An intelligent fan system based on the Internet of Things

Abstract

The invention discloses an intelligent fan system based on the Internet of Things, comprising: deploying an atmospheric environment sensor through a data acquisition module to monitor ammonia concentration Daq, methane concentration Djw, volatile organic compound content Dhf, hydrogen sulfide concentration Dlh and ultrafine particle concentration Dkl in real time; a fan control module adjusts the fan operating power in combination with an air quality comprehensive index Kqzh and a variable frequency speed regulation technology; monitoring the full life cycle state of the fan through an equipment self-check module, calculating and correlating a fan energy consumption index Fjnh and a fan state index Fjzs, obtaining a fan fault warning index Gzyj and evaluating it, and finally issuing a warning and performing fault diagnosis; a remote monitoring module realizes real-time remote monitoring of a fan system through the Internet of Things technology, and a user views a fan scheduling strategy and receives a warning through a mobile terminal; the technical solution effectively solves the problems of inaccurate air quality control, high fan energy consumption, lack of intelligent self-checking function and excessive manual intervention.

Description

Technical Field

[0001] The present invention relates to the technical field of tunnel ventilation, and in particular to an intelligent fan system based on the Internet of Things. Background Art

[0002] The earliest research on tunnel ventilation appeared in railway tunnels, while the technical issues of highway tunnel ventilation began with the research on the Holland Tunnel in New York, USA in 1919. The U.S. Bureau of Mines conducted the first study on automobile CO emissions and the allowable value for human body, determined 400ppm as the design concentration, and calculated the ventilation volume accordingly. In the mid-20th century, with the development of highways, the research on tunnel ventilation was further deepened, and a systematic analysis was conducted on the ventilation mode and the required air volume for the dilution of harmful gases. The theoretical research on highway tunnel ventilation in China began in 1994. In 1999, the "Highway Tunnel Ventilation and Lighting Design Specification" was compiled, which laid the reference basis for tunnel ventilation design. Since 2000, the research on intelligent ventilation control has gradually developed, emphasizing safer, more efficient and energy-saving ventilation systems. By 2010, research in this field gradually broke out.

[0003] As the number and length of tunnels increase, improving traffic and management capabilities is crucial to economic and social development. However, the existing tunnel ventilation system has the following technical shortcomings in practical applications:

[0004] 1. Cleanliness issue: Lack of real-time monitoring leads to inaccurate air quality control.

[0005] 2. Energy saving problem: The fan control method is single and the energy consumption is high.

[0006] 3. Equipment self-inspection problem: Equipment maintenance is passive and lacks intelligent self-inspection function.

[0007] 4. Human resource issues: A lot of manual intervention is required and management efficiency is low. Summary of the invention

[0008] The purpose of the present invention is to provide an intelligent fan system based on the Internet of Things to solve the above problems.

[0009] The present invention is achieved through the following technical solutions:

[0010] An intelligent fan system based on the Internet of Things, including a data acquisition module, a fan control module, an equipment self-checking module, an intelligent scheduling module and a remote monitoring module;

[0011] The data acquisition module is used to deploy atmospheric environment sensors in the tunnel, and monitor and collect air-related data in the tunnel in real time, including information related to polluted gas concentration, and summarize and pre-process the collected air-related data, and finally construct a tunnel air data set;

[0012] The fan control module extracts the tunnel air data set, constructs and evaluates the comprehensive air quality index Kqzh, and then generates a fan operating power-related adjustment strategy based on the evaluation content of the comprehensive air quality index Kqzh and combined with the variable frequency speed regulation technology; finally, the fan operating power-related data is collected and a fan power data set is constructed;

[0013] The equipment self-check module is used to monitor the status of the fan throughout its life cycle. The fan energy consumption index Fjnh and the fan status index Fjzs are calculated by extracting the fan power data set, and then the fan energy consumption index Fjnh is associated with the fan status index Fjzs to obtain and evaluate the fan fault warning index Gzyj. If the fan fault warning index Gzyj is greater than the fault threshold E, the fan is diagnosed and a warning is issued.

[0014] The intelligent scheduling module is used to collect traffic-related data in the tunnel and construct a traffic index Jtzs, then compare and sort the values ​​of the traffic index Jtzs, the comprehensive air quality index Kqzh and the fan status index Fjzs after normalization, and finally generate an intelligent scheduling strategy for the fan based on the sorting results of the traffic index Jtzs, the comprehensive air quality index Kqzh and the fan status index Fjzs;

[0015] The remote monitoring module is used to remotely monitor the fan system in real time through the Internet of Things technology, and use a mobile application to view the current intelligent scheduling strategy of the fan.

[0016] Preferably, the atmospheric environment sensor in the data acquisition module includes an electrochemical sensor, an NDIR methane sensor and a laser scattering dust sensor; the air-related data in the tunnel is monitored and collected at preset time intervals;

[0017] The air-related data include carbon monoxide concentration Daq, methane concentration Djw, dust concentration Dhf, sulfur dioxide concentration Dlh, nitrogen dioxide concentration Dhd and ozone concentration Dkl; finally, the air-related data from different sensors are summarized, and after the preprocessing steps of denoising and data cleaning, a tunnel air data set is constructed.

[0018] Preferably, the fan control module includes an air quality quantification unit, a strategy adjustment unit and a fan operation data acquisition unit;

[0019] The air quality quantification unit is used to calculate the comprehensive air quality index Kqzh; the comprehensive air quality index Kqzh is obtained by extracting the tunnel air data set and combining it with the following formula:

[0020] .

[0021] Preferably, the strategy adjustment unit compares and evaluates the comprehensive air quality index Kqzh by presetting the first comprehensive quality threshold Q1 and the second comprehensive quality threshold Q2, and the first comprehensive quality threshold Q1> the second comprehensive quality threshold Q2, and generates a fan operating power-related adjustment strategy, the specific contents of which are as follows:

[0022] If the comprehensive air quality index Kqzh ≥ the first comprehensive quality threshold Q1, it means that the air quality is abnormal. At this time, mandatory measures are taken to adjust the pollutant concentration, including increasing the fan power by 10% to increase the fan speed on the original basis; at the same time, self-check the fan equipment;

[0023] If the first comprehensive quality threshold Q1> the comprehensive air quality index Kqzh≥ the second comprehensive quality threshold Q2, it means that the air quality is normal, but there is pollution; at this time, adjust the fan speed to match the exhaust volume with the air quality requirements;

[0024] If the second comprehensive quality threshold Q2> the comprehensive air quality index Kqzh, it means that the air quality is normal and no adjustment is required.

[0025] Preferably, the fan operation data acquisition unit collects the power-related data of the running fan by deploying electrical-related sensors; wherein the electrical-related sensors include pressure sensors, voltage sensors, current sensors, temperature sensors, vibration sensors and speed sensors; then the fan operation power-related data collected by the electrical-related sensors are summarized, and then pre-processed and dimensionless processed, and finally a fan power data set is constructed;

[0026] The fan power data set includes the fan lubricating oil pressure Frh, the fan impeller shaft amplitude Fzf, the fan impeller shaft speed Fzs, the fan operating current Fdl, the fan operating voltage Fdy and the motor temperature Fwd.

[0027] Preferably, the equipment self-check module includes a fault warning calculation unit and a fault warning evaluation unit;

[0028] The fault warning calculation unit calculates and obtains the fan energy consumption index Fjnh and the fan status index Fjzs respectively through the following formulas:

[0029] ;

[0030] ;

[0031] In the formula, n represents the total number of sampled data points, i represents the index value of the data point currently being processed, represents the i-th data point of the fan lubricating oil pressure, represents the i-th data point of the fan impeller shaft speed, represents the i-th data point of the fan impeller shaft amplitude;

[0032] The specific calculation formula of the fan fault warning index Gzyj is as follows:

[0033] .

[0034] Preferably, the fault warning evaluation unit evaluates the fan fault warning index Gzyj by a preset fault threshold E and issues a warning. The specific evaluation content is as follows:

[0035] If the fan fault warning index Gzyj ≤ fault threshold E, it means that the fan is operating normally and the fan energy consumption is within the normal range, and no further operation is required;

[0036] If the fan fault warning index Gzyj>fault threshold E, it means that the fan operation status is abnormal and the fan energy consumption exceeds the normal range, that is, the fan has a potential failure risk; at this time, the fan fault diagnosis is performed, including testing the fan operation related data in combination with empirical rules; and a warning is issued at the same time.

[0037] Preferably, the intelligent scheduling module includes a traffic state quantification unit and a scheduling strategy generation unit;

[0038] The traffic state quantification unit deploys photoelectric sensors, laser sensors and ultrasonic speed meters in the tunnel to collect the traffic flow Scl, average vehicle speed Scs and vehicle occupancy rate Szy in a fixed period respectively; after extracting the traffic flow Scl, average vehicle speed Scs and vehicle occupancy rate Szy and performing dimensionless processing, the traffic index Jtz is calculated by the following formula:

[0039] .

[0040] Preferably, the dispatch strategy generation unit normalizes the values ​​of the traffic index Jtzs, the comprehensive air quality index Kqzh and the fan status index Fjzs, and then compares and sorts them, and finally generates an intelligent dispatch strategy for the fan;

[0041] The specific contents are:

[0042] When the traffic index Jtzs is at its maximum value, it means that the traffic flow in the tunnel is large or congested. At this time, it is necessary to maintain smooth ventilation to prevent the accumulation of excessive exhaust gas and pollutants.

[0043] When the comprehensive air quality index Kqzh is at its maximum value, it indicates that the air quality is abnormal, and the ventilation capacity of the fan should be adjusted first, and the pollutant removal speed should be adjusted at the same time;

[0044] When the fan status index Fjzs is at its maximum value, it indicates that the fan is in an abnormal state and the equipment is in an unstable state. At this time, the fan load should be adjusted to prevent further damage.

[0045] Preferably, the remote monitoring module is used to transmit the current intelligent scheduling strategy of the fan to the mobile application through data, and the user can view the real-time scheduling strategy of the fan in the application; at the same time, when the fan fault warning index Gzyj exceeds the fault threshold E, the remote monitoring module automatically sends an early warning to the user's mobile device, prompting the user that the fan has a fault or abnormality.

[0046] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0047] 1. Aiming at the cleaning problem, the present invention deploys atmospheric environment sensors in the tunnel through the data acquisition module to monitor the concentration of polluted gases in the air in real time, including ammonia concentration Daq, methane concentration Djw, volatile organic compound content Dhf, hydrogen sulfide concentration Dlh and ultrafine particle concentration Dkl; uses the fan control module to construct the comprehensive air quality index Kqzh, and combines the variable frequency speed regulation technology to generate the fan operating power related adjustment strategy; effectively solves the problem of inaccurate air quality control caused by the lack of real-time monitoring;

[0048] 2. Aiming at the energy saving problem, the present invention dynamically adjusts the operating power of the fan according to the evaluation content of the comprehensive air quality index Kqzh through the fan control module; at the same time, the traffic index Jtzs, the comprehensive air quality index Kqzh and the fan state index Fjzs are comprehensively sorted to generate an intelligent scheduling strategy to ensure the optimal control of the fan energy consumption under different conditions; effectively reduce energy consumption and solve the problem of single fan control mode and high energy consumption;

[0049] 3. The present invention aims at the problem of equipment self-checking. The solution monitors the operating status of the fan throughout its life cycle through the equipment self-checking module; and further calculates the fan fault warning index Gzyj by calculating the fan energy consumption index Fjnh and the fan status index Fjzs; if the fan fault warning index Gzyj exceeds the fault threshold E, the fault diagnosis is automatically performed and a warning is issued; the passive equipment maintenance is effectively avoided, and the problem of lack of intelligent self-checking function is solved;

[0050] 4. The present invention aims at the manpower problem. The solution realizes real-time remote monitoring of the fan system through a remote monitoring module. Users can view the real-time scheduling strategy of the fan through the mobile application and receive warning notifications when the fan fault warning index Gzyj exceeds the fault threshold E. The need for manual intervention is greatly reduced, the management efficiency is improved, and the problem of more manual intervention and low management efficiency is solved. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] The drawings described herein are used to provide a further understanding of the embodiments of the present invention, constitute a part of this application, and do not constitute a limitation of the embodiments of the present invention. In the drawings:

[0052] Figure 1 It is a schematic diagram of the framework structure of the present invention. DETAILED DESCRIPTION

[0053] In order to make the purpose, technical scheme and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the embodiments and drawings. The schematic implementation modes and descriptions of the present invention are only used to explain the present invention and are not intended to limit the present invention. It should be noted that the present invention is already in the actual development and use stage.

[0054] Example 1

[0055] like Figure 1 As shown, this embodiment includes an intelligent fan system based on the Internet of Things, including a data acquisition module, a fan control module, an equipment self-check module, an intelligent scheduling module and a remote monitoring module;

[0056] The data acquisition module is used to deploy atmospheric environment sensors in the tunnel, and monitor and collect air-related data in the tunnel in real time, including information related to polluted gas concentration, and summarize and pre-process the collected air-related data, and finally construct a tunnel air data set;

[0057] The fan control module extracts the tunnel air data set, constructs and evaluates the comprehensive air quality index Kqzh, and then generates a fan operating power-related adjustment strategy based on the evaluation content of the comprehensive air quality index Kqzh and combined with the variable frequency speed regulation technology; finally, the fan operating power-related data is collected and a fan power data set is constructed;

[0058] The equipment self-check module is used to monitor the status of the fan throughout its life cycle. The fan energy consumption index Fjnh and the fan status index Fjzs are calculated by extracting the fan power data set, and then the fan energy consumption index Fjnh is associated with the fan status index Fjzs to obtain and evaluate the fan fault warning index Gzyj. If the fan fault warning index Gzyj is greater than the fault threshold E, the fan is diagnosed and a warning is issued.

[0059] The intelligent scheduling module is used to collect traffic-related data in the tunnel and construct a traffic index Jtzs, then compare and sort the values ​​of the traffic index Jtzs, the comprehensive air quality index Kqzh and the fan status index Fjzs after normalization, and finally generate an intelligent scheduling strategy for the fan based on the sorting results of the traffic index Jtzs, the comprehensive air quality index Kqzh and the fan status index Fjzs;

[0060] The remote monitoring module is used to remotely monitor the fan system in real time through the Internet of Things technology, and use a mobile application to view the current intelligent scheduling strategy of the fan.

[0061] In this embodiment, the data acquisition module monitors the ammonia concentration Daq, methane concentration Djw, volatile organic compound content Dhf, hydrogen sulfide concentration Dlh and ultrafine particle concentration Dkl in real time by deploying atmospheric environment sensors to ensure the comprehensiveness and accuracy of air quality data; the fan control module adjusts the fan operating power by combining the air quality comprehensive index Kqzh with variable frequency speed regulation technology, effectively optimizing the energy consumption of the fan; the equipment self-check module realizes the status monitoring and fault warning of the whole life cycle by calculating the fan energy consumption index Fjnh, the fan status index Fjzs and the fan fault warning index Gzyj, thereby improving the initiative of equipment maintenance; the intelligent scheduling module generates an intelligent scheduling strategy based on the normalized comparison and sorting of the traffic index Jtzs, the air quality comprehensive index Kqzh and the fan status index Fjzs, effectively balancing the traffic flow, air quality and equipment status; the remote monitoring module realizes real-time remote monitoring of the fan system through the Internet of Things technology, and users can view the real-time scheduling strategy and receive fault warnings through mobile applications, which significantly improves management efficiency and response speed.

[0062] Example 2

[0063] The atmospheric environment sensors in the data acquisition module include electrochemical sensors, NDIR methane sensors and laser scattering dust sensors; the air-related data in the tunnel are monitored and collected at preset time intervals;

[0064] The air-related data include carbon monoxide concentration Daq, methane concentration Djw, dust concentration Dhf, sulfur dioxide concentration Dlh, nitrogen dioxide concentration Dhd and ozone concentration Dkl; finally, the air-related data from different sensors are summarized, and after the preprocessing steps of denoising and data cleaning, a tunnel air data set is constructed.

[0065] In this embodiment, the data acquisition module can accurately collect air-related data in the tunnel by deploying electrochemical sensors, NDIR methane sensors and laser scattering dust sensors; the carbon monoxide concentration Daq is detected by the electrochemical sensor, which can reflect the exhaust emissions of vehicles in the tunnel in real time and ensure the safety of personnel; the methane concentration Djw is monitored by the NDIR methane sensor, which can effectively detect the leakage of methane gas and prevent gas hazards; the dust concentration Dhf is monitored by the laser scattering dust sensor, which can accurately reflect the particulate pollution in the air and optimize the ventilation in the tunnel; the sulfur dioxide concentration Dlh and the nitrogen dioxide concentration Dhd are detected by electrochemical sensors respectively, which can accurately reflect the sulfur oxides and nitrogen oxides emitted by vehicles and prevent acid rain and air pollution; the ozone concentration Dkl is monitored by the ultraviolet light sensor, which can monitor the ozone level in the tunnel and prevent the impact of excessive ozone on human health; by collecting, denoising and cleaning these key air quality parameters, a comprehensive tunnel air data set can be constructed, which provides a reliable basis for subsequent air quality assessment and fan control, and ensures good and safe air quality in the tunnel.

[0066] Example 3

[0067] The fan control module includes an air quality quantification unit, a strategy adjustment unit and a fan operation data acquisition unit;

[0068] The air quality quantification unit is used to calculate the comprehensive air quality index Kqzh; the comprehensive air quality index Kqzh is obtained by extracting the tunnel air data set and combining it with the following formula:

[0069] .

[0070] The strategy adjustment unit compares and evaluates the comprehensive air quality index Kqzh by presetting the first comprehensive quality threshold Q1 and the second comprehensive quality threshold Q2, and the first comprehensive quality threshold Q1> the second comprehensive quality threshold Q2, and generates a fan operating power-related adjustment strategy, the specific contents of which are as follows:

[0071] If the comprehensive air quality index Kqzh ≥ the first comprehensive quality threshold Q1, it means that the air quality is abnormal. At this time, mandatory measures are taken to adjust the pollutant concentration, including increasing the fan power by 10% to increase the fan speed on the original basis; at the same time, self-check the fan equipment;

[0072] If the first comprehensive quality threshold Q1> the comprehensive air quality index Kqzh≥ the second comprehensive quality threshold Q2, it means that the air quality is normal, but there is pollution; at this time, adjust the fan speed to match the exhaust volume with the air quality requirements;

[0073] If the second comprehensive quality threshold Q2> the comprehensive air quality index Kqzh, it means that the air quality is normal and no adjustment is required.

[0074] The fan operation data acquisition unit collects the power-related data of the running fan by deploying electrical related sensors; wherein the electrical related sensors include pressure sensors, voltage sensors, current sensors, temperature sensors, vibration sensors and speed sensors; then the fan operation power-related data collected by the electrical related sensors are summarized, and then pre-processed and dimensionless processed, and finally a fan power data set is constructed;

[0075] The fan power data set includes the fan lubricating oil pressure Frh, the fan impeller shaft amplitude Fzf, the fan impeller shaft speed Fzs, the fan operating current Fdl, the fan operating voltage Fdy and the motor temperature Fwd.

[0076] In this embodiment, the air quality comprehensive index Kqzh is calculated by the air quality quantification unit, so as to accurately evaluate the air quality in the tunnel; the strategy adjustment unit combines the first comprehensive quality threshold Q1 and the second comprehensive quality threshold Q2 to flexibly adjust the operating power of the fan to cope with the air pollution situation and ensure the efficient operation of the fan; if the air quality comprehensive index Kqzh reaches or exceeds the first comprehensive quality threshold Q1, compulsory measures are automatically taken to increase the fan power by 10% and increase the speed, and the equipment is self-checked at the same time to effectively deal with the pollution concentration; the fan operation data acquisition unit deploys pressure sensors and voltage sensors , current sensors, temperature sensors, vibration sensors and speed sensors are used to collect key fan operation data, including fan lubricating oil pressure Frh, fan impeller shaft amplitude Fzf, fan impeller shaft speed Fzs, fan operating current Fdl, fan operating voltage Fdy and motor temperature Fwd, to ensure real-time monitoring of equipment status; these data are aggregated, pre-processed and dimensionlessly processed to construct a fan power data set, which can comprehensively evaluate the power usage and mechanical status of the fan; through the comprehensive calculation and evaluation of these parameters, the fan can be accurately controlled, energy efficiency can be improved, and the safe and stable operation of the equipment can be guaranteed.

[0077] Example 4

[0078] The equipment self-check module includes a fault warning calculation unit and a fault warning evaluation unit;

[0079] The fault warning calculation unit calculates and obtains the fan energy consumption index Fjnh and the fan status index Fjzs respectively through the following formulas:

[0080] ;

[0081] ;

[0082] In the formula, n represents the total number of sampled data points, i represents the index value of the data point currently being processed, represents the i-th data point of the fan lubricating oil pressure, represents the i-th data point of the fan impeller shaft speed, represents the i-th data point of the fan impeller shaft amplitude;

[0083] The specific calculation formula of the fan fault warning index Gzyj is as follows:

[0084] .

[0085] The fault warning evaluation unit evaluates the fan fault warning index Gzyj by the preset fault threshold E and issues a warning. The specific evaluation content is as follows:

[0086] If the fan fault warning index Gzyj ≤ fault threshold E, it means that the fan is operating normally and the fan energy consumption is within the normal range, and no further operation is required;

[0087] If the fan fault warning index Gzyj>fault threshold E, it means that the fan operation status is abnormal and the fan energy consumption exceeds the normal range, that is, the fan has a potential failure risk; at this time, the fan fault diagnosis is performed, including testing the fan operation related data in combination with empirical rules; and a warning is issued at the same time.

[0088] In this embodiment, the fault warning calculation unit and the fault warning evaluation unit work together to realize the monitoring and fault warning of the whole life cycle status of the fan; the fault warning calculation unit collects the fan lubricating oil pressure Frh, the fan impeller shaft speed Fzs and the fan impeller shaft amplitude Fzf, and respectively calculates the fan energy consumption index Fjnh and the fan state index Fjzs, so as to ensure a comprehensive evaluation of the fan energy consumption and mechanical state; the fan lubricating oil pressure Frh is used to judge the health status of the fan lubrication system, the fan impeller shaft speed Fzs reflects the operating efficiency of the fan, and the fan impeller shaft amplitude Fzf is used to detect the vibration condition of the fan to help identify potential mechanical failures; the fan fault warning index Gzyj is obtained by comprehensive calculation of these parameters, reflecting the overall operating status of the fan; the fault warning evaluation unit evaluates Gzyj in combination with the fault threshold E, issues a warning when Gzyj exceeds the threshold, and performs fault diagnosis on the fan in combination with empirical rules, which effectively reduces the risk of equipment failure and ensures the safe operation of the fan.

[0089] Example 5

[0090] The intelligent scheduling module includes a traffic state quantification unit and a scheduling strategy generation unit;

[0091] The traffic state quantification unit deploys photoelectric sensors, laser sensors and ultrasonic speed meters in the tunnel to collect the traffic flow Scl, average vehicle speed Scs and vehicle occupancy rate Szy in a fixed period respectively; after extracting the traffic flow Scl, average vehicle speed Scs and vehicle occupancy rate Szy and performing dimensionless processing, the traffic index Jtz is calculated by the following formula:

[0092] .

[0093] The dispatch strategy generation unit normalizes the values ​​of the traffic index Jtzs, the comprehensive air quality index Kqzh, and the fan status index Fjzs, and then compares and sorts them, and finally generates an intelligent dispatch strategy for the fan;

[0094] The specific contents are:

[0095] When the traffic index Jtzs is at its maximum value, it means that the traffic flow in the tunnel is large or congested. At this time, it is necessary to maintain smooth ventilation to prevent the accumulation of excessive exhaust gas and pollutants.

[0096] When the comprehensive air quality index Kqzh is at its maximum value, it indicates that the air quality is abnormal, and the ventilation capacity of the fan should be adjusted first, and the pollutant removal speed should be adjusted at the same time;

[0097] When the fan status index Fjzs is at its maximum value, it indicates that the fan is in an abnormal state and the equipment is in an unstable state. At this time, the fan load should be adjusted to prevent further damage.

[0098] In this embodiment, the dynamic monitoring and intelligent scheduling of the traffic and air quality in the tunnel are realized through the coordinated work of the traffic state quantification unit and the scheduling strategy generation unit; the traffic state quantification unit collects the vehicle flow Scl, the average vehicle speed Scs and the vehicle occupancy rate Szy through the photoelectric sensor, the laser sensor and the ultrasonic speed meter respectively, which can fully reflect the traffic conditions in the tunnel; the vehicle flow Scl is used to evaluate the traffic flow per unit time, the average vehicle speed Scs is used to measure the traffic efficiency, and the vehicle occupancy rate Szy reflects the congestion degree in the tunnel; by performing dimensionless processing on these parameters and calculating the traffic index Jtz, It can accurately quantify the traffic status in the tunnel; the scheduling strategy generation unit normalizes and sorts the values ​​of the traffic index Jtzs, the comprehensive air quality index Kqzh and the fan status index Fjzs to generate the optimal fan intelligent scheduling strategy; when the traffic index Jtzs is large, the system gives priority to ensuring smooth ventilation to remove exhaust gas, and when the comprehensive air quality index Kqzh is large, the system gives priority to enhancing the fan ventilation capacity, and when the fan status index Fjzs is large, the system will reduce the fan load to prevent equipment damage; through the evaluation of these parameters and the generation of scheduling strategies, the efficient operation of the fan system and equipment protection are achieved.

[0099] Example 6

[0100] The remote monitoring module is used to transmit the current intelligent scheduling strategy of the fan to the mobile application through data, and the user can view the real-time scheduling strategy of the fan in the application; at the same time, when the fan fault warning index Gzyj exceeds the fault threshold E, the remote monitoring module automatically sends an early warning to the user's mobile device to remind the user that the fan has faults and abnormal conditions.

[0101] In this embodiment, efficient remote monitoring of the fan system is achieved by transmitting the current intelligent scheduling strategy of the fan to the user's mobile application in real time; the user can view the real-time scheduling strategy of the fan at any time through the mobile terminal, including the traffic index Jtzs, the comprehensive air quality index Kqzh and the fan status index Fjzs, to help the user understand the operating status of the fan and make remote decisions; when the fan fault warning index Gzyj exceeds the fault threshold E, the remote monitoring module can automatically send a warning notification to the user's mobile terminal, prompting that the fan may have a fault or abnormality; this function significantly improves the convenience and response speed of management, and users can take maintenance measures remotely and in time to avoid further damage to the equipment, thereby improving the operating safety and stability of the fan system.

[0102] The specific implementation methods described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific implementation method of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. An intelligent fan system based on the Internet of Things, characterized in that: Including data acquisition module, fan control module, equipment self-check module, intelligent scheduling module and remote monitoring module; The data acquisition module is used to deploy atmospheric environment sensors in the tunnel, and monitor and collect air-related data in the tunnel in real time, including information related to polluted gas concentration, and summarize and pre-process the collected air-related data, and finally construct a tunnel air data set; The fan control module extracts the tunnel air data set, constructs and evaluates the comprehensive air quality index Kqzh, and then generates a fan operating power-related adjustment strategy based on the evaluation content of the comprehensive air quality index Kqzh and combined with the variable frequency speed regulation technology; finally, the fan operating power-related data is collected and a fan power data set is constructed; The equipment self-check module is used to monitor the status of the fan throughout its life cycle. The fan energy consumption index Fjnh and the fan status index Fjzs are calculated by extracting the fan power data set, and then the fan energy consumption index Fjnh is associated with the fan status index Fjzs to obtain and evaluate the fan fault warning index Gzyj. If the fan fault warning index Gzyj is greater than the fault threshold E, the fan is diagnosed and a warning is issued. The intelligent scheduling module is used to collect traffic-related data in the tunnel and construct a traffic index Jtzs, then compare and sort the values ​​of the traffic index Jtzs, the comprehensive air quality index Kqzh and the fan status index Fjzs after normalization, and finally generate an intelligent scheduling strategy for the fan based on the sorting results of the traffic index Jtzs, the comprehensive air quality index Kqzh and the fan status index Fjzs; The remote monitoring module is used to remotely monitor the fan system in real time through the Internet of Things technology, and use the mobile application to view the current intelligent scheduling strategy of the fan; The atmospheric environment sensors in the data acquisition module include electrochemical sensors, NDIR methane sensors and laser scattering dust sensors; the air-related data in the tunnel are monitored and collected at preset time intervals; The air-related data include carbon monoxide concentration Daq, methane concentration Djw, dust concentration Dhf, sulfur dioxide concentration Dlh, nitrogen dioxide concentration Dhd and ozone concentration Dkl; finally, the air-related data from different sensors are summarized, and after the pre-processing steps of denoising and data cleaning, a tunnel air data set is constructed; The fan control module includes an air quality quantification unit, a strategy adjustment unit and a fan operation data acquisition unit; The air quality quantification unit is used to calculate the comprehensive air quality index Kqzh; the comprehensive air quality index Kqzh is obtained by extracting the tunnel air data set and combining it with the following formula: ； The strategy adjustment unit compares and evaluates the comprehensive air quality index Kqzh by presetting the first comprehensive quality threshold Q1 and the second comprehensive quality threshold Q2, and the first comprehensive quality threshold Q1> the second comprehensive quality threshold Q2, and generates a fan operating power-related adjustment strategy, the specific contents of which are as follows: If the comprehensive air quality index Kqzh ≥ the first comprehensive quality threshold Q1, it means that the air quality is abnormal. At this time, mandatory measures are taken to adjust the pollutant concentration, including increasing the fan power by 10% on the original basis; at the same time, self-check the fan equipment; If the first comprehensive quality threshold Q1> the comprehensive air quality index Kqzh≥ the second comprehensive quality threshold Q2, it means that the air quality is normal, but there is pollution; at this time, adjust the fan speed to match the exhaust volume with the air quality requirements; If the second comprehensive quality threshold Q2> the comprehensive air quality index Kqzh, it means that the air quality is normal and no adjustment is required; The fan operation data acquisition unit collects the power-related data of the running fan by deploying electrical related sensors; wherein the electrical related sensors include pressure sensors, voltage sensors, current sensors, temperature sensors, vibration sensors and speed sensors; then the fan operation power-related data collected by the electrical related sensors are summarized, and then pre-processed and dimensionless processed, and finally a fan power data set is constructed; The fan power data set includes fan lubricating oil pressure Frh, fan impeller shaft amplitude Fzf, fan impeller shaft speed Fzs, fan operating current Fdl, fan operating voltage Fdy and motor temperature Fwd; The equipment self-check module includes a fault warning calculation unit and a fault warning evaluation unit; The fault warning calculation unit calculates and obtains the fan energy consumption index Fjnh and the fan status index Fjzs respectively through the following formulas: ； ； In the formula, n represents the total number of sampled data points, i represents the index value of the data point currently being processed, represents the i-th data point of the fan lubricating oil pressure, represents the i-th data point of the fan impeller shaft speed, represents the i-th data point of the fan impeller shaft amplitude; The specific calculation formula of the fan fault warning index Gzyj is as follows: ； The fault warning evaluation unit evaluates the fan fault warning index Gzyj by the preset fault threshold E and issues a warning. The specific evaluation content is as follows: If the fan fault warning index Gzyj ≤ fault threshold E, it means that the fan is operating normally and the fan energy consumption is within the normal range, and no further operation is required; If the fan fault warning index Gzyj>fault threshold E, it means that the fan operation status is abnormal and the fan energy consumption exceeds the normal range, that is, the fan has a potential failure risk; at this time, the fan fault diagnosis is performed, including testing the fan operation related data in combination with empirical rules; and a warning is issued at the same time.

2. The intelligent fan system based on the Internet of Things according to claim 1, characterized in that: The intelligent scheduling module includes a traffic state quantification unit and a scheduling strategy generation unit; The traffic state quantification unit deploys photoelectric sensors, laser sensors and ultrasonic speed meters in the tunnel to collect the traffic flow Scl, average vehicle speed Scs and vehicle occupancy rate Szy in a fixed period respectively; after extracting the traffic flow Scl, average vehicle speed Scs and vehicle occupancy rate Szy and performing dimensionless processing, the traffic index Jtz is calculated by the following formula: 。 3. The intelligent fan system based on the Internet of Things according to claim 2 is characterized in that: The dispatch strategy generation unit normalizes the values ​​of the traffic index Jtzs, the comprehensive air quality index Kqzh, and the fan status index Fjzs, and then compares and sorts them, and finally generates an intelligent dispatch strategy for the fan; The specific contents are: When the traffic index Jtzs is at its maximum value, it means that the traffic flow in the tunnel is large or congested. At this time, the ventilation should be kept smooth to prevent the accumulation of excessive exhaust gas and pollutants. When the comprehensive air quality index Kqzh is at its maximum value, it indicates that the air quality is abnormal, and the ventilation capacity of the fan should be adjusted first, and the pollutant removal speed should be adjusted at the same time; When the fan status index Fjzs is at its maximum value, it indicates that the fan is in an abnormal state and the equipment is in an unstable state. At this time, the fan load should be adjusted to prevent further damage.

4. The intelligent fan system based on the Internet of Things according to claim 1, characterized in that: The remote monitoring module is used to transmit the current intelligent scheduling strategy of the fan to the mobile application through data, and the user can view the real-time scheduling strategy of the fan in the application; at the same time, when the fan fault warning index Gzyj exceeds the fault threshold E, the remote monitoring module automatically sends an early warning to the user's mobile device to remind the user that the fan has faults and abnormal conditions.

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