Highway tunnel equipment intelligent control method, system and readable storage medium

Abstract

The application discloses a kind of highway tunnel equipment intelligent control method, system and readable storage medium, wherein method includes: target data is collected, wherein the target data at least includes first detection data and second detection data;Based on the first adjustment signal adjustment tunnel in the working content of working equipment, and based on the second detection data detected again input into lighting ventilation network model to obtain second adjustment signal, wherein second detection data at least includes pollutant concentration and actual wind direction wind power;Based on the second adjustment signal, the working content of working equipment is corrected.The application is processed by collecting various data, and the specific equipment in tunnel is intelligently monitored and managed, can realize all-weather real-time monitoring of tunnel air quality and intelligent control of lighting ventilation, while realizing remote monitoring and management of highway tunnel air quality and tunnel equipment unattended, with strong practical value.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and more specifically, to an intelligent control method, system, and readable storage medium for highway tunnel equipment. Background Technology

[0002] In actual operation, highway tunnels are relatively closed road sections. The harmful exhaust gases such as carbon monoxide (CO) and nitrogen oxides, as well as dust particles emitted by vehicles driving in tunnels, can affect the normal driving behavior of drivers and seriously threaten tunnel traffic safety. Therefore, in order to ensure air quality and traffic safety in tunnels, many construction units are paying more and more attention to ventilation control of highway tunnels while ensuring the quality of tunnel construction projects.

[0003] Real-time monitoring of the tunnel's internal operating environment and the implementation of appropriate ventilation measures to effectively dilute harmful gases and dust particles are essential for the safe operation of tunnels. Highway tunnel ventilation methods are divided into two types: natural ventilation and mechanical ventilation.

[0004] Natural ventilation utilizes wind speeds generated by natural conditions to exchange air within the tunnel, requiring no manual operation and is relatively simple. Mechanical ventilation, however, is more complex. In the late 20th century, operators in long tunnels relied heavily on experience, manually adjusting fan operation strategies. In recent years, tunnel designers have improved automation by implementing remote control for fan operation, but tunnel ventilation systems still face significant energy consumption issues. Statistics show that ventilation and lighting systems account for 90% of operational energy consumption, necessitating change. Summary of the Invention

[0005] The purpose of this invention is to provide an intelligent control method, system, and readable storage medium for highway tunnel equipment. By collecting and processing various data, it enables intelligent monitoring and management of specific equipment within the tunnel. It can achieve real-time monitoring of tunnel air quality around the clock and intelligent control of lighting. At the same time, it realizes unattended remote monitoring and management of highway tunnel air quality and tunnel equipment, which has strong practical value.

[0006] The first aspect of this invention provides an intelligent control method for highway tunnel equipment, comprising the following steps:

[0007] Collect target data, wherein the target data includes at least first detection data and second detection data;

[0008] The first detection data is input into the lighting and ventilation network model to obtain the first adjustment signal, wherein the first detection data includes at least the traffic flow, vehicle speed and light intensity outside the tunnel;

[0009] The operation of the working equipment in the tunnel is adjusted based on the first adjustment signal, and the second detection data is input again into the lighting and ventilation network model to obtain the second adjustment signal. The second detection data includes at least the pollutant concentration and the actual wind direction and force.

[0010] The working content of the working equipment is modified based on the second adjustment signal, wherein the modified content includes the lighting brightness and ventilation intensity inside the tunnel.

[0011] In this solution, the target data to be collected includes at least first detection data and second detection data, specifically including:

[0012] Establish a communication connection with the sensor group set at the corresponding location in the highway tunnel to obtain sensor data;

[0013] Based on the sensor data, the system identifies traffic flow and speed inside the tunnel, as well as light intensity and pollutant concentration outside the tunnel, and actual wind direction and force.

[0014] The first detection data is obtained based on the traffic flow and speed inside the tunnel and the light intensity outside the tunnel. The second detection data is obtained based on the pollutant concentration and the actual wind direction and force. The detection time of the second detection data is after the detection time of the first detection data.

[0015] In this solution, sensing data is acquired based on a sensor array, specifically including:

[0016] The system obtains the traffic flow inside the tunnel based on a traffic flow detection sensor, the vehicle speed based on a vehicle speed detection sensor, and the light intensity outside the tunnel based on a light intensity detection sensor.

[0017] The pollutant concentration is obtained based on a pollutant concentration detection sensor, and the actual wind direction and wind force are obtained based on a wind direction and wind force detection sensor.

[0018] In this solution, the step of inputting the first detection data into the lighting and ventilation network model to obtain the first adjustment signal specifically includes:

[0019] The first detection data is input into the lighting and ventilation network model to obtain the lighting signal fuzzy vector;

[0020] Based on the fuzzy vector of the lighting signal, the device number vector is extracted to match the lighting control signal corresponding to each lighting device, wherein the lighting control signal includes at least lighting brightness and lighting direction;

[0021] The first adjustment signal is obtained by generating a lighting control sequence based on the lighting control signals of all lighting devices and the corresponding controlled lighting devices.

[0022] In this scheme, adjusting the working content of the equipment in the tunnel based on the first adjustment signal, and then inputting the detected second detection data back into the lighting and ventilation network model to obtain the second adjustment signal, specifically includes:

[0023] The brightness of the corresponding lighting device is adjusted based on the first adjustment signal, wherein...

[0024] The first detection data detected in the second detection is obtained and input into the lighting and ventilation network model to obtain the lighting signal correction fuzzy vector; the device number vector is extracted based on the lighting signal correction fuzzy vector to match the lighting correction signal corresponding to each lighting device, wherein the lighting correction signal includes at least the lighting brightness correction amount and the lighting direction correction amount; the lighting correction sequence is generated based on the lighting correction signals of all lighting devices and the corresponding controlled lighting devices.

[0025] Based on the second detection data, the corresponding ventilation equipment is controlled to adjust the wind speed and direction, wherein...

[0026] The detected second detection data is input into the lighting and ventilation network model to obtain a ventilation signal fuzzy vector; based on the ventilation signal fuzzy vector, the device number vector is extracted to match the ventilation control signal corresponding to each ventilation device, wherein the ventilation control signal includes at least ventilation force and ventilation direction; based on the ventilation control signals of all ventilation devices and the corresponding controlled ventilation devices, a ventilation control sequence is generated.

[0027] The second adjustment signal is obtained based on the lighting correction sequence and the ventilation control sequence.

[0028] In this scheme, the step of modifying the working content of the working equipment based on the second adjustment signal specifically includes: controlling the wind force and direction by identifying the ventilation control signal corresponding to each ventilation device based on the second adjustment signal, and controlling the lighting by identifying the lighting correction signal corresponding to each lighting device based on the second adjustment signal. The working equipment includes lighting devices and ventilation devices.

[0029] A second aspect of the present invention also provides an intelligent control system for highway tunnel equipment, including a memory and a processor. The memory includes a program for an intelligent control method for highway tunnel equipment. When the program for intelligent control of highway tunnel equipment is executed by the processor, it performs the following steps:

[0030] Collect target data, wherein the target data includes at least first detection data and second detection data;

[0031] The first detection data is input into the lighting and ventilation network model to obtain the first adjustment signal, wherein the first detection data includes at least the traffic flow, vehicle speed and light intensity outside the tunnel;

[0032] The operation of the working equipment in the tunnel is adjusted based on the first adjustment signal, and the second detection data is input again into the lighting and ventilation network model to obtain the second adjustment signal. The second detection data includes at least the pollutant concentration and the actual wind direction and force.

[0033] The working content of the working equipment is modified based on the second adjustment signal, wherein the modified content includes the lighting brightness and ventilation intensity inside the tunnel.

[0034] In this solution, the target data to be collected includes at least first detection data and second detection data, specifically including:

[0035] Establish a communication connection with the sensor group set at the corresponding location in the highway tunnel to obtain sensor data;

[0036] Based on the sensor data, the system identifies traffic flow and speed inside the tunnel, as well as light intensity and pollutant concentration outside the tunnel, and actual wind direction and force.

[0037] The first detection data is obtained based on the traffic flow and speed inside the tunnel and the light intensity outside the tunnel. The second detection data is obtained based on the pollutant concentration and the actual wind direction and force. The detection time of the second detection data is after the detection time of the first detection data.

[0038] In this solution, sensing data is acquired based on a sensor array, specifically including:

[0039] The system obtains the traffic flow inside the tunnel based on a traffic flow detection sensor, the vehicle speed based on a vehicle speed detection sensor, and the light intensity outside the tunnel based on a light intensity detection sensor.

[0040] The pollutant concentration is obtained based on a pollutant concentration detection sensor, and the actual wind direction and wind force are obtained based on a wind direction and wind force detection sensor.

[0041] In this solution, the step of inputting the first detection data into the lighting and ventilation network model to obtain the first adjustment signal specifically includes:

[0042] The first detection data is input into the lighting and ventilation network model to obtain the lighting signal fuzzy vector;

[0043] Based on the fuzzy vector of the lighting signal, the device number vector is extracted to match the lighting control signal corresponding to each lighting device, wherein the lighting control signal includes at least lighting brightness and lighting direction;

[0044] The first adjustment signal is obtained by generating a lighting control sequence based on the lighting control signals of all lighting devices and the corresponding controlled lighting devices.

[0045] In this scheme, adjusting the working content of the equipment in the tunnel based on the first adjustment signal, and then inputting the detected second detection data back into the lighting and ventilation network model to obtain the second adjustment signal, specifically includes:

[0046] The brightness of the corresponding lighting device is adjusted based on the first adjustment signal, wherein...

[0047] The first detection data detected in the second detection is obtained and input into the lighting and ventilation network model to obtain the lighting signal correction fuzzy vector; the device number vector is extracted based on the lighting signal correction fuzzy vector to match the lighting correction signal corresponding to each lighting device, wherein the lighting correction signal includes at least the lighting brightness correction amount and the lighting direction correction amount; the lighting correction sequence is generated based on the lighting correction signals of all lighting devices and the corresponding controlled lighting devices.

[0048] Based on the second detection data, the corresponding ventilation equipment is controlled to adjust the wind speed and direction, wherein...

[0049] The detected second detection data is input into the lighting and ventilation network model to obtain a ventilation signal fuzzy vector; based on the ventilation signal fuzzy vector, the device number vector is extracted to match the ventilation control signal corresponding to each ventilation device, wherein the ventilation control signal includes at least ventilation force and ventilation direction; based on the ventilation control signals of all ventilation devices and the corresponding controlled ventilation devices, a ventilation control sequence is generated.

[0050] The second adjustment signal is obtained based on the lighting correction sequence and the ventilation control sequence.

[0051] In this scheme, the step of modifying the working content of the working equipment based on the second adjustment signal specifically includes: controlling the wind force and direction by identifying the ventilation control signal corresponding to each ventilation device based on the second adjustment signal, and controlling the lighting by identifying the lighting correction signal corresponding to each lighting device based on the second adjustment signal. The working equipment includes lighting devices and ventilation devices.

[0052] A third aspect of the present invention provides a computer-readable storage medium comprising a program for an intelligent control method of highway tunnel equipment, wherein when executed by a processor, the program implements the steps of an intelligent control method of highway tunnel equipment as described in any of the preceding claims.

[0053] This invention discloses an intelligent control method, system, and readable storage medium for highway tunnel equipment. By collecting and processing various data, it enables intelligent monitoring and management of specific equipment within the tunnel. It can achieve real-time monitoring of tunnel air quality around the clock and intelligent control of lighting and ventilation. At the same time, it realizes unattended remote monitoring and management of highway tunnel air quality and tunnel equipment, and has strong practical value. Attached Figure Description

[0054] Figure 1 A flowchart of an intelligent control method for highway tunnel equipment according to the present invention is shown;

[0055] Figure 2 A block diagram of an intelligent control system for highway tunnel equipment according to the present invention is shown. Detailed Implementation

[0056] To better understand the above-mentioned objectives, features, and advantages of the present invention, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be noted that, unless otherwise specified, the embodiments and features described in these embodiments can be combined with each other.

[0057] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and therefore the scope of protection of the invention is not limited to the specific embodiments disclosed below.

[0058] Highway tunnels are an important component of modern transportation infrastructure, playing a crucial role in ensuring driving safety and traffic efficiency. Although some tunnels incorporate external environmental factors into their control strategies, these strategies still tend to be coarse-grained, failing to consider multiple factors within the tunnel simultaneously and thus exhibiting certain limitations.

[0059] Ventilation strategies should not only ensure the safe operation of highway tunnels but also achieve energy efficiency in controlling the operation of the controlled objects. Current highway tunnel ventilation control system designs suffer from two main problems. Firstly, there is the issue of the ability to detect pollutants within the tunnel. Currently, tunnel ventilation is calculated based on baseline emissions from vehicles. However, with the development of my country's automotive industry, these baseline emissions will change, affecting the ability to detect pollutants in the tunnel. Secondly, there is the impact of natural and traffic winds on ventilation effectiveness. Pollutant concentrations, traffic flow characteristics, and the scale of electromechanical equipment within the tunnel all influence ventilation performance, and these factors are highly coupled. Currently, there are numerous highway tunnel monitoring projects in China, and the various monitoring data (i.e., CO, NO2, visibility (VI), and wind speed) are independent and difficult to match. Environmental information processing relies mainly on manual analysis, resulting in poor real-time performance and severely impacting the accuracy of ventilation control. Therefore, this application proposes an intelligent control method, system, and readable storage medium for highway tunnel equipment.

[0060] Specifically, Figure 1 A flowchart of an intelligent control method for highway tunnel equipment according to this application is shown.

[0061] like Figure 1 As shown, this application discloses an intelligent control method for highway tunnel equipment, including the following steps:

[0062] S102, Collect target data, wherein the target data includes at least first detection data and second detection data;

[0063] S104, the first detection data is input into the lighting and ventilation network model to obtain the first adjustment signal, wherein the first detection data includes at least the traffic flow, vehicle speed and light intensity outside the tunnel;

[0064] S106, adjust the working content of the working equipment in the tunnel based on the first adjustment signal, and input the detected second detection data into the lighting and ventilation network model again to obtain the second adjustment signal, wherein the second detection data includes at least the pollutant concentration and the actual wind direction and wind force;

[0065] S108, the working content of the working equipment is modified based on the second adjustment signal, wherein the modified content includes the lighting brightness and ventilation intensity inside the tunnel.

[0066] It should be noted that, in this embodiment, when performing intelligent control of the tunnel, it is necessary to process the actual detection data to enable targeted intelligent control based on real-time data. This achieves all-weather real-time monitoring of tunnel air quality and intelligent control of lighting and ventilation. Specifically, intelligent lighting is implemented first to ensure basic vehicle passage within the tunnel. Firstly, initial detection data for lighting is collected, and then a first adjustment signal is generated to intelligently control the lighting equipment. Then, while ensuring basic vehicle passage, the ventilation status within the tunnel is readjusted to achieve all-weather real-time monitoring of tunnel air quality and intelligent control of lighting, thereby optimizing and maximizing the energy-saving effects of ventilation and lighting. Specifically, target data is collected... In this process, the target data includes at least first detection data and second detection data; the first detection data is input into the lighting and ventilation network model to obtain a first adjustment signal, wherein the first detection data includes at least the traffic flow, vehicle speed, and light intensity outside the tunnel; then, the working content of the working equipment inside the tunnel is adjusted based on the first adjustment signal, and the second detection data is input again into the lighting and ventilation network model to obtain a second adjustment signal, wherein the second detection data includes at least the pollutant concentration and the actual wind direction and force; finally, the working content of the working equipment is corrected based on the second adjustment signal, wherein the correction includes the lighting brightness and ventilation intensity inside the tunnel.

[0067] According to an embodiment of the present invention, the target data to be collected includes at least first detection data and second detection data, specifically including:

[0068] Establish a communication connection with the sensor group set at the corresponding location in the highway tunnel to obtain sensor data;

[0069] Based on the sensor data, the system identifies traffic flow and speed inside the tunnel, as well as light intensity and pollutant concentration outside the tunnel, and actual wind direction and force.

[0070] The first detection data is obtained based on the traffic flow and speed inside the tunnel and the light intensity outside the tunnel. The second detection data is obtained based on the pollutant concentration and the actual wind direction and force. The detection time of the second detection data is after the detection time of the first detection data.

[0071] It should be noted that, in this embodiment, the data is collected specifically through sensors. Therefore, a communication connection must first be established with the sensor group located at the corresponding position in the highway tunnel to obtain sensor data. Since the sensor group includes different types of sensors, the collected data also differs. Therefore, it is necessary to identify the traffic flow, vehicle speed, light intensity outside the tunnel, pollutant concentration, and actual wind direction and force based on the sensor data. Specifically, the first detection data is obtained based on the traffic flow, vehicle speed, and light intensity outside the tunnel, and the second detection data is obtained based on the pollutant concentration and actual wind direction and force. It is worth mentioning that the detection time of the second detection data is after the detection time of the first detection data, which means that, as described in the above embodiment, the first detection data is detected first. The emphasis is on ensuring basic traffic flow first, and then optimizing and maximizing the energy-saving effect of ventilation and lighting, that is, using the second detection data for ventilation control.

[0072] According to an embodiment of the present invention, acquiring sensing data based on a sensor array specifically includes:

[0073] The system obtains the traffic flow inside the tunnel based on a traffic flow detection sensor, the vehicle speed based on a vehicle speed detection sensor, and the light intensity outside the tunnel based on a light intensity detection sensor.

[0074] The pollutant concentration is obtained based on a pollutant concentration detection sensor, and the actual wind direction and wind force are obtained based on a wind direction and wind force detection sensor.

[0075] It should be noted that, in this embodiment, the method of acquiring sensor data and the specific sensor data acquired are described. Specifically, the traffic flow inside the tunnel is acquired based on a traffic flow detection sensor, the vehicle speed is acquired based on a vehicle speed detection sensor, and the light intensity outside the tunnel is acquired based on a light intensity detection sensor; the pollutant concentration is acquired based on a pollution concentration detection sensor, and the actual wind direction and wind force are acquired based on a wind direction and wind force detection sensor.

[0076] According to an embodiment of the present invention, the step of inputting the first detection data into the lighting and ventilation network model to obtain the first adjustment signal specifically includes:

[0077] The first detection data is input into the lighting and ventilation network model to obtain the lighting signal fuzzy vector;

[0078] Based on the fuzzy vector of the lighting signal, the device number vector is extracted to match the lighting control signal corresponding to each lighting device, wherein the lighting control signal includes at least lighting brightness and lighting direction;

[0079] The first adjustment signal is obtained by generating a lighting control sequence based on the lighting control signals of all lighting devices and the corresponding controlled lighting devices.

[0080] It should be noted that, in this embodiment, the lighting and ventilation network model uses a fuzzy comprehensive algorithm as its core and is built based on a neural network model. The specific construction process is not described in detail here. This embodiment does not improve the algorithm or model, but only uses it to obtain the corresponding fuzzy vector for subsequent processing. Specifically, the first detection data is input into the lighting and ventilation network model to obtain the lighting signal fuzzy vector; the device number vector is extracted based on the lighting signal fuzzy vector to match the lighting control signal corresponding to each lighting device, wherein the lighting control signal includes at least lighting brightness and lighting direction; the lighting control sequence is generated based on the lighting control signals of all lighting devices and the corresponding controlled lighting devices to obtain the first adjustment signal.

[0081] According to an embodiment of the present invention, the step of adjusting the working content of the working equipment in the tunnel based on the first adjustment signal, and then inputting the detected second detection data back into the lighting and ventilation network model to obtain a second adjustment signal, specifically includes:

[0082] The brightness of the corresponding lighting device is adjusted based on the first adjustment signal, wherein...

[0083] The first detection data detected in the second detection is obtained and input into the lighting and ventilation network model to obtain the lighting signal correction fuzzy vector; the device number vector is extracted based on the lighting signal correction fuzzy vector to match the lighting correction signal corresponding to each lighting device, wherein the lighting correction signal includes at least the lighting brightness correction amount and the lighting direction correction amount; the lighting correction sequence is generated based on the lighting correction signals of all lighting devices and the corresponding controlled lighting devices.

[0084] Based on the second detection data, the corresponding ventilation equipment is controlled to adjust the wind speed and direction, wherein...

[0085] The detected second detection data is input into the lighting and ventilation network model to obtain a ventilation signal fuzzy vector; based on the ventilation signal fuzzy vector, the device number vector is extracted to match the ventilation control signal corresponding to each ventilation device, wherein the ventilation control signal includes at least ventilation force and ventilation direction; based on the ventilation control signals of all ventilation devices and the corresponding controlled ventilation devices, a ventilation control sequence is generated.

[0086] The second adjustment signal is obtained based on the lighting correction sequence and the ventilation control sequence.

[0087] It should be noted that, in this embodiment, after adjusting the brightness of the lighting equipment using the first adjustment signal, correction is required as the pollutant concentration in the tunnel changes. Specifically, the first detection data detected a second time is acquired and input into the lighting and ventilation network model to obtain a lighting signal correction fuzzy vector; based on the lighting signal correction fuzzy vector, a device number vector is extracted to match the lighting correction signal corresponding to each lighting device, wherein the lighting correction signal includes at least a lighting brightness correction amount and a lighting direction correction amount; based on the lighting correction signals of all lighting devices and the corresponding controlled lighting devices, a lighting correction sequence is generated, thereby using the lighting correction sequence to perform lighting correction on different lighting devices. Furthermore, this embodiment not only states... The correction process is clarified, and ventilation control is also explained. Specifically, based on the second detection data, the corresponding ventilation equipment is controlled to adjust the wind force and direction. The detected second detection data is input into the lighting and ventilation network model to obtain a ventilation signal fuzzy vector. Based on the ventilation signal fuzzy vector, the equipment number vector is extracted to match the ventilation control signal corresponding to each ventilation equipment. The ventilation control signal includes at least ventilation wind force and ventilation direction. Based on the ventilation control signals of all ventilation equipment and the corresponding controlled ventilation equipment, a ventilation control sequence is generated. Since lighting correction signals and ventilation control signals are involved, the second adjustment signal is obtained based on the lighting correction sequence and the ventilation control sequence.

[0088] According to an embodiment of the present invention, the step of correcting the working content of the working equipment based on the second adjustment signal specifically includes: identifying the ventilation control signal corresponding to each ventilation device based on the second adjustment signal to control the wind force and direction, and identifying the lighting correction signal corresponding to each lighting device based on the second adjustment signal to control the lighting, wherein the working equipment includes lighting devices and ventilation devices.

[0089] It should be noted that, in this embodiment, when using the second adjustment signal to adjust the working equipment (lighting equipment and ventilation equipment), the adjustment needs to be made according to the specific control signal in the second adjustment signal. Specifically, the ventilation control signal in the second adjustment signal is used to control the wind force and direction of each ventilation equipment, and the lighting correction signal in the second adjustment signal is used to control the lighting of each lighting equipment.

[0090] Figure 2 A block diagram of an intelligent control system for highway tunnel equipment according to the present invention is shown.

[0091] like Figure 2As shown, this invention discloses an intelligent control system for highway tunnel equipment, including a memory and a processor. The memory includes a program for an intelligent control method for highway tunnel equipment. When the processor executes the program for the intelligent control method for highway tunnel equipment, it performs the following steps:

[0092] Collect target data, wherein the target data includes at least first detection data and second detection data;

[0093] The first detection data is input into the lighting and ventilation network model to obtain the first adjustment signal, wherein the first detection data includes at least the traffic flow, vehicle speed and light intensity outside the tunnel;

[0094] The operation of the working equipment in the tunnel is adjusted based on the first adjustment signal, and the second detection data is input again into the lighting and ventilation network model to obtain the second adjustment signal. The second detection data includes at least the pollutant concentration and the actual wind direction and force.

[0095] The working content of the working equipment is modified based on the second adjustment signal, wherein the modified content includes the lighting brightness and ventilation intensity inside the tunnel.

[0096] It should be noted that, in this embodiment, when performing intelligent control of the tunnel, it is necessary to process the actual detection data to enable targeted intelligent control based on real-time data. This achieves all-weather real-time monitoring of tunnel air quality and intelligent control of lighting and ventilation. Specifically, intelligent lighting is implemented first to ensure basic vehicle passage within the tunnel. Firstly, initial detection data for lighting is collected, and then a first adjustment signal is generated to intelligently control the lighting equipment. Then, while ensuring basic vehicle passage, the ventilation status within the tunnel is readjusted to achieve all-weather real-time monitoring of tunnel air quality and intelligent control of lighting, thereby optimizing and maximizing the energy-saving effects of ventilation and lighting. Specifically, target data is collected... In this process, the target data includes at least first detection data and second detection data; the first detection data is input into the lighting and ventilation network model to obtain a first adjustment signal, wherein the first detection data includes at least the traffic flow, vehicle speed, and light intensity outside the tunnel; then, the working content of the working equipment inside the tunnel is adjusted based on the first adjustment signal, and the second detection data is input again into the lighting and ventilation network model to obtain a second adjustment signal, wherein the second detection data includes at least the pollutant concentration and the actual wind direction and force; finally, the working content of the working equipment is corrected based on the second adjustment signal, wherein the correction includes the lighting brightness and ventilation intensity inside the tunnel.

[0097] According to an embodiment of the present invention, the target data to be collected includes at least first detection data and second detection data, specifically including:

[0098] Establish a communication connection with the sensor group set at the corresponding location in the highway tunnel to obtain sensor data;

[0099] Based on the sensor data, the system identifies traffic flow and speed inside the tunnel, as well as light intensity and pollutant concentration outside the tunnel, and actual wind direction and force.

[0100] The first detection data is obtained based on the traffic flow and speed inside the tunnel and the light intensity outside the tunnel. The second detection data is obtained based on the pollutant concentration and the actual wind direction and force. The detection time of the second detection data is after the detection time of the first detection data.

[0101] It should be noted that, in this embodiment, the data is collected specifically through sensors. Therefore, a communication connection must first be established with the sensor group located at the corresponding position in the highway tunnel to obtain sensor data. Since the sensor group includes different types of sensors, the collected data also differs. Therefore, it is necessary to identify the traffic flow, vehicle speed, light intensity outside the tunnel, pollutant concentration, and actual wind direction and force based on the sensor data. Specifically, the first detection data is obtained based on the traffic flow, vehicle speed, and light intensity outside the tunnel, and the second detection data is obtained based on the pollutant concentration and actual wind direction and force. It is worth mentioning that the detection time of the second detection data is after the detection time of the first detection data, which means that, as described in the above embodiment, the first detection data is detected first. The emphasis is on ensuring basic traffic flow first, and then optimizing and maximizing the energy-saving effect of ventilation and lighting, that is, using the second detection data for ventilation control.

[0102] According to an embodiment of the present invention, acquiring sensing data based on a sensor array specifically includes:

[0103] The system obtains the traffic flow inside the tunnel based on a traffic flow detection sensor, the vehicle speed based on a vehicle speed detection sensor, and the light intensity outside the tunnel based on a light intensity detection sensor.

[0104] The pollutant concentration is obtained based on a pollutant concentration detection sensor, and the actual wind direction and wind force are obtained based on a wind direction and wind force detection sensor.

[0105] It should be noted that, in this embodiment, the method of acquiring sensor data and the specific sensor data acquired are described. Specifically, the traffic flow inside the tunnel is acquired based on a traffic flow detection sensor, the vehicle speed is acquired based on a vehicle speed detection sensor, and the light intensity outside the tunnel is acquired based on a light intensity detection sensor; the pollutant concentration is acquired based on a pollution concentration detection sensor, and the actual wind direction and wind force are acquired based on a wind direction and wind force detection sensor.

[0106] According to an embodiment of the present invention, the step of inputting the first detection data into the lighting and ventilation network model to obtain the first adjustment signal specifically includes:

[0107] The first detection data is input into the lighting and ventilation network model to obtain the lighting signal fuzzy vector;

[0108] Based on the fuzzy vector of the lighting signal, the device number vector is extracted to match the lighting control signal corresponding to each lighting device, wherein the lighting control signal includes at least lighting brightness and lighting direction;

[0109] The first adjustment signal is obtained by generating a lighting control sequence based on the lighting control signals of all lighting devices and the corresponding controlled lighting devices.

[0110] It should be noted that, in this embodiment, the lighting and ventilation network model uses a fuzzy comprehensive algorithm as its core and is built based on a neural network model. The specific construction process is not described in detail here. This embodiment does not improve the algorithm or model, but only uses it to obtain the corresponding fuzzy vector for subsequent processing. Specifically, the first detection data is input into the lighting and ventilation network model to obtain the lighting signal fuzzy vector; the device number vector is extracted based on the lighting signal fuzzy vector to match the lighting control signal corresponding to each lighting device, wherein the lighting control signal includes at least lighting brightness and lighting direction; the lighting control sequence is generated based on the lighting control signals of all lighting devices and the corresponding controlled lighting devices to obtain the first adjustment signal.

[0111] According to an embodiment of the present invention, the step of adjusting the working content of the working equipment in the tunnel based on the first adjustment signal, and then inputting the detected second detection data back into the lighting and ventilation network model to obtain a second adjustment signal, specifically includes:

[0112] The brightness of the corresponding lighting device is adjusted based on the first adjustment signal, wherein...

[0113] The first detection data detected in the second detection is obtained and input into the lighting and ventilation network model to obtain the lighting signal correction fuzzy vector; the device number vector is extracted based on the lighting signal correction fuzzy vector to match the lighting correction signal corresponding to each lighting device, wherein the lighting correction signal includes at least the lighting brightness correction amount and the lighting direction correction amount; the lighting correction sequence is generated based on the lighting correction signals of all lighting devices and the corresponding controlled lighting devices.

[0114] Based on the second detection data, the corresponding ventilation equipment is controlled to adjust the wind speed and direction, wherein...

[0115] The detected second detection data is input into the lighting and ventilation network model to obtain a ventilation signal fuzzy vector; based on the ventilation signal fuzzy vector, the device number vector is extracted to match the ventilation control signal corresponding to each ventilation device, wherein the ventilation control signal includes at least ventilation force and ventilation direction; based on the ventilation control signals of all ventilation devices and the corresponding controlled ventilation devices, a ventilation control sequence is generated.

[0116] The second adjustment signal is obtained based on the lighting correction sequence and the ventilation control sequence.

[0117] It should be noted that, in this embodiment, after adjusting the brightness of the lighting equipment using the first adjustment signal, correction is required as the pollutant concentration in the tunnel changes. Specifically, the first detection data detected a second time is acquired and input into the lighting and ventilation network model to obtain a lighting signal correction fuzzy vector; based on the lighting signal correction fuzzy vector, a device number vector is extracted to match the lighting correction signal corresponding to each lighting device, wherein the lighting correction signal includes at least a lighting brightness correction amount and a lighting direction correction amount; based on the lighting correction signals of all lighting devices and the corresponding controlled lighting devices, a lighting correction sequence is generated, thereby using the lighting correction sequence to perform lighting correction on different lighting devices. Furthermore, this embodiment not only states... The correction process is clarified, and ventilation control is also explained. Specifically, based on the second detection data, the corresponding ventilation equipment is controlled to adjust the wind force and direction. The detected second detection data is input into the lighting and ventilation network model to obtain a ventilation signal fuzzy vector. Based on the ventilation signal fuzzy vector, the equipment number vector is extracted to match the ventilation control signal corresponding to each ventilation equipment. The ventilation control signal includes at least ventilation wind force and ventilation direction. Based on the ventilation control signals of all ventilation equipment and the corresponding controlled ventilation equipment, a ventilation control sequence is generated. Since lighting correction signals and ventilation control signals are involved, the second adjustment signal is obtained based on the lighting correction sequence and the ventilation control sequence.

[0118] According to an embodiment of the present invention, the step of correcting the working content of the working equipment based on the second adjustment signal specifically includes: identifying the ventilation control signal corresponding to each ventilation device based on the second adjustment signal to control the wind force and direction, and identifying the lighting correction signal corresponding to each lighting device based on the second adjustment signal to control the lighting, wherein the working equipment includes lighting devices and ventilation devices.

[0119] It should be noted that, in this embodiment, when using the second adjustment signal to adjust the working equipment (lighting equipment and ventilation equipment), the adjustment needs to be made according to the specific control signal in the second adjustment signal. Specifically, the ventilation control signal in the second adjustment signal is used to control the wind force and direction of each ventilation equipment, and the lighting correction signal in the second adjustment signal is used to control the lighting of each lighting equipment.

[0120] A third aspect of the present invention provides a computer-readable storage medium comprising a program for an intelligent control method of highway tunnel equipment, wherein when executed by a processor, the program implements the steps of an intelligent control method of highway tunnel equipment as described in any of the preceding claims.

[0121] This invention discloses an intelligent control method, system, and readable storage medium for highway tunnel equipment. By collecting and processing various data, it enables intelligent monitoring and management of specific equipment within the tunnel. It can achieve real-time monitoring of tunnel air quality around the clock and intelligent control of lighting and ventilation. At the same time, it realizes unattended remote monitoring and management of highway tunnel air quality and tunnel equipment, and has strong practical value.

[0122] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods, such as: multiple units or components can be combined, or integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the various components shown or discussed can be through some interfaces, and the indirect coupling or communication connection between devices or units can be electrical, mechanical, or other forms.

[0123] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units. They may be located in one place or distributed across multiple network units. Some or all of the units may be selected to achieve the purpose of this embodiment according to actual needs.

[0124] In addition, in the various embodiments of the present invention, each functional unit can be integrated into one processing unit, or each unit can be a separate unit, or two or more units can be integrated into one unit; the integrated unit can be implemented in hardware or in the form of hardware plus software functional units.

[0125] Those skilled in the art will understand that all or part of the steps of the above method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it performs the steps of the above method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as mobile storage devices, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0126] Alternatively, if the integrated units of this invention are implemented as software functional modules and sold or used as independent products, they can also be stored in a computer-readable storage medium. Based on this understanding, the technical solutions of the embodiments of this invention, or the parts that contribute to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as mobile storage devices, ROM, RAM, magnetic disks, or optical disks.

Claims

1. A method for intelligent control of highway tunnel equipment, characterized in that, Includes the following steps: The process involves collecting target data, which includes at least first detection data and second detection data. Specifically, this includes: establishing a communication connection with a sensor group located at a corresponding position in the highway tunnel to acquire sensor data; identifying traffic flow, vehicle speed, external light intensity, pollutant concentration, and actual wind direction and force based on the sensor data. The first detection data is obtained based on the traffic flow, vehicle speed, and external light intensity, while the second detection data is obtained based on the pollutant concentration and actual wind direction and force. The detection time of the second detection data is after the detection time of the first detection data. The first detection data is input into the lighting and ventilation network model to obtain the first adjustment signal, wherein the first detection data includes at least the traffic flow, vehicle speed and light intensity outside the tunnel; The operation of the working equipment in the tunnel is adjusted based on the first adjustment signal, and the second detection data is input again into the lighting and ventilation network model to obtain the second adjustment signal. The second detection data includes at least the pollutant concentration and the actual wind direction and force. The working content of the working equipment is modified based on the second adjustment signal, wherein the modified content includes the lighting brightness and ventilation intensity inside the tunnel.

2. The intelligent control method for highway tunnel equipment according to claim 1, characterized in that, Sensing data is acquired based on a sensor array, specifically including: The system obtains the traffic flow inside the tunnel based on a traffic flow detection sensor, the vehicle speed based on a vehicle speed detection sensor, and the light intensity outside the tunnel based on a light intensity detection sensor. The pollutant concentration is obtained based on a pollutant concentration detection sensor, and the actual wind direction and wind force are obtained based on a wind direction and wind force detection sensor.

3. The intelligent control method for highway tunnel equipment according to claim 2, characterized in that, The step of inputting the first detection data into the lighting and ventilation network model to obtain the first adjustment signal specifically includes: The first detection data is input into the lighting and ventilation network model to obtain the lighting signal fuzzy vector; Based on the fuzzy vector of the lighting signal, the device number vector is extracted to match the lighting control signal corresponding to each lighting device, wherein the lighting control signal includes at least lighting brightness and lighting direction; The first adjustment signal is obtained by generating a lighting control sequence based on the lighting control signals of all lighting devices and the corresponding controlled lighting devices.

4. The intelligent control method for highway tunnel equipment according to claim 3, characterized in that, The process of adjusting the operation of the working equipment in the tunnel based on the first adjustment signal, and then inputting the detected second detection data back into the lighting and ventilation network model to obtain the second adjustment signal, specifically includes: The brightness of the corresponding lighting device is adjusted based on the first adjustment signal, wherein... The first detection data detected in the second detection is obtained and input into the lighting and ventilation network model to obtain the lighting signal correction fuzzy vector; the device number vector is extracted based on the lighting signal correction fuzzy vector to match the lighting correction signal corresponding to each lighting device, wherein the lighting correction signal includes at least the lighting brightness correction amount and the lighting direction correction amount; the lighting correction sequence is generated based on the lighting correction signals of all lighting devices and the corresponding controlled lighting devices. Based on the second detection data, the corresponding ventilation equipment is controlled to adjust the wind speed and direction, wherein... The detected second detection data is input into the lighting and ventilation network model to obtain a ventilation signal fuzzy vector; based on the ventilation signal fuzzy vector, the device number vector is extracted to match the ventilation control signal corresponding to each ventilation device, wherein the ventilation control signal includes at least ventilation force and ventilation direction; based on the ventilation control signals of all ventilation devices and the corresponding controlled ventilation devices, a ventilation control sequence is generated. The second adjustment signal is obtained based on the lighting correction sequence and the ventilation control sequence.

5. The intelligent control method for highway tunnel equipment according to claim 4, characterized in that, The modification of the working content of the working equipment based on the second adjustment signal specifically includes: controlling the wind force and direction by identifying the ventilation control signal corresponding to each ventilation device based on the second adjustment signal, and controlling the lighting by identifying the lighting correction signal corresponding to each lighting device based on the second adjustment signal, wherein the working equipment includes lighting devices and ventilation devices.

6. An intelligent control system for highway tunnel equipment, characterized in that, The system includes a memory and a processor. The memory contains a program for an intelligent control method for highway tunnel equipment. When the processor executes the program, the intelligent control method for highway tunnel equipment performs the following steps: The process involves collecting target data, which includes at least first detection data and second detection data. Specifically, this includes: establishing a communication connection with a sensor group located at a corresponding position in the highway tunnel to acquire sensor data; identifying traffic flow, vehicle speed, external light intensity, pollutant concentration, and actual wind direction and force based on the sensor data. The first detection data is obtained based on the traffic flow, vehicle speed, and external light intensity, while the second detection data is obtained based on the pollutant concentration and actual wind direction and force. The detection time of the second detection data is after the detection time of the first detection data. The first detection data is input into the lighting and ventilation network model to obtain the first adjustment signal, wherein the first detection data includes at least the traffic flow, vehicle speed and light intensity outside the tunnel; The operation of the working equipment in the tunnel is adjusted based on the first adjustment signal, and the second detection data is input again into the lighting and ventilation network model to obtain the second adjustment signal. The second detection data includes at least the pollutant concentration and the actual wind direction and force. The working content of the working equipment is modified based on the second adjustment signal, wherein the modified content includes the lighting brightness and ventilation intensity inside the tunnel.

7. The intelligent control system for highway tunnel equipment according to claim 6, characterized in that, Sensing data is acquired based on a sensor array, specifically including: The system obtains the traffic flow inside the tunnel based on a traffic flow detection sensor, the vehicle speed based on a vehicle speed detection sensor, and the light intensity outside the tunnel based on a light intensity detection sensor. The pollutant concentration is obtained based on a pollutant concentration detection sensor, and the actual wind direction and wind force are obtained based on a wind direction and wind force detection sensor.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a program for an intelligent control method of highway tunnel equipment. When the program is executed by a processor, it implements the steps of an intelligent control method for highway tunnel equipment as described in any one of claims 1 to 5.

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