Long-distance single-end tunnel construction ventilation real-time monitoring system

Abstract

The invention, which relates to the technical field of tunnel construction ventilation, discloses a long-distance single-head tunnel construction ventilation real-time monitoring system comprising a data acquisition module, a monitoring analysis prediction module, a ventilation control module and a terminal interaction module. The data acquisition module is used for finishing arrangement of an environment acquisition sensing array in combination with a BIM construction system, and sampling to obtain an initial air environment parameter sequence, an engineering equipment data set and a ventilation equipment data set; the monitoring analysis prediction module is used for processing to obtain a tunnel construction dynamic data set, a current target gas content curve and a dispersion coefficient of each target gas component along the axial direction of the tunnel, and processing to obtain a target gas content prediction sequence; the ventilation control module is used for judging the target gas content prediction sequence, outputting a ventilation monitoring signal and generating fan combination control data to complete operation adjustment of fan equipment; and the terminal interaction unit is used for providing a display interface, receiving a user operation instruction, outputting data and storing the data.

Description

Technical Field

[0001] This invention relates to the technical field of ventilation during tunnel construction, and more particularly to a real-time monitoring system for ventilation during long-distance single-ended tunnel construction. Background Technology

[0002] Long-distance, single-ended tunnel construction suffers from a lack of natural ventilation due to the closed, blind end of the excavation face. This leads to the accumulation of harmful gases in the work area, and the gathering of personnel and blasting operations during construction can result in insufficient oxygen levels, seriously threatening the health and safety of construction workers and affecting the normal operation and progress of construction equipment. Traditional ventilation methods rely heavily on experience or simple timed ventilation, making it difficult to accurately and in real-time monitor the complex air environment and its changing trends within the tunnel. This often results in either insufficient ventilation leading to safety hazards or excessive ventilation causing energy waste. As tunnel engineering develops towards longer distances and larger cross-sections, the complexity and difficulty of controlling the air environment have significantly increased. Developing an advanced ventilation system capable of real-time monitoring, intelligent analysis and prediction, and dynamic optimization control of the tunnel's internal air environment is a promising area of ​​research.

[0003] Currently, Chinese invention patent application CN201410631765.5 discloses a tunnel construction ventilation monitoring system. This application includes: a host monitoring unit deployed in a remote monitoring room; a handheld mobile terminal carried by tunnel ventilation monitoring personnel; a ventilation status monitoring robot capable of moving back and forth within the tunnel under construction; a sensor mounting frame installed on the inner wall of the tunnel; and an air supply status detection device for real-time monitoring of the air supply status of the tunnel's ventilation ducts. The air supply status detection device is mounted on the sensor mounting frame, which is located in front of the air outlet of the ventilation duct. The ventilation status monitoring robot communicates wirelessly with the host monitoring unit, the handheld mobile terminal, and the air supply status detection device. This invention has a simple structure, reasonable design, is easy to operate, and has good performance. After blasting, it can monitor the distribution of blasting fumes and harmful gases at the tunnel face, as well as the air supply status of the tunnel ventilation ducts, in real time. However, this application focuses on real-time detection but lacks prediction of air composition, relies on manual detection, and does not adequately consider energy conservation and emission reduction. Summary of the Invention

[0004] The technical problem solved by this invention is that, for special construction scenarios such as long-distance single-ended tunnels, existing technologies mostly adopt ventilation by controlling fans to a fixed power, or increasing fan power after monitoring real-time air composition data to detect excessive / insufficient levels. This belongs to a delayed feedback ventilation monitoring and control strategy. It cannot predict composition changes and cannot achieve pre-regulation of ventilation. Furthermore, it suffers from inefficient ventilation and high energy consumption.

[0005] To solve the above-mentioned technical problems, the present invention provides the following technical solution: A real-time monitoring system for ventilation during the construction of long-distance single-ended tunnels includes: Data acquisition module, monitoring, analysis and prediction module, ventilation control module and terminal interaction module; Data acquisition module, monitoring, analysis and prediction module, ventilation control module and terminal interaction module; The data acquisition module is used to combine with the BIM construction system to complete the layout of the environmental acquisition sensor array and sample the initial air environment parameter sequence, engineering equipment dataset and ventilation equipment dataset; The monitoring, analysis and prediction module is used to process the initial air environment parameter sequence to obtain the tunnel construction dynamic dataset, the current target gas content curve and the dispersion coefficient of each target gas component along the tunnel axis, and to process it to obtain the target gas content prediction sequence. The ventilation control module is used to judge the predicted sequence of target gas content, output ventilation monitoring signals, generate fan combination control data, and complete the operation adjustment of fan equipment. The terminal interaction unit is used to provide a display interface and receive user operation commands, as well as to receive and store all output data from the data acquisition module, monitoring analysis and prediction module, and ventilation control module.

[0006] Preferably, the data acquisition module includes a sensor deployment unit, an environmental data unit, and a device data unit; The sensor deployment unit is used to integrate with the BIM construction system to arrange the environmental data acquisition sensor array. The processing logic includes: Based on the BIM construction system, a spatially gridded 3D model of the construction tunnel is obtained. Based on the real-time construction progress, the 3D model of the construction tunnel is divided into key node regions. In each key node region, a vertical double layer is used to determine the target acquisition points. Based on the target acquisition points, the arrangement of the environmental acquisition sensor array is completed. The set of key node regions includes mobile regions and fixed regions; The fixed area includes cross sections sequentially arranged at preset first intervals along the target single-ended tunnel; The mobile area includes the area from the tunneling face, the main tunneling equipment area, the material transfer point area, and the personnel rest area. The vertical double-layer structure includes a lower-level personnel breathing zone and an upper-level gas enrichment zone. The target sampling point corresponding to the lower-level personnel breathing zone is set at a height of 1.5m, and the target sampling point corresponding to the upper-level gas enrichment zone is set at a height of 3.0m. The environmental data acquisition sensor array includes an oxygen sensor, a hydrogen sulfide sensor, a carbon monoxide sensor, a sulfur dioxide sensor, a temperature sensor, a humidity sensor, an ultrasonic anemometer, and a barometer. The environmental data unit is used to continuously sample comprehensive environmental parameters through the environmental acquisition sensor array deployed at each target acquisition point at a preset acquisition time period to obtain an initial air environmental parameter sequence. The comprehensive environmental parameters include the three-dimensional coordinates of the target acquisition point, first timestamp data, oxygen data, hydrogen sulfide data, carbon monoxide data, sulfur dioxide data, temperature data, humidity data, three-dimensional airflow vector data, and air pressure data.

[0007] Preferably, the equipment data unit is used to acquire engineering equipment datasets and ventilation equipment datasets in real time; Based on the standardized engineering equipment operation status analysis interface of each device and the tunnel local area network, the engineering equipment dataset and ventilation equipment data are collected in real time through the OPCUA / Modbus industrial communication protocol. The engineering equipment dataset includes core equipment data and secondary equipment data; Core equipment data includes the second timestamp, tunnel boring machine ID, tunnel boring machine start / stop status, main drive power, propulsion speed, cutterhead torque, and muck removal system load; Secondary equipment data includes a third timestamp, excavation auxiliary equipment ID, excavation auxiliary equipment operation duration, and excavation auxiliary equipment start and stop times. The excavation auxiliary equipment includes concrete spraying trucks, anchor bolt trolleys, and muck transport trucks. The ventilation equipment dataset includes the ID of each fan, the start / stop status of the fan, the real-time power of the fan, the location coordinates of the fan, and the maximum power of the fan.

[0008] Preferably, the monitoring, analysis and prediction module includes a preprocessing unit, a real-time status unit and a status prediction unit; The preprocessing unit integrates the initial air environment parameter sequence with the engineering equipment dataset to obtain a dynamic dataset for tunnel construction. Its processing logic includes: The initial air environment parameter sequence and the engineering equipment dataset are time-stamped to obtain preliminary integrated data. Outlier removal is performed on the preliminary integrated data, and adjacent value interpolation is performed on the missing values ​​based on the time series. Noise removal is completed by median filtering to obtain the dynamic dataset of tunnel construction.

[0009] Preferably, the real-time state unit is used to process the dynamic dataset of tunnel construction to obtain the current target gas content curve and the dispersion coefficient of each target gas component along the tunnel axis under the current working condition. The processing logic includes: Anisotropic Kriging interpolation algorithm is used to fit the discrete integrated environmental parameters in the dynamic data of tunnel construction, and real-time target gas content curves are generated for each target gas component in the integrated environmental parameters along the tunnel axis. The set of nodal rates of change is obtained by differentiating the current target gas content curve at each target sampling point. Based on the Taylor-Aris model and the one-dimensional flow dispersion equation, the dispersion coefficients of each target gas component along the tunnel axis under the current working conditions are calculated using the current target gas content curve and the three-dimensional airflow vector data in the tunnel construction dynamic dataset. The target gas components include oxygen, hydrogen sulfide, carbon monoxide, and sulfur dioxide; The tunnel axis direction is from the single-ended tunnel excavation face to the single-ended tunnel exit direction.

[0010] Preferably, the state prediction unit is used to obtain a target gas content prediction sequence based on the current target gas content curve, and the processing logic includes: Using the current target gas content curve as the initial condition of the one-dimensional convection-diffusion model, the component of the three-dimensional airflow vector data along the tunnel axis in the tunnel construction dynamic dataset is extracted as the convection velocity of the one-dimensional convection-diffusion model. The longitudinal dispersion coefficient is used as the dispersion coefficient of the one-dimensional convection-diffusion model. The concentration distribution curves of each target gas component along the tunnel axis in the next T minutes are calculated through the one-dimensional convection-diffusion model, and the target gas content prediction sequence is obtained. In the target gas content prediction sequence, the vertical axis of each curve represents the content of the corresponding target gas component, and the horizontal axis represents the distance from the tunnel entrance along the tunnel axis.

[0011] Preferably, the ventilation control module includes a ventilation strategy unit and a ventilation execution unit; The ventilation strategy unit judges the target gas content prediction sequence according to the preset rule engine and outputs a ventilation monitoring signal. The processing logic of the preset rule engine includes: When the oxygen prediction value of K consecutive points in the target gas content prediction sequence is less than the preset oxygen content warning line, the ventilation monitoring signal output is a ventilation warning signal; otherwise, the ventilation monitoring signal output is a no-abnormal signal. When the predicted hydrogen sulfide value of K consecutive points in the target gas content prediction sequence is greater than the preset oxygen content warning line, the ventilation monitoring signal output is a ventilation warning signal; otherwise, the ventilation monitoring signal output is a no-abnormal signal. When the predicted carbon monoxide value at K consecutive points in the target gas content prediction sequence is greater than the preset oxygen content warning line, the ventilation monitoring signal output is a ventilation warning signal; otherwise, the ventilation monitoring signal output is a no-abnormal signal. When the predicted sulfur dioxide value of K consecutive points in the target gas content prediction sequence is greater than the preset oxygen content warning line, the ventilation monitoring signal output is a ventilation warning signal; otherwise, the ventilation monitoring signal output is a no-abnormal signal. The ventilation warning signal also includes abnormal point data values, which include the x-coordinate and y-coordinate values ​​of each point in the target gas content prediction sequence that exceeds the corresponding content warning line.

[0012] Preferably, the ventilation strategy unit is used to generate fan combination control data based on ventilation monitoring signals, and the processing logic includes: The abnormal air volume is calculated based on the abnormal point data values ​​in the ventilation early warning signal. The cross-sectional volume is calculated based on the horizontal coordinate coefficient in the abnormal point data value and the three-dimensional model of the construction tunnel. The abnormal air volume is then multiplied by the redundancy amplification factor of 105% to obtain the abnormal air volume. Based on the abscissa corresponding to the abnormal air volume and abnormal point data values, the fan combination control data is calculated and screened through simulated annealing algorithm with the objective function of minimizing the response time of the ventilation warning signal and minimizing the expected energy consumption. The combined wind turbine control data includes the ID of the wind turbine to be started, the location coordinates of the wind turbine, and the target power of the wind turbine.

[0013] Preferably, the ventilation actuator is used to control the operation and adjustment of the fan equipment according to the fan combination control data, and generate ventilation result feedback; Based on the OPCUA / Modbus industrial communication protocol, the combined control data of the wind turbine is encoded to generate a set of control instructions, which are then sent to the corresponding wind turbine equipment for execution via the tunnel local area network to obtain the record of the executed control operations. The tunnel construction dynamic dataset within a 3-minute time window after the execution of the statistical fan combination control data is used to obtain the adjustment feedback dataset. Based on the adjustment feedback dataset and the target gas content prediction sequence, the gas compliance time is calculated and a record of ventilation result feedback is generated. The ventilation results feedback includes those that did not meet expectations and those that met expectations; When the gas compliance time exceeds the preset monitoring response time, the ventilation result feedback indicates that the expected result has not been achieved, and a ventilation warning is issued, indicating that there is an insufficient number of ventilation fans in the tunnel construction. When the time for the gas to reach the standard is less than or equal to the preset monitoring response time, the ventilation result is reported as meeting expectations; The gas compliance time is the length of time from when the ventilation actuator executes the control data of the fan combination until the abnormal point data value is an empty set.

[0014] Preferably, the terminal interaction module includes a display interaction unit and a data storage unit; The display interaction unit is used to provide a display interface and receive user operation instructions, including instructions from the user to set the data acquisition module, monitoring analysis and prediction module, and ventilation control module. The data storage unit stores all the output data from the data acquisition module, monitoring analysis and prediction module, and ventilation control module, and generates historical logs for tunnel construction ventilation monitoring.

[0015] The beneficial effects of this invention are as follows: The data acquisition module, combined with the BIM system, enables refined and dynamic deployment of the environmental acquisition sensor array, allowing for real-time collection of comprehensive environmental parameters and operational data from engineering and ventilation equipment, providing a foundation for subsequent analysis. The monitoring, analysis, and prediction module generates real-time gas content curves through advanced data preprocessing and Kriging interpolation algorithms, and calculates the dispersion coefficient based on a physical model, accurately predicting future gas distribution trends based on real-time data and the model. Finally, the ventilation control module uses the prediction results for proactive early warning, and based on abnormal gas volume calculation and simulated annealing algorithms, intelligently optimizes the fan combination control strategy with the goal of minimizing response speed and energy consumption. Ultimately, by executing control commands and providing feedback on control effects, a closed-loop management system is formed, improving the efficiency and safety of ventilation during tunnel construction. Attached Figure Description

[0016] Figure 1 A schematic diagram of the basic framework of a real-time monitoring system for ventilation during the construction of a long-distance single-ended tunnel, provided in one embodiment of the present invention; Figure 2 This is a schematic diagram of a gas content prediction sequence provided in one embodiment of the present invention. Detailed Implementation

[0017] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0018] Reference Figure 1 and Figure 2 As an embodiment of the present invention, a real-time monitoring system for ventilation during the construction of long-distance single-ended tunnels is provided, comprising: a data acquisition module, a monitoring analysis and prediction module, a ventilation control module, and a terminal interaction module; The data acquisition module includes a sensor deployment unit, an environmental data unit, and an equipment data unit. It is used to combine with the BIM construction system to complete the arrangement of the environmental acquisition sensor array and to sample the initial air environment parameter sequence, engineering equipment dataset, and ventilation equipment dataset. The monitoring, analysis and prediction module includes a preprocessing unit, a real-time status unit and a status prediction unit. It is used to process the initial air environment parameter sequence to obtain the tunnel construction dynamic dataset, the current target gas content curve and the dispersion coefficient of each target gas component along the tunnel axis, and to process it to obtain the target gas content prediction sequence. The ventilation control module includes a ventilation strategy unit and a ventilation execution unit, which are used to judge the predicted sequence of target gas content, output ventilation monitoring signals, generate fan combination control data, and complete the operation adjustment of fan equipment. The terminal interaction unit is used to provide a display interface and receive user operation commands, as well as to receive and store all output data from the data acquisition module, monitoring analysis and prediction module, and ventilation control module.

[0019] In this embodiment, the sensor deployment unit is used to combine with the BIM construction system to complete the arrangement of the environmental acquisition sensor array, and the processing logic includes: Based on the BIM construction system, a spatially gridded 3D model of the construction tunnel is obtained. Based on the real-time construction progress, the 3D model of the construction tunnel is divided into key node regions. In each key node region, a vertical double layer is used to determine the target acquisition points. Based on the target acquisition points, the arrangement of the environmental acquisition sensor array is completed. The set of key node regions includes mobile regions and fixed regions; The fixed area includes cross sections sequentially arranged at preset first intervals along the target single-ended tunnel; The mobile area includes the area from the tunneling face, the main tunneling equipment area, the material transfer point area, and the personnel rest area. The vertical double-layer structure includes a lower-level personnel breathing zone and an upper-level gas enrichment zone. The target sampling point corresponding to the lower-level personnel breathing zone is set at a height of 1.5m, and the target sampling point corresponding to the upper-level gas enrichment zone is set at a height of 3.0m. The environmental data acquisition sensor array includes an oxygen sensor, a hydrogen sulfide sensor, a carbon monoxide sensor, a sulfur dioxide sensor, a temperature sensor, a humidity sensor, an ultrasonic anemometer, and a barometer. The environmental data unit is used to continuously sample comprehensive environmental parameters through the environmental acquisition sensor array deployed at each target acquisition point at a preset acquisition time period to obtain an initial air environmental parameter sequence. The comprehensive environmental parameters include the three-dimensional coordinates of the target acquisition point, first timestamp data, oxygen data, hydrogen sulfide data, carbon monoxide data, sulfur dioxide data, temperature data, humidity data, three-dimensional airflow vector data, and air pressure data.

[0020] The main factors affecting tunnel air quality are exhaust emissions from large machinery during construction, blasting and combustion, and the respiratory effects on construction workers. Therefore, this application specifically deploys sensor arrays in the tunneling machinery area, the tunnel bottom excavation face, and the personnel rest area. Considering the dynamic nature of these areas, they are designated as mobile zones. Furthermore, considering the potential for hydrogen sulfide gas leakage from the rock strata in excavated tunnel areas, a uniformly distributed sensor array is placed in a fixed area to monitor random air quality crisis events. This also helps to obtain overall tunnel air quality information. The data collection and deployment method in this application is well-suited to the characteristics of single-ended tunnel construction, which is beneficial for the efficiency and accuracy of subsequent gas analysis.

[0021] In this embodiment, the equipment data unit is used to collect data in real time to obtain engineering equipment datasets and ventilation equipment datasets. Based on the standardized engineering equipment operation status analysis interface of each device and the tunnel local area network, the engineering equipment dataset and ventilation equipment data are collected in real time through the OPCUA / Modbus industrial communication protocol. The engineering equipment dataset includes core equipment data and secondary equipment data; Core equipment data includes the second timestamp, tunnel boring machine ID, tunnel boring machine start / stop status, main drive power, propulsion speed, cutterhead torque, and muck removal system load; Secondary equipment data includes a third timestamp, excavation auxiliary equipment ID, excavation auxiliary equipment operation duration, and excavation auxiliary equipment start and stop times. The excavation auxiliary equipment includes concrete spraying trucks, anchor bolt trolleys, and muck transport trucks. The ventilation equipment dataset includes the ID of each fan, the start / stop status of the fan, the real-time power of the fan, the location coordinates of the fan, and the maximum power of the fan.

[0022] In this embodiment, the monitoring, analysis and prediction module includes a preprocessing unit, a real-time status unit and a status prediction unit; The preprocessing unit integrates the initial air environment parameter sequence with the engineering equipment dataset to obtain a dynamic dataset for tunnel construction. Its processing logic includes: The initial air environment parameter sequence and the engineering equipment dataset are time-stamped to obtain preliminary integrated data. Outlier removal is performed on the preliminary integrated data, and adjacent value interpolation is performed on the missing values ​​based on the time series. Noise removal is completed by median filtering to obtain the dynamic dataset of tunnel construction.

[0023] In this embodiment, the real-time state unit is used to process the dynamic dataset of tunnel construction to obtain the current target gas content curve and the dispersion coefficient of each target gas component along the tunnel axis under the current working condition. The processing logic includes: Anisotropic Kriging interpolation algorithm is used to fit the discrete integrated environmental parameters in the dynamic data of tunnel construction, and real-time target gas content curves are generated for each target gas component in the integrated environmental parameters along the tunnel axis. The set of nodal rates of change is obtained by differentiating the current target gas content curve at each target sampling point. Based on the Taylor-Aris model and the one-dimensional flow dispersion equation, the dispersion coefficients of each target gas component along the tunnel axis under the current working conditions are calculated using the current target gas content curve and the three-dimensional airflow vector data in the tunnel construction dynamic dataset. The target gas components include oxygen, hydrogen sulfide, carbon monoxide, and sulfur dioxide; The tunnel axis direction is from the single-ended tunnel excavation face to the single-ended tunnel exit direction.

[0024] In this embodiment, the state prediction unit is used to obtain a target gas content prediction sequence based on the current target gas content curve. The processing logic includes: Using the current target gas content curve as the initial condition of the one-dimensional convection-diffusion model, the component of the three-dimensional airflow vector data along the tunnel axis in the tunnel construction dynamic dataset is extracted as the convection velocity of the one-dimensional convection-diffusion model. The longitudinal dispersion coefficient is used as the dispersion coefficient of the one-dimensional convection-diffusion model. The concentration distribution curves of each target gas component along the tunnel axis in the next T minutes are calculated through the one-dimensional convection-diffusion model, and the target gas content prediction sequence is obtained. In the target gas content prediction sequence, the vertical axis of each curve represents the content of the corresponding target gas component, and the horizontal axis represents the distance from the tunnel entrance along the tunnel axis.

[0025] In this embodiment, the ventilation strategy unit judges the target gas content prediction sequence according to the preset rule engine and outputs a ventilation monitoring signal. The processing logic of the preset rule engine includes: When the oxygen prediction value of K consecutive points in the target gas content prediction sequence is less than the preset oxygen content warning line, the ventilation monitoring signal output is a ventilation warning signal; otherwise, the ventilation monitoring signal output is a no-abnormal signal. When the predicted hydrogen sulfide value of K consecutive points in the target gas content prediction sequence is greater than the preset oxygen content warning line, the ventilation monitoring signal output is a ventilation warning signal; otherwise, the ventilation monitoring signal output is a no-abnormal signal. When the predicted carbon monoxide value at K consecutive points in the target gas content prediction sequence is greater than the preset oxygen content warning line, the ventilation monitoring signal output is a ventilation warning signal; otherwise, the ventilation monitoring signal output is a no-abnormal signal. When the predicted sulfur dioxide value of K consecutive points in the target gas content prediction sequence is greater than the preset oxygen content warning line, the ventilation monitoring signal output is a ventilation warning signal; otherwise, the ventilation monitoring signal output is a no-abnormal signal. The ventilation warning signal also includes abnormal point data values, which include the x-coordinate and y-coordinate values ​​of each point in the target gas content prediction sequence that exceeds the corresponding content warning line.

[0026] In this embodiment, the ventilation strategy unit is used to generate fan combination control data based on ventilation monitoring signals, and the processing logic includes: The abnormal air volume is calculated based on the abnormal point data values ​​in the ventilation early warning signal. The cross-sectional volume is calculated based on the horizontal coordinate coefficient in the abnormal point data value and the three-dimensional model of the construction tunnel. The abnormal air volume is then multiplied by the redundancy amplification factor of 105% to obtain the abnormal air volume. Based on the abscissa corresponding to the abnormal air volume and abnormal point data values, the fan combination control data is calculated and screened through simulated annealing algorithm with the objective function of minimizing the response time of the ventilation warning signal and minimizing the expected energy consumption. The combined wind turbine control data includes the ID of the wind turbine to be started, the location coordinates of the wind turbine, and the target power of the wind turbine.

[0027] In this embodiment, the ventilation execution unit is used to control the operation and adjustment of the fan equipment according to the fan combination control data, and generate ventilation result feedback; Based on the OPCUA / Modbus industrial communication protocol, the combined control data of the wind turbine is encoded to generate a set of control instructions, which are then sent to the corresponding wind turbine equipment for execution via the tunnel local area network to obtain the record of the executed control operations. The tunnel construction dynamic dataset within a 3-minute time window after the execution of the statistical fan combination control data is used to obtain the adjustment feedback dataset. Based on the adjustment feedback dataset and the target gas content prediction sequence, the gas compliance time is calculated and a record of ventilation result feedback is generated. The ventilation results feedback includes those that did not meet expectations and those that met expectations; When the gas compliance time exceeds the preset monitoring response time, the ventilation result feedback indicates that the expected result has not been achieved, and a ventilation warning is issued, indicating that there is an insufficient number of ventilation fans in the tunnel construction. When the time for the gas to reach the standard is less than or equal to the preset monitoring response time, the ventilation result is reported as meeting expectations; The gas compliance time is the length of time from when the ventilation actuator executes the control data of the fan combination until the abnormal point data value is an empty set.

[0028] In this embodiment, the terminal interaction module includes a display interaction unit and a data storage unit; The display interaction unit is used to provide a display interface and receive user operation instructions, including instructions from the user to set the data acquisition module, monitoring analysis and prediction module, and ventilation control module. The data storage unit stores all the output data from the data acquisition module, monitoring analysis and prediction module, and ventilation control module, and generates historical logs for tunnel construction ventilation monitoring.

[0029] Those skilled in the art will understand that embodiments of the present invention can provide methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media containing computer-usable program code. The storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Red-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0030] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A real-time monitoring system for ventilation during the construction of long-distance single-ended tunnels, characterized in that, include: Data acquisition module, monitoring, analysis and prediction module, ventilation control module and terminal interaction module; The data acquisition module is used to combine with the BIM construction system to complete the layout of the environmental acquisition sensor array and sample the initial air environment parameter sequence, engineering equipment dataset and ventilation equipment dataset; The monitoring, analysis and prediction module is used to process the initial air environment parameter sequence to obtain the tunnel construction dynamic dataset, the current target gas content curve and the dispersion coefficient of each target gas component along the tunnel axis, and to process it to obtain the target gas content prediction sequence. The ventilation control module is used to judge the predicted sequence of target gas content, output ventilation monitoring signals, generate fan combination control data, and complete the operation adjustment of fan equipment. The terminal interaction unit is used to provide a display interface and receive user operation commands, as well as to receive and store all output data from the data acquisition module, monitoring analysis and prediction module, and ventilation control module.

2. The real-time monitoring system for ventilation during construction of a long-distance single-ended tunnel as described in claim 1, characterized in that: The data acquisition module includes a sensor deployment unit, an environmental data unit, and a device data unit; The sensor deployment unit is used to integrate with the BIM construction system to arrange the environmental data acquisition sensor array. The processing logic includes: Based on the BIM construction system, a spatially gridded 3D model of the construction tunnel is obtained. Based on the real-time construction progress, the 3D model of the construction tunnel is divided into key node regions. In each key node region, a vertical double layer is used to determine the target acquisition points. Based on the target acquisition points, the arrangement of the environmental acquisition sensor array is completed. The set of key node regions includes mobile regions and fixed regions; The fixed area includes cross sections sequentially arranged at preset first intervals along the target single-ended tunnel; The mobile area includes the area from the tunneling face, the main tunneling equipment area, the material transfer point area, and the personnel rest area. The vertical double-layer structure includes a lower-level personnel breathing zone and an upper-level gas enrichment zone. The target sampling point corresponding to the lower-level personnel breathing zone is set at a height of 1.5m, and the target sampling point corresponding to the upper-level gas enrichment zone is set at a height of 3.0m. The environmental data acquisition sensor array includes an oxygen sensor, a hydrogen sulfide sensor, a carbon monoxide sensor, a sulfur dioxide sensor, a temperature sensor, a humidity sensor, an ultrasonic anemometer, and a barometer. The environmental data unit is used to continuously sample comprehensive environmental parameters through the environmental acquisition sensor array deployed at each target acquisition point at a preset acquisition time period to obtain an initial air environmental parameter sequence. The comprehensive environmental parameters include the three-dimensional coordinates of the target acquisition point, first timestamp data, oxygen data, hydrogen sulfide data, carbon monoxide data, sulfur dioxide data, temperature data, humidity data, three-dimensional airflow vector data, and air pressure data.

3. The real-time monitoring system for ventilation during construction of a long-distance single-ended tunnel as described in claim 2, characterized in that: The equipment data unit is used to collect data in real time to obtain engineering equipment datasets and ventilation equipment datasets; Based on the standardized engineering equipment operation status analysis interface of each device and the tunnel local area network, the engineering equipment dataset and ventilation equipment data are collected in real time through the OPCUA / Modbus industrial communication protocol. The engineering equipment dataset includes core equipment data and secondary equipment data; Core equipment data includes the second timestamp, tunnel boring machine ID, tunnel boring machine start / stop status, main drive power, propulsion speed, cutterhead torque, and muck removal system load; Secondary equipment data includes a third timestamp, excavation auxiliary equipment ID, excavation auxiliary equipment operation duration, and excavation auxiliary equipment start and stop times. The excavation auxiliary equipment includes concrete spraying trucks, anchor bolt trolleys, and muck transport trucks. The ventilation equipment dataset includes the ID of each fan, the start / stop status of the fan, the real-time power of the fan, the location coordinates of the fan, and the maximum power of the fan.

4. The real-time monitoring system for ventilation during construction of a long-distance single-ended tunnel as described in claim 1, characterized in that: The monitoring, analysis, and prediction module includes a preprocessing unit, a real-time status unit, and a status prediction unit. The preprocessing unit integrates the initial air environment parameter sequence with the engineering equipment dataset to obtain a dynamic dataset for tunnel construction. Its processing logic includes: The initial air environment parameter sequence and the engineering equipment dataset are time-stamped to obtain preliminary integrated data. Outlier removal is performed on the preliminary integrated data, and adjacent value interpolation is performed on the missing values ​​based on the time series. Noise removal is completed by median filtering to obtain the dynamic dataset of tunnel construction.

5. The real-time monitoring system for ventilation during construction of a long-distance single-ended tunnel as described in claim 4, characterized in that: The real-time state unit is used to process the dynamic dataset of tunnel construction to obtain the current target gas content curve and the dispersion coefficient of each target gas component along the tunnel axis under the current working condition. The processing logic includes: Anisotropic Kriging interpolation algorithm is used to fit the discrete integrated environmental parameters in the dynamic data of tunnel construction, and real-time target gas content curves are generated for each target gas component in the integrated environmental parameters along the tunnel axis. The set of nodal rates of change is obtained by differentiating the current target gas content curve at each target sampling point. Based on the Taylor-Aris model and the one-dimensional flow dispersion equation, the dispersion coefficients of each target gas component along the tunnel axis under the current working conditions are calculated using the current target gas content curve and the three-dimensional airflow vector data in the tunnel construction dynamic dataset. The target gas components include oxygen, hydrogen sulfide, carbon monoxide, and sulfur dioxide; The tunnel axis direction is from the single-ended tunnel excavation face to the single-ended tunnel exit direction.

6. The real-time monitoring system for ventilation during construction of a long-distance single-ended tunnel as described in claim 5, characterized in that: The state prediction unit is used to obtain the target gas content prediction sequence based on the current target gas content curve. The processing logic includes: Using the current target gas content curve as the initial condition of the one-dimensional convection-diffusion model, the component of the three-dimensional airflow vector data along the tunnel axis in the tunnel construction dynamic dataset is extracted as the convection velocity of the one-dimensional convection-diffusion model. The longitudinal dispersion coefficient is used as the dispersion coefficient of the one-dimensional convection-diffusion model. The concentration distribution curves of each target gas component along the tunnel axis in the next T minutes are calculated through the one-dimensional convection-diffusion model, and the target gas content prediction sequence is obtained. In the target gas content prediction sequence, the vertical axis of each curve represents the content of the corresponding target gas component, and the horizontal axis represents the distance from the tunnel entrance along the tunnel axis.

7. The real-time monitoring system for ventilation during construction of a long-distance single-ended tunnel as described in claim 1, characterized in that: The ventilation control module includes a ventilation strategy unit and a ventilation execution unit; The ventilation strategy unit judges the target gas content prediction sequence according to the preset rule engine and outputs a ventilation monitoring signal. The processing logic of the preset rule engine includes: When the oxygen prediction value of K consecutive points in the target gas content prediction sequence is less than the preset oxygen content warning line, the ventilation monitoring signal output is a ventilation warning signal; otherwise, the ventilation monitoring signal output is a no-abnormal signal. When the predicted hydrogen sulfide value of K consecutive points in the target gas content prediction sequence is greater than the preset oxygen content warning line, the ventilation monitoring signal output is a ventilation warning signal; otherwise, the ventilation monitoring signal output is a no-abnormal signal. When the predicted carbon monoxide value at K consecutive points in the target gas content prediction sequence is greater than the preset oxygen content warning line, the ventilation monitoring signal output is a ventilation warning signal; otherwise, the ventilation monitoring signal output is a no-abnormal signal. When the predicted sulfur dioxide value of K consecutive points in the target gas content prediction sequence is greater than the preset oxygen content warning line, the ventilation monitoring signal output is a ventilation warning signal; otherwise, the ventilation monitoring signal output is a no-abnormal signal. The ventilation warning signal also includes abnormal point data values, which include the x-coordinate and y-coordinate values ​​of each point in the target gas content prediction sequence that exceeds the corresponding content warning line.

8. The real-time monitoring system for ventilation during construction of a long-distance single-ended tunnel as described in claim 7, characterized in that: The ventilation strategy unit is used to generate fan combination control data based on ventilation monitoring signals. The processing logic includes: The abnormal air volume is calculated based on the abnormal point data values ​​in the ventilation early warning signal. The cross-sectional volume is calculated based on the horizontal coordinate coefficient in the abnormal point data value and the three-dimensional model of the construction tunnel. The abnormal air volume is then multiplied by the redundancy amplification factor of 105% to obtain the abnormal air volume. Based on the abscissa corresponding to the abnormal air volume and abnormal point data values, the fan combination control data is calculated and screened through simulated annealing algorithm with the objective function of minimizing the response time of the ventilation warning signal and minimizing the expected energy consumption. The combined wind turbine control data includes the ID of the wind turbine to be started, the location coordinates of the wind turbine, and the target power of the wind turbine.

9. The real-time monitoring system for ventilation during construction of a long-distance single-ended tunnel as described in claim 1, characterized in that: The ventilation actuator is used to control the operation and adjustment of the fan equipment according to the fan combination control data and generate ventilation result feedback; Based on the OPCUA / Modbus industrial communication protocol, the combined control data of the wind turbine is encoded to generate a set of control instructions, which are then sent to the corresponding wind turbine equipment for execution via the tunnel local area network to obtain the record of the executed control operations. The tunnel construction dynamic dataset within a 3-minute time window after the execution of the statistical fan combination control data is used to obtain the adjustment feedback dataset. Based on the adjustment feedback dataset and the target gas content prediction sequence, the gas compliance time is calculated and a record of ventilation result feedback is generated. The ventilation results feedback includes those that did not meet expectations and those that met expectations; When the gas compliance time exceeds the preset monitoring response time, the ventilation result feedback indicates that the expected result has not been achieved, and a ventilation warning is issued, indicating that there is an insufficient number of ventilation fans in the tunnel construction. When the time for the gas to reach the standard is less than or equal to the preset monitoring response time, the ventilation result is reported as meeting expectations; The gas compliance time is the length of time from when the ventilation actuator executes the control data of the fan combination until the abnormal point data value is an empty set.

10. The real-time monitoring system for ventilation during construction of a long-distance single-ended tunnel as described in claim 1, characterized in that: The terminal interaction module includes a display interaction unit and a data storage unit; The display interaction unit is used to provide a display interface and receive user operation instructions, including instructions from the user to set the data acquisition module, monitoring analysis and prediction module, and ventilation control module. The data storage unit stores all the output data from the data acquisition module, monitoring analysis and prediction module, and ventilation control module, and generates historical logs for tunnel construction ventilation monitoring.

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