Hierarchical grotto air quality monitoring system

Abstract

The application belongs to the technical field of air quality monitoring, and particularly relates to a hierarchical grotto air quality monitoring system. The system comprises a grotto hierarchical region construction unit configured to obtain a three-dimensional model of a target grotto, calculate a spatial center of the three-dimensional model, and then, taking the spatial center as a spherical center, construct a plurality of hierarchical spherical models from inside to outside with gradually increasing radial distances as radii to divide the three-dimensional model into a plurality of hierarchical regions from inside to outside. The application comprehensively monitors the air quality in the grotto, and in the monitoring process, the hierarchical idea is used to more accurately and finely determine the region where the air quality in the grotto has a problem. The application discards the traditional air quality monitoring method, combines the low air flow rate in the grotto and the spatial structure characteristics of the grotto itself, and performs hierarchical air monitoring, thereby improving the monitoring accuracy.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of air quality monitoring, and particularly relates to a hierarchical cave air quality monitoring system. BACKGROUND

[0002] As an important ancient cultural heritage, caves need to be strictly protected. In the protection of caves, the most important thing besides preventing illegal damage to caves is to prevent cave artworks from being damaged by the surrounding environment. Therefore, it is particularly meaningful to monitor the environment in caves to take targeted measures.

[0003] In the environmental monitoring of caves, the environment in caves is monitored and the environment outside caves is monitored. The environment outside caves is difficult to have a great impact on cave artworks because it is isolated by the cave itself, while the environment in caves is easy to cause damage to cave artworks. Therefore, the environment in caves needs to be scientifically monitored. Among them, air quality monitoring is the most important link.

[0004] The air quality in caves differs from outdoor air quality due to the influence of the composition of the cave itself and the structure of the cave. A new monitoring method needs to be used to monitor the air quality in caves according to the characteristics of the cave to significantly improve the monitoring accuracy while ensuring the monitoring efficiency.

[0005] In the prior art, a patent document with the patent application number 2018102118415 discloses an air quality monitoring system, which comprises a monitoring device controlled by an MSP430 single-chip microcomputer, an air quality sensor, and an upper monitoring system. The monitoring device controlled by the MSP430 single-chip microcomputer is connected to the air quality sensor and the upper monitoring system. The air quality monitoring system disclosed in the patent document can comprehensively monitor the data of negative oxygen ion content, oxygen content, PM2.5 content, PM10 content, carbon monoxide content, nitrogen dioxide content, air temperature, and air humidity in the air, analyze and process the data, and display the data. When an abnormality occurs, an alarm is given. The system can timely upload data to an upper computer. The upper and lower computers can simultaneously display monitoring curves and data, and the function is perfect. The lower monitoring device is a low-power device.

[0006] The above technical solution can comprehensively monitor various components in the air to achieve air quality monitoring, but it cannot adapt to the environment of caves for more accurate air quality monitoring, and cannot determine and analyze air quality abnormalities in the cave environment. SUMMARY

[0007] The main purpose of the present application is to provide a hierarchical cave air quality monitoring system, which comprehensively monitors the air quality in the cave, and uses hierarchical thinking to more accurately and finely determine the area where the air quality in the cave is problematic.

[0008] To achieve the above purpose, the technical scheme of the present application is as follows:

[0009] The hierarchical cave air quality monitoring system comprises a cave hierarchical region construction unit configured to obtain a three-dimensional model of a target cave, calculate the spatial center of the three-dimensional model, and then construct a plurality of hierarchical spherical models from the inside to the outside with the spatial center as the center of the sphere and the gradually increasing radial distance as the radius, so as to divide the three-dimensional model into a plurality of hierarchical regions from the inside to the outside; a hierarchical region key region determination unit configured to determine the key region in each hierarchical region based on the spatial structure characteristics of the three-dimensional model of the target cave, specifically comprising connecting the spatial center in the three-dimensional model of the cave with the farthest point in each different direction of the cave, taking the position of the connecting line passing through each hierarchical region as the intermediate line, and determining the key region in each hierarchical region with a set region size; an air quality hierarchical comparison and analysis unit comprising a sensor group and a data analysis subunit; the sensor group comprises a plurality of sub-sensor groups, each sub-sensor group is arranged in a different key region and other random regions in each hierarchical region except the key region, and each key region or random region comprises at least one sub-sensor group; the data analysis subunit is configured to perform hierarchical region data analysis based on the data information obtained by each sub-sensor, obtain layer analysis results and hierarchical region data analysis, obtain interlayer analysis results, and generate quality monitoring results in combination with the interlayer analysis results and the layer analysis results.

[0010] Further, the reservoir layered area construction unit comprises a three-dimensional model construction subunit and a layered subunit; the three-dimensional model construction subunit is configured to generate a three-dimensional model of the target cave, and specifically comprises: generating an actual three-dimensional model of the cave layer by layer based on images, and then simplifying the boundary of the actual three-dimensional model of the cave to obtain a three-dimensional model of the cave; the boundary simplification process comprises: constantly approaching the boundary of the spatial three-dimensional structure of the actual three-dimensional model of the cave from the outside of a regular three-dimensional structure closest to the spatial three-dimensional structure of the three-dimensional model of the cave, until the inside of the regular three-dimensional structure first contacts the boundary of the spatial three-dimensional structure of the actual three-dimensional model of the cave, and the regular three-dimensional structure at this time is taken as the three-dimensional model of the cave; the layered subunit is configured to take the spatial center of the three-dimensional model as the center of a sphere, expand outward with gradually increasing radial distances as radii, and at the time of expansion, each time the radial distance is increased to meet a set functional constraint relationship, so as to construct a plurality of layered spherical models, and divide the three-dimensional model into a plurality of layered areas from the inside to the outside.

[0011] Further, the functional constraint relationship is represented by the following formula: Wherein, D is the gradually increasing radial distance; S is the area of the regular three-dimensional structure; and d is the diameter of the regular three-dimensional structure.

[0012] Further, the data information obtained by the sensors in each sub-sensor group at least comprises carbon dioxide concentration, oxygen concentration, nitrogen concentration, carbon monoxide concentration, sulfur dioxide concentration, humidity, temperature and sulfur monoxide concentration.

[0013] Further, the generation method of the random area comprises: taking the center of the key area as a starting point coordinate, taking the layered area where the key area is located as a plane coordinate area, using a preset random pathfinding algorithm to generate random pathfinding coordinates starting from the starting point coordinate, taking the first, fifth, ninth, (N-1)*4+1th random pathfinding coordinates as the center of the random area, and generating the random area.

[0014] Further, the random pathfinding algorithm is represented by the following formula: Wherein, Random(x', y', z') is the center coordinate of the generated random area; First(x, y, z) is the center of the key area, i.e. the starting point coordinate; k is the random number of times in a unit time, and a is an adjustment coefficient, and the value range is 3-5.

[0015] Further, the data analysis subunit comprises: an intra-layer analysis subunit, an inter-layer analysis subunit, and a result generation unit; the intra-layer analysis subunit is configured to perform intra-layer regional data analysis based on the data information obtained by each sub-sensor to obtain an intra-layer analysis result; the inter-layer analysis subunit is configured to perform inter-layer data analysis based on the intra-layer analysis result to obtain an inter-layer analysis result; and the result generation unit is configured to generate a quality monitoring result by combining the inter-layer analysis result and the intra-layer analysis result.

[0016] Further, the method for the intra-layer analysis subunit to perform intra-layer regional data analysis based on the data information obtained by each sub-sensor to obtain an intra-layer analysis result comprises: calculating the abnormality degree in the intra-layer region by using the following formula: wherein Data i is the normalized mean value of the data information of a certain type in the intra-layer region, Standard i is the corresponding standard value; n is the number of types of data information; Num is the total number of random regions and key regions in the intra-layer region; and H is the calculated abnormality degree in the intra-layer region.

[0017] Further, the method for the inter-layer analysis subunit to perform inter-layer data analysis based on the intra-layer analysis result to obtain an inter-layer analysis result comprises: substituting the abnormality degree in each intra-layer region and the abnormality degrees of the two adjacent intra-layer regions into an inter-layer abnormality calculation formula to obtain an inter-layer abnormality degree; and the inter-layer abnormality calculation formula is: wherein H m is the abnormality degree of the target intra-layer region, H′ m and H″ m are the abnormality degrees of the two adjacent intra-layer regions, respectively; and K is the inter-layer abnormality degree.

[0018] Further, the method for the result generation unit to generate a quality monitoring result by combining the inter-layer analysis result and the intra-layer analysis result comprises: judging whether each intra-layer region is an abnormal region according to the abnormality degree of the intra-layer region and the corresponding inter-layer abnormality degree, specifically comprising: if the abnormality degree of the intra-layer region and the inter-layer abnormality degree both exceed the respective set judgment threshold values, then the intra-layer region is determined to be an abnormal region.

[0019] The layered cave air quality monitoring system has the beneficial effects that: when the layered cave air quality monitoring system monitors air quality, the spatial structure characteristics of the cave are combined and simplified, so that the three-dimensional structure of the cave can be accurately obtained, and the efficiency of subsequent processing is improved; during monitoring, the layered detection form is creatively used, because the air flow in the cave is very slow, and if a sensor is used to obtain the air quality index of a place, the air quality of the whole cave cannot be monitored; in addition, due to the low air flow in the cave, the layered form can not only accurately monitor the air quality of a region, but also monitor the overall air quality change according to the air quality of each layered region, so that the warning is more scientific and the false alarm is avoided. BRIEF DESCRIPTION OF DRAWINGS

[0020] Figure 1 The system structure diagram of the layered cave air quality monitoring system provided by the embodiment of the present application is shown.

[0021] Figure 2 The structure diagram of the layered region of the layered cave air quality monitoring system provided by the embodiment of the present application is shown. DETAILED DESCRIPTION

[0022] The method of the present application will be further described in detail below in combination with the drawings and the embodiments of the present application.

[0023] Embodiment 1

[0024] As Figure 1As shown, a layered cave air quality monitoring system comprises: a cave layered area construction unit configured to obtain a three-dimensional model of a target cave, calculate the spatial center of the three-dimensional model, and then construct a plurality of layered spherical models with the spatial center as the sphere center, from the inside to the outside, with gradually increasing radial distances as radii, so as to divide the three-dimensional model from the inside to the outside into a plurality of layered areas; a layered area key area determination unit configured to determine the key area in each layered area based on the spatial structure characteristics of the three-dimensional model of the target cave, specifically comprising: connecting the spatial center in the three-dimensional model of the cave and the farthest point in each different direction of the cave, and taking the position of the connecting line passing through each layered area as the key area. The middle line determines the key area in each layered area with a set area size; the air quality stratified comparison and analysis unit includes: a sensor group and a data analysis sub-unit; the sensor group includes multiple sub-sensor groups, each sub-sensor group is respectively arranged in different key areas and other random areas other than the key areas in each layered area, and each key area or random area includes at least one sub-sensor group; the configuration is used to perform data analysis within the layered area based on the data information obtained by each sub-sensor, obtain intra-layer analysis results and inter-layered area data analysis, obtain inter-layer analysis results, and generate quality monitoring results by combining the inter-layer analysis results and the intra-layer analysis results.

[0025] refer to Figure 2 , the establishment of each layered area is based on the continuous expansion of the sphere. Figure 2 The expanding circular area in the figure is the two-dimensional projection of the sphere, and the rectangular area is the two-dimensional projection of the regular three-dimensional structure.

[0026] Due to the low air flow within the caves, widespread deployment of sensors would require them to be located almost everywhere to accurately and timely monitor the air quality. This is because low air flow can lead to high levels of toxic and harmful gases in some areas, or substandard air quality, while meeting standards elsewhere. Failure to monitor air quality in all areas can easily lead to missed areas.

[0027] The present invention adopts a layered approach, which divides the air area within the cave into layers. Air quality data is acquired in key areas or random areas within each layer. This can greatly avoid monitoring omissions and improve accuracy. This is because in the layered approach, even if toxic and harmful gases are present in the intervals between layers, although the air flow is low, the toxic and harmful gases can still be distributed in the adjacent parts. And because the gas diffuses to all surrounding areas, it is easy to monitor.

[0028] In the present application, the definition of the key area is the part through which the line with the largest radial distance in the cave passes. This way of division can reduce the missed monitoring in another aspect. Because the part with the largest radial distance tends to show the direction of air flow in the cave, as air flow moves towards the large space direction, the selection of the key area is consistent with the direction of slow air flow in the cave.

[0029] Embodiment 2

[0030] On the basis of the previous embodiment, the reservoir layered area construction unit comprises a three-dimensional model construction subunit and a layered subunit; the three-dimensional model construction subunit is configured to generate a three-dimensional model of the target cave, specifically comprising: generating an actual three-dimensional model of the cave layer by layer based on images, and then simplifying the boundary of the actual three-dimensional model of the cave to obtain a three-dimensional model of the cave; the process of boundary simplification comprises: constantly approaching the boundary of the spatial stereoscopic structure of the actual three-dimensional model of the cave from the outside of a regular stereoscopic structure closest to the spatial stereoscopic structure of the three-dimensional model of the cave, until the inside of the regular stereoscopic structure first contacts the boundary of the spatial stereoscopic structure of the actual three-dimensional model of the cave, and the regular stereoscopic structure at this time is taken as the three-dimensional model of the cave; the layered subunit is configured to take the spatial center of the three-dimensional model as the center of a sphere, expand outward with gradually increasing radial distances as radii from the inside to the outside, and at the time of expansion, each increased radial distance meets a set functional constraint relationship, so as to construct a plurality of layered spherical models to divide the three-dimensional model into a plurality of layered areas from the inside to the outside.

[0031] Specifically, the construction of the layered area improves the accuracy of monitoring on the one hand, and improves the efficiency on the other hand. Compared with the traditional technology of widely distributed sensor type, such method obtains a lot of sensor data, but it is not conducive to data processing, resulting in high system resource occupation.

[0032] Embodiment 3

[0033] On the basis of the previous embodiment, the functional constraint relationship is represented by the following formula:

[0034] Wherein, D is the gradually increasing radial distance; S is the area of the regular stereoscopic structure; d is the diameter of the regular stereoscopic structure.

[0035] Specifically, through experiments, we found that through the functional constraint relationship, the layered area established can be more scientific, and the accuracy of subsequent monitoring is higher.

[0036] Embodiment 4

[0037] On the basis of the last embodiment, the data information acquired by each sub-sensor in each sub-sensor group at least includes carbon dioxide concentration, oxygen concentration, nitrogen concentration, carbon monoxide concentration, sulfur dioxide concentration, humidity, temperature, and sulfur monoxide concentration.

[0038] Embodiment 5

[0039] On the basis of the last embodiment, the method for generating the random area includes: taking the center of the key area as a starting point coordinate, taking the layered area where the key area is located as a plane coordinate area, using a preset random pathfinding algorithm to generate random pathfinding coordinates starting from the starting point coordinate, taking the first, fifth, ninth, (N-1)*4+1th random pathfinding coordinates as the center of the random area, and generating the random area.

[0040] Embodiment 6

[0041] On the basis of the last embodiment, the random pathfinding algorithm is expressed by the following formula: wherein Random(x', y', z') is the center coordinate of the generated random area; First(x, y, z) is the center of the key area, i.e., the starting point coordinate; k is the random number of times in a unit time, and a is an adjustment coefficient with a value range of 3-5.

[0042] Specifically, the present application creatively uses the randomness of Poisson distribution to construct a random pathfinding algorithm, so that the random area obtained is more random, and the accuracy of monitoring is further improved. In the case of insufficient randomness, the probability of monitoring omission will increase.

[0043] Embodiment 7

[0044] On the basis of the last embodiment, the data analysis subunit includes an intra-layer analysis subunit, an inter-layer analysis subunit, and a result generation unit; the intra-layer analysis subunit is configured to perform intra-layer data analysis based on the data information acquired by each sub-sensor to obtain an intra-layer analysis result; the inter-layer analysis subunit is configured to perform inter-layer data analysis based on the intra-layer analysis result to obtain an inter-layer analysis result; and the result generation unit is configured to generate a quality monitoring result by combining the inter-layer analysis result and the intra-layer analysis result.

[0045] Embodiment 8

[0046] On the basis of the last embodiment, the method for the intra-layer analysis subunit to perform intra-layer data analysis based on the data information acquired by each sub-sensor to obtain an intra-layer analysis result includes: calculating the abnormality degree in the layered area by using the following formula: wherein Data iStandard i is the corresponding standard value; n is the number of categories of data information; Num is the total number of random areas and key areas in the hierarchical area; and H is the abnormality degree calculated in the hierarchical area.

[0047] Embodiment 9

[0048] On the basis of the above embodiment, the inter-layer analysis subunit performs inter-layer data analysis based on the intra-layer analysis result to obtain an inter-layer analysis result, and the method comprises: substituting the abnormality degree of each hierarchical area and the abnormality degrees of two adjacent hierarchical areas into an inter-layer abnormality calculation formula to obtain an inter-layer abnormality degree; and the inter-layer abnormality calculation formula is:

[0049] wherein H m is the abnormality degree of the target hierarchical area, H' m and H" m are the abnormality degrees of the two adjacent hierarchical areas, respectively; and K is the inter-layer abnormality degree.

[0050] Embodiment 10

[0051] On the basis of the above embodiment, the result generation unit combines the inter-layer analysis result and the intra-layer analysis result to generate a quality monitoring result, and the method comprises: judging whether each hierarchical area is an abnormal area according to the abnormality degree of the hierarchical area and the corresponding inter-layer abnormality degree, and specifically comprising: if the abnormality degree of the hierarchical area and the inter-layer abnormality degree both exceed the respective set judgment threshold values, then the hierarchical area is judged to be an abnormal area.

[0052] Specifically, the sub-sensor group further comprises a data noise reduction subunit configured to perform data noise reduction on the data information obtained by the sensors in the sub-sensor group.

[0053] Although the specific embodiments of the present application are described above, those skilled in the art should understand that these specific embodiments are only illustrative, and those skilled in the art can make various omissions, substitutions and changes to the details of the above method and system without departing from the principles and essence of the present application. For example, the above method steps are combined, and the substantially same method is used to perform substantially same function to achieve substantially same result, which belongs to the scope of the present application. Therefore, the scope of the present application is only limited by the appended claims.

Claims

1. The layered cave air quality monitoring system is characterized by: The system includes: a cave layered area construction unit, configured to obtain a three-dimensional model of a target cave, calculate the spatial center of the three-dimensional model, and then use the spatial center as the sphere center, from the inside to the outside, with gradually increasing radial distances as radii, to construct multiple layered spherical models, so as to divide the three-dimensional model from the inside to the outside into multiple layered areas; a layered area key area determination unit, configured to determine the key area in each layered area based on the spatial structure characteristics of the three-dimensional model of the target cave, specifically including: connecting the spatial center in the three-dimensional model of the cave and the farthest point in each different direction of the cave, using the position of the connecting line passing through each layered area as the middle line, and setting the area size, Determine a key area in each layered area; an air quality layered comparative analysis unit, comprising: a sensor group and a data analysis subunit; the sensor group comprises a plurality of sub-sensor groups, each sub-sensor group being respectively arranged in a different key area and other random areas other than the key area within each layered area, and each key area or random area including at least one sub-sensor group; the data analysis subunit is configured to perform data analysis within the layered area based on data information acquired by each sub-sensor, obtain intra-layer analysis results and inter-layered area data analysis, obtain inter-layer analysis results, and generate quality monitoring results by combining the inter-layer analysis results and the intra-layer analysis results; The grotto layered area construction unit includes: a three-dimensional model construction subunit and a layering subunit; the three-dimensional model construction subunit is configured to generate a three-dimensional model of the target grotto, specifically including: generating an actual three-dimensional model of the grotto based on layer-by-layer scanning of the image, and then simplifying the boundary of the actual three-dimensional model of the grotto to obtain the three-dimensional model of the grotto; the process of boundary simplification includes: continuously approaching the boundary of the spatial three-dimensional structure of the actual three-dimensional model of the grotto from the periphery with a regular three-dimensional structure that is closest to the spatial three-dimensional structure of the three-dimensional model of the grotto, until the inner side of the regular three-dimensional structure contacts the boundary of the spatial three-dimensional structure of the actual three-dimensional model of the grotto for the first time, and taking the regular three-dimensional structure at this time as the three-dimensional model of the grotto; the layering subunit is configured to use the spatial center of the three-dimensional model as the sphere center, and expand outward from the inside to the outside with a gradually increasing radial distance as the radius. During the expansion, each increase in the radial distance satisfies the set function constraint relationship, thereby constructing multiple layered spherical models to divide the three-dimensional model from the inside to the outside into multiple layered areas; The data information acquired by the sensors in each sub-sensor group includes at least: carbon dioxide concentration, oxygen concentration, nitrogen concentration, carbon monoxide concentration, sulfur dioxide concentration, humidity, temperature and sulfur monoxide concentration; The method for generating a random area includes: using the center of the key area as the starting point coordinate, using the layered area where the key area is located as the plane coordinate area, using a preset random pathfinding algorithm, starting from the starting point coordinate to generate random pathfinding coordinates, and using the generated 1st, 5th, 9th...(N-1)*4+1st random pathfinding coordinates as the center of the random area to generate the random area; The random pathfinding algorithm is expressed using the following formula: Where Random(x′, y′, z′) is the center coordinate of the generated random area; First(x, y, z) is the center of the key area, that is, the starting point coordinate; k is the number of random times per unit time, and α is the adjustment coefficient, ranging from 3 to 5; The data analysis subunit includes: an intra-layer analysis subunit, an inter-layer analysis subunit and a result generation unit; the intra-layer analysis subunit is configured to perform data analysis within the layered area based on the data information obtained by each sub-sensor to obtain an intra-layer analysis result; the inter-layer analysis subunit is configured to perform inter-layer data analysis based on the intra-layer analysis result to obtain an inter-layer analysis result; the result generation unit is configured to combine the inter-layer analysis result and the intra-layer analysis result to generate a quality monitoring result.

2. The system according to claim 1, wherein The function constraint relationship is expressed using the following formula: Wherein, D is the gradually increasing radial distance of the mirror; S is the area of ​​the regular three-dimensional structure; and d is the diameter of the regular three-dimensional structure.

3. The system according to claim 1, wherein: The intra-layer analysis subunit performs data analysis within the layered area based on the data information acquired by each sub-sensor. The method for obtaining the intra-layer analysis result includes: calculating the abnormality degree within the layered area using the following formula: Among them, Data i is the normalized mean of a certain type of data information in the stratified area, Standard i is its corresponding standard value; n is the number of types of data information; Num is the total number of random areas and key areas in the stratified area; H is the calculated abnormality degree in the stratified area.

4. The system according to claim 3, wherein: The interlayer analysis subunit performs interlayer data analysis based on the intralayer analysis results. The method for obtaining the interlayer analysis results includes: substituting the abnormality degree within each layer area and the abnormality degrees of the two adjacent layer areas into the interlayer abnormality calculation formula to obtain the interlayer abnormality degree; the interlayer abnormality calculation formula is: Among them, H m is the abnormality of the target layer area, H′ m and H″ m are the abnormality of two adjacent stratified areas; K is the inter-layer abnormality.

5. The system according to claim 4, wherein: The result generation unit generates a quality monitoring result in combination with the inter-layer analysis results and the intra-layer analysis results, and the method includes: judging whether the stratified area is an abnormal area based on the abnormality degree of each stratified area and its corresponding inter-layer abnormality degree, specifically including: if the abnormality degree of the stratified area and the inter-layer abnormality degree both exceed the respective set judgment thresholds, then the stratified area is judged to be an abnormal area.

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