Tunnel fire smoke removal method and system based on dynamic zoning sound field

Abstract

The application discloses a tunnel fire smoke removal method and system based on a dynamic partition sound field. The method comprises the following steps: after a mobile device enters a smoke area, a sound source wide spectrum scanning is performed, a monitoring unit is tracked and read, and a light transmittance baseline is established; the visibility improvement rate and the real-time agglomeration efficiency are calculated through a quantitative formula; the priority processing area is screened based on the two indexes, the optimal frequency is calculated by maximizing the entrainment coefficient difference, the measured optimal frequency is determined by combining the numerical optimization algorithm; the power is compensated through the sound intensity-distance attenuation model to form the adaptive closed-loop control of dynamic partition scheduling-frequency optimization-power compensation until the light transmittance reaches the standard. The system comprises a mobile platform, a wide-angle sound source, a laser monitoring unit and a parameter optimization control system. The application realizes the formula optimization of smoke removal parameters, the dynamic regional scheduling and the closed-loop control, and has the advantages of high smoke removal efficiency, fast response, full coverage and the like, and is suitable for the smoke removal scene in the emergency fire of a closed tunnel such as a subway and a railway.

Description

Technical Field

[0001] This invention relates to the technical field of smoke removal or prevention of smoke from the area of ​​origin and tunnel safety escape devices, specifically to a method and system for removing smoke from tunnel fires based on dynamic zoned sound fields. Background Technology

[0002] Enclosed tunnels (highway, subway, urban rail, high-speed rail tunnels, etc.) are characterized by their long, narrow, and poorly ventilated spaces. In the event of a fire, hot smoke and aerosol particles rapidly spread and accumulate longitudinally along the tunnel, unable to dissipate naturally and disrupting escape routes. The strong light-blocking properties of high-concentration smoke cause a sharp drop in visibility within the tunnel, directly obstructing vehicle evacuation, personnel escape routes, and fire rescue paths. This is a core contributing factor to mass casualties in tunnel fires. Smoke removal is the most critical and effective means of improving visibility in a fire. In a fire, the extinction coefficient for safe escape is between 0.15 and 1.2 meters. -1 The minimum escape threshold corresponding to smoke transmittance is 88.7%, and the corresponding minimum visibility threshold is 2.5m. Only when these conditions are met can trapped personnel clearly identify escape indication signals and escape routes, thereby increasing the success rate of escape and rescue.

[0003] Existing tunnel smoke control systems rely on large fans for longitudinal / lateral mechanical smoke extraction. These systems generally suffer from drawbacks such as delayed system startup response, complex duct layout, and fixed, rigid smoke extraction paths. Especially in fire situations where the fire source is unknown and smoke is unevenly distributed, they cannot quickly and accurately target areas with high smoke concentrations. As a result, the overall smoke extraction efficiency is insufficient to meet the timeliness requirements of fire emergency rescue.

[0004] Acoustic agglomeration technology uses a high-intensity sound field to drive smoke aerosol particles to vibrate at high frequencies, collide, agglomerate, and accelerate sedimentation, which can quickly improve visibility in local spaces. It has been gradually applied in fields such as industrial dust removal, air purification, and industrial mist control. However, when this technology is directly applied to enclosed tunnel scenarios, there are insurmountable technical bottlenecks: First, the tunnel has a large longitudinal span, and the sound field coverage of a fixed sound source is limited, making it impossible to achieve long-distance, full-area smoke removal; second, the tunnel cross-section is an irregular, enclosed structure, and conventional sound sources cannot form a uniform and efficient full-section sound field, easily creating smoke removal blind spots at the top and side walls; third, the smoke concentration and aerosol particle size distribution in a fire change dynamically with the development of the fire and the tunnel ventilation status, and traditional fixed-parameter sound sources cannot intelligently match the optimal sound field parameters, resulting in a continuous decay of the smoke removal effect.

[0005] Therefore, developing a mobile acoustic smoke removal technology and system for tunnels, capable of autonomous movement, full-section sound field coverage, multi-parameter adaptive optimization, precise positioning, and targeted smoke control, has become a pressing technical challenge in the field of smoke removal or prevention of smoke escape from the generating area and tunnel safety escape devices. On the other hand, this system can also act as a sound barrier, controlling the smoke generated by a fire to one side of the tunnel's longitudinal direction, preventing the other side from being filled with smoke, thus creating a smoke-free area that facilitates personnel escape, rapid vehicle evacuation, and rescue operations. Summary of the Invention

[0006] To address the shortcomings of existing tunnel fire smoke control technologies, this invention provides a method and system for tunnel fire smoke removal based on dynamic zoned sound fields. The method utilizes a mobile device traversing the smoke zone and a laser monitoring unit to track and read data, obtaining a baseline of visibility. Based on a carrying coefficient difference model, a sound intensity attenuation model, and real-time feedback data, a closed-loop optimization algorithm is constructed to achieve dynamic and coordinated optimization control of sound source frequency, power, and device location, thereby significantly improving tunnel smoke removal efficiency and intelligence.

[0007] In a first aspect, embodiments of this application provide a method for clearing smoke from a tunnel fire based on a dynamic zoned sound field. This method utilizes a mobile device capable of moving along the tunnel, the mobile device comprising a sound source system, a control system, and a loading platform equipped with laser detection units. The method includes the following steps: S1: When a fire occurs, control the mobile device to travel through the entire smoke zone of the tunnel, activate the sound source system for broad-spectrum scanning, and simultaneously acquire the initial light transmittance data in the tunnel. S2: Based on the initial transmittance data, calculate the visibility and initial smoke volume fraction in the smoke area, and establish a baseline for tunnel visibility status and initial smoke volume fraction; S3: Real-time acquisition of current transmittance data for each monitoring area, and combined with the initial smoke volume fraction baseline, to calculate the visibility improvement rate and real-time aggregation efficiency for each monitoring area; S4: Perform adaptive closed-loop control, dynamically and collaboratively optimizing the operating frequency of the sound source, output power, and position of the mobile device based on the visibility improvement rate and the real-time aggregation efficiency. S5: Repeat steps S3 and S4 until the light transmittance of the key areas in the tunnel reaches the safe escape threshold. The adaptive closed-loop control, executed by the parameter optimization module, includes the following steps: S41: Based on the visibility improvement rate or the real-time clustering efficiency, determine the target area that needs to be prioritized from all areas, and control the mobile device to move towards the center of the target area; S42: Based on the principle of maximizing the difference in the carrying coefficient, the theoretical optimal frequency is calculated within the effective frequency range of the sound source system, and the visibility improvement rate of the target area is used as a feedback index. Fine-tuning is performed through a numerical optimization algorithm to determine the measured optimal operating frequency. S43: Based on the longitudinal length between the device and the target area, and using the sound intensity-distance attenuation model, dynamically calculate and adjust the current output power of the sound source system; S44: Based on the measured optimal operating frequency and the current output power, continuously monitor the aggregation efficiency of the target region until the target region is optimized; return to step S41 to select the next target region.

[0008] Preferably, the visibility improvement rate is obtained by the following formula: (1); in, For the first The rate of improvement in visibility in each monitored area For the first In each monitoring area Light transmittance at any given time For the first In each monitoring area Light transmittance at any given time , The total number of monitored areas, This represents the sampling time interval.

[0009] Preferably, the real-time aggregation efficiency is obtained by the following formula: (2); in, For the first Real-time aggregation efficiency of each monitoring area, monitoring area For the first Initial smoke volume fraction for each monitoring area No. Each monitoring area is in The volume fraction of smoke at any given time.

[0010] Preferably, the target area that needs to be prioritized is the area with the lowest real-time visibility or the key area that the system determines is most beneficial to improving personnel escape or fire rescue.

[0011] Preferably, the step of calculating the theoretical optimal frequency within the effective frequency range of the sound source system based on the principle of maximizing the banding coefficient difference includes: Based on the typical particle size distribution of smoke from tunnel fires, two representative particle sizes were selected. and ; The search is performed within a preset frequency range, and the frequency point where the relative carry coefficient reaches its maximum value is taken as the theoretical optimal frequency. Among them, the relative entrainment coefficient It can be obtained through the following formula: (3); in, , Particle size , Particles at frequency The entrainment coefficient in the sound field is obtained by the following formula: (4); in, For sound wave frequency, Particle density, The particle diameter is The dynamic viscosity of the gaseous medium.

[0012] Preferably, the sound intensity-distance attenuation model is expressed by the following formula: (5); in, The power of the sound source that needs to be adjusted. The effective sound intensity threshold for sound wave aggregation. This represents the real-time distance between the mobile platform and the target area. An environmental factor related to sound source directivity and tunnel wall reflection characteristics. is the attenuation index of sound waves in the smoke medium.

[0013] Preferably, the initial smoke volume fraction of each monitoring area is represented by or calculated from the average initial smoke volume fraction within that area.

[0014] Preferably, the fine-tuning using a numerical optimization algorithm includes: Using the visibility or visibility improvement rate in terms of transmittance of the target area as the optimization objective function, the gradient ascent method or the golden section method is used to search within the neighborhood of the theoretically optimal frequency to determine the measured optimal operating frequency.

[0015] Preferably, the condition for determining that the target area has completed optimization is: the real-time aggregation efficiency of the area reaches a preset threshold or the rate of increase tends to zero within several consecutive control cycles.

[0016] Secondly, embodiments of this application provide a tunnel fire smoke removal system based on dynamic zoned sound fields, including: A mobile device that can move along a tunnel; The sound source generating device installed on the mobile device is configured to generate a sound field with a horizontal diffusion angle of not less than 90° along the longitudinal section of the tunnel. The laser monitoring unit, which is arranged on the loading platform of the mobile device, includes a laser light source and a laser power meter. It can read the transmittance of the tunnel section in the vertical direction at the location of the mobile device in real time, and dynamically record it during the movement of the mobile device to obtain the transmittance baseline and real-time transmittance. The control system is integrated into the mobile device and is communicatively connected to the loading platform, the sound source generating device, and the laser monitoring unit. The control system includes a parameter optimization module, which is configured to perform steps S2 to S4 as described in the first aspect.

[0017] Compared to traditional tunnel mechanical smoke extraction technology, this invention establishes a tunnel fire smoke removal method based on dynamic zoned sound field through dual-index quantitative evaluation, maximum optimization of entrainment coefficient, dynamic compensation for sound intensity attenuation, and regional priority closed-loop scheduling. It integrates acoustic smoke removal technology into a mobile device to build an autonomously mobile acoustic smoke removal equipment, fundamentally solving the technical bottlenecks of existing technologies such as slow response, limited coverage, ambiguous parameter adjustment, low smoke removal efficiency, and poor adaptability to operating conditions. It possesses outstanding substantive features and significant progress, with specific beneficial effects as follows: (1) Dual-indicator quantitative closed-loop evaluation ensures accurate and measurable smoke removal effect, completely eliminating fuzzy judgment. The system adopts dual quantitative indicators of visibility improvement rate and real-time aggregation efficiency to replace the traditional single transmittance rough judgment. Relying on the laser monitoring unit and quantitative calculation, it realizes the digital and accurate evaluation of smoke removal effect. It can quickly locate key areas of smoke removal, and the accuracy of smoke removal effect judgment is improved by more than 90% compared with the traditional method. It provides reliable data support for adaptive control and completely solves the problems of "no standard, difficult to quantify, and blind parameter adjustment" in traditional smoke removal. (2) Maximizing the difference in entrainment coefficient and fine-tuning with numerical algorithm to achieve perfect matching between sound wave frequency and smoke particles. To address the shortcomings of traditional acoustic smoke removal methods that rely on blindly sweeping the broad spectrum, resulting in inaccurate frequency matching between the smoke particles and the sound waves, this invention, based on the typical smoke particle size in tunnel fires, accurately calculates the theoretically optimal frequency by maximizing the difference in the entrainment coefficient. This is then combined with the golden section method / gradient ascent method for experimental fine-tuning, ensuring a perfect match between the acoustic frequency and the agglomeration characteristics of the smoke particles. This improves frequency matching efficiency by 60%, increases the smoke particle agglomeration rate by over 35%, and significantly reduces the time required for smoke removal in a single area.

[0018] (3) Dynamic compensation for sound intensity-distance attenuation completely eliminates long-distance smoke removal blind spots in tunnels. An innovative sound intensity-distance attenuation model is introduced, which dynamically compensates the sound source output power based on the real-time distance between the mobile device and the target area. This effectively counteracts the propagation attenuation of sound waves in the tunnel smoke medium, ensuring that the sound intensity across the entire area is always maintained above the effective aggregation threshold of 120dB. The effective smoke removal coverage radius is increased by 50% compared to the traditional fixed power method, completely solving the industry problem of "strong near-field and weak far-field, and failure at long distance" in traditional sound wave smoke removal.

[0019] (4) Regional priority intelligent scheduling, targeting dense smoke areas, doubling the overall smoke removal efficiency. Based on the aggregation efficiency and visibility improvement rate, the system automatically selects priority target areas for treatment, concentrates sound field energy and mobile platform resources to target and deal with the core area of ​​dense smoke, abandons the inefficient mode of traditional uniform smoke removal across the entire area, and reduces the total smoke removal time by more than 40%, thus buying valuable time for personnel escape and fire rescue in tunnel fire emergency scenarios. (5) Autonomous movement + wide-angle acoustic array, smoke removal across the entire tunnel section without blind spots The mobile device travels autonomously within the tunnel without requiring any modifications to the tunnel structure, making it highly adaptable to different locations. The array of sound source generators emits wide-angle sound waves of at least 90°, which, combined with the reflection gain from the tunnel walls, forms a fully enclosed smoke-removing sound field. The sound field radiation angle along the tunnel's longitudinal direction is at least 90°, ensuring sufficient length for individual smoke-removing areas. This creates a smoke-removing sound field covering the tunnel's top and side walls, completely eliminating the smoke-removing dead zones of traditional directional sound sources. It is particularly effective against high-temperature and fine-particle smoke accumulating at the tunnel's top, achieving 100% smoke removal coverage.

[0020] (6) Full-process adaptive closed-loop control, unmanned autonomous operation, and extremely fast and efficient emergency response. The entire system is autonomously executed by the embedded control system through a closed-loop process of data acquisition, effect evaluation, parameter optimization, and execution control. After a fire is triggered, the mobile device can enter the smoke zone within 10 seconds without human intervention. This overcomes the shortcomings of traditional fire-fighting equipment that rely on manual operation and have a slow response. It is suitable for emergency scenarios such as sudden, high-risk, and inaccessible tunnel fires, and significantly improves the level of automation and intelligence. (7) Laser monitoring + dual redundant communication ensures accurate monitoring and stable and reliable operation. The mobile device moves through the smoke area, and the laser monitoring unit tracks and reads the initial transmittance data of each monitoring area in the tunnel simultaneously to achieve full-area smoke monitoring with a transmittance detection resolution of 0.1%. The control system adopts wireless + wired dual redundant communication to avoid signal interruption under fire conditions, improves system operation stability by more than 95%, and ensures stable and continuous operation even in extreme fire environments with high temperature, dense smoke, and strong interference. (8) Based on laser monitoring, the system can autonomously determine the fire situation and smoke flow direction, and achieve precise smoke separation to help ensure unobstructed escape routes for personnel. According to the laser monitoring unit, when the mobile device passes through the smoke zone, it can dynamically determine the direction and speed of the smoke flow in the tunnel in a timely manner, and position the mobile device to a key position to improve the efficiency of personnel escape and fire rescue. It plays the role of smoke isolation sound curtain, controlling the smoke generated by the fire in a local area in the longitudinal direction of the tunnel. When the smoke flows through the sound field, it will agglomerate, and small particles with strong extinction properties will condense into large particles and partially settle. The visibility of smoke downstream of the smoke removal area is greatly improved, providing favorable conditions for personnel escape, rapid vehicle departure and rescue work. (9) Adaptive matching of working conditions, strong versatility, and adaptable to all types of enclosed tunnels. Based on the differences in smoke particle size and concentration generated by the combustion of different combustibles such as cables, vehicles, and building materials in the tunnel, the sound wave frequency, output power, and device position can be adaptively adjusted. It is perfectly adapted to various tunnel fire smoke removal scenarios such as subway tunnels, railway tunnels, and highway tunnels. There is no need to readjust the equipment for different working conditions, and its versatility and practicality are greatly improved. (10) The sound field has high energy utilization rate, low consumption and high efficiency, which meets the requirements of tunnel emergency equipment. Wide-angle sound waves combined with tunnel wall reflections form a closed sound field, reducing sound energy loss by 40% and significantly improving energy utilization. While ensuring efficient smoke removal, the sound source power can be dynamically reduced to lower equipment power consumption and energy consumption, making it more suitable for the long-term stable operation of tunnel emergency equipment. Attached Figure Description

[0021] Exemplary embodiments of the present invention can be more fully understood by referring to the accompanying drawings. The drawings are provided to further illustrate the embodiments of this application and form part of the specification. They are used together with the embodiments of this application to explain the present invention and do not constitute a limitation thereof. In the drawings, the same reference numerals generally represent the same components or steps.

[0022] Figure 1 A flowchart of a tunnel fire smoke removal method based on dynamic zoned sound field provided as an exemplary embodiment of this application; Figure 2 A front view of a tunnel fire smoke removal system based on a dynamic zoned sound field, provided as an exemplary embodiment of this application; Figure 3 A side view of a tunnel fire smoke dynamic zoning acoustic agglomeration smoke removal system provided as an exemplary embodiment of this application. Attached Figure Description 1-Tunnel wall; 2-Loading platform; 3-Control system; 4-Support; 5-Laser source; 6-Laser power meter module; 7-Sound source system clearing sound source generating device array. Detailed Implementation

[0024] 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 so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.

[0025] In the description of this invention, it should be noted that the terms "center," "upper," "lower," "left," "right," "vertical," "horizontal," "inner," and "outer," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are used only for the convenience of describing the invention and for simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the invention. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0026] Furthermore, the technical features involved in the different embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0027] Example 1 Reference Figure 1 This embodiment discloses a tunnel fire smoke removal method based on dynamic zoned sound field, a mobile device that can move along the tunnel, the mobile device including a sound source system 7, a control system 3 and a loading platform 2 equipped with a laser detection unit.

[0028] This embodiment is applied to a closed tunnel in an urban subway system. The tunnel is 1200m long and adopts a unidirectional layout, with multiple monitoring areas set along the longitudinal direction of the tunnel, such as... Figure 3 As shown, the length of the monitoring area is the maximum length corresponding to the maximum emission angle of the noise source generator, denoted as L. max There are a total of N groups, numbered sequentially from 1# to N#, corresponding to the following mileage markers: Monitoring Area #1: K0+L max Monitoring Area #2: K0+2L max Monitoring Area #3: K0+3L max Monitoring areas: K0+NL max ; At the same time, L is divided with the location of each monitoring area as the center. max A tunnel section of a certain length is considered as one target area, as detailed below: Target area #1: K0+0.5L max ~K0+1.5L max Target area #2: K0+1.5L max~K0+2.5L max Target area #3: K0+2.5L max ~K0+3.5L max N# Target region: K0 + (N - 0.5)L max K0+(N+0.5)L max .

[0029] The fire occurred in tunnel K0+1.5L. max The location and the fire ignited the cable, producing a large amount of smoke aerosol, with the smoke concentrated at K0+L. max ~K0+3L max When the section (i.e. the core area of ​​dense smoke, covering target areas 1#, 2#, and 3#) triggers the tunnel fire alarm system, the system of this invention will be activated immediately.

[0030] The preset values ​​for the core control parameters in this embodiment are as follows: (1) The sampling time interval Δt=0.5s is set to balance the real-time performance of smoke removal and the calculation load of the controller, and to avoid system lag caused by frequent calculations; (2) The visibility improvement rate preset threshold is 0.2% / s. When it is lower than this value, the smoke removal effect is deemed insufficient and should be dealt with first. (3) The threshold for achieving the aggregation efficiency is 85%. When the real-time aggregation efficiency reaches this value, the smoke removal in the area is considered complete. (4) Light transmittance safety escape threshold = 88.7%. This parameter is the tunnel fire emergency standard and meets the visibility requirements for safe escape of personnel. (5) Effective sound intensity threshold =120dB, which is the lowest effective agglomeration sound intensity of smoke particles verified in the laboratory; (6) Tunnel environmental coefficient =0.8, this parameter is adapted to the sound wave reflection characteristics of the concrete tunnel wall, and is used to compensate for the reflected sound energy in turn; (7) Sound wave attenuation index =1.2, this parameter is set to adapt to the sound wave attenuation of fire smoke aerosol medium; (8) Representative smoke particle size =1.0μm, =5.0μm, these two particle sizes are the two most common types of particles in tunnel fires, and can cover the mainstream smoke particles; for example, the particle size in gasoline vehicle fires is 5.0μm. The particle size of fires involving new energy battery vehicles is ; Specifically, the tunnel fire smoke removal method based on dynamic zoned sound field includes the following steps: S1: When a fire occurs, control the mobile device to drive into the tunnel smoke area, start the sound source system to scan the wide spectrum, and simultaneously acquire the initial light transmittance data in the tunnel. In this embodiment, a laser monitoring unit mounted on the mobile device acquires initial transmittance data for multiple pre-divided monitoring areas within the smoke zone, and calculates the initial smoke volume fraction baseline value for each monitoring area accordingly. Specifically, after a fire signal is triggered, the control system immediately issues a command, and the mobile device starts its drive motor, traveling along the tunnel at a safe speed of 0.8 m / s, accurately entering the smoke-concentrated area K0+L within 8 seconds. max ~K0+3L max Section, stopping at the initial work point K0+2L max Location; The sound source system is simultaneously activated, performing a broad-spectrum scan within the effective frequency range of 0.5~5kHz to rapidly excite the full-frequency sound field. Simultaneously, the initial transmittance data of monitoring units 1#~N# is tracked and read. as follows: Monitoring Area 1 (K0+L) max ): ; Monitoring area #2 (K0+L) max ): ; Monitoring Area 3 (K0+L) max ): Monitoring area #4 (K0+L) max ): ; Monitoring Area 5 (K0+L) max ): Monitoring area #6 (K0+L) max ): ; Specifically, in this embodiment, the broad-spectrum scanning is performed as follows: the control system controls the sound source system to start from a low frequency of 0.5kHz, gradually increasing the frequency to 5kHz in 0.1kHz increments, and emitting for a duration Δτ at each frequency. During this time, the laser monitoring unit synchronously records the transmittance data at that frequency, thereby establishing an initial frequency-transmittance database. The purpose of the scan is to quickly acquire the initial optical state of the entire smoke area at different frequencies, providing a baseline comparison for subsequent dynamic zoning and frequency optimization.

[0031] In this embodiment, setting the parameters as described above enables rapid acquisition of the initial smoke distribution across the entire area through transmittance tracking, providing raw data for establishing a visibility baseline and avoiding poor smoke removal adaptability caused by directly fixing the frequency. Simultaneously, in step S1, only the raw transmittance of the monitored area is collected, providing a basis for subsequent calculations of the target area data.

[0032] S2: Based on the initial transmittance data, calculate the visibility and initial smoke volume fraction in the smoke area, and establish a baseline for tunnel visibility status and initial smoke volume fraction; Specifically, based on the initial transmittance data and the initial smoke volume fraction baseline value, a dual-index quantitative evaluation is performed on all monitoring areas. According to the evaluation results, the area with the highest processing priority is determined as the current target area, and the mobile device is dispatched to move to the target area. In this embodiment, the key location of the smoke removal area that improves the efficiency of personnel escape and fire rescue is determined. Based on the high-precision positioning system, the mobile device is controlled to drive into the designated location in the tunnel smoke area, and the sound source system is started to work according to the theoretical optimal frequency determined by the principle of maximizing the difference of the carrying coefficient. Specifically, the control system calculates the initial smoke volume fraction of the corresponding target area based on the single-area data of the monitoring area using the transmittance-volume fraction formula. The transmittance-smoke volume fraction is as follows:

[0033] in, For the action of sound waves Volume fraction of fire smoke in the constant-time reunion room; This represents the initial volume fraction of the fire smoke. The initial transmittance of the fire smoke; for Light transmittance at any given time.

[0034] To reduce tunnel light transmittance Using 100% as a baseline, the initial smoke volume fraction for each monitoring area was calculated. The results are as follows: Target Area #1: =68.5%; Target Area #2: =81.2%; Target Area #3: =92.1%; Target area #4: =78.3%; Target area #5: =56.7%; Target area #6: =42.5%; Establishing a tunnel visibility baseline based on the aforementioned data allows for the capture of the initial spatial pattern of non-uniform smoke distribution within the tunnel, providing a personalized comparison benchmark for subsequent calculations. Simultaneously, it provides indispensable initial data for calculating the core performance indicator, real-time smoke aggregation efficiency, thus fundamentally transforming the smoke removal effect from qualitative to quantitative assessment. Finally, this baseline establishes a stable and unified decision-making reference coordinate system for the entire adaptive closed-loop control system, ensuring the coherence and consistency of the optimization logic. The tunnel visibility baseline provides the absolute benchmark and data origin for the entire adaptive closed-loop control algorithm, serving as the data foundation for the entire system to achieve a perception-evaluation-decision intelligent closed loop.

[0035] Based on the tunnel visibility baseline, it can be determined that target area #3 has the lowest initial light transmittance and the highest initial smoke volume fraction, making it the core area of ​​dense smoke. This step establishes a quantitative evaluation benchmark, which not only solves the problems of traditional smoke removal lacking reference standards and having unquantifiable effects, but also provides a foundation for subsequent treatment steps.

[0036] S3: Real-time acquisition of current transmittance data for each monitoring area, and combined with the initial smoke volume fraction baseline, to calculate the visibility improvement rate and real-time aggregation efficiency for each monitoring area; Specifically, for the monitoring area corresponding to the current target area, its transmittance data is acquired in real time, and the real-time smoke volume fraction and real-time aggregation efficiency are calculated. For other monitoring areas, their current state is estimated using the most recent historical data and the smoke diffusion model. In the dynamic processing stage, the mobile device is performing acoustic aggregation operation on a certain target area. Here, the current transmittance data of each monitoring area is acquired in real time. Specifically, this means acquiring the laser transmittance data of the monitoring area corresponding to the target area where the mobile device is currently located (i.e., the area where the sound source is acting). For other monitoring areas that are not in the operation position, their current transmittance data is obtained in the following way: the control system calls the most recent valid measurement value of the area and combines it with the pre-established smoke diffusion model (considering factors such as wind speed and temperature gradient) to perform time recursion estimation, thereby obtaining the approximate real-time state of each monitoring area in the world, which is then used for dual-index quantitative evaluation and priority ranking in step S3.

[0037] Specifically, the control system uses a sampling period of Δt=0.5s (i.e., sampling time interval) to collect the current transmittance of each monitoring area at time t=0.5s in real time. Substitute the values ​​into the formula to calculate the corresponding dual indicators for the target area: 1. Visibility is obtained through the following steps: According to Lambert-Beer's law, when a beam of light passes through fire smoke, we have: ; but: ; further: ; in, is the transmittance; K is the extinction coefficient of the smoke; L is the distance from the laser to the receiver of the laser monitoring unit, i.e., the range; C is a constant, C=8 for luminous objects and C=3 for non-luminous objects.

[0038] Meanwhile, the rate of visibility improvement is calculated using the following formula: (1); in, For the first The rate of improvement in visibility in each monitored area For the first In each monitoring area Light transmittance at any given time For the first In each monitoring area Light transmittance at any given time , The total number of monitored areas, This represents the sampling time interval.

[0039] 2. Real-time reunion efficiency is calculated using formula (2): (2); in, For the first Real-time aggregation efficiency of each monitoring area, monitoring area For the first Initial smoke volume fraction for each monitoring area No. Each monitoring area is in The volume fraction of smoke at any given time.

[0040] Based on the aforementioned data, the real-time reunification efficiency of target area #3 was calculated as follows: =12.0% < 85% (compliance threshold), which is much lower than other areas; this step uses a dual-indicator joint evaluation. A single light transmittance cannot reflect the smoke removal rate. The dual indicators can accurately identify inefficient areas and provide a basis for priority scheduling.

[0041] Establish visibility baseline: Area 3 (K0+2L) max The initial light transmittance was the lowest (5%), and the smoke concentration was the highest.

[0042] S4: Perform adaptive closed-loop control, dynamically and collaboratively optimizing the operating frequency of the sound source, output power, and position of the mobile device based on the visibility improvement rate and the real-time aggregation efficiency. Specifically, the adaptive closed-loop control in step S4 is executed by the parameter optimization module, and includes the following steps: S41: Based on the visibility improvement rate or the real-time clustering efficiency, determine the target area that needs to be prioritized from all monitored areas, and control the mobile device to move towards the center of the target area; Specifically, the control system makes a dual-indicator judgment: the target area #3 is the target area that needs to be prioritized for processing; at the same time, a movement command is immediately issued, and the moving device moves precisely at a low speed of 0.5m / s, and stops at the center of the area #3 after traveling 12m, with a positioning accuracy of ±0.1m; in this way, the system can focus on the core area of ​​dense smoke, concentrate the sound field energy to efficiently remove smoke, avoid the low efficiency caused by the average force applied to the whole area, and move to the center of the area to ensure the uniformity of the sound field coverage.

[0043] S42: Based on the principle of maximizing the difference in the carrying coefficient, the theoretical optimal frequency is calculated within the effective frequency range of the sound source system, and the visibility improvement rate of the target area is used as a feedback index. Fine-tuning is performed through a numerical optimization algorithm to determine the measured optimal operating frequency. Since the agglomeration of smoke particles is mainly driven by the relative velocity difference between large and small particles, and the entrainment effect of sound waves on particles is directly related to particle size, this invention selects the median diameter... and mass median diameter As a representative particle size, maximizing the difference in entrainment coefficients between the two particles at the same frequency is equivalent to maximizing the driving force for most particle collisions and aggregation, thereby rapidly improving the overall aggregation efficiency. Specifically, a typical particle size for tunnel fires is selected. ,and Substituting into the entrainment coefficient formula (4): (4); Substitute this into the relative entrainment coefficient formula (3): (3); in, , Particle size , Particles at frequency The entrainment coefficient in the sound field, For sound wave frequency, Particle density, The particle diameter is The dynamic viscosity of the gaseous medium.

[0044] Through the above calculations, traversing the range of 0.5~5kHz, when... At 2.2kHz, the relative entrainment coefficient reaches its maximum value, which is the theoretical optimal frequency.

[0045] After obtaining the theoretical optimal frequency, with the optimization objective of maximizing the visibility improvement rate in target area #3, the golden section method (or gradient ascent method) is used to search step by step within the neighborhood of the theoretical optimal frequency (e.g., 2.0~2.4kHz), with a step size of 0.05kHz. Finally, the measured optimal operating frequency is determined to be 2.3kHz.

[0046] By theoretically calculating and locking in the optimal frequency range, numerical optimization corrects the theoretical errors caused by tunnel wall reflection and smoke concentration, so that the frequency is perfectly matched with the actual smoke particles, thereby improving the aggregation efficiency by more than 35%.

[0047] S43: Based on the longitudinal length between the device and the target area, and using the sound intensity-distance attenuation model, dynamically calculate and adjust the current output power of the sound source system; The real-time distance between the mobile device and the target area #3 was measured to be L=12m. Substituting this distance into the sound intensity-distance attenuation model formula (5): (5); Then, the compensated sound source output power P=138dB is calculated; the control system immediately adjusts the sound source power from the initial 130dB to 138dB; in this way, the distance attenuation of the sound wave in the smoke medium can be offset, thereby ensuring that the sound intensity in the target area is always ≥120dB effective threshold, and finally the long-distance smoke removal blind zone can be eliminated.

[0048] S44: Based on the measured optimal operating frequency and the current output power, continuously monitor the aggregation efficiency of the target region until the target region is optimized; return to step S41 to select the next target region.

[0049] The sound source system operates continuously at the measured optimal frequency of 2.3kHz and the compensated power of 138dB. The control system refreshes the aggregation efficiency of target area #3 every 0.5s. The target area is considered to have completed optimization when one of the following conditions is met: (1) the real-time aggregation efficiency reaches or exceeds the preset threshold (e.g., 85%); (2) within N consecutive control cycles (e.g., 3 cycles), the rate of increase in aggregation efficiency is lower than the preset convergence threshold (e.g., 0.5% / cycle). In a preferred embodiment, after 18s of continuous smoke removal, the real-time aggregation efficiency of target area #3 is... =88.2%≥85% (the threshold for achieving the target), it is determined that the optimization of target area #3 is complete; at this time, return to step S41, select the next inefficient area (target area #2), and repeat the above adaptive closed-loop control.

[0050] S5: Repeat steps S3 and S4 until the light transmittance of key areas in the tunnel reaches the safe escape threshold. Specifically, the control system executes steps S3 to S4 in a loop, sequentially completing the smoke removal optimization of target areas 3#, 2#, and 1#, with the overall smoke removal taking 112 seconds. After completion, the light transmittance of the key escape areas in the tunnel reaches over 72%, meeting the 70% safety escape threshold, and the smoke removal operation is completed.

[0051] Specifically, critical areas refer to areas that play a decisive role in the safe evacuation of personnel in a tunnel fire emergency escape scenario, either pre-delineated or dynamically determined by the system based on sensor data. These areas include, but are not limited to, areas surrounding emergency escape routes and safety exits, and areas along the firefighters' internal attack routes. The safety escape threshold is a pre-set minimum light transmittance value (e.g., 0.5). When the light transmittance of all these critical areas remains above this threshold, the system determines that the smoke extraction task is complete.

[0052] This embodiment operates autonomously in a closed loop throughout the entire process without manual intervention. The smoke removal efficiency is 40% higher than that of traditional fuzzy adjustment, and there are no blind spots in smoke removal across the entire area.

[0053] Example 2 Based on the same inventive concept, this application also provides a tunnel fire smoke removal system with dynamic zoned sound field for performing the method described in the above embodiments. The solution provided by this system is similar to the solution described in the above method; therefore, the specific limitations in the embodiments of the tunnel fire smoke removal system based on dynamic zoned sound field provided below can be found in the limitations of the tunnel fire smoke removal method based on dynamic zoned sound field described above, and will not be repeated here.

[0054] Reference Figure 2-3 , Figure 2 A tunnel fire smoke removal system based on dynamic zoned sound field is shown. Figure 3 The diagram shows the longitudinal extension of the system along the tunnel. The system is based on a mobile device that can move along the tunnel. The mobile device includes: a loading platform 2, a laser monitoring unit, a sound source system 7 (which may be an array of sound source generators), and a control system 3. The distributed monitoring unit consists of a support 4, a laser light source 5, and a laser power meter module 6.

[0055] Specifically, the loading platform 2 can be a rail-mounted mobile platform or a tracked (wheeled) mobile trolley, which are suitable for tunnels with tracks or highway tunnels without tracks, respectively.

[0056] During operation, the tunnel-mounted acoustic smoke removal system is applied inside the enclosed tunnel. The tunnel wall 1 is a concrete retaining structure, and the track is fixedly laid along the tunnel's extension direction. In the event of a fire, the control system 3 issues control commands to drive the loading platform 2 to move autonomously on the track and precisely enter the smoke-concentrated area. The laser light source 5 is rigidly fixed to the top of the loading platform 2 via a bracket 4, maintaining coaxial alignment with the laser power meter module 6, and real-time monitoring of the light transmittance data of the tunnel cross-section, transmitting it to the control system 3. The sound source generator array 7 is fixedly installed at the front of the loading platform 2, emitting wide-angle sound waves towards the tunnel cross-section. The tunnel wall 1 is used to reflect the sound waves, working in conjunction with the sound source generator array 7 to form a closed smoke removal sound field covering the entire tunnel cross-section. The control system 3 performs adaptive closed-loop control based on the light transmittance data fed back from the monitoring unit, dynamically adjusting the operating frequency and output power of the sound source generator array 7, while simultaneously controlling the movement position and stopping posture of the loading platform 2, achieving efficient and precise elimination of smoke from tunnel fires.

[0057] For example, the sound source generating device is a directional diffusion pneumatic sound source or an electric sound source. The sound field emission angle of the loudspeaker of the sound source generating device is configured to generate a sound field with a horizontal diffusion angle of not less than 90° along the longitudinal section of the tunnel. As the sound field emission angle of the loudspeaker increases, the length of the individual target area increases, but it also causes the sound field intensity to decrease at a distance from the area.

[0058] It should be noted that the flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code, which contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than those marked in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0059] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0060] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. The apparatus embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and there may be other division methods in actual implementation. Furthermore, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Additionally, the coupling or direct coupling or communication connection shown or discussed may be through some communication interface; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0061] The units described as clearing components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0062] In addition, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0063] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application, and they should all be covered within the scope of the claims and specification of this application.

Claims

1. A method for clearing smoke from a tunnel fire based on a dynamic zoned sound field, comprising a mobile device movable along the tunnel, the mobile device including a loading platform equipped with a sound source system, a control system, and a laser detection unit, characterized in that... The method includes the following steps: S1: When a fire occurs, control the mobile device to travel through the entire smoke zone of the tunnel, activate the sound source system for broad-spectrum scanning, and simultaneously acquire the initial light transmittance data in the tunnel. S2: Based on the initial transmittance data, calculate the visibility and initial smoke volume fraction in the smoke area, and establish a baseline for tunnel visibility status and initial smoke volume fraction; S3: Real-time acquisition of current transmittance data for each monitoring area, and combined with the initial smoke volume fraction baseline, to calculate the visibility improvement rate and real-time aggregation efficiency for each monitoring area; S4: Perform adaptive closed-loop control, dynamically and collaboratively optimizing the operating frequency of the sound source, output power, and position of the mobile device based on the visibility improvement rate and the real-time aggregation efficiency. S5: Repeat steps S3 and S4 until the light transmittance of key areas in the tunnel reaches the safe escape threshold. The adaptive closed-loop control, executed by the parameter optimization module, includes the following steps: S41: Based on the visibility improvement rate or the real-time clustering efficiency, determine the target area that needs to be prioritized from all monitored areas, and control the mobile device to move towards the center of the target area; S42: Based on the principle of maximizing the difference in the carrying coefficient, the theoretical optimal frequency is calculated within the effective frequency range of the sound source system, and the visibility improvement rate of the target area is used as a feedback index. Fine-tuning is performed through a numerical optimization algorithm to determine the measured optimal operating frequency. S43: Based on the longitudinal length between the device and the target area, and using the sound intensity-distance attenuation model, dynamically calculate and adjust the current output power of the sound source system; S44: Based on the measured optimal operating frequency and the current output power, continuously monitor the aggregation efficiency of the target region until the target region is optimized; return to step S41 to select the next target region.

2. The method according to claim 1, characterized in that, The visibility improvement rate is obtained using the following formula: （1）； in, For the first The rate of improvement in visibility in each monitored area For the first In each monitoring area Light transmittance at any given time For the first In each monitoring area Light transmittance at any given time , The total number of monitored areas, This represents the sampling time interval.

3. The method according to claim 2, characterized in that, The real-time reunification efficiency is obtained using the following formula: （2）； in, For the first Real-time aggregation efficiency of each monitoring area, monitoring area For the first Initial smoke volume fraction for each monitoring area No. Each monitoring area is in The volume fraction of smoke at any given time.

4. The method according to claim 2 or 3, characterized in that, The priority areas to be addressed are those with the lowest real-time visibility or those critical areas identified by the system as being most beneficial for improving personnel escape or fire rescue.

5. The method according to claim 1, characterized in that, The calculation of the theoretically optimal frequency within the effective frequency range of the sound source system based on the principle of maximizing the bandgap difference includes: Based on the typical particle size distribution of smoke from tunnel fires, two representative particle sizes were selected. and ; The search is performed within a preset frequency range, and the frequency point where the relative carry coefficient reaches its maximum value is taken as the theoretical optimal frequency. Among them, the relative entrainment coefficient It can be obtained through the following formula: （3）； in, , Particle size , Particles at frequency The entrainment coefficient in the sound field is obtained by the following formula: （4）； in, For sound wave frequency, Particle density, The particle diameter is The dynamic viscosity of the gaseous medium.

6. The method according to claim 1, characterized in that, The sound intensity-distance attenuation model is expressed by the following formula: （5）； in, The power of the sound source that needs to be adjusted. The effective sound intensity threshold for sound wave aggregation. This represents the real-time distance between the mobile platform and the target area. An environmental factor related to sound source directivity and tunnel wall reflection characteristics. is the attenuation index of sound waves in the smoke medium.

7. The method according to claim 1, characterized in that, The initial smoke volume fraction of each monitoring area is represented by or calculated from the average initial smoke volume fraction within that area.

8. The method according to claim 1, characterized in that, The fine-tuning through numerical optimization algorithms includes: Using the visibility rate, characterized by light transmittance, in the target area as the optimization objective function, the gradient ascent method or the golden section method is used to search within the neighborhood of the theoretically optimal frequency to determine the measured optimal operating frequency.

9. The method according to claim 1, characterized in that, The criteria for determining whether the target area has been optimized are: the real-time aggregation efficiency of the area reaches a preset threshold or the rate of improvement tends to zero within several consecutive control cycles.

10. A tunnel fire smoke removal system based on dynamic zoned sound field for implementing the method of any one of claims 1-9, characterized in that, include: A mobile device that can move along a tunnel; The sound source generating device installed on the mobile device is configured to generate a sound field with a horizontal diffusion angle of not less than 90° along the longitudinal section of the tunnel. The laser monitoring unit, which is arranged on the loading platform of the mobile device, includes a laser light source and a laser power meter. It can read the transmittance of the tunnel section in the vertical direction at the location of the mobile device in real time, and dynamically record it during the movement of the mobile device to obtain the transmittance baseline and real-time transmittance. The control system is integrated into the mobile device and is communicatively connected to the loading platform, the sound source generating device, and the laser monitoring unit. The control system includes a parameter optimization module, which is configured to perform steps S2 to S4 as described in claim 1.

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