Three-dimensional intelligent control visualization method and system for highway tunnel group based on digital twinning

Abstract

The present application relates to the technical field of image data processing, in particular to a highway tunnel group three-dimensional intelligent control visualization method and system based on digital twinning, comprising: dividing the highway tunnel group into multiple detection units and obtaining the state sequence of each detection unit; determining the abnormal starting detection unit based on the state sequence; determining the group tunnel connection state value; recording the detection unit in the downstream direction that appears consistent state change with the abnormal starting detection unit as the actual affected detection unit; determining the traffic reflection expansion degree; determining the joint control reflection influence degree; determining the front cutting ratio; determining the control chain focus degree according to the space control chain where the control equipment is located; determining the three-dimensional control display priority according to the joint control reflection influence degree, the front cutting ratio and the control chain focus degree; driving the digital twinning scene linkage display and control according to the three-dimensional control display priority; the present application can realize accurate intelligent control in the tunnel group scene.

Description

Technical Field

[0001] This invention relates to the field of image data processing technology, specifically to a three-dimensional intelligent control and visualization method and system for highway tunnel groups based on digital twins. Background Technology

[0002] Highway tunnel complexes, especially short-interval continuous tunnel sections, are characterized by strong traffic flow continuity, frequent alternation between tunnel entrances and connecting sections, and the easy propagation of abnormal conditions across tunnels. A local anomaly (such as an accident, congestion, or smoke) often does not remain confined to the tunnel itself but can spread along the direction of traffic or backflow to adjacent tunnels, transition sections outside the tunnel entrance, and connecting sections, affecting traffic organization and electromechanical control in multiple tunnels. Therefore, operational monitoring and coordinated control in tunnel complex scenarios are more complex than in single tunnels, placing higher demands on the rapid identification and precise intervention of anomaly propagation.

[0003] Currently, digital twin technology is being gradually introduced into the field of highway tunnel management. By constructing a 3D visualization platform, video events, traffic flow, environmental parameters, and electromechanical equipment status are mapped to digital space for daily monitoring, alarm display, and equipment management. However, most existing digital twin platforms for tunnel groups remain at the level of "single-point status display": they can display parking, congestion, smoke, equipment failure, or lighting anomalies in a specific tunnel, but lack the ability to determine how anomalies propagate along the continuous traffic relationship of the tunnel group and which control objects will be further affected. Due to the lack of analysis on the continuous operational relationship between adjacent tunnels, tunnel entrances, and connecting sections, existing platforms cannot accurately determine which detection units and electromechanical objects will be affected by an anomaly based on the propagation basis and control range of a local anomaly in the tunnel group. This results in inaccurate spatial projection range in the 3D scene and overly broad or narrow linkage control objects, making it difficult to support precise intelligent control in tunnel group scenarios. Summary of the Invention

[0004] This invention provides a three-dimensional intelligent control and visualization method and system for highway tunnel groups based on digital twins to solve existing problems.

[0005] The present invention adopts the following technical solution for the three-dimensional intelligent control and visualization method and system for highway tunnel groups based on digital twins: One embodiment of the present invention provides a three-dimensional intelligent control and visualization method for highway tunnel groups based on digital twins, the method comprising the following steps: The highway tunnel group is divided into multiple detection units along the direction of traffic, and the state sequence of each detection unit is obtained; The abnormal starting detection unit is determined based on the state sequence of each detection unit; the tunnel connection status value is determined based on the proportion of the number of detection units downstream of the abnormal starting detection unit to the total number of detection units in the entire road section, and the connection interruption situation that occurs in the downstream detection units. The detection units in the downstream direction that exhibit a state change consistent with the abnormal initial detection unit within a preset time window are recorded as the actual affected detection units; the traffic projection expansion degree is determined based on the proportion of the number of actual affected detection units to the number of downstream detection units and the tunnel connection status value. The impact of joint control projection is determined based on the functional range of the control equipment in the highway tunnel group, the spatial coverage relationship between the actual affected detection units, and the traffic projection expansion. The leading edge cutoff ratio is determined based on the spatial coverage relationship between the effective range of the control equipment, the actual affected detection units, and the unaffected detection units in the downstream direction. The focus of the control chain is determined based on the spatial control chain in which the control equipment is located; where the spatial control chain refers to a continuous section of the tunnel along the direction of travel, consisting of the entrance, connecting section, and tunnel section. The priority of 3D control and display is determined based on the impact of joint control projection, the front-end cutoff ratio, and the focus of the control chain. The digital twin scene is linked for display and control based on the priority of 3D control and display.

[0006] Furthermore, the specific steps involved in dividing the highway tunnel group into multiple detection units along the driving direction and obtaining the state sequence of each detection unit are as follows: The highway tunnel group is divided into multiple inspection units; each inspection unit includes at least an external transition section, an entrance section, an internal section, an inter-tunnel connection section, and a control equipment operating section. Acquire video event detection data, traffic flow detection data, environmental detection data, and electromechanical equipment status data from each detection unit, and organize the acquired data into a status sequence for each detection unit according to a unified timeline.

[0007] Furthermore, the specific steps for determining the tunnel connection status value based on the proportion of the number of detection units downstream of the abnormal initiation detection unit to the total number of detection units in the entire road section, and the connection interruption situations that occur in the downstream detection units, are as follows: The total number of all detection units downstream of the anomaly initiation detection unit is recorded as the total number of downstream units; the total number of all detection units in the entire road segment is recorded as the total number of road segment units; the coverage ratio is obtained by dividing the total number of downstream units by the total number of road segment units. From all the detection units downstream of the anomaly initiation detection unit, select the detection units that do not show the same state change as the anomaly initiation detection unit within the preset time window, and record them as interruption detection units; divide the total length of the continuous segment of all interruption detection units by the total length of all detection units downstream to obtain the interruption ratio; Multiplying the coverage ratio by an exponential function with the natural constant e as the base and the negative interruption ratio as the exponent, we obtain the tunnel group coverage status value.

[0008] Furthermore, the specific steps for determining the traffic projection expansion degree based on the proportion of the number of actually affected detection units to the number of downstream detection units and the tunnel group connection status value are as follows: Divide the total number of actual affected detection units by the total number of downstream units to obtain the effective expansion ratio; The normalized value of the product of the tunnel connection status value and the effective expansion ratio is determined as the traffic projection expansion degree.

[0009] Furthermore, the specific steps for determining the joint control projection impact degree based on the operational range of the control equipment in the highway tunnel group, the spatial coverage relationship between the actual affected detection units, and the traffic projection expansion degree are as follows: Each control device in the highway tunnel group is taken as a candidate control object, and the candidate control object whose scope of action covers at least one actual affected detection unit is selected from all candidate control objects and denoted as the target control object. Divide the total number of target control objects by the total number of candidate control objects to obtain the object action matching ratio; Subtract the traffic projection extension from 1 to get the difference, multiply the difference by the object action matching ratio to get the product, and sum the product with the traffic projection extension to determine the joint control projection influence degree.

[0010] Furthermore, the specific steps for determining the leading edge cutoff ratio based on the spatial coverage relationship between the effective range of the control device and the actual affected detection units and the unaffected detection units in the downstream direction are as follows: From all target control objects, select the target control objects whose scope of action simultaneously covers at least one actual affected detection unit and at least one detection unit located downstream of the actual affected detection unit that has not yet been affected, and denot them as front-end cutoff objects. The front truncation ratio is obtained by dividing the total number of front truncation objects by the total number of target control objects.

[0011] Furthermore, the specific steps for determining the control chain focus based on the spatial control chain where the control device is located are as follows: Group all target control objects according to the spatial control chain in which they are located; Obtain the spatial control chain containing the largest number of target control objects, and divide the number of target control objects contained in the spatial control chain by the total number of target control objects to obtain the control chain focus.

[0012] Furthermore, the specific steps for determining the 3D control and display priority based on the joint control projection impact, the leading edge cutoff ratio, and the control chain focus are as follows: The product of the exponential function value (with the natural constant e as the base and the difference between the control chain focus degree and 1 as the exponent) and the joint control projection influence degree and the front cutoff ratio is determined as the three-dimensional control display priority.

[0013] Furthermore, the specific steps of driving the digital twin scene linkage display and control based on the three-dimensional control and display priority are as follows: When the priority of 3D control and display is greater than the preset high threshold, the corresponding section and device will be automatically highlighted and a linkage command will be issued. When the priority of 3D display control is greater than the preset low threshold and less than or equal to the preset high threshold, the highlight and prompt will be retained and will be activated after manual confirmation. When the priority of 3D control and display is less than or equal to the preset low threshold, an alarm will only be triggered in the initial segment.

[0014] This invention proposes a three-dimensional intelligent control and visualization system for highway tunnel groups based on digital twins, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the computer program is executed by the processor, it implements the steps of the three-dimensional intelligent control and visualization method for highway tunnel groups based on digital twins.

[0015] The beneficial effects of the technical solution of this invention are as follows: This invention proposes a three-dimensional intelligent control and visualization method and system for highway tunnel groups based on digital twins. By dividing the highway tunnel group into continuous detection units along the driving direction (including transition sections outside the tunnel entrance, tunnel entrance sections, internal sections, and connecting sections between tunnels), and identifying the starting point of anomalies based on the state sequence of the detection units, it analyzes the actual propagation range and interruption of anomalies downstream. This accurately determines whether local anomalies have the basis for continuous propagation across tunnels and connecting sections, overcoming the shortcomings of existing platforms that can only display single points and cannot determine the propagation path. Simultaneously, this invention introduces multi-level quantitative indicators such as tunnel group connection status, traffic projection expansion, object action matching ratio, leading edge truncation ratio, and control chain focus, to comprehensively evaluate... The impact of anomalies on downstream detection units and the coverage of control equipment ultimately yield a 3D control and display priority. This priority can accurately locate the spatial sections and electromechanical equipment that truly require intervention, avoiding the drawbacks of simultaneously highlighting the entire tunnel group or only highlighting the anomaly starting point, thus improving the accuracy of spatial projection in the 3D twin scene. In addition, based on different threshold ranges of the 3D control and display priority, the system can automatically execute hierarchical response strategies such as highlight linkage, manual confirmation of local linkage, or only local alarm. This can quickly cut off the spread of accidents in the event of severe anomalies, and avoid unnecessary full-line linkage in the event of minor anomalies. This significantly improves the intelligent control accuracy and the work efficiency of on-duty personnel in tunnel group scenarios, and is especially suitable for intelligent management and digital twin visualization systems for short-spacing highway tunnel groups. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 This is a flowchart illustrating the steps of the three-dimensional intelligent control and visualization method for highway tunnel groups based on digital twins in this invention. Figure 2 This is a block diagram of the three-dimensional intelligent control and visualization system for highway tunnel groups based on digital twins, as described in this invention. Detailed Implementation

[0018] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of the digital twin-based three-dimensional intelligent control and visualization method and system for highway tunnel groups proposed according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0019] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0020] The following description, in conjunction with the accompanying drawings, details the specific scheme of the three-dimensional intelligent control and visualization method and system for highway tunnel groups based on digital twins provided by this invention.

[0021] Current digital twin platforms for highway tunnels are typically built around an overview of a single or multiple tunnels. They can map video events, traffic flow, environmental parameters, and electromechanical equipment status onto a 3D scene for daily monitoring, anomaly alarms, and equipment management. Such platforms can display information such as parking, congestion, smoke, equipment failure, or lighting anomalies occurring in a tunnel, but their functionality is mostly limited to "single-point status display."

[0022] In scenarios involving short-spaced highway tunnel clusters, local anomalies often extend beyond the tunnel itself. For example, an accident in a preceding tunnel can cause vehicles behind to slow down and form a queue, with the tail of the queue potentially spilling back to the transition section outside the tunnel entrance and adjacent connecting sections. Simultaneously, smoke diffusion, reduced visibility, lighting imbalances, and equipment linkage requirements can also cross tunnel entrances and connecting sections, continuing to affect traffic flow and electromechanical control in adjacent tunnels. While existing technologies can display the status of multiple tunnels in a 3D scene, they generally lack the ability to determine "how anomalies propagate along the continuous traffic relationships within the tunnel cluster and which control objects will be further affected." Therefore, practical applications often result in issues such as the entire tunnel cluster being simultaneously highlighted, batch alarms from control objects, or only highlighting the origin of the anomaly without effectively presenting the spatial objects that truly require coordinated control.

[0023] Existing digital twin platforms for highway tunnel clusters lack the capability to analyze the continuous operational relationships between adjacent tunnels, portal sections, and connecting sections. They cannot determine which detection units and electromechanical objects will be further affected by a local anomaly based on its propagation and control range within the tunnel cluster. This results in inaccurate spatial projection within the 3D scene and an overly broad or narrow range of linked control objects, making it difficult to support precise intelligent control in tunnel cluster scenarios.

[0024] The main objective of this invention is to transform the actual impact of a local anomaly in a highway tunnel group from "single-point anomaly display" to "cross-tunnel spatial projection and control object projection," enabling the system to determine which tunnel entrances, connecting sections, tunnel sections, and electromechanical objects should be highlighted first based on the inheritance and expansion relationship of the anomaly in the tunnel group, and further drive the corresponding speed limit, guidance, ventilation, lighting, and broadcast control.

[0025] This invention takes a short-spacing highway tunnel group consisting of three consecutive tunnels on a mountainous expressway as an example. The tunnel group includes an upstream transition section outside the tunnel entrance, a first tunnel, a short connecting section between the exit of the first tunnel and the entrance of the second tunnel, a second tunnel, a connecting section between the exit of the second tunnel and the entrance of the third tunnel, and a third tunnel. Each tunnel is equipped with video event detection equipment, traffic detectors, visibility detectors, CO detectors, fans, lighting zones, lane indicators, variable speed limit signs, information boards, and broadcasting equipment. In actual operation, if a parking accident occurs in a lane of the second tunnel, the speed of vehicles near the accident point will first decrease, followed by an increase in traffic occupancy, and the queue length may extend to the upstream transition section outside the tunnel entrance. If this is accompanied by smoke or decreased visibility, the ventilation zones and broadcast targets may also continue to be linked along adjacent detection units. Therefore, the digital twin platform cannot simply limit anomalies to the tunnel where the accident point is located; it should further determine the supporting infrastructure, projection range, and control priorities within the tunnel group.

[0026] Please see Figure 1 The diagram illustrates a flowchart of a three-dimensional intelligent control visualization method for highway tunnel groups based on digital twins, according to an embodiment of the present invention. The method includes the following steps: Step S001: Divide the highway tunnel group into multiple detection units along the driving direction and obtain the state sequence of each detection unit.

[0027] Step S001 specifically includes: Step S0011: Divide the highway tunnel group into multiple detection units; wherein, the multiple detection units include at least an external transition section, a tunnel entrance section, an internal section, an inter-tunnel connection section, and a control equipment operating section.

[0028] It should be noted that, taking a tunnel group consisting of three consecutive highway tunnels as an example, the detection units are divided according to the significance of continuous operation.

[0029] Transition section outside the tunnel entrance: refers to the open road section before the entrance of the first tunnel, used to reflect the speed adaptation and queuing changes of vehicles before and after entering and exiting the tunnel.

[0030] Tunnel entrance section: refers to a section of the tunnel within a certain length near the tunnel entrance or exit, used to monitor changes in light and wind speed, as well as traffic and environmental conditions in accident-prone areas.

[0031] Tunnel section: refers to a continuous section inside the tunnel divided into fixed lengths (such as 100 meters), used to reflect the status of traffic, environment and equipment operation inside the tunnel.

[0032] Inter-tunnel connection section: refers to the open-air or tunnel section between two adjacent tunnels, used to reflect the connection between adjacent tunnels.

[0033] Control equipment effective range: refers to the effective spatial influence range of each electromechanical device (such as speed limit sign, information board, fan, lighting zone, broadcast, etc.), used to match the coverage relationship between the detection unit and the control equipment.

[0034] Step S0012: Acquire video event detection data, traffic flow detection data, environmental detection data, and electromechanical equipment status data of each detection unit, and organize the acquired data into a status sequence of each detection unit according to a unified timeline.

[0035] It should be noted that the video event detection data includes at least parking events, low-speed congestion events, wrong-way driving events, pedestrian events, littering events, smoke events, and fire events, and records the corresponding detection unit, occurrence time, event type, and event confidence result. Video event detection data is collected in real time by fixed cameras installed inside the tunnel and near the entrance, and deep learning algorithms are used to automatically identify events such as parking, congestion, wrong-way driving, pedestrians, littering, smoke, and fire, outputting the event type, occurrence time, corresponding detection unit, and confidence level (between 0 and 1).

[0036] Traffic flow detection data includes at least the average vehicle speed, traffic volume, occupancy rate, and queue length calculated from the number of consecutive low-speed units or spillover distance for each detection unit. Traffic flow detection data is acquired via microwave radar, loop detectors, or video analytics, with each detection unit recording traffic flow data once per minute.

[0037] Environmental monitoring data includes at least visibility, CO concentration, wind speed and direction, and illuminance. This data is acquired using specialized sensors such as visibility meters, CO detectors, anemometers, and illuminometers.

[0038] The status data of electromechanical equipment includes at least the start / stop status of the fan, the status of the lighting zones, the status of the lane indicators, the status of the speed limit signs, the status of the information boards, and the status of the broadcasts. This status data is read via a PLC or the equipment's own feedback interface.

[0039] The above data is aligned along a unified timeline (e.g., at the second or minute level) to form a state sequence for each detection unit at each timestamp. Each state sequence includes at least the detection unit identifier, the tunnel or connecting section to which it belongs, the timestamp, traffic status, environmental status, event status, and control status. These are then arranged chronologically along the road's driving direction to obtain the final overall state sequence.

[0040] After the above processing, the system no longer directly processes the scattered monitoring data, but obtains the overall state sequence for the continuous operation of the tunnel group, providing a unified input for subsequent connection analysis and spatial projection analysis.

[0041] For example, assume that the system is divided into 5 detection units along the driving direction: Unit 1 (transition section outside the tunnel entrance), Unit 2 (first tunnel section), Unit 3 (connecting section), Unit 4 (second tunnel section), and Unit 5 (second tunnel exit section). At a certain moment (e.g., 10:00:00), the system collects the following status data for each unit: Unit 1 average speed 80 km / h, occupancy rate 15%, no events; Unit 2 average speed 78 km / h, occupancy rate 18%, no events; Unit 3 average speed 75 km / h, occupancy rate 22%, no events; Unit 4 average speed 0 km / h, occupancy rate 95%, a parking event was detected with a confidence level of 0.96; Unit 5 average speed 60 km / h, occupancy rate 45%, no events. The status data of each detection unit constitutes a status sequence. Arranging all units sequentially along the driving direction (from Unit 1 to Unit 5) yields the overall status sequence at that moment. If multiple time points are recorded continuously, a sequence like this will be generated at each time point, forming a temporally continuous set of total state sequences for subsequent anomaly propagation analysis.

[0042] Step S002: Determine the abnormal starting detection unit based on the state sequence of each detection unit; determine the tunnel group connection status value based on the proportion of the number of detection units downstream of the abnormal starting detection unit to the total number of detection units in the entire road section, and the connection interruption situation that occurs in the downstream detection units.

[0043] It should be noted that the system scans the state sequence of each detection unit in real time. When a detection unit meets any of the following conditions, it is determined to be an abnormal starting detection unit: The video event detection data contains events such as parking, driving in the wrong direction, pedestrians, littering, smoke, or fire, and the event confidence is greater than a preset threshold (e.g., 0.8). In traffic flow detection data, the average vehicle speed is lower than the preset low speed threshold (e.g., 20 km / h) for two consecutive time windows (each time window is 1 minute long, i.e., two consecutive 1-minute cycles), while the occupancy rate is higher than the preset high occupancy rate threshold (e.g., 50%), and this state occurs for the first time in this unit. If the visibility in the environmental monitoring data is below the preset safety threshold (e.g., 50m) or the CO concentration exceeds the preset threshold (e.g., 100ppm), and the upstream adjacent unit of this unit is in normal condition (to exclude secondary anomalies caused by propagation).

[0044] If multiple detection units that meet the above conditions appear simultaneously, the unit that appears first in the driving direction is selected as the initial anomaly detection unit to avoid duplicate analysis. After determining the initial anomaly unit, the system uses it as the starting point for subsequent continuation analysis, scalability calculation, and priority evaluation.

[0045] Whether a local anomaly in a highway tunnel complex needs to be expanded for display in a 3D twin scene is not solely determined by the anomaly's intensity, but rather by whether the anomaly forms a continuous connection between adjacent detection units, and whether these connecting units fall within the effective range of the corresponding electromechanical objects. If the anomaly is confined to a single detection unit, the system should issue a local alarm; if the anomaly has formed a continuous connection between adjacent tunnel entrances, connecting sections, and tunnel sections, the system should further determine which lane indicators, speed limit signs, fans, lighting zones, and broadcast objects the anomaly will affect, thereby forming a 3D spatial representation with practical control significance.

[0046] The tunnel group continuity status is used to determine whether the current anomaly has moved from the "local unit state change" stage to the "cross-unit continuous propagation" stage. For highway tunnel groups, adjacent units are only spatially close, which does not necessarily mean that their states are connected. For example, if a short-term low speed occurs in a section of a tunnel, but the upstream transition section and connecting section do not show a decrease in vehicle speed or an increase in occupancy, then the anomaly cannot be considered to have a basis for outward propagation. Only when the adjacent detection unit downstream of the anomaly unit shows a state change consistent with the anomaly within the allowable time window, and there is no obvious interruption in between, can the tunnel group continuity relationship be established.

[0047] Based on the proportion of the number of detection units downstream of the initial detection unit to the total number of detection units in the entire road section, and the interruption of connection in the downstream detection units, the tunnel connection status value is determined, specifically including: Step S0021: Count the number of all detection units downstream of the anomaly initiation detection unit and record it as the total number of downstream units; count the total number of all detection units in the entire road segment and record it as the total number of road segment units; divide the total number of downstream units by the total number of road segment units to obtain the coverage ratio.

[0048] It should be noted that: "the entire road segment" refers to the entire highway tunnel group where the anomaly initiation detection unit is located, and "downstream direction" refers to the direction along the driving direction from the anomaly initiation detection unit to the end of the road segment.

[0049] For example, suppose a highway tunnel group is divided into 8 detection units along the entire road segment, numbered 1, 2, 3, 4, 5, 6, 7, and 8 in the direction of travel. Through state sequence analysis, the anomaly originating detection unit is determined to be unit 3. The downstream detection units are units 4, 5, 6, 7, and 8, totaling 5 units; the total number of detection units for the entire road segment is 8. According to the formula, the coverage ratio = total number of downstream units ÷ total number of units in the road segment = 5 ÷ 8 = 0.625. This value indicates that the downstream units from the anomaly originating point account for 62.5% of the entire road segment, theoretically indicating a wide maximum coverage area for the anomaly to propagate downstream. If the anomaly originating unit is unit 7, then only unit 8 is downstream, resulting in a total of 1 downstream unit, and a coverage ratio of 1 ÷ 8 = 0.125, indicating a smaller theoretical propagation range.

[0050] Step S0022: From all the detection units downstream of the abnormal start detection unit, select the detection units that have not shown the same state change as the abnormal start detection unit within the preset time window, and record them as interruption detection units; divide the total length of the continuous segment of all interruption detection units by the total length of all detection units downstream to obtain the interruption ratio.

[0051] It should be noted that the interruption ratio is used to quantify the degree of "interruption" that occurs during the downstream propagation of an anomaly. The preset time window refers to the maximum time interval (e.g., 30 seconds) during which a downstream detection unit can exhibit a state change consistent with that of the anomaly initiating detection unit. "Consistent state changes" include: decreased vehicle speed, increased occupancy, extended queues, decreased visibility, increased CO concentration, and the occurrence of a corresponding event of the same type or directly related to the event of the anomaly initiating unit (e.g., a parking event corresponds to a low-speed congestion event downstream, a smoke event corresponds to decreased visibility downstream, etc.). If a downstream detection unit does not exhibit any of the above state changes within the window, it is determined to be an interrupted detection unit. Multiple consecutive interrupted detection units constitute an interruption segment. If multiple discontinuous interruption segments exist downstream, the lengths of all interruption segments are summed to obtain the total length of the interruption segment. This total length is then divided by the total length of all detection units downstream to obtain the interruption ratio. A larger ratio indicates a more severe interruption and a more discontinuous anomaly propagation.

[0052] For example, assume the anomaly initiation detection unit is unit 3, with 5 downstream detection units (numbered 4 to 8), each unit 100 meters long, for a total downstream length of 500 meters, and a preset time window of 30 seconds. The anomaly initiation unit detects a parking event (confidence level 0.96). Detection results: Unit 4 experiences a decrease in vehicle speed within 20 seconds (normal); Unit 5 shows no decrease in vehicle speed, no increase in occupancy rate, and no low-speed congestion event detected within 35 seconds (interruption); Unit 6 shows no change within 40 seconds (interruption); Unit 7 detects a low-speed congestion event within 15 seconds (confidence level 0.85), considered a corresponding event (normal); Unit 8 shows an increase in occupancy rate within 25 seconds (normal). Therefore, the interruption detection units are units 5 and 6, forming a continuous interruption segment with a length of 200 meters, and the interruption ratio = 200 / 500 = 0.4. If no change occurs in unit 8, and units 5-6 and unit 8 constitute two discontinuous interruption sections respectively, then the total length of the interruption section is 200+100=300 meters, and the interruption ratio is 300 / 500=0.6.

[0053] Step S0023: Multiply the coverage ratio by an exponential function value with the natural constant e as the base and the negative interruption ratio as the exponent to obtain the tunnel group coverage status value.

[0054] Specifically, no. The tunnel connection status value corresponding to each abnormal initiation detection unit is: ; in, This represents the connection status value of the tunnel group. This represents the coverage ratio of the downstream detection unit to the current anomaly. To accommodate interruptions. This represents an exponential function with the natural constant e as its base. The logic of this expression lies in: [The text abruptly ends here, likely due to an incomplete sentence or missing information.] The larger the value, the wider the theoretical coverage of the anomaly's downstream propagation; the interruption ratio... The larger, the better The smaller the value, the more severe the interruption in the transmission process; the combined effect of both factors makes... Reduced. Conversely, if multiple downstream detection units continuously exhibit state changes consistent with the anomaly, and with few intermediate interruptions, then The increasing trend indicates that the anomaly has the potential to propagate across tunnels.

[0055] Step S003: Record the detection units in the downstream direction that show the same state change as the abnormal starting detection unit within the preset time window as the actual affected detection units; determine the traffic projection expansion degree based on the proportion of the number of actual affected detection units to the number of detection units in the downstream direction and the tunnel connection status value.

[0056] It should be noted that in tunnel cluster scenarios, after an anomaly has a basis for being received, it is necessary to further determine the actual number of detection units affected and whether these affected detection units are within the scope of the corresponding control objects. If the anomaly extends along the driving direction, but the detection units it extends to do not fall within the scope of speed limit signs, information boards, fan zones, or broadcasts, then the 3D scene does not need to highlight these control objects; conversely, if the anomaly has extended to some detection units, and these units happen to be within the scope of the corresponding electromechanical objects, then the relevant objects should be used as the objects to be linked for projection.

[0057] Traffic projection expansion is used to characterize the actual spatial extent of an anomaly's expansion within a sequence of detection units. Its focus is not on the state value of a single detection unit, but rather on whether the anomaly has created a continuous zone of low speed, congestion, queue extension, or environmental degradation in downstream detection units.

[0058] The traffic projection expansion is determined based on the proportion of the number of actual affected detection units to the number of downstream detection units and the tunnel connection status value, specifically including: Step S0031: Divide the total number of actual affected detection units by the total number of downstream units to obtain the effective expansion ratio.

[0059] It should be noted that the effective spread ratio reflects the actual coverage of the anomaly propagating downstream; a higher value indicates a higher proportion of downstream detection units affected by the anomaly. For example, if there are a total of 5 downstream units, and 3 units are actually affected, then the effective spread ratio is 0.6.

[0060] Step S0032: The normalized value of the product of the tunnel connection status value and the effective expansion ratio is determined as the traffic projection expansion degree.

[0061] Specifically, no. The traffic projection expansion corresponding to each anomaly initiation detection unit is: ); in, For the first Traffic projection expansion of each anomaly initial detection unit, This represents the connection status value of the tunnel group. The effective expansion ratio. This formula means that the traffic projection expansion will only significantly increase when the tunnel group connection is established and the anomaly forms a continuous expansion across multiple downstream detection units. `norm` represents the normalization process, linearly mapping the product result to the [0,1] interval.

[0062] Step S004: Determine the impact of joint control projection based on the operational range of the control equipment in the highway tunnel group, the spatial coverage relationship between the actual affected detection units, and the traffic projection expansion.

[0063] It should be noted that the controlled objects in a highway tunnel complex have clearly defined areas of effect. For example, variable speed limit signs primarily affect upstream traffic units, ventilation fans and lighting zones correspond to specific sections within the tunnel, while information boards and broadcasts may cover the tunnel entrance and connecting sections. When the actual affected detection units highly overlap with the areas of effect of these controlled objects, it indicates that the anomaly not only involves the expansion of traffic conditions but also creates a projection requirement at the control level.

[0064] Step S004 specifically includes: Step S0041: Take each control device in the highway tunnel group as a candidate control object, and select the candidate control object whose scope of action covers at least one actual affected detection unit from all candidate control objects, and denot it as the target control object.

[0065] It should be noted that candidate control objects include, but are not limited to, variable speed limit signs, information boards, lane indicators, fan zones, lighting zones, and broadcasting equipment; the effective coverage area refers to the spatial overlap between the effective influence area of ​​the control equipment (such as the upstream road section affected by the speed limit sign, the tunnel section affected by the fan, and the tunnel entrance or connecting section covered by the broadcasting system) and the geographical location of the actual affected detection unit. This screening process ensures that only control objects that can truly intervene in the current anomaly are included in subsequent analysis.

[0066] Step S0042: Divide the total number of target control objects by the total number of candidate control objects to obtain the object action matching ratio.

[0067] It should be noted that the object action matching ratio reflects the density of coverage of the currently affected detection units by existing control equipment. A higher ratio indicates that the affected area falls within the coverage range of more control equipment, and the need for the anomaly to transition from the traffic level to the control level is stronger. Conversely, a lower ratio indicates that even if the anomaly has expanded, available control resources are relatively limited, and the system should focus on providing alerts through other means (such as broadcasts and information boards). For example, if there are 20 candidate control objects, and 8 of them are target control objects whose coverage ranges actually cover the affected detection units, then the object action matching ratio is 0.4.

[0068] Step S0043: Subtract the traffic projection extension degree from 1 to obtain the difference, and multiply the difference by the object action matching ratio to obtain the product. Sum the product with the traffic projection extension degree to determine the joint control projection influence degree.

[0069] Specifically, no. The impact of the joint projection corresponding to each anomaly initiation detection unit is: ; In the formula, For the first The impact of the joint control projection of each abnormal initial detection unit. To expand the scope of traffic projection, The matching ratio applies to the object. In this formula, Characterizing anomalies along the extended basis already formed in the detection unit sequence, This refers to the remaining projection space that has not yet been directly covered by the extended foundation, and then... We need to assess whether this area can be further converted into projection requirements for the controlled object. When the anomaly's expansion range is large, and the affected units and the controlled object's scope of action highly overlap, It then rises.

[0070] Step S005: Determine the leading edge cutoff ratio based on the spatial coverage relationship between the effective range of the control equipment, the actual affected detection units, and the unaffected detection units in the downstream direction.

[0071] It should be noted that even if multiple spatial objects are within the joint control projection range, the digital twin scene should not simultaneously highlight all objects with the same intensity. For on-duty personnel, the priority should not be all affected objects, but rather key objects located at the forefront of the anomaly's propagation that can prevent further spread of the impact. For example, when an accident in the second tunnel causes a queue to overflow to the connecting section but not yet enter the first tunnel, the speed limit signs, information boards, and lane indicators in front of the upstream tunnel entrance should be highlighted first; if smoke and reduced visibility are concentrated near a certain tunnel entrance, the fan zones, lighting zones, and broadcast objects adjacent to that entrance should be displayed first. In other words, the priority of 3D control display depends not only on the impact of the joint control projection itself, but also on whether the affected objects are at the control cutoff position between "affected units" and "unaffected but about to be affected units," and whether these objects are concentrated in the same tunnel entrance-connecting section-tunnel control chain.

[0072] Step S005 specifically includes: Step S0051: From all target control objects, select the target control objects whose scope of action simultaneously covers at least one actual affected detection unit and at least one detection unit located downstream of the actual affected detection unit that has not yet been affected, and denot them as front-end cutoff objects.

[0073] It should be noted that "front-end interception objects" refer to control devices that can affect both already affected detection units and unaffected detection units located downstream (i.e., ahead of the anomaly propagation direction). These objects are at the "front" of anomaly propagation; controlling them can intervene in advance to prevent the anomaly from spreading downstream, effectively cutting off the propagation chain. The purpose of selecting front-end interception objects is to prioritize these interception-capable devices in the 3D twin scene, helping personnel quickly locate key control points and avoiding indiscriminate highlighting of all affected devices. For example, when the queue has overflowed to the connecting section but has not yet entered the upstream tunnel, a speed limit sign located before the upstream tunnel exit simultaneously covers both the already congested connecting section and the tunnel entrance section that is about to be affected; this falls under the category of front-end interception objects.

[0074] Step S0052: Divide the total number of front truncation objects by the total number of target control objects to obtain the front truncation ratio.

[0075] It should be noted that the front truncation ratio reflects the proportion of key devices among all target control objects capable of simultaneously covering both affected and unaffected downstream units. A higher ratio indicates a more concentrated concentration of effective devices that can be used to intercept the continued propagation of anomalies downstream; the system should prioritize highlighting these devices in the 3D scene. Conversely, a low front truncation ratio indicates that most target control objects can only cover the already affected areas and cannot intervene downstream in advance; the system's focus should shift to other strategies. For example, if there are 10 target control objects in total, and 4 of them are front truncation objects, then the front truncation ratio is 0.4.

[0076] Step S006: Determine the focus of the control chain based on the spatial control chain in which the control equipment is located; wherein, the spatial control chain refers to the continuous tunnel segment from the entrance to the connecting section to the tunnel along the direction of travel.

[0077] Step S006 specifically includes: Step S0061: Group all target control objects according to their spatial control chain.

[0078] It should be noted that a spatial control chain refers to a group of spatial segments arranged continuously along the direction of traffic, typically including entrance segments, connecting segments, and tunnel segments, forming basic units such as "entrance—connecting segment—tunnel". Detection units within the same spatial control chain are spatially continuous, and the corresponding control equipment (such as speed limit signs, information boards, fans, lighting, etc.) often jointly serve traffic flow control on that chain. Grouping all target control objects according to their respective spatial control chains helps identify the spatial concentration of control resources: if a large number of target control objects are concentrated on a single control chain, it indicates that the scope of the anomaly's impact is highly correlated with that chain, and operators can focus their attention on the equipment on that chain; conversely, if target control objects are scattered across multiple control chains, it indicates that the scope of the anomaly's impact is wider, and the system should reduce the overall highlight intensity or adopt a time-sharing highlight strategy to avoid information overload. This grouping result provides a basis for subsequent calculations of control chain focus.

[0079] Step S0062: Obtain the spatial control chain containing the largest number of target control objects, and divide the number of target control objects contained in the spatial control chain by the total number of target control objects to obtain the control chain focus.

[0080] It should be noted that control chain focus is used to measure the spatial concentration of target control objects. Specifically, first, identify the spatial control chain containing the largest number of target control objects (i.e., the chain with the most interconnectable devices). Then, divide the number of target control objects on that chain by the total number of all target control objects; the resulting ratio is the control chain focus. A higher value indicates that the target control objects are highly concentrated on a single continuous control chain, facilitating quick viewing and operation by operators from the same perspective, and the system should give it a higher display priority. Conversely, a low focus indicates that the target control objects are scattered across multiple control chains, and highlighting them simultaneously can cause visual confusion; the system should appropriately reduce the overall priority or adopt a phased highlighting strategy. For example, if there are a total of 12 target control objects, and a spatial control chain contains 7 of them, then the control chain focus is 7 / 12 ≈ 0.583.

[0081] Step S007: Determine the priority of 3D control and display based on the joint control projection impact, front-end cutoff ratio, and control chain focus.

[0082] Step S007 specifically includes: The product of the exponential function value (with the natural constant e as the base and the difference between the control chain focus degree and 1 as the exponent) and the joint control projection influence degree and the front cutoff ratio is determined as the three-dimensional control display priority.

[0083] Specifically, read the first The impact of the joint projection corresponding to each abnormal initial detection unit Focus the control chain Convert to control display hold coefficient This coefficient is used to characterize that the more concentrated the affected objects are in the same portal-connecting section-tunnel continuous control chain, the more fully the priority display requirements of the front-line objects are preserved.

[0084] No. The priority of the 3D control display corresponding to each anomaly initiation detection unit is: ; In the formula, Indicates the first The priority of 3D control and display corresponding to each anomaly initiation detection unit. To enhance the impact of joint control and projection, For the front-end cutoff ratio, To control the chain focus. In this formula, The proportion of joint control projection demand that indicates anomalies has been formed, directly corresponding to key control and display objects at the forefront of dissemination; This reflects whether these frontier objects are concentrated in the same portal-connecting section-tunnel continuous control chain. If If it approaches 1, then A value approaching 1 indicates a high concentration of frontier objects, meaning their control and display requirements are largely preserved; if... Decrease, then The corresponding decrease indicates that although there are leading-edge objects, they are dispersed across multiple control chains and should not all be highlighted simultaneously with high priority. and When both are relatively high, When the system is elevated, it should prioritize highlighting key 3D objects located at the propagation front and concentrated in the same control chain; when and When both are low, The resulting 3D display priority is no longer based solely on the number of objects or spatial clustering, but rather prioritizes highlighting key 3D objects that can prevent anomalies from spreading further to adjacent tunnels, portal sections, and connecting sections.

[0085] Step S008: Drive the digital twin scene linkage display and control according to the 3D control and display priority.

[0086] Step S008 specifically includes: Step S0081: When the priority of 3D control display is greater than the preset high threshold, automatically highlight the corresponding section and device and issue a linkage command.

[0087] Specifically, when the 3D control display priority When the priority is greater than a preset high threshold (e.g., 0.7), it indicates that the anomaly already has a clear basis for cross-tunnel propagation, and the front truncated objects are highly concentrated on the same control chain, so both the urgency and effectiveness of coordinated intervention are high. At this time, the system automatically highlights the abnormal starting section, downstream receiving section, and corresponding speed limit signs, information boards, lane indicators, fan zones, lighting zones, and broadcast objects in the digital twin scene, and directly issues coordinated instructions to the control unit (such as reducing speed limits, turning on fans, releasing guidance information, etc.), without manual confirmation, so as to quickly cut off the propagation of anomalies.

[0088] Step S0082: When the 3D control display priority is greater than a preset low threshold and less than or equal to the preset high threshold, retaining the highlighting and prompt, and performing coordination after manual confirmation.

[0089] Specifically, when the 3D control display priority is greater than a preset low threshold (e.g., 0.3) and less than or equal to the preset high threshold (0.7), the anomaly has a certain propagation risk, but has not yet reached the emergency level for automatic coordination. At this time, the system retains the highlighting and control prompts for relevant sections and equipment in the 3D scene, but does not automatically issue instructions. Instead, the on-duty personnel perform local coordinated control after confirmation according to the on-site situation (such as manually turning on information boards, adjusting speed limits, etc.). This interval is designed to balance automation and manual judgment, avoiding unnecessary full-line coordination caused by false detection or minor anomalies.

[0090] Step S0083: When the 3D control display priority is less than or equal to the preset low threshold, only a local alarm is issued in the starting section.

[0091] Specifically, when the 3D control display priority is less than or equal to the preset low threshold (0.3), the influence range of the anomaly is limited to the initial detection unit, and there is a lack of cross-unit continuous receiving or effective control object coverage. At this time, the system only issues a local alarm in the abnormal starting section (such as pop-up prompt, edge highlighting), does not expand the projection range to the upstream and downstream, and does not trigger any coordination instructions. This strategy can reduce interference to on-duty personnel and focus their attention on events that really require intervention.

[0092] The high and low thresholds can be determined by means of empirical calibration, simulation optimization or adaptive dynamic adjustment. For example, through off-line simulation of typical tunnel groups or review of historical events, the 3D control display priority under different abnormal scenarios is counted The distribution of thresholds, combined with actual linkage effects and feedback from on-duty personnel, allows for the setting of high thresholds (e.g., 0.7) and low thresholds (e.g., 0.3). Alternatively, traffic simulation software can be coupled with a digital twin platform to calculate thresholds with optimal overall efficiency as the goal; or the thresholds can be dynamically adjusted during operation based on real-time traffic flow, weather conditions, etc. In practical applications, a combination of empirical calibration and on-site debugging is recommended. Initial thresholds are set based on historical data, and then fine-tuned based on feedback after deployment. This embodiment does not limit the specific value of the thresholds; only preset values ​​are required.

[0093] During the execution process, the system records the execution results of the controlled object and the changes in the status of the detection unit after execution. If the speed, occupancy rate, visibility, and equipment status in the upstream tunnel section, connecting section, or adjacent tunnel return to normal after execution, the subsequent projection range is reduced accordingly. If the queue length continues to increase, visibility further decreases, or new affected units appear after execution, the new status changes are re-incorporated into the detection unit status sequence, and the aforementioned connection analysis and projection analysis are executed again to continuously correct the tunnel group joint control projection chain.

[0094] In summary, compared to single-tunnel 3D display solutions, this embodiment can establish a connection between local anomalies and the continuous passage relationships between adjacent tunnels, portal sections, and connecting sections; compared to digital twin solutions that only provide an overview of multiple tunnels, it can further determine which spatial sections and electromechanical objects the current anomaly should be projected onto; compared to solutions that perform group control according to fixed rules, it can prioritize highlighting the truly affected 3D objects based on the actual propagation basis of the anomaly in the tunnel group and the scope of the object's influence, and execute linked controls such as speed limits, guidance, ventilation, lighting, or broadcasting accordingly, thereby improving the accuracy of intelligent control of tunnel groups and the pertinence of visualization.

[0095] This invention also proposes a three-dimensional intelligent control and visualization system for highway tunnel groups based on digital twins. Please refer to [link / reference]. Figure 2 The diagram illustrates a block diagram of a three-dimensional intelligent control and visualization system for highway tunnel groups based on digital twins, according to an embodiment of the present invention. The system includes: The segmentation module 100 is used to divide the highway tunnel group into multiple detection units along the driving direction and obtain the state sequence of each detection unit; Analysis module 200 is used to determine the abnormal starting detection unit based on the state sequence of each detection unit; and to determine the tunnel connection status value based on the proportion of the number of detection units downstream of the abnormal starting detection unit to the total number of detection units in the entire road section, as well as the connection interruption situation that occurs in the downstream detection units. The detection units in the downstream direction that exhibit a state change consistent with the abnormal initial detection unit within a preset time window are recorded as the actual affected detection units; the traffic projection expansion degree is determined based on the proportion of the number of actual affected detection units to the number of downstream detection units and the tunnel connection status value. The impact of joint control projection is determined based on the functional range of the control equipment in the highway tunnel group, the spatial coverage relationship between the actual affected detection units, and the traffic projection expansion. The leading edge cutoff ratio is determined based on the spatial coverage relationship between the effective range of the control equipment, the actual affected detection units, and the unaffected detection units in the downstream direction. The focus of the control chain is determined based on the spatial control chain in which the control equipment is located; where the spatial control chain refers to a continuous section of the tunnel along the direction of travel, consisting of the entrance, connecting section, and tunnel section. The priority of 3D control and display is determined based on the impact of joint control projection, the front-end cutoff ratio, and the focus of the control chain. Display module 300 is used to drive the linkage display and control of digital twin scenes according to the 3D control display priority.

[0096] It should be noted that the system provided in the above embodiments is only an example of the division of the above functional modules. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the computer device can be divided into different functional modules to complete all or part of the functions described above. In addition, the 3D intelligent control and visualization system for highway tunnel groups based on digital twins and the 3D intelligent control and visualization method for highway tunnel groups based on digital twins provided in the above embodiments belong to the same concept. The specific implementation process is detailed in the method embodiments and will not be repeated here.

[0097] It should be noted that the order of the above embodiments of the present invention is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0098] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

[0099] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for three-dimensional intelligent control visualization of a highway tunnel group based on digital twinning, characterized in that, The method includes the following steps: The highway tunnel group is divided into multiple detection units along the direction of traffic, and the state sequence of each detection unit is obtained; The anomaly initiation detection unit is determined based on the state sequence of each detection unit; Based on the proportion of the number of detection units downstream of the initial detection unit to the total number of detection units in the entire road section, and the interruption of connection in the downstream detection units, the tunnel connection status value is determined, specifically including: The total number of all detection units downstream of the anomaly initiation detection unit is recorded as the total number of downstream units; the total number of all detection units in the entire road segment is recorded as the total number of road segment units; the coverage ratio is obtained by dividing the total number of downstream units by the total number of road segment units. From all the detection units downstream of the anomaly initiation detection unit, select the detection units that do not show the same state change as the anomaly initiation detection unit within the preset time window, and record them as interruption detection units; divide the total length of the continuous segment of all interruption detection units by the total length of all detection units downstream to obtain the interruption ratio; Multiply the coverage ratio by an exponential function value with the natural constant e as the base and the negative interruption ratio as the exponent to obtain the tunnel group coverage status value. The detection units in the downstream direction that exhibit a state change consistent with the abnormal initial detection unit within a preset time window are recorded as the actual affected detection units; the traffic projection expansion degree is determined based on the proportion of the number of actual affected detection units to the number of downstream detection units and the tunnel connection status value. The impact of joint control projection is determined based on the functional range of the control equipment in the highway tunnel group, the spatial coverage relationship between the actual affected detection units, and the traffic projection expansion. The leading edge cutoff ratio is determined based on the spatial coverage relationship between the effective range of the control equipment, the actual affected detection units, and the unaffected detection units in the downstream direction. Based on the spatial control chain in which the control equipment is located, the focus of the control chain is determined, specifically including: All target control objects are grouped according to their spatial control chain; where the spatial control chain refers to a continuous segment of opening-connecting section-tunnel along the direction of travel; Obtain the spatial control chain containing the largest number of target control objects, and divide the number of target control objects contained in the spatial control chain by the total number of target control objects to obtain the control chain focus. The priority of 3D control and display is determined based on the impact of joint control projection, the front-end cutoff ratio, and the focus of the control chain. The digital twin scene is linked for display and control based on the priority of 3D control and display.

2. The method for three-dimensional intelligent control and visualization of highway tunnel groups based on digital twins according to claim 1, characterized in that, The specific steps involved in dividing the highway tunnel group into multiple detection units along the driving direction and obtaining the state sequence of each detection unit are as follows: The highway tunnel group is divided into multiple inspection units; each inspection unit includes at least an external transition section, an entrance section, an internal section, an inter-tunnel connection section, and a control equipment operating section. Acquire video event detection data, traffic flow detection data, environmental detection data, and electromechanical equipment status data from each detection unit, and organize the acquired data into a status sequence for each detection unit according to a unified timeline.

3. The method for three-dimensional intelligent control and visualization of highway tunnel groups based on digital twins according to claim 1, characterized in that, The specific steps for determining the traffic projection expansion based on the proportion of the number of actually affected detection units to the number of downstream detection units and the tunnel connection status value are as follows: Divide the total number of actual affected detection units by the total number of downstream units to obtain the effective expansion ratio; The normalized value of the product of the tunnel connection status value and the effective expansion ratio is determined as the traffic projection expansion degree.

4. The method for three-dimensional intelligent control and visualization of highway tunnel groups based on digital twins according to claim 3, characterized in that, The specific steps for determining the impact of joint control projection based on the operational range of the control equipment in the highway tunnel group, the spatial coverage relationship between the actual affected detection units, and the traffic projection expansion are as follows: Each control device in the highway tunnel group is taken as a candidate control object, and the candidate control object whose scope of action covers at least one actual affected detection unit is selected from all candidate control objects and denoted as the target control object. Divide the total number of target control objects by the total number of candidate control objects to obtain the object action matching ratio; Subtract the traffic projection extension from 1 to get the difference, multiply the difference by the object action matching ratio to get the product, and sum the product with the traffic projection extension to determine the joint control projection influence degree.

5. The method for three-dimensional intelligent control and visualization of highway tunnel groups based on digital twins according to claim 4, characterized in that, The process of determining the leading edge cutoff ratio based on the spatial coverage relationship between the effective range of the control equipment and the actual affected detection units as well as the unaffected detection units in the downstream direction includes the following specific steps: From all target control objects, select the target control objects whose scope of action simultaneously covers at least one actual affected detection unit and at least one detection unit located downstream of the actual affected detection unit that has not yet been affected, and denot them as front-end cutoff objects. The front truncation ratio is obtained by dividing the total number of front truncation objects by the total number of target control objects.

6. The method for three-dimensional intelligent control and visualization of highway tunnel groups based on digital twins according to claim 1, characterized in that, The specific steps for determining the 3D control and display priority based on the joint control projection impact, the leading edge cutoff ratio, and the control chain focus are as follows: The product of the exponential function value (with the natural constant e as the base and the difference between the control chain focus degree and 1 as the exponent) and the joint control projection influence degree and the front cutoff ratio is determined as the three-dimensional control display priority.

7. The method for three-dimensional intelligent control and visualization of highway tunnel groups based on digital twins according to claim 6, characterized in that, The specific steps involved in driving the coordinated display and control of the digital twin scene based on the priority of 3D control and display are as follows: When the priority of 3D control and display is greater than the preset high threshold, the corresponding section and device will be automatically highlighted and a linkage command will be issued. When the priority of 3D display control is greater than the preset low threshold and less than or equal to the preset high threshold, the highlight and prompt will be retained and will be activated after manual confirmation. When the priority of 3D control and display is less than or equal to the preset low threshold, an alarm will only be triggered in the initial segment.

8. A three-dimensional intelligent control and visualization system for highway tunnel groups based on digital twins, comprising a memory, a processor, and a computer program stored in the memory and capable of running on the processor, characterized in that, When the computer program is executed by the processor, it implements the steps of the three-dimensional intelligent control and visualization method for highway tunnel groups based on digital twins as described in any one of claims 1-7.

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