Old goaf water quality detection system based on multi-parameter detection technology

Abstract

The invention discloses an old goaf water quality detection system based on a multi-parameter detection technology, relates to the technical field of water quality detection, and improves the accuracy of a water quality detection result. According to the invention, a plurality of detection space areas are divided in a three-dimensional visual space, a plurality of local fluctuation fragments are marked in each old goaf signal frequency spectrum and then are input into the three-dimensional visual space, and the local fluctuation fragments in each detection space area are compared with each other; all detection space areas are fused according to a comparison result, information filtering is carried out on the spectrum video of the old goaf according to a fusion result, a plurality of water quality detection spaces are divided in a three-dimensional visual space, and all the water quality detection spaces are rotated correspondingly according to the flight route of the unmanned aerial vehicle. And mapping each surface of the water quality detection space, corresponding to the same old goaf position, of different unmanned aerial vehicles to a three-dimensional neural network so as to obtain a water quality distribution display block of each position of the old goaf.

Description

Technical Field

[0001] The invention relates to the technical field of water quality detection, in particular to an old airspace water quality detection system based on multi-parameter detection technology. Background Art

[0002] In underground operations such as mining and coal mining, water quality testing in old empty areas is a crucial and challenging task. Old empty areas are usually residual spaces formed after mining. Long-term accumulation of water may lead to serious safety accidents, such as water seepage accidents. Traditional water quality testing methods mainly rely on manual sampling and laboratory analysis, which is not only time-consuming and labor-intensive, but also unable to achieve large-scale, high-precision, and real-time water quality monitoring.

[0003] The existing water quality detection system has the following problems: Insufficient three-dimensional visualization capabilities: The existing water quality detection system lacks effective three-dimensional visualization tools, making it difficult to intuitively display the water quality distribution in old empty areas.

[0004] Limited data fusion capability: Data collected by different drones are prone to errors during the fusion process and cannot accurately reflect the water quality of the old airspace area. At the same time, since the accumulated water in the old airspace area is mixed with a large amount of suspended matter or particulate matter, it is very easy to cause deviations in the detection results.

[0005] For this purpose, a water quality detection system for old airspace area based on multi-parameter detection technology is provided. Summary of the invention

[0006] In order to solve the above technical problems, the purpose of the present invention is to provide an old empty area water quality detection system based on multi-parameter detection technology.

[0007] In order to achieve the above object, the present invention provides the following technical solutions: A water quality detection system for old empty areas based on multi-parameter detection technology, comprising a cloud computing platform, wherein the cloud computing platform is communicatively connected with a water quality information acquisition module, a water quality information filtering module and a water quality information deconstruction module; The water quality information collection module is used to set up several drones, install multi-spectrometers and remote sensing devices on the drones, and set the flight routes of the drones, so that the drones can collect the spectrum video of the old empty area and the spectrum of the pulsed laser reflection signal of the old empty area along the flight routes of the drones; 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 flight route of the drone to generate the old empty area signal spectrum, divide a number of detection space areas in the three-dimensional visualization space, and mark a number of local fluctuation fragments in the old empty area signal spectrum and input them into the three-dimensional visualization space, compare the local fluctuation fragments in each detection space area with each other, merge each detection space area with each other according to the comparison result, and filter the old empty area spectrum video according to the fusion result; The water quality information deconstruction module is used to establish a three-dimensional neural network, and embed the three-dimensional visualization space into the three-dimensional neural network, map the spectral video of the old airspace area into the three-dimensional visualization space, and divide a number of water quality detection spaces in the three-dimensional visualization space. Each water quality detection space is rotated according to the flight route of the drone, and then each surface of the water quality detection space corresponding to the same old airspace location of different drones is mapped onto the three-dimensional neural network, so as to obtain the water quality distribution display blocks of the old airspace at various locations.

[0008] Furthermore, the acquisition process of the spectrum video of the old empty area and the spectrum of the pulsed laser reflection signal includes: Several drones are set up, and a drone flight route with a different starting point and end point is set for each drone. Each drone flies over the old airspace along the drone flight route at the same time. During the flight of the drone, each drone obtains a spectral video of the old airspace within the data acquisition range of the multi-spectral imager, and transmits a pulse laser signal to the old airspace through a remote sensing device, and synchronously generates a corresponding pulse laser reflection signal spectrum.

[0009] Furthermore, the process of setting the detection space area in the three-dimensional visualization space includes: A three-dimensional visualization space is established, and the spectrum of pulsed laser reflection signals collected by each drone is stitched together in sequence according to the flight route of the drone to obtain the spectrum of the old airspace signal; Several local fluctuation segments are marked in the signal spectrum of each old empty area, and all the signal spectra of the old empty area are mapped in the three-dimensional visualization space. According to the amplitude length and the time length occupied by each local fluctuation segment, several intersecting or overlapping detection space areas are divided in the three-dimensional visualization space.

[0010] Furthermore, the process of fusing the various detection space regions includes: Establish a three-dimensional coordinate system, map the local fluctuation segments contained in the overlapping or intersecting detection space regions 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 fit of the corresponding two detection space regions; A fitting threshold is set, and according to the size relationship between the relative fitting degree and the fitting threshold, a fusion operation is performed on the corresponding detection space area until the remaining detection space area cannot be fused, and the remaining detection space area in the three-dimensional visualization space is recorded as the suspended object display area.

[0011] Furthermore, the process of filtering the information of the water quality information spectrum video includes: The spectral videos of the old air zone collected by each drone are spliced ​​according to the flight route of the drone to obtain a spectral video of water quality information, and the spectral video of water quality information is mapped in the three-dimensional visualization space. Then, according to the spatial position of the suspended matter display area in the three-dimensional visualization space, the video area corresponding to the corresponding position of the spectral video of water quality information is deleted, thereby completing the information filtering of the spectral video of water quality information.

[0012] Furthermore, the three-dimensional neural network consists of six main detection network surfaces, wherein the planes formed by the six main detection network surfaces are perpendicular to each other, and the main detection network surface is composed of a plurality of secondary inclined detection surfaces.

[0013] Furthermore, the process of rotating each water quality detection space includes: Obtain the RGB color spaces of several types of water quality in corresponding spectral frequency bands through the Internet, divide the water quality information spectrum video in the three-dimensional visualization space into several water quality detection spaces, and mark the distribution of various water qualities contained in each water quality detection space in the water quality information spectrum video according to the RGB color spaces of various water qualities in various spectral frequency bands. At the same time, map the water quality information spectrum video corresponding to the UAV flight route in the three-dimensional visualization space, and select the shooting center point at the center position of the UAV flight route; A shooting frame is set at the top of the three-dimensional visualization space with the shooting center point as the center of the circle and the data collection range of the drone as the diameter. The entire water quality information spectrum video is traversed in sequence along the drone flight route at the top of the three-dimensional visualization space through the shooting frame, and then the collection range cone of the corresponding drone at each time node is traversed in the three-dimensional visualization space; Obtain the angle from the vertex of the collection range cone to each associated water quality detection space, rotate the water quality detection space according to the angle so that it is relatively parallel to the bottom of the three-dimensional visualization space, obtain the angle between the rotation angle of the water quality detection space and each main detection network surface at each time node, and then vertically map the water quality distribution of each surface of the water quality detection space onto the corresponding secondary inclined detection surface according to the angle; The water quality detection space corresponding to the same location of different drones and the water quality distribution on the inclined detection surface of the same main detection network surface are vertically mapped onto the same main detection network surface, and then the overlapping water quality distribution results on each main detection network surface are spliced ​​to obtain the water quality distribution display block at the corresponding position of the old airspace, and each water quality distribution display block is labeled with the corresponding water quality information name.

[0014] Furthermore, a common time slider and a single time slider are set for each water quality distribution display block. When the common time slider slides, the water quality distribution and water quality information names in all water quality distribution display blocks change synchronously. When the single time slider slides, the water quality distribution and water quality information names in the other water quality distribution display blocks remain unchanged, and only the water quality distribution and water quality information names in the water quality distribution display block associated with the single time slider change synchronously.

[0015] Compared with the prior art, the present invention has the following beneficial effects: 1. The present invention establishes a three-dimensional visualization space, splices the pulse laser reflection signal spectrum according to the flight route of the drone to generate the old empty area signal spectrum. In the three-dimensional visualization space, several detection space areas are divided, and several local fluctuation fragments are marked. By comparing and integrating the data of each detection space area, the suspended matter and particulate matter in the old empty area water are reduced. The interference of the water quality detection process is reduced, and the data foundation is laid for the accuracy of the subsequent water quality detection results, thereby improving the accuracy of the water quality detection results.

[0016] 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. It divides the space into several water quality detection spaces. By rotating and mapping different drones to correspond to the water quality detection space at the same old airspace location, it achieves high-precision three-dimensional visualization of water quality distribution while improving the efficiency of water quality detection. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 It is a schematic diagram of the present invention. DETAILED DESCRIPTION

[0018] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the specific implementation mode, structure, characteristics and effects of the present invention are described in detail below in combination with the accompanying drawings and preferred embodiments.

[0019] like Figure 1 As shown, a water quality detection system for old empty areas based on multi-parameter detection technology includes a cloud computing platform, and the cloud computing platform is communicatively connected with a water quality information acquisition module, a water quality information filtering module and a water quality information deconstruction module; The water quality information collection module is used to set up several drones, install multi-spectrometers and remote sensing devices on the drones, and set the flight routes of the drones, so that the drones can collect the spectrum video of the old empty area and the spectrum of the pulsed laser reflection signal of the old empty area along the flight routes of the drones; 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 flight route of the drone to generate the old empty area signal spectrum, divide a number of detection space areas in the three-dimensional visualization space, and mark a number of local fluctuation fragments in each old empty area signal spectrum and input them into the three-dimensional visualization space, compare the local fluctuation fragments in each detection space area with each other, merge each detection space area with each other according to the comparison result, and filter the old empty area spectrum video according to the fusion result; The water quality information deconstruction module is used to establish a three-dimensional neural network, and embed the three-dimensional visualization space into the three-dimensional neural network, map the spectral video of the old airspace area into the three-dimensional visualization space, and divide a number of water quality detection spaces in the three-dimensional visualization space. Each water quality detection space is rotated according to the flight route of the drone, and then each surface of the water quality detection space corresponding to the same old airspace location of different drones is mapped onto the three-dimensional neural network, so as to obtain the water quality distribution display blocks of the old airspace at various locations.

[0020] Further, the working principle of the present invention is described below by way of examples: n drones are set, each drone is equipped with a wireless communication device, a multispectral imager and a remote sensing device, each drone is connected to the water quality information collection module through the wireless communication device, and then the water quality information collection module sets an identity number for each drone, and the identity number is 54184, for example; Set a drone flight route with different starting and ending points and the same flight speed for each drone, where the width of the drone flight route is equal to the data collection range of the multispectral imager and the remote sensing device, and the size and shape of the area formed by each drone flight route is equal to the area and plane shape of the old airspace; The water quality information collection module sends the UAV flight route to each UAV, and then each UAV flies over the old airspace area simultaneously along the UAV flight route. During the flight of the UAV, each UAV obtains the spectral video of the old airspace area within the data collection range of the multi-spectral imager, and transmits a pulse laser signal to the old airspace area through a remote sensing device, and synchronously generates the corresponding pulse laser reflection signal spectrum; When the UAV reaches the end point along the UAV flight route, the spectrum video of the old airspace and the spectrum of the pulsed laser reflection signal are marked with corresponding identity numbers and uploaded to the water quality information collection module.

[0021] Further, after all drones upload the spectrum video of the old empty area and the spectrum of the pulsed laser reflection signal, the water quality information collection module sends all the spectrum videos of the old empty area and the spectrum of the pulsed laser reflection signal to the water quality information filtering module; Since old empty areas are generally formed by multiple factors such as mining and groundwater, there are a lot of suspended matter and particulate matter in the water in the old empty areas, such as ore powder, mud and other suspended matter. When the drone collects the spectral video of the old empty area through the multi-spectral imager, the suspended matter and particulate matter will scatter and absorb the spectral signal, resulting in errors in the imaging results of the spectral video of the old empty area; Then the water quality information filtering module establishes a three-dimensional visualization space, and splices the pulse laser reflection signal spectra collected by each drone in sequence according to the drone flight route to obtain the corresponding old airspace signal spectrum; When the UAV transmits a pulse laser signal to the old empty area, the corresponding signal spectrum of the old empty area undergoes the first obvious fluctuation change after the pulse laser signal passes through the water surface, until the pulse laser signal reaches the bottom of the old empty area, the signal spectrum of the old empty area undergoes the second obvious fluctuation change. During the two obvious fluctuation changes, when the pulse laser signal passes through the suspended matter and particulate matter in the accumulated water of the old empty area, the corresponding signal spectrum of the old empty area undergoes a series of local fluctuations that are smaller than the obvious fluctuations when the pulse laser signal passes through the water surface and reaches the bottom of the old empty area. Then, several local fluctuation segments are marked in the signal spectrum of each old empty area, and all the signal spectrum of the old empty area is mapped in the three-dimensional visualization space. According to the amplitude length and the time length occupied by each local fluctuation segment, several mutually intersecting or overlapping detection space areas are divided in the three-dimensional visualization space. It should be noted that each detection space area can only contain one local fluctuation segment in the initial state; Since the position of the drone is constantly changing during the process of collecting the pulse laser reflection signal spectrum corresponding to the suspended matter or particulate matter in the water accumulation in the old airspace through the remote sensing device, each local fluctuation segment in the three-dimensional visualization space presents 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 partial differences in the pulse laser reflection signal spectrum corresponding to the same suspended matter or particulate matter collected by each drone; Establish a three-dimensional coordinate system, map the local fluctuation segments contained in the overlapping or intersecting detection space regions 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 fit of the corresponding two detection space regions; Set the fitting degree threshold, merge the detection space areas whose relative fitting degree is greater than or equal to the fitting degree threshold, and synchronize the internal local fluctuation fragments; For the detection space area whose relative fit is less than the fit threshold, no operation is performed; Repeat the above-mentioned detection space area operation until the remaining detection space area cannot be fused, and then record the remaining detection space area in the three-dimensional visualization space as the suspended object display area.

[0022] The spectral videos of the old air zone collected by each drone are spliced ​​according to the flight route of the drone to obtain a spectral video of water quality information, and the spectral video of water quality information is mapped in the three-dimensional visualization space. Then, according to the spatial position of the suspended matter display area in the three-dimensional visualization space, the video area corresponding to the corresponding position of the spectral video of water quality information is deleted, thereby completing the information filtering of the spectral video of water quality information.

[0023] Furthermore, the water quality information filtering module sends the three-dimensional visualization space and all the water quality information spectrum videos that have completed information filtering to the water quality information deconstruction module; The water quality information deconstruction module is provided with a three-dimensional neural network, and the three-dimensional neural network is composed of six main detection network surfaces, wherein the planes formed by the six main detection network surfaces are perpendicular to each other; The main detection network surface is composed of a plurality of secondary tilt detection surfaces. It should be noted that the intersection angle between each secondary tilt detection surface and its related main detection network surface is [-45°, 45°]; The three-dimensional visualization space is embedded in the three-dimensional neural network, and according to the spectral bands emitted by the multi-spectral imager, the RGB color space of several types of water quality in the corresponding spectral bands is obtained through the Internet; Since each water quality information spectral video is collected by different drones flying along different drone flight routes, the spectral video clips corresponding to the water quality information at the same position in the old airspace are collected by different drones at different positions within the same time period in different water quality information spectral videos, resulting in differences in the spectral video clips generated at the same position in different water quality information spectral videos; The water quality information spectrum video in the three-dimensional visualization space is divided into a number of water quality detection spaces, each of which is in the shape of a cube and generally has a size of 3*3*3mm; According to the RGB color space of various water qualities in various spectral frequency bands, the distribution of various water qualities contained in each water quality detection space is marked in the water quality information spectrum video. At the same time, the water quality information spectrum video corresponding to the UAV flight route is mapped in the three-dimensional visualization space, and the shooting center point at the center of the UAV flight route is selected; A shooting frame is set at the top of the three-dimensional visualization space with the shooting center point as the center of the circle and the data collection range of the drone as the diameter. The entire water quality information spectrum video is traversed in sequence along the drone flight route at the top of the three-dimensional visualization space through the shooting frame, and then the collection range cone of the corresponding drone at each time node is traversed in the three-dimensional visualization space; It should be noted that, since the spectral signal propagates in a fan-shaped manner in the old airspace, the data collection range of the UAV at each time node is conical; Obtain the angle from the vertex of the collection range cone to each associated water quality detection space, rotate the water quality detection space according to the angle so that it is relatively parallel to the bottom of the three-dimensional visualization space, obtain the angle between the rotation angle of the water quality detection space and each main detection network surface at each time node, and then vertically map the water quality distribution of each surface of the water quality detection space onto the corresponding secondary inclined detection surface according to the angle; The water quality detection space corresponding to the same position of different drones and the water quality distribution on the inclined detection surface of the same main detection network surface are vertically mapped onto the same main detection network surface, and then the overlapping water quality distribution results on each main detection network surface are spliced ​​to obtain the water quality distribution display block corresponding to the old airspace area, and each water quality distribution display block is labeled with the corresponding water quality information name; The water quality distribution display block includes water quality distributions in several colors, and each color corresponds to a type of water quality information, such as lead, mercury, cadmium, etc.

[0024] Furthermore, a common time slider and a single time slider are set for each water quality distribution display block. When the common time slider slides, the water quality distribution and water quality information names in all water quality distribution display blocks change synchronously. When the single time slider slides, the water quality distribution and water quality information names in the other water quality distribution display blocks remain unchanged, and only the water quality distribution and water quality information names in the water quality distribution display block associated with the single time slider change synchronously.

[0025] The above description is only a preferred embodiment of the present invention and does not limit the present invention in any form. Although the present invention has been disclosed as a preferred embodiment as above, it is not used to limit the present invention. Any technical personnel in this field can make some changes or modify the technical contents disclosed above into equivalent embodiments without departing from the scope of the technical solution of the present invention. However, any brief modifications, equivalent changes and modifications made to the above embodiments based on the technical essence of the present invention without departing from the content of the technical solution of the present invention are still within the scope of the technical solution of the present invention.

Claims

1. A water quality detection system for old empty areas based on multi-parameter detection technology, including a cloud computing platform, characterized in that: The cloud computing platform is communicatively connected with a water quality information acquisition module, a water quality information filtering module and a water quality information decomposition module; The water quality information collection module is used to set up several drones, install multi-spectrometers and remote sensing devices on the drones, and set the flight routes of the drones, so that the drones can collect the spectrum video of the old empty area and the spectrum of the pulsed laser reflection signal of the old empty area along the flight routes of the drones; 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 flight route of the drone to generate the old empty area signal spectrum, divide a number of detection space areas in the three-dimensional visualization space, and mark a number of local fluctuation fragments in the old empty area signal spectrum and input them into the three-dimensional visualization space, compare the local fluctuation fragments in each detection space area with each other, merge each detection space area with each other according to the comparison result, and filter the old empty area spectrum video according to the fusion result; The water quality information deconstruction module is used to establish a three-dimensional neural network, and embed the three-dimensional visualization space into the three-dimensional neural network, map the spectral video of the old airspace area into the three-dimensional visualization space, and divide a number of water quality detection spaces in the three-dimensional visualization space. Each water quality detection space is rotated according to the flight route of the drone, and then each surface of the water quality detection space corresponding to the same old airspace location of different drones is mapped onto the three-dimensional neural network, so as to obtain the water quality distribution display blocks of the old airspace at various locations.

2. According to claim 1, a water quality detection system for old empty areas based on multi-parameter detection technology is characterized in that: The acquisition process of the spectrum video of the old empty area and the spectrum of the pulsed laser reflection signal includes: A drone flight route with a different starting point and end point is set for each drone. Each drone flies over the old airspace simultaneously along the drone flight route, obtains spectral video of the old airspace within the data acquisition range of the multi-spectral imager, transmits pulse laser signals to the old airspace through the remote sensing device, and synchronously generates a pulse laser reflection signal spectrum.

3. The old empty area water quality detection system based on multi-parameter detection technology according to claim 2 is characterized in that: The process of setting up the detection space area in the 3D visualization space includes: A three-dimensional visualization space is established, and the pulse laser reflection signal spectra collected by each drone are stitched together in sequence according to the drone flight route to obtain the old airspace signal spectrum; Several local fluctuation segments are marked in the signal spectrum of each old empty area, and all the signal spectra of the old empty area are mapped in the three-dimensional visualization space. According to the amplitude length and the time length occupied by each local fluctuation segment, several intersecting or overlapping detection space areas are divided in the three-dimensional visualization space.

4. The old empty area water quality detection system based on multi-parameter detection technology according to claim 3 is characterized in that: The process of fusion between various detection space areas includes: Establish a three-dimensional coordinate system, map the local fluctuation segments contained in the overlapping or intersecting detection space regions 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 fit of the corresponding two detection space regions; Set the fitting degree threshold, merge the detection space areas whose relative fitting degree is greater than or equal to the fitting degree threshold, and synchronize the internal local fluctuation fragments; For the detection space area whose relative fit is less than the fit threshold, no operation is performed; The operation of merging the detection space areas is repeated until the remaining detection space areas cannot be fused, and then the remaining detection space areas in the three-dimensional visualization space are recorded as the suspended object display area.

5. The old empty area water quality detection system based on multi-parameter detection technology according to claim 4 is characterized in that: The process of filtering the information of the water quality information spectrum video includes: The spectral videos of each old airspace area are spliced ​​according to the flight route of the drone to obtain a spectral video of water quality information, and the spectral video of water quality information is mapped in the three-dimensional visualization space. Then, according to the spatial position of the suspended matter display area in the three-dimensional visualization space, the video area corresponding to the corresponding position of the spectral video of water quality information is deleted.

6. The old empty area water quality detection system based on multi-parameter detection technology according to claim 5 is characterized in that: The three-dimensional neural network consists of six main detection network surfaces, and each main detection network surface is composed of a number of secondary tilt detection surfaces.

7. The old empty area water quality detection system based on multi-parameter detection technology according to claim 6 is characterized in that: The process of rotating each water quality testing space includes: Obtain the RGB color spaces of several types of water quality in corresponding spectral frequency bands through the Internet, divide the water quality information spectrum video in the three-dimensional visualization space into several water quality detection spaces, and mark the distribution of various water qualities contained in each water quality detection space in the water quality information spectrum video according to the RGB color spaces of various water qualities in various spectral frequency bands. At the same time, map the water quality information spectrum video corresponding to the UAV flight route in the three-dimensional visualization space, and select the shooting center point at the center position of the UAV flight route; With the shooting center point as the center of the circle and the data collection range of the drone as the diameter, the collection range cone of the corresponding drone at each time node is traversed in the three-dimensional visualization space; Obtain the angle from the vertex of the collection range cone to each associated water quality detection space, rotate the water quality detection space according to the angle, obtain the angle between the rotation angle of the water quality detection space and each main detection network surface at each time node, and then vertically map the water quality distribution of each surface of the water quality detection space onto the corresponding secondary inclined detection surface according to the angle; The water quality detection space corresponding to the same location of different drones and the water quality distribution on the inclined detection surface of the same main detection network surface are vertically mapped onto the same main detection network surface, and then the overlapping water quality distribution results on each main detection network surface are spliced ​​to obtain the water quality distribution display block at the corresponding position of the old airspace, and each water quality distribution display block is labeled with the corresponding water quality information name.

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

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