Tunnel segmented exhaust method and system based on train working conditions, medium and product

Abstract

The invention discloses a tunnel segmented exhaust method and system based on train working conditions, a medium and a product, and relates to the technical field of ventilation. The method comprises the steps that a target tunnel is divided into a plurality of control sections according to fan installation configuration of the target tunnel; acquiring train passing data and train configuration information of the control section, and calculating a flue gas growth value of the control section in a preset time period; collecting flue gas concentration data of the control section, and calculating a ventilation demand index of the control section in combination with the flue gas growth value; collecting wind direction parameters of the control sections, determining a ventilation control relationship between the adjacent control sections based on the ventilation demand indexes and the wind direction parameters, and generating a ventilation cooperation scheme; according to the ventilation cooperation scheme, operation parameters of the tunnel fans in all the control sections are calculated, and control instructions corresponding to the operation parameters are sent to the tunnel fans. By implementing the application, the fan operation can be adjusted according to the actual train operation condition, and the ventilation effect is optimized.

Description

Technical Field

[0001] The present application relates to the field of ventilation technology, and in particular to a tunnel segmented exhaust method, system, medium and product based on train operating conditions. Background Art

[0002] With the rapid development of railway transportation industry, tunnels are an important part of railway lines, and the performance of their ventilation systems directly affects the safety of train operation and the comfort of passengers. Especially in long and large tunnels, the exhaust gas and dust emitted by trains will affect the safety of tunnels, making the control of tunnel ventilation systems a technical problem that needs to be solved urgently.

[0003] In related technologies, tunnel ventilation control systems mainly use a timing control method to turn ventilation equipment on or off according to a pre-set time period. This control method installs a fixed number of axial flow fans in the tunnel and performs ventilation operations according to a preset operation schedule. At the same time, some systems are also equipped with simple environmental monitoring devices. When the air quality index is detected to exceed the threshold, the ventilation equipment in the corresponding area is started for ventilation.

[0004] However, since the operation of ventilation equipment is completely dependent on a preset schedule or a single environmental parameter threshold, the ventilation pressure will increase dramatically during periods of intensive train operation, while the ventilation capacity of the ventilation equipment will be insufficient in a short period of time, resulting in poor ventilation effect. Summary of the invention

[0005] The present application provides a tunnel segmented exhaust method, system, medium and product based on train operating conditions, which are used to adjust fan operation and optimize ventilation effects.

[0006] In the first aspect, the present application provides a tunnel segmented exhaust method based on train operating conditions, which is applied to a ventilation control system, and the method includes: dividing the target tunnel into multiple control sections according to the fan installation configuration of the target tunnel; obtaining the train passage data and train configuration information of the control section, and calculating the smoke growth value of the control section within a preset time period; collecting the smoke concentration data of the control section, and calculating the ventilation demand index of the control section in combination with the smoke growth value; collecting the wind direction parameters of the control section, and determining the ventilation control relationship between adjacent control sections based on the ventilation demand index and wind direction parameters, and generating a ventilation coordination plan; according to the ventilation coordination plan, calculating the operating parameters of the tunnel fans in each control section, and sending control instructions corresponding to the operating parameters to the tunnel fans.

[0007] In the above embodiment, the ventilation control system divides the tunnel into multiple control sections, and performs ventilation control in combination with train traffic data, smoke data and wind direction parameters. This distributed control method realizes the effect of dynamically adjusting the operation of the fan according to the actual ventilation demand, and can accurately calculate the ventilation demand of each section. Through coordinated control between sections, it ensures the optimization of ventilation effect, thus avoiding the problem of unsatisfactory ventilation effect caused by traditional timing and quantitative control methods.

[0008] In combination with some embodiments of the first aspect, in some embodiments, the step of dividing the target tunnel into multiple control sections according to the fan installation configuration of the target tunnel specifically includes: determining the installation position and rated power parameters of each tunnel fan unit in the target tunnel according to the fan installation configuration of the target tunnel; calculating the installation spacing and ventilation coverage range of each tunnel fan unit based on the installation position and rated power parameters; dividing all tunnel fan units into multiple fan control groups by taking adjacent tunnel fan units whose installation spacing is less than a preset distance threshold and whose ventilation coverage ranges overlap as a fan control group; and dividing the target tunnel into multiple control sections based on the multiple fan control groups.

[0009] In the above embodiment, the ventilation control system divides the control sections based on the fan installation position, power parameters, installation spacing and ventilation coverage range, so that the fans in each control section can form an effective ventilation collaborative working group, ensuring the effective superposition of ventilation effects between adjacent fans and improving the overall ventilation efficiency.

[0010] In combination with some embodiments of the first aspect, in some embodiments, the wind direction parameters of the control section are collected, and based on the ventilation demand index and the wind direction parameters, the ventilation control relationship between adjacent control sections is determined, and the steps of generating a ventilation coordination plan specifically include: sorting the control sections in descending order according to the ventilation demand index, and determining the target control section with the largest ventilation demand; collecting the wind direction parameters of the target control section, determining the ventilation airflow direction of the target control section between adjacent control sections, and determining multiple coordinated control sections based on the ventilation airflow direction; based on the ventilation demand index, calculating the demand index difference between the target control section and the coordinated control section; when the demand index difference is greater than a preset difference threshold, adjusting the ventilation demand index of the target control section and the coordinated control section to generate a ventilation coordination plan.

[0011] In the above embodiment, the ventilation control system achieves optimal allocation of ventilation resources by focusing on controlling the section with the greatest ventilation demand and making coordinated adjustments in conjunction with the ventilation airflow directions of adjacent sections. When the demand index deviation is large, the system will adjust the ventilation plan to ensure that the high-demand section obtains sufficient ventilation support.

[0012] In combination with some embodiments of the first aspect, in some embodiments, the steps of obtaining train passage data and train configuration information of a control section, and calculating the smoke growth value of the control section within a preset time period specifically include: obtaining train operation data on a rail transit dispatching system, and determining the train passage data and train configuration information of the control section based on the train operation data; when it is determined that the passing train is a diesel locomotive based on the train passage data, determining the smoke emission rate of the diesel locomotive based on the train configuration information; and calculating the smoke growth value of the control section within a preset time period based on the train passage data and the smoke emission rate.

[0013] In the above embodiment, the ventilation control system analyzes the smoke emissions of the diesel locomotive and accurately calculates the smoke growth value, thereby providing reliable data support for ventilation control. According to the characteristics of different types of trains, corresponding ventilation control strategies can be adopted to improve the accuracy of ventilation.

[0014] In combination with some embodiments of the first aspect, in some embodiments, after the steps of obtaining train operation data on the rail transit dispatching system and determining the train traffic data and train configuration information of the control section based on the train operation data, the method also includes: when it is determined that the passing train is an electric locomotive based on the train traffic type, determining the operating speed and vehicle type information of the electric locomotive based on the train configuration information; determining the airflow disturbance coefficient of the electric locomotive based on the operating speed and vehicle type information; collecting the smoke concentration data of the control section, and calculating the smoke growth value of the control section within a preset time period according to the smoke concentration data and the airflow disturbance coefficient.

[0015] In the above embodiment, the ventilation control system analyzes the airflow disturbance effect generated by the operation of the electric locomotive and calculates its influence on smoke diffusion, thereby achieving a more comprehensive ventilation demand assessment.

[0016] In combination with some embodiments of the first aspect, in some embodiments, after the step of calculating the operating parameters of the tunnel fans in each control section according to the ventilation coordination plan, and sending control instructions corresponding to the operating parameters to the tunnel fans, the method also includes: obtaining the real-time smoke concentration in each control section and constructing a smoke change curve; calculating the ventilation effect score of each control section based on the smoke change curve; taking the control section with a ventilation effect score lower than a preset score threshold as an abnormal control section, and detecting the fan operating status of all tunnel fans in the abnormal control section; when the fan operating status is abnormal, sending maintenance prompt information to the management terminal.

[0017] In the above embodiment, the ventilation control system realizes intelligent monitoring and timely maintenance of the ventilation system by real-time monitoring of the smoke change curve and the ventilation effect score, promptly discovers abnormal conditions and performs fan status detection, thereby preventing ventilation failures and ensuring the continuous and stable operation of the ventilation system.

[0018] In combination with some embodiments of the first aspect, in some embodiments, after the step of calculating the operating parameters of the tunnel fans in each control section according to the ventilation coordination plan, and sending control instructions corresponding to the operating parameters to the tunnel fans, the method also includes: obtaining the operating time and energy consumption data of each tunnel fan in the target control section; calculating the performance attenuation coefficient of the tunnel fan based on the operating time and energy consumption data; calculating the estimated maintenance time of the tunnel fan based on the performance attenuation coefficient and the maintenance record of the tunnel fan, and generating a maintenance plan.

[0019] In the above embodiment, the ventilation control system formulates a maintenance plan by analyzing the fan operating time, energy consumption data and performance degradation, thereby achieving preventive maintenance of the fan equipment, reducing maintenance costs and improving the reliability of the ventilation system.

[0020] In a second aspect, an embodiment of the present application provides a ventilation control system, which comprises: one or more processors and a memory; the memory is coupled to the one or more processors, the memory is used to store computer program code, the computer program code comprises computer instructions, and the one or more processors call the computer instructions to cause the ventilation control system to execute the method described in the first aspect and any possible implementation method of the first aspect.

[0021] In a third aspect, an embodiment of the present application provides a computer program product comprising instructions, and when the above-mentioned computer program product is run on a ventilation control system, the above-mentioned ventilation control system executes the method described in the first aspect and any possible implementation method of the first aspect.

[0022] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, comprising instructions, which, when executed on a ventilation control system, enable the ventilation control system to execute the method described in the first aspect and any possible implementation of the first aspect.

[0023] It can be understood that the ventilation control system provided in the second aspect, the computer program product provided in the third aspect, and the computer storage medium provided in the fourth aspect are all used to execute the method provided in the embodiment of the present application. Therefore, the beneficial effects that can be achieved can refer to the beneficial effects in the corresponding method, which will not be repeated here.

[0024] One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages: 1. Due to the adoption of a distributed ventilation control method based on section division, by acquiring train traffic data, smoke concentration and wind direction parameters in real time, and combining the ventilation demand index for coordinated control, the fan operating parameters can be flexibly adjusted according to the actual operating conditions, effectively solving the problem of unsatisfactory ventilation effect caused by relying solely on timing control or single environmental parameter threshold control in related technologies, thereby realizing the reasonable allocation of ventilation resources and the optimization of ventilation effects, so that the ventilation system can accurately obtain and process the actual ventilation needs of each section, avoiding unnecessary energy waste.

[0025] 2. Due to the adoption of the section collaborative control method based on the ventilation demand index, by determining the target section with the largest ventilation demand, analyzing the ventilation airflow direction and demand index difference between it and the adjacent sections, and dynamically adjusting the ventilation plan, it is possible to achieve precise allocation of ventilation resources and realize the overall collaborative optimization of the ventilation system, ensuring that high-demand sections can obtain sufficient ventilation support. At the same time, by guiding the ventilation airflow between sections, the overall ventilation efficiency is improved.

[0026] 3. Due to the adoption of a ventilation control method that analyzes the airflow disturbance effect of electric locomotives, by analyzing the train running speed and vehicle type information, calculating the airflow disturbance coefficient, and combining the smoke concentration data to evaluate the ventilation demand, it can more accurately reflect the impact of train operation on the tunnel environment, and effectively solve the problem of insufficient ventilation control accuracy caused by ignoring the airflow disturbance effect of electric locomotives in related technologies, thereby achieving more precise and scientific ventilation control and improving the accuracy and efficiency of ventilation control. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 It is a flow chart of a tunnel segmented exhaust method based on train working conditions in an embodiment of the present application; Figure 2 is another flow chart of the tunnel segmented exhaust method based on train working conditions in an embodiment of the present application; Figure 3 It is a schematic diagram of the structure of a physical device of the ventilation control system in the embodiment of the present application. DETAILED DESCRIPTION

[0028] The terms used in the following embodiments of the present application are only for the purpose of describing specific embodiments, and are not intended to be used as limitations to the present application. As used in the specification of the present application, the singular expressions "one", "a kind of", "above", "the" and "this" are intended to also include plural expressions, unless there is a clear indication to the contrary in the context. It should also be understood that the term "and / or" used in the present application refers to any or all possible combinations comprising one or more of the listed items.

[0029] In the following, the terms "first" and "second" are used for descriptive purposes only and are not to be understood as suggesting or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features, and in the description of the embodiments of the present application, unless otherwise specified, "plurality" means two or more.

[0030] For ease of understanding, the application scenarios of the embodiments of the present application are introduced below.

[0031] A special railway tunnel is 15 kilometers long, with an average of 180 trains passing through it every day, of which 30% are diesel locomotives. The tunnel is equipped with 24 sets of axial flow fans for ventilation. Due to the long length of the tunnel and the piston effect caused by the dense traffic of trains, the air quality inside the tunnel is difficult to control. Especially during the peak summer season, when multiple diesel locomotives pass through for a short period of time, the CO concentration in some sections has reached 150ppm, far exceeding the safety standard of 100ppm, seriously threatening driving safety.

[0032] In the related art, basic ventilation in the tunnel can be achieved by using a fixed mode ventilation control based on threshold triggering. The following describes a scenario using the tunnel segmented exhaust method based on train working conditions in the related art.

[0033] A single-track railway tunnel adopts the traditional timed and quantitative ventilation control method. According to the preset schedule, the system starts the ventilation equipment every 2 hours from 7 am to 11 pm every day, and each operation lasts for 30 minutes. At the same time, a CO concentration detector is installed every 500 meters in the tunnel. When the CO concentration at a certain point exceeds 120ppm, the two nearest axial flow fans are started for ventilation. However, due to the fixed ventilation time and parameters, the ventilation cannot be adjusted according to the actual train operation density, resulting in insufficient ventilation during peak periods and energy waste during trough periods.

[0034] By adopting the tunnel segmented exhaust method based on train working conditions in the embodiment of the present application, accurate ventilation regulation is achieved by real-time calculation of the ventilation demand index of each section and dynamic adjustment of the fan operating parameters, thus improving ventilation efficiency and reducing energy consumption. The following describes the scenario in which the tunnel segmented exhaust method based on train working conditions in the present application is used.

[0035] A double-track railway tunnel uses this solution to achieve intelligent ventilation control. The system obtains train dispatch information in real time, calculates the exhaust emissions of diesel locomotives and the piston effect generated by electric locomotives in advance, and makes accurate ventilation demand predictions. For example, the system detects that two diesel freight trains will pass through Section 3 within 10 minutes, and calculates that the expected smoke growth value of this section is 25ppm / min. At the same time, the distributed sensor network monitors that the current airflow direction of this section is flowing from north to south, and the adjacent Section 4 has a low ventilation demand index. The system generates a collaborative solution: reduce the operating power of the north side fan of Section 3 to 60% in advance, and adjust the south side fan of Section 3 to 90% power operation, while ensuring the ventilation effect and avoiding the spread of pollutants to Section 4. Throughout the process, the system continuously monitors the real-time concentration changes in each section to ensure that the ventilation effect is always in the best state.

[0036] It can be seen that the adoption of the tunnel segmented exhaust method based on train operating conditions in the embodiment of the present application can not only achieve effective ventilation, but also effectively solve the problem of poor ventilation effect in traditional solutions, thereby achieving the optimization of ventilation effect and energy utilization.

[0037] For ease of understanding, the following describes the process of the method provided by this implementation in combination with the above scenario. Figure 1 , which is a flow chart of a tunnel segmented exhaust method based on train operating conditions in an embodiment of the present application.

[0038] S101. Divide the target tunnel into a plurality of control sections according to the fan installation configuration of the target tunnel.

[0039] Among them, the target tunnel refers to the specific tunnel that needs ventilation control; the fan installation configuration refers to the installation position, power parameters, quantity distribution and other information of the fans in the tunnel; the control section refers to the independent ventilation control unit divided based on the fan distribution characteristics, and each control section contains one or more fan units working in coordination.

[0040] When initializing the tunnel ventilation control plan, the ventilation control system needs to first establish the control section division. Specifically, the system first obtains the installation information of all fans in the tunnel, analyzes the spacing between adjacent fans and the overlap of ventilation coverage, and divides adjacent fans with similar ventilation capacity and overlapping coverage into the same control section, eventually forming multiple relatively independent control sections.

[0041] In some embodiments, the division of control sections can be achieved in a variety of ways: Optionally, the system can calculate the effective ventilation radius of each fan based on the rated power and installation position of the fan, divide adjacent fans whose ventilation radius overlap exceeds a preset threshold into a group, and complete the section division by traversing all fans; Optionally, the system can divide the tunnel according to the geometric characteristics of the tunnel (such as slope, curve, etc.) and the distribution characteristics of ventilation demand, combined with the installation position and manual settings of the fan, and then verify and optimize the section division scheme through actual ventilation effects. It is understandable that other division methods based on fan characteristics or tunnel characteristics can also be used, which are not limited here.

[0042] S102: Obtain train traffic data and train configuration information of the control section, and calculate the smoke growth value of the control section within a preset time period.

[0043] Among them, the train passage data represents the operating status information of the train in the control section, including the train position, running speed, passing time, etc.; the train configuration information refers to the basic parameters such as the train type, power system, emission standards, etc.; the preset time period represents the fixed time period for calculating the smoke growth value; the smoke growth value refers to the expected increase in smoke concentration in the section due to the train operation within the preset time period.

[0044] When evaluating ventilation needs, the ventilation control system needs to predict the impact of train operation on the air quality in the section. Specifically, the system first obtains real-time train operation data from the rail transit dispatching system, determines the emission characteristics of each train in combination with the train configuration information, and then calculates the cumulative growth of smoke within a preset time period (such as 15 minutes) based on the train's residence time and operating status in the section, and finally obtains the predicted value of smoke growth in the section.

[0045] In some embodiments, the calculation of smoke growth value can be realized in a variety of ways: Optionally, the system can establish an emission model based on train type and running status, obtain the cumulative emission in a time period by integral calculation, and then calculate the concentration growth value in combination with the spatial volume of the section. It is understandable that other mathematical models or statistical methods can also be used to predict smoke growth, which is not limited here.

[0046] S103, collecting smoke concentration data of the control section, and calculating the ventilation demand index of the control section in combination with the smoke growth value.

[0047] Among them, the smoke concentration data represents the smoke content information in the section collected in real time by sensors; the ventilation demand index refers to a dimensionless indicator that quantitatively represents the ventilation urgency of the section after comprehensively considering the current smoke concentration and the expected growth value.

[0048] The ventilation control system needs to evaluate the ventilation needs of the section based on real-time monitoring and prediction data. Specifically, the system collects real-time smoke concentration data through a sensor network distributed in the section, and weights and combines these data with the smoke growth value calculated in the previous step. Through the preset demand index calculation model, a quantitative index reflecting the current ventilation urgency of the section is obtained.

[0049] In some embodiments, the calculation of the ventilation demand index can be achieved in a variety of ways: Optionally, the system can use a weighted summation method to assign different weights to the current smoke concentration value and the predicted growth value, and obtain the final demand index through standardization; Optionally, the system can establish a multi-factor evaluation model, using smoke concentration, growth forecast, air quality standards and other indicators as a benchmark, and obtain the demand index through comprehensive scoring. It is understandable that other quantitative analysis methods can also be used to calculate the ventilation demand index, which is not limited here.

[0050] Among them, when the ventilation demand index is calculated by weighted summation, the three key indicators of smoke concentration, growth forecast and train speed are integrated into a unified evaluation index by establishing a mathematical model. The system uses an exponential standardization function f(x)=100*(1-e^(-1.5x)) to map the values ​​of each parameter to the range of 0-100. This function has the characteristic of gradually slowing down as the input value increases, which can better reflect the actual impact of parameter changes on ventilation demand.

[0051] In the specific calculation, first divide the current smoke concentration C by the safety concentration threshold Cs (100ppm) to get the relative concentration value, divide the predicted 15-minute smoke growth value ΔC by the allowable growth threshold ΔCs (30ppm) to get the relative growth rate, and divide the train speed V by the tunnel speed limit Vs (80km / h) to get the relative speed value, and substitute them into the standardized function to get the corresponding score. Then, according to the influence of each factor on the ventilation demand, the corresponding weight coefficient is assigned: the current concentration and growth forecast each account for 40%, reflecting their dominant role in ventilation control; the train speed accounts for 20%, reflecting its auxiliary influence on the ventilation demand.

[0052] Finally, the ventilation demand index is obtained by weighted summation: NDI=w1*f(C / Cs)+w2*f(ΔC / ΔCs)+w3*f(V / Vs).

[0053] For example, when the smoke concentration is 75ppm at a certain moment, the predicted 15-minute growth value is 20ppm, and the train speed is 60km / h, the following equations are used to calculate: f(75 / 100)=68.32, f(20 / 30)=63.21, f(60 / 80)=68.32, and the final ventilation demand index NDI=0.4*68.32+0.4*63.21+0.2*68.32=66.21. This index value is in the medium demand range (50-80), indicating that the current section needs to maintain normal ventilation mode.

[0054] The system continuously evaluates changes in ventilation demand by updating the calculation every 5 minutes. When the index exceeds 80, the emergency ventilation mode is activated in time. When it is below 50, the ventilation intensity can be appropriately reduced to save energy. This evaluation method based on mathematical models not only ensures the objectivity and accuracy of the evaluation, but also realizes the intelligence and precision of ventilation control.

[0055] S104, collecting wind direction parameters of the control sections, determining the ventilation control relationship between adjacent control sections based on the ventilation demand index and the wind direction parameters, and generating a ventilation coordination plan.

[0056] Among them, the wind direction parameter represents the direction and speed information of air flow within the section; the ventilation control relationship refers to the ventilation airflow exchange mode and influence degree between adjacent sections; the ventilation coordination plan refers to the overall ventilation control strategy formulated based on the ventilation needs and airflow characteristics of multiple sections.

[0057] The ventilation control system needs to coordinate the ventilation relationship between each section to achieve overall optimal control. Specifically, the system first collects the wind direction sensor data of each section, analyzes the airflow characteristics between sections, and then combines the ventilation demand index of each section and the influence of airflow direction on ventilation effect to establish a ventilation coordination relationship model between sections, and finally generates an overall control solution that can achieve multi-section coordinated ventilation.

[0058] Among them, the ventilation coordination solution constructs a mathematical model based on the pressure gradient control theory, which transforms the ventilation coordination problem of multiple sections into a pressure balance control problem.

[0059] The system determines the required fan power through the section pressure difference calculation model ΔP=ΔPf+ΔPk+ΔPw. The total pressure difference ΔP is composed of the friction pressure loss ΔPf along the way, the local pressure loss ΔPk and the pressure increment ΔPw provided by the fan.

[0060] The pressure increment ΔPw required by the fan can be calculated by ΔPw=ΔP-(ΔPf+ΔPk). Since the relationship between the fan power P and the pressure increment and air volume is P=ΔPw·Q / η (η is the fan efficiency), the power ratio between adjacent sections can be expressed as: Pi / Pj=(ΔPwi·Qi) / (ΔPwj·Qj); The system relates this power ratio to the ventilation demand index (NDI): Pi / Pj=(NDIi / NDIj)^α By combining the above equations, the relationship between pressure increment and ventilation demand can be obtained: ΔPwi / ΔPwj=(NDIi / NDIj)^α·(Qj / Qi).

[0061] For example, when the ventilation demand index of the core section is 85 and that of the adjacent section is 45, assuming that the air volume ratio of the two sections is Qj / Qi=0.8 and α is 1.5, then: ΔPwi / ΔPwj=(85 / 45)^1.5·0.8=2.31 Based on this pressure proportional relationship, combined with the measured pressure losses ΔPf and ΔPk, the system can accurately calculate the pressure increment required by the fan in each section, and then determine the required fan power. This collaborative control method based on pressure balance ensures that the pressure gradient between the sections of the ventilation system is maintained within a reasonable range of 5-10Pa, achieving the optimal allocation of ventilation resources.

[0062] S105. According to the ventilation coordination plan, the operating parameters of the tunnel fans in each control section are calculated, and control instructions corresponding to the operating parameters are sent to the tunnel fans.

[0063] Among them, the operating parameters refer to the specific parameter indicators for controlling the operation of the fan, including speed, power, start and stop status, etc.; the control instructions refer to the specific execution commands used to adjust the operating status of the fan.

[0064] The ventilation control system needs to convert the ventilation plan into specific equipment control instructions. Specifically, the system calculates the specific operating parameters that need to be adjusted for each fan based on the ventilation requirements of each section specified in the ventilation coordination plan, combined with the performance characteristics and working curve of the fan, and then converts these parameters into a standard control instruction format, which is sent to the control unit of each fan through the communication network to achieve precise adjustment of the fan's operating status.

[0065] In some embodiments, the system may adopt a fuzzy control method to dynamically adjust the operating parameters of the fan according to the degree of ventilation demand, and use a feedback control method, that is, continuously optimize the control effect through real-time feedback.

[0066] Among them, the feedback control method adopts the closed-loop control principle to achieve precise adjustment of the fan. The ventilation control system will first establish a dynamic model of the ventilation effect, taking the smoke concentration as the control target and the fan speed as the control quantity. The controller calculates the deviation from the target value by collecting smoke sensor data in real time, and calculates the required control quantity adjustment value according to the proportional-integral-differential (PID) control algorithm. Among them, the proportional term is used to quickly respond to the deviation, the integral term eliminates the steady-state error, and the differential term improves the dynamic characteristics. The controller will continuously adjust the PID parameters according to the actual ventilation effect. When it is detected that the ventilation effect is not ideal, the system will automatically increase the control action to improve the response characteristics. The whole process forms a closed-loop control by calculating the deviation, the control quantity and executing the adjustment to achieve dynamic optimization of the ventilation effect.

[0067] For example, in the actual ventilation control process, the system uses a numerical control method based on iterative calculation to achieve precise regulation. The control process first establishes the fan characteristic equation: ΔP=k*Q², where k is the system characteristic coefficient (determined by the tunnel geometry parameters and air physical parameters), and Q is the air volume. At the same time, there is a functional relationship between air volume and fan power: Q=a*P^0.5, where a is the fan characteristic coefficient.

[0068] Through these two basic equations, the system can accurately calculate the pressure distribution under different power configurations. In the tunnel space, the pressure at any position y can be calculated through the linear distribution model P(y)=P0-ΔP*(y / L), where P0 is the starting pressure and L is the section length. The system calculates the pressure distribution every 30 seconds and adjusts the fan power in real time through the proportional-integral-differential (PID) control algorithm. The control equation is: P(t)=Kp*e(t)+Ki*∫e(t)dt+Kd*de(t) / dt, where e(t) is the pressure deviation, and Kp, Ki, and Kd are the proportional, integral, and differential coefficients, respectively.

[0069] Taking a typical control scenario as an example, when the demand index of section 3 reaches 85, the system detects that the current smoke concentration is 85ppm and the airflow velocity is 3.5m / s. First, it is calculated based on the characteristic coefficient k=0.024Pa / (m³ / s)² and the fan coefficient a=50m³ / s: at 90% power, the leading fan can provide 47.4m³ / s of air volume and generate a pressure rise of 53.8Pa; while the cooperative fan at 60% power provides 38.7m³ / s of air volume and generates a pressure rise of 35.9Pa.

[0070] The system dynamically adjusts these parameters to ensure that the pressure difference between sections is stable at around 7.2 Pa, achieving precise ventilation control. This refined control method based on mathematical models not only ensures the accuracy of ventilation effects, but also optimizes energy utilization.

[0071] In the above embodiment, accurate prediction of ventilation demand is achieved through comprehensive analysis of train operation data and environmental monitoring data. In practical applications, the fan of the ventilation control system also needs to be maintained predictively to ensure the normal operation of the ventilation function. The following is a supplement to the scenario of this embodiment.

[0072] The intelligent ventilation system based on this solution further introduces the function of predictive maintenance of equipment. The system continuously tracks the operating data of 12 groups of fans to establish a performance attenuation model. On one occasion, the system detected that the energy consumption data of fan No. 2 in section 5 had abnormal fluctuations in recent operation: the power consumption at the same speed increased by 15% compared with the standard value, and the amplitude of the vibration spectrum in the characteristic frequency band increased. The system calculated that the performance attenuation coefficient of the fan has reached 0.82, and predicted that a bearing failure may occur in the next 72 hours. The system adjusted the ventilation coordination plan in advance, appropriately increased the load sharing of adjacent fans, and automatically generated a maintenance work order, suggesting that the bearing be replaced during the nearest off-peak period of train operation. This predictive maintenance strategy not only avoids the ventilation risks caused by unexpected equipment shutdown, but also achieves the optimal coordination between maintenance work and train operations.

[0073] After combining the above scenarios, the following is a more detailed description of the process of the method provided by this implementation. Figure 2 , is another flow chart of the tunnel segmented exhaust method based on train conditions in an embodiment of the present application.

[0074] S201. Determine the installation position and rated power parameters of each tunnel fan unit in the target tunnel according to the fan installation configuration of the target tunnel.

[0075] Among them, the fan installation configuration represents the installation details of all fans in the tunnel; the tunnel fan unit refers to a single complete ventilation equipment and its supporting control device; the installation position represents the specific spatial coordinates of the fan in the tunnel; the rated power parameter refers to the designed operating power value of the fan under standard working conditions.

[0076] Before the ventilation control system starts to divide the sections, it needs to obtain the basic configuration information of the fans. Specifically, the system first reads the overall structural information of the tunnel and the fan installation drawings from the engineering database, extracts the installation coordinates, height, direction and other location information of each fan, and obtains the fan model specifications, rated power, maximum air volume and other technical parameters to establish a complete fan basic information database.

[0077] S202. Based on the installation position and rated power parameters, calculate the installation spacing and ventilation coverage of each tunnel fan unit.

[0078] Among them, the installation spacing refers to the straight-line distance between adjacent fans; the ventilation coverage range refers to the spatial range that can be reached by a single fan for effective ventilation.

[0079] The ventilation control system needs to evaluate the spatial characteristics and ventilation capacity of the fan distribution. Specifically, the system calculates the three-dimensional spatial distance between any two adjacent fans based on the fan position coordinates obtained in the previous step. At the same time, based on the rated power and installation angle of each fan, combined with the geometric characteristics of the tunnel (such as cross-sectional area, slope, etc.), the effective ventilation coverage of each fan is calculated to form a complete fan spatial distribution characteristic map.

[0080] Among them, the calculation of the effective ventilation coverage range can adopt the attenuation model based on fluid mechanics, and the effective range of the fan can be determined by establishing the velocity field distribution function.

[0081] The system uses the axial velocity attenuation formula v(x)=v0*(d0 / x)^n to describe the change of air flow velocity with distance, where v0 is the outlet velocity, d0 is the fan impeller diameter, x is the axial distance from the fan, and n is the attenuation index (generally 1.2-1.5, related to the cross-sectional characteristics of the tunnel).

[0082] When the airflow velocity decays to the specified critical value vc (usually 0.5m / s), it is defined as the boundary of the effective coverage range. In the calculation of the lateral coverage range, the system uses a Gaussian distribution model to describe the diffusion characteristics of the velocity field: v(r)=v(x)*exp(-r² / 2σ²), where r is the radial distance to the axis and σ is the diffusion coefficient (related to the fan power and installation height). By combining the axial and radial velocity distribution equations, the three-dimensional coverage model of the fan can be obtained.

[0083] For example, for an axial fan with a rated power of 75kW and an impeller diameter of 2.8m, when the outlet velocity is 25m / s, its effective axial coverage distance is about 120m, and the maximum lateral coverage radius is about 30m. The system simplifies the coverage range into an ellipsoid for numerical expression, which is convenient for subsequent segment division calculations.

[0084] In order to realize the constraint effect of the tunnel wall, the system also introduces a wall correction coefficient: λw=1+(A0 / AT)^0.5, where A0 is the wind outlet area of ​​the fan and AT is the cross-sectional area of ​​the tunnel.

[0085] The final effective coverage range is obtained by multiplying the original calculation result by the wall correction coefficient. The correction realizes the influence of tunnel limit on airflow diffusion, making the calculation result more consistent with the actual situation.

[0086] S203, taking adjacent tunnel fan units whose installation interval is less than a preset distance threshold and whose ventilation coverage areas overlap as one fan control group, and dividing all tunnel fan units into multiple fan control groups.

[0087] Among them, the preset distance threshold represents the standard value for determining whether the distance between fans is too close; the fan control group refers to a collection of fans that can work together; and the ventilation coverage overlap refers to the intersection of the effective ventilation areas of adjacent fans.

[0088] The ventilation control system needs to group fans based on spatial distribution characteristics. Specifically, the system first compares the installation spacing of all adjacent fan pairs, selects fan pairs with spacing less than a preset threshold, and then analyzes the overlap of the ventilation coverage of these fan pairs, and divides the adjacent fans that meet the overlapping conditions into the same control group, and finally forms multiple relatively independent fan control groups. In some embodiments, the ventilation control system can provide a grouping reference for tunnel fan units, and then perform grouping settings and adjustments based on user input controls on the graphical interface.

[0089] S204. Based on the multiple fan control groups, divide the target tunnel into multiple control sections.

[0090] Among them, the control section represents the tunnel space unit that needs to be uniformly controlled for ventilation; multiple fan control groups refer to the fan sets obtained by the division in the previous step.

[0091] The ventilation control system needs to convert the division results of the fan control group into a zoning scheme for the tunnel space. Specifically, the system analyzes the spatial distribution range and ventilation coverage area of ​​each fan control group, combines the structural characteristics of the tunnel (such as cross passages, emergency exits and other key nodes), determines the specific boundary position of each control section, and forms a complete tunnel zoning control scheme.

[0092] In some embodiments, the division of control sections can be achieved in a variety of ways: Optionally, the system can determine the optimal dividing point between adjacent sections based on the coverage of the fan control group through a boundary optimization algorithm to achieve balance and rationality of the section division; Optionally, the system can determine the control section in combination with the natural segmentation of the tunnel (such as the slope change point, the location of the cross channel, etc.) and the distribution characteristics of the fan control group. It is understandable that other spatial division methods can also be used to determine the boundaries of the control section, which is not limited here.

[0093] S205: Acquire train operation data on the rail transit dispatching system, and determine the train traffic data and train configuration information of the control section based on the train operation data.

[0094] Among them, the rail transit dispatching system refers to the central control system that manages train operations; train operation data represents the real-time position, speed, direction and other information of the train; train passage data refers to the time, speed and other records of the train passing through a specific section; train configuration information includes basic parameters such as train type, length and weight.

[0095] The ventilation control system needs to obtain real-time train operation status information. Specifically, the system obtains real-time train operation data, including train location, running speed, estimated arrival time, etc., through the data interface with the rail transit dispatching system, and then screens out the train passage records related to the section according to the spatial scope of the control section, and extracts the basic configuration information of these trains to prepare for the subsequent ventilation demand calculation.

[0096] S206. When it is determined that the passing train is a diesel locomotive based on the train passing data, determine the smoke emission rate of the diesel locomotive based on the train configuration information.

[0097] Among them, diesel locomotives refer to trains that use diesel, gasoline and other fuels; the smoke emission rate refers to the amount of smoke generated per unit time; and the train configuration information includes engine parameters and emission standards.

[0098] The ventilation control system needs to evaluate the pollutant emissions of diesel locomotives. Specifically, the system first identifies the diesel locomotive through train traffic data, and then calculates the real-time smoke emission rate of the locomotive based on the engine model, power level and emission standard in its configuration information and the current operating conditions (such as idling, acceleration, constant speed, etc.).

[0099] The calculation of flue gas emission rate adopts a dynamic model based on engine operating conditions. The system first establishes the emission benchmark equation for diesel locomotives: E=Ef*P*L, where E is the emission (g / h), Ef is the emission factor (g / kWh), P is the engine power (kW), and L is the load factor. The emission factor represents the mass (g) of pollutants generated for every kilowatt-hour (kWh) of energy consumed. Different pollutants have different emission factor values, for example: CO (carbon monoxide): 3.0-4.0g / kWh; NOx (nitrogen oxides): 4.5-5.5g / kWh; PM (particulate matter): 0.2-0.4g / kWh; HC (hydrocarbons): 0.5-0.8g / kWh.

[0100] The load factor of the engine under different working conditions is described by a piecewise function: L=0.2 at idle, L=0.8-1.0 during acceleration, and L=0.5-0.7 at constant speed. The specific value is calculated in real time based on the train speed and slope data.

[0101] The system uses the modified Euler method for numerical integration, and the calculation time step is 1 second: Et+1=Et+ΔE*Δt. The emission increment ΔE needs to consider the temperature correction factor KT=(T / T0)^0.5 and the altitude correction factor KH=(P / P0)^1.2, where T and P are the real-time temperature and atmospheric pressure respectively, and T0 and P0 are standard state parameters.

[0102] For example, for a certain model of 2000kW diesel locomotive, its CO emission factor is 3.5g / kWh. When running at a constant speed of 60km / h on a 3-degree slope, the load factor is 0.65. After taking into account the corrections for the temperature of 28°C and the altitude of 500m, the calculated emission rate is about 4.8kg / h.

[0103] In order to improve the calculation accuracy, the system also introduces the engine warm-up correction coefficient Kw=1+0.3*exp(-t / τ), where t is the running time after the engine is started and τ is the characteristic time constant (about 300 seconds).

[0104] The final emission rate is obtained by multiplying the baseline value by each correction coefficient. This dynamic calculation method based on the influence of multiple factors can accurately reflect the emission characteristics of diesel locomotives during actual operation.

[0105] S207. Calculate the smoke growth value of the control section within a preset time period according to the train traffic data and the smoke emission rate.

[0106] Among them, the preset time period represents the fixed time interval for calculating the smoke growth; the smoke growth value refers to the expected increase in smoke concentration in the section; and the train passage data is used to determine the train's residence time and operating status in the section.

[0107] The ventilation control system needs to predict the cumulative effect of air pollution caused by the operation of diesel locomotives. Specifically, the system determines the locomotive's running trajectory and time distribution in the section based on the train traffic data, combines the smoke emission rate calculated in the previous step, and calculates the cumulative emission in a preset time period (such as 15 minutes) through time integration. Combined with the section space volume and natural ventilation effect, the expected growth value of smoke concentration is finally obtained.

[0108] In some embodiments, the calculation of the smoke growth value can be achieved in a variety of ways: Optionally, the system can establish a smoke diffusion model based on mass conservation, and obtain the concentration growth value through numerical integration based on the emission source intensity and ventilation dilution effect.

[0109] The smoke diffusion model solves the convection-diffusion governing equation: c / t+▽·(uc)=▽·(D▽c)+S is used to predict the pollutant concentration distribution. The diffusion coefficient tensor D uses the expression Dij=Cμ(k² / ε)Sij based on the k-ε turbulence model, which enables the model to accurately describe the turbulent diffusion characteristics in complex flow fields. The turbulent kinetic energy k and dissipation rate ε are solved through additional transport equations, and the model constant Cμ takes the standard value of 0.09. The strain rate tensor Sij is calculated based on the spatial gradient of the velocity field, reflecting the deformation characteristics of the local flow field.

[0110] In numerical processing, the time term of the control equation is discretized using an explicit format with second-order accuracy, and the space term is discretized in an appropriate format according to the physical characteristics. Due to its hyperbolic characteristics, the convection term uses the upwind format to avoid numerical oscillation; the diffusion term uses central difference to ensure calculation accuracy. The computational domain adopts a non-uniform grid division strategy, and appropriately densifies the grid near the wall area and the pollution source, which not only ensures the calculation accuracy but also improves the efficiency.

[0111] The processing of boundary conditions also fully considers physical characteristics: the measured concentration distribution is given at the inlet, the free outflow condition is adopted at the outlet, and the zero flux condition is imposed on the wall. Such boundary condition settings are in line with actual engineering conditions. The source term model specifically considers the influence of train movement and describes the emission source as a three-dimensional Gaussian distribution that moves with the train. The distribution function G(x, y, z) contains diffusion parameters σx, σy, and σz in three directions. These parameters will be dynamically adjusted according to local airflow conditions, so that the model can accurately reflect the diffusion process of pollutants in the tunnel space.

[0112] For example, when a certain type of diesel locomotive passes through a tunnel at a speed of 60 km / h, the system uses a 100×20×20 non-uniform grid and a time step of 0.5 seconds for calculation. Through this numerical simulation, the predicted value of pollutant concentration at any location in the tunnel within the next 15 minutes can be obtained. The relative error of the prediction result can usually be controlled within 10%, providing a reliable basis for ventilation control decision-making.

[0113] S208. When it is determined that the passing train is an electric locomotive based on the train passing type, the running speed and vehicle type information of the electric locomotive are determined based on the train configuration information.

[0114] Among them, electric locomotive refers to a train driven by electricity; operating speed refers to the real-time running speed of the locomotive within the section; vehicle model information includes parameters such as the locomotive's external dimensions and aerodynamic characteristics.

[0115] The ventilation control system needs to evaluate the impact of electric locomotive operation on tunnel ventilation. Specifically, the system first identifies the type of electric locomotive, then extracts the key parameters of the locomotive from the train configuration information, and combines the real-time operating speed to prepare for the subsequent calculation of the airflow disturbance effect.

[0116] S209. Determine the airflow disturbance coefficient of the electric locomotive based on the running speed and vehicle type information.

[0117] Among them, the airflow disturbance coefficient represents the degree of influence of locomotive operation on tunnel air flow; the operating speed is used to calculate the intensity of the piston effect; and the vehicle model information is used to determine the aerodynamic characteristics of the locomotive.

[0118] The ventilation control system needs to quantify the ventilation effect generated by the operation of electric locomotives. Specifically, the system calculates the piston effect intensity generated by the operation of the locomotive based on the locomotive's operating speed and vehicle model parameters, combined with the ratio of the tunnel cross-sectional area to the locomotive cross-sectional area, and converts it into a standardized airflow disturbance coefficient to evaluate its impact on tunnel ventilation.

[0119] In some embodiments, the calculation of the airflow disturbance coefficient can be implemented in a variety of ways: Optionally, the system can use a computational fluid dynamics model to combine the aerodynamic effects of the locomotive movement to obtain an accurate disturbance coefficient; Optionally, the system can use a simplified empirical formula to quickly estimate the disturbance intensity based on the speed and section ratio. It is understandable that other calculation methods can also be used to determine the airflow disturbance coefficient, which is not limited here.

[0120] Among them, a specific calculation method uses a modified aerodynamic model. First, the basic disturbance coefficient expression is established: γ=(1-β)^(-2)·(v / v0)^α, where β is the cross-sectional blocking ratio (the ratio of the locomotive cross-sectional area to the tunnel cross-sectional area), v is the train speed, v0 is the characteristic speed (taken as 15m / s), and α is the speed exponent (the default is 1.6). This expression reflects the basic physical mechanism of the piston effect: the disturbance intensity increases with the increase of the cross-sectional blocking ratio and increases in a power law with the increase of speed.

[0121] The basic disturbance coefficient also needs to be corrected by aerodynamics, and the correction items include the head shape coefficient Kh and the tail interference coefficient Kt: γ'=γ(1+Kh+Kt). The head shape coefficient is calculated by the ratio of the locomotive head length to the equivalent diameter: Kh=0.5(L / D)^(-0.2), which reflects the weakening effect of the head streamline design on disturbance; the tail interference coefficient is related to the characteristics of the wake area: Kt=0.3Re^(-0.1), Re is the Reynolds number based on the vehicle length.

[0122] For example, when a certain type of locomotive has a cross-sectional blocking ratio of 0.6 and runs at a speed of 80 km / h, the calculated basic disturbance coefficient is about 2.8, and the final disturbance coefficient after aerodynamic correction is 3.5, indicating that the locomotive operation under this condition has a significant disturbance effect on the tunnel airflow. To improve the calculation accuracy, the model can also analyze the influence of the roughness of the tunnel wall. By introducing the friction correction coefficient Kf=1+0.15(ε / D)^0.3, where ε is the equivalent roughness, the disturbance coefficient is further adjusted.

[0123] Finally, the model will be verified and corrected with actual measured data as a reference. The system collects measured data through the air velocity sensor installed in the tunnel and calculates the actual disturbance coefficient γr=(vr-v0) / v0, where vr is the measured air velocity when the train passes, and v0 is the background air velocity. By comparing the deviation between the theoretical calculated value γ' and the measured value γr, a correction function μ=γr / γ' is established. This correction function will be updated as data continues to accumulate, so that the model can continuously optimize itself during long-term operation and improve prediction accuracy.

[0124] S210, collecting smoke concentration data of the control section, and calculating the smoke growth value of the control section within a preset time period according to the smoke concentration data and the airflow disturbance coefficient.

[0125] Among them, the flue gas concentration data represents the real-time pollutant concentration measured by the sensor; the preset time period also represents a fixed calculation cycle.

[0126] The ventilation control system needs to evaluate the comprehensive impact of electric locomotive operation on air quality. Specifically, the system collects real-time smoke concentration data through a sensor network distributed in the control section, combines the airflow disturbance coefficient calculated in the previous step, and combines the local ventilation effect and dust lifting effect brought by locomotive operation to calculate the smoke concentration change trend within the preset time period.

[0127] In some embodiments, the calculation of the smoke growth value can be achieved in a variety of ways: Optionally, the system can establish a concentration change model based on airflow disturbance and predict the concentration change trend through numerical calculation; Optionally, the system can establish a statistical relationship model between the operation of electric locomotives and the change of smoke concentration based on historical data analysis. It is understandable that other prediction methods can also be used to calculate the smoke growth value, which is not limited here.

[0128] The calculation of the smoke growth value may adopt a dual-model fusion method, with the airflow disturbance coefficient γ obtained in step S209 being used as a key input parameter.

[0129] The first model is a dynamic model based on airflow disturbance: dc / dt=-v·▽c+D▽²c+S(t). Among them, c is the smoke concentration, v is the airflow velocity field (v=v0+γΔv, v0 is the background airflow velocity, Δv is the velocity change caused by the disturbance), D is the turbulent diffusion coefficient, which is determined by D=νt / Sct, νt is the turbulent viscosity coefficient, Sct is the turbulent Schmidt number (taken as 0.7), and S(t) is the pollutant source term, which represents the pollutant generation rate per unit time. The disturbance coefficient γ directly adopts the result calculated in S209 to characterize the intensity of the influence of train movement on the airflow field.

[0130] The second model is a time series prediction model based on historical data, which uses support vector regression (SVR) to construct a mapping relationship: c(t+τ)=f(c(t), v(t), γ), τ is the prediction time step (usually 60s); c(t) is the smoke concentration at time t (ppm); v(t) is the airflow velocity at time t (m / s). The model uses the current smoke concentration, airflow velocity and disturbance coefficient as input variables to predict the concentration value at future times. The kernel function uses the radial basis function: K(x, y)=exp(-||xy||² / 2σ²), σ is the kernel width parameter, determined by cross-validation (typical value is 0.8-1.2); ||xy|| represents the Euclidean distance of the input vector.

[0131] The prediction results of the two models are combined through adaptive weights: c_pred=w1c_physics+w2c_svr.

[0132] The weight update equation is: w1(k+1)=w1(k)+η[c_true(k)-c_pred(k)]·[c_physics(k)-c_svr(k)]; w2(k+1)=1-w1(k+1).

[0133] Where: k represents the discrete time step; η is the learning rate (range 0.01-0.05); c_true is the measured concentration value; c_physics and c_svr are the predicted values ​​of the kinetic model and SVR model respectively.

[0134] For example, when a certain type of locomotive passes through a tunnel at a speed of 60 km / h, the airflow disturbance coefficient γ calculated in step S209 is 2.6. At this time, the measured smoke concentration is 40 ppm. The dynamic model predicts that the concentration will rise to 48 ppm after 15 minutes based on the disturbance coefficient, and the predicted value of the SVR model is 45 ppm. Considering the current weights w1=0.45 and w2=0.55, the fusion prediction value is 46.35 ppm, the measured value is 47 ppm, and the prediction error is within 5%.

[0135] S211. Collect smoke concentration data of the control section, and calculate the ventilation demand index of the control section in combination with the smoke growth value.

[0136] Referring to step S103 , the ventilation control system calculates the ventilation demand index of the control section.

[0137] S212, collecting wind direction parameters of the control sections, determining the ventilation control relationship between adjacent control sections based on the ventilation demand index and the wind direction parameters, and generating a ventilation coordination plan.

[0138] Referring to step S104, the ventilation control system generates a ventilation coordination plan.

[0139] In some embodiments, the ventilation control system performs segmented collaborative processing based on the ventilation demand, that is, the control sections are sorted in descending order according to the ventilation demand index to determine the target control section with the largest ventilation demand; the wind direction parameters of the target control section are collected to determine the ventilation airflow direction of the target control section between adjacent control sections, and multiple collaborative control sections are determined based on the ventilation airflow direction; based on the ventilation demand index, the demand index difference between the target control section and the collaborative control section is calculated; when the demand index difference is greater than a preset difference threshold, the ventilation demand index of the target control section and the collaborative control section is adjusted to generate a ventilation coordination plan.

[0140] Among them, the ventilation demand index represents the real-time demand intensity for ventilation within the section; the target control section refers to the section with the highest current ventilation demand; the wind direction parameter is used to indicate the direction and speed of air flow; the collaborative control section refers to the adjacent section with airflow exchange with the target section; the demand index difference refers to the relative difference in ventilation demand between different sections; the preset difference threshold represents the standard value for triggering collaborative control.

[0141] After the ventilation system has been running for a period of time, the ventilation effect needs to be evaluated and optimized. Specifically, the system first sorts and analyzes the ventilation needs of all control sections to find the target section with the highest ventilation demand, and then obtains the airflow direction information of the section through the sensor network. Combined with the spatial structural characteristics of the tunnel, it determines the upstream and downstream collaborative sections that have a ventilation coupling relationship with the target section. The system further calculates the demand difference between the target section and the collaborative section. When the difference exceeds the threshold preset by the system, it indicates that the ventilation demand between the sections is seriously unbalanced. At this time, the system will adjust the ventilation parameter configuration of each section, such as the fan operating power, the number of openings, etc., to generate a collaborative control solution that can balance the ventilation effects of each section.

[0142] For example, ventilation system fault diagnosis uses a multi-layer detection framework based on deep learning. The first layer uses a convolutional neural network (CNN) to extract time-frequency features. The network structure contains three convolutional layers (the convolution kernel sizes are 5×5, 3×3, and 3×3, respectively) and two pooling layers. The input is the time-frequency graph of the fan vibration signal (obtained by short-time Fourier transform, with a time window of 1024 points and an overlap rate of 50%). The second layer uses a long short-term memory network (LSTM) to identify sequential fault modes, including two LSTM layers (128 units each) and one fully connected layer (64 neurons). The fault diagnosis accuracy is evaluated by the confusion matrix.

[0143] Taking a fan bearing failure as an example, the system first converts the collected vibration signal into a 64×64 time-frequency graph, and extracts it through CNN to obtain a 128-dimensional feature vector. These features are sent to the LSTM network to output the probabilities of 8 possible fault types. The training data set contains 1,000 sets of samples for normal operation and 7 typical fault states, and is trained using the Adam optimizer (learning rate 0.001, batch size 32). On the validation set, the system's recognition accuracy for bearing inner ring faults reached 96.5%, outer ring faults 95.8%, and rolling element faults 94.2%.

[0144] The system also introduces a confidence assessment mechanism: information entropy H=-∑pi·log(pi) is calculated based on the probability distribution of the network output. When H is greater than the threshold Hc (taken as 1.5), the system will remain in observation mode without immediately sounding an alarm. At the same time, the model parameters are continuously updated through online learning: incremental training is performed every 24 hours using newly added labeled data, and the learning rate is dynamically adjusted to η=η0 / (1+kt), where k is the decay coefficient (taken as 0.1).

[0145] S213. According to the ventilation coordination plan, the operating parameters of the tunnel fans in each control section are calculated, and control instructions corresponding to the operating parameters are sent to the tunnel fans.

[0146] Referring to step S105, the ventilation control system sends a control instruction to the tunnel fan.

[0147] In some embodiments, the ventilation control system will also test the ventilation effect, that is, obtain the real-time smoke concentration in each control section and construct a smoke change curve; calculate the ventilation effect score of each control section based on the smoke change curve; take the control section with a ventilation effect score lower than a preset score threshold as an abnormal control section, and detect the fan operating status of all tunnel fans in the abnormal control section; when the fan operating status is abnormal, send maintenance reminder information to the management terminal.

[0148] Among them, the flue gas change curve represents the changing trend of pollutant concentration over time; the ventilation effect score refers to a comprehensive indicator for quantitatively evaluating the ventilation effect; the preset score threshold is a standard value used to judge whether the ventilation effect meets the standard; the abnormal control section represents the section where the ventilation effect does not meet the standard and needs special attention; the fan operation status is used to describe the working condition of the equipment; the maintenance prompt information refers to the alarm notification that equipment maintenance is required; the management terminal represents the operating platform that receives and displays system information.

[0149] The ventilation system needs to establish a real-time monitoring and effect evaluation mechanism to ensure that the system continues to operate efficiently. Specifically, the system continuously collects smoke concentration data through a sensor network distributed in each control section, and records data points at fixed time intervals (such as 5 minutes) to form a time series curve that reflects the change law of pollutants. The system calculates the ventilation effect score from multiple dimensions such as concentration drop rate, control stability, and target value achievement according to pre-set scoring rules. When it is found that the score of a section is lower than the threshold set by the system, the abnormal diagnosis program is started to conduct a comprehensive inspection of all fan equipment in the section, including electrical parameters, mechanical characteristics, control response and other aspects. If the detection finds that the fan is abnormal, the system will generate detailed maintenance prompt information including fault type, severity, recommended measures and expected impact, and push it to relevant maintenance personnel in real time through the management terminal.

[0150] Among them, the ventilation effect score adopts a percentage-based weighted calculation method, which mainly combines three core indicators. The first is the concentration reduction efficiency, which is obtained by calculating the reduction in pollutant concentration per unit time. The system uses an exponential decay model to fit the measured data, and converts the ratio of the actual reduction rate to the theoretical optimal rate into a score, with a weight accounting for 40%. The second is control stability, which is evaluated by calculating the standard deviation of concentration fluctuations. The system sets ±5% of the target concentration as the stable interval, and determines the score based on the time proportion of the measured value in the stable interval, with a weight accounting for 35%. Finally, energy consumption efficiency is calculated by comparing the ratio of actual energy consumption to theoretical minimum energy consumption. The system calculates the theoretical minimum energy consumption based on a thermodynamic model, compares the actual energy consumption with it, and maps it to the score interval, with a weight accounting for 25%. For example, in a certain section, the concentration drops from 100ppm to 30ppm within 15 minutes, the standard deviation during the period is 2ppm, and the energy consumption ratio is 1.2, then the final score is 85 points.

[0151] Among them, the training of the exponential decay model is based on historical ventilation data. The input includes the initial pollutant concentration, fan operating parameters and the measured concentration change curve. The attenuation coefficient and time constant are determined by least squares fitting. The training goal is to minimize the mean square error between the model prediction value and the measured value. The exponential decay model adopts the mathematical form of C(t)=C0*e^(-λt), where C(t) is the pollutant concentration at time t, C0 is the initial concentration, and λ is the attenuation coefficient. By inputting the current pollutant concentration and fan parameters, the exponential decay model can output a predicted concentration change curve for evaluating the ventilation effect.

[0152] In addition, the abnormal diagnosis program that is activated when the score is low adopts a multi-level progressive diagnosis strategy, which gradually conducts in-depth analysis from the equipment level to the system level. The abnormal diagnosis program will first collect the basic operating data of the fan, including electrical parameters such as current, voltage, and power factor, as well as mechanical parameters such as speed, vibration, and temperature, and extract the characteristic spectrum through fast Fourier transform. The program then compares these features with the pre-established normal operating mode library to calculate the deviation value of each parameter. When a parameter is found to be out of the normal range, the program starts the deep diagnosis module, which analyzes the correlation between parameters through a neural network model and combines the expert rule library to infer the possible fault type and cause. Finally, the program generates a diagnostic report with specific maintenance recommendations based on the nature and severity of the fault. For example, when an abnormal peak is detected in the fan vibration spectrum in a specific frequency band, accompanied by an increase in bearing temperature, the system will determine that it is a bearing fault and recommend replacement.

[0153] S214. Obtain the operating time and energy consumption data of each tunnel fan in the target control section.

[0154] Among them, the operating time indicates the cumulative working time of the fan; the energy consumption data indicates the power consumption during the operation of the fan; and the target control section refers to the specific section that requires maintenance evaluation.

[0155] The ventilation control system needs to collect fan operating status data for performance evaluation. Specifically, the system collects the operating time statistics and real-time power data of each fan through the fan controller, records the energy consumption trend of the fan under different working conditions, and establishes a complete operating status database to provide basic data support for subsequent performance evaluation.

[0156] In some embodiments, the collection and processing of operating data can be achieved in a variety of ways: Optionally, the system can record the operating time and power curve of the fan through real-time monitoring equipment, and evaluate the energy consumption trend through data analysis; Optionally, the system can establish an equipment operation log to regularly count the cumulative working time and average energy consumption level of the fan.

[0157] S215. Calculate the performance attenuation coefficient of the tunnel fan based on the operating time and energy consumption data.

[0158] Among them, the performance attenuation coefficient indicates the degree of reduction in the actual performance of the fan relative to the rated performance; the operating time is used to evaluate the service life of the equipment; and the energy consumption data is used to determine changes in operating efficiency.

[0159] The ventilation control system needs to evaluate the performance status of the fan. Specifically, the system calculates the performance degradation degree, i.e., the performance degradation coefficient, based on the collected operating time and energy consumption data by comparing the actual operating efficiency of the fan with the rated efficiency. At the same time, the system comprehensively determines the performance degradation coefficient of each fan by combining the service life of the equipment and the working environment factors.

[0160] Among them, the calculation of performance attenuation coefficient is mainly based on energy efficiency comparison method and state parameter analysis method. Energy efficiency comparison method quantifies performance attenuation by calculating the ratio of actual operating efficiency to theoretical efficiency. This method is simple and intuitive to calculate, but it is easily affected by environmental factors and measurement errors. State parameter analysis method establishes a multi-dimensional evaluation index system, analyzes the changing trends of operating parameters such as vibration, temperature, and noise, and uses weighted summation or fuzzy evaluation methods to calculate the attenuation coefficient. This method has more comprehensive and accurate evaluation results, but requires more sensor support and more complex data processing. In engineering practice, the two methods are usually used in combination. For example, during the operation of a certain fan, the energy efficiency comparison shows a 12% drop in performance, while the state parameter analysis shows a 15% attenuation. The system will obtain the final attenuation coefficient through weighted averaging and make corrections based on expert experience to improve the accuracy and reliability of the evaluation.

[0161] In addition, for the collection of energy consumption data, the ventilation control system will collect electrical parameters such as voltage, current and power factor in real time by installing an electric energy metering device in the fan power supply line. The system first obtains the instantaneous power value, obtains the cumulative energy consumption through integral calculation, and records the corresponding relationship between the fan's operating conditions (such as speed, air volume, etc.) and energy consumption. In practical applications, the system usually uses 15 minutes as the basic statistical period to calculate the average power and energy consumption per unit time, and compares these data with the rated parameters of the fan to establish an energy consumption characteristic curve. For example, a fan with a rated power of 75kW has a measured power of 78kW when operating under standard conditions. Through continuous monitoring, a trend of gradually increasing energy consumption can be found. This abnormal growth often indicates a decline in equipment performance.

[0162] S216. Calculate the estimated maintenance time of the tunnel fan based on the performance attenuation coefficient and the maintenance record of the tunnel fan, and generate a maintenance plan.

[0163] Among them, the estimated maintenance time indicates the recommended time point for the next maintenance; the maintenance record includes the time, content and effect of all previous maintenance; the maintenance plan refers to the equipment maintenance arrangement in the future.

[0164] The ventilation control system needs to formulate a scientific equipment maintenance plan. Specifically, the system analyzes the equipment maintenance cycle based on the performance attenuation coefficient calculated in the previous step and the historical maintenance records of the fan, predicts the best time for the next maintenance, and reasonably allocates maintenance resources to generate a detailed maintenance plan.

[0165] Among them, the calculation of the expected maintenance time adopts a dynamic programming model based on equipment health. The equipment health index HDI is obtained through multi-parameter weighted fusion: HDI=∑wiPi, where Pi is the health score of each monitoring parameter (vibration, temperature, current, etc.), and the weight wi is determined by fuzzy hierarchical analysis. The score of each parameter adopts an exponential decay function: Pi=100·exp(-λi·ti), λi is the degradation rate parameter, and ti is the operating time.

[0166] The maintenance time prediction adopts a two-stage method. In the first stage, the Wiener process model is used to predict the health decay trajectory: dHDI=(μ+σB(t))dt, where μ is the drift parameter, σ is the diffusion coefficient, and B(t) is the standard Brownian motion. The parameters μ and σ are obtained by maximum likelihood estimation: after logarithmic transformation of historical data, linear regression is used to estimate μ, and the residual standard deviation is σ.

[0167] In the second stage, the optimal maintenance time point is determined by first arrival time analysis: define the failure threshold HDIf of HDI (usually 60), and calculate the expected time to reach the threshold E[T]=(HDI0-HDIf) / |μ|. Considering cost optimization, the maintenance decision model is introduced: C(t)=cp·P(t<T)+cu·P(t> T)+cm·e^(-rt), where cp is the preventive maintenance cost, cu is the unplanned downtime cost, cm is the regular maintenance cost, and r is the discount rate. The optimal maintenance time t* is determined by minimizing the total cost C(t).

[0168] For example, the current health of a wind turbine is 85, and the historical data fitting results in μ=-0.015 / day, σ=0.008. It is predicted that without maintenance, the health will drop to the failure threshold in about 180 days. Considering cp=5000 yuan, cu=20000 yuan, cm=8000 yuan, r=0.1 / year, the optimal maintenance time is calculated to be about 150 days.

[0169] In the embodiment of the present application, a ventilation demand prediction mechanism based on train operation data is adopted, a distributed ventilation control strategy between sections is established, and equipment performance evaluation and predictive maintenance functions are introduced. Therefore, accurate prediction of ventilation demand, coordinated control between sections and intelligent maintenance of equipment can be achieved, which effectively solves the problems of unsatisfactory ventilation effect, serious energy waste and passive equipment maintenance in traditional fixed-mode ventilation schemes, achieves improvement of ventilation effect and reduction of operating energy consumption, and ensures reliable operation of the system.

[0170] The ventilation control system in the embodiment of the present invention is described below from the perspective of hardware processing. Figure 3 , which is a schematic diagram of the structure of a physical device of a ventilation control system in an embodiment of the present application.

[0171] It should be noted that Figure 3 The structure of the ventilation control system shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention.

[0172] like Figure 3 As shown, the ventilation control system includes a central processing unit (CPU) 301, which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM) 302 or the program loaded from the storage part 308 to the random access memory (RAM) 303, such as executing the method described in the above embodiment. In the RAM 303, various programs and data required for system operation are also stored. The CPU 301, the ROM 302 and the RAM 303 are connected to each other through the bus 304. The input / output (I / O) interface 305 is also connected to the bus 304.

[0173] The following components are connected to the I / O interface 305: an input section 306 including an audio input device, a button switch, etc.; an output section 307 including a liquid crystal display (LCD) and an audio output device, an indicator light, etc.; a storage section 308 including a hard disk, etc.; and a communication section 309 including a network interface card such as a LAN (Local Area Network) card, a modem, etc. The communication section 309 performs communication processing via a network such as the Internet. A drive 310 is also connected to the I / O interface 305 as needed. A removable medium 311, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 310 as needed so that a computer program read therefrom is installed into the storage section 308 as needed.

[0174] In particular, according to an embodiment of the present invention, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present invention includes a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a computer program for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through the communication part 309, and / or installed from a removable medium 311. When the computer program is executed by the central processing unit (CPU) 301, various functions defined in the present invention are performed.

[0175] It should be noted that specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a flash memory, an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present invention, a computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in combination with an instruction execution system, apparatus, or device.

[0176] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present invention. Each box in the flowchart or block diagram may represent a module, a program segment, or a part of a code, and the above-mentioned module, program segment, or a part of a code contains one or more executable instructions for implementing the specified logical functions. It should also be noted that in some alternative implementations, the functions marked in the box may also occur in an order different from that marked in the accompanying drawings.

[0177] Specifically, the ventilation control system of this embodiment includes a processor and a memory. The memory stores a computer program. When the computer program is executed by the processor, the tunnel segmented exhaust method based on the train working condition provided in the above embodiment is implemented.

[0178] As another aspect, the present invention further provides a computer-readable storage medium, which may be included in the ventilation control system described in the above embodiment; or may exist independently without being assembled into the ventilation control system. The above storage medium carries one or more computer programs, and when the above one or more computer programs are executed by a processor of the ventilation control system, the ventilation control system implements the tunnel segmented exhaust method based on train working conditions provided in the above embodiment.

[0179] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present application.

[0180] As used in the above embodiments, the term "when..." may be interpreted to mean "if..." or "after..." or "in response to determining..." or "in response to detecting...", depending on the context. Similarly, the phrases "upon determining..." or "if (the stated condition or event) is detected" may be interpreted to mean "if determining..." or "in response to determining..." or "upon detecting (the stated condition or event)" or "in response to detecting (the stated condition or event)", depending on the context.

[0181] Those skilled in the art can understand that to implement all or part of the processes in the above-mentioned embodiments, the processes can be completed by computer programs to instruct related hardware, and the programs can be stored in computer-readable storage media. When the programs are executed, they can include the processes of the above-mentioned method embodiments. The aforementioned storage media include: ROM or random access memory RAM, magnetic disk or optical disk and other media that can store program codes.

Claims

1. A tunnel segmented exhaust method based on train working conditions, characterized in that: Applied to a ventilation control system, the method comprises: Dividing the target tunnel into a plurality of control sections according to the fan installation configuration of the target tunnel; Acquire train traffic data and train configuration information of the control section, and calculate the smoke growth value of the control section within a preset time period; Collecting smoke concentration data of the control section, and calculating the ventilation demand index of the control section in combination with the smoke growth value; Collecting the wind direction parameters of the control section, determining the ventilation control relationship between adjacent control sections based on the ventilation demand index and the wind direction parameters, and generating a ventilation coordination plan; According to the ventilation coordination scheme, the operating parameters of the tunnel fans in each of the control sections are calculated, and control instructions corresponding to the operating parameters are sent to the tunnel fans.

2. The method according to claim 1, characterized in that The step of dividing the target tunnel into a plurality of control sections according to the fan installation configuration of the target tunnel specifically comprises: Determine the installation position and rated power parameters of each tunnel fan unit in the target tunnel according to the fan installation configuration of the target tunnel; Based on the installation position and the rated power parameter, calculating the installation spacing and ventilation coverage of each of the tunnel fan units; Adjacent tunnel fan units whose installation spacing is less than a preset distance threshold and whose ventilation coverage areas overlap are defined as one fan control group, and all tunnel fan units are divided into a plurality of fan control groups; Based on the multiple fan control groups, the target tunnel is divided into multiple control sections.

3. The method according to claim 1, characterized in that The step of collecting the wind direction parameters of the control section, determining the ventilation control relationship between adjacent control sections based on the ventilation demand index and the wind direction parameters, and generating a ventilation coordination plan specifically includes: Sorting the control sections in descending order according to the ventilation demand index to determine the target control section with the largest ventilation demand; Collecting wind direction parameters of the target control section, determining the ventilation airflow direction of the target control section between adjacent control sections, and determining a plurality of coordinated control sections based on the ventilation airflow direction; Based on the ventilation demand index, calculating a demand index difference between the target control section and the coordinated control section; When the demand index difference is greater than a preset difference threshold, the ventilation demand indexes of the target control section and the cooperative control section are adjusted to generate a ventilation coordination plan.

4. The method according to claim 1, characterized in that: The step of obtaining the train traffic data and train configuration information of the control section and calculating the smoke growth value of the control section within a preset time period specifically includes: Acquire train operation data on the rail transit dispatching system, and determine the train traffic data and train configuration information of the control section based on the train operation data; When it is determined based on the train travel data that the passing train is a diesel locomotive, determining a smoke emission rate of the diesel locomotive based on the train configuration information; The smoke growth value of the control section within a preset time period is calculated according to the train passage data and the smoke emission rate.

5. The method according to claim 4, characterized in that After the step of acquiring the train operation data on the rail transit dispatching system and determining the train passage data and train configuration information of the control section based on the train operation data, the method further includes: When it is determined that the passing train is an electric locomotive based on the train passing type, the running speed and vehicle type information of the electric locomotive are determined based on the train configuration information; Determining an airflow disturbance coefficient of the electric locomotive based on the running speed and the vehicle type information; The smoke concentration data of the control section is collected, and the smoke growth value of the control section within a preset time period is calculated according to the smoke concentration data and the airflow disturbance coefficient.

6. The method according to claim 1, characterized in that After the step of calculating the operating parameters of the tunnel fans in each of the control sections according to the ventilation coordination scheme, and sending control instructions corresponding to the operating parameters to the tunnel fans, the method further includes: Obtaining the real-time smoke concentration in each of the control sections and constructing a smoke change curve; Calculating the ventilation effect score of each of the control sections based on the smoke change curve; The control section where the ventilation effect score is lower than the preset score threshold is taken as the abnormal control section, and the fan operation status of all tunnel fans in the abnormal control section is detected; When the operation state of the fan is abnormal, a maintenance reminder message is sent to the management terminal.

7. The method according to claim 1, characterized in that After the step of calculating the operating parameters of the tunnel fans in each of the control sections according to the ventilation coordination scheme, and sending control instructions corresponding to the operating parameters to the tunnel fans, the method further includes: Obtaining the operating time and energy consumption data of each tunnel fan in the target control section; Calculating a performance attenuation coefficient of the tunnel fan based on the operating time and the energy consumption data; According to the performance attenuation coefficient and the maintenance record of the tunnel fan, the estimated maintenance time of the tunnel fan is calculated and a maintenance plan is generated.

8. A ventilation control system, characterized in that: The ventilation control system includes: one or more processors and a memory; the memory is coupled to the one or more processors, the memory is used to store computer program code, the computer program code includes computer instructions, and the one or more processors call the computer instructions to enable the ventilation control system to execute the method described in any one of claims 1-7.

9. A computer-readable storage medium comprising instructions, characterized in that: When the instruction is executed on a ventilation control system, the ventilation control system is caused to execute the method according to any one of claims 1 to 7.

10. A computer program product, characterized in that When the computer program product is run on a ventilation control system, the ventilation control system is caused to perform the method according to any one of claims 1 to 7.

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