A highway tunnel operation state dynamic monitoring method and system

Abstract

The application relates to the technical field of highway tunnel monitoring, and discloses a highway tunnel operation state dynamic monitoring method and system, which comprises the following steps: collecting environment information and vehicle information of a highway tunnel within a preset time length; setting a speed limit initial value according to the environment information; judging whether the speed limit initial value needs to be adjusted according to the total weight of vehicles, if the judgment is that the speed limit initial value needs to be adjusted, calculating the similarity between the total weight of vehicles and the number of vehicles and the historical total weight of vehicles and the historical number of vehicles in historical data, adjusting the speed limit initial value according to the similarity to obtain a speed limit adjustment value; judging whether the speed limit adjustment value needs to be adjusted according to the frequency of vehicles and the speed limit adjustment value, if the judgment is that the speed limit adjustment value needs to be adjusted, adjusting the speed limit adjustment value according to the frequency of vehicles to obtain a speed limit final value. The application guarantees driving safety and tunnel safety, and simultaneously considers traffic efficiency.

Description

Technical Field

[0001] This invention relates to the field of highway tunnel monitoring technology, and more specifically, to a method and system for dynamic monitoring of the operating status of highway tunnels. Background Technology

[0002] As an important component of transportation infrastructure, the safety and efficiency of highway tunnels are crucial for ensuring smooth transportation.

[0003] Currently, there are some technical limitations and shortcomings in the monitoring of highway tunnel operation. On the one hand, traditional monitoring methods often focus on monitoring single factors, such as traffic flow or environmental factors like ventilation and lighting within the tunnel, lacking comprehensive and integrated collection of environmental and vehicle information. However, the operation of highway tunnels is affected by multiple factors. Rainfall affects road surface slipperiness, pore water pressure is related to tunnel structural stability, and the number, total weight, and frequency of vehicles reflect traffic flow and load conditions. Monitoring a single factor cannot provide sufficient data support for comprehensive and accurate decision-making. On the other hand, in terms of speed limit setting strategies, existing methods mostly adopt fixed speed limits or set speed limits based on simple environmental conditions, lacking dynamic adjustment mechanisms. This approach does not fully consider the actual safety needs of vehicles traveling under different environments and the changes in the tunnel structure's load-bearing capacity. For example, in heavy rain, road surface friction decreases, and vehicle braking distance increases. Fixed speed limits cannot be adjusted in time according to changes in rainfall, potentially leading to safety hazards. When the tunnel structure faces potential risks due to changes in pore water pressure, fixed speed limits cannot effectively reduce the impact of vehicles passing through the tunnel structure. Furthermore, there is insufficient control over the balance between the actual load-bearing capacity of the tunnel and traffic flow. Current technologies rarely dynamically adjust speed limits by comparing the current total weight and number of vehicles with historical data. This makes it difficult to reasonably control traffic flow while ensuring the tunnel's structural safety, easily leading to excessive traffic restrictions or neglect of tunnel safety risks. In addition, when traffic flow is high, i.e., vehicle frequency is high, current technologies lack effective means to further optimize speed limit strategies based on vehicle frequency. This makes it difficult to maintain traffic order within the tunnel, easily causing congestion, reducing traffic efficiency, and also impacting vehicle safety.

[0004] Therefore, it is necessary to provide a method and system for dynamic monitoring of highway tunnel operation status to solve the problems of current highway tunnel operation status monitoring methods, such as lack of comprehensive collection of environmental and vehicle information, lack of dynamic adjustment mechanism for speed limit setting strategies, insufficient control over the balance between tunnel load-bearing capacity and traffic flow, and lack of optimization of speed limit strategies based on vehicle frequency when traffic flow is high. Summary of the Invention

[0005] In view of this, the present invention proposes a method and system for dynamic monitoring of the operation status of highway tunnels, aiming to solve the problems of the current highway tunnel operation status monitoring methods lacking comprehensive collection of environmental and vehicle information, lacking dynamic adjustment mechanism for speed limit setting strategies, insufficient control over the balance between tunnel load-bearing capacity and traffic flow, and lacking optimization of speed limit strategies based on vehicle frequency when traffic flow is high.

[0006] On the one hand, this invention proposes a method for dynamic monitoring of the operating status of highway tunnels, including:

[0007] Collect environmental and vehicle information of the highway tunnel within a preset time period; wherein, the environmental information includes rainfall and pore water pressure, and the vehicle information includes the number of vehicles, total vehicle weight, and vehicle frequency;

[0008] Set the initial speed limit value based on the environmental information;

[0009] Based on the total vehicle weight, determine whether to adjust the initial speed limit. If it is determined to be adjusted, calculate the similarity between the total vehicle weight and the number of vehicles and the historical total vehicle weight and the historical number of vehicles in the historical data. Adjust the initial speed limit based on the similarity to obtain the adjusted speed limit value.

[0010] Based on the vehicle frequency and the speed limit adjustment value, it is determined whether the speed limit adjustment value should be adjusted. If it is determined that an adjustment is needed, the speed limit adjustment value is adjusted according to the vehicle frequency to obtain the final speed limit value.

[0011] Furthermore, setting the initial speed limit value based on the environmental information includes:

[0012] Set a rainfall threshold and a pore water pressure range. If the rainfall is less than the rainfall threshold and the pore water pressure is within the pore water pressure range, then the initial speed limit value is the first initial value.

[0013] If the rainfall is greater than or equal to the rainfall threshold and the pore water pressure is within the pore water pressure range, then the initial value of the speed limit is the second initial value.

[0014] If the rainfall is less than the rainfall threshold and the pore water pressure is not within the pore water pressure range, then the initial value of the speed limit is the second initial value.

[0015] If the rainfall is greater than or equal to the rainfall threshold and the pore water pressure is not within the pore water pressure range, then the initial value of the speed limit is the third initial value.

[0016] Among them, the first initial value > the second initial value > the third initial value.

[0017] Furthermore, when determining whether to adjust the initial speed limit based on the total weight of the vehicle, the following steps are included:

[0018] If the total weight of the vehicle is greater than a preset vehicle weight threshold, then it is determined that the initial speed limit value should be adjusted.

[0019] If the total weight of the vehicle is less than or equal to a preset vehicle weight threshold, then it is determined that the initial speed limit value will not be adjusted.

[0020] Furthermore, when calculating the similarity between the total vehicle weight and the number of vehicles and the historical total vehicle weight and the historical number of vehicles in the historical data, the following steps are included:

[0021] The historical data includes the total weight and number of vehicles within a preset historical time period; wherein, the preset historical time period is equal to the duration of the preset time period.

[0022] The total weight of vehicles and the number of vehicles within the current preset time period are used to form a two-dimensional vector x=[W, N], and the total weight of historical vehicles and the number of historical vehicles within the j-th historical preset time period are used to form a two-dimensional vector hj=[Whj, Nhj].

[0023] The similarity between two-dimensional vector x and two-dimensional vector hj is calculated using Euclidean distance.

[0024] The calculated similarities are used to construct a similarity sequence.

[0025] Furthermore, when adjusting the initial speed limit value based on similarity to obtain the adjusted speed limit value, the process includes:

[0026] Pre-set a similarity threshold;

[0027] The speed limit adjustment value is obtained based on the relationship between the similarity threshold and the similarity in the similarity sequence.

[0028] Furthermore, when obtaining the speed limit adjustment value based on the relationship between the similarity threshold and the similarity in the similarity sequence, the process includes:

[0029] If there exists a similarity value in the similarity sequence that is greater than or equal to the similarity threshold, then the historical speed limit final value corresponding to the maximum similarity value is used as the second speed limit adjustment value.

[0030] If the similarity scores in the similarity sequence are all less than the similarity threshold, then the historical speed limit final value corresponding to the maximum similarity score will be used as the first speed limit adjustment value.

[0031] Furthermore, when determining whether to adjust the speed limit adjustment value based on the vehicle frequency and the speed limit adjustment value, the following steps are included:

[0032] If the speed limit adjustment value is the second speed limit adjustment value, then it is determined that the speed limit adjustment value will not be adjusted, and the second speed limit adjustment value will be used as the final speed limit value.

[0033] If the speed limit adjustment value is the first speed limit adjustment value, then it is determined whether to adjust the speed limit adjustment value based on the vehicle frequency.

[0034] Furthermore, when determining whether to adjust the speed limit adjustment value based on the vehicle frequency if the speed limit adjustment value is the first speed limit adjustment value, the following steps are included:

[0035] If the vehicle frequency is greater than a preset frequency threshold, then it is determined that the speed limit adjustment value should be adjusted.

[0036] If the vehicle frequency is less than or equal to a preset frequency threshold, it is determined that the speed limit adjustment value will not be adjusted.

[0037] Furthermore, when adjusting the speed limit adjustment value according to the vehicle frequency to obtain the final speed limit value, the process includes:

[0038] Calculate the frequency difference between the vehicle frequency and the frequency threshold, obtain an adjustment coefficient based on the frequency difference, and adjust the first speed limit adjustment value based on the adjustment coefficient;

[0039] The final speed limit value is the product of the adjustment coefficient and the first speed limit adjustment value. The adjustment coefficient is inversely proportional to the frequency difference, and the range of the adjustment coefficient is 1 > adjustment coefficient > 0.

[0040] Compared with existing technologies, the advantages of this invention are as follows: First, by collecting environmental and vehicle information of highway tunnels within a preset time period, a comprehensive and accurate data foundation is provided for subsequent decision-making. Rainfall information relates to road surface slipperiness, pore water pressure affects tunnel structural stability, and vehicle quantity, total weight, and frequency reflect traffic flow and load conditions. Accurate understanding of vehicle weight and quantity also helps to understand the actual load-bearing capacity of the tunnel. Second, setting initial speed limits based on environmental information ensures adaptation to local conditions and time. Safe driving speeds vary under different rainfall and pore water pressure conditions. Third, determining whether to adjust the initial speed limit based on vehicle weight, and adjusting it by calculating similarity with historical data, if the current vehicle weight increases significantly compared to historical data, it indicates an increased load on the tunnel. Adjusting the speed limit through similarity analysis can ensure tunnel structural safety while reasonably controlling traffic flow, avoiding excessive restrictions or neglect, and achieving a balance between traffic and tunnel safety. Finally, the final speed limit is determined and adjusted again based on vehicle frequency and the adjusted speed limit value, further optimizing the speed limit strategy. High vehicle frequency means high traffic volume. At this time, reasonable adjustment of the final speed limit can maintain traffic order in the tunnel, avoid congestion, improve traffic efficiency, and at the same time ensure vehicle driving safety, thus achieving a balance between safety and efficiency.

[0041] On the other hand, this application also provides a dynamic monitoring system for the operating status of highway tunnels, including:

[0042] The data acquisition module is configured to collect environmental and vehicle information of the highway tunnel within a preset time period; wherein the environmental information includes rainfall and pore water pressure, and the vehicle information includes the number of vehicles, total vehicle weight, and vehicle frequency.

[0043] The processing module is configured to set an initial speed limit value based on the environmental information;

[0044] The judgment module is configured to determine whether to adjust the initial speed limit value based on the total weight of the vehicles. If the judgment is to adjust, the module calculates the similarity between the total weight of the vehicles and the number of vehicles and the historical total weight of the vehicles and the historical number of vehicles in the historical data, and adjusts the initial speed limit value based on the similarity to obtain the adjusted speed limit value.

[0045] The adjustment module is configured to determine whether to adjust the speed limit adjustment value based on the vehicle frequency and the speed limit adjustment value. If it is determined that adjustment is needed, the speed limit adjustment value is adjusted according to the vehicle frequency to obtain the final speed limit value.

[0046] It is understood that the dynamic monitoring method and system for highway tunnel operation status provided in this application have the same beneficial effects, and will not be elaborated further here. Attached Figure Description

[0047] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings:

[0048] Figure 1 A flowchart of a method for dynamic monitoring of highway tunnel operation status provided in an embodiment of the present invention;

[0049] Figure 2 This is a functional block diagram of a dynamic monitoring system for the operation status of a highway tunnel provided in an embodiment of the present invention. Detailed Implementation

[0050] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the disclosure to those skilled in the art. It should be noted that, unless otherwise specified, embodiments and features in the embodiments of the present invention can be combined with each other. The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0051] In some embodiments of this application, see Figure 1 As shown in the figure, this embodiment provides a method for dynamic monitoring of the operating status of highway tunnels, including the following steps:

[0052] S100. Collect environmental and vehicle information of the highway tunnel within a preset time period; wherein, the environmental information includes rainfall and pore water pressure, and the vehicle information includes the number of vehicles, total vehicle weight, and vehicle frequency.

[0053] S200. Set the initial speed limit value according to the environmental information;

[0054] S300. Determine whether to adjust the initial speed limit value based on the total vehicle weight. If it is determined to be adjusted, calculate the similarity between the total vehicle weight and the number of vehicles and the historical total vehicle weight and the historical number of vehicles in the historical data. Adjust the initial speed limit value based on the similarity to obtain the adjusted speed limit value.

[0055] S400. Determine whether to adjust the speed limit adjustment value based on the vehicle frequency and the speed limit adjustment value. If it is determined that adjustment is needed, adjust the speed limit adjustment value based on the vehicle frequency to obtain the final speed limit value.

[0056] Understandably, firstly, collecting environmental and vehicle information about highway tunnels within a preset timeframe provides a comprehensive and accurate data foundation for subsequent decision-making. Rainfall information relates to road surface slipperiness, pore water pressure affects tunnel structural stability, and vehicle quantity, total weight, and frequency reflect traffic flow and load conditions. For example, in heavy rain, increased rainfall reduces road surface friction, increasing vehicle braking distance. Setting initial speed limits based on rainfall data effectively ensures driving safety. Accurate knowledge of vehicle weight and quantity also helps understand the actual load-bearing capacity of the tunnel. Secondly, setting initial speed limits based on environmental information ensures adaptation to local conditions and time. Safe driving speeds vary under different rainfall and pore water pressure conditions. For instance, higher pore water pressure may indicate a certain risk to the tunnel structure. Lowering the initial speed limit in this case reduces the impact of vehicles passing through the tunnel structure, prevents potential accidents, and protects tunnel facilities and the safety of passing vehicles and personnel. Furthermore, the initial speed limit is adjusted based on the total vehicle weight, and this adjustment is made by calculating the similarity with historical data. If the current total vehicle weight increases significantly compared to historical data, it indicates an increase in the tunnel's load. Adjusting the speed limit through similarity analysis can ensure the tunnel's structural safety while reasonably controlling traffic flow, avoiding excessive restrictions or neglect, and achieving a balance between traffic and tunnel safety. Finally, the final speed limit is determined and adjusted again based on vehicle frequency and the adjusted speed limit value, further optimizing the speed limit strategy. High vehicle frequency means high traffic flow; in this case, reasonably adjusting the final speed limit can maintain traffic order within the tunnel, avoid congestion, improve traffic efficiency, and ensure vehicle safety, achieving a balance between safety and efficiency.

[0057] Specifically, when no traffic accidents or traffic jams occur, the initial speed limit, the adjusted speed limit, and the final speed limit meet the national laws and regulations regarding the minimum speed for highways.

[0058] In some embodiments of this application, setting the initial speed limit value based on the environmental information includes:

[0059] Set a rainfall threshold and a pore water pressure range. If the rainfall is less than the rainfall threshold and the pore water pressure is within the pore water pressure range, then the initial speed limit value is the first initial value.

[0060] If the rainfall is greater than or equal to the rainfall threshold and the pore water pressure is within the pore water pressure range, then the initial value of the speed limit is the second initial value.

[0061] If the rainfall is less than the rainfall threshold and the pore water pressure is not within the pore water pressure range, then the initial value of the speed limit is the second initial value.

[0062] If the rainfall is greater than or equal to the rainfall threshold and the pore water pressure is not within the pore water pressure range, then the initial value of the speed limit is the third initial value.

[0063] Among them, the first initial value > the second initial value > the third initial value.

[0064] Understandably, firstly, this invention dynamically adjusts the initial speed limit based on different environmental conditions, improving road safety. By comprehensively considering rainfall and pore water pressure, the speed limit setting is made more scientific and reasonable, avoiding accidents caused by excessive speed in severe weather or adverse geological conditions. Secondly, the refined setting can take into account different levels of environmental changes, such as different rainfall levels and pore water pressure states, ensuring that an appropriate initial speed limit value is provided under various conditions, guaranteeing smooth and safe traffic.

[0065] Specifically, the rainfall threshold is set at 50 mm / h, and the pore water pressure range is set at 0.1-0.3 MPa. During a continuous rainfall event, if the monitored rainfall is 40 mm / h and the pore water pressure is 0.2 MPa, meeting the conditions of rainfall being less than the rainfall threshold and pore water pressure within the range, then the initial speed limit is set to the first initial value, such as 80 km / h. This setting allows the vehicle to maintain a reasonable driving speed in relatively good conditions, ensuring driving safety and efficiency.

[0066] In some embodiments of this application, determining whether to adjust the initial speed limit based on the total vehicle weight includes:

[0067] If the total weight of the vehicle is greater than a preset vehicle weight threshold, then it is determined that the initial speed limit value should be adjusted.

[0068] If the total weight of the vehicle is less than or equal to a preset vehicle weight threshold, then it is determined that the initial speed limit value will not be adjusted.

[0069] Understandably, firstly, determining whether to adjust the initial speed limit based on the total vehicle weight allows for precise responses to changes in tunnel load-bearing pressure. When the total vehicle weight exceeds a preset threshold, it indicates a significant increase in the tunnel's load. Adjusting the initial speed limit at this point effectively reduces the risk of structural damage while avoiding excessive speed limits that could negatively impact traffic efficiency. For example, if the preset vehicle weight threshold for a given time period is 500 tons, and the detected total vehicle weight is 510 tons, the speed limit adjustment mechanism is automatically triggered, lowering the initial speed limit from 80 km / h to 70 km / h. This adjustment ensures tunnel structural safety while maintaining basic traffic capacity through differentiated speed limits, avoiding resource waste or safety hazards caused by uniform speed limits. This invention, through the linkage between a real-time weighing system and speed limit rules, ensures the timeliness and accuracy of dynamic responses.

[0070] In some embodiments of this application, calculating the similarity between the total vehicle weight and the number of vehicles and the historical total vehicle weight and the historical number of vehicles in historical data includes:

[0071] The historical data includes the total weight and number of vehicles within a preset historical time period; wherein, the preset historical time period is equal to the duration of the preset time period.

[0072] The total weight of vehicles and the number of vehicles within the current preset time period are used to form a two-dimensional vector x=[W, N], and the total weight of historical vehicles and the number of historical vehicles within the j-th historical preset time period are used to form a two-dimensional vector hj=[Whj, Nhj].

[0073] The similarity between two-dimensional vector x and two-dimensional vector hj is calculated using Euclidean distance.

[0074] The calculated similarities are used to construct a similarity sequence.

[0075] Understandably, by calculating the similarity between total vehicle weight and number and historical data, it is possible to accurately grasp the degree of fit between current traffic conditions and typical historical conditions, providing a more targeted and scientific basis for speed limit adjustments. This similarity analysis avoids the one-sidedness of adjusting speed limits solely based on current data, fully considering the impact of historical traffic patterns on current speed limit strategies, making speed limit adjustments more aligned with actual traffic needs, ensuring tunnel structural safety while minimizing unnecessary interference with normal traffic flow.

[0076] Specifically, the preset duration is set to 1 hour. In the current hour, the total weight W of the monitored vehicles is 480 tons, and the number of vehicles N is 120, forming a two-dimensional vector x = [480, 120]. From historical data, one-hour data points from the same day of each week over the past three months are selected as historical preset duration data, resulting in 12 sets of historical data. In the 5th set of historical data, the historical total vehicle weight Wh5 is 470 tons, and the historical number of vehicles Nh5 is 115, forming a two-dimensional vector h5 = [470, 115]. The similarity between two-dimensional vector x and two-dimensional vector h5 is calculated using Euclidean distance, and the similarity to other historical two-dimensional vectors is also calculated to construct a similarity sequence. When calculating the similarity, the Euclidean distance d between two-dimensional vector x and two-dimensional vector h5 is calculated first, and the similarity between two-dimensional vector x and two-dimensional vector h5 is 1 / (1+d).

[0077] In some embodiments of this application, adjusting the initial speed limit value based on similarity to obtain an adjusted speed limit value includes:

[0078] Pre-set a similarity threshold;

[0079] The speed limit adjustment value is obtained based on the relationship between the similarity threshold and the similarity in the similarity sequence.

[0080] Understandably, by pre-setting a similarity threshold and comparing it with the calculated similarity sequence, the speed limit adjustment value can be determined scientifically and accurately. This adjustment method based on historical data similarity avoids the subjectivity and arbitrariness of speed limit adjustments, making the speed limit strategy more consistent with the changing patterns of actual traffic conditions.

[0081] In some embodiments of this application, obtaining the speed limit adjustment value based on the relationship between the similarity threshold and the similarity in the similarity sequence includes:

[0082] If there exists a similarity value in the similarity sequence that is greater than or equal to the similarity threshold, then the historical speed limit final value corresponding to the maximum similarity value is used as the second speed limit adjustment value.

[0083] If the similarity scores in the similarity sequence are all less than the similarity threshold, then the historical speed limit final value corresponding to the maximum similarity score will be used as the first speed limit adjustment value.

[0084] In some embodiments of this application, determining whether to adjust the speed limit adjustment value based on the vehicle frequency and the speed limit adjustment value includes:

[0085] If the speed limit adjustment value is the second speed limit adjustment value, then it is determined that the speed limit adjustment value will not be adjusted, and the second speed limit adjustment value will be used as the final speed limit value.

[0086] If the speed limit adjustment value is the first speed limit adjustment value, then it is determined whether to adjust the speed limit adjustment value based on the vehicle frequency.

[0087] Understandably, determining whether to adjust the speed limit again based on vehicle frequency and the adjusted speed limit value can further optimize the speed limit strategy and improve the safety and efficiency of tunnel operation. When the adjusted speed limit value is the second adjusted speed limit value, it indicates that the current traffic conditions are highly consistent with historical typical conditions, and safe passage can be guaranteed without additional adjustments. When it is the first adjusted speed limit value, dynamically adjusting the final speed limit value in combination with vehicle frequency can avoid congestion caused by dense traffic flow and prevent excessively low speed limits from affecting traffic efficiency, achieving a balance between safety and smooth flow.

[0088] Specifically, for example, if the preset similarity threshold is 0.85, and the similarity between the current traffic situation and a historical time period is 0.88 (second speed limit adjustment scenario), then the final speed limit of 60 km / h from that historical time period is directly adopted. As another example, if the similarity is 0.82 (first speed limit adjustment scenario), and the monitored vehicle frequency is 5 vehicles per minute, then the final speed limit is further adjusted to 65 km / h, which alleviates traffic pressure while ensuring structural safety. This layered adjustment mechanism, through data-driven decision-making, makes the speed limit strategy more aligned with real-time traffic needs, reduces accident risks, and improves the overall operational efficiency of the tunnel.

[0089] Preferably, in the implementation of "calculating the similarity between the total vehicle weight and number of vehicles and the historical total vehicle weight and number of vehicles in historical data" and "adjusting the initial speed limit value according to the similarity to obtain the adjusted speed limit value", if there are two or more tied maximum values ​​in the similarity sequence (i.e., multiple historical data have the same similarity to the current data and are all the highest values ​​in the sequence), the target historical data needs to be further filtered according to the following method to ensure the safety and rationality of the speed limit adjustment:

[0090] First, the core logic relating vehicle weight, number of vehicles, and speed limits is established: the greater the total vehicle weight, the higher the load on the tunnel structure, requiring a lower speed limit; the greater the number of vehicles, the higher the risk of traffic congestion and rear-end collisions, requiring a lower speed limit. This logic serves as the core basis for historical data screening and takes precedence over simple similarity score judgments.

[0091] Secondly, all historical data with the highest similarity are divided into two categories according to their deviation from the total vehicle weight (W) and number of vehicles (N) within the current preset time period: (1) Low load and low flow: historical vehicle weight Wh ≤ current vehicle weight W, and historical vehicle number Nh ≤ current vehicle number N; (2) High load and high flow: historical vehicle weight Wh > current vehicle weight W, and historical vehicle number Nh > current vehicle number N; (3) High load and low flow: historical vehicle weight Wh > current vehicle weight W, and historical vehicle number Nh ≤ current vehicle number N; (4) Low load and high flow: historical vehicle weight Wh ≤ current vehicle weight W, and historical vehicle number Nh > current vehicle number N.

[0092] Finally, the final historical speed limit value corresponding to the historical data of high load and high flow rate category is selected first as the speed limit adjustment value for the current scenario (either the first or second speed limit adjustment value). If there is no high load and high flow rate category data among the historical data with the highest similarity, the lowest historical final speed limit value among the historical data with the highest similarity is selected as the speed limit adjustment value for the current scenario.

[0093] In some embodiments of this application, when determining whether to adjust the speed limit adjustment value based on the vehicle frequency if the speed limit adjustment value is a first speed limit adjustment value, the method includes:

[0094] If the vehicle frequency is greater than a preset frequency threshold, then it is determined that the speed limit adjustment value should be adjusted.

[0095] If the vehicle frequency is less than or equal to a preset frequency threshold, it is determined that the speed limit adjustment value will not be adjusted.

[0096] Understandably, firstly, determining whether to adjust the speed limit based on a comparison between vehicle frequency and a preset frequency threshold allows for a dynamic response to real-time changes in traffic flow. When the vehicle frequency exceeds the threshold, it indicates high traffic density within the tunnel. Adjusting the speed limit at this time effectively avoids the risk of rear-end collisions or traffic congestion, while maintaining traffic efficiency through differentiated speed limits. When the frequency does not exceed the threshold, maintaining the current speed limit reduces the impact of frequent speed limit changes on driving behavior. For example, if the preset frequency threshold is 4 vehicles per minute, and a vehicle frequency of 6 vehicles per minute is detected (exceeding the threshold), the speed limit is lowered from 70 km / h to 65 km / h, increasing safe following distance by reducing vehicle speed. If the vehicle frequency is 3 vehicles per minute (not exceeding the threshold), the speed limit is maintained at 70 km / h to avoid affecting traffic flow due to excessive intervention. This frequency-based dynamic adjustment mechanism ensures driving safety in high-traffic scenarios while also considering efficiency needs in low-traffic scenarios, achieving dual optimization of traffic flow and structural safety.

[0097] In a specific embodiment, during the morning rush hour (7:00-9:00), the tunnel entrance monitoring system collects vehicle frequency data in real time. When the frequency exceeds 4 vehicles / minute for 5 consecutive minutes, the speed limit adjustment process is automatically triggered, lowering the final speed limit from 70 km / h to 65 km / h. The adjusted speed limit is then displayed in real time on the variable message sign. After the morning rush hour ends and the frequency drops below 3 vehicles / minute, the original speed limit is automatically restored. This process, through the linkage between the frequency threshold and the speed limit, effectively alleviates traffic pressure during peak hours while avoiding wasted speed limit usage during off-peak hours, thus improving the overall operational efficiency of the tunnel.

[0098] In some embodiments of this application, adjusting the speed limit adjustment value according to the vehicle frequency to obtain the final speed limit value includes:

[0099] Calculate the frequency difference between the vehicle frequency and the frequency threshold, obtain an adjustment coefficient based on the frequency difference, and adjust the first speed limit adjustment value based on the adjustment coefficient;

[0100] The final speed limit value is the product of the adjustment coefficient and the first speed limit adjustment value. The adjustment coefficient is inversely proportional to the frequency difference, and the range of the adjustment coefficient is 1 > adjustment coefficient > 0.

[0101] Understandably, by calculating the frequency difference between the vehicle frequency and the frequency threshold and obtaining an adjustment coefficient accordingly, and then adjusting the first speed limit adjustment value to obtain the final speed limit value, the speed limit can be dynamically adjusted more accurately and scientifically based on the actual traffic flow. This avoids the irrationality of fixed speed limits under different traffic flow scenarios. During high traffic flow, the speed limit is reduced to increase the safe distance between vehicles and ensure driving safety; during low traffic flow, a higher speed limit is maintained to ensure smooth traffic flow. This achieves dual optimization of traffic flow and structural safety, and improves the overall operating efficiency of the tunnel.

[0102] Specifically, for example, the currently set frequency threshold is 4 vehicles per minute, and the first speed limit adjustment value is 70 km / h. If at a certain moment a vehicle frequency of 5 vehicles per minute is detected, the frequency difference is 1 vehicle per minute. Based on preset rules, an adjustment coefficient of 0.9 is calculated. Therefore, the final speed limit is the product of the adjustment coefficient 0.9 and the first speed limit adjustment value of 70 km / h, which is 63 km / h. This allows for dynamic adjustment of the speed limit based on real-time vehicle frequency to adapt to different traffic conditions.

[0103] Preferably, a difference range is set. If the frequency difference is greater than the maximum value of the difference range, the first speed limit adjustment value is adjusted by a first adjustment coefficient, the value of which is in the range of (0, 0.3). If the frequency difference is within the difference range, the first speed limit adjustment value is adjusted by a second adjustment coefficient, the value of which is in the range of (0.3, 0.8). If the frequency difference is less than the minimum value of the difference range, the first speed limit adjustment value is adjusted by a third adjustment coefficient, the value of which is in the range of (0.8, 1).

[0104] For example, the current frequency threshold is set to 6 vehicles per minute, the first speed limit adjustment value is 80 km / h, and the set difference range is (3, 5).

[0105] When the frequency difference exceeds the maximum value of the difference range (in cases with heavy traffic): If the monitored vehicle frequency is 12 vehicles per minute at a certain moment, the frequency difference is 12 - 6 = 6 vehicles / minute. 6 is greater than the maximum value of 5 in the difference range (3, 5) (this assumes the difference range does not include boundary values; actual application follows specific regulations). According to the rules, the first speed limit is adjusted using the first adjustment coefficient. If the first adjustment coefficient is set to 0.2 (within the range (0, 0.3]), then the final speed limit is 0.2 × 80 = 16 km / h.

[0106] When the frequency difference is within the range: If the monitored vehicle frequency is 10 vehicles per minute at a certain moment, the frequency difference is 10-6=4 vehicles / minute, and 4 is within the difference range (3, 5). The first speed limit adjustment value is adjusted by the second adjustment coefficient. If the second adjustment coefficient is 0.5 (within the range (0.3, 0.8]), then the final speed limit value is 0.5×80=40 km / h.

[0107] When the frequency difference is less than the minimum value of the difference range (when there are few vehicles): If the frequency of vehicles monitored at a certain moment is 8 vehicles per minute, the frequency difference is 8-6=2 vehicles / minute. 2 is less than the minimum value of 3 in the difference range (3,5). The first speed limit adjustment value is adjusted by the third adjustment coefficient. The third adjustment coefficient is taken as 0.9 (within the value range (0.8,1)). The final speed limit value is 0.9×80=72 km / h.

[0108] On the other hand, see Figure 2 As shown, this application also provides a dynamic monitoring system for the operating status of highway tunnels, used to apply the above-mentioned dynamic monitoring method for the operating status of highway tunnels, including:

[0109] The data acquisition module is configured to collect environmental and vehicle information of the highway tunnel within a preset time period; wherein the environmental information includes rainfall and pore water pressure, and the vehicle information includes the number of vehicles, total vehicle weight, and vehicle frequency.

[0110] The processing module is configured to set an initial speed limit value based on the environmental information;

[0111] The judgment module is configured to determine whether to adjust the initial speed limit value based on the total weight of the vehicles. If the judgment is to adjust, the module calculates the similarity between the total weight of the vehicles and the number of vehicles and the historical total weight of the vehicles and the historical number of vehicles in the historical data, and adjusts the initial speed limit value based on the similarity to obtain the adjusted speed limit value.

[0112] The adjustment module is configured to determine whether to adjust the speed limit adjustment value based on the vehicle frequency and the speed limit adjustment value. If it is determined that adjustment is needed, the speed limit adjustment value is adjusted according to the vehicle frequency to obtain the final speed limit value.

[0113] Understandably, the data acquisition module comprehensively collects environmental information (rainfall and pore water pressure) and vehicle information (number of vehicles, total vehicle weight, and vehicle frequency) for the highway tunnel within a preset time period, providing a rich and accurate data foundation for subsequent monitoring and decision-making. Accurate rainfall information reflects the potential impact of weather conditions outside the tunnel on driving, pore water pressure is related to the tunnel's structural safety, and vehicle-related information directly reflects the actual traffic situation inside the tunnel. The comprehensive collection of this data acts like equipping the system with "eagle eyes," allowing it to clearly understand the tunnel's operational status. The processing module sets initial speed limits based on environmental information. Considering that rainfall may affect road surface friction and abnormal pore water pressure may indicate potential structural hazards in the tunnel, setting initial speed limits based on these factors ensures driving safety while maximizing the smoothness of tunnel traffic. For example, lowering the initial speed limit during periods of heavy rainfall can prevent accidents caused by slippery road surfaces. The judgment module adjusts the initial speed limit based on the total vehicle weight, further optimizing the rationality of the speed limit setting. By calculating the similarity between the total vehicle weight and number of vehicles and historical data, the adjustments become more targeted and accurate. If the total weight of vehicles increases significantly compared to historical data, it indicates an increased vehicle load within the tunnel. In this case, appropriately lowering the speed limit adjustment value can effectively prevent tunnel structural damage and traffic accidents that may be caused by overloaded vehicles. The adjustment module then makes further adjustments based on vehicle frequency and the speed limit adjustment value to arrive at a final speed limit value, making the speed limit setting more closely reflect real-time traffic conditions. If the vehicle frequency is too high, it indicates heavy traffic flow. In this case, the speed limit adjustment value is adjusted again to obtain a reasonable final speed limit value, ensuring orderly vehicle passage, avoiding congestion, and ensuring driving safety in complex traffic environments. In summary, through the close collaboration of various modules, the system dynamically adjusts the speed limit, comprehensively ensuring the safe and efficient operation of highway tunnels.

[0114] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program goods. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program goods embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0115] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program goods according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0116] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0117] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0118] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for dynamic monitoring of the operating status of highway tunnels, characterized in that, include: Collect environmental and vehicle information of the highway tunnel within a preset time period; wherein, the environmental information includes rainfall and pore water pressure, and the vehicle information includes the number of vehicles, total vehicle weight, and vehicle frequency; Set the initial speed limit value based on the environmental information; Based on the total vehicle weight, determine whether to adjust the initial speed limit. If it is determined to be adjusted, calculate the similarity between the total vehicle weight and the number of vehicles and the historical total vehicle weight and the historical number of vehicles in the historical data. Adjust the initial speed limit based on the similarity to obtain the adjusted speed limit value. Based on the vehicle frequency and the speed limit adjustment value, it is determined whether the speed limit adjustment value should be adjusted. If it is determined that an adjustment is needed, the speed limit adjustment value is adjusted according to the vehicle frequency to obtain the final speed limit value.

2. The method for dynamic monitoring of highway tunnel operation status according to claim 1, characterized in that, Setting the initial speed limit value based on the environmental information includes: Set a rainfall threshold and a pore water pressure range. If the rainfall is less than the rainfall threshold and the pore water pressure is within the pore water pressure range, then the initial speed limit value is the first initial value. If the rainfall is greater than or equal to the rainfall threshold and the pore water pressure is within the pore water pressure range, then the initial value of the speed limit is the second initial value. If the rainfall is less than the rainfall threshold and the pore water pressure is not within the pore water pressure range, then the initial value of the speed limit is the second initial value. If the rainfall is greater than or equal to the rainfall threshold and the pore water pressure is not within the pore water pressure range, then the initial value of the speed limit is the third initial value. Among them, the first initial value > the second initial value > the third initial value.

3. The method for dynamic monitoring of highway tunnel operation status according to claim 1, characterized in that, When determining whether to adjust the initial speed limit based on the total weight of the vehicle, the following steps are included: If the total weight of the vehicle is greater than a preset vehicle weight threshold, then it is determined that the initial speed limit value should be adjusted. If the total weight of the vehicle is less than or equal to a preset vehicle weight threshold, then it is determined that the initial speed limit value will not be adjusted.

4. The method for dynamic monitoring of the operating status of highway tunnels according to claim 1, characterized in that, The calculation of the similarity between the total vehicle weight and number of vehicles and the historical total vehicle weight and number of vehicles in historical data includes: The historical data includes the total weight and number of vehicles within a preset historical time period; wherein, the preset historical time period is equal to the duration of the preset time period. The total weight of vehicles and the number of vehicles within the current preset time period are used to form a two-dimensional vector x=[W, N], and the total weight of historical vehicles and the number of historical vehicles within the j-th historical preset time period are used to form a two-dimensional vector hj=[Whj, Nhj]. The similarity between two-dimensional vector x and two-dimensional vector hj is calculated using Euclidean distance. The calculated similarities are used to construct a similarity sequence.

5. The method for dynamic monitoring of the operating status of highway tunnels according to claim 4, characterized in that, When adjusting the initial speed limit value based on similarity to obtain the adjusted speed limit value, the following steps are included: Pre-set a similarity threshold; The speed limit adjustment value is obtained based on the relationship between the similarity threshold and the similarity in the similarity sequence.

6. The method for dynamic monitoring of the operating status of highway tunnels according to claim 5, characterized in that, The step of obtaining the speed limit adjustment value based on the similarity threshold and the similarity in the similarity sequence includes: If there exists a similarity value in the similarity sequence that is greater than or equal to the similarity threshold, then the historical speed limit final value corresponding to the maximum similarity value is used as the second speed limit adjustment value. If the similarity scores in the similarity sequence are all less than the similarity threshold, then the historical speed limit final value corresponding to the maximum similarity score will be used as the first speed limit adjustment value.

7. The method for dynamic monitoring of the operating status of highway tunnels according to claim 6, characterized in that, When determining whether to adjust the speed limit adjustment value based on the vehicle frequency and the speed limit adjustment value, the following steps are included: If the speed limit adjustment value is the second speed limit adjustment value, then it is determined that the speed limit adjustment value will not be adjusted, and the second speed limit adjustment value will be used as the final speed limit value. If the speed limit adjustment value is the first speed limit adjustment value, then it is determined whether to adjust the speed limit adjustment value based on the vehicle frequency.

8. The method for dynamic monitoring of the operating status of highway tunnels according to claim 7, characterized in that, If the speed limit adjustment value is the first speed limit adjustment value, then when determining whether to adjust the speed limit adjustment value based on the vehicle frequency, the following steps are included: If the vehicle frequency is greater than a preset frequency threshold, then it is determined that the speed limit adjustment value should be adjusted. If the vehicle frequency is less than or equal to a preset frequency threshold, it is determined that the speed limit adjustment value will not be adjusted.

9. The method for dynamic monitoring of the operating status of highway tunnels according to claim 8, characterized in that, When adjusting the speed limit adjustment value according to the vehicle frequency to obtain the final speed limit value, the following steps are included: Calculate the frequency difference between the vehicle frequency and the frequency threshold, obtain an adjustment coefficient based on the frequency difference, and adjust the first speed limit adjustment value based on the adjustment coefficient; The final speed limit value is the product of the adjustment coefficient and the first speed limit adjustment value. The adjustment coefficient is inversely proportional to the frequency difference, and the range of the adjustment coefficient is 1 > adjustment coefficient > 0.

10. A dynamic monitoring system for the operating status of a highway tunnel, used to apply the dynamic monitoring method for the operating status of a highway tunnel as described in any one of claims 1-9, characterized in that, include: The data acquisition module is configured to collect environmental and vehicle information of the highway tunnel within a preset time period; wherein the environmental information includes rainfall and pore water pressure, and the vehicle information includes the number of vehicles, total vehicle weight, and vehicle frequency. The processing module is configured to set an initial speed limit value based on the environmental information; The judgment module is configured to determine whether to adjust the initial speed limit value based on the total weight of the vehicles. If the judgment is to adjust, the module calculates the similarity between the total weight of the vehicles and the number of vehicles and the historical total weight of the vehicles and the historical number of vehicles in the historical data, and adjusts the initial speed limit value based on the similarity to obtain the adjusted speed limit value. The adjustment module is configured to determine whether to adjust the speed limit adjustment value based on the vehicle frequency and the speed limit adjustment value. If it is determined that adjustment is needed, the speed limit adjustment value is adjusted according to the vehicle frequency to obtain the final speed limit value.

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