Environment monitoring system based on Internet of Things

Abstract

The invention discloses an environment monitoring system based on Internet of Things, which comprises a data acquisition module, a data transmission module and an environment detection module, and is characterized in that the data acquisition module is used for collecting comprehensive environment data in a tunnel, and the data transmission module is used for selecting data for fusion; the data acquisition module is used for acquiring data and optimizing a data transmission path, data transmission nodes and a data transmission network, the environment detection module is used for detecting flying dust and air components in a tunnel in real time, anchoring the flying dust and spraying water mist to inhibit the flying dust, and the data acquisition module, the data transmission module and the environment detection module are in communication connection with one another. The data acquisition module comprises a sensor module and a data collection module, the sensor module is used for collecting air quality data and dust raising data in the tunnel, the data collection module is used for collecting comprehensive data of the tunnel, and the system has the advantages of being high in network transmission efficiency and capable of accurately restraining dust raising.

Description

Technical Field

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

[0002] In order to shorten the distance, many projects in my country's transportation route network pass through mountains. Due to the complex mountain environment, the construction environment needs to be checked in real time during the construction process to avoid construction errors or safety hazards. However, in the process of data transmission in the prior art, since some transportation routes are located in the mountains, the environment is complex, and the amount of data collected is large, the data transmission lines will be congested and blocked, resulting in poor network transmission efficiency, and the detection data cannot be transmitted to the host computer in time. In addition, a large amount of dust will be generated during the construction phase. The prior art generally adopts real-time spraying of a large amount of water mist to suppress dust. Since the prior art cannot accurately spray water mist to suppress dust, and the amount of water sprayed by water mist cannot be accurately adjusted, a large amount of water will appear on the construction site, causing the construction site to be wet, which not only wastes a lot of water resources but also affects 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. Contents of the invention

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

[0004] In order to solve the above technical problems, the present invention provides the following technical solutions: 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 node 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 connected to each other for communication.

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

[0006] The dust processing module includes a dust analysis submodule and a fixed-point spraying submodule. The dust analysis submodule 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 a threshold value. The fixed-point spraying submodule is used to adjust the nozzle to spray water mist at a fixed point to reduce dust according to the actual anchored position.

[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 the data features of the collected tunnel environment data, the data fusion module is used to fuse the extracted fusible data features, and the transmission optimization module is used to optimize the data transmission route.

[0009] According to the above technical solution, the environment detection module includes an air detection module, and the air detection module is used to detect whether the concentrations of dust, harmful gases and carbon dioxide in the air exceed a threshold value.

[0010] According to the above technical solution, the environment detection module also includes a dust processing module, which is used to anchor the position of the dust and spray water mist to reduce dust.

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

[0012] 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;

[0013] Step S2: After the data collection is completed, the system triggers the data transmission clustering module to start 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;

[0014] Step S3: During the data transmission process, the system starts the transmission optimization module, starts analyzing the real-time transmission capacity and data volume 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, the dust treatment module is started to analyze the distribution of the dust, and the nozzle is adjusted to spray water mist at a fixed point to reduce the dust 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, build a tunnel model according to the real-time scanning data of the tunnel, retrieve the data acquisition nodes, identify the position of each data acquisition node in the tunnel model, establish a coordinate system, measure the distance between two adjacent data acquisition nodes in the tunnel model, arrange the distances between the acquisition nodes in ascending order, select the data acquisition nodes whose distance to the current data acquisition node is less than a threshold and the nearest system-set number, if the current data acquisition node does not have a data acquisition node whose distance is less than the threshold, then set the data acquisition node as an independent cluster family, otherwise the data acquisition nodes are clustered;

[0018] Step S22: call all data acquisition nodes in the same cluster family, identify the type of data collected, and when the same data acquisition nodes exist in the same cluster family, extract the data features of the current data acquisition node, and compare the data features of all the same data acquisition nodes. If the same data features exist, call 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 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 the formula In the formula, D represents the bandwidth occupancy rate of the transmission line currently in use, m represents the bandwidth occupied by the transmission line currently in use, α represents the applicable loss coefficient of the transmission line currently in use, and 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, the data acquisition node closest to the current cluster head in the cluster family is called as the secondary cluster head and is allocated the same resource configuration as the cluster head. Otherwise, the current cluster head continues to be used.

[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 by the formula Wherein, 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, and the cluster head transmission node transmits data to the secondary cluster head transmission node through the transmission route within the cluster family 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 family closest to the current cluster family is called to establish a virtual link. The current cluster family transmits data to the host computer through the independent cluster family transmission node through the virtual link.

[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 family closest to the current cluster family is called, and if the distance between the current transmission node and the current cluster family is greater than the threshold, the relay node is called, and a virtual link is established to connect the current transmission node and the current cluster family, 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 family 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 laser dust sensor detection data. 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 amount of dust in the segmented area is identified, and the amount of water spraying required for the current dust concentration is calculated by the formula Where 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 number of dust 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 water 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: the present invention, by clustering according to the distance between adjacent data acquisition nodes, can enable the data collected by the data acquisition nodes of the same cluster group to be quickly transmitted to the cluster head, thereby reducing the data transmission time of the data in the same cluster group, further accelerating the data transmission efficiency; by fusing and compressing the collected data, it can reduce the resources occupied by the data during the transmission process, thereby being able to transmit more data at the same time, further improving the data transmission efficiency; by detecting the bandwidth occupancy rate of the internal transmission line of the cluster group in which the current cluster head transmits data in the current cluster group, it can start allocating the transmission route of the secondary cluster head and the secondary cluster head when the bandwidth occupancy rate is greater than the threshold value, thereby avoiding the excessive bandwidth occupancy rate of the internal transmission line of the cluster group, causing data congestion, further improving the data transmission efficiency; by part of the cluster head transmission node Data is transferred to the secondary cluster head node for transmission, which can avoid data transmission congestion in the cluster head transmission node, resulting in a large amount of data accumulation, leading to slow data transmission speed, and further improving data transmission efficiency. When the remaining throughput efficiency of the current cluster family transmission node is insufficient, the data in the current cluster family transmission node is divided and then transmitted through the independent cluster family transmission node. This can avoid the blockage of the independent cluster family transmission node caused by excessive data in the current cluster family transmission node, resulting in a decrease in transmission speed, greatly improving the data insertion loss rate. The required water spraying amount is quickly obtained through calculation, the current area is anchored in the dust distribution model, and water mist is sprayed at a fixed point in the current area to reduce dust. By constructing the dust distribution, the spraying water amount can be quickly analyzed, the dust distribution position can be accurately anchored, and then water mist can be accurately sprayed for dust reduction. BRIEF DESCRIPTION OF THE DRAWINGS

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

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

[0027] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0028] See also Figure 1The present invention provides a technical solution: an environment monitoring system based on the Internet of Things, including a data acquisition module, a data transmission module and an environment 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 node 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 connected to each other in communication

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

[0030] The dust treatment module includes a dust analysis submodule and a fixed-point spraying submodule. The dust analysis submodule 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 submodule is used to adjust the nozzle to spray water mist at a fixed point to reduce dust according to the actual anchor position.

[0031] 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.

[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 collected tunnel environment data, the data fusion module is used to fuse the extracted fusible data features, and the transmission optimization module is used to optimize the data transmission route.

[0033] The environment detection module includes an air detection module, which is used to detect whether the concentrations of dust, harmful gases and carbon dioxide in the air exceed the threshold value.

[0034] The environment detection module also includes a dust processing module, which is used to anchor the position of dust and spray water mist to reduce dust.

[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 data transmission clustering module to start 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, starts analyzing the real-time transmission capacity and data volume 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 too high, the dust treatment module is started to analyze the distribution of the dust, and the nozzle is adjusted to spray water mist at a fixed point to reduce the dust according to the dust distribution position.

[0040] Step S2 further comprises the following steps:

[0041] Step S21: retrieve the real-time scanning data of the tunnel, build a tunnel model according to the real-time scanning data of the tunnel, retrieve the data acquisition nodes, identify the position of each data acquisition node in the tunnel model, establish a coordinate system, measure the distance between two adjacent data acquisition nodes in the tunnel model, arrange the distances between the acquisition nodes in ascending order, select the data acquisition nodes whose distance to the current data acquisition node is less than a threshold and the nearest system-set number, if there is no data acquisition node whose distance to the current data acquisition node is less than the threshold, then set the data acquisition node as an independent cluster family, otherwise, cluster the data acquisition nodes, by clustering according to the distance between adjacent data acquisition nodes, the data collected by the data acquisition nodes in the same cluster family can be quickly transmitted to the cluster head, reducing the data transmission time in the same cluster family, and further accelerating the data transmission efficiency;

[0042] Step S22: call all data acquisition nodes in the same cluster family, identify the type of data collected, and when the same data acquisition node exists in the same cluster family, extract the data features of the current data acquisition node, and compare the data features of all the same data acquisition nodes. If the same data features exist, call the same data features of each data acquisition node for fusion and compression. By fusing and compressing the collected data, the resources occupied by the data during transmission can be reduced, so that more data can be transmitted at the same time, further improving the data transmission efficiency.

[0043] Step S3 further comprises 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 the formula Where D represents the bandwidth occupancy rate of the transmission line currently in use, m represents the bandwidth occupied by the transmission line currently in use, α represents the applicable loss coefficient of the transmission line currently in use, and 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, the data acquisition node closest to the current cluster head in the cluster group is called as the secondary cluster head and the same resource configuration as 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 group that transmits data with the current cluster head in the current cluster group, the secondary cluster head and the transmission route of the secondary cluster head can be allocated when the bandwidth occupancy rate is greater than the threshold, avoiding excessive bandwidth occupancy of the internal transmission line of the cluster group, causing data congestion, and further improving the efficiency of data transmission.

[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 by the formula Wherein, 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, and the cluster head transmission node transmits data to the secondary cluster head transmission node through the transmission route within the cluster family 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 family closest to the current cluster family is called to establish a virtual link. The current cluster family transmits data to the host computer through the independent cluster family transmission node through the virtual link. By transferring part of the data of the cluster head transmission node to the secondary cluster head node for transmission, data transmission congestion in the cluster head transmission node can be avoided, resulting in a large amount of data accumulation, resulting in slow data transmission speed, and further improving 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 family closest to the current cluster family is called. If the distance between the current transmission node and the current cluster family 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 family, 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 family 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 divided, and the divided data is transmitted through the virtual link. When the remaining throughput efficiency of the current cluster family transmission node is insufficient, the data in the current cluster family transmission node is divided and then transmitted through the independent cluster family transmission node. This can avoid the congestion of the independent cluster family transmission node caused by excessive data in the current cluster family transmission node, resulting in a decrease in transmission speed, thereby greatly improving the data insertion loss rate.

[0047] 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 laser dust sensor detection data. A dust distribution model is established according to the identified dust position distribution, and the dust distribution model is segmented according to the construction scope. The amount of dust in the segmented area is identified, and the amount of water spraying required for the current dust concentration is calculated by the formula. Where S represents the amount of water sprayed according to the current dust concentration, M represents the number of dust particles in the current area, κ represents the influence coefficient of the number of dust particles in the current area, μ represents the influence coefficient of the current dust concentration on the amount of water sprayed, Q represents the rated amount of water sprayed once, and V represents the volume of the current area. The required amount of water sprayed can be quickly obtained by calculation, and the current area can be anchored in the dust distribution model. Water mist can be sprayed at a fixed point to reduce dust in the current area. By constructing the dust distribution, the amount of water sprayed can be quickly analyzed, and the dust distribution position can be accurately anchored, so that 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, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

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

Claims

1. An environmental monitoring system based on the Internet of Things, 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 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 connected to each other in communication; The transmission optimization module includes a path optimization submodule, a node optimization submodule and a network optimization submodule. The path optimization submodule is used to optimize the data transmission route, the node optimization submodule is used to select a better transmission node, and the network optimization submodule is used to adjust the network resource configuration of data transmission according to the size of real-time transmission data; The dust processing module includes a dust analysis submodule and a fixed-point spraying submodule. The dust analysis submodule 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 a threshold value. The fixed-point spraying submodule is used to adjust the nozzle to spray water mist at a fixed point to reduce dust according to the actual anchored position; 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 tunnel environment data. The data fusion module is used to fuse the extracted fusible data features. The transmission optimization module is used to optimize the data transmission route. The environment detection module also includes a dust processing module, which is used to anchor the position of dust and spray water mist to reduce dust; The operating method of the environmental monitoring system mainly comprises the following steps: 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; Step S2: After the data collection is completed, the system triggers the data transmission clustering module to start 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; Step S3: During the data transmission process, the system starts the transmission optimization module, starts analyzing the real-time transmission capacity and data volume of each transmission node, and optimizes the data transmission according to the analysis results; Step S4: when it is detected that the dust content in the air is too high, the dust treatment module is started to analyze the distribution of the dust, and the nozzle is adjusted to spray water mist at a fixed point to reduce the dust according to the distribution position of the dust; The step S2 further comprises the following steps: Step S21: retrieve the real-time scanning data of the tunnel, build a tunnel model according to the real-time scanning data of the tunnel, retrieve the data acquisition nodes, identify the position of each data acquisition node in the tunnel model, establish a coordinate system, measure the distance between two adjacent data acquisition nodes in the tunnel model, arrange the distances between the acquisition nodes in ascending order, select the data acquisition nodes whose distance to the current data acquisition node is less than a threshold and the nearest system-set number, if the current data acquisition node does not have a data acquisition node whose distance is less than the threshold, then set the data acquisition node as an independent cluster family, otherwise the data acquisition nodes are clustered; Step S22: call all data collection nodes in the same cluster family, identify the data type collected, and if the same data collection node exists in the same cluster family, extract the data features of the current data collection node, compare the data features of all the same data collection nodes, and if the same data features exist, call the same data features of each data collection node to merge and compress them; The step S3 further comprises the following steps: 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 the formula In the formula, D represents the bandwidth occupancy rate of the transmission line currently in use, m represents the bandwidth occupied by the transmission line currently in use, α represents the applicable loss coefficient of the transmission line currently in use, and 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, the data acquisition node closest to the current cluster head in the cluster family is called as the secondary cluster head and is allocated the same resource configuration as the cluster head. Otherwise, the current cluster head continues to be used. 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 by 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 called, and the cluster head transmission node transmits data to the secondary cluster head transmission node through the transmission route within the cluster family 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 family closest to the current cluster family is called to establish a virtual link. The current cluster family transmits data to the host computer through the independent cluster family transmission node through the virtual link; In the 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 group closest to the current cluster group is called, and if the distance between the current transmission node and the current cluster group is greater than the threshold, the relay node is called, a virtual link is established to connect the current transmission node and the current cluster group, 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 group for data transmission, and 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; 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 laser dust sensor detection data. 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 amount of dust in the segmented area is identified, and the amount of water spraying required for the current dust concentration is calculated by the formula Where 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 number of dust 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 water for one spraying, and V represents the volume of the current area.

Images