detectors outside / inside the tunnel, status acquisition modules for speed limit signs / variable speed limit boards, and so on.

[0027] The intelligent safety management and control system for tunnel entrances based on multi-source sensing and linkage control can be configured as a standalone server, a server cluster, or a cloud server. A cloud server, also known as a cloud computing server or cloud host, is a host product within the cloud computing service system, designed to address the management difficulties and weak service scalability inherent in traditional physical hosts and Virtual Private Servers (VPS) services. Besides... Figure 1 In addition to the architecture shown, the intelligent safety management and control system for tunnel entrances based on multi-source perception and linkage control can also be set up on a computer terminal with strong computing power.

[0028] It should be understood that Figure 1 The data acquisition equipment and the intelligent safety management system for tunnel entrances based on multi-source sensing and linkage control shown are merely illustrative. Depending on the implementation requirements, any number of data acquisition devices and the intelligent safety management system for tunnel entrances based on multi-source sensing and linkage control can be included.

[0029] Figure 2 A flowchart illustrating the intelligent safety management method for tunnel entrances based on multi-source sensing and linkage control, provided in an embodiment of this application. Figure 2 As shown, the method may include the following steps: Step 201: Obtain traffic operation perception data and tunnel entrance safety facility operation status data within the tunnel entrance area.

[0030] Step 202: Based on the traffic operation perception data and the safety facility operation status data, identify the reachable causal relationship between changes in traffic operation status and the operation status of safety facilities, and construct a traffic coupling system for tunnel entrance facilities.

[0031] Step 203: Using the Interpretive Structural Modeling algorithm, based on the directed dependencies between data in the facility transportation coupling system, construct a multi-level coupling state parameter set with causal hierarchical constraints. The multi-level coupling state parameter set includes a bottom triggering factor layer, an intermediate propagation path layer, and a top system influence layer.

[0032] Step 204: Based on the preset stable operating range of the coupled system, perform sequential probability determination on the time-series evolution process of the multi-level coupled state parameter set to determine whether the facility traffic coupled system deviates from the stable operating range and enters the unstable evolution range.

[0033] Step 205: When it is determined that the facility traffic coupling system has entered the unstable evolution range, the linkage control operation mode of the tunnel portal safety facilities is triggered. The linkage control operation mode includes adjusting the coordinated intervention sequence of speed limit, lighting and information guidance facilities according to the importance of each level of state.

[0034] As can be seen from the above process, this application constructs a coupled system of traffic operation and safety facilities in real time using multi-source sensing data. It employs an interpretive structural model to clearly delineate a three-tiered structure: bottom-level triggering, intermediate propagation, and top-level influence. Furthermore, it introduces sequential probability judgment to achieve early and gradual prediction of system instability trends, significantly outperforming traditional single-threshold triggering methods. When the system enters the instability evolution range, the coordinated intervention sequence and intensity of speed limits, lighting, and information guidance facilities are dynamically adjusted according to the importance of each level, forming a targeted and timely closed-loop control. This method effectively solves the problems of unclear causal relationships, delayed prediction, and rigid intervention strategies in existing technologies. It can provide early warning of potential accident risks, reduce false triggering rates, and significantly improve the intelligence, accuracy, and overall effectiveness of traffic safety management in tunnel entrance areas.

[0035] The following describes in detail each step of the above process and the effects that can be further produced, with reference to the embodiments.

[0036] First, the above step 201, namely "acquiring traffic operation perception data and tunnel entrance safety facility operation status data in the tunnel entrance area", will be described in detail with reference to the embodiments.

[0037] Traffic operation sensing data primarily reflects the dynamic characteristics of vehicle operation within a certain range upstream and downstream of the tunnel entrance. This type of data is usually not the raw single-vehicle trajectory, but rather statistically significant feature data formed after time statistics and spatial aggregation processing. Time statistics processing mainly focuses on aggregation and feature extraction along the time series dimension. It typically uses fixed time windows (e.g., 30 seconds, 1 minute, 5 minutes, 15 minutes, etc., selected based on the sensitivity of traffic dynamics at the tunnel entrance) as units, performing statistical calculations on all raw data within the window. Through time aggregation, the original high-frequency, highly fluctuating vehicle-by-vehicle data is smoothed into relatively stable time series features, facilitating the capture of short-term traffic flow mutations (such as congestion formation and dissipation) and periodic patterns (such as morning and evening rush hours). Spatial aggregation processing integrates data from multiple points at adjacent detection points or the same road segment in the spatial dimension, eliminating the influence of potential local anomalies or coverage blind spots at individual detection points. Time statistics and spatial aggregation are usually combined; that is, time window statistics are first performed at each detection point / section, and then spatial aggregation is performed on multiple adjacent sections to finally obtain the spatiotemporal aggregated statistical characteristics of the tunnel entrance area.

[0038] The specific content of traffic operation perception data includes the composition ratio of vehicle types, such as the percentage of passenger cars, large buses, trucks, and hazardous materials vehicles; the distribution of vehicle speed changes, such as average speed, 85th percentile speed, speed standard deviation, and the degree of acceleration and deceleration; and statistical characteristics of headway, such as average headway, minimum headway distribution, and following stability indicators. These characteristics can comprehensively depict the current traffic flow's congestion level, turbulence risk, possibility of abnormal aggregation, and potential conflict intensity, serving as key inputs for determining whether abrupt changes in traffic conditions have occurred.

[0039] The operational status data of tunnel entrance safety facilities comes directly from the various active control devices deployed at the tunnel entrance, reflecting the current actual operational configuration of these facilities. This data includes the current control level of speed limit facilities, such as the currently implemented speed limit threshold value and the magnitude of the most recent adjustment; the operational status of lighting facilities, such as the current brightness level, illuminance distribution range, or power output percentage; and the guidance intensity of information guidance facilities, such as the information display frequency of variable message signs, the type of currently displayed content, the switching status of warning information, and the cycle period. This data essentially constitutes the facilities' own "health records" and "execution logs," used to determine whether the safety facilities have responded to previous traffic changes and whether the current response intensity matches the actual traffic risk.

[0040] The acquisition of these two types of data must be strictly synchronized in time and spatially cover the core impact area of ​​the tunnel entrance, typically focusing on the upstream 200 to 500 meters, the tunnel entrance itself, and a certain distance downstream. Only when traffic operation perception data and safety facility operation status data are simultaneously collected in real time and aligned in time sequence can the system further identify whether and how changes in traffic conditions have affected the facility status. This characteristic lays the most fundamental data foundation for subsequent causal tracing, hierarchical modeling, and coordinated decision-making, and is a key starting point for realizing the transformation from passive response to proactive prevention and intelligent closed-loop systems.

[0041] The following describes in detail step 202, namely, "based on the traffic operation perception data and the safety facility operation status data, identifying the reachable causal relationship between changes in traffic operation status and the operation status of safety facilities, and constructing a traffic coupling system for tunnel entrance facilities", with reference to the embodiments.

[0042] This step shifts from simple data collection to systematic modeling of the complex dynamic system at the tunnel entrance. The aim is to clearly reveal whether and how any significant changes in traffic operation status trigger subsequent adjustments to the operation status of safety facilities, thereby providing a causal basis for early warning and intelligent linkage control.

[0043] This identification process first requires strict time synchronization and alignment of the two types of data. Since traffic operation perception data typically exists in the form of statistical characteristics within fixed time windows, while safety facility operation status data often appears as discrete configuration switching events, it is necessary to align the two within a unified temporal framework. A sliding time window approach is usually adopted, for example, with a basic window length of 5 to 30 minutes, to ensure that the correspondence between traffic status and facility status is observed on the same time scale. Only after completing temporal alignment can it be accurately determined which type of change occurred first and which type of change occurred later.

[0044] The core idea behind identifying reachable causal relationships is to use discrete switching events of safety facility operating states as reference anchors to trace the sequence of changes in traffic operation perception data. Specifically, when a safety facility undergoes a clear state transition—for example, a speed limit threshold decreasing from 80 km / h to 60 km / h, lighting brightness increasing from level three to level five, or an information guidance sign switching to an upcoming congestion warning—the system automatically searches backwards to check whether significant and continuous changes occurred in traffic operation perception parameters in the period preceding the event. If characteristics such as a significant decrease in average vehicle speed, a significant shortening of headway, an abnormal increase in the proportion of large vehicles, and a sharp increase in speed standard deviation are found, and these changes repeatedly precede the facility switching event, while the time lead meets a pre-set precedence consistency constraint, then it is determined that the change in traffic operation state has a reachable causal impact on the operating state of the safety facility.

[0045] The determination of reachability causality emphasizes the word "reachability," requiring not only temporal sequence but also statistical consistency and repeatability of this sequence across multiple independent events. Only when changes in the same type of traffic state parameter repeatedly precede changes in the state of similar facilities within multiple time windows, and the lead time falls within a reasonable physical range, can a reliable causal transmission path be confirmed. This event-driven reverse tracing method effectively filters out random fluctuations or irrelevant noise, highlighting the true traffic incentives driving facility adjustments.

[0046] After identifying reachable causal relationships, all traffic operation state parameters and safety facility operation state parameters confirmed to have causal influence are incorporated into the same system state set, forming a tunnel entrance facility-traffic coupled system. This system is essentially a complex network containing multiple variables, strong coupling, and dynamic evolution. Traffic flow parameters act as potential triggering sources, while safety facility parameters act as response ends, interacting with each other through identified causal chains. The construction of this coupled system enables subsequent steps to conduct explanatory structural model analysis, instability trend prediction, and hierarchical linkage control based on clear causal hierarchical relationships, thereby achieving a fundamental shift from passive response to proactive prevention.

[0047] As an implementable approach, this application, based on the traffic operation perception data and the safety facility operation status data, identifies the reachable causal relationship between changes in traffic operation status and the operation status of safety facilities, and constructs a tunnel entrance facility traffic coupling system. This includes: time-series alignment of the traffic operation perception data and the safety facility operation status data within a preset time window; generating corresponding discrete switching events for safety facility operation status based on changes in the operation parameters of each facility in the safety facility operation status data when they cross preset state boundaries or state codes; using these discrete switching events as references, tracing the sequence of changes in traffic operation status parameters in the traffic operation perception data backward within the time advancement window; determining that a reachable causal relationship exists between the traffic operation status parameters and the safety facility operation status when changes in traffic operation status parameters occur multiple times within the time window prior to switching events in the safety facility operation status, and satisfy a preset time-ahead consistency constraint; and incorporating the traffic operation status parameters and safety facility operation status parameters that satisfy the reachable causal relationship into the system state set of the tunnel entrance facility traffic coupling system.

[0048] Specifically, based on changes in the operating parameters of each facility across preset state boundaries or state codes in the safety facility operation status data, corresponding discrete switching events for the safety facility operation status are generated. This step transforms continuous or quasi-continuous facility status data into a clear sequence of discrete events. For example, when the speed limit threshold jumps from 80 km / h to 60 km / h, the lighting brightness level increases from level three to level five, or the information guidance board switches from regular information to a "slow down due to congestion ahead" warning, a discrete switching event record is generated. These events serve as the "result end" anchor points for causal analysis, with clear occurrence times and change types, and are the starting point for subsequent reverse tracing. Preset state boundaries or state codes refer to predefined boundary points or discrete state sets during the system design phase. For example, speed limit thresholds are divided into boundaries in steps of 10 km / h or 20 km / h; lighting levels are divided into integers from 1 to 10; and information content is coded with limited categories such as "normal / warning / accident / control".

[0049] For each facility switching event, the system automatically traces back a predefined time range, such as five to thirty minutes before the event, and extracts the complete trajectory of changes in traffic operation perception parameters within that interval. These parameters include key indicators such as average vehicle speed, speed standard deviation, headway distribution, and the proportion of large vehicles. By comparing the parameter sequences before and after the event, the system can clearly observe which traffic characteristics showed significant trend changes or abrupt changes before the switching event occurred.

[0050] When changes in traffic operation status parameters occur multiple times within the specified time window prior to the switching events of safety facility operation status, and a preset time-priority consistency constraint is met, a reachable causal relationship is determined to exist between the two. The key to this determination lies in "multiple precedences" and the "time-priority consistency constraint." "Multiple precedences" means that in at least 60% to 80% of historically accumulated independent switching events, changes in the same type of traffic parameter occur before facility switching. The time-priority consistency constraint requires that the lead time fall within a reasonable physical range, such as 10 seconds to 5 minutes, avoiding overly brief coincidences or overly distant correlations. Only when both repeatability and time reasonableness are satisfied can a reachable causal relationship between the traffic parameter and the facility status be confirmed.

[0051] Finally, all traffic operation state parameters and safety facility operation state parameters that satisfy the reachability causal relationship are incorporated into the system state set of the tunnel portal facility-traffic coupling system. This set is no longer a simple data stack, but forms a dynamic network structure with a clear causal orientation. Traffic parameters serve as potential triggering sources, and facility parameters as response ends; the two are interconnected through identified causal chains, providing a solid systemic foundation for subsequent hierarchical decomposition using interpretive structural models, sequential probabilistic instability determination, and the implementation of hierarchical linkage control.

[0052] The following describes in detail step 203, namely, "using the Interpretive Structural Model algorithm to construct a multi-level coupling state parameter set with causal hierarchical constraints based on the directed dependencies between data in the facility transportation coupling system, wherein the multi-level coupling state parameter set includes a bottom triggering factor layer, an intermediate propagation path layer, and a top system influence layer," with reference to the embodiments.

[0053] This step further abstracts and structures the system state set obtained through causal identification from a planar, interconnected network of variables into a multi-level system with clear hierarchical relationships and a clear direction of causal transmission. This provides a scientific and interpretable structural basis for subsequent prediction of instability trends and hierarchical linkage control.

[0054] Explained structural modeling (ESM) is a classic systems engineering analysis method. Its core idea is to simplify complex causal networks into a hierarchical structure by progressively decomposing the directed dependencies between elements within a system. In this invention, firstly, all parameter pairs with confirmed reachable causal relationships are extracted from the constructed tunnel entrance facility traffic coupling system. Then, a directed graph is constructed based on the directed dependencies between these parameters. The arrows represent the direction of causal influence transmission; for example, "sudden drop in vehicle speed" points to "increased speed dispersion," which in turn points to "increased probability of potential accident risk."

[0055] The specific process of using the interpretive structural model algorithm usually includes the following steps: First, establish the adjacency matrix between each parameter, where the elements in the matrix represent whether there is a direct influence relationship; then, solve the reachability matrix through Boolean operations to obtain whether there is a path reachability relationship between any two parameters; next, use the reachability matrix to perform hierarchical partitioning, peeling away layer by layer from the bottom layer until the top-level elements are separated; finally, based on the hierarchical partitioning results and the original directed dependencies, draw a structural model diagram with strict causal hierarchy.

[0056] Using this algorithm, this invention divides all key state parameters in a coupled system into three layers of multi-level coupled state parameter sets with clear causal constraints. The first layer is the bottom-level triggering factor layer, which mainly contains the most direct and original indicators of sudden changes in traffic operation, such as a sharp drop in average vehicle speed, a sudden increase in the proportion of large vehicles, a significant shortening of headway, and a rapid increase in the speed standard deviation. These factors are usually the initial triggers for abnormal traffic flow conditions and have the strongest "source" attribute.

[0057] The second layer is the intermediate propagation path layer, located between the bottom and top layers, acting as a bridge between them, amplifying or transmitting signals. These parameters reflect how triggering factors evolve, spread, and interact within the traffic flow, such as decreased vehicle following stability, continuously expanding speed dispersion, intensified traffic flow turbulence, and rapid increase in queue length. The characteristic of intermediate layer parameters is that they are directly driven by the bottom layer and further influence the comprehensive risk indicators of the top layer, serving as the main channel for the propagation of instability within the system.

[0058] The third layer is the top-level system impact layer, located at the very top of the entire structure. It is directly related to the final manifestation of the overall safety risk at the tunnel entrance, such as the estimation of potential conflict point density, comprehensive assessment of accident probability, and severe event risk index. Although these indicators are furthest from the original triggering factors, they are the most direct and critical representations of system instability and the ultimate basis for judging whether the entire coupled system has truly entered a dangerous state.

[0059] By interpreting the multi-level coupled state parameter set constructed by the structural model, it is no longer a simple list of parameters, but forms a complete causal chain and hierarchical constraint relationship from "bottom-level triggering" to "intermediate transmission" and then to "top-level influence." This structured expression makes the evolution path of system instability clearly visible, provides a scientific basis for subsequent sequential probability determination, and lays the theoretical foundation for hierarchical differentiated linkage control strategies: the dominant role of instability at different levels will correspond to different priority intervention targets and control intensities. The introduction of this technical feature significantly enhances the systematicity, interpretability, and engineering guidance value of the entire control method.

[0060] The following describes in detail step 204, namely, "based on a preset stable operating range of the coupled system, performing sequential probability determination on the temporal evolution process of the multi-level coupled state parameter set to determine whether the facility traffic coupled system deviates from the stable operating range and enters the unstable evolution range," with reference to the embodiments.

[0061] This step transforms the previously constructed multi-level coupled state parameter set from a static hierarchical structure into a dynamic time-series evolution process monitoring, and introduces the idea of ​​sequential probability determination, so as to progressively and cumulatively assess the degree of risk of deviating from the normal state before the system becomes completely unstable, thereby significantly improving the timeliness and sensitivity of early warning.

[0062] The pre-defined stable operating range of the coupled system refers to a set of multi-dimensional threshold ranges determined during the system design phase through extensive historical data statistics, expert experience, and simulation experiments. This range defines the reasonable fluctuation boundaries that key state parameters at each level should fall into under normal traffic conditions. For example, the speed drop at the bottom triggering factor layer should generally not exceed 30% of the normal value, the speed dispersion at the intermediate propagation path layer should be kept at a low level, and the accident risk probability estimate at the top system influence layer should be below a certain safety threshold. When all level parameters are simultaneously within their respective stable ranges, the entire facility traffic coupled system is considered to be in a healthy and stable operating state. Once the parameter sequence begins to deviate continuously from these ranges, it means that the system may be evolving towards an unstable evolution range, which corresponds to a higher risk accumulation state, or even approaching the precursor to an actual accident.

[0063] Sequential probability determination is a dynamic decision-making method that differs from traditional fixed-threshold determination. Instead of making a rigid "yes or no" decision on parameter values ​​at a single moment, it treats the temporal evolution of multi-level coupled state parameters as a continuous observation sequence, accumulating evidence in real time to update the probability of whether the system is currently in a stable or unstable state. Common implementation methods include sequential probability ratio testing, Bayesian sequential update, or cumulative sum testing algorithms.

[0064] As an implementable approach, the sequential probability determination of the temporal evolution process of the multi-level coupled state parameter set includes: selecting representative state parameters from the multi-level coupled state parameter set, respectively located at the bottom triggering factor layer, the intermediate propagation path layer, and the top system influence layer, to construct corresponding level determination parameter sequences; for each level determination parameter sequence, based on the preset stable operating range of the coupled system, constructing stability and instability hypotheses respectively, and performing hourly updated sequential probability calculations on the determination parameter sequences; when the sequential probability calculation result of any level satisfies the corresponding instability determination condition, generating the instability determination result for that level; and comprehensively determining whether the tunnel portal facility traffic coupling system has entered the instability evolution range based on the combination relationship of the instability determination results of each level.

[0065] The specific implementation process first selects representative state parameters from the multi-level coupled state parameter set, specifically those located at the bottom triggering factor layer, the intermediate propagation path layer, and the top system influence layer. These parameters are the most sensitive and representative indicators of the essential characteristics of each layer. For example, the bottom layer could select the sudden drop in vehicle speed or the abrupt change rate of the proportion of large vehicles; the intermediate layer could select the increasing trend of speed dispersion or the decrease index of car-following stability; and the top layer could select the probability estimate of potential accident risks or the comprehensive index of conflict point density. For the parameters selected at each layer, the system constructs an independent time-series decision parameter sequence. This sequence is continuously updated with a fixed sampling period, recording the complete trajectory of the parameter evolution over time.

[0066] Next, for each level of the decision parameter sequence, the system constructs two opposing statistical hypotheses based on the pre-defined stable operating range of the coupled system. The stability hypothesis assumes that the current sequence is still within the normal fluctuation range, and the system as a whole is safe; the instability hypothesis assumes that the sequence has already deviated from or is deviating from the stable range, and the system risk is accumulating and increasing. For example, As new observational data arrives hourly, the system performs sequential probability calculations on the sequence, typically using methods such as sequential probability ratio tests or Bayesian sequential updates. This process accumulates evidence in real time and updates the likelihood ratio or posterior probability value to indicate which hypothesis the current data supports. This calculation is continuously incremental; with each new data point, the probability value is updated based on the new evidence, without waiting for a fixed sample size. Two mutually exclusive statistical hypotheses are established for each level of the decision parameter sequence: the stability hypothesis (H0): the current and future observational sequences remain within the normal fluctuation range of the coupled system, and the entire system is in a safe and stable operating state. For example, for the bottom-level vehicle speed drop sequence, H0 assumes its mean is close to historical normal levels, the fluctuations conform to a normal distribution, and there is no systematic deviation trend; the instability hypothesis (H1): the current sequence has already deviated significantly from the stable operating range, and the system is in a stage of risk accumulation or instability evolution. For example, the vehicle speed drop amplitude continues to increase and exceeds the upper limit of normal fluctuations, showing a systematic downward trend. These two hypotheses are usually mathematically characterized by different probability distributions or parameter ranges: H0: The parameters follow a distribution that occurs during normal operation (e.g., mean μ0, small variance σ0²). H1: The parameters follow the distribution during the unstable evolution (e.g., mean μ1 > μ0, or biased towards the dangerous direction, increased variance, etc.). When the sequential probability calculation result of any level reaches the pre-set instability judgment condition, such as the cumulative likelihood ratio exceeding a certain warning threshold and persisting for several sampling points, or the posterior instability probability exceeding 80% to 90%, the system generates an instability judgment result for that level. This result indicates that the level has shown a significant trend of deviating from the stable range. Different levels can generate instability signals independently, but the true system-level hazard judgment depends on the combination relationship of instability signals from multiple levels.

[0067] Finally, based on the combination pattern of the instability determination results at the bottom, intermediate, and top levels, a comprehensive judgment is made as to whether the entire tunnel entrance facility traffic coupling system has entered the instability evolution range. The instability determination results at each level can be weighted and summed to determine whether the tunnel entrance facility traffic coupling system has entered the instability evolution range. Preferably, the comprehensive judgment based on the combination relationship of the instability determination results at each level includes: obtaining the instability determination results at the bottom triggering factor layer, the intermediate propagation path layer, and the top system influence layer; determining whether the instability determination result at the bottom triggering factor layer forms a continuous causal transmission path to the top system influence layer through the intermediate propagation path layer; when the continuous causal transmission path is established and the instability determination result corresponding to the top system influence layer meets preset confirmation conditions, the facility traffic coupling system is determined to have entered the instability evolution range; when the continuous causal transmission path is not formed, the facility traffic coupling system is determined to be in a stable or transitional operating state.

[0068] The specific judgment process first obtains the instability judgment results for each of the three layers. These results are derived from the aforementioned sequential probability calculation. Each layer generates a clear binary or probabilistic conclusion during real-time monitoring: whether the layer has been determined to be in an unstable state. For example, the bottom layer may issue an instability signal due to a sustained excessive drop in vehicle speed, the middle layer may become unstable due to an accumulated increase in speed dispersion, and the top layer may become unstable because the probability of potential accident risk exceeds the warning line. The system simultaneously collects these three independent judgment results as the raw input for subsequent comprehensive judgment.

[0069] Next, the system focuses on determining whether the instability assessment result of the bottom-level triggering factor layer has formed a continuous causal transmission path to the top-level system influence layer through the intermediate propagation path layer. This judgment is the core logic of the entire decision-making mechanism and a design principle highly consistent with the hierarchical structure of the explanatory structural model. The conditions for a continuous causal transmission path to exist are: the bottom-level instability signal must appear before or simultaneously with the intermediate layer in time, the intermediate layer instability signal must appear before or simultaneously with the top layer in time, and the changes in the intermediate layer parameters can reasonably explain the physical mechanism of the transmission of bottom-level instability to the top layer. For example, when the bottom layer detects a sharp increase in the proportion of large vehicles, leading to a sudden drop in vehicle speed, the intermediate layer subsequently experiences a decrease in car-following stability and an increase in speed dispersion, and ultimately the accident risk probability of the top layer begins to increase significantly. This bottom-up, layer-by-layer transmission is considered a continuous path. If the transmission chain is interrupted at a certain intermediate layer, for example, the bottom layer becomes unstable but the intermediate layer remains stable, then the path does not exist.

[0070] Assuming the continuous causal transmission path is established, the system further checks whether the instability determination result corresponding to the top-level system's influence layer meets the preset confirmation conditions. These confirmation conditions are typically a high threshold for the top-level instability probability, such as a posterior probability exceeding 85% to 95%, or a sequential likelihood ratio consistently exceeding the upper bound threshold for a certain period. Only when the top-level instability signal is fully confirmed is the entire coupled system considered to have truly entered the instability evolution range. At this point, the system will issue the highest-level warning and immediately trigger subsequent hierarchical linkage control strategies.

[0071] Conversely, if a continuous causal transmission path from the bottom to the top is not established, even if instability signals appear at individual levels, the system will not determine that the whole system has entered an unstable evolution range. Instead, it will classify the facility-traffic coupling system as a stable operating state or only in a transitional operating state. In this case, the system will continue to maintain high-frequency monitoring but will not initiate large-scale intervention measures. This rule effectively reduces the false trigger rate and avoids frequent interventions caused by local anomalies or noise.

[0072] This comprehensive judgment mechanism, based on hierarchical combination relationships and the integrity of causal transmission paths, is the most significant innovation of this invention, distinguishing it from traditional single-threshold or simple multi-parameter fusion methods. It fully respects the physical laws governing the evolution of traffic risks at tunnel entrances, namely, that danger typically amplifies and propagates from sudden traffic changes at the lower levels to systemic risks at the top. Only when this complete transmission chain is observed and confirmed at the top level are strong intervention measures taken, thus achieving a high degree of unity between the timeliness, accuracy, and interpretability of early warnings, providing truly intelligent closed-loop decision-making capabilities for tunnel entrance safety management.

[0073] The greatest advantage of this sequential judgment lies in its "early detection and gradual warning" characteristics. Compared to a one-time threshold judgment, it can capture abnormal trends through probability accumulation when parameters just begin to deviate from the stable range but have not yet reached a dangerous level, thus advancing the warning time by several minutes or even longer. Furthermore, because the judgment is based on joint evidence from multiple levels of sequences, the judgment that the system as a whole has entered the unstable evolution range is more reliable when deviations gradually propagate from the bottom, middle, and top layers, avoiding false alarms caused by sudden changes in a single parameter.

[0074] The following describes in detail step 205, namely, "when it is determined that the facility traffic coupling system has entered the unstable evolution range, the linkage control operation mode of the tunnel entrance safety facilities is triggered, wherein the linkage control operation mode includes adjusting the coordinated intervention sequence of speed limit, lighting and information guidance facilities according to the importance of each level of state".

[0075] Once the system determines that the coupled system has entered the unstable evolution range, it no longer adopts the traditional fixed sequence or single facility intervention method. Instead, it dynamically and intelligently adjusts the intervention priority, activation timing and coordination strength of speed limit, lighting and information guidance facilities according to the dominance and importance of each level of state during the instability process, so as to achieve the best risk suppression effect.

[0076] The conditions for triggering the linkage control operation mode are very clear: this mode will only be officially activated when the bottom triggering factor layer, the intermediate propagation path layer, and the top system influence layer form a continuous causal transmission path, and the instability judgment result of the top system influence layer meets the preset high-confidence confirmation conditions. This strict triggering logic ensures that intervention measures will not be frequently activated due to local anomalies, avoiding interference with normal traffic flow due to misoperation, while also ensuring rapid response in truly high-risk scenarios.

[0077] The most innovative aspect of the coordinated control operation mode lies in the differentiated and dynamic adjustment of the intervention sequence based on the importance of each level's state. Specifically, based on the instability determination results corresponding to each level in the multi-level coupled state parameter set, the level that plays a dominant role in the current instability evolution process is determined. When the dominant level is determined to be the bottom triggering factor level, speed limiting facilities used to directly regulate traffic behavior are triggered first. When the dominant level is determined to be the intermediate propagation path level, lighting facilities and information guidance facilities used to weaken the state transmission effect are triggered first. When the dominant level is determined to be the top system influence level, the operating states of speed limiting, lighting, and information guidance facilities are synchronously adjusted according to preset coordinated control rules.

[0078] Specifically, when the underlying triggering factor layer is determined to be the dominant layer, it indicates that the traffic flow has experienced its most primitive and drastic abrupt change, such as a sharp drop in vehicle speed, an abnormal increase in the proportion of large vehicles, or a drastic reduction in headway. At this point, the system prioritizes and triggers the regulation of speed limit facilities with maximum force, such as rapidly lowering the speed limit threshold, implementing stricter tiered speed limits, or enforcing vehicle-type-specific speed limits, directly suppressing further deterioration of traffic flow from the source, thereby cutting off the initial link in the risk transmission.

[0079] When the intermediate propagation path layer is determined to be the dominant layer, it indicates that the triggering at the lower level has begun to amplify and spread within the traffic flow, such as a continuous increase in speed dispersion and a significant decrease in car-following stability. At this point, the system prioritizes the coordinated intervention of lighting and information guidance facilities: firstly, it increases the lighting brightness level to improve driver visual adaptability, and at the same time, it frequently broadcasts targeted warnings and guidance information through information guidance boards, such as "The distance to the vehicle ahead is too close, please maintain a safe distance" and "Slow down and maintain a safe distance," with the aim of weakening the propagation effect of the unstable state in the traffic flow and preventing the risk from accumulating further at the top level.

[0080] When the top-level system's impact layer is determined to be the dominant layer or has reached the highest level of confirmation, it indicates that the overall risk of the system is approaching a critical point. At this point, the system no longer relies solely on a single facility, but instead, according to the preset highest-level collaborative control rules, significantly adjusts the operating status of three types of facilities simultaneously: drastically reducing speed limit thresholds, increasing lighting brightness to full power, switching to the strongest warning information content and increasing the frequency of its release, forming a multi-facility coordinated intervention to minimize the remaining risk window and strive to achieve the most effective passive protection before an accident occurs.

[0081] By dynamically adjusting the collaborative intervention sequence based on hierarchical importance, the coordinated control model breaks through the rigid limitations of traditional fixed strategies, achieving intelligent responses of "targeted treatment and precise policy implementation." Different instability-dominant levels correspond to different priority intervention targets and intensity gradients, ensuring that every control resource is allocated to the most urgent and effective aspects, thereby significantly improving the targeting, timeliness, and overall effectiveness of interventions. This technical feature ultimately transforms all the results of causal identification, hierarchical modeling, and sequential probability determination into an executable engineering control strategy, completing a full closed loop from risk perception to proactive prevention and control. This is the key to achieving true intelligence in the field of tunnel portal safety.

[0082] The methods provided in this application can be applied to various scenarios, including but not limited to: First, during peak hours at highway tunnel entrances, when sudden changes in upstream traffic flow cause a sharp drop in vehicle speed and a shortened headway, the system can predict instability trends in advance through multi-level coupling analysis, dynamically adjust speed limits and lighting facilities, and prioritize triggering information guidance boards to issue "slow down" warnings, effectively reducing the risk of rear-end collisions and improving traffic efficiency. Second, in complex environments at mountain or urban tunnel entrances, facing the intensified "black hole effect" caused by rain and fog, the method can identify causal transmission paths based on real-time perception data, link up to increase lighting brightness and simultaneously control speed limits, weakening the hidden dangers caused by visual adaptation difficulties and ensuring drivers' safe entry and exit from tunnels. Finally, in special scenarios such as long tunnels or undersea tunnels, when the proportion of large vehicles increases and potential conflicts arise, the system comprehensively considers the top-level instability judgment results and implements a full-facility collaborative intervention mode, such as significantly lowering the speed limit threshold and frequently issuing guidance information, achieving closed-loop prevention and control from source to end, and significantly improving overall safety and emergency response capabilities.

[0083] The foregoing has described specific embodiments of this specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims may be performed in a different order than that shown in the embodiments and may still achieve the desired result. Furthermore, the processes depicted in the drawings do not necessarily require the specific or sequential order shown to achieve the desired result. In some embodiments, multitasking and parallel processing are possible or may be advantageous.

[0084] According to another embodiment, an intelligent safety management and control system for tunnel entrances based on multi-source sensing and linkage control is provided. For example... Figure 3 As shown, the system 300 includes: The tunnel data acquisition unit 301 is configured to acquire traffic operation perception data and tunnel portal safety facility operation status data within the tunnel portal area.

[0085] The facility traffic coupling system construction unit 302 is configured to identify the reachable causal relationship between changes in traffic operation status and the operation status of safety facilities based on the traffic operation perception data and the operation status data of safety facilities, and to construct a tunnel entrance facility traffic coupling system.

[0086] The coupling state parameter set construction unit 303 is configured to use the Interpretive Structural Model algorithm to construct a multi-level coupling state parameter set with causal hierarchical constraints based on the directed dependencies between data in the facility traffic coupling system. The multi-level coupling state parameter set includes a bottom triggering factor layer, an intermediate propagation path layer, and a top system influence layer.

[0087] The sequential probability determination unit 304 is configured to perform sequential probability determination on the temporal evolution process of the multi-level coupling state parameter set based on a preset stable operating range of the coupling system, so as to determine whether the facility traffic coupling system deviates from the stable operating range and enters the unstable evolution range.

[0088] The linkage control triggering unit 305 is configured to trigger the linkage control operation mode of the tunnel portal safety facilities when the facility traffic coupling system is determined to have entered the unstable evolution range. The linkage control operation mode includes adjusting the coordinated intervention sequence of speed limit, lighting and information guidance facilities according to the importance of each level of state.

[0089] As an feasible approach, traffic operation perception data is statistical feature data formed by time statistics and spatial aggregation processing of traffic monitoring data based on tunnel entrances and their upstream and downstream road sections. It is used to characterize the composition of vehicle types, the distribution of vehicle speed changes, and the statistical characteristics of headway.

[0090] As an implementable approach, safety facility operation status data is used to characterize the current configuration status of speed limit, lighting, and information guidance facilities; the safety facility operation status data includes: speed limit thresholds and their variation range reflecting the control level of speed limit facilities; brightness level distribution characteristics reflecting the operation status of lighting facilities; and information release frequency and information content switching status reflecting the guidance intensity of information guidance facilities.

[0091] As an implementable approach, the facility traffic coupling system construction unit 302, when constructing a tunnel entrance facility traffic coupling system based on the traffic operation perception data and the safety facility operation status data, can be configured to: time-align the traffic operation perception data and the safety facility operation status data; generate corresponding discrete switching events for the safety facility operation status based on changes in the operation parameters of each facility in the safety facility operation status data that cross preset state boundaries or state codes; use the discrete switching events for the safety facility operation status as a reference to trace the sequence of changes in the traffic operation status parameters in the traffic operation perception data backward within the time advancement window; when changes in the traffic operation status parameters occur multiple times within the time window before the switching events of the safety facility operation status, and satisfy a preset time-ahead consistency constraint, determine that there is a reachable causal relationship between the traffic operation status parameters and the safety facility operation status; and include the traffic operation status parameters and safety facility operation status parameters that satisfy the reachable causal relationship into the system state set of the tunnel entrance facility traffic coupling system.

[0092] As an implementable approach, the sequential probability determination unit 304 can be configured to perform sequential probability determination on the temporal evolution process of a multi-level coupled state parameter set as follows: In the multi-level coupled state parameter set, representative state parameters located at the bottom triggering factor layer, the intermediate propagation path layer, and the top system influence layer are selected respectively to construct corresponding level determination parameter sequences; for each level determination parameter sequence, based on the preset stable operating range of the coupled system, stability and instability hypotheses are constructed respectively, and sequential probability calculations are performed on the determination parameter sequences on an hourly basis; when the sequential probability calculation result of any level satisfies the corresponding instability determination condition, an instability determination result for that level is generated; based on the combination relationship of the instability determination results of each level, a comprehensive judgment is made as to whether the tunnel entrance facility traffic coupling system has entered the instability evolution range.

[0093] As an implementable approach, the sequential probability determination unit 304, when comprehensively judging whether the tunnel entrance facility traffic coupling system has entered the instability evolution range based on the combination relationship of the instability determination results at each level, can be configured as follows: acquiring the instability determination results of the bottom triggering factor layer, the intermediate propagation path layer, and the top system influence layer respectively; determining whether the instability determination result of the bottom triggering factor layer forms a continuous causal transmission path to the top system influence layer through the intermediate propagation path layer; determining that the facility traffic coupling system has entered the instability evolution range when the continuous causal transmission path is established and the instability determination result corresponding to the top system influence layer meets the preset confirmation conditions; and determining that the facility traffic coupling system is in a stable or transitional operating state when the continuous causal transmission path has not been formed.

[0094] As an implementable approach, the linkage control triggering unit 305 can be configured to, when adjusting the coordinated intervention sequence of speed limits, lighting, and information guidance facilities according to the importance of each level of state, determine the dominant level in the current instability evolution process based on the instability determination results corresponding to each level in the multi-level coupled state parameter set; when the dominant level is determined to be the bottom triggering factor level, the speed limit facilities used to directly regulate traffic behavior are triggered first; when the dominant level is determined to be the intermediate propagation path level, the lighting facilities and information guidance facilities used to weaken the state transmission effect are triggered first; when the dominant level is determined to be the top system influence level, the operating state of the speed limit, lighting, and information guidance facilities is adjusted synchronously according to the preset coordinated control rules.

[0095] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on its differences from other embodiments. In particular, the system embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions in the method embodiments. The system embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. Components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without creative effort.

[0096] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties. Furthermore, the collection, use and processing of the relevant data must comply with the relevant laws, regulations and standards of the relevant countries and regions, and corresponding operation entry points are provided for users to choose to authorize or refuse.

[0097] In addition, embodiments of this application also provide a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the steps of the method described in any of the foregoing method embodiments.

[0098] And an electronic device comprising: one or more processors; and a memory associated with the one or more processors, the memory being used to store program instructions that, when read and executed by the one or more processors, perform the steps of the method described in any of the foregoing method embodiments.

[0099] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the method described in any of the foregoing method embodiments.

[0100] in, Figure 4 An exemplary architecture of an electronic device is shown, which may include a processor 410, a video display adapter 411, a disk drive 412, an input / output interface 413, a network interface 414, and a memory 420. The processor 410, video display adapter 411, disk drive 412, input / output interface 413, network interface 414, and memory 420 can communicate with each other via a communication bus 430.

[0101] The processor 410 can be implemented using a general-purpose CPU, microprocessor, application-specific integrated circuit (ASIC), or one or more integrated circuits to execute relevant programs and implement the technical solution provided in this application.

[0102] The memory 420 can be implemented in the form of ROM (Read Only Memory), RAM (Random Access Memory), static storage device, dynamic storage device, etc. The memory 420 can store the operating system 421 for controlling the operation of the electronic device 400, and the basic input / output system (BIOS) 422 for controlling the low-level operations of the electronic device 400. Additionally, it can store a web browser 423, a data storage management system 424, and a tunnel entrance intelligent safety management system 425 based on multi-source sensing and linkage control, etc. The aforementioned tunnel entrance intelligent safety management system 425 based on multi-source sensing and linkage control can be the application program that specifically implements the aforementioned steps in this embodiment. In summary, when implementing the technical solution provided in this application through software or firmware, the relevant program code is stored in the memory 420 and executed by the processor 410.

[0103] Input / output interface 413 is used to connect input / output modules to realize information input and output. Input / output modules can be configured as components in the device (not shown in the figure) or externally connected to the device to provide corresponding functions. Input devices may include keyboards, mice, touch screens, microphones, various sensors, etc., and output devices may include displays, speakers, vibrators, indicator lights, etc.

[0104] Network interface 414 is used to connect a communication module (not shown in the figure) to enable communication and interaction between this device and other devices. The communication module can communicate via wired means (such as USB, Ethernet cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.).

[0105] Bus 430 includes a pathway for transmitting information between various components of the device, such as processor 410, video display adapter 411, disk drive 412, input / output interface 413, network interface 414, and memory 420.

[0106] It should be noted that although the above-described device only shows the processor 410, video display adapter 411, disk drive 412, input / output interface 413, network interface 414, memory 420, bus 430, etc., in specific implementations, the device may also include other components necessary for normal operation. Furthermore, those skilled in the art will understand that the above-described device may only include the components necessary for implementing the solution of this application, and does not necessarily include all the components shown in the figures.

[0107] As can be seen from the above description of the embodiments, those skilled in the art can clearly understand that this application can be implemented by means of software plus necessary general-purpose hardware platforms. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a computer program product. This computer program product can be stored in a storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in various embodiments or some parts of the embodiments of this application.

[0108] The technical solutions provided in this application have been described in detail above. Specific examples have been used to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. Furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A method for intelligent safety management and control of tunnel entrances based on multi-source sensing and linkage control, characterized in that, The method includes: Acquire traffic operation perception data and tunnel portal safety facility operation status data within the tunnel portal area; Based on the traffic operation perception data and the safety facility operation status data, the reachable causal relationship between changes in traffic operation status and the operation status of safety facilities is identified, and a traffic coupling system for tunnel entrance facilities is constructed. Using the Interpretive Structural Modeling algorithm, a multi-level coupling state parameter set with causal hierarchical constraints is constructed based on the directed dependencies between data in the facility traffic coupling system. The multi-level coupling state parameter set includes a bottom triggering factor layer, an intermediate propagation path layer, and a top system influence layer. Based on the preset stable operating range of the coupled system, a sequential probability determination is performed on the temporal evolution process of the multi-level coupled state parameter set to determine whether the facility traffic coupled system deviates from the stable operating range and enters the unstable evolution range. When the traffic coupling system of the facility is determined to have entered the unstable evolution range, the linkage control operation mode of the tunnel portal safety facilities is triggered. The linkage control operation mode includes adjusting the coordinated intervention sequence of speed limit, lighting and information guidance facilities according to the importance of each level of state.

2. The method according to claim 1, characterized in that, The traffic operation perception data is statistical feature data formed by time statistics and spatial aggregation processing of traffic monitoring data at the tunnel entrance and its upstream and downstream road sections. It is used to characterize the composition of vehicle types, the distribution of vehicle speed changes, and the statistical characteristics of vehicle headway.

3. The method according to claim 1, characterized in that, The operational status data of the safety facilities is used to characterize the current configuration status of speed limits, lighting, and information guidance facilities; The operational status data of the safety facilities includes: Speed ​​limit thresholds and their variation ranges reflect the control level of speed limiting facilities; and, The distribution characteristics of brightness levels reflecting the operating status of lighting facilities; and, The frequency of information dissemination and the switching status of information content reflect the guidance intensity of information guidance facilities.

4. The method according to claim 1, characterized in that, The process of identifying the reachable causal relationship between changes in traffic operation status and the operational status of safety facilities based on the traffic operation perception data and the operational status data of safety facilities, and constructing a traffic coupling system for tunnel entrance facilities, includes: Within a preset time window, traffic operation perception data and safety facility operation status data are time-series aligned; Based on the changes in the operating parameters of each facility in the safety facility operation status data when they cross preset status boundaries or status codes, corresponding discrete switching events of the safety facility operation status are generated. Using the discrete switching events of the safety facility's operating status as a reference, the sequence of changes in traffic operation status parameters in the traffic operation perception data is traced back in reverse within the time advancement window; When changes in traffic operation status parameters occur multiple times within the time window before the switching events of safety facility operation status, and the preset time-ahead consistency constraint is met, it is determined that there is a reachable causal relationship between the traffic operation status parameters and the safety facility operation status. Traffic operation state parameters and safety facility operation state parameters that satisfy the aforementioned reachability causality relationship are incorporated into the system state set of the tunnel portal facility traffic coupling system.

5. The method according to claim 1, characterized in that, The sequential probability determination of the temporal evolution process of the multi-level coupled state parameter set includes: In the set of multi-level coupled state parameters, representative state parameters located in the bottom triggering factor layer, the intermediate propagation path layer and the top system influence layer are selected respectively to construct the corresponding decision parameter sequence. For the decision parameter sequence at each level, based on the preset stable operating range of the coupled system, stability hypothesis and instability hypothesis are constructed respectively, and sequential probability calculation is performed on the decision parameter sequence with hourly updates. When the sequential probability calculation result of any level satisfies the corresponding instability judgment condition, the instability judgment result of that level is generated. Based on the combination of instability assessment results at each level, a comprehensive judgment is made as to whether the traffic coupling system at the tunnel entrance has entered the instability evolution range.

6. The method according to claim 5, characterized in that, The method of comprehensively judging whether the traffic coupling system at the tunnel entrance has entered the instability evolution range based on the combination relationship of the instability judgment results at each level includes: Obtain the instability determination results for the bottom triggering factor layer, the intermediate propagation path layer, and the top system influence layer, respectively; Determine whether the instability determination result of the bottom triggering factor layer forms a continuous causal transmission path to the top system influence layer through the intermediate propagation path layer; When the continuous causal transmission path is determined to be valid, and the instability determination result corresponding to the top-level system influence layer meets the preset confirmation conditions, the facility traffic coupling system is determined to have entered the instability evolution range. When the continuous causal transmission path is not formed, the facility traffic coupling system is determined to be in a stable or transitional operating state.

7. The method according to claim 1, characterized in that, The coordinated intervention sequence for adjusting speed limits, lighting, and information guidance facilities based on the importance of each level of status includes: Based on the instability determination results corresponding to each level in the multi-level coupled state parameter set, the level that plays a dominant role in the current instability evolution process is determined. When the dominant level is determined to be the bottom triggering factor level, speed limit facilities used to directly regulate traffic behavior are triggered first. When the dominant level is determined to be an intermediate propagation path layer, lighting facilities and information guidance facilities used to weaken the state transmission effect are triggered first. When the dominant level is determined to be the top-level system influence layer, the operating status of speed limits, lighting, and information guidance facilities is adjusted synchronously according to the preset collaborative control rules.

8. A tunnel entrance intelligent safety management and control system based on multi-source sensing and linkage control, characterized in that, The system includes: The tunnel data acquisition unit is configured to acquire traffic operation perception data and tunnel portal safety facility operation status data within the tunnel portal area; The facility traffic coupling system construction unit is configured to identify the reachable causal relationship between changes in traffic operation status and the operation status of safety facilities based on the traffic operation perception data and the operation status data of safety facilities, and to construct a tunnel portal facility traffic coupling system. The coupling state parameter set construction unit is configured to use the Interpretive Structural Model algorithm to construct a multi-level coupling state parameter set with causal hierarchical constraints based on the directed dependencies between data in the facility traffic coupling system. The multi-level coupling state parameter set includes a bottom triggering factor layer, an intermediate propagation path layer, and a top system influence layer. The sequential probability determination unit is configured to perform sequential probability determination on the temporal evolution process of the multi-level coupling state parameter set based on the preset stable operating range of the coupling system, so as to determine whether the facility traffic coupling system deviates from the stable operating range and enters the unstable evolution range. The linkage control triggering unit is configured to trigger the linkage control operation mode of the tunnel portal safety facilities when the facility traffic coupling system is determined to have entered the unstable evolution range. The linkage control operation mode includes adjusting the coordinated intervention sequence of speed limit, lighting and information guidance facilities according to the importance of each level of state.

9. An electronic device, characterized in that, include: One or more processors; and a memory associated with the one or more processors, the memory being used to store program instructions that, when read and executed by the one or more processors, perform the steps of the method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the steps of the method according to any one of claims 1 to 7.

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