A convenient water quality monitoring method and system with high spectral reflectance

Abstract

The present invention discloses a convenient high-spectral reflectance water quality monitoring method and system, which belongs to the technical field of water quality monitoring. The method comprises the following steps: S 1. Receive a user's query instruction and display a monitoring map to the user based on the query instruction; the monitoring map includes at least the monitored water body information and N Monitoring point information; S 2. Obtain the requirements of the current monitoring point that the user wants to view, and generate water quality information at the current monitoring point based on the requirements; S 3. Display the water quality information of the current monitoring point to the user, and push the monitoring points of interest to the user; S 4. Repetition S 2 and S 3, until receiving the user's end query instruction. The water quality data displayed to the user by the present invention is the water quality data collected and updated in real time at the current monitoring point, ensuring that the user obtains the latest and most accurate information; this is of great significance for timely discovery and response to water quality problems.

Description

Technical Field

[0001] The present invention belongs to the technical field of water quality monitoring, and in particular relates to a convenient high-spectral reflectance water quality monitoring method and system. Background Art

[0002] Traditional water quality monitoring methods mostly rely on laboratory analysis or fixed-site monitoring. Although these methods can provide relatively accurate data, they have disadvantages such as expensive equipment, complex operation, and limited monitoring range. In particular, for sudden water pollution incidents that require rapid response, traditional methods often find it difficult to achieve real-time monitoring and early warning. Traditional water quality monitoring methods also have the phenomenon of information islands, that is, data between different monitoring points are difficult to share and communicate, resulting in the inability to fully utilize data resources.

[0003] At the same time, the channels for users to obtain water quality information are relatively scattered, and they may need to query through multiple departments or platforms, which increases the complexity and difficulty of obtaining information. This way of obtaining information is not only inefficient, but also prone to data distortion due to untimely information updates or errors in the transmission process. Summary of the invention

[0004] Purpose of the invention: In order to solve the above problems, the present invention provides a convenient water quality monitoring method and system with high spectral reflectance.

[0005] Technical solution: A convenient water quality monitoring method with high spectral reflectance, comprising the following steps:

[0006] S 1. Receive a user's query instruction and display a monitoring map to the user based on the query instruction; the monitoring map includes at least the monitored water body information and N Monitoring point information includes location information and level information of the monitoring point;

[0007] S 2. Obtain the needs of the current monitoring point that the user wants to view, and generate water quality information at the current monitoring point based on the needs; the water quality information is obtained by inputting the hyperspectral data of the current monitoring point in real time as input data into a preset water quality inversion model, and the output result is obtained after calculation and processing by the water quality inversion model;

[0008] S 3. Display the water quality information of the current monitoring point to the user; push the monitoring point of interest to the user based on the water quality information, location information and grade information of the current monitoring point;

[0009] S 4. Repetition S 2 and S3. Until receiving the user's end query instruction.

[0010] In a further embodiment, the step S The process of establishing the monitoring map in 1 is as follows:

[0011] Acquire basic geographic data of the monitored water body, wherein the basic geographic data includes water body shape data, water body flow direction data, and terrain around the water body; collect regional information around the monitored water body along the water body flow direction; the regional information includes at least basic information and location information of the region; and construct a monitoring map based on the basic geographic data and regional information;

[0012] According to the basic information of the area, the area is divided into at least: a key water quality sensitive area, a potential pollution source area, and a general area; monitoring points are set in the key water quality sensitive area, the potential pollution source area, and the general area, and the level information of the key water quality sensitive area, the potential pollution source area, and the general area is set to the first level, the second level, and the third level in descending order of priority;

[0013] Different display labels are given to the first-level, second-level, and third-level monitoring points on the monitoring map.

[0014] In a further embodiment, the step S The construction process of the preset water quality inversion model in 2 is as follows:

[0015] Acquire an original hyperspectral reflectance set of the monitored water body at each monitoring point in multiple scenarios, and preprocess the original hyperspectral reflectance set to obtain an effective hyperspectral reflectance set;

[0016] Collecting surface water samples synchronized with the original hyperspectral reflectance set at each monitoring point, and measuring actual water quality data of each monitoring point based on the surface water samples;

[0017] Based on the monitoring point, the effective hyperspectral reflectance set corresponding to the monitoring point, and the actual water quality data, a monitoring point-water quality-hyperspectral reflectance data set is constructed, and a multi-scenario water quality inversion model set is constructed based on the data set; wherein the multi-scenario water quality inversion model set includes at least water quality inversion models corresponding to different weathers; the different weathers include at least: sunny days, cloudy days, rainy days, and snowy days.

[0018] In a further embodiment, the acquisition of the original hyperspectral reflectance set includes the following process:

[0019] Use a portable hyperspectral acquisition device to collect data information in different weather conditions, where the data information includes: downward irradiance on the water surface , skylight radiance , and water-level radiance ; and use the following formula to calculate the original hyperspectral reflectance ;

[0020] ;

[0021] In the formula, is the reflectivity of the air-water interface to sky light;

[0022] ;

[0023] In the formula, , , For different wind and wave speeds The value of is the wind and wave speed, , , for different wind and wave speed thresholds;

[0024] The original hyperspectral reflectance calculated under different weather conditions is updated to the original hyperspectral reflectance set.

[0025] In a further embodiment, the step S 2 generates water quality information at the current monitoring point based on the demand, including the following processes:

[0026] Obtaining the location information of the current monitoring point, and analyzing the weather information of the current monitoring point in the current time period based on the location information;

[0027] Select a corresponding water quality inversion model based on the weather information; obtain real-time hyperspectral data at the current monitoring point in the current time period; input the real-time hyperspectral data into the water quality inversion model, and output water quality information ;in, express m Different water quality parameter data.

[0028] In a further embodiment, the method further comprises the following steps:

[0029] Based on different water quality parameter data and scoring models, the water quality score of the current monitoring point is calculated. ; Wherein, the scoring model is as follows:

[0030] ;

[0031] In the formula, For the j The weight coefficients of the water quality parameters are For the j Water quality parameter data The corresponding score value;

[0032] Based on the grade information of the current monitoring point, assigning a threshold range and a weight coefficient of a water quality parameter that matches the grade information thereof;

[0033] Compare each water quality parameter data with its corresponding threshold range to obtain a comparison result, and assign a corresponding score value to the water quality parameter data based on the comparison result;

[0034] The water quality fraction Display it to the user in a visual way and push the monitoring points of interest to the user.

[0035] In a further embodiment, the step S 3 pushes the monitoring point of interest to the user based on the water quality information, location information and grade information of the current monitoring point, including the following processes:

[0036] Get the water quality score of the current monitoring point And level information and make a judgment to get the judgment result:

[0037] If the water quality score at the current monitoring point If the score does not exceed the threshold, based on the level information of the current monitoring point, both upstream and downstream along the flow direction of the water body are pushed X A monitoring point of interest with different level information from the current monitoring point;

[0038] On the contrary, based on the level information of the current monitoring point, both upstream and downstream along the flow direction of the water body are pushed Y The monitoring points of interest have the same level information as the current monitoring point.

[0039] In another technical solution, a portable water quality monitoring system with high spectral reflectance is provided, which is used to implement the portable water quality monitoring method with high spectral reflectance as described above, including:

[0040] A portable hyperspectral acquisition device is used to collect hyperspectral data of the current monitoring point in real time;

[0041] Weather acquisition module, used to obtain weather information of the current monitoring point in real time;

[0042] The mobile terminal is used to analyze the hyperspectral data and weather information and output water quality information.

[0043] In a further embodiment, the mobile terminal comprises:

[0044] The first module is configured to receive a user's query instruction and display a monitoring map to the user based on the query instruction; the monitoring map includes at least monitored water body information and NMonitoring point information includes location information and level information of the monitoring point;

[0045] The second module is configured to obtain the needs of the current monitoring point that the user wants to view, and generate water quality information at the current monitoring point based on the needs; the water quality information is obtained by inputting the hyperspectral data of the current monitoring point in real time as input data into a preset water quality inversion model, and the output result is obtained after calculation and processing by the water quality inversion model;

[0046] The third module is configured to display water quality information at the current monitoring point to the user; and push the monitoring point of interest to the user based on the water quality information, location information and grade information of the current monitoring point;

[0047] The fourth module is configured to receive a user's query end instruction.

[0048] Beneficial effects:

[0049] (1) The present invention uses a portable hyperspectral measurement instrument to achieve rapid measurement directly on site, and transmits the measurement data to a cloud server in real time for processing; by constructing a water quality inversion model, the hyperspectral reflectance data is converted into water quality parameters (such as total nitrogen, chlorophyll content, dissolved oxygen, etc.) that are easy for users to understand, and these data are integrated on a software platform;

[0050] Users only need to install the software platform on their terminals to view surrounding water quality information anytime and anywhere, without having to obtain information through multiple channels or complex processes. This water quality monitoring method not only improves the efficiency and accuracy of information acquisition, but also reduces monitoring costs and expands the monitoring scope, providing strong technical support for water quality monitoring and environmental protection work.

[0051] (2) Based on the user's query instructions, this method displays a monitoring map to the user, which displays the information of the monitored water body and the settings. N Based on the current monitoring point that the user wants to view, the system displays detailed water quality information to the user. By analyzing the water quality information, location information, and grade information of the current monitoring point, the system pushes information of other monitoring points with similar water quality characteristics or potential risks to the user, helping the user to fully understand the surrounding water quality conditions.

[0052] The water quality data displayed to the user by this method is the water quality data collected and updated in real time at the current monitoring point, ensuring that the user obtains the latest and most accurate information; this is of great significance for timely discovery and response to water quality problems. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 is a flow chart of the method;

[0054] Figure 2 is the water body hyperspectral data before standardized preprocessing in the present invention;

[0055] Figure 3 The water body hyperspectral data after standardized preprocessing in the present invention;

[0056] Figure 4 It is the aquatic grass coverage abnormal hyperspectral image in the present invention;

[0057] Figure 5 It is the abnormal high spectrum map of floating plate coverage in the present invention;

[0058] Figure 6 It is a screenshot of the visualization of water quality information of the smart terminal in the present invention. DETAILED DESCRIPTION

[0059] Example 1

[0060] Traditional water quality information monitoring methods often require users to actively query multiple data sources or consult relevant departments, which is a cumbersome and time-consuming process. In order to solve this problem, this embodiment provides a convenient high-spectral reflectance water quality monitoring method (hereinafter referred to as this method), including the following steps:

[0061] S 1. Receive a user's query instruction and display a monitoring map to the user based on the query instruction; the monitoring map includes at least the monitored water body information and N The monitoring point information includes the location information and level information of the monitoring point.

[0062] S 2. Obtain the needs of the current monitoring point that the user wants to view, and generate water quality information at the current monitoring point based on the needs; the water quality information is obtained by inputting the hyperspectral data of the current monitoring point in real time as input data into a preset water quality inversion model, and the output result is obtained after calculation and processing by the water quality inversion model.

[0063] S 3. Display the water quality information of the current monitoring point to the user; push the monitoring point of interest to the user based on the water quality information, location information and grade information of the current monitoring point.

[0064] S 4. Repetition S 2 and S 3. Until receiving the user's end query instruction.

[0065] The above users are but not limited to the staff of environmental protection departments, water companies, scientific research institutions and educational institutions. Based on the user's query instructions, this method displays a monitoring map to the user, which displays the information of the monitored water body and the settings. N Based on the current monitoring point that the user wants to view, detailed water quality information is displayed to the user. In addition, by analyzing the water quality information, location information and grade information of the current monitoring point, information of other monitoring points with similar water quality characteristics or potential risks is pushed to the user, helping the user to fully understand the surrounding water quality conditions.

[0066] The water quality data displayed to the user by this method is the water quality data collected and updated in real time at the current monitoring point, ensuring that the user obtains the latest and most accurate information. This is of great significance for timely discovery and response to water quality problems.

[0067] This method supports multiple query channels and terminal devices, such as web pages, mobile phones APP , WeChat public accounts, etc. Users can choose the appropriate query method according to their needs and habits; they can intuitively view the water quality of the area they want to pay attention to, which greatly improves the query and monitoring efficiency and convenience.

[0068] This method can update water quality data in real time, ensuring that users obtain the latest and most accurate information. Compared with the information lag problem that may exist in traditional methods, this method provides higher real-time performance.

[0069] This method provides customized water quality information push based on the user's preferences and concerns; this means that users no longer need to be overwhelmed by a large amount of irrelevant information, but can directly obtain the content that interests them.

[0070] Further, the steps S The process of establishing the monitoring map in 1 is as follows:

[0071] Acquire basic geographic data of the monitored water body, wherein the basic geographic data includes water body shape data, water body flow direction data, and terrain around the water body; collect regional information around the monitored water body along the water body flow direction; the regional information includes at least basic information and location information of the region; and construct a monitoring map based on the basic geographic data and regional information;

[0072] According to the basic information of the area, the area is divided into at least: a key water quality sensitive area, a potential pollution source area, and a general area; monitoring points are set in the key water quality sensitive area, the potential pollution source area, and the general area, and the level information of the key water quality sensitive area, the potential pollution source area, and the general area is set to the first level, the second level, and the third level in descending order of priority;

[0073] Different display labels are given to the first-level, second-level, and third-level monitoring points on the monitoring map.

[0074] When constructing monitoring maps, satellite remote sensing technology, drone aerial photography or ground measurement equipment are used to collect data on the shape of the monitored water body, the direction of the water body flow, and the terrain data around the water body. The precise outline of the water body is automatically identified and extracted through image processing algorithms; the geographical name database and GIS Data is collected and the names of villages and towns, administrative boundaries and important landmarks within a certain range around the monitored water body are marked on the map. In addition, basic information on the areas near the monitored water body that require special attention for water quality is collected, including but not limited to the distribution of people in need of drinking water, the location of farms, industrial emission sources, etc. All the above information is integrated into the digital map platform to generate a detailed map containing the shape of the water body, tributary network, surrounding villages and towns, and place names, and supports zoom viewing and interactive operations at multiple scales.

[0075] The above regional basic information is classified into at least key water quality sensitive areas (such as drinking water intake points, ecological protection areas, etc.), potential pollution source areas (such as farms, factories, living areas, etc.), and general areas (such as ordinary water areas, ecological tourism areas, etc.). The water quality supervision priority in key water quality sensitive areas, potential pollution source areas, and general areas is reduced in turn, that is, their level information is divided into level one, level two, and level three accordingly. Based on the different levels of the area where each monitoring point is located, each monitoring point is intuitively displayed on the map in the form of different colors, icons, or labels.

[0076] At least one monitoring point is set up in key water quality sensitive areas, potential pollution source areas, and general areas. Each monitoring point is equipped with a monitoring device, such as a portable three-channel hyperspectrometer. Connect the portable three-channel hyperspectrometer to a smartphone or other terminal. The portable three-channel hyperspectrometer includes a radiometer placed on the top, a radiometer 1 placed on the side at an upward angle of 45° to the horizontal plane, and a radiometer 2 at a downward angle of 45° to the horizontal plane; they collect the downward irradiance of the water surface ( ), skylight radiance ( ) and the water-surface radiance ( ), radiometer 1 and radiometer 2 are located in the same plane. The portable three-channel hyperspectral instrument can collect wavelengths of 400~900 nm , the spectral resolution is 1 nm High spectral reflectance of water bodies.

[0077] In the above technical scheme, by setting up monitoring points in different types of areas, comprehensive coverage of the water quality of the entire monitored water body is achieved; this helps to obtain more extensive and comprehensive water quality data. Key water quality sensitive areas and potential pollution source areas are areas that are most sensitive to water quality changes and are easily polluted. Setting up monitoring points in these areas can more accurately locate water quality problems and promptly discover and deal with potential water quality risks. Different regulatory priorities are set according to the importance and pollution risks of the region to achieve a reasonable allocation of monitoring resources; more monitoring resources and manpower are invested in key water quality sensitive areas and potential pollution source areas to ensure that the water quality safety of these areas is better guaranteed. By giving priority to high-risk areas, water quality supervision work can be carried out more efficiently. Setting an appropriate number of monitoring points in general areas and appropriately lowering the supervision priority can reduce the supervision cost while ensuring the monitoring effect; this helps to achieve sustainable development of water quality supervision.

[0078] Further, the steps S The construction process of the preset water quality inversion model in 2 is as follows:

[0079] Acquire an original hyperspectral reflectance set of the monitored water body at each monitoring point in multiple scenarios, and preprocess the original hyperspectral reflectance set to obtain an effective hyperspectral reflectance set;

[0080] Collecting surface water samples synchronized with the original hyperspectral reflectance set at each monitoring point, and measuring actual water quality data of each monitoring point based on the surface water samples;

[0081] Based on the monitoring point, the effective hyperspectral reflectance set corresponding to the monitoring point, and the actual water quality data, a monitoring point-water quality-hyperspectral reflectance data set is constructed, and a multi-scenario water quality inversion model set is constructed based on the data set; wherein the multi-scenario water quality inversion model set includes at least water quality inversion models corresponding to different weathers; the different weathers include at least: sunny days, cloudy days, rainy days, and snowy days.

[0082] In the above technical solution, water quality inversion models under different weather scenarios are inverted under different weather scenarios. Taking sunny and rainy days as examples, the study found that environmental factors such as light intensity, atmospheric conditions, and water surface conditions are significantly different between sunny and rainy days. On sunny days, the spectral characteristics are stable and clear, which is conducive to building a simple and accurate model; on rainy days, due to the weakening of light, increased atmospheric interference, and dynamic changes in the water surface, the spectral signals are complex and changeable, and it is necessary to adjust the model parameters or adopt a more complex model structure to capture these changes, so as to ensure the accuracy and reliability of the inversion results.

[0083] Therefore, different models or model parameters are required to invert water quality parameters using hyperspectral data obtained on sunny and rainy days. To this end, in this technical solution, the corresponding hyperspectral reflectance sets are first obtained under different weather scenes. Then, while obtaining the hyperspectral reflectance of the water body to be tested, 5 L The organic glass water sampler collects water around the monitoring point (0-10 cm ) of the surface water column (0-50 cm ) as synchronous water samples. In addition, record the sampling time, wind speed, transparency, weather and temperature and other environmental factors. Obtain the total nitrogen, total phosphorus and chlorophyll in the synchronous water samples a , transparency, suspended matter, permanganate index, turbidity and other water quality parameter data. The monitoring point, the effective hyperspectral reflectance set obtained at the monitoring point, and the actual water quality data are used to construct a monitoring point-water quality-hyperspectral reflectance data set, and the data set is randomly divided into a training data set and a validation data set in a ratio of 7:3, which are used for water quality inversion model construction and independent verification, respectively. Using the Gaussian regression algorithm, BP Three machine learning models, neural network algorithm and random forest algorithm, and two deep learning models, long short-term memory model and deep neural network, are used to generate water quality inversion models for multiple scenarios. RMSE ) and the mean relative error ( MRE ) and other methods to evaluate water quality inversion models and select the optimal water quality inversion model.

[0084] Furthermore, the acquisition of the original hyperspectral reflectance set includes the following process:

[0085] Use a portable hyperspectral acquisition device to collect data information in different weather conditions, where the data information includes: downward irradiance on the water surface , skylight radiance , and water-level radiance ; and use the following formula to calculate the original hyperspectral reflectance ;

[0086] ;

[0087] In the formula, is the reflectivity of the air-water interface to sky light;

[0088] ;

[0089] In the formula, , , For different wind and wave speeds The value of is the wind and wave speed, , , for different wind and wave speed thresholds;

[0090] The original hyperspectral reflectance calculated under different weather conditions is updated to the original hyperspectral reflectance set.

[0091] In this example, considering the different effects of different wind and wave intensities on water body hyperspectral acquisition, when the wind and wave is less than 2.5 m / s hour, r The value is 0.022; when the wind and waves are between 2.5 and 5 m / s hour, r The value is 0.025; when the wind and waves are greater than 5 m / s hour, r The value is 0.026.

[0092] Downward irradiance on the water surface collected by a portable three-channel hyperspectral instrument , skylight radiance , and water-level radiance Data, using the above formula to calculate the original hyperspectral reflectance Based on the data collected in multiple scenes, the original hyperspectral reflectance set is obtained.

[0093] When the portable three-channel hyperspectral instrument collects data, due to the flare caused by some waves or observation angles in the actual scene, the water spectrum is overexposed, covering up the water information, so the original hyperspectral reflectance There will be some invalid data; for this reason, the original hyperspectral reflectance Perform preprocessing to obtain effective hyperspectral reflectance. The preprocessing method is as follows:

[0094] (1) In order to further eliminate the influence of flare, R Water spectra with a value greater than 0.2 are treated as abnormal spectra and removed.

[0095] (2) In order to reduce the error caused by different background light environments in the measurement of hyperspectral data, the collected hyperspectral data are subjected to standardized preprocessing: standardized preprocessing refers to the use of 575~585 nm The average value is used as a reference, and the whole band is standardized. The formula is:

[0096] ;

[0097] In the formula, is the normalized hyperspectral reflectance, For the i nm The high spectral reflectance at i Between 400~900 nm .

[0098] (3) When the surface of the water body to be tested is covered by garbage, plants and other floating objects (such as water plants and floating boards) and hides the water surface, it is necessary to remove abnormal data from the water body hyperspectrum based on the spectral shape; when the water body to be tested produces flare spectrum due to the observation angle and water surface waves, it is necessary to remove the 550 nm The spectra with high spectral reflectance of water bodies>0.15 were removed.

[0099] Further, the steps S 2 generates water quality information at the current monitoring point based on the demand, including the following processes:

[0100] Obtaining the location information of the current monitoring point, and analyzing the weather information of the current monitoring point in the current time period based on the location information;

[0101] Select a corresponding water quality inversion model based on the weather information; obtain real-time hyperspectral data at the current monitoring point in the current time period; input the real-time hyperspectral data into the water quality inversion model, and output

[0102] Water quality information ;in, express m Different water quality parameter data.

[0103] The user clicks to query the water quality information on the current monitoring point, obtains the location information of the current monitoring point from the monitoring map, and the monitoring device at the location of the current monitoring point obtains the weather information of the current monitoring point at the current time point released by the official meteorological agency or weather forecast application through the Internet, determines whether the weather information is sunny or rainy or other weather, and selects the corresponding water quality inversion model based on the weather judgment result. The real-time hyperspectral data obtained in real time is input into the water quality inversion model to output water quality information . express m Different water quality parameter data, such as is the chlorophyll concentration, is the total nitrogen concentration. Water quality information Presented to users in a visual form, such as a table.

[0104] Further comprising the following steps:

[0105] Based on different water quality parameter data and scoring models, the water quality score of the current monitoring point is calculated. ;

[0106] The scoring model is as follows:

[0107] ;

[0108] In the formula, For the j The weight coefficients of the water quality parameters are For the j Water quality parameter data The corresponding score value;

[0109] Based on the grade information of the current monitoring point, assigning a threshold range and a weight coefficient of a water quality parameter that matches the grade information thereof;

[0110] Compare each water quality parameter data with its corresponding threshold range to obtain a comparison result, and assign a corresponding score value to the water quality parameter data based on the comparison result;

[0111] The water quality fraction Display it to the user in a visual way and push the monitoring points of interest to the user.

[0112] The following examples illustrate the above technical solution:

[0113] When the current monitoring point selected by the user belongs to the first-level monitoring point, that is, the key water quality sensitive area; set the full score of each water quality parameter to 10. Taking the water quality parameters of total nitrogen, total phosphorus, and permanganate index as an example, set the total nitrogen concentration of the first-level monitoring point to ≤0.2 mg / L (≤0.2 mg / L The threshold range) is full score, and the total phosphorus concentration is ≤0.1 mg / L Full marks, permanganate index concentration ≤ 2 mg / L The total nitrogen concentration increases by 0.1 mg / L 3 points will be deducted for every 0.1 increase in total phosphorus concentration. mg / L 3 points will be deducted for every 1 point increase in the permanganate index concentration. mg / L Subtract 3 points. Set the corresponding weight coefficients of total nitrogen, total phosphorus, and permanganate index to 0.4, 0.4, and 0.2 (the sum of the three weights is 1).

[0114] Under actual monitoring conditions, if the actual concentrations of total nitrogen, total phosphorus, and permanganate index detected at the current monitoring point are: 0.25 mg / L , 0.1 mg / L , 3 mg / L , then the water quality score =0.4×8.5+0.4×10+0.2×7=8.8.

[0115] Water quality score The highest score is 10. The water quality is divided into 5 levels from 0 to 10, that is, (8, 10] belongs to level 1, (6, 8] belongs to level 2, and so on. Different response strategies are set for the 5 levels based on actual planning. It is displayed to users in a visual form so that they can monitor the water quality.

[0116] When the monitored water body has many monitoring points, the comprehensive scores of each monitoring point are calculated and compared to achieve an overall assessment of the water quality in the area; this helps to identify hot spots with poor water quality and provide data support for regional water quality improvement plans; at the same time, by comparing the changes in scores at different monitoring points, the effectiveness of different governance measures can be evaluated, providing a basis for optimizing governance strategies.

[0117] Further, the steps S 3 pushes the monitoring point of interest to the user based on the water quality information, location information and grade information of the current monitoring point, including the following processes:

[0118] Get the water quality score of the current monitoring point And level information and make a judgment to get the judgment result:

[0119] If the water quality score at the current monitoring point If the score does not exceed the threshold, based on the level information of the current monitoring point, both upstream and downstream along the flow direction of the water body are pushed X A monitoring point of interest with different level information from the current monitoring point;

[0120] On the contrary, based on the level information of the current monitoring point, both upstream and downstream along the flow direction of the water body are pushed Y The monitoring points of interest have the same level information as the current monitoring point.

[0121] The following is a detailed description of this technical solution:

[0122] In the above, based on the water quality score The value of water quality is divided into 5 levels from 0 to 10 (8 to 10 belongs to level 1, 6 to 8 belongs to level 2, and so on); the score threshold is set to 6, and the water quality score is If the value is less than 6, that is, ≤6, the water quality is level 3, 4, or 5, indicating that the water quality is poor, then the upstream of the current monitoring point will be pushed to the user. X Downstream X Secondary or tertiary monitoring points. Water quality score If it is greater than 6, the water quality is level 1 or 2, indicating that the water quality is good, and the upstream of the current monitoring point is pushed to the user. Y Downstream Y The user can check the corresponding water quality based on the pushed monitoring points.

[0123] The above push method is set up to push information according to the specific conditions of the grade information of the monitoring point and the water quality score, ensuring that the information received by the user is highly relevant to the water quality conditions that the user cares about; this personalized push method can reduce the interference of irrelevant information and improve the user experience; when the water quality is found to be poor, timely push information from other nearby monitoring points can help users quickly understand the surrounding water quality conditions, so as to take necessary measures to protect their own health or the environment; this immediacy is particularly important for responding to sudden water quality incidents; automated push logic can reduce the need for manual intervention and reduce operating costs. At the same time, through algorithm optimization and data analysis, the push strategy can be continuously improved to improve the overall efficiency and accuracy of the system.

[0124] Example 2

[0125] This embodiment provides a portable water quality monitoring system with high spectral reflectance, which is used to implement a portable water quality monitoring method with high spectral reflectance as described in Embodiment 1, including:

[0126] A portable hyperspectral acquisition device is used to collect hyperspectral data of the current monitoring point in real time;

[0127] Weather acquisition module, used to obtain weather information of the current monitoring point in real time;

[0128] The mobile terminal is used to analyze the hyperspectral data and weather information and output water quality information.

[0129] Furthermore, the mobile terminal includes:

[0130] The first module is configured to receive a user's query instruction and display a monitoring map to the user based on the query instruction; the monitoring map includes at least monitored water body information and N Monitoring point information includes location information and level information of the monitoring point;

[0131] The second module is configured to obtain the needs of the current monitoring point that the user wants to view, and generate water quality information at the current monitoring point based on the needs; the water quality information is obtained by inputting the hyperspectral data of the current monitoring point in real time as input data into a preset water quality inversion model, and the output result is obtained after calculation and processing by the water quality inversion model;

[0132] The third module is configured to display water quality information at the current monitoring point to the user; and push the monitoring point of interest to the user based on the water quality information, location information and grade information of the current monitoring point;

[0133] The fourth module is configured to receive a user's query end instruction.

Claims

1. A convenient water quality monitoring method with high spectral reflectance, characterized in that: The following steps are involved: S 1. Receive a user's query instruction and display a monitoring map to the user based on the query instruction; The monitoring map at least includes the monitored water body information and N Monitoring point information includes location information and level information of the monitoring point; S 2. Obtain the requirements of the current monitoring point that the user wants to view, and generate water quality information at the current monitoring point based on the requirements; The water quality information is obtained by inputting the hyperspectral data monitored in real time at the current monitoring point as input data into a preset water quality inversion model, and the output result is obtained after calculation and processing by the water quality inversion model; S 3. Display water quality information at the current monitoring point to users; Pushing the monitoring point of interest to the user based on the water quality information, location information and grade information of the current monitoring point; S 4. Repetition S 2 and S 3. Until receiving the user's end query instruction; step S The construction process of the preset water quality inversion model in 2 is as follows: Acquire an original hyperspectral reflectance set of the monitored water body at each monitoring point in multiple scenarios, and preprocess the original hyperspectral reflectance set to obtain an effective hyperspectral reflectance set; Collecting surface water samples synchronized with the original hyperspectral reflectance set at each monitoring point, and measuring actual water quality data of each monitoring point based on the surface water samples; Based on the monitoring point, the effective hyperspectral reflectance set corresponding to the monitoring point, and the actual water quality data, a data set of monitoring point-water quality-hyperspectral reflectance is constructed, and a multi-scenario water quality inversion model set is constructed based on the data set; wherein the multi-scenario water quality inversion model set includes at least water quality inversion models corresponding to different weather conditions; the different weather conditions include at least: sunny days, cloudy days, rainy days, and snowy days; step S 2 generates water quality information at the current monitoring point based on the demand, including the following processes: Obtaining the location information of the current monitoring point, and analyzing the weather information of the current monitoring point in the current time period based on the location information; Select a corresponding water quality inversion model based on the weather information; obtain real-time hyperspectral data at the current monitoring point in the current time period; input the real-time hyperspectral data into the water quality inversion model, and output water quality information ;in, express m Different water quality parameter data; Further comprising the following steps: Based on different water quality parameter data and scoring models, the water quality score of the current monitoring point is calculated. ; Wherein, the scoring model is as follows: ； In the formula, For the j The weight coefficients of the water quality parameters are For the j Water quality parameter data The corresponding score value; Based on the grade information of the current monitoring point, assigning a threshold range and a weight coefficient of a water quality parameter that matches the grade information thereof; Compare each water quality parameter data with its corresponding threshold range to obtain a comparison result, and assign a corresponding score value to the water quality parameter data based on the comparison result; The water quality fraction Display it to users in a visual way and push monitoring points of interest to users; step S 3 pushes the monitoring point of interest to the user based on the water quality information, location information and grade information of the current monitoring point, including the following processes: Get the water quality score of the current monitoring point And level information and make a judgment to get the judgment result: If the water quality score at the current monitoring point If the score does not exceed the threshold, based on the level information of the current monitoring point, both upstream and downstream along the flow direction of the water body are pushed X A monitoring point of interest with different level information from the current monitoring point; On the contrary, based on the level information of the current monitoring point, both upstream and downstream along the flow direction of the water body are pushed Y The monitoring points of interest have the same level information as the current monitoring point.

2. A portable high-spectral reflectance water quality monitoring method as claimed in claim 1, characterized in that: step S The process of establishing the monitoring map in 1 is as follows: Acquire basic geographic data of the monitored water body, the basic geographic data including water body shape data, water body flow direction data and the terrain around the water body; collect regional information around the monitored water body along the water body flow direction; The regional information at least includes basic information and location information of the region; Constructing a monitoring map based on the geographic basic data and regional information; According to the basic information of the area, the area is divided into at least: a key water quality sensitive area, a potential pollution source area, and a general area; monitoring points are set in the key water quality sensitive area, the potential pollution source area, and the general area, and the level information of the key water quality sensitive area, the potential pollution source area, and the general area is set to the first level, the second level, and the third level in descending order of priority; Different display labels are given to the first-level, second-level, and third-level monitoring points on the monitoring map.

3. A portable water quality monitoring method with high spectral reflectance as claimed in claim 1, characterized in that: The acquisition of the original hyperspectral reflectance set includes the following processes: Use a portable hyperspectral acquisition device to collect data information in different weather conditions, where the data information includes: downward irradiance on the water surface , skylight radiance , and water-level radiance ; and use the following formula to calculate the original hyperspectral reflectance ; ； In the formula, is the reflectivity of the air-water interface to sky light; ； In the formula, , , For different wind and wave speeds The value of is the wind and wave speed, , , for different wind and wave speed thresholds; The original hyperspectral reflectance calculated under different weather conditions is updated to the original hyperspectral reflectance set.

4. A portable water quality monitoring system with high spectral reflectance, used to implement a portable water quality monitoring method with high spectral reflectance as claimed in any one of claims 1 to 3, characterized in that: include: A portable hyperspectral acquisition device is used to collect hyperspectral data of the current monitoring point in real time; Weather acquisition module, used to obtain weather information of the current monitoring point in real time; The mobile terminal is used to analyze the hyperspectral data and weather information and output water quality information.

5. A portable high spectral reflectance water quality monitoring system as claimed in claim 4, characterized in that: The mobile terminal comprises: The first module is configured to receive a user's query instruction and display a monitoring map to the user based on the query instruction; the monitoring map includes at least monitored water body information and N Monitoring point information includes location information and level information of the monitoring point; The second module is configured to obtain the needs of the current monitoring point that the user wants to view, and generate water quality information at the current monitoring point based on the needs; the water quality information is obtained by inputting the hyperspectral data of the current monitoring point in real time as input data into a preset water quality inversion model, and the output result is obtained after calculation and processing by the water quality inversion model; The third module is configured to display water quality information at the current monitoring point to the user; and push the monitoring point of interest to the user based on the water quality information, location information and grade information of the current monitoring point; The fourth module is configured to receive a user's query end instruction.

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