CN117552826A - Intelligent tunnel energy-saving ventilation control system and method - Google Patents
Intelligent tunnel energy-saving ventilation control system and method Download PDFInfo
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- CN117552826A CN117552826A CN202311539411.3A CN202311539411A CN117552826A CN 117552826 A CN117552826 A CN 117552826A CN 202311539411 A CN202311539411 A CN 202311539411A CN 117552826 A CN117552826 A CN 117552826A
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- 238000009423 ventilation Methods 0.000 title claims abstract description 117
- 238000000034 method Methods 0.000 title claims abstract description 33
- 230000007613 environmental effect Effects 0.000 claims abstract description 57
- 238000001514 detection method Methods 0.000 claims abstract description 19
- 238000012544 monitoring process Methods 0.000 claims abstract description 9
- 238000004458 analytical method Methods 0.000 claims abstract description 8
- 238000013499 data model Methods 0.000 claims abstract description 7
- 238000013178 mathematical model Methods 0.000 claims abstract description 7
- 230000001105 regulatory effect Effects 0.000 claims abstract description 7
- 230000001276 controlling effect Effects 0.000 claims abstract description 5
- 230000005540 biological transmission Effects 0.000 claims description 7
- 238000011217 control strategy Methods 0.000 claims description 6
- 239000000779 smoke Substances 0.000 claims description 6
- 238000003860 storage Methods 0.000 claims description 6
- 239000000428 dust Substances 0.000 description 23
- 239000007921 spray Substances 0.000 description 10
- 238000004364 calculation method Methods 0.000 description 7
- 230000008878 coupling Effects 0.000 description 5
- 238000010168 coupling process Methods 0.000 description 5
- 238000005859 coupling reaction Methods 0.000 description 5
- 239000002245 particle Substances 0.000 description 5
- 238000005507 spraying Methods 0.000 description 5
- 238000004422 calculation algorithm Methods 0.000 description 4
- 230000008569 process Effects 0.000 description 4
- 238000012545 processing Methods 0.000 description 4
- 230000009471 action Effects 0.000 description 3
- 238000013461 design Methods 0.000 description 3
- 230000000694 effects Effects 0.000 description 3
- 238000012423 maintenance Methods 0.000 description 3
- 238000004519 manufacturing process Methods 0.000 description 3
- 206010035653 pneumoconiosis Diseases 0.000 description 3
- 238000004088 simulation Methods 0.000 description 3
- 230000000007 visual effect Effects 0.000 description 3
- 238000004891 communication Methods 0.000 description 2
- 238000009826 distribution Methods 0.000 description 2
- 238000005265 energy consumption Methods 0.000 description 2
- 230000006870 function Effects 0.000 description 2
- 238000007726 management method Methods 0.000 description 2
- 230000009323 psychological health Effects 0.000 description 2
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 2
- 238000005299 abrasion Methods 0.000 description 1
- 238000009412 basement excavation Methods 0.000 description 1
- 238000012512 characterization method Methods 0.000 description 1
- 238000004140 cleaning Methods 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 230000034994 death Effects 0.000 description 1
- 231100000517 death Toxicity 0.000 description 1
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- 230000000750 progressive effect Effects 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 208000023504 respiratory system disease Diseases 0.000 description 1
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- E—FIXED CONSTRUCTIONS
- E21—EARTH OR ROCK DRILLING; MINING
- E21F—SAFETY DEVICES, TRANSPORT, FILLING-UP, RESCUE, VENTILATION, OR DRAINING IN OR OF MINES OR TUNNELS
- E21F1/00—Ventilation of mines or tunnels; Distribution of ventilating currents
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- E—FIXED CONSTRUCTIONS
- E21—EARTH OR ROCK DRILLING; MINING
- E21F—SAFETY DEVICES, TRANSPORT, FILLING-UP, RESCUE, VENTILATION, OR DRAINING IN OR OF MINES OR TUNNELS
- E21F1/00—Ventilation of mines or tunnels; Distribution of ventilating currents
- E21F1/003—Ventilation of traffic tunnels
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- E—FIXED CONSTRUCTIONS
- E21—EARTH OR ROCK DRILLING; MINING
- E21F—SAFETY DEVICES, TRANSPORT, FILLING-UP, RESCUE, VENTILATION, OR DRAINING IN OR OF MINES OR TUNNELS
- E21F17/00—Methods or devices for use in mines or tunnels, not covered elsewhere
- E21F17/18—Special adaptations of signalling or alarm devices
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Abstract
The invention relates to an intelligent tunnel energy-saving ventilation control system and method, wherein the system comprises the following components: the sensor detection module comprises a plurality of sensors which are arranged in the tunnel and used for detecting environmental parameters in the tunnel, and the sensors are used for detecting the environmental parameters in the tunnel; the data acquisition module is used for acquiring the environmental parameters in the tunnel and the running state data of the ventilation equipment, acquiring the environmental parameters in the tunnel through a sensor in the tunnel and acquiring the running state data of the ventilation equipment through a monitoring system of the ventilation equipment; the data modeling module is used for analyzing the collected environmental parameters in the tunnel and the operation state data of the ventilation equipment and establishing a corresponding mathematical model according to the analysis result; analyzing a plurality of historical data and real-time data by using the data model to identify associations and laws between the data; and the ventilation control module is used for correspondingly regulating and controlling the ventilation equipment according to the collected environmental parameters in the tunnel and the running state data of ventilation setting.
Description
Technical Field
The invention belongs to the technical field of tunnel energy-saving control, and particularly relates to an intelligent tunnel energy-saving ventilation control system and method.
Background
In the prior art, tunnel dust is tiny particles generated in the tunnel face excavation process, and the particle size is generally smaller than the micron level, so that the tunnel dust is very easy to enter a human body to cause respiratory diseases such as pneumoconiosis and the like. The related data show that the number of deaths caused by pneumoconiosis in underground engineering is twice that caused by accidents, and the average life of pneumoconiosis patients is reduced by 10-15 years compared with normal people. In addition, dust particles enter underground mechanical equipment to accelerate the abrasion of the mechanical equipment, so that the equipment is damaged, and the production cost is increased; and higher dust concentrations risk causing dust explosions. Therefore, the dust is seriously harmful to the safety production of tunnels, the use and maintenance of mechanical equipment and the physical and psychological health of workers, and the related research of dust removal and dust suppression of tunnels has important significance for reducing the concentration of underground dust, protecting the physical and psychological health of operators and guaranteeing the safety production of tunnels.
The invention patent with publication number of CN116992795A discloses a tunnel ventilation-spray dust removal simulation method and system based on DEM-CFD coupling, which specifically comprises the following steps:
acquiring tunnel calculation domain parameters, CFD flow field parameters, DEM dust distribution parameters and DEM spraying facility parameters to form a dynamic environment model information data set of the tunnel; constructing a tunnel three-dimensional numerical calculation model based on the acquired tunnel dynamic environment model information data set; according to engineering practice, carrying out ventilation-spraying dust removal working condition design; setting a calculation model type and calculation model parameters, representing dust particles and liquid drop particles with high precision by a DEM method, and accurately simulating the tunnel flow field environment by a CFD method; carrying out tunnel ventilation-spray dust removal DEM-CFD coupling simulation numerical calculation aiming at the ventilation-spray dust removal working condition; and (3) carrying out result analysis to obtain the optimal ventilation-spray dust removal working condition based on the actual engineering environment. According to the invention, the three-dimensional model data of the tunnel is accurately obtained, a high-precision numerical model of ventilation-spray dust removal of the tunnel is established, and high-precision characterization of flow field, dust distribution and spray systems in the tunnel is realized, so that a calculation model has higher accuracy, and finally, the dust removal effect of the ventilation-spray dust removal facility is analyzed and optimized on the basis of multiple numerical simulation calculation results under different working conditions, so that an optimal ventilation-spray dust removal scheme suitable for actual operation conditions of the tunnel is obtained, and the application of actual engineering is guided or reference is provided for related engineering.
The spraying mode that adopts among the above-mentioned prior art handles the dust in the tunnel, and spraying mode handles the dust and not only needs to consume a large amount of water that sprays, sprays the back in the tunnel moreover, is difficult to get rid of in the tunnel that a large amount of ponding can be detained, can't guarantee the safe construction of tunnel or the safe traffic of vehicle. This is a disadvantage of the prior art.
In view of the above, the present invention provides an intelligent tunnel energy-saving ventilation control system and method; to solve the above-mentioned drawbacks of the prior art, it is highly desirable.
Disclosure of Invention
The invention aims to provide an intelligent tunnel energy-saving ventilation control system and method for solving the technical problems.
In order to achieve the above purpose, the present invention provides the following technical solutions:
an intelligent tunnel energy-saving ventilation control system, comprising:
the sensor detection module comprises a plurality of sensors which are arranged in the tunnel and used for detecting environmental parameters in the tunnel, the sensors are used for detecting the environmental parameters in the tunnel, and detected parameter data are converted into electric signals;
the data acquisition module acquires the environmental parameters in the tunnel and the running state data of the ventilation equipment, acquires the environmental parameters in the tunnel through a sensor in the tunnel, and acquires the running state data of the ventilation equipment through a monitoring system of the ventilation equipment;
the data modeling module analyzes the collected environmental parameters in the tunnel and the running state data of the ventilation equipment, and establishes a corresponding mathematical model according to the analysis result; analyzing a plurality of historical data and real-time data by using the data model to identify the association and rule between the data and making predictions according to the identified association and rule between the data;
and the ventilation control module is used for correspondingly regulating and controlling the ventilation equipment according to the collected environmental parameters in the tunnel and the running state data of the ventilation setting and the prediction made by combining data modeling.
Preferably, the ventilation control system further includes:
the fault detection module is used for acquiring the running state data of the ventilation equipment in the tunnel, comparing the acquired running state data with a preset safety threshold according to the acquired result, and sending out an alarm signal when the acquired running state data is not in the safety threshold range. So as to realize fault detection and alarm of ventilation equipment in the tunnel.
Preferably, the sensor detection module comprises a smoke sensor, a temperature sensor, a co/vi detector, a wind speed and direction detector, a fan vibration sensor, an air pressure sensor, a humidity sensor and a visual sensor; the corresponding environmental parameters in the tunnel are detected by the above-mentioned sensors or detectors.
Preferably, the data acquisition module further comprises transmission and storage of acquired data, transmission and storage of acquired environmental parameters and equipment state information, and transmission of the data to the ventilation control module or the cloud platform through the internet of things, so that the data can be remotely accessed and processed.
Preferably, the ventilation control module selects a corresponding ventilation control strategy according to the sensor data acquired in real time and the set safety threshold parameter. To maintain a comfortable environment within the tunnel.
The invention also provides an intelligent tunnel energy-saving ventilation control method, which comprises the following steps:
s1: detecting the environmental parameters in the tunnel by a plurality of sensors arranged in the tunnel and used for detecting the environmental parameters in the tunnel, and converting the detected parameter data into electric signals;
s2: collecting environmental parameters in a tunnel and running state data of ventilation equipment, collecting the environmental parameters in the tunnel through a sensor in the tunnel, and obtaining the running state data of the ventilation equipment through a monitoring system of the ventilation equipment;
s3: analyzing the collected environmental parameters in the tunnel and the running state data of the ventilation equipment, and establishing a corresponding mathematical model according to the analysis result; analyzing a plurality of historical data and real-time data by using the data model to identify the association and rule between the data and making predictions according to the identified association and rule between the data;
s4: and a ventilation control step, wherein the ventilation equipment is correspondingly regulated and controlled according to the collected environmental parameters in the tunnel and the running state data of ventilation setting and the predictions made by combining data modeling.
Preferably, the ventilation control method further includes the steps of:
s5: and a fault detection step, wherein the operation state data of the ventilation equipment in the tunnel is acquired, the operation state data is compared with a preset safety threshold according to the acquired result, and an alarm signal is sent out when the acquired operation state data is not in the safety threshold range. So as to realize fault detection and alarm of ventilation equipment in the tunnel.
Preferably, the step of detecting by the sensor comprises a smoke sensor, a temperature sensor, a co/vi detector, a wind speed and direction detector, a fan vibration sensor, an air pressure sensor, a humidity sensor and a vision sensor; the corresponding environmental parameters in the tunnel are detected by the above-mentioned sensors or detectors.
Preferably, the step of data acquisition further comprises transmitting and storing acquired data, transmitting and storing acquired environmental parameters and equipment state information, and transmitting the data to a ventilation control module or a cloud platform through the internet of things, so that the data can be remotely accessed and processed.
Preferably, the ventilation control step selects a corresponding ventilation control strategy according to the sensor data acquired in real time and the set safety threshold parameter. To maintain a comfortable environment within the tunnel.
The method has the advantages that in the technical scheme, parameters in the tunnel environment and operation state data of the ventilation equipment are collected, and mathematical modeling is carried out according to the collected data; control of ventilation equipment in the tunnel is achieved so that the tunnel is in a comfortable environment. The method and the device are completely dependent on ventilation equipment to clean the environment in the tunnel, so that the problem of accumulated water caused by cleaning the environment in the tunnel in a spraying mode in the prior art is effectively avoided; meanwhile, in the method, through establishing a model, the ventilation equipment is correspondingly controlled according to the environmental parameters in the tunnel, so that the method is energy-saving and environment-friendly.
In addition, the invention has reliable design principle, simple structure and very wide application prospect.
It can be seen that the present invention has outstanding substantial features and significant advances over the prior art, as well as its practical advantages.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are required to be used in the embodiments or the description of the prior art will be briefly described below, and it is obvious that the drawings in the following description are only embodiments of the present invention, and that other drawings can be obtained according to the provided drawings without inventive effort for a person skilled in the art.
Fig. 1 is a schematic block diagram of an intelligent tunnel energy-saving ventilation control system provided by the invention.
Fig. 2 is a flow chart of an intelligent tunnel energy-saving ventilation control method provided by the invention.
The system comprises a 1-sensor detection module, a 2-data acquisition module, a 3-data modeling module, a 4-ventilation control module and a 5-fault detection module.
Detailed Description
The present invention will be described in detail below by way of specific examples with reference to the accompanying drawings, the following examples being illustrative of the present invention and the present invention is not limited to the following embodiments.
Example 1:
as shown in fig. 1, the intelligent tunnel energy-saving ventilation control system provided in this embodiment includes:
the sensor detection module 1 comprises a plurality of sensors which are arranged in the tunnel and are used for detecting environmental parameters in the tunnel, the sensors are used for detecting the environmental parameters in the tunnel, and detected parameter data are converted into electric signals; the sensor detection module 1 comprises a smoke sensor, a temperature sensor, a co/vi detector, a wind speed and direction detector, a fan vibration sensor, an air pressure sensor, a humidity sensor and a visual sensor; the corresponding environmental parameters in the tunnel are detected by the above-mentioned sensors or detectors.
The data acquisition module 2 is used for acquiring the environmental parameters in the tunnel and the running state data of the ventilation equipment, acquiring the environmental parameters in the tunnel through a sensor in the tunnel and acquiring the running state data of the ventilation equipment through a monitoring system of the ventilation equipment; the data acquisition module 2 further comprises transmission and storage of acquired data, transmission and storage of acquired environmental parameters and equipment state information, and transmission of the data to the ventilation control module or the cloud platform through the Internet of things, so that the data can be accessed and processed remotely. Environmental parameters such as temperature, humidity, air pressure, gas concentration, vehicle flow and the like in the tunnel are monitored in real time. The data acquired by the sensor can reflect the actual situation inside the tunnel. And collecting the operation state information of the ventilation equipment, wherein the operation state information comprises the rotating speed of a fan, the opening and closing degree of a valve, current and voltage and the like.
The data modeling module 3 analyzes the collected environmental parameters in the tunnel and the running state data of the ventilation equipment, and establishes a corresponding mathematical model according to the analysis result; analyzing a plurality of historical data and real-time data by using the data model to identify the association and rule between the data and making predictions according to the identified association and rule between the data; and predicting ventilation requirements and energy consumption conditions in a future period of time according to the current environmental conditions and real-time data. And the parameter settings of the ventilation system, such as the rotating speed of a fan, the opening and closing degree of a valve and the like, are optimized by utilizing an algorithm so as to achieve the optimal ventilation effect and energy utilization efficiency.
And the ventilation control module 4 is used for correspondingly regulating and controlling the ventilation equipment according to the collected environmental parameters in the tunnel and the running state data of the ventilation setting and the predictions made by combining data modeling. And the ventilation control module 4 selects a corresponding ventilation control strategy according to the sensor data acquired in real time and the set safety threshold parameters. To maintain a comfortable environment within the tunnel. The ventilation control module supports remote monitoring and management functions so that a system administrator can monitor the running state of the ventilation equipment, view sensor data, remotely set parameters and the like at any time and any place. Thus, the operation and maintenance efficiency and convenience of the system can be improved.
The fault detection module 5 is used for collecting the operation state data of the ventilation equipment in the tunnel, comparing the operation state data with a preset safety threshold according to the collected result, and sending out an alarm signal when the collected operation state data is not in the safety threshold range. So as to realize fault detection and alarm of ventilation equipment in the tunnel.
Example 2:
as shown in fig. 2, the method for controlling energy-saving ventilation of an intelligent tunnel provided in this embodiment includes the following steps:
s1: detecting the environmental parameters in the tunnel by a plurality of sensors arranged in the tunnel and used for detecting the environmental parameters in the tunnel, and converting the detected parameter data into electric signals; the step of detecting by the sensor comprises a smoke sensor, a temperature sensor, a co/vi detector, a wind speed and direction detector, a fan vibration sensor, an air pressure sensor, a humidity sensor and a visual sensor; the corresponding environmental parameters in the tunnel are detected by the above-mentioned sensors or detectors.
S2: collecting environmental parameters in a tunnel and running state data of ventilation equipment, collecting the environmental parameters in the tunnel through a sensor in the tunnel, and obtaining the running state data of the ventilation equipment through a monitoring system of the ventilation equipment; the data acquisition step further comprises the steps of transmitting and storing acquired data, transmitting and storing acquired environmental parameters and equipment state information, and transmitting the data to a ventilation control module or a cloud platform through the Internet of things, so that the data can be remotely accessed and processed. Environmental parameters such as temperature, humidity, air pressure, gas concentration, vehicle flow and the like in the tunnel are monitored in real time. The data acquired by the sensor can reflect the actual situation inside the tunnel. And collecting the operation state information of the ventilation equipment, wherein the operation state information comprises the rotating speed of a fan, the opening and closing degree of a valve, current and voltage and the like.
S3: analyzing the collected environmental parameters in the tunnel and the running state data of the ventilation equipment, and establishing a corresponding mathematical model according to the analysis result; analyzing a plurality of historical data and real-time data by using the data model to identify the association and rule between the data and making predictions according to the identified association and rule between the data; and predicting ventilation requirements and energy consumption conditions in a future period of time according to the current environmental conditions and real-time data. And the parameter settings of the ventilation system, such as the rotating speed of a fan, the opening and closing degree of a valve and the like, are optimized by utilizing an algorithm so as to achieve the optimal ventilation effect and energy utilization efficiency.
S4: and a ventilation control step, wherein the ventilation equipment is correspondingly regulated and controlled according to the collected environmental parameters in the tunnel and the running state data of ventilation setting and the predictions made by combining data modeling. And the ventilation control step is to select a corresponding ventilation control strategy according to the sensor data acquired in real time and the set safety threshold parameters. To maintain a comfortable environment within the tunnel. This step supports remote monitoring and management functions so that a system administrator can monitor the operating state of the ventilation device, view sensor data, remotely set parameters, etc. at any time and any place. Thus, the operation and maintenance efficiency and convenience of the system can be improved.
S5: and a fault detection step, wherein the operation state data of the ventilation equipment in the tunnel is acquired, the operation state data is compared with a preset safety threshold according to the acquired result, and an alarm signal is sent out when the acquired operation state data is not in the safety threshold range. So as to realize fault detection and alarm of ventilation equipment in the tunnel.
In this specification, each embodiment is described in a progressive manner, and each embodiment is mainly described in a different point from other embodiments, so that the same or similar parts between the embodiments are referred to each other. For the method disclosed in the embodiment, since it corresponds to the system disclosed in the embodiment, the description is relatively simple, and the relevant points refer to the description of the method section.
Those of skill would further appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both, and that the various illustrative elements and steps are described above generally in terms of functionality in order to clearly illustrate the interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
In the several embodiments provided by the present invention, it should be understood that the disclosed systems, and methods may be implemented in other ways. For example, the system embodiments described above are merely illustrative, e.g., the division of the elements is merely a logical functional division, and there may be additional divisions when actually implemented, e.g., multiple elements or components may be combined or integrated into another system, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be through some interface, system or unit indirect coupling or communication connection, which may be in electrical, mechanical or other form.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional module in the embodiments of the present invention may be integrated in one processing unit, or each module may exist alone physically, or two or more modules may be integrated in one unit.
Similarly, each processing unit in the embodiments of the present invention may be integrated in one functional module, or each processing unit may exist physically, or two or more processing units may be integrated in one functional module.
The steps of a method or algorithm described in connection with the embodiments disclosed herein may be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. The software modules may be disposed in Random Access Memory (RAM), memory, read Only Memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.
Finally, it is further noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
The foregoing disclosure is merely illustrative of the preferred embodiments of the invention and the invention is not limited thereto, since modifications and variations may be made by those skilled in the art without departing from the principles of the invention.
Claims (10)
1. An intelligent tunnel energy-saving ventilation control system, which is characterized by comprising:
the sensor detection module comprises a plurality of sensors which are arranged in the tunnel and used for detecting environmental parameters in the tunnel, the sensors are used for detecting the environmental parameters in the tunnel, and detected parameter data are converted into electric signals;
the data acquisition module acquires the environmental parameters in the tunnel and the running state data of the ventilation equipment, acquires the environmental parameters in the tunnel through a sensor in the tunnel, and acquires the running state data of the ventilation equipment through a monitoring system of the ventilation equipment;
the data modeling module analyzes the collected environmental parameters in the tunnel and the running state data of the ventilation equipment, and establishes a corresponding mathematical model according to the analysis result; analyzing a plurality of historical data and real-time data by using the data model to identify the association and rule between the data and making predictions according to the identified association and rule between the data;
and the ventilation control module is used for correspondingly regulating and controlling the ventilation equipment according to the collected environmental parameters in the tunnel and the running state data of the ventilation setting and the prediction made by combining data modeling.
2. The intelligent tunnel energy-saving ventilation control system of claim 1, further comprising:
the fault detection module is used for acquiring the running state data of the ventilation equipment in the tunnel, comparing the acquired running state data with a preset safety threshold according to the acquired result, and sending out an alarm signal when the acquired running state data is not in the safety threshold range.
3. The intelligent tunnel energy-saving ventilation control system according to claim 2, wherein the sensor detection module comprises a smoke sensor, a temperature sensor, a co/vi detector, a wind speed and direction detector, a fan vibration sensor, an air pressure sensor, a humidity sensor and a vision sensor.
4. The intelligent tunnel energy-saving ventilation control system according to claim 3, wherein the data acquisition module further comprises a transmission and storage module for transmitting and storing the acquired environmental parameters and equipment state information, and transmitting the data to the ventilation control module or the cloud platform through the internet of things, so that the data can be remotely accessed and processed.
5. The intelligent tunnel energy-saving ventilation control system according to claim 4, wherein the ventilation control module selects the corresponding ventilation control strategy according to the sensor data collected in real time and the set safety threshold parameter.
6. The intelligent tunnel energy-saving ventilation control method is characterized by comprising the following steps of:
s1: detecting the environmental parameters in the tunnel by a plurality of sensors arranged in the tunnel and used for detecting the environmental parameters in the tunnel, and converting the detected parameter data into electric signals;
s2: collecting environmental parameters in a tunnel and running state data of ventilation equipment, collecting the environmental parameters in the tunnel through a sensor in the tunnel, and obtaining the running state data of the ventilation equipment through a monitoring system of the ventilation equipment;
s3: analyzing the collected environmental parameters in the tunnel and the running state data of the ventilation equipment, and establishing a corresponding mathematical model according to the analysis result; analyzing a plurality of historical data and real-time data by using the data model to identify the association and rule between the data and making predictions according to the identified association and rule between the data;
s4: and a ventilation control step, wherein the ventilation equipment is correspondingly regulated and controlled according to the collected environmental parameters in the tunnel and the running state data of ventilation setting and the predictions made by combining data modeling.
7. The intelligent tunnel energy-saving ventilation control method according to claim 6, further comprising the steps of:
s5: and a fault detection step, wherein the operation state data of the ventilation equipment in the tunnel is acquired, the operation state data is compared with a preset safety threshold according to the acquired result, and an alarm signal is sent out when the acquired operation state data is not in the safety threshold range.
8. The intelligent tunnel energy-saving ventilation control method according to claim 7, wherein the step of detecting the sensor comprises a smoke sensor, a temperature sensor, a co/vi detector, a wind speed and direction detector, a fan vibration sensor, an air pressure sensor, a humidity sensor and a vision sensor.
9. The intelligent tunnel energy-saving ventilation control method according to claim 8, wherein the step of data acquisition further comprises transmitting and storing the acquired data, transmitting and storing the acquired environmental parameters and equipment state information, and transmitting the data to a ventilation control module or a cloud platform through the internet of things, so that the data can be remotely accessed and processed.
10. The intelligent tunnel energy-saving ventilation control method according to claim 9, wherein the ventilation control step selects a corresponding ventilation control strategy according to sensor data collected in real time and a set safety threshold parameter.
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| CN202311539411.3A CN117552826A (en) | 2023-11-17 | 2023-11-17 | Intelligent tunnel energy-saving ventilation control system and method |
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117909667A (en) * | 2024-03-19 | 2024-04-19 | 中铁四局集团有限公司 | Internet of things acquisition method and acquisition device suitable for complex environment |
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117909667A (en) * | 2024-03-19 | 2024-04-19 | 中铁四局集团有限公司 | Internet of things acquisition method and acquisition device suitable for complex environment |
| CN117909667B (en) * | 2024-03-19 | 2024-06-07 | 中铁四局集团有限公司 | Internet of things acquisition method and acquisition device suitable for complex environment |
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