Tunnel fan energy-saving control method and system, storage medium and program product

Abstract

The invention discloses a tunnel fan energy-saving control method and system, a storage medium and a program product, and relates to the field of general control or regulation systems.The method comprises the steps that historical train flow data are obtained, and the predicted train flow of the next time period is obtained through calculation according to the historical train flow data; collecting real-time gas concentration data and wind speed and direction data; calculating a theoretical gas concentration increment based on an aerodynamic model, the predicted train flow, the real-time gas concentration data and the wind speed and direction data; calculating according to the theoretical gas concentration increment and a pollutant diffusion model to obtain a pollutant diffusion trend; determining a target section according to the pollutant diffusion trend and a preset gas concentration threshold value; according to the position information of the target section and the pollutant diffusion trend, the minimum starting number of tunnel fans and the corresponding minimum rotating speed are obtained through calculation; and generating a fan control instruction according to the minimum number of started fans and the minimum rotating speed. According to the method, the effect of reducing the energy consumption of the tunnel fan while the ventilation effect of the tunnel is maintained is achieved.

Description

Technical Field

[0001] The present application belongs to the field of general control or regulation systems, and in particular, relates to a tunnel fan energy-saving control method, system, storage medium and program product. Background Art

[0002] During tunnel construction and operation, fans are needed for ventilation to ensure the air quality and construction environment in the tunnel. At present, existing tunnel fans are generally operated at a fixed frequency and controlled by manual or remote opening or closing commands. This control method is difficult to achieve the most efficient exhaust of the shaft, and it is also difficult to perform intelligent control based on factors such as gas concentration, wind speed and direction, train type and traffic flow, resulting in low ventilation efficiency and high energy consumption.

[0003] In related technologies, gas concentration sensors, wind speed and direction sensors, etc. can be installed in the tunnel to collect environmental parameters in the tunnel in real time, and the operating status of the fan can be automatically adjusted according to the collected data, thereby realizing automatic control of tunnel ventilation and improving ventilation efficiency.

[0004] However, when a train passes through, the air in the tunnel will be compressed, causing the air circulation rate to increase, making it difficult to predict the changing trend of pollutant concentrations, and thus making it difficult to maintain the ventilation effect of the tunnel in the most energy-efficient way, thereby increasing the energy consumption of the tunnel fan. Summary of the invention

[0005] The present application provides a tunnel fan energy-saving control method, system, storage medium and program product, which are used to reduce the energy consumption of the tunnel fan while maintaining the ventilation effect of the tunnel.

[0006] In a first aspect, the present application provides a tunnel fan energy-saving control method, which obtains historical train flow data within a preset time period from a database, and calculates the predicted train flow for the next time period based on the historical train flow data; Collect real-time gas concentration data and wind speed and direction data within a preset period of time. The real-time gas concentration data includes carbon monoxide concentration, nitrogen dioxide concentration, ozone concentration and dust concentration; The theoretical gas concentration increment for the next period is calculated based on the aerodynamic model, predicted train flow, real-time gas concentration data, and wind speed and direction data; The pollutant diffusion trends in different sections of the tunnel are calculated based on the theoretical gas concentration increment and pollutant diffusion model; Compare the gas concentration value corresponding to the pollutant diffusion trend with the preset gas concentration threshold to obtain the target section that needs to adjust ventilation; According to the location information of the target section and the diffusion trend of pollutants, the minimum number of tunnel fans to be opened and the corresponding minimum speed are calculated; A fan control instruction is generated according to the minimum number of units opened and the minimum speed, so that the gas concentration in the target section of the tunnel fans with the minimum number of units opened is controlled according to the fan control instruction so as not to exceed a preset gas concentration threshold.

[0007] The above technical solution is adopted to obtain historical train flow data from the database to calculate the predicted train flow, and combine the real-time collected gas concentration and wind speed and direction data to calculate the theoretical gas concentration increment for the next period based on the aerodynamic model, and then use the pollutant diffusion model to analyze the pollutant diffusion trend of each section of the tunnel, so that the system can predict the distribution changes of pollutants in advance. On this basis, the target section is determined by comparing the pollutant diffusion trend with the preset gas concentration threshold, and the minimum number of open units and the minimum speed are calculated based on the location information of the target section and the pollutant diffusion trend, so as to generate accurate fan control instructions. This prediction-based control method makes the ventilation volume adjustment more accurate. While ensuring that the gas concentration in the target section does not exceed the preset threshold, it achieves energy consumption reduction by minimizing the number of fans opened and the speed, thereby achieving the effect of reducing the energy consumption of the tunnel fan while maintaining the ventilation effect of the tunnel.

[0008] In conjunction with some embodiments of the first aspect, in some embodiments, the theoretical gas concentration increment for the next period is calculated based on the aerodynamic model, predicted train flow, real-time gas concentration data, and wind speed and direction data, specifically including: Determine the train operation plan for the next period based on the predicted train flow, which includes train passing time, train type and running speed; Based on the aerodynamic model, the waveform parameters of the air compression wave and expansion wave generated by each type of train at different running speeds are calculated, and the waveform parameters include the peak value, trough value and duration; According to the waveform parameters of air compression wave and expansion wave, combined with the geometric characteristics of the tunnel, the instantaneous airflow velocity field at each moment in the next period is calculated; The instantaneous airflow velocity field is superimposed and calculated with the real-time gas concentration data and wind speed and direction data to obtain the gas convection diffusion field; The mass conservation equation is established according to the convection diffusion field, and the theoretical predicted value of gas concentration in each section of the tunnel in the next period is obtained by solving the mass conservation equation. The difference between the theoretical predicted value of gas concentration and the current measured concentration value is taken as the theoretical gas concentration increment.

[0009] The above technical solution is adopted to determine the train operation plan according to the predicted train flow, calculate the waveform parameters of the air compression wave and expansion wave under different train types and operating speeds based on the aerodynamic model, calculate the instantaneous airflow velocity field in combination with the geometric characteristics of the tunnel, and superimpose the real-time gas concentration data and wind speed and direction data to obtain the convection diffusion field, and finally obtain the theoretical gas concentration increment by solving the mass conservation equation. This calculation method takes into account the impact of dynamic airflow changes caused by train operation on pollutant diffusion, making the prediction of gas concentration increment more accurate. By establishing a physical model to describe the essential process of pollutant diffusion, the calculation results can reflect the actual impact of factors such as train operation and tunnel structure on gas concentration distribution, improve the pertinence and effectiveness of subsequent ventilation control, better adapt to the impact of changes in train speed and vehicle type, and improve the control system's ability to adapt to dynamic working conditions.

[0010] In conjunction with some embodiments of the first aspect, in some embodiments, the pollutant diffusion trends in different sections of the tunnel are calculated based on the theoretical gas concentration increment and the pollutant diffusion model, specifically including: The tunnel is divided into several calculation sections according to the structural characteristics of the tunnel, and a pollutant transmission network model of the calculation section is established. A virtual monitoring point is set at the boundary of each calculation section. For each calculation section, the initial diffusion rate of pollutants is calculated based on the theoretical gas concentration increment; According to the pollutant diffusion model, the transfer function of various pollutants between adjacent calculation sections is constructed. The transfer function includes the temperature gradient coefficient, humidity influence factor and wall adsorption coefficient. The pollutant concentration of each virtual monitoring point is iteratively calculated using the transfer function to obtain the diffusion law of the pollutant; Based on the diffusion law, the least square method is used to fit the pollutant concentration change trend curve of each calculation section; According to the slope and curvature of the pollutant concentration change trend curve, the pollutant diffusion trend of each calculation section is determined.

[0011] The above technical solution is adopted to divide the tunnel into calculation sections and establish a pollutant transmission network model. The initial diffusion rate of pollutants is calculated at the virtual monitoring point. The transfer function including the temperature gradient coefficient, humidity influence factor and wall adsorption coefficient is used to describe the transfer law of pollutants between adjacent sections. The pollutant diffusion trend is obtained through iterative calculation and curve fitting. The influence of environmental factors such as temperature, humidity and wall adsorption on pollutant diffusion is considered, making the prediction of diffusion trend more in line with the actual situation. By setting virtual monitoring points at the boundary of the calculation section for iterative calculation, the number of actual monitoring points required is reduced, while the calculation accuracy is guaranteed. The pollutant concentration change trend curve obtained by least squares fitting can smooth the noise influence in discrete data, extract the main law of pollutant diffusion, and provide a reliable basis for subsequent ventilation control. It reduces the limitation of traditional diffusion models that ignore the influence of environmental factors and improves the accuracy of diffusion trend prediction.

[0012] In combination with some embodiments of the first aspect, in some embodiments, the method for establishing a pollutant transmission network model is: dividing the space of the tunnel into multiple grid nodes according to a preset spacing to form a computing unit, collecting historical monitoring data of the computing unit, including historical monitoring data gas concentration data, airflow parameter data and environmental parameter data, dividing the historical monitoring data into a training data set and a verification data set according to a preset ratio, based on the training data set, taking the gas concentration data, airflow parameter data and environmental parameter data at the current moment as input features, and taking the gas concentration data at the next moment as output features, training to obtain the pollutant transmission network model, and using the verification data set to verify the pollutant transmission network model, when the root mean square error between the predicted result and the measured result of the pollutant transmission network model is less than the preset error value, outputting the pollutant transmission network model.

[0013] The above technical solution is adopted to divide the tunnel space into grid nodes to form calculation units, and the pollutant transmission network model is trained using the gas concentration data, airflow parameter data and environmental parameter data in the historical monitoring data. The model is verified using the verification data set to ensure that the root mean square error of the model prediction results meets the accuracy requirements. This data-driven modeling method captures the nonlinear characteristics and complex correlations in the pollutant transmission process by learning from a large amount of historical data. Compared with the simplified theoretical model, this method makes full use of the information contained in the measured data to make the model more in line with the actual working conditions. The root mean square error is used as an evaluation indicator in the model verification process, and a clear accuracy control standard is established to improve the reliability of the model in practical applications, thereby improving the accuracy of the prediction of pollutant transmission characteristics.

[0014] In combination with some embodiments of the first aspect, in some embodiments, according to the location information of the target section and the pollutant diffusion trend, the minimum number of tunnel fans to be opened and the corresponding minimum speed are calculated, specifically including: Obtain the fan distribution information upstream and downstream of the target section, which includes the location coordinates, rated power and maximum air volume of the fan; Determine the minimum ventilation volume of the target section based on the pollutant diffusion trend; Calculate the relationship curve between ventilation volume and energy consumption of a single fan at different speeds; According to the minimum ventilation requirements, the dynamic programming method is used to calculate the total energy consumption of different fan combinations; Select the fan combination with the lowest total energy consumption to obtain the minimum number of fans turned on and the lowest speed.

[0015] By adopting the above technical solution, by obtaining the fan distribution information upstream and downstream of the target section and determining the minimum ventilation volume according to the pollutant diffusion trend, combined with the ventilation volume and energy consumption relationship curve of a single fan at different speeds, the dynamic programming method is used to calculate the total energy consumption of different fan combination schemes, so as to select the fan combination scheme with the minimum total energy consumption, which can achieve the optimal control of fan operation under the premise of meeting the ventilation demand. By considering the spatial distribution characteristics and performance parameters of the fan, a mapping relationship between the ventilation volume demand and the fan operation parameters is established, reducing the energy waste caused by too many fans being turned on or too high a speed. By optimizing different fan combination schemes through the dynamic programming algorithm, the most energy-saving operation scheme that can meet the minimum ventilation volume requirements of the target section can be found, while ensuring the ventilation effect and reducing the system operation cost.

[0016] In combination with some embodiments of the first aspect, in some embodiments, after generating the fan control instruction according to the minimum number of units started and the minimum speed, the method further includes: Collect the real-time gas concentration change rate in the target section after the tunnel fan is adjusted; When the real-time gas concentration change rate is greater than a preset change rate threshold, the pulsation period and pulsation amplitude of the gas concentration in the target section are calculated; When it is determined that there is an eddy current dead zone in the target section according to the pulsation period and the pulsation amplitude, the fan group to be adjusted is determined according to the position of the eddy current dead zone; The pulse ventilation control of the regulated fan unit is carried out by alternating start and stop to eliminate the vortex dead zone.

[0017] By adopting the above technical solution, the ventilation dead zone problem can be discovered in time by collecting the real-time gas concentration change rate of the target section after the fan is adjusted, and calculating the pulsation characteristics of the gas concentration when the change rate exceeds the threshold. When it is determined that there is a vortex dead zone, the pulse ventilation control method of alternating start and stop can break the vortex structure and improve the ventilation effect. By monitoring the dynamic change characteristics of gas concentration, quantitative indicators for vortex dead zone identification are established. Pulse ventilation control produces a forced disturbance effect by periodically changing the airflow field structure, which can destroy the stable existence conditions of the vortex dead zone, promote gas mixing and diffusion, improve the control accuracy and ventilation efficiency of the ventilation system, and ensure the uniformity of air quality in the tunnel space.

[0018] In combination with some embodiments of the first aspect, in some embodiments, pulse ventilation control is performed on the regulated fan group in an alternating start-stop manner, specifically including: Divide the fan group into a number of fan subgroups, each fan subgroup contains at least two adjacent tunnel fans; Calculate one quarter of the pulsation period as the basic switching time; The fan sub-groups are rotated in turn according to a preset sequence and the rotation operation is repeated until the eddy current dead zone is eliminated. The rotation operation of each fan sub-group is to increase the speed of the fan sub-group to the maximum speed and run it for a basic switching time, and then reduce the speed of the fan sub-group to the minimum speed and run it for a basic switching time.

[0019] By adopting the above technical solution, the fan group is divided into subgroups containing adjacent fans, and the basic switching time is determined based on the pollutant concentration pulsation cycle. The fan subgroups are rotated in a preset sequence to achieve precise control of the vortex dead zone. By coordinating the speed changes of multiple fan subgroups, orderly airflow pulsations are formed in time and space, producing a directional airflow disturbance effect. Using one quarter of the pulsation cycle as the basic switching time ensures that the disturbance intensity matches the inherent characteristics of the vortex structure and improves the vortex breaking efficiency. The alternating operation mode of the fan subgroups maintains sufficient disturbance intensity, achieving rational use of energy while ensuring ventilation effects.

[0020] In the second aspect, an embodiment of the present application provides a tunnel fan energy-saving control system, which includes: one or more processors and a memory; the memory is coupled to one or more processors, the memory is used to store computer program code, the computer program code includes computer instructions, and one or more processors call computer instructions to enable the 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-readable storage medium, comprising instructions, which, when executed on a system, causes the system to execute the method described in the first aspect and any possible implementation of the first aspect.

[0022] In a fourth aspect, an embodiment of the present application provides a computer program product, characterized in that when the computer program product runs on a system, the system executes the method described in any possible implementation manner in the first aspect.

[0023] One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages: 1. The present application provides a tunnel fan energy-saving control method, which obtains historical train flow data from a database to calculate the predicted train flow, combines the real-time collected gas concentration and wind speed and direction data, calculates the theoretical gas concentration increment for the next period based on the aerodynamic model, and then uses the pollutant diffusion model to analyze the pollutant diffusion trend of each section of the tunnel, so that the system can predict the distribution changes of pollutants in advance. On this basis, the target section is determined by comparing the pollutant diffusion trend with the preset gas concentration threshold, and the minimum number of open units and the minimum speed are calculated based on the location information of the target section and the pollutant diffusion trend, thereby generating accurate fan control instructions. This prediction-based control method makes the ventilation volume adjustment more accurate. While ensuring that the gas concentration in the target section does not exceed the preset threshold, the energy consumption is reduced by minimizing the number of fans opened and the speed, thereby achieving the effect of reducing the energy consumption of the tunnel fan while maintaining the ventilation effect of the tunnel.

[0024] 2. The present application provides a method for energy-saving control of tunnel fans. By obtaining the fan distribution information upstream and downstream of the target section and determining the minimum ventilation volume according to the pollutant diffusion trend, the relationship curve between the ventilation volume and energy consumption of a single fan at different speeds is combined, and the dynamic programming method is used to calculate the total energy consumption of different fan combination schemes, thereby selecting the fan combination scheme with the minimum total energy consumption, which can achieve optimal control of fan operation while meeting the ventilation requirements. By considering the spatial distribution characteristics and performance parameters of the fan, a mapping relationship between the ventilation volume demand and the fan operating parameters is established, reducing energy waste caused by too many fans being turned on or too high a speed. By optimizing different fan combination schemes through a dynamic programming algorithm, the most energy-saving operation scheme that can meet the minimum ventilation volume requirements of the target section can be found, while ensuring the ventilation effect and reducing the system operating cost.

[0025] 3. The present application provides a method for energy-saving control of tunnel fans. By collecting the real-time gas concentration change rate of the target section after the fan is adjusted, and calculating the pulsation characteristics of the gas concentration when the change rate exceeds the threshold, the ventilation dead zone problem can be discovered in time. When it is determined that there is a vortex dead zone, the alternating start-stop pulse ventilation control method is adopted to break the vortex structure and improve the ventilation effect. By monitoring the dynamic change characteristics of the gas concentration, a quantitative index for the identification of vortex dead zones is established. Pulse ventilation control periodically changes the airflow field structure to produce a forced disturbance effect, which can destroy the stable existence conditions of the vortex dead zone, promote gas mixing and diffusion, improve the control accuracy and ventilation efficiency of the ventilation system, and ensure the uniformity of the air quality in the tunnel space. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 It is a flow chart of a tunnel fan energy-saving control method in an embodiment of the present application.

[0027] Figure 2 It is a flow chart of a control method based on eddy current dead zone identification and elimination in an embodiment of the present application.

[0028] Figure 3 It is a schematic diagram of the physical device structure of a tunnel fan energy-saving control system provided in an embodiment of the present application. DETAILED DESCRIPTION

[0029] 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 and appended claims of the present application, the singular expressions "one", "a kind of", "said", "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 listed items.

[0030] 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.

[0031] The following uses an embodiment and combines Figure 1 , a tunnel fan energy-saving control method in an embodiment of the present application is described: See also Figure 1 , which is a flow chart of a tunnel fan energy-saving control method in an embodiment of the present application.

[0032] S101, obtaining historical train flow data within a preset time period from a database, and calculating the predicted train flow for the next time period based on the historical train flow data; The system obtains the historical train flow data within the preset time period from the database, and calculates the predicted train flow for the next time period based on the historical train flow data. Specifically, the system can set a preset time period, such as 1 hour, 2 hours, etc., and then query the number of trains passing at each moment in the preset time period from the database to form historical train flow data. Then, the system can use the time series prediction algorithm to model the historical train flow data. Commonly used time series prediction algorithms include autoregressive moving average model (ARMA), autoregressive difference moving average model (ARIMA), long short-term memory network (LSTM), etc. The train flow prediction model is obtained through training, and the time interval of the next time period is input to obtain the predicted train flow for the next time period.

[0033] In practical applications, the operation of tunnel trains usually has certain periodic laws, such as dense trains during peak hours in the morning and evening, and sparse trains during non-peak hours, so using historical data to predict traffic flow has certain feasibility and accuracy. However, sudden events (such as train failures, bad weather, etc.) will disrupt the normal train operation rules, resulting in large deviations in train flow prediction. To solve this problem, the system can introduce an emergency detection mechanism to monitor the train operation status in real time. If abnormal conditions such as train delays and suspensions are detected, the parameters of the prediction model are adaptively adjusted to generate updated train flow prediction results.

[0034] S102, collecting real-time gas concentration data and wind speed and direction data within a preset time period; The system collects real-time gas concentration data and wind speed and direction data within a preset time period. The real-time gas concentration data includes carbon monoxide concentration, nitrogen dioxide concentration, ozone concentration and dust concentration.

[0035] The system collects real-time gas concentration data and wind speed and direction data within a preset time period. The real-time gas concentration data includes carbon monoxide concentration, nitrogen dioxide concentration, ozone concentration and dust concentration. Specifically, the system can deploy several sensors in the tunnel, including CO sensors, NO2 sensors, O3 sensors, PM2.5 sensors and wind speed and direction sensors, and collect various parameters at certain time intervals (for example, 1 minute) to form real-time environmental monitoring data. The collected data is uploaded to the tunnel environment monitoring platform in real time through the wireless communication network for subsequent data analysis and processing.

[0036] To ensure the reliability and integrity of monitoring data, the system can regularly calibrate and maintain sensors and replace aging or damaged sensors in a timely manner. At the same time, the system can use multi-sensor data fusion technology to perform weighted averaging of multiple sensor data for the same monitoring indicator to reduce the measurement error of a single sensor. When a sensor is missing data, the system can also repair the data through interpolation methods to ensure data continuity.

[0037] S103, calculating the theoretical gas concentration increment for the next period based on the aerodynamic model, predicted train flow, real-time gas concentration data, and wind speed and direction data; The system calculates the theoretical gas concentration increment for the next time period based on the aerodynamic model, predicted train flow, real-time gas concentration data, and wind speed and direction data. Specifically, the system determines the train operation plan for the next time period based on the predicted train flow, and the train operation plan includes the train passing time, train type, and running speed; calculates the waveform parameters of the air compression wave and expansion wave generated by each type of train at different running speeds based on the aerodynamic model, and the waveform parameters include the peak value, trough value, and duration; calculates the instantaneous airflow velocity field at each moment in the next time period based on the waveform parameters of the air compression wave and expansion wave combined with the geometric characteristics of the tunnel; superimposes the instantaneous airflow velocity field with the real-time gas concentration data and wind speed and direction data to obtain the convection diffusion field of the gas; establishes a mass conservation equation based on the convection diffusion field, and obtains the theoretical predicted value of the gas concentration in each section of the tunnel in the next time period by solving the mass conservation equation; and takes the difference between the theoretical predicted value of the gas concentration and the current measured concentration value as the theoretical gas concentration increment.

[0038] This step uses the aerodynamic model, combined with the predicted train flow, real-time gas concentration data and wind speed and direction data, to calculate the theoretical gas concentration increment in each area of ​​the tunnel in the next period. Specifically, the system first determines the train operation plan in the next period based on the predicted train flow, including parameters such as the passing time of each train, train type and running speed. Then, based on the aerodynamic model, the system calculates the waveform characteristics of the air compression wave and expansion wave generated by different types of trains at different speeds, including the peak value, trough value and duration. Next, the system calculates the instantaneous airflow velocity field distribution at each moment in the tunnel in the next period based on the waveform parameters of the compression wave and expansion wave and the geometric characteristics of the tunnel. Finally, the system superimposes the instantaneous airflow field with the real-time gas concentration data and wind speed and direction data for analysis, establishes and solves the mass conservation equation, and obtains the theoretical predicted value of the gas concentration in each section of the tunnel in the next period, and uses the difference between it and the current measured concentration value as the theoretical gas concentration increment.

[0039] In the implementation of this step, the selection and parameter setting of the aerodynamic model are crucial. Commonly used tunnel air flow models include one-dimensional unsteady flow models, three-dimensional turbulence models, etc. Different models have their own advantages and disadvantages in calculation accuracy and efficiency. The system needs to select a suitable aerodynamic model based on the actual conditions of the tunnel, such as cross-sectional shape, length, fan layout, etc., and determine and verify the model parameters through field measured data. At the same time, the aerodynamic characteristics of the train also need to be differentiated and modeled according to different vehicle models and speed levels. For complex train formations and crossing scenarios, the system also needs to consider the aerodynamic interference effects between vehicles.

[0040] The theoretical calculation results of this step provide important prior knowledge for the subsequent pollutant diffusion trend analysis, but due to the complexity of the actual situation, the calculation results may deviate from the actual situation. In order to improve the prediction accuracy, the system can introduce data assimilation technology to dynamically integrate the model calculation results with the measured data, and continuously update and correct the model parameters. In addition, for the special environment of the local area inside the tunnel, such as the opening, slope and obstacles, the system can also make targeted airflow parameter corrections to improve the reliability of the overall calculation results.

[0041] S104. Calculate the pollutant diffusion trends in different sections of the tunnel based on the theoretical gas concentration increment and the pollutant diffusion model; The system calculates the pollutant diffusion trends in different sections of the tunnel based on the theoretical gas concentration increment and the pollutant diffusion model, specifically including: dividing the tunnel into several calculation sections according to the structural characteristics of the tunnel, and establishing a pollutant transmission network model for the calculation sections, with virtual monitoring points set at the boundaries of each calculation section; for each calculation section, calculating the initial diffusion rate of pollutants based on the theoretical gas concentration increment; constructing the transfer function of various pollutants between adjacent calculation sections based on the pollutant diffusion model, the transfer function includes the temperature gradient coefficient, the humidity influence factor and the wall adsorption coefficient; using the transfer function to iteratively calculate the pollutant concentration of each virtual monitoring point to obtain the diffusion law of the pollutants; based on the diffusion law, the least squares method is used to fit the pollutant concentration change trend curve of each calculation section; according to the slope and curvature of the pollutant concentration change trend curve, the pollutant diffusion trend of each calculation section is determined.

[0042] Among them, the method for establishing the pollutant transmission network model is: dividing the space of the tunnel into multiple grid nodes according to a preset spacing to form a computing unit, collecting historical monitoring data of the computing unit, including historical monitoring data gas concentration data, airflow parameter data and environmental parameter data, dividing the historical monitoring data into a training data set and a verification data set according to a preset ratio, based on the training data set, taking the gas concentration data, airflow parameter data and environmental parameter data at the current moment as input features, and taking the gas concentration data at the next moment as output features, training to obtain the pollutant transmission network model, and using the verification data set to verify the pollutant transmission network model, when the root mean square error between the predicted result and the measured result of the pollutant transmission network model is less than the preset error value, the pollutant transmission network model is output.

[0043] In this step, the system needs to predict the pollutant diffusion trend in different sections of the tunnel based on the theoretical gas concentration increment calculated in the previous step and the pollutant diffusion model. First, the system divides the tunnel into several calculation sections according to the structural characteristics of the tunnel, such as length, cross section, bifurcation, etc., and sets virtual monitoring points at the boundaries of each section. Then, the system calculates the initial diffusion rate of pollutants in each section based on the theoretical gas concentration increment. Next, the system constructs a mathematical function for pollutant transfer between adjacent sections based on the pollutant diffusion model, which comprehensively considers multiple influencing factors such as temperature gradient, humidity, and wall adsorption. Finally, the system uses the transfer function to iteratively calculate the pollutant concentration of each virtual monitoring point, obtain the diffusion law of pollutants in time and space, and further fit the pollutant concentration change trend curve of each section, and judge the diffusion trend of pollutants according to the slope and curvature of the curve.

[0044] In terms of the choice of pollutant diffusion model, the system can use theoretical models such as the classical convection-diffusion equation and the concentration kinetics equation, or it can combine the actual monitoring data of the tunnel environment and apply machine learning algorithms to build a data-driven diffusion prediction model. For example, the system can use support vector machines, random forests and other algorithms to train a spatiotemporal prediction model of pollutant concentration based on the historical concentration data and wind speed and direction data of each monitoring point inside the tunnel. At the same time, in order to improve the generalization performance of the model, the system can also introduce transfer learning technology to pre-train and fine-tune the model using monitoring data from other similar tunnel environments.

[0045] The pollutant diffusion trend predicted in this step is crucial for the subsequent ventilation control strategy. However, due to the dynamic changes and complexity of the environmental parameters inside the tunnel, the static pollutant diffusion model may be difficult to accurately depict the actual situation. To address this problem, the system can introduce an online learning mechanism to continuously optimize and update the model based on real-time monitoring data. For example, when an abnormal change in the pollutant concentration in a certain section is detected, the system can appropriately adjust the transfer function parameters of the section to reflect the influence of factors such as wall adsorption and chemical reactions. In addition, the system can also use incremental learning algorithms to quickly update local model parameters without retraining the entire model to adapt to the dynamic changes in the tunnel environment.

[0046] S105, comparing the gas concentration value corresponding to the pollutant diffusion trend with the preset gas concentration threshold to obtain the target section that needs to adjust ventilation; In this step, the system compares the pollutant diffusion trend of each section predicted in the previous step with the preset gas concentration threshold, identifies the section that exceeds the threshold, and determines it as the target section that needs to focus on ventilation adjustment. The preset gas concentration threshold is usually set according to industry standards. The system can set different concentration thresholds for different types of pollutants (such as carbon monoxide, nitrogen dioxide, etc.).

[0047] The system can use a variety of judgment criteria when performing threshold comparison. For example, the average or maximum value of the pollutant concentration in the section can be compared with the threshold, and factors such as the duration and frequency of exceeding the standard can also be considered. For sections where the pollutant concentration shows a rapid upward trend, even if the current concentration does not exceed the standard, the system can mark it as a potential target section and start ventilation measures in advance. In addition, for multiple adjacent target sections, the system can merge them and formulate a unified ventilation strategy to reduce the frequent start and stop of the fan and improve energy efficiency.

[0048] S106, calculating the minimum number of tunnel fans to be turned on and the corresponding minimum speed according to the location information of the target section and the pollutant diffusion trend; The system calculates the minimum number of tunnel fans to be opened and the corresponding minimum speed based on the location information of the target section and the pollutant diffusion trend. Specifically, it includes: obtaining the fan distribution information upstream and downstream of the target section, which includes the location coordinates, rated power and maximum air volume of the fan; determining the minimum ventilation volume of the target section according to the pollutant diffusion trend; calculating the relationship curve between the ventilation volume and energy consumption of a single fan at different speeds; according to the minimum ventilation volume requirements, using the dynamic programming method to calculate the total energy consumption of different fan combinations; selecting the fan combination with the minimum total energy consumption to obtain the minimum number of fans opened and the minimum speed.

[0049] This step optimizes the opening plan of the tunnel fan according to the target section information identified in the previous step, and minimizes the number of fans started and the operating power as much as possible while meeting the ventilation requirements, so as to achieve the purpose of energy saving and efficiency improvement. First, the system obtains the fan distribution information upstream and downstream of the target section, including the installation position, rated power, maximum output and other parameters of each fan. Then, based on the pollutant diffusion trend of the target section, such as diffusion direction and rate, the system estimates the minimum ventilation volume required for the section in the next period. Next, the system calculates the ventilation volume-energy consumption curve of a single fan at different speeds, which reflects the energy-saving characteristics of the fan. Finally, the system uses a dynamic programming algorithm to solve the fan combination optimization problem, obtains the fan opening plan with the minimum total energy consumption, and determines the minimum number of fans to be opened and the minimum operating speed of each fan.

[0050] In practical applications, fan combination optimization needs to consider a variety of constraints. For example, the start and stop process of the fan usually requires a certain amount of time and energy consumption, and frequent start and stop operations will shorten the service life of the fan. Therefore, when formulating the fan start plan, the system needs to reasonably set the minimum operating time and minimum shutdown time of the fan to reduce frequent start and stop situations. At the same time, for long and large tunnels, there are multiple fan locations. The system needs to comprehensively consider the synergy between the fans and optimize the ventilation effect of the entire tunnel. In some cases, turning on a fan far away from the target section may be more energy-efficient and efficient than turning on a nearby fan.

[0051] S107, generating a fan control instruction according to the minimum number of opened fans and the minimum rotation speed, so that the tunnel fans with the minimum number of opened fans control the gas concentration in the target section according to the fan control instruction so as not to exceed a preset gas concentration threshold.

[0052] In this step, the system generates corresponding fan control instructions based on the optimal fan start-up plan calculated in the previous step, and sends them to the fan control unit in the tunnel. The control instructions include parameters such as the fan start and stop status, operating speed, and operating time. After receiving the instructions, the fan control unit transmits the control signal to the inverter to adjust the working state of the motor, thereby changing the ventilation volume of the fan. Through this process, the system achieves precise ventilation control of the target section, so that the pollutant concentration in the section is maintained below the preset threshold.

[0053] In the process of generating and issuing control instructions, the system needs to ensure the accuracy and timeliness of the instructions. On the one hand, the parameters of the control instructions need to match the actual working characteristics of the fan to avoid exceeding the working range of the fan. Therefore, the system needs to calibrate and verify the rated parameters and operating curves of the fan to ensure the executability of the control instructions. On the other hand, due to the rapid changes in the internal environment of the tunnel, the control instructions need to be transmitted and executed in a timely manner to ensure the ventilation effect. The system can use communication protocols such as industrial Ethernet and fieldbus to achieve real-time communication and data interaction between the fan control unit and the central control system.

[0054] At the same time, in order to ensure the reliability and stability of ventilation control, the system can also monitor and feedback the operating status of the fan in real time. The fan control unit needs to be equipped with corresponding sensors, such as speed sensors, vibration sensors, etc., to collect the operating parameters of the fan in real time and upload them to the central control system. The system analyzes the working status of the fan based on the monitoring data. If abnormal conditions are found, such as speed deviation, fault alarm, etc., the control strategy is adjusted in time or the standby fan is started to ensure the continuous operation of the ventilation system.

[0055] In the above embodiment, the train flow is predicted by obtaining historical train flow data from the database, combining the real-time collected gas concentration and wind speed and direction data, and calculating the theoretical gas concentration increment for the next period based on the aerodynamic model, and then using the pollutant diffusion model to analyze the pollutant diffusion trend of each section of the tunnel, so that the system can predict the distribution changes of pollutants in advance. On this basis, the target section is determined by comparing the pollutant diffusion trend with the preset gas concentration threshold, and the minimum number of open units and the minimum speed are calculated based on the location information of the target section and the pollutant diffusion trend, thereby generating accurate fan control instructions. This prediction-based control method makes the ventilation volume adjustment more accurate, while ensuring that the gas concentration in the target section does not exceed the preset threshold, by minimizing the number of fans opened and the speed to achieve energy consumption reduction, thereby achieving the effect of reducing the energy consumption of the tunnel fan while maintaining the ventilation effect of the tunnel.

[0056] The above embodiments mainly describe the basic process of the prediction-based tunnel fan energy-saving control method. By analyzing and predicting data such as train flow, gas concentration, wind speed and direction, the fan operation is optimized. However, in practical applications, due to the complexity of the tunnel structure and the uneven distribution of airflow, local eddy current dead zones may still occur, affecting the ventilation effect. In order to solve this problem, a control method based on eddy current dead zone identification and elimination is introduced below. This method, as a supplement and improvement to the basic control process, can further improve the control accuracy and efficiency of the ventilation system. Figure 2 , a control method based on eddy current dead zone identification and elimination in an embodiment of the present application is described: please refer to Figure 2 , which is a flow chart of a control method based on eddy current dead zone identification and elimination in an embodiment of the present application.

[0057] S201, collecting the real-time gas concentration change rate of the target section after the tunnel fan is adjusted; The system collects the real-time gas concentration change rate of the target section after the tunnel fan is adjusted. Specifically, after executing step S107 to optimize the control of the fan in the target section, the system continuously monitors the change of gas concentration in the section and calculates the change rate of gas concentration per unit time as a dynamic indicator for evaluating the ventilation effect. Since fan adjustment will cause the redistribution of airflow in the tunnel, causing the pollutant concentration in the local area to fluctuate, the gas concentration change rate can more sensitively reflect the airflow disturbance in the section.

[0058] In order to calculate the real-time gas concentration change rate, the system can use a sliding window method, with a fixed time interval (such as 1 minute) as the window length, to continuously collect gas concentration values ​​at multiple times, and obtain the average change rate within the time period by the ratio of the difference between the mean concentrations of two adjacent time windows and the time interval. Considering that the fluctuation of gas concentration has a certain degree of randomness, the system can also set a smoothing coefficient for the change rate, and perform a weighted average of the change rates of multiple consecutive time windows to reduce the impact of accidental factors.

[0059] S202, when the real-time gas concentration change rate is greater than a preset change rate threshold, calculating the pulsation period and pulsation amplitude of the gas concentration in the target section; When the real-time gas concentration change rate is greater than the preset change rate threshold, the system calculates the pulsation period and pulsation amplitude of the gas concentration in the target section. Among them, the preset change rate threshold is a reference value set according to the tunnel ventilation design standard and operating experience, which represents the gas concentration fluctuation range under normal ventilation conditions. When the actual change rate exceeds the threshold, it means that there may be abnormal conditions such as airflow turbulence or pollutant accumulation in the target section, and further analysis of the change pattern of gas concentration is required.

[0060] The pulsation period refers to the time interval of periodic fluctuations in gas concentration on a relatively stable time scale, reflecting the circulation period of pollutants inside the tunnel. The pulsation amplitude refers to the concentration difference between two adjacent peaks (or troughs), reflecting the fluctuation range of pollutant concentration. The system can use spectrum analysis to convert the time series data of gas concentration into frequency domain space, obtain the dominant frequency component through power spectrum density estimation, and then calculate the corresponding pulsation period based on the dominant frequency. At the same time, the system can use peak and valley detection algorithm to automatically identify the local extreme points of concentration series data, and calculate the pulsation amplitude through the concentration difference of adjacent extreme points.

[0061] In practical applications, since air flow pulsation is a complex fluid mechanics phenomenon, it is affected by many factors (such as tunnel section shape, fan layout, traffic conditions, etc.), and its manifestations and laws may be diverse. In order to improve the recognition accuracy of pulsation characteristics, the system can establish a pulsation feature library for different working conditions and use pattern matching methods to achieve automatic recognition. For example, clustering algorithms are used to classify historical data and extract typical features of various pulsation patterns. When new detection data arrives, its similarity with various patterns is calculated and assigned to the category with the greatest similarity. In addition, the system can also use machine learning algorithms (such as support vector machines, random forests, etc.) to establish a discriminant model for pulsation characteristics, and dynamically update the judgment rules for pulsation characteristics through continuous training and optimization of the model.

[0062] S203, when it is determined that there is an eddy current dead zone in the target section according to the pulsation period and the pulsation amplitude, determining the wind turbine group to be adjusted according to the position of the eddy current dead zone; When judging the existence of eddy current dead zone in the target section based on the pulsation period and pulsation amplitude, the system determines the fan group to be adjusted based on the location of the eddy current dead zone. Among them, the eddy current dead zone refers to a closed or semi-closed airflow loop formed in a local area of ​​the tunnel, which leads to insufficient gas exchange in the area, difficulty in the diffusion of pollutants, and continuous increase in concentration. The causes of the formation of eddy current dead zones are relatively complex, which are related to structural factors such as the slope and corners of the tunnel, as well as dynamic factors such as vehicle operation and fan layout.

[0063] The general basis for judging the existence of eddy dead zones is: long pulsation period (such as more than 10 minutes) and large pulsation amplitude (such as peak-to-valley difference exceeding 50%). This means that the pollutants have been circulating in this area for a long time and the cumulative effect is more significant, and it is likely that a relatively stable eddy structure has been formed. In addition, the system can also comprehensively consider the temperature and humidity distribution in the section, and analyze the airflow streamlines and flow velocity distribution in the section in combination with the fluid mechanics model to further verify the existence and scope of the eddy dead zone.

[0064] The following methods can be used to locate the eddy current dead zone: deploy a high-density sensor network in the target section to achieve refined monitoring of gas concentration; perform three-dimensional spatial interpolation on the gas concentration distribution in the section to obtain a continuous concentration distribution surface; use image processing methods (such as threshold segmentation, edge detection, etc.) to analyze the concentration distribution surface to identify high-value and low-value areas of pollutant concentration; match the high-value concentration area with the tunnel CAD model to determine the specific location and range of the eddy current dead zone.

[0065] On this basis, the system can combine the arrangement of fans near the eddy area and select the fans that have the greatest impact on the ventilation effect in the area as the fan group to be adjusted. For example, the fans closest to the upstream and downstream of the eddy area, the fans at the corners of the eddy area, etc. By focusing on these fans, the local ventilation effect can be improved to the greatest extent.

[0066] S204, performing pulse ventilation control on the regulated fan unit by means of alternating start and stop, so as to eliminate the eddy current dead zone.

[0067] The system adopts an alternating start-stop method to perform pulse ventilation control on the regulated fan group to eliminate the vortex dead zone, specifically including: dividing the fan group into several fan sub-groups, each fan sub-group contains at least two adjacent tunnel fans; calculating one quarter of the pulsation cycle as the basic switching time; rotating each fan sub-group in turn according to a preset sequence, and repeating the rotation operation until the vortex dead zone is eliminated. The rotation operation of each fan sub-group is to increase the speed of the fan sub-group to the maximum speed and run it for a basic switching time, and then reduce the speed of the fan sub-group to the minimum speed and run it for a basic switching time.

[0068] The system uses an alternating start-stop method to perform pulse ventilation control on the regulated fan group to eliminate the vortex dead zone. Specifically, the following steps are included: the fan group is divided into several fan sub-groups, each fan sub-group contains at least two adjacent tunnel fans; one quarter of the pulsation cycle is calculated as the basic switching time; each fan sub-group is rotated in turn according to a preset sequence, and the rotation operation is repeated until the vortex dead zone is eliminated. The rotation operation of each fan sub-group is to increase the speed of the fan sub-group to the maximum speed and run for a basic switching time, and then reduce the speed of the fan sub-group to the minimum speed and run for a basic switching time.

[0069] The basic idea is to form an alternating pressure field and velocity field around the eddy area through the pulsed operation of the fan group, breaking the original eddy balance structure and accelerating the diffusion of pollutants in the dead zone. Due to the inertial effect of the eddy, it is difficult to completely eliminate the eddy dead zone by the continuous operation of a single fan. Therefore, the alternating start and stop control of the combined fan is adopted, and the airflow effect of multiple points and directions is utilized to improve the local ventilation conditions more efficiently and flexibly.

[0070] In practical applications, the division of fan subgroups needs to take into account the feasibility and economy of variable frequency speed regulation to avoid frequent start and stop to cause damage to the equipment. The selection of basic switching time needs to balance the needs of pulse effect and energy consumption optimization. It is necessary to make the fan run long enough to form an effective local airflow impact, and to shorten the high-speed operation time of the fan as much as possible to save electricity consumption. In addition, the system can dynamically adjust the switching frequency and switching amplitude (such as the speed change gradient) of the fan subgroup according to the pollutant concentration level in the eddy area to achieve more refined ventilation control.

[0071] On this basis, the system can further optimize the rotation order and combination of fan subgroups. For example, according to the relative relationship between the vortex area and the fan position, different control strategies are adopted for upstream and downstream fans. Downstream fans focus on eliminating vortex wakes, while upstream fans focus on blocking the source of vortices. For example, for vortex dead zones of different intensity levels, fan combinations of different sizes are matched, more fan combinations are enabled in strong vortex areas, and fewer fan combinations are enabled in weak vortex areas. The system can establish a fan combination optimization model, and through statistical analysis of historical control effects, train the best fan combination solutions for various vortex dead zones, form an intelligent strategy library, and quickly match and call according to the real-time monitored vortex state information.

[0072] In the above embodiment, by collecting the real-time gas concentration change rate of the target section after the fan is adjusted, and calculating the pulsation characteristics of the gas concentration when the change rate exceeds the threshold, the ventilation dead zone problem can be discovered in time. When it is determined that there is a vortex dead zone, the alternating start-stop pulse ventilation control method is adopted to break the vortex structure and improve the ventilation effect. By monitoring the dynamic change characteristics of the gas concentration, a quantitative index for the identification of the vortex dead zone is established. Pulse ventilation control produces a forced disturbance effect by periodically changing the airflow field structure, which can destroy the stable existence conditions of the vortex dead zone, promote gas mixing and diffusion, improve the control accuracy and ventilation efficiency of the ventilation system, and ensure the uniformity of the air quality in the tunnel space.

[0073] The following describes the system in the embodiment of the present invention from the perspective of hardware processing. Figure 3 , which is a schematic diagram of the physical device structure of a tunnel fan energy-saving control system provided in an embodiment of the present application.

[0074] It should be noted that Figure 3 The structure of the 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.

[0075] like Figure 3As shown, the 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 in the above embodiment. In RAM 303, various programs and data required for system operation are also stored. CPU 301, ROM 302 and RAM 303 are connected to each other through a bus 304. Input / output (I / O) interface 305 is also connected to bus 304.

[0076] The following components are connected to the I / O interface 305: an input section 306 including a camera, an infrared sensor, etc.; an output section 307 including a liquid crystal display (LCD) and a speaker, 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.

[0077] 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.

[0078] It should be noted that the computer-readable medium shown in the embodiment of the present invention may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More 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 of the above. In the present invention, a computer-readable storage medium may be any tangible medium containing or storing a program, which may be used by or in combination with an instruction execution system, device or device. In the present invention, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, which carries a computer-readable computer program. Such a propagated data signal may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the foregoing.

[0079] 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. Among them, each box in the flowchart or block diagram can represent a module, a program segment, or a part of the code, and the above-mentioned module, program segment, or a part of the code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flowchart, and the combination of boxes in the block diagram or flowchart, can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0080] As another aspect, the present invention further provides a computer-readable storage medium, which may be included in the system described in the above embodiment; or may exist independently without being assembled into the 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 a system, the system implements the method provided in the above embodiment.

[0081] 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.

[0082] 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.

[0083] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions may be transmitted from a website site, computer, server or data center to another website site, computer, server or data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium may be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more available media integrated. The available medium may be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid-state hard disk), etc.

[0084] 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 fan energy-saving control method, characterized in that: include: Acquire historical train flow data within a preset time period from a database, and calculate the predicted train flow for the next time period based on the historical train flow data; Collecting real-time gas concentration data and wind speed and direction data within the preset time period, wherein the real-time gas concentration data includes carbon monoxide concentration, nitrogen dioxide concentration, ozone concentration and dust concentration; Calculating the theoretical gas concentration increment for the next time period based on the aerodynamic model, the predicted train flow, the real-time gas concentration data and the wind speed and direction data; The pollutant diffusion trends in different sections of the tunnel are calculated based on the theoretical gas concentration increment and the pollutant diffusion model; Comparing the gas concentration value corresponding to the pollutant diffusion trend with the preset gas concentration threshold to obtain the target section that needs to adjust ventilation; According to the location information of the target section and the diffusion trend of the pollutants, the minimum number of tunnel fans to be opened and the corresponding minimum speed are calculated; A fan control instruction is generated according to the minimum number of opened fans and the minimum rotation speed, so that the tunnel fans with the minimum number of opened fans control the gas concentration in the target section according to the fan control instruction so that the gas concentration does not exceed the preset gas concentration threshold.

2. The method according to claim 1, characterized in that The step of calculating the theoretical gas concentration increment for the next time period based on the aerodynamic model, the predicted train flow, the real-time gas concentration data and the wind speed and direction data specifically includes: Determine the train operation plan for the next period according to the predicted train flow, wherein the train operation plan includes train passing time, train type and running speed; Calculate the waveform parameters of air compression waves and expansion waves generated by each type of train at different running speeds based on the aerodynamic model, wherein the waveform parameters include wave peak value, wave trough value and duration; Calculating the instantaneous airflow velocity field at each moment in the next time period according to the waveform parameters of the air compression wave and the expansion wave and in combination with the geometric characteristics of the tunnel; The instantaneous airflow velocity field is superimposed and calculated with the real-time gas concentration data and the wind speed and direction data to obtain a gas convection diffusion field; A mass conservation equation is established according to the convection diffusion field, and a theoretical predicted value of the gas concentration in each section of the tunnel in the next time period is obtained by solving the mass conservation equation; The difference between the theoretical predicted value of the gas concentration and the current measured concentration value is taken as the theoretical gas concentration increment.

3. The method according to claim 1, characterized in that The pollutant diffusion trends in different sections of the tunnel are calculated based on the theoretical gas concentration increment and the pollutant diffusion model, specifically including: Dividing the tunnel into a number of calculation sections according to the structural characteristics of the tunnel, and establishing a pollutant transmission network model of the calculation sections, with a virtual monitoring point being set at the boundary of each calculation section; For each of the calculation sections, calculating the initial diffusion rate of the pollutant based on the theoretical gas concentration increment; Constructing a transfer function of each type of pollutant between adjacent calculation sections according to the pollutant diffusion model, wherein the transfer function includes a temperature gradient coefficient, a humidity influence factor, and a wall adsorption coefficient; Iteratively calculating the pollutant concentration of each virtual monitoring point using the transfer function to obtain the diffusion law of the pollutant; Based on the diffusion law, the least square method is used to fit the pollutant concentration change trend curve of each calculation section; The pollutant diffusion trend of each of the calculation sections is determined according to the slope and curvature of the pollutant concentration change trend curve.

4. The method according to claim 1, characterized in that: The method for establishing the pollutant transmission network model is as follows: dividing the space of the tunnel into multiple grid nodes according to a preset spacing to form a computing unit, collecting historical monitoring data of the computing unit, including historical monitoring data gas concentration data, airflow parameter data and environmental parameter data, dividing the historical monitoring data into a training data set and a verification data set according to a preset ratio, based on the training data set, taking the gas concentration data, airflow parameter data and environmental parameter data at the current moment as input features, and taking the gas concentration data at the next moment as output features, training to obtain the pollutant transmission network model, and using the verification data set to verify the pollutant transmission network model, when the root mean square error between the predicted result of the pollutant transmission network model and the measured result is less than a preset error value, outputting the pollutant transmission network model.

5. The method according to claim 1, characterized in that The step of calculating the minimum number of tunnel fans to be opened and the corresponding minimum speed according to the location information of the target section and the diffusion trend of the pollutants specifically includes: Obtaining wind turbine distribution information upstream and downstream of the target section, wherein the wind turbine distribution information includes location coordinates, rated power, and maximum air volume of the wind turbine; Determining the minimum ventilation volume of the target section according to the pollutant diffusion trend; Calculate the relationship curve between ventilation volume and energy consumption of a single fan at different speeds; According to the minimum ventilation volume requirement, a dynamic programming method is used to calculate the total energy consumption of different fan combination schemes; Select the fan combination scheme with the minimum total energy consumption to obtain the minimum number of fans turned on and the lowest speed.

6. The method according to claim 1, characterized in that After generating the fan control instruction according to the minimum number of units started and the minimum speed, the method further includes: Collecting the real-time gas concentration change rate of the target section after the tunnel fan is adjusted; When the real-time gas concentration change rate is greater than a preset change rate threshold, calculating the pulsation period and pulsation amplitude of the gas concentration in the target section; In the case where it is determined that there is an eddy current dead zone in the target section according to the pulsation period and the pulsation amplitude, determining the wind turbine group to be adjusted according to the position of the eddy current dead zone; The fan unit to be adjusted is subjected to pulse ventilation control by alternately starting and stopping to eliminate the eddy current dead zone.

7. The method according to claim 6, characterized in that The pulse ventilation control of the fan group to be adjusted by using an alternating start-stop method specifically includes: Dividing the fan group into a plurality of fan subgroups, each of the fan subgroups comprising at least two adjacent tunnel fans; Calculating one quarter of the pulsation period as a basic switching time; The fan sub-groups are rotated in turn according to a preset sequence, and the rotation operation is repeated until the eddy current dead zone is eliminated. The rotation operation of each fan sub-group is to increase the speed of the fan sub-group to the maximum speed and run it for a basic switching time, and then reduce the speed of the fan sub-group to the minimum speed and run it for a basic switching time.

8. A tunnel fan energy-saving control system, characterized in that: The system 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 includes computer instructions, and the one or more processors call the computer instructions to cause the system to execute the method as described in any one of claims 1-7.

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

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