A new high ground temperature tunnel local ventilation cooling system

Abstract

This invention discloses a novel local ventilation and cooling system for high-temperature tunnels, relating to the field of high-temperature tunnel ventilation technology. It includes a temperature monitoring module, a cold air delivery activation module, a cold air output regulation module, a regional cold air distribution module, an energy efficiency optimization module, and a real-time monitoring and emergency response module. The temperature monitoring module involves arranging a temperature sensor network inside the tunnel. This invention monitors the tunnel temperature in real time through the temperature sensor network and model, dynamically adjusting the cold air output to ensure precise cooling. A vortex tube array directly generates cold air by introducing high-pressure air, which is then transported to the cooling area through ducts. The discharged hot air undergoes energy recovery through phase change heat absorption materials or a hot air power generation system. Intelligent algorithms predict temperature trends and adjust the cold air in advance to prevent safety hazards. A real-time monitoring platform tracks temperature and equipment status, automatically issues alarms, and executes emergency measures to ensure the system operates within safe limits.

Description

Technical Field

[0001] This invention relates to the field of ventilation technology for high-temperature tunnels, and specifically to a novel local ventilation and cooling system for high-temperature tunnels. Background Technology

[0002] High-temperature tunnel ventilation and cooling refers to the process of reducing the temperature of the working face by using effective ventilation and cooling technologies during the excavation of high-temperature underground spaces, such as tunnels, coal mines, and deep underground urban spaces, in order to ensure the safety and comfort of the working environment.

[0003] Current high-temperature tunnel ventilation technologies primarily achieve cooling by delivering cool outside airflow to the working face. However, this technology has limitations when the tunnel is long or the underground space is large. As the cool outside airflow is gradually heated within the ductwork during its journey to the working face, its temperature reaches a high level by the time it arrives, making effective cooling impossible. This necessitates compensating by increasing ventilation or fan power, but this results in significant energy consumption and low energy conversion efficiency. Furthermore, this ventilation and cooling method is highly dependent on ambient temperature. Higher ambient temperatures further exacerbate the problem. Therefore, designing and implementing more efficient ventilation and cooling systems in high-temperature environments is a pressing issue that needs to be addressed.

[0004] The information disclosed in the background section is only intended to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0005] The purpose of this invention is to provide a novel local ventilation and cooling system for high-temperature tunnels. By deploying a temperature sensor network and establishing a temperature model, it achieves real-time monitoring of temperature distribution within the tunnel and rapid response to abnormal areas. The system dynamically adjusts the output and distribution of cold air based on temperature changes, avoiding excessive or insufficient cold air supply. A vortex tube array directly generates cold air by introducing high-pressure air, which is then transported to the cooling area through ducts. The discharged hot air undergoes energy recovery through phase change heat absorption materials or a hot air power generation system. Intelligent algorithms analyze temperature change patterns, predict future temperature trends, and adjust the cold air output in advance to avoid safety hazards. A real-time monitoring platform continuously tracks temperature and equipment status, automatically issues alarms, and executes emergency procedures to ensure the tunnel temperature remains within a safe range, improving the system's reliability and safety, thus addressing the problems mentioned in the background technology.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a novel local ventilation and cooling system for high-temperature tunnels, comprising a temperature monitoring module, a cold air delivery start-up module, a cold air output regulation module, a regional cold air distribution module, an energy efficiency optimization module, and a real-time monitoring and emergency response module;

[0007] The temperature monitoring module deploys a network of temperature sensors inside the tunnel to monitor the temperature distribution in real time. The temperature data collected by the temperature sensors is transmitted to the central control system through a data acquisition system. The temperature data is used to build a temperature model inside the tunnel and identify areas with abnormal temperatures.

[0008] The cold air delivery start-up module, based on preliminary temperature monitoring data, starts the vortex tube array cold air delivery mechanism. The vortex tube array uses compressed air to generate two airflows, cold air and hot air, and delivers the cold air part to the tunnel through the pipeline.

[0009] The cold air output regulation module, when the temperature inside the tunnel changes, the central control system analyzes the current temperature inside the tunnel based on real-time temperature data and dynamically adjusts the output of cold air to achieve timely temperature response;

[0010] The regional cold air distribution module further subdivides the various areas within the tunnel during the cold air delivery process. By adjusting valves and distributors, it precisely controls the distribution of cold air, determines the specific needs of each area based on temperature monitoring data, and accurately controls the amount of cold air distributed to different areas.

[0011] The energy efficiency optimization module uses intelligent algorithms to analyze the patterns of tunnel temperature changes and predict future temperature trends, thereby adjusting the cold air output of the vortex tube array in advance to further optimize energy efficiency.

[0012] The real-time monitoring and emergency response module continuously tracks the temperature inside the tunnel and the working status of the vortex tube array through a real-time monitoring platform. Once an abnormality is detected, it automatically issues an alarm and executes emergency response measures.

[0013] Preferably, a temperature model of the tunnel interior is established using temperature data to identify areas of temperature anomalies. The specific steps are as follows:

[0014] A temperature sensor network is deployed inside the tunnel, and the temperature sensors collect temperature data from various locations in the tunnel in real time.

[0015] Using the processed temperature data, the temperature gradient at each sensor location is calculated to reflect the spatial variation of temperature. Simultaneously, the rate of temperature change is calculated to identify the dynamic characteristics of temperature changes. The expression for calculating the temperature gradient is: In the formula, T i(t) represents the temperature reading of the i-th sensor at time t, where T i ′(t) is the processed temperature reading of the i-th sensor at time t. R represents the temperature gradient at the i-th sensor location, reflecting the rate of temperature change in space. i (t) represents the temperature change rate at the i-th sensor location, indicating how the temperature changes over time, d i The distance between adjacent sensors;

[0016] Based on the temperature gradient and the rate of temperature change, a temperature anomaly factor is calculated at each sensor location to identify temperature anomaly regions. The temperature anomaly factor is a combined function of the temperature gradient and the rate of temperature change, reflecting the comprehensive characteristics of temperature changes in both space and time. The calculation expression is as follows: F i (t) represents the temperature anomaly factor at the i-th sensor location, where α and β are adjustment parameters used to balance the effects of temperature gradient and rate of change; S i (t) is the weighting factor;

[0017] Based on the calculated temperature anomaly factor, temperature anomaly regions inside the tunnel are identified. Thresholds are set to distinguish between normal and abnormal regions, and a model of the temperature anomaly regions is constructed to depict the spatial distribution and variation characteristics of temperature anomalies, providing support for subsequent ventilation and cooling strategies. The expression for constructing the temperature anomaly region model is: A(t)={i|F i (t)>F th In the formula, A(t) represents the set of temperature anomaly regions identified at time t, containing the locations of all sensor sensors where temperature anomaly factors exceed the threshold, and F th This is the threshold for the temperature anomaly factor.

[0018] Preferably, the output flow rate and temperature of the cold air from the vortex tube array can be initially controlled by adjusting the flow rate and pressure of the compressed air to ensure that the basic cooling requirements are met in the initial state.

[0019] Preferably, when the temperature inside the tunnel changes, the central control system analyzes the current temperature situation inside the tunnel based on real-time temperature data and dynamically adjusts the output of cold air to achieve timely temperature response. The specific steps are as follows:

[0020] The temperature change rate is calculated for each region based on temperature data to determine areas requiring priority cooling. The temperature change rate can be used to assess abrupt temperature changes within a region. The calculation expression is as follows: R i (t) represents the rate of temperature change of sensor i at time t, and Δt represents the time interval;

[0021] Based on the temperature change rate of each region, the required amount of cold air to be supplied is calculated to ensure a rapid response to temperature changes. The calculation expression is as follows: In the formula, Q air (t) represents the output of cold air at time t, κ represents the cold air transport efficiency coefficient, and V x Represents the volume of region x. This represents the temperature response coefficient, which reflects the sensitivity of temperature to the demand for cold air.

[0022] Based on the demand for cold air and the temperature distribution in different areas of the tunnel, the output path of the cold air is optimized to reduce energy consumption and improve cooling efficiency. Simultaneously, a feedback control mechanism continuously monitors temperature changes and dynamically adjusts the output of cold air. The specific expression is as follows: In the formula, P opt For the optimal cold air output path, L x，P E represents the transport distance from path P to region x. x Let μ be the energy transfer efficiency of region x, and μ be the feedback adjustment coefficient. This represents the actual output of cold air.

[0023] Preferably, the tunnel is further subdivided into different zones, and the distribution of cold air is precisely controlled by adjusting valves and distributors. Based on temperature monitoring data, the specific needs of each zone are determined, and the amount of cold air distributed to different zones is precisely controlled. The specific steps are as follows:

[0024] Based on the tunnel's length, shape, purpose, and internal temperature distribution, the tunnel is divided into several independent zones;

[0025] Based on real-time temperature data, the central control system analyzes the cooling demand of each area, assesses the degree of deviation of the temperature of each area from the set safe and comfortable range, as well as the rate and trend of temperature change, in order to determine the amount of cold air required for each area.

[0026] After the cold air distribution strategy is formulated, the central control system sends instructions to the regulating valves and distributors in each area. The regulating valves and distributors adjust the flow and direction of the cold air to achieve precise cold air distribution to each area.

[0027] Once the cold air distribution is complete, the tunnel ventilation system does not stop monitoring, but continues to monitor the temperature changes in each area in real time. Based on the data continuously fed back by the temperature sensors, the central control system can promptly identify any temperature anomalies or changes in demand in any area.

[0028] Preferably, if the temperature in a certain area changes abruptly or the cooling demand in other areas changes, the tunnel ventilation system responds immediately, dynamically adjusting the settings of regulating valves and distributors to redistribute the cool air.

[0029] Preferably, an intelligent algorithm is used to analyze the pattern of tunnel temperature changes, predict future temperature trends, and adjust the cold air output of the vortex tube array in advance. The specific steps are as follows:

[0030] Intelligent algorithms are used to extract features and recognize patterns from temperature data. Feature extraction refers to extracting key numerical indicators and statistical characteristics from raw data, while pattern recognition refers to identifying the patterns and trends of temperature changes inside the tunnel by analyzing these features.

[0031] Based on the extracted features and identified patterns, temperature trend prediction is performed using machine learning or deep learning algorithms. The prediction model is trained with historical data to learn the complex relationships of temperature changes and the influence of potential factors, and predicts the temperature change trends of various areas of the tunnel in the future.

[0032] Based on the temperature trend forecast, the cold air output of the vortex tube array is adjusted in advance. By adjusting the operating parameters of the vortex tube array, an appropriate amount of cold air output is pre-set to cope with the upcoming temperature changes.

[0033] During the implementation of forecasting and regulation, the deviation between actual temperature changes and forecast results is continuously monitored. Through real-time monitoring and data feedback, the accuracy of the forecasting model is evaluated, and dynamic adjustments and optimizations are made based on the actual situation.

[0034] Preferably, if the prediction is found to be inconsistent with the actual situation, the model parameters are automatically updated or a more suitable algorithm is selected for prediction. Through continuous optimization and feedback adjustment mechanisms, the best energy efficiency performance is always maintained under different environments and operating conditions.

[0035] The technical effects and advantages provided by the present invention in the above technical solution are as follows:

[0036] This invention, through the deployment of a temperature sensor network and the establishment of a temperature model, enables the system to monitor the temperature distribution within the tunnel in real time, identify areas of abnormal temperature, and respond rapidly to temperature changes. This precise monitoring and dynamic adjustment mechanism allows for the adjustment of the output and distribution of cold air according to actual needs, preventing excessive or insufficient cold air supply. The vortex tube array directly generates cold air by introducing high-pressure air, which is then transported to the cooling areas via ductwork. The discharged hot air undergoes energy recovery through phase change heat absorption materials or a hot air power generation system. Furthermore, the system further subdivides the tunnel into different zones and precisely controls the distribution of cold air through regulating valves and distributors, ensuring that each zone receives the appropriate flow of cold air according to its temperature requirements. This optimized cold air management significantly reduces energy consumption, improves overall energy efficiency, and reduces operating costs, especially during long-term, high-intensity tunnel construction.

[0037] This invention significantly improves the reliability and safety of the system through intelligent algorithms and a real-time monitoring platform. The intelligent algorithm analyzes the patterns of temperature changes within the tunnel and predicts future temperature trends, thereby adjusting the output of cooling air in advance to avoid safety hazards caused by sudden temperature changes. The real-time monitoring platform continuously tracks the temperature conditions within the tunnel and the operating status of the vortex tube array. Once an abnormal temperature or equipment malfunction is detected, the system automatically issues an alarm and executes emergency measures, such as adjusting the cooling air output or activating backup equipment. This real-time monitoring and rapid response capability ensures that the system maintains the temperature within the tunnel within a safe and comfortable range under various operating conditions, effectively preventing safety accidents such as worker heatstroke and equipment overheating caused by high temperatures. Attached Figure Description

[0038] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this invention. For those skilled in the art, other drawings can be obtained based on these drawings.

[0039] Figure 1 This is a schematic diagram of a novel local ventilation and cooling system for high-temperature tunnels according to the present invention.

[0040] Figure 2 This is a schematic diagram illustrating an example of localized cooling at the tunnel excavation face according to the present invention.

[0041] Figure 3 This is a schematic diagram of the vortex tube cooling principle of the present invention. Detailed Implementation

[0042] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, they are provided so that the description of this disclosure will be more complete and fully convey the concept of the exemplary embodiments to those skilled in the art.

[0043] This invention provides, for example Figures 1 to 3 The novel high-temperature tunnel local ventilation and cooling system shown includes a temperature monitoring module, a cold air delivery start-up module, a cold air output regulation module, a regional cold air distribution module, an energy efficiency optimization module, and a real-time monitoring and emergency response module.

[0044] The temperature monitoring module deploys a network of temperature sensors inside the tunnel to monitor the temperature distribution in real time. The temperature data collected by the temperature sensors is transmitted to the central control system through a data acquisition system. The temperature data is used to build a temperature model inside the tunnel and identify areas with abnormal temperatures (high temperatures).

[0045] Accurate temperature data acquisition is the foundation for dynamically adjusting the output of cold air, ensuring the system's real-time performance and response speed.

[0046] A temperature model of the tunnel interior is established using temperature data to identify areas of temperature anomalies. The specific steps are as follows:

[0047] A temperature sensor network is deployed inside the tunnel, and the temperature sensors collect temperature data from various locations in the tunnel in real time.

[0048] These data are transmitted to the central control system via a data acquisition system. To ensure the accuracy and validity of the data, the temperature data is first preprocessed, including noise removal, outlier elimination, and data smoothing.

[0049] Using the processed temperature data, the temperature gradient at each sensor location is calculated to reflect the spatial variation of temperature. Simultaneously, the rate of temperature change is calculated to identify the dynamic characteristics of temperature changes. The expression for calculating the temperature gradient is: In the formula, T i (t) represents the temperature reading of the i-th sensor at time t, where T i ′(t) is the processed temperature reading of the i-th sensor at time t. R represents the temperature gradient at the i-th sensor location, reflecting the rate of temperature change in space. i (t) represents the temperature change rate at the i-th sensor location, indicating how the temperature changes over time, d i The distance between adjacent sensors;

[0050] Based on the temperature gradient and the rate of temperature change, a temperature anomaly factor is calculated at each sensor location to identify temperature anomaly regions. The temperature anomaly factor is a combined function of the temperature gradient and the rate of temperature change, reflecting the comprehensive characteristics of temperature changes in both space and time. The calculation expression is as follows: F i (t) represents the temperature anomaly factor at the i-th sensor location, measuring the degree of temperature anomaly at that location. α and β are adjustment parameters used to balance the effects of temperature gradient and rate of change; S i (t) is a weighting factor that takes into account the special characteristics of the sensor location, such as the influence of terrain or wind flow in the tunnel.

[0051] Based on the calculated temperature anomaly factor, temperature anomaly regions inside the tunnel are identified. Thresholds are set to distinguish between normal and abnormal regions, and a model of the temperature anomaly regions is constructed. This model is used to depict the spatial distribution and variation characteristics of temperature anomalies, providing support for subsequent ventilation and cooling strategies. The expression for constructing the temperature anomaly region model is: A(t)={i|Fi (t)>F th}, where A(t) represents the set of temperature anomaly regions identified at time t, including the locations of all sensor sensors where temperature anomaly factors exceed the threshold, and F th This is the threshold for the temperature anomaly factor; if the value exceeds this threshold, the area is considered a temperature anomaly area.

[0052] The cold air delivery start-up module, based on preliminary temperature monitoring data, starts the vortex tube array cold air delivery mechanism. The vortex tube array uses compressed air to generate two airflows, cold air and hot air, and delivers the cold air part to the tunnel through a dedicated pipeline.

[0053] The advantages of scroll tube arrays lie in their elimination of the need for complex refrigerant and compressor systems, their small size, ease of installation, and ability to operate stably in high-temperature environments. The output flow rate and temperature of the cooled air from the scroll tube array can be initially controlled by adjusting the flow rate and pressure of the compressed air, ensuring that basic cooling requirements are met in the initial state.

[0054] The cold air output regulation module, when the temperature inside the tunnel changes, the central control system analyzes the current temperature inside the tunnel based on real-time temperature data and dynamically adjusts the output of cold air to achieve timely temperature response;

[0055] When the temperature sensor detects an increase in temperature in a certain area, the system will increase the supply of cool air to that area; conversely, when the temperature drops, the system will reduce the output of cool air. This dynamic adjustment mechanism can quickly respond to temperature changes, avoid energy waste, and ensure that the temperature of the work surface is always within a safe and comfortable range.

[0056] When the temperature inside the tunnel changes, the central control system analyzes the current temperature situation inside the tunnel based on real-time temperature data and dynamically adjusts the output of cold air to achieve timely temperature response. The specific steps are as follows:

[0057] The temperature change rate for each region is calculated based on temperature data to identify areas requiring priority cooling. The temperature change rate can be used to assess abrupt temperature changes within a region. The calculation expression is as follows: R i (t) represents the rate of temperature change of sensor i at time t, and Δt represents the time interval;

[0058] By calculating the temperature change rate of each sensor, the system can determine which areas experience the fastest temperature rise and thus prioritize delivering cold air to these areas.

[0059] Based on the temperature change rate of each region, the required amount of cold air to be transported is calculated. The amount of cold air is related to the temperature change rate, the region volume, and the current temperature to ensure a rapid response to temperature changes. The calculation expression is as follows: In the formula, Q air (t) represents the output of cold air at time t, κ represents the cold air transport efficiency coefficient, and V x Represents the volume of region x. This represents the temperature response coefficient, which reflects the sensitivity of temperature to the demand for cold air.

[0060] The cold air delivery efficiency coefficient is a parameter describing the cooling effect of cold air from its source (such as a vortex tube array) to various target areas within a tunnel. This coefficient reflects the energy loss of the cold air during delivery, including air friction, heat transfer losses through the pipes, and airflow diffusion. A higher delivery efficiency coefficient means that the cold air maintains a higher cooling capacity during delivery, and can more effectively reduce the temperature of the target area. Conversely, a low delivery efficiency coefficient indicates that the cold air has lost much of its cooling effect before reaching its destination, which may require increasing the airflow rate or adjusting the delivery path to ensure sufficient cooling. Therefore, determining the cold air delivery efficiency coefficient typically requires consideration of the cold air delivery system design, material selection, pipe length, and the actual environmental conditions inside the tunnel.

[0061] The volume of region x refers to the three-dimensional spatial size of a specific area within the tunnel. This volume is a key physical parameter that determines the amount of cold air required for that region, as a larger volume necessitates more cold air to achieve the same temperature reduction. The volumes of different regions within the tunnel may vary, depending on factors such as the tunnel's shape, size, and distribution. During cold air distribution, the system needs to calculate the appropriate cold air flow rate based on the volume of each region to ensure effective temperature control. For example, smaller regions may require less cold air flow, while larger regions require more cold air to achieve the same cooling effect. Therefore, region volume is a crucial factor influencing cold air distribution and temperature regulation strategies.

[0062] Based on the demand for cold air and the temperature distribution in different areas of the tunnel, the output path of the cold air is optimized to reduce energy consumption and improve cooling efficiency. Simultaneously, a feedback control mechanism continuously monitors temperature changes and dynamically adjusts the output of cold air. The specific expression is as follows: In the formula, P opt For the optimal cold air output path, L x，P E represents the transport distance from path P to region x. x Let μ be the energy transfer efficiency of region x, and μ be the feedback adjustment coefficient. This represents the actual output of cold air.

[0063] By minimizing energy consumption and output errors, the system can achieve efficient cold air delivery and temperature control, ensuring that the temperature inside the tunnel is always kept within a safe and comfortable range.

[0064] The regional cold air distribution module further subdivides the various areas within the tunnel during the cold air delivery process. By adjusting valves and distributors, it precisely controls the distribution of cold air, determines the specific needs of each area based on temperature monitoring data, and accurately controls the amount of cold air distributed to different areas.

[0065] This not only improves the efficiency of cold air utilization, but also provides targeted cooling services when temperatures change in different areas, reducing unnecessary consumption of cold air.

[0066] The tunnel is further subdivided into different zones. By adjusting valves and distributors, the distribution of cold air is precisely controlled. Based on temperature monitoring data, the specific needs of each zone are determined, and the amount of cold air distributed to different zones is precisely controlled. The specific steps are as follows:

[0067] Based on the tunnel's length, shape, purpose, and internal temperature distribution, the tunnel is divided into several independent zones;

[0068] The division of these zones is typically based on the distribution of temperature sensors, combined with the requirements of the working environment within the tunnel. Each zone represents an area requiring individual temperature control, which may vary due to different geological conditions, construction activities, or equipment layouts. The purpose of zoning is to achieve more precise distribution of cool air, ensuring that each zone receives the required amount of cool air according to actual needs.

[0069] Based on real-time temperature data, the central control system analyzes the cooling demand of each area. The system assesses the degree of deviation of the temperature of each area from the set safe and comfortable range, as well as the speed and trend of temperature change, in order to determine the amount of cold air required for each area.

[0070] High-temperature areas or areas with rapidly rising temperatures are given priority. The system calculates the required amount of cold air based on demand and formulates an allocation strategy. This allocation strategy aims to ensure that the temperature in all areas remains within a safe and comfortable range with minimal energy consumption, thereby optimizing the overall environment of the tunnel.

[0071] After the cold air distribution strategy is formulated, the central control system sends instructions to the regulating valves and distributors in each area. The regulating valves and distributors adjust the flow and direction of the cold air to achieve precise cold air distribution to each area.

[0072] Each regulating valve and distributor can be operated independently to ensure that cool air enters each zone according to a pre-set strategy. Through this precise control, the system can flexibly adjust the flow and path of cool air according to the specific needs of each zone, thereby ensuring that each zone receives an appropriate supply of cool air and achieves the best cooling effect.

[0073] Once the cold air distribution is complete, the system does not stop monitoring, but continues to monitor the temperature changes in each area in real time. Through the data continuously fed back by the temperature sensors, the central control system can promptly identify any temperature anomalies or changes in demand in any area.

[0074] If the temperature in a certain area changes abruptly or the cooling demand in other areas changes, the system will respond immediately, dynamically adjusting the settings of the regulating valves and distributors to redistribute the cool air;

[0075] This dynamic adjustment mechanism ensures that the system can respond quickly to any changes, maintain the stability and comfort of the temperature inside the tunnel, and maximize energy conservation and optimize resource use.

[0076] The energy efficiency optimization module uses intelligent algorithms to analyze the patterns of tunnel temperature changes and predict future temperature trends, thereby adjusting the cold air output of the vortex tube array in advance to further optimize energy efficiency.

[0077] This proactive adjustment reduces system response delays and avoids over-adjustment during sudden temperature changes. Furthermore, by optimizing compressed air usage, the system can reduce energy consumption, decrease equipment wear, extend the lifespan of the vortex tube array, and improve overall system energy efficiency.

[0078] Intelligent algorithms are used to analyze the patterns of temperature changes in the tunnel, predict future temperature trends, and adjust the cold air output of the vortex tube array in advance. The specific steps are as follows:

[0079] Intelligent algorithms are used to extract features and recognize patterns from temperature data. Feature extraction refers to extracting key numerical indicators and statistical characteristics from raw data, such as the magnitude of temperature change, rate of change, and periodic fluctuations. Pattern recognition refers to identifying the patterns and trends of temperature changes inside the tunnel by analyzing these features.

[0080] The system can identify potential factors affecting temperature, such as diurnal temperature variation, seasonal changes, and the impact of construction activities. By deeply mining data features and identifying patterns, the system can better understand the temperature variation patterns within tunnels, providing support for predicting future trends.

[0081] Based on the extracted features and identified patterns, temperature trend prediction is performed using machine learning or deep learning algorithms. The prediction model is trained with historical data to learn the complex relationships of temperature changes and the influence of potential factors, and can predict the temperature change trends of various areas of the tunnel in a specific future time period.

[0082] This forecast not only considers current temperature data but also incorporates various factors such as time series models, environmental variables, and construction plans, improving the accuracy and reliability of the prediction. By predicting future temperature trends in advance, the system can anticipate potential temperature fluctuations and prepare for the release of cold air.

[0083] Based on the temperature trend prediction results, the cold air output of the vortex tube array can be adjusted in advance. By adjusting the operating parameters of the vortex tube array, such as the flow rate and pressure of compressed air, an appropriate amount of cold air output can be preset to cope with the upcoming temperature changes.

[0084] This proactive adjustment strategy avoids the delays caused by sudden temperature changes, ensuring the timeliness and accuracy of cold air delivery. Pre-adjusting cold air output not only improves temperature control efficiency but also reduces unnecessary energy consumption and resource waste, optimizing the overall environment within the tunnel.

[0085] During the implementation of forecasting and regulation, the deviation between actual temperature changes and forecast results is continuously monitored. Through real-time monitoring and data feedback, the accuracy of the forecasting model is evaluated, and dynamic adjustments and optimizations are made based on the actual situation.

[0086] If a discrepancy is found between the prediction and the actual situation, the system automatically updates the model parameters or selects a more suitable algorithm for prediction. This continuous optimization and feedback adjustment mechanism ensures that the system maintains optimal energy efficiency under different environments and operating conditions, providing stable and efficient temperature control services.

[0087] The real-time monitoring and emergency response module continuously tracks the temperature inside the tunnel and the working status of the vortex tube array through a real-time monitoring platform. Once an abnormality is detected, it automatically issues an alarm and executes emergency response measures.

[0088] A real-time monitoring platform continuously tracks temperature changes and the operational status of the vortex tube array in various areas within the tunnel, including the output of cold air, the pressure of compressed air, and the operating efficiency of the vortex tube array. Equipped with multiple sensors and data acquisition devices, the platform can collect and analyze environmental and equipment operation data within the tunnel in a timely manner. When the system detects that the temperature exceeds the preset safety range, or that the vortex tube array exhibits abnormal operating conditions (such as malfunction, abnormally reduced output, or unstable compressed air pressure), the system automatically issues an alarm to notify management personnel and immediately activates preset emergency response measures. These measures may include adjusting the output of cold air, switching to backup equipment, repairing the faulty vortex tube array, or increasing ventilation to quickly restore temperature balance within the tunnel and ensure the safety of workers and equipment. Through this intelligent monitoring and emergency response mechanism, the system can identify and address potential risks immediately, effectively preventing safety accidents caused by equipment failure or abnormal temperature, and improving the overall safety and operational efficiency of tunnel construction. The operation of the real-time monitoring platform ensures that the system can dynamically adjust and maintain optimal operating conditions in the complex and ever-changing tunnel environment, ensuring a consistently safe and comfortable working environment for construction personnel.

[0089] Meanwhile, regular maintenance and management plans, including inspections and maintenance of temperature sensors, vortex tube arrays, and their piping systems, ensure the long-term stable operation of the entire ventilation system, reduce the occurrence of failures, and improve the reliability and safety of the system.

[0090] The new ventilation system effectively cools high-temperature tunnels or other underground spaces by introducing highly efficient vortex tube cooling technology. Its working principle includes the following aspects:

[0091] Firstly, this new ventilation system adds a vortex tube array and a compressed air supply system to the traditional fan and ductwork. The fan delivers low-temperature outdoor air to the tunnel excavation face through the ductwork, while the compressed air supply provides compressed air to the vortex tube array. The vortex tubes utilize the vortex effect of the compressed air to generate a low-temperature cold airflow, which is then introduced into the tunnel, effectively reducing the temperature at the working face. The output of the cold air from the vortex tubes is adjusted according to the on-site temperature requirements, ensuring that the temperature drops to a set range, typically reaching as low as -40℃. At the vortex tube outlet, a heat recovery system and energy recovery device are also equipped, achieving energy saving and efficiency improvement through heat emission and energy conversion.

[0092] Vortex tube arrays can be placed anywhere in a tunnel. High-pressure air generated by an air compressor is input into the vortex array to produce both high-temperature and low-temperature air (even below -10 degrees Celsius). The low-temperature air is transported to the cooling area through ducts, while the high-temperature air undergoes energy recovery. Vortex tubes utilize vortex tube arrays to achieve ventilation and cooling, and recover heat energy through phase change energy storage materials, significantly improving the overall energy efficiency of the system.

[0093] Secondly, the system incorporates a multi-point temperature monitoring system within the tunnel to detect temperature changes in real time. Based on this data, the central control system automatically adjusts the cooling air output and temperature of the vortex tubes to ensure that the temperature in each area remains within the set range, guaranteeing a safe and comfortable working environment. The system design allows for flexible responses to different cooling needs by increasing or decreasing the number of vortex tubes or adjusting the compressed air flow rate, making it suitable for various underground construction scenarios.

[0094] Overall, the new ventilation system, by combining vortex tube cold air technology with traditional ventilation methods, provides a highly efficient, flexible, and energy-saving solution for cooling high-temperature tunnels. It can effectively address the high-temperature challenges in tunnels and underground spaces, and improve the safety and comfort of the construction environment.

[0095] This invention, by deploying a network of temperature sensors and establishing a temperature model, enables the system to monitor the temperature distribution within the tunnel in real time, identify areas of abnormal temperature, and respond rapidly to temperature changes. Through this precise monitoring and dynamic adjustment mechanism, the output and distribution of cold air can be adjusted according to actual needs, avoiding excessive or insufficient cold air supply. The use of a vortex tube array makes cold air delivery more efficient, as cold air can be directly transported to areas requiring cooling through dedicated pipelines, reducing transmission losses. Furthermore, the system further subdivides the tunnel into different zones and precisely controls the distribution of cold air through regulating valves and distributors, ensuring that each zone receives the appropriate flow of cold air according to its temperature requirements. This optimized cold air management significantly reduces energy consumption, improves overall energy efficiency, and reduces operating costs, especially during long-term, high-intensity tunnel construction.

[0096] This invention significantly improves the reliability and safety of the system through intelligent algorithms and a real-time monitoring platform. The intelligent algorithm analyzes the patterns of temperature changes within the tunnel and predicts future temperature trends, thereby adjusting the output of cooling air in advance to avoid safety hazards caused by sudden temperature changes. The real-time monitoring platform continuously tracks the temperature conditions within the tunnel and the operating status of the vortex tube array. Once an abnormal temperature or equipment malfunction is detected, the system automatically issues an alarm and executes emergency measures, such as adjusting the cooling air output or activating backup equipment. This real-time monitoring and rapid response capability ensures that the system maintains the temperature within the tunnel within a safe and comfortable range under various operating conditions, effectively preventing safety accidents such as worker heatstroke and equipment overheating caused by high temperatures.

[0097] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0098] The foregoing has only described certain exemplary embodiments of the present invention by way of illustration. Undoubtedly, 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 foregoing 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.

[0099] It should be noted that, in this document, the use of relational terms such as "first" and "second" is merely to distinguish one entity or operation from another, and does not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes the element.

[0100] It should be understood that in the various embodiments of this application, the order of the above-mentioned processes does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0101] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0102] 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.

[0103] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0104] The units described as separate 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.

[0105] 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.

[0106] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0107] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A novel local ventilation and cooling system for high-temperature tunnels, characterized in that, It includes a temperature monitoring module, a cold air delivery start-up module, a cold air output regulation module, a regional cold air distribution module, an energy efficiency optimization module, and a real-time monitoring and emergency response module; The temperature monitoring module deploys a network of temperature sensors inside the tunnel to monitor the temperature distribution in real time. The temperature data collected by the temperature sensors is transmitted to the central control system through a data acquisition system. The temperature data is used to build a temperature model inside the tunnel and identify areas with abnormal temperatures. The cold air delivery start-up module, based on preliminary temperature monitoring data, starts the vortex tube array cold air delivery mechanism. The vortex tube array uses compressed air to generate two airflows, cold air and hot air, and delivers the cold air part to the tunnel through the pipeline. The cold air output regulation module, when the temperature inside the tunnel changes, the central control system analyzes the current temperature inside the tunnel based on real-time temperature data and dynamically adjusts the output of cold air to achieve timely temperature response; The regional cold air distribution module further subdivides the various areas within the tunnel during the cold air delivery process. By adjusting valves and distributors, it precisely controls the distribution of cold air, determines the specific needs of each area based on temperature monitoring data, and accurately controls the amount of cold air distributed to different areas. The energy efficiency optimization module uses intelligent algorithms to analyze the patterns of temperature changes in the tunnel and predict future temperature trends, thereby adjusting the cold air output of the vortex tube array in advance. The real-time monitoring and emergency response module continuously tracks the temperature inside the tunnel and the working status of the vortex tube array through a real-time monitoring platform. Once an abnormality is detected, it automatically issues an alarm and executes emergency response measures.

2. The novel local ventilation and cooling system for high-temperature tunnels according to claim 1, characterized in that, A temperature model of the tunnel interior is established using temperature data to identify areas of temperature anomalies. The specific steps are as follows: A temperature sensor network is deployed inside the tunnel, and the temperature sensors collect temperature data from various locations in the tunnel in real time. Using the processed temperature data, the temperature gradient at each sensor location is calculated to reflect the spatial variation of temperature. Simultaneously, the rate of temperature change is calculated to identify the dynamic characteristics of temperature changes. The expression for calculating the temperature gradient is: In the formula, T i (t) represents the temperature reading of the i-th sensor at time t, where T i ′(t) is the processed temperature reading of the i-th sensor at time t. R represents the temperature gradient at the i-th sensor location, reflecting the rate of temperature change in space. i (t) represents the temperature change rate at the i-th sensor location, indicating how the temperature changes over time, d i The distance between adjacent sensors; Based on the temperature gradient and the rate of temperature change, a temperature anomaly factor is calculated at each sensor location to identify temperature anomaly regions. The temperature anomaly factor is a combined function of the temperature gradient and the rate of temperature change, reflecting the comprehensive characteristics of temperature changes in both space and time. The calculation expression is as follows: F i (t) represents the temperature anomaly factor at the i-th sensor location, where α and β are adjustment parameters used to balance the effects of temperature gradient and rate of change; S i (t) is the weighting factor; Based on the calculated temperature anomaly factor, temperature anomaly regions inside the tunnel are identified. Thresholds are set to distinguish between normal and abnormal regions, and a model of the temperature anomaly regions is constructed to depict the spatial distribution and variation characteristics of temperature anomalies, providing support for subsequent ventilation and cooling strategies. The expression for constructing the temperature anomaly region model is: A(t)={i|F i (t)>F th In the formula, A(t) represents the set of temperature anomaly regions identified at time t, containing the locations of all sensor sensors where temperature anomaly factors exceed the threshold, and F th This is the threshold for the temperature anomaly factor.

3. The novel local ventilation and cooling system for high-temperature tunnels according to claim 1, characterized in that, The output flow rate and temperature of the cold air from the vortex tube array can be initially controlled by adjusting the flow rate and pressure of the compressed air, ensuring that basic cooling requirements are met in the initial state.

4. A novel local ventilation and cooling system for high-temperature tunnels according to claim 2, characterized in that, When the temperature inside the tunnel changes, the central control system analyzes the current temperature situation inside the tunnel based on real-time temperature data and dynamically adjusts the output of cold air to achieve timely temperature response. The specific steps are as follows: The temperature change rate is calculated for each region based on temperature data to determine areas requiring priority cooling. The temperature change rate can be used to assess abrupt temperature changes within a region. The calculation expression is as follows: R i (t) represents the rate of temperature change of sensor i at time t, and Δt represents the time interval; Based on the temperature change rate of each region, the required amount of cold air to be supplied is calculated to ensure a rapid response to temperature changes. The calculation expression is as follows: In the formula, Q air (t) represents the output of cold air at time t, κ represents the cold air transport efficiency coefficient, and V x Represents the volume of region x. This represents the temperature response coefficient, which reflects the sensitivity of temperature to the demand for cold air. Based on the demand for cold air and the temperature distribution in different areas of the tunnel, the output path of the cold air is optimized to reduce energy consumption and improve cooling efficiency. Simultaneously, a feedback control mechanism continuously monitors temperature changes and dynamically adjusts the output of cold air. The specific expression is as follows: In the formula, P opt For the optimal cold air output path, L x，P E represents the transport distance from path P to region x. x Let μ be the energy transfer efficiency of region x, and μ be the feedback adjustment coefficient. This represents the actual output of cold air.

5. A novel local ventilation and cooling system for high-temperature tunnels according to claim 1, characterized in that, The tunnel is further subdivided into different zones. By adjusting valves and distributors, the distribution of cold air is precisely controlled. Based on temperature monitoring data, the specific needs of each zone are determined, and the amount of cold air distributed to different zones is precisely controlled. The specific steps are as follows: Based on the tunnel's length, shape, purpose, and internal temperature distribution, the tunnel is divided into several independent zones; Based on real-time temperature data, the central control system analyzes the cooling demand of each area, assesses the degree of deviation of the temperature of each area from the set safe and comfortable range, as well as the rate and trend of temperature change, in order to determine the amount of cold air required for each area. After the cold air distribution strategy is formulated, the central control system sends instructions to the regulating valves and distributors in each area. The regulating valves and distributors adjust the flow and direction of the cold air to achieve precise cold air distribution to each area. Once the cold air distribution is complete, the tunnel ventilation system does not stop monitoring, but continues to monitor the temperature changes in each area in real time. Based on the data continuously fed back by the temperature sensors, the central control system can promptly identify any temperature anomalies or changes in demand in any area.

6. A novel local ventilation and cooling system for high-temperature tunnels according to claim 5, characterized in that, If the temperature in a certain area changes abruptly or the cooling demand in other areas changes, the tunnel ventilation system will respond immediately, dynamically adjusting the settings of regulating valves and distributors to redistribute the cool air.

7. A novel local ventilation and cooling system for high-temperature tunnels according to claim 1, characterized in that, Intelligent algorithms are used to analyze the patterns of temperature changes in the tunnel, predict future temperature trends, and adjust the cold air output of the vortex tube array in advance. The specific steps are as follows: Intelligent algorithms are used to extract features and recognize patterns from temperature data. Feature extraction refers to extracting key numerical indicators and statistical characteristics from raw data, while pattern recognition refers to identifying the patterns and trends of temperature changes inside the tunnel by analyzing these features. Based on the extracted features and identified patterns, temperature trend prediction is performed using machine learning or deep learning algorithms. The prediction model is trained with historical data to learn the complex relationships of temperature changes and the influence of potential factors, and predicts the temperature change trends of various areas of the tunnel in the future. Based on the temperature trend forecast, the cold air output of the vortex tube array is adjusted in advance. By adjusting the operating parameters of the vortex tube array, an appropriate amount of cold air output is pre-set to cope with the upcoming temperature changes. During the implementation of forecasting and regulation, the deviation between actual temperature changes and forecast results is continuously monitored. Through real-time monitoring and data feedback, the accuracy of the forecasting model is evaluated, and dynamic adjustments and optimizations are made based on the actual situation.

8. A novel local ventilation and cooling system for high-temperature tunnels according to claim 7, characterized in that, If the prediction is found to be inconsistent with the actual situation, the model parameters are automatically updated or a more suitable algorithm is selected for prediction. Through continuous optimization and feedback adjustment mechanisms, the best energy efficiency performance is always maintained under different environments and operating conditions.

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