Internet of Things-based environmental monitoring system

Abstract

The present invention discloses an environment monitoring system based on the Internet of Things, which includes a data acquisition module, a data transmission module, and an environment detection module. It is characterized in that: the data acquisition module is used to collect comprehensive environment data in the tunnel; the data transmission module is used to select data for fusion and optimize the data transmission path, data transmission nodes, and data transmission network; the environment detection module is used to detect the dust and air components in the tunnel in real time, anchor the dust, and spray water mist to suppress the dust. The data acquisition module, the data transmission module, and the environment detection module are communicatively connected to each other. The data acquisition module includes a sensor module and a data collection module. The sensor module is used to collect air quality data and dust data in the tunnel, and the data collection module is used to collect comprehensive data of the tunnel. The present invention has the characteristics of high network transmission efficiency and accurate dust suppression.

Description

Technical Field

[0001] The present invention relates to the technical field of environmental monitoring, and particularly to an environmental monitoring system based on the Internet of Things. Background Art

[0002] In China's transportation route network, in order to shorten the journey, many projects pass through mountains. Due to the complex mountain environment, it is necessary to inspect the construction environment in real time during the construction process to avoid errors in construction or potential safety hazards. However, in the process of data transmission in the prior art, since some traffic routes are located in the mountains, the environment is complex, and the amount of collected data is large, the data transmission line will be congested, resulting in poor network transmission efficiency, and the detected data cannot be transmitted to the upper computer in time. Moreover, a large amount of dust will be generated during the construction stage. The prior art generally uses a large amount of water mist to be sprayed in real time to suppress dust. Since the prior art cannot accurately spray water mist to suppress dust and the amount of water mist sprayed cannot be accurately adjusted, a large amount of water appears in the construction site, resulting in the construction site being wet, which not only wastes a large amount of water resources but also affects the construction. Therefore, it is very necessary to design an environmental monitoring system based on the Internet of Things with high network transmission efficiency and accurate dust suppression.

[0003] The purpose of the present invention is to provide an environmental monitoring system based on the Internet of Things to solve the problems mentioned in the above background art.

[0004] To solve the above technical problems, the present invention provides the following technical solution: An environmental monitoring system based on the Internet of Things, including a data acquisition module, a data transmission module, and an environmental detection module, characterized in that: the data acquisition module is used to collect comprehensive environmental data in the tunnel, the data transmission module is used to select data for fusion, and optimize the data transmission path, data transmission nodes, and data transmission network, the environmental detection module is used to detect the dust and air components in the tunnel in real time, anchor the dust and spray water mist to suppress the dust, and the data acquisition module, the data transmission module, and the environmental detection module are communicatively connected to each other

[0005] The transmission optimization module includes a path optimization sub-module, a node optimization sub-module, and a network optimization sub-module. The path optimization sub-module is used to optimize the data transmission route, the node optimization sub-module is used to select a better transmission node, and the network optimization sub-module is used to adjust the network resource configuration of data transmission according to the size of the real-time transmitted data;

[0006] The dust treatment module includes a dust analysis sub-module and a fixed-point spraying sub-module. The dust analysis sub-module is used to analyze the location of the dust and the location where the dust is generated when the dust density in the air is detected to be greater than the threshold, and the fixed-point spraying sub-module is used to adjust the nozzle to spray water mist at a fixed point for dust reduction according to the actual position anchored.

[0007] According to the above technical solution, the data acquisition module includes a sensor module and a data collection module. The sensor module is used to collect air quality data and dust data in the tunnel, and the data collection module is used to collect comprehensive data of the tunnel.

[0008] According to the above technical solution, the data transmission module includes a data feature extraction module, a data fusion module, and a transmission optimization module. The data feature extraction module is used to extract data features of the collected environmental data in the tunnel. The data fusion module is used to fuse the extractable data features. The transmission optimization module is used to optimize the data transmission route.

[0009] According to the above technical solution, the environmental detection module includes an air detection module. The air detection module is used to detect whether the dust, harmful gases, and carbon dioxide concentration in the air exceed the threshold.

[0010] According to the above technical solution, the environmental detection module further includes a dust treatment module. The dust treatment module is used to anchor the position of the dust and spray water mist for dust reduction.

[0011] According to the above technical solution, the operation method of the environmental monitoring system mainly includes the following steps:

[0012] Step S1: Through the sensor module, collect air data, dust data, and comprehensive environmental data in the tunnel. Through the data collection module, scan the comprehensive data in the tunnel in real time;

[0013] Step S2: After the data collection is completed, the system triggers the start of the data transmission clustering module through an electrical signal, starts to analyze the data of each data acquisition point for clustering, and fuses the data according to the clustering result;

[0014] Step S3: During the data transmission process, the system starts the transmission optimization module, starts to analyze the real-time transmission capabilities and data volumes of each transmission node, and optimizes the data transmission according to the analysis results;

[0015] Step S4: When it is detected that the dust content in the air is too high, start the dust treatment module, start to analyze the distribution of the dust, and adjust the nozzle to spray water mist for dust reduction according to the dust distribution position.

[0016] According to the above technical solution, step S2 further includes the following steps:

[0017] Step S21: Retrieve the real-time scanning data of the tunnel, construct a tunnel model based on the real-time scanning data of the tunnel, retrieve the data acquisition nodes, identify the positions of each data acquisition node in the tunnel model, establish a coordinate system, measure the distance between adjacent two data acquisition nodes in the tunnel model, sort the distances between the acquisition nodes in ascending order, select the system-set number of data acquisition nodes whose distances from the current data acquisition node are less than the threshold and are the closest. If there are no data acquisition nodes with distances less than the threshold for the current data acquisition node, then set this data acquisition node as an independent cluster. Otherwise, cluster the data acquisition nodes;

[0018] Step S22: Retrieve all the data acquisition nodes in the same cluster, identify the types of data collected. When there are the same data acquisition nodes in the same cluster, extract the data features of the current data acquisition node, compare the data features of all the same data acquisition nodes. If there are the same data features, then retrieve the same data features of each data acquisition node for fusion and compression.

[0019] According to the above technical solution, step S3 further includes the following steps:

[0020] Step S31: Retrieve the transmission line in the middle of adjacent two data acquisition nodes in the current cluster, identify the occupied bandwidth of the currently used transmission line, and calculate the bandwidth occupancy rate of the currently used transmission line through a formula In the formula, D represents the bandwidth occupancy rate of the currently used transmission line, m represents the occupied bandwidth of the currently used transmission line, α represents the applicable loss coefficient of the currently used transmission line, and M represents the rated bandwidth of the currently used transmission line. If the bandwidth occupancy rate D of the currently used transmission line is greater than the system-set threshold at this time, then retrieve the data acquisition node closest to the current cluster head in the cluster as the secondary cluster head and allocate the same resource configuration as the cluster head. Otherwise, continue to use the current cluster head;

[0021] Step S32: Retrieve the transmission node responsible for data transmission in the current cluster, identify the data throughput of the current transmission node, and calculate the remaining throughput efficiency of the current transmission node through a formula In the formula, F represents the remaining throughput efficiency of the current transmission node, W represents the rated throughput of the current transmission node, T represents the rated data throughput time of the current transmission node, and P represents the data throughput of the current transmission node. When the remaining throughput efficiency of the cluster head transmission node is less than the system-set threshold, the secondary cluster head transmission node is called. The cluster head transmission node transmits the data to the secondary cluster head transmission node through the transmission route within the cluster for transmission. When the remaining throughput efficiency of the secondary cluster head node is less than the system-set threshold, the transmission node of the independent cluster closest to the current cluster is called, and a virtual link is established. The current cluster transmits the data to the host computer through the virtual link via the transmission node of the independent cluster.

[0022] According to the above technical solution, in step S32, when the remaining throughput efficiency of the secondary cluster head transmission node is less than the system threshold, the transmission node of the independent cluster closest to the current cluster is called. If the distance between the current transmission node and the current cluster is greater than the threshold, the relay node is called to establish a virtual link to connect the current transmission node and the current cluster, and data is transmitted through the virtual link. Otherwise, a virtual link is directly established to connect the current transmission node and the current cluster for data transmission. When the remaining throughput efficiency of the secondary node is greater than the system threshold, a virtual link is established to connect the cluster head transmission node and the secondary cluster head transmission node, and the data in the cluster head transmission node is segmented, and the segmented data is transmitted through the virtual link.

[0023] According to the above technical solution, in step S4, the dust sensor is called to detect the dust in the tunnel, the dust scanning data is called, and the dust content in the air is identified. If the dust content in the air is less than the system-set threshold, the system continues to monitor. Otherwise, the detection data of the laser dust sensor is called to identify the dust distribution in the detection data of the laser dust sensor. According to the identified dust position distribution, a dust distribution model is established. According to the construction scope, the dust distribution model is segmented, the dust quantity in the segmented area is identified, and the water spraying amount required for the current dust concentration is calculated by the formula. In the formula, S represents the water spraying amount required for the current dust concentration, M represents the number of dust particles in the current area, κ represents the influence coefficient of the dust quantity in the current area, μ represents the influence coefficient of the current dust concentration on the water spraying amount, Q represents the rated water quantity for one spraying, and V represents the volume of the current area.

[0024] Compared with the prior art, the beneficial effects achieved by the present invention are as follows: In the present invention, by clustering according to the distance between adjacent data acquisition nodes, the data collected by the data acquisition nodes in the same cluster can be quickly transmitted to the cluster head, reducing the data transmission time in the same cluster and further accelerating the data transmission efficiency. By fusing and compressing the collected data, the resources occupied during data transmission can be reduced, and thus more data can be transmitted simultaneously, further improving the data transmission efficiency. By detecting the bandwidth occupancy rate of the internal transmission line of the cluster in which the current cluster head transmits data, when the bandwidth occupancy rate is greater than the threshold, the secondary cluster head and the transmission route of the secondary cluster head can be allocated to avoid excessive bandwidth occupancy rate of the internal transmission line of the cluster and data congestion, further improving the data transmission efficiency. By transferring some of the data of the cluster head transmission node to the secondary cluster head node for transmission, it is possible to avoid data transmission congestion at the cluster head transmission node, resulting in a large accumulation of data and slow data transmission speed, further improving the data transmission efficiency. When the remaining throughput efficiency of the current cluster transmission node is insufficient, by dividing the data in the current cluster transmission node and then transmitting it through the transmission nodes of the independent cluster, it is possible to avoid data congestion at the transmission nodes of the independent cluster due to the excessive amount of data in the current cluster transmission node, resulting in a decrease in the transmission speed, greatly improving the data insertion loss speed. By quickly calculating the required spraying water volume, anchoring the current area in the dust distribution model, and spraying water mist for dust reduction at the fixed point in the current area, by constructing the dust distribution, the sprayed water volume can be quickly analyzed, the dust distribution position can be accurately anchored, and thus water mist can be accurately sprayed for dust reduction. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] The drawings are used to provide a further understanding of the present invention and constitute a part of the specification. They are used together with the embodiments of the present invention to explain the present invention and do not constitute a limitation to the present invention. In the drawings:

[0026] Figure 1 is a schematic diagram of the system module composition of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0027] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0028] Please refer to Figure 1, the present invention provides a technical solution: an Internet of Things-based environmental monitoring system, including a data collection module, a data transmission module, and an environmental detection module, characterized in that: the data collection module is used to collect comprehensive environmental data in the tunnel, the data transmission module is used to select data for fusion, and optimize the data transmission path, data transmission nodes, and data transmission network, the environmental detection module is used to detect the dust and air components in the tunnel in real time, anchor the dust and spray water mist to suppress the dust, and the data collection module, the data transmission module, and the environmental detection module are communicatively connected to each other

[0029] The transmission optimization module includes a path optimization sub-module, a node optimization sub-module, and a network optimization sub-module. The path optimization sub-module is used to optimize the data transmission route, the node optimization sub-module is used to select better transmission nodes, and the network optimization sub-module is used to adjust the network resource configuration of data transmission according to the size of the real-time transmitted data;

[0030] The dust treatment module includes a dust analysis sub-module and a fixed-point spraying sub-module. The dust analysis sub-module is used to analyze the location of the dust and the location where the dust is generated when it is detected that the dust density in the air is greater than the threshold. The fixed-point spraying sub-module is used to adjust the nozzle to spray water mist for dust reduction according to the actual anchored position.

[0031] The data collection module includes a sensor module and a data collection module. The sensor module is used to collect air quality data and dust data in the tunnel, and the data collection module is used to collect comprehensive data of the tunnel.

[0032] The data transmission module includes a data feature extraction module, a data fusion module, and a transmission optimization module. The data feature extraction module is used to extract the data features of the environmental data in the tunnel collected, the data fusion module is used to fuse the extractable data features, and the transmission optimization module is used to optimize the data transmission route.

[0033] The environmental detection module includes an air detection module, and the air detection module is used to detect whether the dust, harmful gases, and carbon dioxide concentration in the air exceed the threshold.

[0034] The environmental detection module also includes a dust treatment module, and the dust treatment module is used to anchor the position of the dust and spray water mist for dust reduction.

[0035] The operation method of the environmental monitoring system mainly includes the following steps:

[0036] Step S1: Collect air data, dust data, and comprehensive environmental data in the tunnel through the sensor module, and scan the comprehensive data in the tunnel in real time through the data collection module;

[0037] Step S2: After the data collection is completed, the system triggers the start of the data transmission clustering module through an electrical signal, starts to analyze the data of each data collection point for clustering, and fuses the data according to the clustering results;

[0038] Step S3: During the data transmission process, the system starts the transmission optimization module, begins to analyze the real-time transmission capabilities and data volumes of each transmission node, and optimizes the data transmission according to the analysis results;

[0039] Step S4: When it is detected that the dust content in the air is excessive, the dust treatment module is started, the distribution of the dust is analyzed, and the spray nozzles are adjusted to spray water mist at fixed points according to the dust distribution position for dust reduction.

[0040] Step S2 further includes the following steps:

[0041] Step S21: Retrieve the real-time scan data of the tunnel, construct a tunnel model based on the real-time scan data of the tunnel, retrieve the data collection nodes, identify the positions of each data collection node in the tunnel model, establish a coordinate system, measure the distance between adjacent two data collection nodes in the tunnel model, sort the distances between the collection nodes in ascending order, select the system-set number of data collection nodes whose distances from the current data collection node are less than the threshold and are the closest. If there are no data collection nodes with distances less than the threshold for the current data collection node, then set this data collection node as an independent cluster. Otherwise, cluster the data collection nodes. By clustering according to the distances between adjacent data collection nodes, the data collected by the data collection nodes in the same cluster can be quickly transmitted to the cluster head, reducing the data transmission time in the same cluster and further accelerating the data transmission efficiency;

[0042] Step S22: Retrieve all the data collection nodes in the same cluster, identify the types of data collected. When there are identical data collection nodes in the same cluster, extract the data features of the current data collection node, compare the data features of all the identical data collection nodes. If there are identical data features, then retrieve the identical data features of each data collection node for fusion and compression. By fusing and compressing the collected data, the resources occupied during data transmission can be reduced, and thus more data can be transmitted simultaneously, further improving the data transmission efficiency.

[0043] Step S3 further includes the following steps:

[0044] Step S31: Retrieve the transmission line between two adjacent data collection nodes in the current cluster, identify the occupied bandwidth of the currently used transmission line, and calculate the bandwidth occupancy rate of the currently used transmission line through a formula Where D represents the bandwidth occupancy rate of the transmission line currently in use, m represents the occupied bandwidth of the transmission line currently in use, α represents the applicable loss coefficient of the transmission line currently in use, M represents the rated bandwidth of the transmission line currently in use. If the bandwidth occupancy rate D of the transmission line currently in use is greater than the system-set threshold at this time, the data acquisition node closest to the current cluster head in the cluster is retrieved as the secondary cluster head, and the same resource configuration as that of the cluster head is allocated. Otherwise, the current cluster head continues to be used. By detecting the bandwidth occupancy rate of the internal transmission line of the cluster that transmits data with the current cluster head as the cluster head, it is possible to allocate the secondary cluster head and the transmission route of the secondary cluster head when the bandwidth occupancy rate is greater than the threshold, avoiding excessive bandwidth occupancy of the internal transmission line of the cluster and causing data congestion, and further improving the data transmission efficiency;

[0045] Step S32: Retrieve the transmission node responsible for data transmission in the current cluster, identify the data throughput of the current transmission node, and calculate the remaining throughput efficiency of the current transmission node through the formula Where F represents the remaining throughput efficiency of the current transmission node, W represents the rated throughput of the current transmission node, T represents the rated data throughput time of the current transmission node, and P represents the data throughput of the current transmission node. When the remaining throughput efficiency of the cluster head transmission node is less than the system-set threshold, the secondary cluster head transmission node is retrieved. The cluster head transmission node transmits the data to the secondary cluster head transmission node through the transmission route within the cluster for transmission. When the remaining throughput efficiency of the secondary cluster head node is less than the system-set threshold, the transmission node of the independent cluster closest to the current cluster is retrieved, and a virtual link is established. The current cluster transmits the data to the host computer through the virtual link via the transmission node of the independent cluster. By transferring part of the data of the cluster head transmission node to the secondary cluster head node for transmission, it is possible to avoid data transmission congestion at the cluster head transmission node, resulting in a large accumulation of data and slow data transmission speed, and further improving the data transmission efficiency.

[0046] In step S32, when the remaining throughput efficiency of the secondary cluster head transmission node is less than the system threshold, the transmission node of the independent cluster closest to the current cluster is retrieved. If the distance between the current transmission node and the current cluster is greater than the threshold, the relay node is retrieved to establish a virtual link to connect the current transmission node and the current cluster, and data is transmitted through the virtual link. Otherwise, a virtual link is directly established to connect the current transmission node and the current cluster for data transmission. When the remaining throughput efficiency of the secondary node is greater than the system threshold, a virtual link is established to connect the cluster head transmission node and the secondary cluster head transmission node, the data in the cluster head transmission node is segmented, and the segmented data is transmitted through the virtual link. By segmenting the data in the current cluster transmission node when its remaining throughput efficiency is insufficient and then transmitting it through the transmission node of the independent cluster, it is possible to avoid congestion of the transmission node of the independent cluster caused by excessive data volume in the current cluster transmission node, resulting in a decrease in the transmission speed, and greatly improving the data insertion loss speed.

[0047] In step S4, the dust sensor is retrieved to detect the dust in the tunnel, the dust scanning data is retrieved, and the dust content in the air is identified. If the dust content in the air is less than the system-set threshold, the system continues to monitor. Otherwise, the detection data of the laser dust sensor is retrieved to identify the dust distribution in the detection data of the laser dust sensor, a dust distribution model is established according to the identified dust position distribution, the dust distribution model is segmented according to the construction scope, the dust quantity in the segmented area is identified, and the amount of spraying water required for the current dust concentration is calculated through a formula. In the formula, S represents the amount of spraying water required for the current dust concentration, M represents the number of dust particles in the current area, κ represents the influence coefficient of the dust quantity in the current area, μ represents the influence coefficient of the current dust concentration on the amount of spraying water, Q represents the rated amount of spraying water at one time, and V represents the volume of the current area. By calculating, the required amount of spraying water can be quickly obtained, the current area is anchored in the dust distribution model, and the current area is sprayed with water mist for dust reduction at a fixed point. By constructing the dust distribution, the amount of spraying water can be quickly analyzed, the dust distribution position can be accurately anchored, and then the water mist can be accurately sprayed for dust reduction.

[0048] It should be noted that in this article, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article or device.

[0049] Finally, it should be noted that the above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or perform equivalent replacements for some of the technical features. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. An Internet of Things-based environmental monitoring system, comprising a data acquisition module, a data transmission module, and an environmental detection module, characterized in that: The data acquisition module is used to collect the comprehensive environmental data in the tunnel. The data transmission module is used to select data for fusion and optimize the data transmission path, data transmission nodes and data transmission network. The environmental detection module is used to detect the dust and air components in the tunnel in real time, anchor the dust and spray water mist to suppress the dust. The data acquisition module, data transmission module and environmental detection module are communicatively connected to each other; The data transmission module includes a data feature extraction module, a data fusion module and a transmission optimization module. The data feature extraction module is used to extract the data features of the environmental data in the tunnel collected. The data fusion module is used to fuse the extractable data features. The transmission optimization module is used to optimize the data transmission route; The environmental detection module further includes a dust treatment module. The dust treatment module is used to anchor the position of the dust and spray water mist to reduce the dust; The transmission optimization module includes a path optimization sub-module, a node optimization sub-module and a network optimization sub-module. The path optimization sub-module is used to optimize the data transmission route. The node optimization sub-module is used to select a better transmission node. The network optimization sub-module is used to adjust the network resource configuration of data transmission according to the size of the real-time transmitted data; The dust treatment module includes a dust analysis sub-module and a fixed-point spraying sub-module. The dust analysis sub-module is used to analyze the location of the dust and the dust generation location when the dust density in the air is detected to be greater than the threshold. The fixed-point spraying sub-module is used to adjust the nozzle to spray water mist at a fixed point for dust reduction according to the anchored actual position; The operation method of the environmental monitoring system mainly includes the following steps: Step S1: Through the sensor module, collect the air data, dust data and comprehensive environmental data in the tunnel. Through the data collection module, scan the comprehensive data in the tunnel in real time; Step S2: After the data collection is completed, the system triggers the start of the data transmission clustering module through an electrical signal, starts to analyze the data of each data acquisition point for clustering, and fuses the data according to the clustering result; Step S3: During the data transmission process, the system starts the transmission optimization module, starts to analyze the real-time transmission capacity and data volume of each transmission node, and optimizes the data transmission according to the analysis result; Step S4: When the dust content in the air is detected to be excessive, start the dust treatment module, start to analyze the distribution of the dust, and adjust the nozzle to spray water mist at a fixed point for dust reduction according to the dust distribution position; The step S2 further includes the following steps: Step S21: Retrieve the real-time scanning data of the tunnel, construct a tunnel model based on the real-time scanning data of the tunnel, retrieve the data acquisition nodes, identify the positions of the data acquisition nodes in the tunnel model, establish a coordinate system, measure the distances between adjacent data acquisition nodes in the tunnel model, sort the distances between the acquisition nodes in ascending order, select a system-set number of data acquisition nodes whose distances from the current data acquisition node are less than the threshold and are the closest. If there are no data acquisition nodes with distances less than the threshold for the current data acquisition node, set this data acquisition node as an independent cluster. Otherwise, cluster the data acquisition nodes; Step S22: Retrieve all the data acquisition nodes in the same cluster, identify the types of data collected. When there are identical data acquisition nodes in the same cluster, extract the data features of the current data acquisition node, compare the data features of all identical data acquisition nodes. If there are identical data features, retrieve the identical data features of each data acquisition node for fusion and compression; Step S3 further includes the following steps: Step S31: Retrieve the transmission line between two adjacent data acquisition nodes in the current cluster, identify the occupied bandwidth of the currently used transmission line, and calculate the bandwidth occupancy rate of the currently used transmission line through a formula , where D represents the bandwidth occupancy rate of the currently used transmission line, m represents the occupied bandwidth of the currently used transmission line, represents the applicable loss coefficient of the currently used transmission line, and M represents the rated bandwidth of the currently used transmission line. If the bandwidth occupancy rate D of the currently used transmission line is greater than the system-set threshold at this time, retrieve the data acquisition node closest to the current cluster head in the cluster as the secondary cluster head and allocate the same resource configuration as the cluster head; otherwise, continue to use the current cluster head; Step S32: Retrieve the transmission node responsible for data transmission of the current cluster, identify the data throughput of the current transmission node, and calculate the remaining throughput efficiency of the current transmission node through a formula , where F represents the remaining throughput efficiency of the current transmission node, W represents the rated throughput of the current transmission node, T represents the rated data throughput time of the current transmission node, and P represents the data throughput of the current transmission node. When the remaining throughput efficiency of the cluster head transmission node is less than the system-set threshold, retrieve the secondary cluster head transmission node. The cluster head transmission node transmits the data to the secondary cluster head transmission node through the transmission route within the cluster for transmission. When the remaining throughput efficiency of the secondary cluster head node is less than the system-set threshold, retrieve the transmission node of the independent cluster closest to the current cluster, establish a virtual link, and the current cluster transmits the data to the host computer through the virtual link via the transmission node of the independent cluster In step S32, when the remaining throughput efficiency of the secondary cluster head transmission node is less than the system threshold, retrieve the transmission node of the independent cluster closest to the current cluster. If the distance between the current transmission node and the current cluster is greater than the threshold, retrieve the relay node, establish a virtual link to connect the current transmission node and the current cluster, and perform data transmission through the virtual link. Otherwise, directly establish a virtual link to connect the current transmission node and the current cluster for data transmission. When the remaining throughput efficiency of the secondary node is greater than the system threshold, establish a virtual link to connect the cluster head transmission node and the secondary cluster head transmission node, split the data in the cluster head transmission node, and transmit the split data through the virtual link; In step S4, a dust sensor is retrieved to detect the dust in the tunnel, the dust scanning data is retrieved, the dust content in the air is identified. If the dust content in the air is less than the system-set threshold value, the system continues to monitor. Otherwise, the detection data of the laser dust sensor is retrieved, the dust distribution in the detection data of the laser dust sensor is identified, a dust distribution model is established based on the identified dust position distribution, the dust distribution model is segmented according to the construction scope, the dust quantity in the segmented area is identified, and the water spraying amount required for the current dust concentration is calculated through a formula In the formula, S represents the water spraying amount required for the current dust concentration, M represents the number of dust particles in the current area, represents the influence coefficient of the dust quantity in the current area, represents the influence coefficient of the current dust concentration on the water spraying amount, Q represents the rated water spraying amount at one time, and V represents the volume of the current area.

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