Multi-region coordinated long-distance tunnel ventilation control device and method

Abstract

The present application provides a multi-region collaborative long-distance tunnel ventilation control device and method, which relates to the field of tunnel ventilation technology, including: a regional division module reads and analyzes the data information of the tunnel, uses the analysis results to aggregate and divide the composite region, and establishes the regional segmentation results; a distribution module configures independent ventilation devices and sensor networks based on the regional segmentation results, sets preliminary control decisions to operate independent ventilation devices; a dynamic perception module activates the sensor network, establishes dynamic perception data of the tunnel, and establishes regional ventilation requirements based on the dynamic perception data; a collaborative compensation module calls the central control system to establish regional collaborative scheduling decision results; and a control module controls independent ventilation devices to manage tunnel ventilation based on the regional collaborative scheduling decision results. The present application solves the technical problem that traditional tunnel ventilation control cannot respond to environmental changes in different areas of the tunnel in a timely manner, resulting in uneven ventilation effects, and improves tunnel ventilation efficiency and ventilation uniformity.

Description

Technical Field

[0001] The present application relates to the field of tunnel ventilation technology, and in particular to multi-zone coordinated long-distance tunnel ventilation control equipment and methods. Background Art

[0002] In tunnels, as vehicles travel and natural ventilation is limited, pollutants such as exhaust gas and dust are easily accumulated, seriously affecting the environmental safety inside the tunnel. Therefore, how to timely remove pollutants and maintain air circulation through an effective ventilation system has become an important issue in long-distance tunnel management.

[0003] At present, the existing long-distance tunnel ventilation control systems mostly adopt centralized control methods, usually based on the length of the tunnel, traffic flow and meteorological conditions for preset model management. This method usually arranges fixed ventilators and a small number of monitoring equipment in the tunnel, which are uniformly managed by the central control system. However, due to the complex internal structure of the tunnel, the functional characteristics, physical conditions and environmental requirements of different areas are different. A single centralized control is difficult to cope with the changing regional needs, resulting in uneven ventilation effects. Secondly, the existing system responds slowly to environmental changes and relies on static models for ventilation adjustment. It is difficult to perceive and adjust the specific needs of the area in real time, resulting in excessive energy consumption or insufficient local ventilation. Summary of the invention

[0004] The present application provides a multi-zone coordinated long-distance tunnel ventilation control device and method, which solves the technical problem that traditional tunnel ventilation control adopts a single centralized control, lacks dynamic adjustment means, and cannot respond to environmental changes in different areas of the tunnel in time, resulting in uneven ventilation effects. It achieves the technical effect of dynamically responding to the ventilation needs of different areas of a long-distance tunnel and improving the tunnel ventilation efficiency and ventilation uniformity.

[0005] In view of the above problems, on the one hand, the present application provides a multi-region collaborative long-distance tunnel ventilation control device, the device comprising: a region division module, used to read the data information of the tunnel, and analyze the data information, and use the data information analysis results to perform composite regional aggregation division of functional characteristics, physical structure characteristics, and environmental characteristics, and establish a regional segmentation result, and the regional segmentation result carries a ventilation importance mark; a distribution module, used to configure independent ventilation devices and sensor networks based on the regional segmentation results, and the independent ventilation devices and sensor networks are both controlled by local controllers, and the local controllers are communicated with the central control system, and the local controllers are used to set preliminary control decisions for independent ventilation devices and execute independent ventilation device operations; a dynamic perception module, used to activate the sensor network, establish dynamic perception data of the tunnel, and establish regional ventilation requirements based on the dynamic perception data; a collaborative compensation module, used to call the central control system to make regional collaborative scheduling decisions on regional ventilation requirements based on the ventilation importance mark, and establish regional collaborative scheduling decision results; a control module, used to control the independent ventilation devices to perform tunnel ventilation management based on the regional collaborative scheduling decision results.

[0006] On the other hand, the present application also provides a multi-region collaborative long-distance tunnel ventilation control method, the method comprising: reading the data information of the tunnel, and parsing the data information, using the data information analysis results to perform composite regional aggregation division of functional characteristics, physical structure characteristics, and environmental characteristics, and establish regional segmentation results, wherein the regional segmentation results carry a ventilation importance mark; configuring independent ventilation devices and sensor networks based on the regional segmentation results, and the independent ventilation devices and sensor networks are both controlled by local controllers, the local controllers are communicatively connected to the central control system, and the local controllers are used to set preliminary control decisions for the independent ventilation devices and execute the operation of the independent ventilation devices; activating the sensor network, establishing dynamic perception data of the tunnel, and establishing regional ventilation requirements based on the dynamic perception data; calling the central control system to perform regional collaborative scheduling decisions on regional ventilation requirements based on the ventilation importance mark, and establish regional collaborative scheduling decision results; and controlling the independent ventilation devices to perform tunnel ventilation management based on the regional collaborative scheduling decision results.

[0007] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0008] The regional division module is used to read the data information of the tunnel and analyze the data information. The data information analysis results are used to perform composite regional aggregation division of functional characteristics, physical structure characteristics, and environmental characteristics, and establish regional segmentation results. The regional segmentation results are marked with ventilation importance. By refining the regional division and giving the ventilation importance marks of each region, the ventilation needs of different regions can be more effectively identified, the configuration of ventilation resources can be optimized, the ventilation efficiency can be improved, and energy consumption can be reduced. The distribution module is used to configure independent ventilation devices and sensor networks based on the regional segmentation results. The independent ventilation devices and sensor networks are controlled by local controllers. The local controllers are connected to the central control system in communication. The local controllers are used to set the preliminary control decisions of the independent ventilation devices and execute the operation of the independent ventilation devices. Through the independent ventilation devices, the flexibility and adaptability of the entire ventilation equipment are enhanced, so that the ventilation needs of each area can be responded to quickly, the delay is reduced, and the rapid response to local conditions is ensured. The dynamic perception module is used to activate the sensor network, establish the dynamic perception data of the tunnel, and establish the regional ventilation needs based on the dynamic perception data. By acquiring dynamic data in real time, it can make rapid adjustments according to the actual environmental changes in the tunnel, thereby improving the response speed of the ventilation equipment. The collaborative compensation module is used to call the central control system to make regional collaborative scheduling decisions based on the ventilation importance identification of the regional ventilation demand, and establish the regional collaborative scheduling decision results; through the collaborative compensation mechanism, the ventilation strategy can be adjusted according to the importance of each area to ensure that resources can be more reasonably allocated to the priority demand areas, thereby improving the ventilation management effect of the entire tunnel. The control module is used to control the independent ventilation devices for tunnel ventilation management based on the regional collaborative scheduling decision results. Through the cooperation of the central control system and the local controller, the combination of global regulation and local autonomous adjustment is realized, so that the ventilation equipment can effectively respond to the ventilation needs of different areas and improve management efficiency.

[0009] In summary, this application integrates the above modules to build an intelligent and flexible tunnel ventilation control device, which can perform precise zoning management according to the functional characteristics and environmental requirements of different areas, and dynamically adjust the ventilation strategy through real-time data, respond to the ventilation needs of each area in a timely manner, and ensure that the air quality in the tunnel meets the standards. At the same time, the cooperation between the central control system and the local controller realizes the organic combination of global regulation and local autonomous adjustment, effectively optimizes resource allocation, improves overall ventilation efficiency, reduces energy waste, and significantly improves the intelligence level and environmental adaptability of tunnel ventilation management.

[0010] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] Figure 1 A schematic diagram of the structure of a multi-zone coordinated long-distance tunnel ventilation control device provided in an embodiment of the present application;

[0012] Figure 2 A schematic diagram of a process for making linkage decisions for downstream independent ventilation devices using an airflow model and upstream and downstream associations in a multi-zone coordinated long-distance tunnel ventilation control device provided in an embodiment of the present application;

[0013] Figure 3 A schematic diagram of a process for establishing a regional segmentation result in a multi-region collaborative long-distance tunnel ventilation control device provided in an embodiment of the present application;

[0014] Figure 4 A schematic flow chart of a multi-zone coordinated long-distance tunnel ventilation control method provided in an embodiment of the present application.

[0015] Explanation of the reference numerals: area division module 10 , distribution module 20 , dynamic perception module 30 , collaborative compensation module 40 , control module 50 . DETAILED DESCRIPTION

[0016] The embodiment of the present application provides a multi-region collaborative long-distance tunnel ventilation control device and method, which divides the region according to regional characteristics, configures independent ventilation devices and sensor networks for each region; uses local controllers to control the operation of independent ventilation devices, and collects tunnel environmental data in real time through sensor networks, calls the central control system to make regional collaborative scheduling decisions based on ventilation importance, and realizes tunnel ventilation management. It solves the technical problem that traditional tunnel ventilation control uses a single centralized control and lacks dynamic adjustment means, and cannot respond to environmental changes in different areas of the tunnel in a timely manner, resulting in uneven ventilation effects, and achieves the technical effect of dynamically responding to the ventilation needs of different areas in long-distance tunnels and improving tunnel ventilation efficiency and ventilation uniformity.

[0017] Embodiment 1, as Figure 1 As shown, the embodiment of the present application provides a multi-region coordinated long-distance tunnel ventilation control device, the device comprising:

[0018] The area division module 10 is used to read the data information of the tunnel, analyze the data information, and use the data information analysis results to perform composite area aggregation division of functional characteristics, physical structure characteristics, and environmental characteristics to establish area segmentation results, which are marked with ventilation importance.

[0019] Specifically, the area division module 10 obtains various data information in the tunnel from the tunnel management system, including the air quality, temperature, humidity, ventilation conditions, traffic flow, structural information, etc. inside the tunnel. These data are used to analyze the characteristics of different areas in the tunnel.

[0020] Parse these data information to identify the purpose and function of different parts of the tunnel (such as driving lanes, emergency passages), the physical properties of the tunnel (such as the length, width, structural complexity of the tunnel, etc.) and the environmental conditions inside the tunnel (such as temperature, humidity, concentration of air pollutants, etc.), and extract the functional characteristics, physical structure characteristics, and environmental characteristics of the tunnel. Based on the functional characteristics, physical structure characteristics, and environmental characteristics in the analysis results, the tunnel is partitioned through a weighted fusion algorithm, and the tunnel is divided into multiple areas with similar characteristics to obtain regional segmentation results. According to the degree of influence of air flow in each area on the overall air quality of the tunnel, a ventilation importance level, i.e., ventilation importance identification, is assigned to each area in the regional segmentation results. For example, areas with high pollutant concentrations or poor air circulation will be marked as high importance so that ventilation is given priority.

[0021] Through zone division, a basis can be provided for subsequent ventilation management, ensuring that ventilation control can be reasonably divided and scheduled according to the actual needs and importance of each area.

[0022] The distribution module 20 is used to configure the independent ventilation device and the sensor network based on the regional segmentation result, and the independent ventilation device and the sensor network are controlled by a local controller, and the local controller is communicated with the central control system. The local controller is used to set the preliminary control decision of the independent ventilation device and execute the operation of the independent ventilation device.

[0023] Specifically, independent ventilation devices are independently operated ventilation equipment configured for each area, which can be operated separately for different areas. The sensor network is a system composed of multiple sensors, which is used to monitor air quality, temperature and other parameters in different areas of the tunnel in real time. The local controller is a local control device used to control the independent ventilation devices and sensor network. It is connected to the central control system, but can also make some decisions independently. The preliminary control decision is the first round of control plan made by the local controller based on real-time data, which is used to directly manage the ventilation devices in the area.

[0024] In the distribution module 20, an independent ventilation device and a corresponding sensor network are configured for each area in the area segmentation result. The independent ventilation device can operate independently according to the specific needs of the area without relying on the unified control of the entire tunnel. All independent ventilation devices are initially managed by local controllers, which can collect sensor data and make preliminary ventilation control decisions. For example, a certain area of ​​the tunnel requires more ventilation due to dense vehicles, while another area has less traffic and better air. Different ventilation strategies can be implemented for these two areas through distributed independent ventilation devices. These local controllers maintain communication with the central control system so that the central system can make global adjustments when necessary.

[0025] Distributed control of ventilation devices is achieved through independent ventilation devices, sensor networks and local controllers, ensuring that each area can perform preliminary autonomous control based on local needs while maintaining communication with the central system, improving the flexibility and responsiveness of ventilation control in the entire tunnel.

[0026] The dynamic sensing module 30 is used to activate the sensor network, establish dynamic sensing data of the tunnel, and establish regional ventilation requirements based on the dynamic sensing data.

[0027] Specifically, the dynamic perception module 30 activates the sensor network distributed in the tunnel to obtain the environmental change information of each area in real time. The sensor network includes several inlet and outlet flow sensors, several CO2 concentration sensors, several temperature sensors, several humidity sensors, etc., which can measure the traffic flow of the tunnel, CO2 concentration in the air, temperature, humidity and other real-time environmental data to form multiple sets of dynamic perception data. According to the dynamic perception data, the actual demand for ventilation in each area of ​​the tunnel is judged, and the regional ventilation demand is established. For example, if the sensor network detects that the air pollution level in a certain area is high or the temperature is too high, it is judged that the area needs to strengthen ventilation, thereby triggering subsequent ventilation operations.

[0028] By sensing the environmental data of each area in real time, it is ensured that adjustments can be made according to the real-time environmental data during the ventilation control process, thereby improving the real-time performance of ventilation management.

[0029] The collaborative compensation module 40 is used to call the central control system to make a regional collaborative scheduling decision based on the regional ventilation demand marked with the ventilation importance, and establish a regional collaborative scheduling decision result.

[0030] Specifically, the collaborative compensation module 40 makes global ventilation scheduling decisions through the central control system according to the ventilation importance identification of each area and the corresponding ventilation demand, giving priority to areas with higher ventilation importance and allocating more ventilation resources to the area. For example, in a tunnel, if some areas have a lot of pollution due to dense traffic, the central control system will give priority to the operation of ventilation equipment in these areas and reduce the ventilation intensity of other areas with less ventilation demand, so as to achieve optimal use of resources. Through regional collaborative scheduling decisions, the module outputs the results of regional collaborative scheduling decisions, including specific ventilation control strategies and control instructions.

[0031] Through regional collaborative scheduling decisions based on ventilation importance identification, the central system can comprehensively consider the needs and importance of each region, make the best resource allocation and control strategy on a global scale, and achieve overall ventilation efficiency improvement.

[0032] The control module 50 is used to control the independent ventilation devices to perform tunnel ventilation management based on the regional collaborative scheduling decision results.

[0033] Specifically, the control module 50 executes the regional collaborative scheduling decision results of the central control system, directly controls each independent ventilation device, and performs specific ventilation management for each area of ​​the tunnel according to the instructions of the collaborative compensation module 40. For example, if the central control system determines that a certain area needs to increase the ventilation volume, the control module 50 will correspondingly increase the power of the ventilation equipment in the area to increase air circulation. For areas with better air quality, the control module 50 will reduce the ventilation intensity to save energy.

[0034] Furthermore, the collaborative compensation module 40 described in the embodiment of the present application is also used to perform the following steps:

[0035] Call the event parsing model, use the event parsing model to analyze the regional ventilation demand, establish regional events, and use regional events to establish event priorities; obtain the maximum power of independent ventilation devices, perform adaptation evaluation based on the maximum power and regional ventilation demand, and establish adaptation priority based on the adaptation evaluation results; perform compensation priority scheduling analysis based on event priority, adaptation priority, and ventilation importance identification, establish a dynamic priority queue, and use the dynamic priority queue to complete regional collaborative scheduling decisions.

[0036] Specifically, the event parsing model is a model for analyzing and understanding events occurring in different areas. The collaborative compensation module 40 calls the event parsing model to parse events occurring in different areas of the tunnel. These events may include fires, accidents, traffic jams, etc. The event parsing model will establish regional events for each area based on the nature and impact of the events. These regional events are events that occur in the area and affect ventilation needs. Different priorities are assigned to each regional event based on the importance and urgency of the event, and events that have a greater impact on ventilation are given priority. For example, a fire event will have a higher priority than a traffic jam because it poses a greater threat to personnel safety and requires emergency ventilation.

[0037] Read the technical parameters provided by the equipment manufacturer to determine the maximum power of each independent ventilation device. The maximum power determines the maximum ventilation volume that each ventilation device can provide. Based on these maximum powers, determine whether the current ventilation device can meet the ventilation needs of the area. If the maximum power of the ventilation device can meet or exceed the current ventilation needs, the adaptability is high; if it cannot meet the needs, the adaptability is low. According to the degree of adaptability, assign an adaptation priority to each area. Areas with higher priorities mean that the existing ventilation devices can meet the needs well, while areas with lower priorities may require further scheduling or compensation.

[0038] The compensation priority scheduling analysis is carried out based on the three priorities of event priority, adaptation priority and ventilation importance. Generally, priority is given to those areas with high event priority and high adaptation priority. If the event priority of a certain area is very high (such as fire), even if the adaptation priority is low, other methods (such as dispatching ventilation resources in adjacent areas) will be tried to compensate the area.

[0039] A dynamic priority queue is established by comprehensively considering event priority, adaptation priority and ventilation importance. This queue is updated in real time and is constantly adjusted as event priority, adaptation priority and ventilation importance change to ensure that the working status of ventilation equipment is dynamically adjusted according to the latest sensor data, event development and equipment status. For example, if a fire in a certain area is under control, the priority will be lowered, and tasks in other areas will automatically be promoted in the queue.

[0040] By building a dynamic priority queue, it can be ensured that urgent and well-adapted areas can get ventilation resources first, and in the case of insufficient adaptation, resources from other areas can be dispatched to compensate, so as to ensure the safety of the tunnel and stable air quality.

[0041] Furthermore, the collaborative compensation module 40 described in the embodiment of the present application is also used to perform the following steps:

[0042] Establish upstream and downstream associations of the region based on the regional segmentation results; locate the ventilation demand of the first region based on the dynamic priority queue, and use the ventilation demand of the first region to perform independent fan response fitting to establish an airflow model; use the airflow model and upstream and downstream associations to make linkage decisions for downstream independent ventilation devices, and establish linkage decision results; obtain control parameters for independent fan response fitting, and use the control parameters and linkage decision results to establish regional collaborative scheduling decision results.

[0043] Specifically, due to the circulation of air, different areas in the tunnel will affect each other, and the ventilation conditions in the upstream area will affect the air flow in the downstream area. Therefore, the correlation between various areas needs to be considered in ventilation control. First, the module divides the upstream and downstream areas according to the regional segmentation results and the direction of air flow, and establishes the upstream and downstream associations of each area. Among them, the upstream area is the area where the air enters or is affected by the main ventilation equipment, and the downstream is the area affected by the air in the upstream area. For example, the exhaust gas emitted by vehicles begins to accumulate at the entrance of the tunnel, so the area near the entrance of the tunnel is the upstream, and the area along the direction of the tunnel airflow is the downstream.

[0044] Then, the first area ventilation demand is located through the dynamic priority queue, and the first area ventilation demand is the area ventilation demand with the highest priority (first place) in the current dynamic priority queue. The air volume or air flow required for the area is determined according to the first area ventilation demand, and then the fan simulation model continuously adjusts the fan speed, air volume, power and other operating parameters to find the fan control parameters that can highly match the first area ventilation demand.

[0045] According to the physical structure characteristics of the tunnel and the working status of the fan, the flow of air in the tunnel is simulated using the principles of aerodynamics, and an air flow model is established. This model combines the correlation between the upstream and downstream areas to simulate the flow of air in the tunnel, predict how the air status in the upstream area affects the downstream, and provide a decision-making basis for ventilation control.

[0046] The fan control parameters that are highly matched to the ventilation demand of the first area are input into the airflow model. According to the prediction results of the airflow model, the linkage decision of the downstream independent ventilation device is made, and the linkage decision result is established to ensure that the fans in the downstream area can work together with the fans in the upstream area. For example, if the upstream fan increases the ventilation intensity, the downstream fan may need to reduce power to avoid excessive ventilation or waste of energy. Finally, the control parameters of the independent fan response fitting are obtained, and combined with the linkage decision results, a complete regional collaborative scheduling decision result is formed to achieve optimal ventilation management.

[0047] Further, such as Figure 2As shown, the linkage decision of the downstream independent ventilation device using the airflow model and the upstream and downstream association also includes:

[0048] The airflow flow model is used to predict the impact of downstream status; if the prediction result of the downstream impact status is a first-level result, the original operation plan of the downstream independent ventilation device is maintained; if the prediction result of the downstream impact status is a second-level result, the airflow balance adjustment of the downstream independent ventilation device is performed, and a linkage decision to increase the operation intensity is established; if the prediction result of the downstream impact status is a second-level result and the downstream air quality is within the target range, it is determined whether there is excessive disturbance of the downstream airflow; if there is excessive disturbance of the airflow, a linkage decision to reduce the intensity of the downstream independent ventilation device is generated.

[0049] Specifically, the speed, direction, pressure and other parameters of the air flow inside the tunnel are first simulated through the airflow model to predict the impact of changes in the upstream ventilation device (such as fan acceleration or deceleration) on the downstream area. According to the prediction results of the airflow model, the impact state of the downstream area is divided into different levels to guide subsequent ventilation scheduling.

[0050] When the ventilation demand in the upstream area changes only slightly, the upstream fan maintains a low intensity operation, the airflow intensity remains relatively stable, and the impact on the downstream area is very weak. The corresponding first-level result of the downstream impact state prediction result shows that the downstream area will not be significantly affected. At this time, no additional adjustment is required, and the original operation plan of the downstream ventilation device can be maintained.

[0051] When the ventilation demand in the upstream area increases, the fan operation intensity increases, and the airflow begins to accelerate, but it is not enough to cause a large pressure or airflow change in the downstream. At this time, the ventilation demand increases, but it is still within the control range. The corresponding second-level result of the downstream impact state prediction result shows that the downstream area is slightly affected and the airflow state fluctuates to a certain extent. At this time, it is necessary to perform airflow balance adjustment and generate a linkage decision to increase the fan operation intensity to maintain the balance of air flow in the entire tunnel. The linkage decision includes increasing the speed of the downstream fan to increase the air volume, or adjusting the fan operation time period to increase the operation during peak hours. As the ventilation demand in the upstream area increases, the continuous increase in the fan operation intensity will lead to an increase in the airflow change in the downstream area, and the generated linkage decision will gradually increase the fan operation intensity to meet the ventilation needs of the downstream area.

[0052] When the downstream impact state prediction result is the second-level result and the downstream air quality is within the target range, it is necessary to further determine whether the adjustment has caused excessive airflow disturbance. Excessive airflow disturbance refers to unnecessary fluctuations in airflow or air turbulence caused by excessive operation of ventilation equipment, which may have an adverse effect on ventilation efficiency and energy consumption. The airflow flow model is used to detect whether abnormal airflow fluctuations occur in the downstream area. If the airflow changes too drastically, a linkage decision is generated to reduce the downstream independent ventilation device and reduce the working intensity of the fan to avoid wasting energy and reducing ventilation efficiency. The linkage decision includes reducing the fan speed; or adjusting the fan's duty cycle to reduce the frequency of continuous operation, etc.

[0053] Exemplarily, based on the prediction results of the airflow model, the changes in the upstream and downstream airflow and the changes in air quality are determined, and based on the different changes, the specific downstream impact state level is determined.

[0054] When the wind speed change in the upstream area is less than 5% and the air flow fluctuation in the downstream area is less than 10%, it indicates that the air flow and air quality remain stable and there is no significant change. At this time, the downstream impact status level is level 1. The response plan is: the downstream fans can maintain the current operating status without making any adjustments, and continue to monitor the changes in air flow and air quality.

[0055] When the upstream wind speed increases by 5% to 15%, the airflow in the downstream area fluctuates between 10% and 20%, and the air quality changes slightly but is still within an acceptable range, the downstream impact status level is Level 2. The response plan is to slightly increase the operating intensity and increase the air volume to adapt to the enhanced airflow from the upstream to prevent the air quality in the downstream area from deteriorating, while continuing to monitor the flow of airflow to ensure that the adjustment of wind speed and air volume does not cause over-ventilation or airflow reversal in local areas, and ensure the smoothness of air flow.

[0056] As the wind speed changes in the upstream area gradually increase (every increase of 10%), the airflow fluctuations in the downstream area also gradually increase (every increase of 10%). The downstream impact state level is gradually increased according to the severity of the changes, and the fan operation intensity in the response plan is adaptively increased.

[0057] The above steps predict the impact of the upstream ventilation device on the downstream area by combining the airflow model and the upstream and downstream associations. According to the different levels of downstream state prediction, corresponding ventilation adjustments are made to optimize the operation of the fan, thereby ensuring the balance of air flow and maintaining the air quality in the tunnel within the target range.

[0058] Further, such as Figure 3 As shown, the composite region aggregation division of functional characteristics, physical structure characteristics, and environmental characteristics is performed by using the data information analysis results to establish the region segmentation results, and further includes:

[0059] Functional features are extracted from the data information analysis results, and the functional features include import and export area features, driving area features, emergency parking area features, and equipment area features; physical structure features are extracted from the data analysis results, and the physical structure features include curve radius features and slope features; environmental features are extracted from the data analysis results, and the environmental features include temperature and humidity features; functional features, physical structure features, and environmental features are weightedly fused with the same position features to establish regional segmentation results.

[0060] Specifically, the regional segmentation results are established using the data information analysis results to determine the specific regional ventilation requirements according to different regions. First, it is necessary to extract functional characteristics, physical structure characteristics, and environmental characteristics from the data information analysis results. The functional characteristics of the tunnel describe the specific uses of each area in the tunnel, including the entrance and exit area characteristics, driving area characteristics, emergency parking area characteristics, and equipment area characteristics. The functional areas of the tunnel can be extracted by analyzing the tunnel design drawings, sensor data, and actual usage. Different areas have different functions and different ventilation requirements. The entrance and exit areas are the key areas for tunnel ventilation. When vehicles enter and exit, they will bring in outside air and generate a large amount of exhaust gas. The inlet and outlet flow sensors and CO2 concentration sensors are used to detect the air exchange volume at the entrance and exit, and identify that this area needs stronger ventilation. The driving area is the main area of ​​the tunnel. When vehicles pass through, they will emit exhaust gas. The vehicle flow sensor and exhaust gas sensor are used to monitor the vehicle flow and exhaust gas concentration to determine the ventilation requirements of the driving area. The emergency parking area can be identified and its characteristics can be extracted based on the tunnel design and emergency plan. The emergency parking area requires spare strong ventilation equipment to quickly discharge exhaust gas in an emergency. The equipment area is the area in the tunnel used to place equipment (such as power distribution equipment, pump room, etc.). The ventilation requirement of these areas is to ensure that the equipment does not affect performance due to overheating. The characteristics of such areas can be extracted based on temperature sensors and equipment operating status.

[0061] The physical structural features of a tunnel include curve radius features and slope features, which affect the distribution and flow of airflow. The physical structural features can be extracted by reading the tunnel's architectural design data, laser scanning, and 3D modeling tools. In the curved areas of a tunnel, the airflow may swirl or slow down due to the curvature of the curve, and the ventilation efficiency is relatively low. The required ventilation volume is calculated using the curve curvature data to ensure that the air flow in the curved area is not stagnant. In areas with larger slopes, pollutants in the air may accumulate in low places due to the effect of gravity. By extracting the slope information, it is calculated whether the airflow needs to be strengthened in these areas with larger slopes to reduce the accumulation of exhaust gas.

[0062] The environmental characteristics inside and outside the tunnel mainly include temperature and humidity characteristics, which directly affect the air quality and the working efficiency of the ventilation equipment. These characteristics can be extracted through environmental sensors in the sensor network, such as temperature sensors, humidity sensors, and gas concentration detectors. If the temperature in the tunnel is too high, the air mobility increases, and the ventilation intensity needs to be reduced. On the contrary, in cold conditions, ventilation may need to be enhanced. The temperature data in the tunnel can be obtained through the temperature sensor, and the appropriate ventilation intensity can be set according to different temperature ranges. In a humid environment, humidity will affect the viscosity of the air, resulting in a decrease in ventilation efficiency. The humidity in the tunnel can be monitored through a humidity sensor, and the operating status of the fan can be adjusted according to the humidity changes.

[0063] Different weights are assigned to different features according to the degree of influence of each feature on ventilation demand. The extracted functional features, physical structure features and environmental features are standardized, and different types of data are normalized to eliminate the impact of dimension on data results. For the same location, the feature standardization result is multiplied by the corresponding weight to obtain the weighted feature value corresponding to the location, which reflects the degree of ventilation demand at that location. The weighted scoring threshold is set according to historical experience or rules, and different areas of the tunnel are divided into high-demand, medium-demand and low-demand ventilation areas. Based on the weighted score, the tunnel is divided into several ventilation areas according to the weighted score threshold, and the regional segmentation result is established.

[0064] The above steps comprehensively evaluate the functional, structural and environmental characteristics of different areas in the tunnel through feature extraction and weighted fusion, thereby determining the regional segmentation results. These regional segmentation results show the specific ventilation requirements of each area in the tunnel, providing a clear regional division and control basis for the ventilation management of the entire tunnel.

[0065] Furthermore, the device described in the embodiment of the present application also includes:

[0066] The early warning module is used to obtain the operating load of the independent ventilation device by using the central control system, establish the cluster load, accumulate the early warnings for the cluster load to be executed continuously, report the early warning results, and use the early warning results to report ventilation control abnormalities.

[0067] Specifically, the early warning module continuously monitors the working status of all independent ventilation devices in the tunnel, detects potential equipment problems in advance and issues alarms. First, the module obtains the operating load data of each ventilation device through the central control system, including the motor power, speed, temperature, operating time, etc. of the fan. The operating load data reflects the current working intensity and pressure of the ventilation equipment. If the equipment is in a high-level operating state for a long time, it may cause increased wear or failure of the equipment.

[0068] Summarize the loads of all independent ventilation devices to establish a cluster load. The cluster load reflects the overall operating pressure of the entire tunnel ventilation equipment. By analyzing the cluster load, it is possible to understand whether there is a risk of overall overload in the tunnel.

[0069] According to the tunnel design standards and power supply capacity, determine the warning upper limit of the cluster load, that is, the cluster load threshold. Continuously monitor the cluster load data generated during continuous operation. When the cluster load reaches or exceeds the cluster load threshold, the warning mechanism is triggered immediately. From the first time the cluster load exceeds the cluster load threshold, the duration of this state is recorded and accumulated. Based on the duration of the overload state, a warning result is generated, which includes specific overload conditions such as duration and overload amplitude. Subsequently, ventilation control abnormalities are reported based on the warning results, and management personnel are notified or emergency measures are automatically triggered, such as reducing the load of certain fans, or readjusting the ventilation strategy to prevent equipment damage or ventilation failure.

[0070] Through the early warning module, potential risks can be discovered in a timely manner, failures caused by overload can be effectively prevented, and the stable operation of tunnel ventilation equipment can be ensured.

[0071] Furthermore, the device described in the embodiment of the present application also includes:

[0072] The self-check module is used to perform adaptation self-check of the independent ventilation device according to the early warning result and establish the adaptation self-check result.

[0073] The feedback module is used to receive the adaptation self-test result and feed back the adaptation self-test result to the user end.

[0074] Specifically, when the ventilation abnormality signal sent by the early warning module is received, the self-test module is started. The self-test module automatically performs adaptive self-test on the independent ventilation device according to the early warning results. The adaptive self-test process includes checking the various working parameters of the independent ventilation device (such as speed, power, temperature, operating time, etc.), and comparing them with the standard parameters, evaluating the current health status of the independent ventilation device, and judging whether the independent ventilation device meets the current ventilation requirements, generating the adaptive self-test results, and sending them to the feedback module. For example, when the early warning module detects that the ventilation device in a certain area is overloaded and may affect normal operation, the self-test module is started to conduct in-depth inspections on related equipment to check its health status, configuration rationality, etc. It is found that the blades of a certain fan are worn, resulting in a decrease in air volume, or the fixed frequency setting is no longer suitable for the current flow demand.

[0075] The feedback module receives the adaptation self-test results and feeds this information back to the user end. The user end may be a software interface, a monitoring platform of a central control system, or a mobile application used by maintenance personnel. Through the user end, managers can understand the operating status of each ventilation device in real time and take corresponding measures based on the self-test results, such as arranging equipment maintenance, adjusting the equipment working mode, etc.

[0076] Through the self-check module and feedback module, potential hidden dangers of independent ventilation devices can be effectively identified and timely feedback can be given to users to promote continuous optimization of the equipment, thereby enhancing the reliability and stability of the entire ventilation equipment and significantly improving the efficiency and quality of tunnel ventilation management.

[0077] In summary, the multi-zone coordinated long-distance tunnel ventilation control device provided in the embodiment of the present application has the following technical effects:

[0078] The regional division module 10 is used to read the data information of the tunnel, analyze the data information, and use the data information analysis results to perform composite regional aggregation division of functional characteristics, physical structure characteristics, and environmental characteristics, and establish regional segmentation results, and the regional segmentation results are marked with ventilation importance; by refining the regional division and giving the ventilation importance marks of each region, the ventilation needs of different regions can be more effectively identified, the configuration of ventilation resources can be optimized, the ventilation efficiency can be improved, and energy consumption can be reduced. The distribution module 20 is used to configure independent ventilation devices and sensor networks based on the regional segmentation results, and the independent ventilation devices and sensor networks are controlled by local controllers, and the local controllers are connected to the central control system by communication, and the local controllers are used to set the preliminary control decisions of the independent ventilation devices and execute the operation of the independent ventilation devices; through the independent ventilation devices, the flexibility and adaptability of the entire ventilation equipment are enhanced, so that the ventilation needs of each area can be responded to quickly, the delay is reduced, and the rapid response to local conditions is ensured. The dynamic perception module 30 is used to activate the sensor network, establish the dynamic perception data of the tunnel, and establish the regional ventilation needs based on the dynamic perception data; by acquiring dynamic data in real time, it can make rapid adjustments according to the actual environmental changes in the tunnel, thereby improving the response speed of the ventilation equipment. The collaborative compensation module 40 is used to call the central control system to make regional collaborative scheduling decisions based on the ventilation importance identification of the regional ventilation demand, and establish the regional collaborative scheduling decision results; through the collaborative compensation mechanism, the ventilation strategy can be adjusted according to the importance of each area to ensure that resources can be more reasonably allocated to the priority demand area, thereby improving the ventilation management effect of the entire tunnel. The control module 50 is used to control the independent ventilation device to manage the tunnel ventilation based on the regional collaborative scheduling decision results. Through the cooperation of the central control system and the local controller, the combination of global regulation and local autonomous adjustment is realized, so that the ventilation equipment can effectively respond to the ventilation needs of different areas and improve the management efficiency. In addition, the early warning module uses the central control system to obtain the operating load of the independent ventilation device, establish a cluster load, perform continuous early warning accumulation on the cluster load, report the early warning results, and use the early warning results to report ventilation control abnormalities. The self-test module performs adaptive self-test of the independent ventilation device according to the early warning results and establishes adaptive self-test results; the feedback module receives the adaptive self-test results and feeds the adaptive self-test results back to the user end. Through early warning, self-inspection and feedback mechanisms, the operation process of the ventilation equipment is continuously monitored, equipment failures and mismatches in ventilation demand are discovered in a timely manner, and management personnel are reminded to make maintenance adjustments to ensure the stable operation of the entire tunnel ventilation equipment.

[0079] In general, the embodiment of the present application integrates the above modules to build an intelligent and flexible tunnel ventilation control device, which can perform precise zoning management according to the functional characteristics and environmental requirements of different areas, and dynamically adjust the ventilation strategy through real-time data to ensure that the ventilation needs of each area are responded to in a timely manner and that the air quality in the tunnel meets the standards. At the same time, the cooperation between the central control system and the local controller realizes the organic combination of global regulation and local autonomous adjustment, effectively optimizes resource allocation, improves overall ventilation efficiency, reduces energy waste, and significantly improves the intelligence level and environmental adaptability of tunnel ventilation management.

[0080] Embodiment 2, as Figure 4 As shown, the embodiment of the present application provides a multi-region coordinated long-distance tunnel ventilation control method, the method comprising:

[0081] Step S1: Read the data information of the tunnel, and analyze the data information, and use the data information analysis results to perform composite regional aggregation division of functional characteristics, physical structure characteristics, and environmental characteristics, and establish regional segmentation results, which are marked with ventilation importance.

[0082] Step S2: Based on the regional segmentation results, the independent ventilation device and the sensor network are configured, and both the independent ventilation device and the sensor network are controlled by a local controller, the local controller is communicated with the central control system, and the local controller is used to set the preliminary control decision of the independent ventilation device to execute the operation of the independent ventilation device.

[0083] Step S3: activating the sensor network, establishing dynamic sensing data of the tunnel, and establishing regional ventilation requirements based on the dynamic sensing data.

[0084] Step S4: calling the central control system to make regional coordinated scheduling decisions based on the ventilation importance identification of the regional ventilation demand, and establishing the regional coordinated scheduling decision results.

[0085] Step S5: Control the independent ventilation device to perform tunnel ventilation management based on the regional collaborative scheduling decision results.

[0086] Furthermore, step S4 of the embodiment of the present application also includes:

[0087] Call the event parsing model, use the event parsing model to analyze the regional ventilation demand, establish regional events, and use regional events to establish event priorities; obtain the maximum power of independent ventilation devices, perform adaptation evaluation based on the maximum power and regional ventilation demand, and establish adaptation priority based on the adaptation evaluation results; perform compensation priority scheduling analysis based on event priority, adaptation priority, and ventilation importance identification, establish a dynamic priority queue, and use the dynamic priority queue to complete regional collaborative scheduling decisions.

[0088] Furthermore, step S4 of the embodiment of the present application also includes:

[0089] Establish upstream and downstream associations of the region based on the regional segmentation results; locate the ventilation demand of the first region based on the dynamic priority queue, and use the ventilation demand of the first region to perform independent fan response fitting to establish an airflow model; use the airflow model and upstream and downstream associations to make linkage decisions for downstream independent ventilation devices, and establish linkage decision results; obtain control parameters for independent fan response fitting, and use the control parameters and linkage decision results to establish regional collaborative scheduling decision results.

[0090] Furthermore, the embodiment of the present application uses the airflow model and the upstream and downstream association to make a linkage decision for the downstream independent ventilation device, and further includes:

[0091] The airflow flow model is used to predict the impact of downstream status; if the prediction result of the downstream impact status is a first-level result, the original operation plan of the downstream independent ventilation device is maintained; if the prediction result of the downstream impact status is a second-level result, the airflow balance adjustment of the downstream independent ventilation device is performed, and a linkage decision to increase the operation intensity is established; if the prediction result of the downstream impact status is a second-level result and the downstream air quality is within the target range, it is determined whether there is excessive disturbance of the downstream airflow; if there is excessive disturbance of the airflow, a linkage decision to reduce the intensity of the downstream independent ventilation device is generated.

[0092] Furthermore, the embodiment of the present application uses the data information analysis results to perform composite regional aggregation division of functional characteristics, physical structure characteristics, and environmental characteristics to establish regional segmentation results, and also includes:

[0093] Functional features are extracted from the data information analysis results, and the functional features include import and export area features, driving area features, emergency parking area features, and equipment area features; physical structure features are extracted from the data analysis results, and the physical structure features include curve radius features and slope features; environmental features are extracted from the data analysis results, and the environmental features include temperature and humidity features; functional features, physical structure features, and environmental features are weightedly fused with the same position features to establish regional segmentation results.

[0094] Furthermore, the method described in the embodiment of the present application also includes:

[0095] The central control system is used to obtain the operating load of the independent ventilation device, establish the cluster load, perform continuous early warning accumulation on the cluster load, report the early warning result, and use the early warning result to report ventilation control abnormalities.

[0096] Furthermore, the method described in the embodiment of the present application also includes:

[0097] According to the early warning results, the independent ventilation device is adapted and self-checked, and the adaptation and self-checking results are established; the adaptation and self-checking results are received, and the adaptation and self-checking results are fed back to the user end.

[0098] Through the above-mentioned detailed description of the multi-zone coordinated long-distance tunnel ventilation control equipment in this specification, those skilled in the art can clearly understand the multi-zone coordinated long-distance tunnel ventilation control method in this embodiment. For the method disclosed in Example 2, since it corresponds to the equipment disclosed in Example 1 and has corresponding execution steps and beneficial effects, please refer to the equipment part description for relevant matters.

[0099] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. Multi-area coordinated long-distance tunnel ventilation control equipment, characterized in that: The device comprises: The regional division module is used to read the data information of the tunnel, analyze the data information, and use the data information analysis results to perform composite regional aggregation division of functional characteristics, physical structure characteristics, and environmental characteristics, and establish regional segmentation results, which are marked with ventilation importance; A distribution module, configured to configure an independent ventilation device and a sensor network based on the regional segmentation result, wherein the independent ventilation device and the sensor network are controlled by a local controller, the local controller is in communication with the central control system, and the local controller is used to set a preliminary control decision of the independent ventilation device and execute the operation of the independent ventilation device; A dynamic sensing module, used to activate the sensor network, establish dynamic sensing data of the tunnel, and establish regional ventilation requirements based on the dynamic sensing data; The collaborative compensation module is used to call the central control system to make regional collaborative scheduling decisions based on the ventilation importance identification of the regional ventilation demand and establish the regional collaborative scheduling decision results; The control module is used to control independent ventilation devices to perform tunnel ventilation management based on the regional collaborative scheduling decision results.

2. The multi-zone coordinated long-distance tunnel ventilation control device according to claim 1, characterized in that: The collaborative compensation module is also used for: Call the event analysis model, use the event analysis model to analyze the regional ventilation demand, establish regional events, and use regional events to establish event priorities; Acquire the maximum power of the independent ventilation device, perform adaptation evaluation based on the maximum power and the regional ventilation demand, and establish the adaptation priority according to the adaptation evaluation result; Compensation priority scheduling analysis is performed based on event priority, adaptation priority, and ventilation importance identification, and a dynamic priority queue is established to complete regional collaborative scheduling decisions.

3. The multi-zone coordinated long-distance tunnel ventilation control device according to claim 2, characterized in that: The collaborative compensation module is also used for: Establish upstream and downstream associations of regions based on the region segmentation results; The ventilation demand of the first area is located based on the dynamic priority queue, and the ventilation demand of the first area is used to fit the response of the independent fan to establish the airflow model; Use the airflow model and upstream and downstream associations to make linkage decisions for downstream independent ventilation devices and establish linkage decision results; The control parameters of the independent wind turbine response fitting are obtained, and the regional collaborative scheduling decision results are established using the control parameters and the linkage decision results.

4. The multi-zone coordinated long-distance tunnel ventilation control device according to claim 3, characterized in that: The method of using the airflow model and the upstream and downstream association to make a linkage decision for the downstream independent ventilation device also includes: Use airflow models to predict downstream state impacts; If the downstream impact status prediction result is the first level result, the original operation plan of the downstream independent ventilation device shall be maintained; If the downstream impact state prediction result is the second-level result, the airflow balance adjustment of the downstream independent ventilation device is performed to establish a linkage decision to increase the operating intensity; If the downstream impact state prediction result is a second-level result and the downstream air quality is within the target range, determine whether there is excessive disturbance of the downstream airflow; If there is excessive airflow disturbance, a coordinated decision is made to reduce the downstream independent ventilation device.

5. The multi-zone coordinated long-distance tunnel ventilation control device according to claim 1, characterized in that: The method of using the data information analysis results to respectively aggregate and divide the composite regions of functional characteristics, physical structure characteristics, and environmental characteristics to establish the regional segmentation results also includes: Extracting functional features from the data information analysis results, wherein the functional features include import and export area features, driving area features, emergency parking area features, and equipment area features; Extracting physical structural features from the data analysis results, wherein the physical structural features include curve radius features and slope features; Extracting environmental features from the data analysis results, wherein the environmental features include temperature and humidity features; The functional features, physical structure features, and environmental features are weightedly fused with the same location features to establish the regional segmentation result.

6. The multi-zone coordinated long-distance tunnel ventilation control device according to claim 1, characterized in that: The device also includes: The early warning module is used to obtain the operating load of the independent ventilation device by using the central control system, establish the cluster load, accumulate the early warnings for the cluster load to be executed continuously, report the early warning results, and use the early warning results to report ventilation control abnormalities.

7. The multi-zone coordinated long-distance tunnel ventilation control device according to claim 6, characterized in that: The device also includes: A self-check module, used to perform adaptation self-check of the independent ventilation device according to the early warning result, and establish the adaptation self-check result; The feedback module is used to receive the adaptation self-test result and feed back the adaptation self-test result to the user end.

8. A multi-region coordinated long-distance tunnel ventilation control method, characterized in that: The method is performed by the multi-zone coordinated long-distance tunnel ventilation control device according to any one of claims 1 to 7, and the method comprises: Read the data information of the tunnel, analyze the data information, and use the data information analysis results to perform composite regional aggregation division of functional characteristics, physical structure characteristics, and environmental characteristics, and establish regional segmentation results, wherein the regional segmentation results are marked with ventilation importance; Based on the regional segmentation result, an independent ventilation device and a sensor network are configured, and both the independent ventilation device and the sensor network are controlled by a local controller, the local controller is in communication connection with a central control system, and a preliminary control decision of the independent ventilation device is set by the local controller to execute the operation of the independent ventilation device; activating a sensor network to establish dynamic sensing data of the tunnel, and establishing regional ventilation requirements based on the dynamic sensing data; Call the central control system to make regional coordinated dispatch decisions on regional ventilation demand based on ventilation importance identification, and establish regional coordinated dispatch decision results; Based on the regional collaborative scheduling decision results, independent ventilation devices are controlled to manage tunnel ventilation.

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