An old goaf water quality detection system based on multi-parameter detection technology

Abstract

The present invention discloses an old goaf water quality detection system based on multi-parameter detection technology, which relates to the technical field of water quality detection and improves the accuracy of water quality detection results. The present invention divides a number of detection space regions in a three-dimensional visualization space, marks a number of local fluctuation segments on the signal spectrum of each old goaf and inputs them into the three-dimensional visualization space, compares the local fluctuation segments in each detection space region with each other, fuses each detection space region according to the comparison result, filters the information of the old goaf spectral video according to the fusion result, divides a number of water quality detection spaces in the three-dimensional visualization space, rotates each water quality detection space corresponding to the UAV flight route, and then maps each surface of the water quality detection spaces corresponding to the same old goaf position by different UAVs onto a three-dimensional neural network, so as to obtain the water quality distribution display blocks at each position of the old goaf.

Description

Technical Field

[0001] The present invention relates to the technical field of water quality detection, and specifically to an old goaf water quality detection system based on multi-parameter detection technology. Background Art

[0002] In underground operations such as mines and coal mining, the water quality detection of old goafs is a crucial and challenging task. Old goafs are usually the remaining spaces formed after mine exploitation. Long-term water accumulation may lead to serious safety accidents, such as water inrush accidents. Traditional water quality detection methods mainly rely on manual sampling and laboratory analysis. This method is not only time-consuming and laborious, but also unable to achieve large-scale, high-precision, and real-time water quality monitoring.

[0003] The existing water quality detection systems have the following problems:

[0004] Insufficient 3D visualization ability: The existing water quality detection systems lack effective 3D visualization tools and are difficult to intuitively display the water quality distribution in old goafs.

[0005] Limited data fusion ability: The data collected by different drones are prone to errors during the fusion process and cannot accurately reflect the water quality conditions in old goafs. At the same time, due to a large amount of suspended matter or particulate matter mixed in the accumulated water in old goafs, it is extremely easy to cause deviations in the detection results.

[0006] Therefore, an old goaf water quality detection system based on multi-parameter detection technology is provided. Summary of the Invention

[0007] In order to solve the above technical problems, the purpose of the present invention is to provide an old goaf water quality detection system based on multi-parameter detection technology.

[0008] In order to achieve the above purpose, the present invention provides the following technical solutions:

[0009] An old goaf water quality detection system based on multi-parameter detection technology, including a cloud computing platform, which is communicatively connected to a water quality information collection module, a water quality information filtering module, and a water quality information deconstruction module;

[0010] The water quality information collection module is used to set a number of drones, install multi-spectrometers and remote sensing devices on the drones, and set the flight routes of the drones. Then, the drones collect the old goaf spectral videos and pulse laser reflection signal spectra in the old goaf along the flight routes of the drones;

[0011] The water quality information filtering module is used to establish a three-dimensional visualization space, splice the pulse laser reflection signal spectrum according to the UAV flight route to generate the goaf signal spectrum, divide a number of detection space regions in the three-dimensional visualization space, mark a number of local fluctuation segments on the goaf signal spectrum and then input them into the three-dimensional visualization space, compare the local fluctuation segments in each detection space region with each other, fuse each detection space region according to the comparison result, and filter the information of the goaf spectral video according to the fusion result;

[0012] The water quality information deconstruction module is used to establish a three-dimensional neural network, embed the three-dimensional visualization space into the three-dimensional neural network, map the goaf spectral video to the three-dimensional visualization space, divide a number of water quality detection spaces in the three-dimensional visualization space, rotate each water quality detection space according to the UAV flight route, and then map each surface of the water quality detection spaces corresponding to the same goaf position by different UAVs onto the three-dimensional neural network, so as to obtain the water quality distribution display blocks at each position of the goaf.

[0013] Furthermore, the acquisition process of the goaf spectral video and the pulse laser reflection signal spectrum includes:

[0014] Set a number of UAVs, set different UAV flight routes with different starting points and ending points for each UAV, each UAV flies over the goaf along the UAV flight route at the same time. During the flight of the UAVs, each UAV acquires the goaf spectral video within the data acquisition range of the multispectral imager, and emits a pulse laser signal to the goaf through the remote sensing device, and synchronously generates the corresponding pulse laser reflection signal spectrum.

[0015] Furthermore, the process of setting the detection space regions in the three-dimensional visualization space includes:

[0016] Establish a three-dimensional visualization space, and splice the pulse laser reflection signal spectra collected by each UAV in sequence according to the UAV flight route to obtain the goaf signal spectrum;

[0017] Mark a number of local fluctuation segments on each goaf signal spectrum, map all the goaf signal spectra to the three-dimensional visualization space, and divide a number of intersecting or overlapping detection space regions in the three-dimensional visualization space according to the amplitude length and the time length occupied by each local fluctuation segment.

[0018] Furthermore, the process of fusing between each detection space region includes:

[0019] A three-dimensional coordinate system is established, and the local fluctuation segments included in the detection space regions with overlapping or intersecting relationships are mapped onto the three-dimensional coordinate system to obtain the volume values of the two local fluctuation segments in the three-dimensional coordinate system. Then, the ratio of the local fluctuation segment with a relatively smaller volume value to the local fluctuation segment with a relatively larger volume value is selected, and the ratio is recorded as the relative fitting degree of the corresponding two detection space regions.

[0020] A fitting degree threshold is set. According to the size relationship between the relative fitting degree and the fitting degree threshold, a fusion operation is performed on the corresponding detection space regions until the remaining detection space regions cannot be fused anymore. The remaining detection space regions in the three-dimensional visualization space are recorded as the suspended matter display regions.

[0021] Furthermore, the process of information filtering for the water quality information spectral video includes:

[0022] The spectral videos of the goafs collected by each drone are stitched together according to the drone flight routes to obtain the water quality information spectral video. The water quality information spectral video is mapped onto the three-dimensional visualization space. Then, according to the spatial position of the suspended matter display region in the three-dimensional visualization space, the video regions corresponding to the corresponding positions of the water quality information spectral video are deleted, thereby completing the information filtering of the water quality information spectral video.

[0023] Furthermore, the three-dimensional neural network consists of six main detection network planes, where the planes formed by the six main detection network planes are perpendicular to each other in pairs, and the main detection network plane is composed of several sub-inclined detection planes.

[0024] Furthermore, the process of rotating each water quality detection space includes:

[0025] The RGB color spaces of several water qualities in the corresponding spectral frequency bands are obtained through the Internet. The water quality information spectral video in the three-dimensional visualization space is divided into several water quality detection spaces. According to the RGB color spaces of various water qualities in various spectral frequency bands, the distributions of various water qualities included in each water quality detection space are marked in the water quality information spectral video. At the same time, the drone flight routes corresponding to the water quality information spectral video are mapped onto the three-dimensional visualization space, and the shooting center point at the center position of the drone flight route is selected.

[0026] Taking the shooting center point as the center and the data acquisition range of the drone as the diameter, a shooting frame is set at the top of the three-dimensional visualization space. The shooting frame traverses the entire water quality information spectral video along the drone flight route at the top of the three-dimensional visualization space, and then the acquisition range cones corresponding to the drone at each time node are traversed in the three-dimensional visualization space.

[0027] Obtain the angles from the vertex of the acquisition range cone to the associated water quality detection spaces, rotate the water quality detection spaces according to the angles so that they are in a relatively parallel state with the bottom of the three-dimensional visualization space, obtain the included angles between the rotation angles of the water quality detection spaces at each time node and each main detection network plane, and then vertically map the water quality distribution on each surface of the water quality detection space to the corresponding secondary inclined detection surfaces according to the included angles;

[0028] Vertically map the water quality distributions on the inclined detection surfaces of the water quality detection spaces corresponding to the same position by different drones and the same main detection network plane to the same main detection network plane, then splice the overlapping water quality distribution results on each main detection network plane, and further obtain the water quality distribution display blocks corresponding to the positions in the goaf, and label the corresponding water quality information names for each water quality distribution display block.

[0029] Furthermore, set a common time slider and a single-body time slider for each water quality distribution display block. When the common time slider slides, the water quality distributions and water quality information names in all water quality distribution display blocks change synchronously;

[0030] When the single-body time slider slides, the water quality distributions and water quality information names in the remaining water quality distribution display blocks remain unchanged, and only the water quality distributions and water quality information names in the water quality distribution display block associated with the single-body time slider change synchronously.

[0031] Compared with the prior art, the beneficial effects of the present invention are:

[0032] 1. The present invention establishes a three-dimensional visualization space, splices the pulse laser reflection signal spectra according to the UAV flight route to generate the goaf signal spectra. Divide several detection space regions in the three-dimensional visualization space and mark several local fluctuation segments. By comparing and fusing the data of each detection space region, while reducing the interference of suspended substances and particulate matters in the goaf water on the water quality detection process, it lays a data foundation for the accuracy of subsequent water quality detection results, thereby improving the accuracy of water quality detection results.

[0033] 2. The water quality information deconstruction module establishes a three-dimensional neural network and embeds the three-dimensional visualization space into the three-dimensional neural network. Divide several water quality detection spaces in the space. By rotating and mapping the water quality detection spaces corresponding to the same goaf position by different UAVs, while realizing the three-dimensional visualization display of high-precision water quality distribution, it improves the water quality detection efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 is the schematic diagram of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0035] To further elaborate on the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following specifically describes in detail the specific implementation manner, structure, features and effects of the present invention in combination with the accompanying drawings and preferred embodiments.

[0036] As Figure 1 shown, a goaf water quality detection system based on multi-parameter detection technology includes a cloud computing platform, and the cloud computing platform is communicatively connected to a water quality information collection module, a water quality information filtering module, and a water quality information deconstruction module;

[0037] The water quality information collection module is used to set a number of unmanned aerial vehicles (UAVs), install multi-spectral spectrometers and remote sensing devices on the UAVs, and set the UAV flight routes. Then, the UAVs collect the goaf spectral videos and pulse laser reflection signal spectra of the goaf along the UAV flight routes;

[0038] The water quality information filtering module is used to establish a three-dimensional visualization space, splice the pulse laser reflection signal spectra according to the UAV flight routes to generate goaf signal spectra, divide a number of detection space regions in the three-dimensional visualization space, mark a number of local fluctuation segments on each goaf signal spectrum and then input them into the three-dimensional visualization space, compare the local fluctuation segments in each detection space region with each other, fuse each detection space region according to the comparison result, and filter the information of the goaf spectral video according to the fusion result;

[0039] The water quality information deconstruction module is used to establish a three-dimensional neural network, embed the three-dimensional visualization space into the three-dimensional neural network, map the goaf spectral video to the three-dimensional visualization space, divide a number of water quality detection spaces in the three-dimensional visualization space, rotate each water quality detection space according to the UAV flight routes, and then map each surface of the water quality detection spaces corresponding to the same goaf position by different UAVs onto the three-dimensional neural network, so as to obtain the water quality distribution display blocks at each position of the goaf.

[0040] Furthermore, the working principle of the present invention is illustrated by the following embodiments:

[0041] Set n UAVs, and the UAVs are installed with wireless communication devices, multi-spectral imagers and remote sensing devices. Each UAV is communicatively connected to the water quality information collection module through the wireless communication device. Then, the water quality information collection module sets identity numbers for each UAV respectively, and the identity numbers are, for example, 54184;

[0042] Set different starting and ending points for each UAV, as well as the same flight speed for the UAV flight routes. The width of the UAV flight routes is equal to the data acquisition ranges of the multispectral imager and the remote sensing device, and the area size and shape formed by each UAV flight route are equal to the floor area and planar shape of the goaf.

[0043] The water quality information acquisition module sends the UAV flight routes to each UAV. Then, each UAV flies over the goaf along the UAV flight routes simultaneously. During the flight of the UAVs, each UAV obtains the spectral video of the goaf within the data acquisition range of the multispectral imager, and emits pulsed laser signals to the goaf through the remote sensing device, and synchronously generates the corresponding pulsed laser reflection signal spectrum.

[0044] When the UAV reaches the end point along the UAV flight route, label the corresponding identity numbers on the spectral video of the goaf and the pulsed laser reflection signal spectrum and upload them to the water quality information acquisition module.

[0045] Furthermore, when all UAVs upload the spectral video of the goaf and the pulsed laser reflection signal spectrum, the water quality information acquisition module sends all the spectral videos of the goaf and the pulsed laser reflection signal spectrum to the water quality information filtering module.

[0046] Since goafs are generally formed by the combined effects of multiple factors such as mine exploitation and groundwater, there are a large number of suspended solids and particulate matters in the goaf water, such as suspended solids like ore powder and mud blocks. When the UAV acquires the spectral video of the goaf through the multispectral imager, the suspended solids and particulate matters will scatter and absorb the spectral signals, resulting in errors in the imaging results of the spectral video of the goaf.

[0047] Furthermore, the water quality information filtering module establishes a three-dimensional visualization space, and splices the pulsed laser reflection signal spectra collected by each UAV in sequence according to the UAV flight routes to obtain the corresponding goaf signal spectrum.

[0048] During the process of the UAV emitting pulsed laser signals to the goaf, when the pulsed laser signal passes through the water surface, the corresponding goaf signal spectrum undergoes the first obvious fluctuation change. Until the pulsed laser signal reaches the bottom of the goaf, the goaf signal spectrum undergoes the second obvious fluctuation change. During the process of these two obvious fluctuation changes, when the pulsed laser signal passes through the suspended solids and particulate matters in the goaf water, a series of local fluctuations smaller than the obvious fluctuations when the pulsed laser signal passes through the water surface and reaches the bottom of the goaf appear in the corresponding goaf signal spectrum.

[0049] Furthermore, several local fluctuation segments are marked on the signal spectrum of each goaf area, and the signal spectra of all goaf areas are mapped into a three-dimensional visualization space. According to the amplitude length and the occupied time length of each local fluctuation segment, several intersecting or overlapping detection space regions are divided in the three-dimensional visualization space. It should be noted that each detection space region can only contain one local fluctuation segment in the initial state;

[0050] Since the position of the drone itself is also continuously changing during the process of collecting the pulse laser reflection signal spectrum corresponding to the suspended substances or particulate matters in the goaf water accumulation through the remote sensing device, the local fluctuation segments in the three-dimensional visualization space are in a three-dimensional shape, and the plane width is equal to the time length. At the same time, the starting and ending positions of each drone are different, so there are some differences in the pulse laser reflection signal spectra collected by each drone corresponding to the same suspended substance or particulate matter;

[0051] A three-dimensional coordinate system is established, and the local fluctuation segments included in the detection space regions with overlapping or intersecting relationships are mapped into the three-dimensional coordinate system to obtain the volume values of the two local fluctuation segments in the three-dimensional coordinate system. Furthermore, the ratio of the local fluctuation segment with a relatively smaller volume value to the local fluctuation segment with a relatively larger volume value is selected, and the ratio is recorded as the relative fitting degree of the corresponding two detection space regions;

[0052] A fitting degree threshold is set, and the detection space regions with a relative fitting degree greater than or equal to the fitting degree threshold are merged, and the local fluctuation segments inside are synchronously fused;

[0053] For the detection space regions with a relative fitting degree less than the fitting degree threshold, no operation is performed;

[0054] Repeat the above operations on the detection space regions until the remaining detection space regions cannot be fused. Then, the remaining detection space regions in the three-dimensional visualization space are recorded as the suspended substance display regions.

[0055] The spectral videos of the goaf water quality collected by each drone are spliced according to the drone flight route to obtain the spectral video of water quality information. The spectral video of water quality information is mapped into the three-dimensional visualization space. Then, according to the spatial position of the suspended substance display region in the three-dimensional visualization space, the video regions corresponding to the corresponding positions of the spectral video of water quality information are deleted, thus completing the information filtering of the spectral video of water quality information.

[0056] Furthermore, the water quality information filtering module sends the three-dimensional visualization space and the spectral video of water quality information that has completed all information filtering to the water quality information deconstruction module;

[0057] The water quality information deconstruction module is provided with a three-dimensional neural network, which is composed of six main detection network planes, and the planes formed by the six main detection network planes are perpendicular to each other in pairs;

[0058] The main detection network plane is simultaneously composed of a number of sub-inclined detection planes. It should be noted that the included angle between each sub-inclined detection plane and its related main detection network plane is in the range of [-45°, 45°];

[0059] Embed the three-dimensional visualization space into the three-dimensional neural network, and according to the spectral bands emitted by the hyperspectral imager, obtain the RGB color space of several water qualities in the corresponding spectral bands through the Internet;

[0060] Since each water quality information spectral video is collected during the flight of different drones along different drone flight routes, for the spectral video segments of the same position in the old goaf area corresponding to the water quality information, they are collected at different positions by different drones within the same time period in different water quality information spectral videos, resulting in differences in the spectral video segments generated at the same position in different water quality information spectral videos;

[0061] Divide the water quality information spectral video in the three-dimensional visualization space into several water quality detection spaces. The water quality detection spaces are in the shape of a cube, and the size is generally 3*3*3mm;

[0062] According to the RGB color space of various water qualities in various spectral bands, mark the distribution of various water qualities contained in each water quality detection space in the water quality information spectral video. At the same time, map the drone flight route corresponding to the water quality information spectral video into the three-dimensional visualization space, and select the shooting center point at the center position of the drone flight route;

[0063] Taking the shooting center point as the center and the data collection range of the drone as the diameter, set a shooting frame at the top of the three-dimensional visualization space. Traverse the entire water quality information spectral video along the drone flight route through the shooting frame, and then traverse the collection range cones of the corresponding drone at each time node in the three-dimensional visualization space;

[0064] It should be noted that since the spectral signal propagates in a fan shape in the old goaf area, the data collection range of the drone at each time node is conical;

[0065] Obtain the angles from the vertex of the acquisition range cone to the associated water quality detection spaces, rotate the water quality detection spaces according to the angles so that they are in a relatively parallel state with the bottom of the 3D visualization space, obtain the included angles between the rotation angles of the water quality detection spaces at each time node and each main detection network plane, and then vertically map the water quality distributions of each surface of the water quality detection space onto the corresponding secondary inclined detection surfaces according to the included angles;

[0066] Vertically map the water quality distributions of the water quality detection spaces corresponding to the same position of different UAVs and the inclined detection surfaces of the same main detection network plane onto the same main detection network plane, then splice the overlapping water quality distribution results on each main detection network plane, and then obtain the water quality distribution display blocks corresponding to the positions in the goaf area, and label the corresponding water quality information names for each water quality distribution display block;

[0067] The water quality distribution display block contains water quality distributions of several colors, and each color corresponds to a water quality information, such as lead, mercury, cadmium, etc.

[0068] Furthermore, set a common time slider and a single-body time slider for each water quality distribution display block. When the common time slider slides, the water quality distributions and water quality information names in all water quality distribution display blocks change synchronously;

[0069] When the single-body time slider slides, the water quality distributions and water quality information names in the remaining water quality distribution display blocks remain unchanged, and only the water quality distributions and water quality information names in the water quality distribution display block associated with the single-body time slider change synchronously.

[0070] The above is only a preferred embodiment of the present invention, and does not impose any form of limitation on the present invention. Although the present invention has been disclosed above with a preferred embodiment, it is not intended to limit the present invention. Any person skilled in the art can make some changes or modifications into equivalent embodiments by using the disclosed technical content within the scope of the technical solution of the present invention. However, as long as it does not depart from the content of the technical solution of the present invention, any brief modifications, equivalent changes and modifications made to the above embodiments based on the technical essence of the present invention still fall within the scope of the technical solution of the present invention.

Claims

1. A goaf water quality detection system based on multi-parameter detection technology, including a cloud computing platform, characterized in that, The cloud computing platform is communicatively connected to a water quality information acquisition module, a water quality information filtering module, and a water quality information deconstruction module; The water quality information acquisition module is used to set up a number of unmanned aerial vehicles (UAVs), install multi-spectral spectrometers and remote sensing devices on the UAVs, and set the flight routes of the UAVs. Then, the UAVs collect the spectral videos of the goaf and the spectral of the pulse laser reflection signals along the flight routes of the UAVs; The water quality information filtering module is used to establish a three-dimensional visualization space, splice the spectral of the pulse laser reflection signals according to the flight routes of the UAVs to generate the goaf signal spectrum, divide a number of detection space regions in the three-dimensional visualization space, mark a number of local fluctuation segments on the goaf signal spectrum and then input them into the three-dimensional visualization space, compare the local fluctuation segments in each detection space region with each other, fuse each detection space region according to the comparison result, and filter the information of the goaf spectral video according to the fusion result; The water quality information deconstruction module is used to establish a three-dimensional neural network, embed the three-dimensional visualization space into the three-dimensional neural network, map the goaf spectral video to the three-dimensional visualization space, divide a number of water quality detection spaces in the three-dimensional visualization space, rotate each water quality detection space according to the flight routes of the UAVs, and then map each surface of the water quality detection spaces corresponding to the same goaf position by different UAVs onto the three-dimensional neural network, so as to obtain the water quality distribution display blocks at each position of the goaf.

2. The water quality detection system for old goafs based on multi-parameter detection technology according to claim 1, wherein, The acquisition process of the goaf spectral video and the spectral of the pulse laser reflection signals includes: Set different flight routes of the UAVs with different starting points and ending points for each UAV. Each UAV flies over the goaf along the flight route of the UAV, obtains the goaf spectral video within the data acquisition range of the multi-spectral imager, and emits pulse laser signals to the goaf through the remote sensing device and synchronously generates the spectral of the pulse laser reflection signals.

3. The water quality detection system for goaf based on multi-parameter detection technology according to claim 2, characterized in that The process of setting the detection space regions in the three-dimensional visualization space includes: Establish a three-dimensional visualization space, and splice the spectral of the pulse laser reflection signals collected by each UAV in sequence according to the flight routes of the UAVs to obtain the goaf signal spectrum; Mark a number of local fluctuation segments on each goaf signal spectrum, map all the goaf signal spectra into the three-dimensional visualization space, and divide a number of intersecting or overlapping detection space regions in the three-dimensional visualization space according to the amplitude length and the time length occupied by each local fluctuation segment.

4. The water quality detection system for old goafs based on multi-parameter detection technology according to claim 3, characterized in that, The process of fusing between each detection space region includes: Establish a three-dimensional coordinate system, map the local fluctuation segments included in the detection space regions with overlapping or intersecting relationships into the three-dimensional coordinate system, obtain the volume values of the two local fluctuation segments in the three-dimensional coordinate system, and then select the ratio of the local fluctuation segment with a relatively smaller volume value to the local fluctuation segment with a relatively larger volume value, and record the ratio as the relative fitting degree of the corresponding two detection space regions; Set a fitting degree threshold, merge the detection space regions with a relative fitting degree greater than or equal to the fitting degree threshold, and synchronously fuse the internal local fluctuation segments; For the detection space region with a relative fitness less than the fitness threshold, no operation is performed; Repeat the operation of detecting space region merging until the remaining detection space regions cannot be fused, and then record the remaining detection space regions in the three-dimensional visualization space as the suspended matter display regions.

5. The water quality detection system for old goafs based on multi-parameter detection technology according to claim 4, wherein, The process of information filtering for the water quality information spectral video includes: Stitch the spectral videos of each goaf according to the UAV flight route to obtain the water quality information spectral video, map the water quality information spectral video to the three-dimensional visualization space, and then delete the video regions corresponding to the positions of the water quality information spectral video according to the spatial positions of the suspended matter display regions in the three-dimensional visualization space.

6. The water quality detection system for old goafs based on multi-parameter detection technology according to claim 5, characterized in that, The three-dimensional neural network consists of six main detection network surfaces, and each main detection network surface is composed of several sub-inclined detection surfaces.

7. The water quality detection system for goaf based on multi-parameter detection technology according to claim 6, characterized in that, The process of rotating each water quality detection space includes: Obtain the RGB color spaces of several water qualities in the corresponding spectral bands through the Internet, divide the water quality information spectral video in the three-dimensional visualization space into several water quality detection spaces, mark the distribution of various water qualities contained in each water quality detection space according to the RGB color spaces of various water qualities in various spectral bands, and at the same time map the UAV flight route corresponding to the water quality information spectral video to the three-dimensional visualization space, and select the shooting center point at the center position of the UAV flight route; Taking the shooting center point as the center and the data acquisition range of the UAV as the diameter, traverse the acquisition range cones corresponding to the UAV at each time node in the three-dimensional visualization space; Obtain the angles from the vertices of the acquisition range cones to the associated water quality detection spaces, rotate the water quality detection spaces according to the angles, obtain the included angles between the rotation angles of the water quality detection spaces at each time node and the main detection network surfaces, and then vertically map the water quality distributions of each surface of the water quality detection space to the corresponding sub-inclined detection surfaces according to the included angles; Vertically map the water quality detection spaces corresponding to the same position of different UAVs and the water quality distributions on the inclined detection surfaces of the same main detection network surface to the same main detection network surface, and then splice the overlapping water quality distribution results on each main detection network surface to obtain the water quality distribution display blocks corresponding to the positions of the goafs, and mark the corresponding water quality information names for each water quality distribution display block.

8. The water quality detection system for old goafs based on multi-parameter detection technology according to claim 7, characterized in that, Set a common time slider and a single-time slider for each water quality distribution display block. When the common time slider slides, the water quality distributions and water quality information names in all water quality distribution display blocks change synchronously; When the single-time slider slides, the water quality distributions and water quality information names in the remaining water quality distribution display blocks remain unchanged, and only the water quality distributions and water quality information names in the water quality distribution display block associated with the single-time slider change synchronously.

Images