Urban tunnel intelligent safety operation and maintenance method and system combined with multiple data

Abstract

The invention discloses an urban tunnel intelligent safety operation and maintenance method and system combined with multiple data, particularly relates to the field of intelligent traffic operation and maintenance, and is used for solving the problems of prediction deviation and congestion management caused by tunnel flow fluctuation. In combination with traffic parameters of a multi-point detector, a wave similarity analysis model is utilized to accurately evaluate a propagation path and a target section of fluctuation; meanwhile, by means of a time sequence superposition correction mechanism, a historical fluctuation rule and real-time data are fused, a traffic flow transmission prediction curve after dynamic correction is generated, and balance between physical constraint and prediction precision is achieved; finally, key indexes are output through dynamic updating of the flow prediction model, efficient linkage of signal coordination and induction management and control modules is guided, congestion accumulation and traffic risks caused by fluctuation propagation can be reduced to the maximum extent, the tunnel operation and maintenance efficiency is optimized, and the traffic safety guarantee capacity is enhanced.

Description

Technical Field

[0001] The present invention relates to the field of intelligent traffic operation and maintenance, and more specifically, to a method and system for intelligent and safe operation and maintenance of urban tunnels combining multiple data. Background Art

[0002] In the urban transportation network, tunnels are important transportation hubs that carry a large amount of vehicle traffic and connect complex ground road networks and highway systems. In order to improve traffic efficiency and ensure safe operation, short-cycle traffic lights are usually set at the entrance of the tunnel to dynamically adjust the vehicle entry flow. However, this frequent signal switching will cause vehicles to flow into the tunnel in waves, forming periodic traffic pulses, which will significantly increase the complexity of traffic management inside the tunnel. The nonlinear propagation characteristics of wave traffic may cause local congestion in the tunnel, uneven traffic flow, and accident risks, which brings huge challenges to intelligent safety operation and maintenance.

[0003] Under the control of short-cycle signals, the vehicle flow at the tunnel entrance shows periodic fluctuations and propagates in waves deep into the tunnel, forming a dynamic and complex traffic situation. However, the existing technology lacks accurate identification of entrance fluctuation characteristics and modeling of propagation laws. It is impossible to combine the real-time traffic data provided by multi-point detectors in the tunnel to dynamically evaluate the fluctuation propagation path and its impact on the congestion risk of the target section, which leads to the inability of the traffic prediction model to effectively correct the dynamic impact of entrance fluctuations on the traffic inside the tunnel. Therefore, there is an urgent need for an intelligent method based on multi-data fusion to improve the accuracy of traffic prediction and congestion risk assessment in the tunnel by accurately identifying entrance fluctuations and dynamically modeling the fluctuation propagation laws, so as to provide support for intelligent and safe operation and maintenance.

[0004] In order to solve the above problems, a technical solution is now provided. Summary of the invention

[0005] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides an intelligent and safe operation and maintenance method and system for urban tunnels combined with multiple data. By constructing a fluctuation feature mapping matrix, the entrance peak characteristics are systematically expressed for the first time, and combined with the traffic parameters of the multi-point detector, the wave similarity analysis model is used to accurately evaluate the propagation path and target section of the fluctuation; at the same time, with the help of a time series superposition correction mechanism, the historical fluctuation law is integrated with the real-time data to generate a dynamically corrected vehicle flow transfer prediction curve, thereby achieving a balance between physical constraints and prediction accuracy; finally, by dynamically updating the flow prediction model, key indicators such as the maximum flow upper limit and the critical duration of queue diffusion are accurately output, and the efficient linkage of signal coordination and induction control modules is guided, which can significantly improve the timeliness and accuracy of flow prediction in the tunnel under complex fluctuation scenarios, minimize the congestion accumulation and traffic risks caused by fluctuation propagation, optimize the tunnel operation and maintenance efficiency, and enhance the traffic safety assurance capability, so as to solve the problems raised in the above-mentioned background technology.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] The intelligent safety operation and maintenance method of urban tunnels combined with multiple data includes the following steps:

[0008] S1. Use the short-period signal switching log and vehicle detection data, use the wave amplitude extraction algorithm to determine the time window and peak scale of the rapid influx of vehicles in each cycle, and generate a fluctuation feature mapping matrix;

[0009] S2. Combine the mapping matrix with the traffic detection data in the tunnel, predict the congestion propagation path of the wave in the tunnel through wave similarity analysis, and identify the target section and the corresponding time window;

[0010] S3. Iterate and correct the traffic density, queue length and amplitude attenuation of the target section, use the historical fluctuation case library to perform time series superposition calculation, and output the traffic flow prediction curve with real-time fluctuation feedback;

[0011] S4. Update the traffic prediction model parameters of the tunnel operation and maintenance system according to the traffic flow prediction curve, and issue dynamic control instructions in a timely manner through external signal light coordination and lane induction linkage mechanism.

[0012] In a preferred embodiment, step S1 includes the following contents:

[0013] First, obtain the start time of each signal cycle from the short-period signal switching log at the tunnel entrance and end time ,in Indicates signal cycle; at the same time, real-time vehicle entry data in each signal cycle is obtained from the vehicle detection equipment;

[0014] For each signal cycle , the vehicle entry data in the corresponding period is segmented and counted at fixed time intervals to generate a vehicle entry rate sequence ,in Indicates time periods; then, define the wave amplitude is the cumulative increment of vehicle entry rate in the corresponding signal period;

[0015] After the wave amplitude is determined, if the wave amplitude value exceeds the peak threshold , it is considered that there is a significant increase in vehicle flow in the corresponding signal period; next, calculate the peak time in the corresponding signal period , which is defined as the period of the signal The specific time point within which the cumulative increment reaches the maximum value;

[0016] For each signal cycle ,like , then add the corresponding record item to the fluctuation feature mapping matrix.

[0017] In a preferred embodiment, step S2 includes the following contents:

[0018] In the multi-point detector, first define the traffic parameter set based on time series ,in represents the detector position number, Represents a discretized time index;

[0019] Related data retrieval and alignment: traverse the traffic parameter set of multiple point detectors and filter out the traffic parameters that are adjacent to or covered by the peak time. Detector position within the time period The corresponding time series ; Align the discretized time index with the peak time. When there is a time difference between the sampling interval of the detector and the input data, the detector sequence is intercepted using the same discretization scale and stored in the intermediate data table;

[0020] Correlation kernel function and wave similarity calculation: Define the correlation kernel function between each peak record and its aligned intermediate data table To measure the similarity between the peak and the actual traffic flow, let Indicates the detector At the moment The traffic parameter mapping vector at Indicates peak entry The incremental feature map in the fluctuation feature map matrix; the associated kernel function is as follows: ;in and is a positive constant used to regulate the decay rate of the difference in traffic characteristics in the exponential term.

[0021] In a preferred embodiment, step S2 further includes the following contents:

[0022] Based on the correlation kernel function, Calculate several moments The similarity mean or maximum value is used to quantify the degree of agreement between the current peak and the flow rate measured by the detector, and obtain the fluctuation shape matching degree;

[0023] Congestion propagation path assessment: After obtaining the wave shape matching degree, based on the tunnel geographical topology order and the spatial distribution of multi-point detectors, the potential traffic queue extension caused by the wave crest is evaluated section by section; If the fluctuation shape matching degree exceeds a certain critical threshold, it is considered that the vehicle speed at this detector position has dropped significantly or the occupancy rate has continued to rise under the influence of the current peak. Marked as potential congestion trigger points; then numbered according to the order of the detectors in the tunnel , recursively from the entrance to the depth of the tunnel and records it at each location The specific time interval of the congestion trigger point , and finally a set of target sections for congestion accumulation is obtained and its corresponding time window ;

[0024] All the obtained target sections, congestion triggering points and corresponding time windows are written into the congestion propagation matrix in a tabular form.

[0025] In a preferred embodiment, step S3 includes the following contents:

[0026] Historical case retrieval and mapping: Based on the records in the congestion propagation matrix , Target Segment and congestion triggering time interval , select from the historical fluctuation case library The corresponding reference items with similar entrance fluctuation shapes and similar segment lengths and queue evolution characteristics are denoted as ; Then, retrieve the corresponding historical traffic delay function ;in Indicates a reference item After entering the tunnel The growth rate of the queue within the duration;

[0027] Fluctuation correction based on the principle of time series superposition: The traffic density detected in the target section and waiting time , define a set of fluctuation correction equations based on time series superposition; in Time On, order ;in express Position at time The correction factor of is a pre-designed wave superposition function; through a similar form of recursion, along to The fluctuation correction amount is calculated point by point in the direction, and the following progression is performed between adjacent positions: and Distance ,but ;in The detectors are indexed sequentially in the tunnel. is the segment length correction function.

[0028] In a preferred embodiment, step S3 further includes the following contents:

[0029] Comprehensive consideration of attenuation factor and traffic capacity: When superimposing, if the wave amplitude is less than the peak threshold or the congestion time window exceeds a specific range, the attenuation factor is introduced. , to reflect the impact of the entrance peak gradually decreasing as the traffic flow travels further, and to add the upper limit of the capacity of the middle and rear sections when including the large flow bottleneck section , constrain the cumulative value of the superposition function not to exceed the upper limit of the physical capacity;

[0030] Generation of traffic flow prediction curve: After completing time series superposition and fluctuation correction, the target section Each position In continuous moments The corrected density and queue duration are obtained, and the traffic flow transfer prediction curve is fitted. ; Each The traffic flow transfer prediction curve sequence corresponding to the target section is stored in the real-time fluctuation prediction table.

[0031] In a preferred embodiment, step S4 includes the following contents:

[0032] Dynamic extraction of key parameters of the traffic prediction model: predicting the target segment in the table based on real-time fluctuations , corresponding to the continuous moments And the revised traffic flow prediction curve First, we extract the core indicators that can reflect the tunnel capacity and queue evolution intensity under the entrance fluctuation state, including the maximum flow limit , Queue diffusion critical time .

[0033] In a preferred embodiment, step S4 further includes the following contents:

[0034] Numerical adjustment and parameter mapping: Based on the core indicators recorded by the key parameters of the traffic model and combined with the traffic flow prediction curve sequence, the original traffic flow prediction model is numerically mapped and improved in the following ways: ;in Indicates that the current prediction model is at time The core status is a nonlinear correction function.

[0035] In a preferred embodiment, step S4 further includes the following contents:

[0036] External module linkage strategy: When the updated traffic prediction model is obtained After that, the following two types of information are immediately injected into the corresponding modules according to the tunnel operation plan and external signal light control requirements:

[0037] 1) Entrance signal light coordination module: Based on the newly added maximum flow upper limit and queue diffusion critical time indicators in the flow prediction model, it is determined whether a tighter or looser signal switching cycle needs to be implemented in the future according to the preset judgment criteria, and the signal coordination data object is entered in a structured form;

[0038] 2) Tunnel internal guidance control module: According to the potential congestion accumulation period estimated by the new model, add corresponding speed limit instructions and lane guidance configurations in the lane guidance rule object;

[0039] Finally, the updated traffic prediction model and the signal coordination data and lane induction rules derived therefrom are written into a data table named updated traffic model.

[0040] The intelligent safety operation and maintenance system for urban tunnels combined with multiple data includes: pulse extraction module, path calculation module, timing correction module and dynamic update module;

[0041] Pulse extraction module: uses short-period signal switching logs and vehicle detection data, applies wave amplitude extraction algorithm to identify the surge time window and peak size of vehicles in each cycle, generates fluctuation feature mapping matrix, and passes the fluctuation feature mapping matrix to the path calculation module;

[0042] Path calculation module: combines the mapping matrix with the traffic detection data in the tunnel, predicts the congestion propagation path of the wave in the tunnel through wave similarity analysis, identifies the target section and the corresponding time window, and transmits the identification result to the timing correction module;

[0043] Time series correction module: It iteratively corrects the target sections and time windows marked in the congestion propagation matrix, combines the historical fluctuation case library, uses time series superposition calculation to analyze the traffic density, queue length and amplitude attenuation law, generates a traffic flow transfer prediction curve with real-time fluctuation feedback, and transmits the traffic flow transfer prediction curve to the dynamic update module;

[0044] Dynamic update module: Updates tunnel operation and maintenance model parameters according to the traffic flow prediction curve, and generates traffic light cycle adjustment strategies and lane induction instructions.

[0045] The technical effects and advantages of the intelligent safety operation and maintenance method and system for urban tunnels combined with multiple data of the present invention are as follows:

[0046] The present invention systematically expresses the entrance peak characteristics for the first time by constructing a fluctuation feature mapping matrix, and combines the traffic parameters of multi-point detectors to accurately evaluate the propagation path and target section of the fluctuation using the wave similarity analysis model; at the same time, with the help of the time series superposition correction mechanism, the historical fluctuation law is integrated with the real-time data to generate a dynamically corrected vehicle flow transfer prediction curve, thus achieving a balance between physical constraints and prediction accuracy; finally, through the dynamic update of the traffic prediction model, key indicators such as the maximum flow upper limit and the critical duration of queue diffusion are accurately output, guiding the efficient linkage of signal coordination and induced control modules, which can significantly improve the timeliness and accuracy of traffic flow prediction in tunnels under complex fluctuation scenarios, minimize the congestion accumulation and traffic risks caused by fluctuation propagation, optimize tunnel operation and maintenance efficiency, and enhance traffic safety assurance capabilities. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 It is a flow chart of the intelligent safety operation and maintenance method of urban tunnels combined with multiple data of the present invention;

[0048] Figure 2 It is a structural schematic diagram of the intelligent safety operation and maintenance system for urban tunnels combined with multiple data according to the present invention. DETAILED DESCRIPTION

[0049] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0050] Embodiment 1: Figure 1 The present invention provides an intelligent and safe operation and maintenance method for urban tunnels combining multiple data, including:

[0051] S1. Use the short-period signal switching log and vehicle detection data, use the wave amplitude extraction algorithm to determine the time window and peak scale of the rapid influx of vehicles in each cycle, and generate a fluctuation feature mapping matrix;

[0052] S2. Combine the mapping matrix with the traffic detection data in the tunnel, predict the congestion propagation path of the wave in the tunnel through wave similarity analysis, and identify the target section and the corresponding time window;

[0053] S3. Iterate and correct the traffic density, queue length and amplitude attenuation of the target section, use the historical fluctuation case library to perform time series superposition calculation, and output the traffic flow prediction curve with real-time fluctuation feedback;

[0054] S4. Update the traffic prediction model parameters of the tunnel operation and maintenance system according to the traffic flow prediction curve, and issue dynamic control instructions in a timely manner through external signal light coordination and lane induction linkage mechanism.

[0055] As urban traffic networks become increasingly complex, traffic tunnels, as key traffic hubs, have a direct impact on overall traffic flow and urban operations due to their operational efficiency and safety. However, the frequent switching of short-cycle signal lights at the entrance of the tunnel causes vehicles to flow into the tunnel in waves, forming a nonlinear and highly volatile traffic pattern. This volatility not only poses significant challenges to traditional traffic flow prediction models, but may also lead to prediction errors, which in turn affect real-time traffic management and safety warnings in the tunnel. Therefore, there is an urgent need for a method that can accurately identify and quantify vehicle flow fluctuations caused by short-cycle signal regulation in order to optimize the traffic flow prediction model in the tunnel and improve the response speed and decision-making accuracy of the traffic management system.

[0056] Step S1 is based on the short-period signal switching log at the tunnel entrance and the real-time output of the vehicle detection equipment. By constructing a wave amplitude extraction algorithm, the vehicle entry time in each signal cycle is segmented and counted, and the traffic surge in each time window is marked using the peak threshold. The resulting short-period fluctuation sequence is then stored in the fluctuation feature mapping matrix. Specifically, it includes the following contents:

[0057] First, obtain the start time of each signal cycle from the short-period signal switching log at the tunnel entrance and end time ,in Indicates At the same time, the real-time vehicle entry data in each signal cycle is obtained from the vehicle detection equipment. ,in Indicates a specific point in time. In order to ensure the consistency and accuracy of the data, the original data needs to be synchronized and format standardized to eliminate the clock deviation and data format differences that may exist between different devices.

[0058] For each signal cycle , the vehicles in the corresponding period enter the data At fixed time intervals (For example, once per second) to generate a vehicle entry rate sequence ,in Indicates Then, define the wave amplitude is the cumulative increment of vehicle entry rate in the corresponding signal period, that is, ;in The signal period This metric is used to quantify the degree of surge in the vehicle entry rate within each signal period.

[0059] After the wave amplitude is determined, if the wave amplitude value exceeds the peak threshold , it is considered that there is a significant increase in vehicle traffic in the corresponding signal period. Next, calculate the peak time in the corresponding signal period , which is defined as the period of the signal The specific time point within , where the cumulative increment reaches the maximum value: ; This peak moment represents the strongest moment of the surge in vehicle entry rate and has key identifying significance.

[0060] For each signal cycle ,like , then add the corresponding record item to the fluctuation feature mapping matrix middle: in, is the signal cycle number, is the wave amplitude, For the peak moment, and are the start and end time of the signal cycle respectively. Through this process, the fluctuation feature mapping matrix will systematically record all significant vehicle flow surge events and form a complete short-period fluctuation sequence.

[0061] To ensure that the subsequent steps can efficiently call and reference the fluctuation characteristic information, the fluctuation characteristic mapping matrix is ​​stored in a dedicated database in the form of a structured data table.

[0062] Step S1 constructs a fluctuation feature mapping matrix by combining the short-cycle signal switching log with the real-time vehicle detection data, accurately identifying the traffic surge and its peak time in each signal cycle. This method not only achieves a detailed quantification of vehicle flow fluctuations, but also ensures the structured storage of data, which facilitates efficient calling and analysis in subsequent steps. Specific benefits include: significantly improving the accuracy of the traffic prediction model under short-cycle signal regulation, reducing misjudgments and delays caused by prediction bias; providing a reliable data basis for the accurate evaluation of congestion propagation paths, thereby optimizing traffic control strategies in tunnels; and enhancing the scalability and data processing efficiency of the system through structured data management. These advantages jointly promote the overall effectiveness of the intelligent and safe operation and maintenance system of urban tunnels, ensuring the efficiency and safety of tunnel operations.

[0063] In the stage of creating the fluctuation feature mapping matrix, the surge in vehicle traffic caused by short-cycle signal switching has been carefully quantified and stored in a unified data structure, the fluctuation feature mapping matrix. Next, it is necessary to combine the fluctuation feature mapping matrix with the multi-dimensional traffic parameters provided by the multi-point detectors in the tunnel, and use the correlation kernel function configured in the wave similarity analysis module to identify and evaluate the congestion propagation paths that may be caused by these significant peaks, thereby laying the foundation for subsequent time series superposition correction and traffic prediction. In order to enable the subsequent steps to accurately obtain the congestion evolution information of each target section, this process will produce a data structure containing the congestion propagation time window and section identification, in preparation for the next stage of performing fluctuation correction and transmission prediction.

[0064] After obtaining the fluctuation feature mapping matrix, step S2 calls the actual traffic parameters uploaded by the multi-point detectors in the tunnel, uses the wave similarity analysis module based on the built-in correlation kernel function to evaluate the congestion propagation path triggered under the current fluctuation form, and estimates the congestion transmission for each peak in the mapping matrix, extracting the target section and corresponding time window where congestion accumulation occurs. Specifically, it includes the following contents:

[0065] In the multi-point detector, first define the traffic parameter set based on time series ,in represents the detector position number, represents the discretized time index. At the same time, the fluctuation feature mapping matrix retains several entries, among which the peak moment is used to locate the central moment of vehicle flow surge. In this step, for each peak moment, the following operations need to be performed:

[0066] Linked Data Retrieval and Alignment: Traversing Traffic Parameter Sets from Multiple Point Detectors , filter out the peak moments that are adjacent to or covered by Detector position within the time period The corresponding time series To ensure data consistency, the discretized time index needs to be aligned with the peak time. When there is a time difference between the sampling interval of the detector and the input data, the detector sequence is intercepted and stored in the intermediate data table using the same discretization scale. , providing directly callable aligned traffic features for subsequent analysis.

[0067] Correlation kernel function and wave similarity calculation: Define the correlation kernel function between each peak record and its aligned intermediate data table To measure the similarity between the peak and the actual traffic flow, let Indicates the detector At the moment The traffic parameter mapping vector at Indicates peak entry The incremental feature map in the fluctuation feature map matrix. The correlation kernel function is as follows: ;in and is a positive constant used to regulate the decay rate of the difference in traffic characteristics in the exponential term.

[0068] Based on the correlation kernel function, Calculate several moments The similarity mean or maximum value is used to quantify the degree of agreement between the current peak and the actual flow rate measured by the detector, obtain the fluctuation shape matching degree, and record the result as .

[0069] Congestion propagation path evaluation: After obtaining the wave shape matching degree, based on the tunnel geographical topology order and the spatial distribution of multi-point detectors, the potential traffic queue extension caused by the wave crest is evaluated section by section. , if the fluctuation pattern matching degree exceeds a certain critical threshold , it is considered that the detector position is likely to experience a significant decrease in vehicle speed or a continuous increase in occupancy rate under the influence of the current peak, and then Marked as a potential congestion trigger point. Then, numbered according to the order of the detectors in the tunnel , recursively from the entrance to the depth of the tunnel and records it at each location The specific time interval of the congestion trigger point , and finally a set of target sections for congestion accumulation is obtained and its corresponding time window .

[0070] All the target sections, congestion trigger points and corresponding time windows obtained above are written into the congestion propagation matrix in a table form , whose structure includes ,in Corresponding to the signal period identification in the fluctuation characteristic mapping matrix, Used to determine the detector segments that may be covered by congestion, Characterizes the congestion time window. After this table object is associated with the fluctuation feature mapping matrix, it will be read and used by the subsequent time series superposition correction and prediction steps. This ensures that the quantitative information of the congestion propagation path can be accurately called when making corrections based on the fluctuation characteristics in the next stage.

[0071] Step S2 establishes a close mapping relationship between the fluctuation characteristics at the entrance and the spatiotemporal flow data of the multi-point detector. It not only calculates the wave similarity, but also mines the congestion accumulation sections and time windows that may be triggered by the peak inside the tunnel based on the correlation kernel function. Corresponding one-to-one with the fluctuation characteristic mapping matrix of step S1, the congestion propagation matrix generated by step S2 stores the congestion probability information and target section positioning that match each peak entry, providing a key quantitative basis for subsequent time series superposition correction and transmission prediction. In the face of frequent switching scenarios of short-cycle signals, the results of this step can more accurately capture the impact of flow fluctuations on the traffic situation inside the tunnel, thereby laying a solid data foundation for subsequent fluctuation correction and dynamic induction decision-making.

[0072] A multi-point detector is a network of traffic parameter collection devices deployed at multiple key locations inside a tunnel, designed to capture multi-dimensional traffic operation status data including vehicle flow, vehicle speed, lane occupancy, queue length, etc. in real time. Its core function is to accurately characterize the spatiotemporal traffic status in the tunnel through a distributed sensing layout, and provide dynamic data sequences for each monitoring point in different time periods. These detectors usually include a variety of sensing technologies such as ground sensing coils, microwave radars, and video monitoring devices, which can record changes in traffic parameters in a continuous or discrete form, providing basic support for congestion propagation path assessment and traffic management. Through the collaborative work of multi-point detectors, a fully covered data collection network can be formed in space, thereby realizing real-time monitoring and multi-level analysis of traffic conditions in tunnels.

[0073] In step S1, the vehicle fluctuation information caused by short-cycle signal switching is recorded in the fluctuation feature mapping matrix; in step S2, the fluctuation feature mapping matrix is ​​matched with the traffic data of the multi-point detector through the correlation kernel function to generate a congestion propagation matrix, which clarifies the target section and time window where congestion accumulation may occur. At this stage, it is necessary to iteratively correct the traffic density and queue length of the target section based on the above data structure and the typical fluctuation evolution curve stored in the historical fluctuation case library using the principle of time series superposition, and comprehensively consider the dynamic factors such as the entrance amplitude attenuation rate and the mid-to-back section traffic capacity, and finally form a traffic flow transmission prediction curve with real-time fluctuation feedback.

[0074] Step S3 compares the obtained congestion propagation path and the target section results with the historical fluctuation case library, calls the fluctuation correction mechanism based on the principle of time series superposition, iteratively corrects the traffic density and vehicle queue time of each target section, and comprehensively considers the dynamic changes of the entrance amplitude attenuation rate and the traffic capacity of the middle and rear sections to form a traffic flow transmission prediction curve with real-time fluctuation feedback, so as to reflect how the entrance wave peak is deferred or dissipated at different positions of the tunnel depth. Specifically, it includes the following contents:

[0075] Historical case retrieval and mapping: Based on the records in the congestion propagation matrix , Target Segment and congestion triggering time interval , select from the historical fluctuation case library The corresponding reference items with similar entrance fluctuation shapes and similar segment lengths and queue evolution characteristics are denoted as Then, the corresponding historical traffic delay function is retrieved ;in Indicates a reference item After entering the tunnel The queue growth rate within the time length is used to reflect the accumulation or attenuation of the entrance amplitude in the depth of the tunnel. This delay function is temporarily stored in an intermediate data object named historicalDelayMap.

[0076] Fluctuation correction based on the principle of time series superposition: The traffic density detected in the target section and waiting time , define a set of fluctuation correction equations based on time series superposition. For example, in Time On, order ;in express Position at time The correction factor of It is a pre-designed volatility superposition function used to quantify the historical delay function Cumulative impacts revealed vs. currently observed occupancy characteristics The superposition coupling between them can calculate a comprehensive correction value to reflect the superposition effect of historical fluctuation laws on the current traffic status. It aims to capture the complex relationship between peak accumulation, attenuation and queue growth rate, so as to make a more realistic correction to the traffic prediction model. The specific form can be defined as: ;in represents the delay function extracted from historical fluctuation cases, quantifying the cumulative queue growth rate caused by the peak on the propagation path, Current target segment In time The traffic density, Indicates the maximum traffic density of the segment, used to normalize the current density level. is the queue attenuation factor, which controls the speed at which the fluctuation superposition gradually decreases as the queue length increases. Density amplification factor, used to quantify the impact of traffic density on overlay correction.

[0077] By similar recursion, along to The fluctuation correction amount is calculated point by point in the direction, and the following progression is performed between adjacent positions: and Distance ,but ;in The detectors are indexed sequentially in the tunnel, here is a segment length correction function, which is calculated by the spatial distance Put the upstream position The fluctuations in time Deliver to the moment , simulating the propagation attenuation of the wave crest at the depth of the tunnel, thus reflecting the transmission law of the wave crest at the depth of the tunnel. The specific form can be defined as: ;in, It is the distance attenuation factor, which is used to control the decreasing rate of the peak intensity with increasing distance. Its value is determined by the tunnel structure and flow characteristics.

[0078] Comprehensive consideration of attenuation factor and traffic capacity: When performing the above superposition, if the wave amplitude is less than the peak threshold or the congestion time window exceeds a specific range, an additional attenuation factor needs to be introduced , to reflect the impact of the entrance peak gradually decreasing as the traffic flow travels further, and to add the upper limit of the capacity of the middle and rear sections when including the large flow bottleneck section , constraining the cumulative value of the superposition function to not exceed the upper limit of the physical capacity. For example, when calculating When the fluctuation is If the value of ; thereby correcting or reducing the excessive accumulated volatility effects.

[0079] Generation of traffic flow prediction curve: After completing time series superposition and fluctuation correction, the target section Each position In continuous moments The corrected density and queue duration are obtained, and then the traffic flow transfer prediction curve is fitted. ; to show how the entrance peak is delayed or fades with tunnel depth. With target segment Corresponding traffic flow transfer prediction curve sequence Stored in the real-time fluctuation prediction table realTimeWavePredict, it provides directly referenced transfer prediction results for the next step of implementing key parameter updates and external control strategy linkage.

[0080] Through retrieval and mapping based on historical fluctuation cases, combined with the principle of time series superposition, multiple iterative corrections are made to the traffic density and queue duration of the target section. Step S3 finally generates a traffic flow prediction curve that covers the attenuation law of the cumulative effect of the entrance peak. This curve intuitively reveals how congestion evolves or decays in different sections after vehicles rush in from the entrance in a pulsed manner. The structured storage method of this result ensures that the next step can easily call relevant prediction data, so that in step S4, the key parameters of the traffic prediction model are dynamically updated and the external signal lights and induction strategies in the tunnel are linked, so as to achieve more accurate congestion prevention and control and safe operation and maintenance in an environment with frequent switching of short-cycle signals.

[0081] In step S1 and step S2, the construction from the fluctuation feature mapping matrix to the congestion propagation matrix has been completed, and the target section and its corresponding time window have been identified; in step S3, the traffic density and queue length of the above target section are subjected to multiple rounds of fluctuation correction using the historical fluctuation case library and the principle of time series superposition, and finally a real-time fluctuation prediction table is generated to record the traffic flow transmission prediction results. Next, it is necessary to update the key parameters of the tunnel flow prediction model in real time based on the prediction curve described in the real-time fluctuation prediction table, and distribute the updated model information to the external signal light coordination module and the tunnel internal induction control module, so as to take effective flow control measures before predicting the impending traffic accumulation.

[0082] Step S4 updates the key parameters of the traffic prediction model of the tunnel intelligent operation and maintenance system in real time based on the obtained traffic flow transfer prediction curve, and distributes the update information to the external signal light coordination module and the tunnel internal induction control module at the same time, so that the operation and maintenance platform can quickly and dynamically adjust the signal light timing and tunnel lane guidance measures before predicting the impending traffic accumulation, so as to continuously optimize the overall traffic status and minimize the congestion or safety risks caused by traffic flow fluctuations. Specifically, it includes the following contents:

[0083] Dynamic extraction of key parameters of the traffic prediction model: predicting the target segment in the table based on real-time fluctuations , corresponding to the continuous moments And the revised traffic flow prediction curve First, we extract several core indicators that can reflect the tunnel capacity and queue evolution intensity under the entrance fluctuation state, including the maximum flow limit , Queue diffusion critical time Etc. and archived in the defined data structure traffic model key parameters. This ensures that the input of the subsequent numerical adjustment phase is consistent with the prediction results of the third step.

[0084] Numerical adjustment and parameter mapping: For each indicator recorded by the key parameters of the traffic model, such as the maximum traffic limit and the critical time of queue diffusion, combined with the traffic flow transfer prediction curve sequence generated in step S3, the original traffic flow prediction model is numerically mapped and improved in the following form: ;in Indicates that the current prediction model is at time The core status It is a nonlinear correction function, which is used to absorb the congestion accumulation and fluctuation attenuation information calculated in step S3, and superimpose it on the original model, so that the model can respond more sensitively to the dynamic impact of the entrance wave on traffic operation. The specific form can be defined as: ;in It is the fluctuation response intensity coefficient, which is used to adjust the overall amplitude of the correction amount and is determined according to historical data statistics. is the time decay factor that controls the model's sensitivity to longer-term fluctuations.

[0085] External module linkage strategy: When the updated traffic prediction model is obtained After that, the following two types of information are immediately injected into the corresponding modules according to the tunnel operation plan and external signal light control requirements:

[0086] 1). Entrance signal light coordination module: Based on the newly added maximum flow upper limit, queue diffusion critical time and other indicators in the traffic prediction model, it is determined whether a tighter or looser signal switching cycle is required in the future according to the preset judgment criteria, and the signal coordination data object is entered in a structured form; the signal coordination data is used to record and transmit the adjustment information of the signal light control module, such as the recommended signal cycle or switching strategy.

[0087] 2) Tunnel internal guidance control module: According to the potential congestion accumulation period estimated by the new model, the corresponding speed limit instructions and lane guidance configurations are added to the lane guidance rule object to complete the pre-deployment before the specific time interval arrives.

[0088] Finally, the updated traffic prediction model and the signal coordination data and lane induction rules derived from it are written into a data table called the updated traffic model. This table is bidirectionally associated with the real-time fluctuation prediction table so that if new waves continue to surge, the traffic prediction model can be quickly retrieved and further iterated and modified.

[0089] By deeply analyzing the traffic flow information contained in the real-time fluctuation prediction table, step S4 completes the real-time correction of the key parameters of the tunnel traffic prediction model, generates an updated model that is closely coupled to the entrance fluctuation state, and distributes the corresponding parameters to the external signal light coordination module and the tunnel internal induction control module in real time. This can effectively improve the accuracy of traffic flow prediction in scenarios with frequent short-cycle signal switching, and provides an accurate basis for formulating reasonable speed limit, diversion and signal cycle plans before the peak impact arrives, thereby preventing or alleviating potential congestion accumulation and ensuring traffic efficiency and safety. The output updated traffic model is bidirectionally connected to the real-time fluctuation prediction table in a structured manner, and the overall traffic prediction architecture can be further corrected and optimized according to the subsequent newly added fluctuation information, so that the tunnel operation and maintenance platform can maintain agile response and continuous optimization capabilities to fluctuation risks in a highly dynamic traffic environment.

[0090] Embodiment 2: Figure 2 The invention provides an intelligent safety operation and maintenance system for urban tunnels combined with multiple data, including: a pulse extraction module, a path calculation module, a timing correction module and a dynamic update module;

[0091] Pulse extraction module: uses short-period signal switching logs and vehicle detection data, applies wave amplitude extraction algorithm to identify the surge time window and peak size of vehicles in each cycle, generates fluctuation feature mapping matrix, and passes the fluctuation feature mapping matrix to the path calculation module;

[0092] Path calculation module: combines the mapping matrix with the traffic detection data in the tunnel, predicts the congestion propagation path of the wave in the tunnel through wave similarity analysis, identifies the target section and the corresponding time window, and transmits the identification result to the timing correction module;

[0093] Time series correction module: It iteratively corrects the target sections and time windows marked in the congestion propagation matrix, combines the historical fluctuation case library, uses time series superposition calculation to analyze the traffic density, queue length and amplitude attenuation law, generates a traffic flow transfer prediction curve with real-time fluctuation feedback, and transmits the traffic flow transfer prediction curve to the dynamic update module;

[0094] Dynamic update module: Updates tunnel operation and maintenance model parameters according to the traffic flow prediction curve, and generates traffic light cycle adjustment strategies and lane induction instructions.

[0095] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0096] The above description is only by way of illustration of certain exemplary embodiments of the present invention. It is undoubted that those skilled in the art can modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the above drawings and descriptions are illustrative in nature and should not be construed as limiting the scope of protection of the claims of the present invention.

[0097] It should be noted that, in this article, if there are relational terms such as first and second, etc., they are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "including a..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0098] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. An intelligent and safe operation and maintenance method for urban tunnels combining multiple data, characterized in that: Includes steps: S1. Use the short-period signal switching log and vehicle detection data, use the wave amplitude extraction algorithm to determine the time window and peak scale of the rapid influx of vehicles in each cycle, and generate a fluctuation feature mapping matrix; S2. Combine the mapping matrix with the traffic detection data in the tunnel, predict the congestion propagation path of the wave in the tunnel through wave similarity analysis, and identify the target section and the corresponding time window; S3. Iterate and correct the traffic density, queue length and amplitude attenuation of the target section, use the historical fluctuation case library to perform time series superposition calculation, and output the traffic flow prediction curve with real-time fluctuation feedback; S4. Update the traffic prediction model parameters of the tunnel operation and maintenance system according to the traffic flow prediction curve, and issue dynamic control instructions in a timely manner through external signal light coordination and lane induction linkage mechanism.

2. The intelligent safety operation and maintenance method for urban tunnels combined with multiple data according to claim 1 is characterized in that: Step S1 includes the following contents: First, obtain the start time of each signal cycle from the short-period signal switching log at the tunnel entrance and end time ,in Indicates signal cycle; at the same time, real-time vehicle entry data in each signal cycle is obtained from the vehicle detection equipment; For each signal cycle , the vehicle entry data in the corresponding period is segmented and counted at fixed time intervals to generate a vehicle entry rate sequence ,in Indicates time periods; then, define the wave amplitude is the cumulative increment of vehicle entry rate in the corresponding signal period; After the wave amplitude is determined, if the wave amplitude value exceeds the peak threshold , it is considered that there is a significant increase in vehicle traffic in the corresponding signal period; Next, calculate the peak time within the corresponding signal period , which is defined as the period of the signal The specific time point within which the cumulative increment reaches the maximum value; For each signal cycle ,like , then add the corresponding record item to the fluctuation feature mapping matrix.

3. The intelligent safety operation and maintenance method for urban tunnels combined with multiple data according to claim 2 is characterized in that: Step S2 includes the following contents: In the multi-point detector, first define the traffic parameter set based on time series ,in represents the detector position number, Represents a discretized time index; Related data retrieval and alignment: traverse the traffic parameter set of multiple point detectors and filter out the traffic parameters that are adjacent to or covered by the peak time. Detector position within the time period The corresponding time series ; Align the discretized time index with the peak time. When there is a time difference between the sampling interval of the detector and the input data, the detector sequence is intercepted using the same discretization scale and stored in the intermediate data table. Correlation kernel function and wave similarity calculation: Define the correlation kernel function between each peak record and its aligned intermediate data table To measure the similarity between the peak and the actual traffic flow, let Indicates the detector At the moment The traffic parameter mapping vector at Indicates peak entry The incremental feature map in the fluctuation feature map matrix; the associated kernel function is as follows: ;in and is a positive constant used to regulate the decay rate of the difference in traffic characteristics in the exponential term.

4. The intelligent safety operation and maintenance method for urban tunnels combined with multiple data according to claim 3 is characterized in that: Step S2 also includes the following contents: Based on the correlation kernel function, Calculate several moments The similarity mean or maximum value is used to quantify the degree of agreement between the current peak and the flow rate measured by the detector, and obtain the fluctuation shape matching degree; Congestion propagation path assessment: After obtaining the wave shape matching degree, the potential traffic queue extension caused by the wave crest is evaluated section by section based on the tunnel geographical topology order and the spatial distribution of multi-point detectors; For each detector position If the fluctuation shape matching degree exceeds a certain critical threshold, it is considered that the vehicle speed at this detector position has dropped significantly or the occupancy rate has continued to rise under the influence of the current peak. Mark as potential congestion trigger points; Then, the detectors are numbered in the order in which they are located in the tunnel. , recursively from the entrance to the depth of the tunnel and records it at each location The specific time interval of the congestion trigger point , and finally a set of target sections for congestion accumulation is obtained and its corresponding time window ; All the obtained target sections, congestion triggering points and corresponding time windows are written into the congestion propagation matrix in a tabular form.

5. The intelligent safety operation and maintenance method for urban tunnels combined with multiple data according to claim 4 is characterized in that: Step S3 includes the following contents: Historical case retrieval and mapping: Based on the records in the congestion propagation matrix , Target Segment and congestion triggering time interval , select from the historical fluctuation case library The corresponding reference items with similar entrance fluctuation shapes and similar segment lengths and queue evolution characteristics are denoted as ; Then, the corresponding historical traffic delay function is retrieved ;in Indicates a reference item After entering the tunnel The growth rate of the queue within the duration; Fluctuation correction based on the principle of time series superposition: The traffic density detected in the target section and waiting time , define a set of fluctuation correction equations based on time series superposition; in Time On, order ;in express Position at time The correction factor of is a pre-designed wave superposition function; through a similar form of recursion, along to The fluctuation correction amount is calculated point by point in the direction, and the following progression is performed between adjacent positions: and Distance ,but ;in The detectors are indexed sequentially in the tunnel. is the segment length correction function.

6. The method for intelligent and safe operation and maintenance of urban tunnels combining multiple data according to claim 5 is characterized in that: Step S3 also includes the following: Comprehensive consideration of attenuation factor and traffic capacity: When superimposing, if the wave amplitude is less than the peak threshold or the congestion time window exceeds a specific range, the attenuation factor is introduced. , to reflect the impact of the entrance peak gradually decreasing as the traffic flow travels further, and to add the upper limit of the capacity of the middle and rear sections when including the large flow bottleneck section , constrain the cumulative value of the superposition function not to exceed the upper limit of the physical capacity; Generation of traffic flow prediction curve: After completing time series superposition and fluctuation correction, the target section Each position In continuous moments The corrected density and queue duration are obtained, and the traffic flow transfer prediction curve is fitted. ; Each The traffic flow transfer prediction curve sequence corresponding to the target section is stored in the real-time fluctuation prediction table.

7. The method for intelligent and safe operation and maintenance of urban tunnels combining multiple data according to claim 6 is characterized in that: Step S4 includes the following contents: Dynamic extraction of key parameters of the traffic prediction model: predicting the target segment in the table based on real-time fluctuations , corresponding to the continuous moments And the revised traffic flow prediction curve First, we extract the core indicators that can reflect the tunnel capacity and queue evolution intensity under the entrance fluctuation state, including the maximum flow limit , Queue diffusion critical time .

8. The method for intelligent and safe operation and maintenance of urban tunnels combining multiple data according to claim 7 is characterized in that: Step S4 also includes the following contents: Numerical adjustment and parameter mapping: Based on the core indicators recorded by the key parameters of the traffic model and combined with the traffic flow prediction curve sequence, the original traffic flow prediction model is numerically mapped and improved in the following ways: ;in Indicates that the current prediction model is at time The core status is a nonlinear correction function.

9. The method for intelligent and safe operation and maintenance of urban tunnels combining multiple data according to claim 8 is characterized in that: Step S4 also includes the following contents: External module linkage strategy: When the updated traffic prediction model is obtained After that, the following two types of information are immediately injected into the corresponding modules according to the tunnel operation plan and external signal light control requirements: 1) Entrance signal light coordination module: Based on the newly added maximum flow upper limit and queue diffusion critical time indicators in the flow prediction model, it is determined whether a tighter or looser signal switching cycle needs to be implemented in the future according to the preset judgment criteria, and the signal coordination data object is entered in a structured form; 2) Tunnel internal guidance control module: According to the potential congestion accumulation period estimated by the new model, add corresponding speed limit instructions and lane guidance configurations in the lane guidance rule object; Finally, the updated traffic prediction model and the signal coordination data and lane induction rules derived therefrom are written into a data table named updated traffic model.

10. An intelligent safety operation and maintenance system for urban tunnels combined with multiple data, used to implement the intelligent safety operation and maintenance method for urban tunnels combined with multiple data as claimed in any one of claims 1 to 9, characterized in that: include: Pulse extraction module, path calculation module, timing correction module and dynamic update module; Pulse extraction module: uses short-period signal switching logs and vehicle detection data, applies wave amplitude extraction algorithm to identify the surge time window and peak size of vehicles in each cycle, generates fluctuation feature mapping matrix, and passes the fluctuation feature mapping matrix to the path calculation module; Path calculation module: combines the mapping matrix with the traffic detection data in the tunnel, predicts the congestion propagation path of the wave in the tunnel through wave similarity analysis, identifies the target section and the corresponding time window, and transmits the identification result to the timing correction module; Time series correction module: It iteratively corrects the target sections and time windows marked in the congestion propagation matrix, combines the historical fluctuation case library, uses time series superposition calculation to analyze the traffic density, queue length and amplitude attenuation law, generates a traffic flow transfer prediction curve with real-time fluctuation feedback, and transmits the traffic flow transfer prediction curve to the dynamic update module; Dynamic update module: Updates tunnel operation and maintenance model parameters according to the traffic flow prediction curve, and generates traffic light cycle adjustment strategies and lane induction instructions.

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