Device and method for simultaneously monitoring water quality and water level

Abstract

The invention discloses a water quality and water level simultaneous monitoring device and method and belongs to the technical field of water quality and water level monitoring. The device comprises a shell, and a water quality data acquisition module, a water level data acquisition module, a power supply module and a PC (Personal Computer) upper computer which are positioned in the shell, power is supplied through the power supply module, the water quality data acquisition module is a hyperspectral camera system, the water level data acquisition module is a radar position finder, and control and data processing functions are integrated in the upper computer. A hyperspectral camera system and a radar position finder are fused and work cooperatively, the hyperspectral camera system monitors water quality, and the radar position finder obtains water level data. And meanwhile, a water level dynamic compensation mechanism is introduced, so that accurate quantification is realized, and the influence of water level change on water quality parameter inversion is compensated. When the real-time water level deviates from the reference water level, an inversion result is corrected in real time according to a dynamic compensation algorithm, spectral signal distortion caused by water level fluctuation is effectively inhibited, the accuracy and reliability of monitoring data are ensured, powerful support is provided for water environment monitoring and management, and the application range is wide.

Description

Technical Field

[0001] The present invention belongs to the technical field of water quality and water level monitoring, and relates to a device and method for simultaneously monitoring water quality and water level. Background Art

[0002] As an indispensable survival resource for human society, monitoring water quality has become one of the important technical means for the rational utilization of water resources. Water quality monitoring is a process of monitoring and measuring the types of pollutants in water bodies, the concentrations of various pollutants and their changing trends, and evaluating the water quality status. Water quality monitoring can provide a basis for the judgment of water pollution and also provide data for the direction of water quality treatment.

[0003] Traditional water quality monitoring methods usually rely on sampling bottles to collect water samples. Researchers need to go to different locations of the water area to be measured to extract samples and bring the samples back to the laboratory for analysis and research. This method has the disadvantages of complex operation, long time consumption, and inability to monitor in real time. Especially when facing the monitoring requirements of a large-scale water area, the limited sampling point density and low-frequency detection are difficult to ensure the spatio-temporal representativeness of the monitoring data, resulting in low overall monitoring efficiency.

[0004] Currently, using hyperspectral systems for water quality monitoring can achieve in-situ rapid detection of water quality parameters, with technical advantages of pollution-free, fast, real-time, and all-weather, which is an important technical breakthrough in the field of water quality monitoring. However, they are usually only used for water quality monitoring and cannot simultaneously monitor the water level. This limitation of single function may lead to insufficient information and prediction accuracy in the monitoring process. Especially when a comprehensive assessment of the water body condition is required, the change of the water level in the water area will affect the accuracy of water quality monitoring.

[0005] Therefore, in order to more comprehensively understand the water body condition and improve the monitoring accuracy, there is an urgent need for a comprehensive monitoring device and method that can simultaneously monitor water quality and water level. Such a device will be able to better meet the needs of water body environmental monitoring and management, and provide more reliable technical support for protecting water resources and maintaining ecological balance. Summary of the Invention

[0006] In view of the above problems existing in the prior art, the present invention provides a device and method for simultaneously monitoring water quality and water level. The present invention integrates a hyperspectral camera system and a radar altimeter, and the two work together. The former monitors water quality, and the latter obtains water level data. At the same time, a water level dynamic compensation mechanism is introduced to achieve precise quantification and compensation of the influence of water level changes on the inversion of water quality parameters. When the real-time water level deviates from the reference water level, the system corrects the inversion result in real time according to the dynamic compensation algorithm, effectively suppressing the spectral signal distortion caused by water level fluctuations, breaking through the limitation of water level interference in the traditional single monitoring mode, ensuring the accuracy and reliability of the monitoring data, and improving the accuracy and comprehensiveness of water quality monitoring.

[0007] To achieve the above object, the present invention adopts the following technical solutions.

[0008] A device for simultaneously monitoring water quality and water level, the device for simultaneously monitoring water quality and water level includes a housing 9 and a water quality data acquisition module, a water level data acquisition module, a power supply module, and a PC host computer 8 located inside the housing 9. Each part is powered by the power supply module, and both the water quality data acquisition module and the water level data acquisition module are connected to the PC host computer 8. The main body of the water quality data acquisition module is a set of hyperspectral camera system with a spectral range of 400 - 1000 nm, the main body of the water level data acquisition module is a radar altimeter 7, the main body of the power supply module is a built-in battery or an external power adapter, and the control and data processing functions are integrated in the PC host computer 8.

[0009] The hyperspectral camera system of the water quality data acquisition module includes an optical lens 1, an optical slit 2, an optical lens 3, a transmission grating 4, an optical lens 5, and a CMOS image sensor 6. The specific layout and functions of each component are described as follows:

[0010] The optical lens 1 is located at the forefront of the hyperspectral camera system and is arranged in sequence with the optical slit 2, the optical lens 3, and the transmission grating 4 on the axis a. The optical lens 5 and the CMOS image sensor 6 are arranged in sequence on the axis b, and the optical lens 5 is located below and behind the transmission grating 4. The CMOS image sensor 6 is connected to the PC host computer 8. Among them, the axis a is the optical axis of the optical lens 1, the optical slit 2, the optical lens 3, and the transmission grating 4, and the axis b is the optical axis of the optical lens 5. The axis a and the axis b are in the same plane and are parallel. Further, the axis b is below the axis a, and the distance between the two is determined by the diffraction angle of the transmission grating 4 and the focal length of the optical lens 5, Δd = f·tanθ, where f is the focal length of the optical lens 5 and tanθ is the diffraction angle of the transmission grating 4.

[0011] Further, the optical lens 1 is an optical element for focusing light onto subsequent components, achieving high-quality light focusing, thereby improving the clarity and resolution of the image.

[0012] Further, the slit width of the optical slit 2 is between 10 um and 200 um and is located at the focal positions of the optical lens 1 and the optical lens 3. It is mainly used to precisely control the width and direction of the light entering the camera, and can improve the spectral resolution by adjusting its size and position, enabling the camera to capture the light signal emitted by the target object more precisely.

[0013] Further, the optical lens 3 is used to collimate and adjust light so as to disperse it on the transmission grating 4. By focusing the light on the transmission grating 4 and controlling the incident angle of the light, it is ensured that the light can be correctly dispersed and clear and accurate spectral information can be formed.

[0014] Further, due to the diffraction effect caused by its structure, the transmission grating 4 disperses the incident light into spectra of different wavelengths. When the light passes through the transmission grating 4, according to the structure of the transmission grating 4, lights of different wavelengths will diffract at different angles, and the light can be dispersed into a continuous spectrum, and lights of different wavelengths will be generated at different diffraction angles.

[0015] Further, the optical lens 5 can refocus all the light dispersed by the transmission grating 4, ensuring that the light has sufficient focus and clarity when passing through the subsequent CMOS image sensor 6, further optimizing the light transmission efficiency and image quality, so as to generate high-quality hyperspectral images on the sensor.

[0016] Further, the CMOS image sensor 6 is used to receive the light processed by the optical system and convert it into a digital signal, realizing the accurate measurement and recording of the light intensity and wavelength information, and providing accurate hyperspectral image data.

[0017] Further, the radar altimeter 7 in the water level data acquisition module is a millimeter-level liquid level radar, which is mainly used to provide accurate liquid level information of water area monitoring points. Its radar probe is parallel to the axis a and perpendicular to the water surface during monitoring, realizing non-contact liquid level monitoring. This radar altimeter can penetrate obstacles such as light, rain, dust, fog or frost, realizing all-weather and all-time monitoring. As a small and highly integrated sensor, it has a flexible interface and can exchange data with a PC host computer through a standard communication protocol (such as RS485). The main technical parameters of the radar altimeter include a measurement frequency of 80 GHz, a data refresh rate of 200 ms, an operating current of 30 mA, a distance measurement accuracy of ±2 mm, a range of 0.2 - 40 meters, etc. The non-contact measurement method of this radar altimeter reduces the limitation on the water quality environment and is applicable to various water body monitoring occasions such as reservoirs, pools, and water towers, providing strong support for the synchronous monitoring of water quality and water level.

[0018] Further, the power supply module can select an internal battery or an external power adapter to supply power to the CMOS image sensor 6, the radar altimeter 7 and the PC host computer 8 in the water quality data acquisition module.

[0019] Further, the PC host computer 8 integrates control and data analysis functions. On the one hand, it precisely controls the CMOS image sensor 6 and the radar altimeter 7. On the other hand, it stores and efficiently processes various data from the water quality data acquisition module and the water level data acquisition module.

[0020] A method for simultaneously monitoring water quality and water level, which is implemented based on the above-mentioned device for simultaneously monitoring water quality and water level, specifically includes the following steps:

[0021] The first step is multi-dimensional data acquisition. Specifically as follows:

[0022] 1.1) Select sampling points: Select 5 or more sampling points with significant water level differences in the target water area. The water level difference between adjacent points satisfies: ΔH≥0.1m, covering typical water level states in the dry season, normal season, and flood season, so as to fully reflect the characteristics of water bodies under different water level conditions.

[0023] 1.2) Install the device on a stable platform (such as a tripod or buoy), ensuring that the optical lens 1 of the hyperspectral camera system and the probe of the radar altimeter 7 are both perpendicular to the water surface to ensure the accuracy of data acquisition.

[0024] 1.3) At the sampling points selected in 1.1), synchronously collect hyperspectral data, water level data, and water quality true value data obtained through laboratory analysis to provide comprehensive and accurate basic data for subsequent model establishment and data analysis.

[0025] The second step is spectral data preprocessing. Specifically as follows:

[0026] 2.1) Radiometric calibration: By eliminating environmental interference and equipment noise, ensure that the spectral data truly reflects the optical characteristics of water bodies and provides standardized input for water quality parameter inversion. After the original spectral data is corrected for dark current and radiometrically calibrated, the radiance L corr is calculated as: In the formula, where: L raw is the original digital quantization value, L dark is the dark current value (measured under no light conditions), G is the gain coefficient (calibrated in the laboratory), and T int is the integration time compensation factor.

[0027] 2.2) Characteristic band screening: Aimed at reducing the data dimension, improving the operation efficiency and generalization ability of the model, a two-stage optimization strategy is adopted.

[0028] (1) Based on partial least squares (PLS) for preliminary dimensionality reduction, calculate the variable importance in projection (VIP) index for each wavelength: Select the bands with VIP>1.2 to enter the second-stage optimization. In the formula, p is the total number of wavelengths, ω jk is the weight coefficient of the jth wavelength in the kth principal component, and SSk is the variance explained by the k-th principal component, and SS total is the total variance, and m is the total number of samples.

[0029] (2) The improved successive projections algorithm (SPA) is used for secondary screening, and the objective function is: In the formula, X unsel is the matrix of unselected bands, X sel is the matrix of selected bands, and η is the regression coefficient. Through iterative calculation, finally 5 - 8 characteristic bands are retained. While reducing the spectral data volume by more than 70%, more than 95% of the effective information is retained, realizing the efficient compression and feature extraction of spectral data.

[0030] The third step is to establish a water quality inversion model and a water level dynamic compensation mechanism. Specifically as follows:

[0031] 3.1) Data standardization processing: To eliminate the dimensional difference between spectral reflectance and water level data and improve the convergence speed and stability of the model, the data is standardized. The formula is: , where R(λ k ) is the original reflectance value of the k-th characteristic band, μ R is the arithmetic mean of all characteristic bands, σ R is the standard deviation of the reflectance of all characteristic bands, H is the original water level value directly output by the radar altimeter 7, μ H is the arithmetic mean of all water level data (reference water level), and σ H is the standard deviation of all water level data.

[0032] 3.2) Establish a multiple regression model: The standardized reflectance of characteristic bands and water level values are jointly modeled, introducing the main effect term of water level and the reflectance - water level interaction term to quantify the direct and non - linear effects of water level on water quality parameters. The model expression is: In the formula, Y is the predicted value of the target water quality parameter, R′(λ k ) is the standardized reflectance of the k-th characteristic band, H′ is the standardized water level value, β0 is the model intercept term, β k is the main effect coefficient of the reflectance of the k-th characteristic band, reflecting the independent influence of this band on water quality parameters, γ is the main effect coefficient of water level, reflecting the independent influence of water level on water quality parameters, δ k is the interaction coefficient of reflectance and water level, characterizing the modulation effect of water level change on the spectrum - water quality relationship, ε is the random error term, and n is the total number of characteristic bands.

[0033] 3.3) Establish a water level dynamic compensation mechanism:

[0034] When the real - time water level H t and the reference water level μ HThe absolute value of the difference |ΔH| exceeds the threshold ξ H or the absolute value of the water level change rate exceeds the threshold ξ v , a compensation mechanism combining nonlinear static compensation and dynamic rate adjustment is introduced: the compensation term constructed by the exponential function approximates linear compensation at small water level deviations and exhibits asymptotic saturation characteristics at large deviations, and at the same time, the transient response compensation of the water level rise and fall process is realized through the dynamic compensation term. Dynamically adjust the model parameters or inversion results based on the water level change characteristics, reduce the negative impact of water level changes on the inversion accuracy of water quality parameters, and ensure that the model can maintain high prediction accuracy under different water level conditions. In the above 3.3), the threshold ξ H ranges from 0.05 m to 0.5 m; the threshold ξ v ranges from 0.001 m / s to 0.5 m / s.

[0035] The compensation formula is: In the formula, Y′ is the predicted value of the water quality parameter after introducing water level dynamic compensation, Y is the original predicted value without introducing water level dynamic compensation, α is the amplitude sensitivity coefficient, τ is the nonlinear attenuation factor, ΔH = H t -u H is the deviation between the real-time water level and the reference water level, H t is the real-time monitored water level value, χ is the rate sensitivity coefficient, is the water level change rate.

[0036] 3.4) Evaluation of the accuracy of the hyperspectral water quality inversion model and the effect of water level dynamic compensation: After introducing the water level dynamic compensation mechanism, the determination coefficient R 2 and the root mean square error (RMSE) are selected to comprehensively evaluate the accuracy of the inversion model and the compensation effect, quantify the improvement amplitude of the model accuracy, and verify the effectiveness and stability of the compensation mechanism.

[0037] The determination coefficient R 2 is a statistic used to evaluate the fitting effect of the regression model, reflecting the correlation between the predicted value and the true value. The closer R 2 is to 1, the better the model fitting effect. In the formula, is the measured value, Y i is the predicted value, is the mean of the measured values, and m is the total number of samples.

[0038] The root mean square error (RMSE) measures the root mean square difference between the predicted value and the true value, and can intuitively reflect the average deviation degree between the predicted value and the true value. In the formula, is the measured value, Y i is the predicted value, and m is the total number of samples.

[0039] Further, the coefficient of determination R 2 and the root mean square error (RMSE) are used as the test indexes for the fitting accuracy of the inversion model. The improvement of both can comprehensively evaluate the comprehensive improvement effect of the compensation model in terms of accuracy improvement and law fitting.

[0040] Step 4: Device installation and monitoring implementation. Specifically as follows:

[0041] 4.1) At the selected monitoring point location, use measuring tools such as a meter stick to measure the distance from the water surface to the bottom of the water, providing accurate spatial position information for subsequent monitoring;

[0042] 4.2) Use a bracket or platform to firmly fix the device, ensuring that the optical lens 1 of the hyperspectral camera system and the probe of the radar altimeter 7 are both perpendicular to the water surface. Use the radar altimeter 7 to measure the height of the device from the water surface, and add it to the current water level to obtain the distance from the device to the bottom of the water;

[0043] 4.3) Set parameters such as the current distance from the device to the bottom of the water, the required water quality parameters, and the monitoring interval time on the PC host computer 8, and start the monitoring program to simultaneously monitor the water quality and water level.

[0044] Advantages of the present invention.

[0045] (1) By integrating the hyperspectral camera system and the radar altimeter, the present invention realizes the simultaneous monitoring of water quality and water level, without the need to additionally configure separate devices or sensors, significantly improving the monitoring efficiency and convenience, and saving labor, material and time costs. The present invention can not only provide comprehensive water environment information, including water quality parameters and water level data of the water body, but also improve the accuracy of the water quality inversion model by means of the water level dynamic compensation algorithm, so as to more accurately grasp the overall situation and dynamic changes of the water body, providing strong support for water environment monitoring and management.

[0046] (2) By monitoring the water quality and water level, the present invention can detect abnormal situations or water body pollution events in a timely manner, enabling water quality and water level monitoring personnel to quickly take corresponding measures, such as flood warning, pollution source tracking, emergency treatment, etc., to protect the safety of water resources and the environment. At the same time, due to the integration of water quality monitoring and water level monitoring functions, the cost of purchasing and installing multiple separate devices or sensors is avoided, and it is applicable to the water body monitoring needs of various different scales and site conditions, with a wide range of applications. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 is a general framework schematic diagram of a device and method for simultaneously monitoring water quality and water level in an embodiment of the present invention.

[0048] Figure 2 is a specific structural schematic diagram of a device for simultaneously monitoring water quality and water level in an embodiment of the present invention.

[0049] Figure 3 Schematic diagram of the characteristic spectra of the standard light sources 577nm and 579nm of the image obtained by the hyperspectral camera system provided in the embodiment of the present invention.

[0050] Figure 4 One of the flowcharts of a water quality and water level simultaneous monitoring device and method provided in the embodiment of the present invention.

[0051] In the figure: 1 optical lens; 2 optical slit; 3 optical lens; 4 transmission grating; 5 optical lens; 6 CMOS image sensor; 7 radar altimeter; 8 PC host computer; 9 housing. Detailed implementation manners

[0052] In order to more clearly illustrate the technical problems, technical solutions and beneficial effects solved by the present invention, the present invention will be further described in detail below with reference to the accompanying drawings and specific implementation manners.

[0053] The following gives a preferred example of the present invention with reference to the drawings in the present invention, mainly for further detailed description, rather than for limiting the scope of the present invention:

[0054] As Figure 1 shown, this embodiment is composed of a water quality data acquisition module, a water level data acquisition module, a PC host computer 8 and a housing 9. The water quality data acquisition module is composed of an optical lens 1, an optical slit 2, an optical lens 3, a transmission grating 4, an optical lens 5 and a CMOS image sensor 6. The water level data acquisition module is composed of a radar altimeter 7.

[0055] In this example, as Figure 2 shown, in the hyperspectral camera system of the water quality data acquisition module, the optical lens 1 realizes high-quality light focusing. The optical slit 2 is used to control the width and direction of the light entering the camera. The optical lens 3 is used to collimate and adjust the light for dispersion on the grating. The transmission grating 4 disperses the incident light into spectra of different wavelengths. The optical lens 5 refocuses the dispersed light. The CMOS image sensor 6 is used to receive the light processed by the optical system and convert it into a digital signal.

[0056] Specifically, the optical lens 1, the optical lens 3 and the optical lens 5 use fixed-focus lenses with a focal length of 25mm and an F number of 1.8; the slit width of the optical slit 2 is 30um; the transmission grating 4 uses a high-performance volume phase holographic grating with a grating density of 600 lines / mm; the CMOS image sensor 6 uses a CMOS industrial area array camera with a target surface size of 1 inch and a resolution of 5472×3648.

[0057] Specifically, the optical lens 1, optical slit 2, optical lens 3, and transmission grating 4 are arranged in sequence front and back on the axis a. The optical lens 5 and the CMOS image sensor 6 are arranged in sequence front and back on the axis b. The optical lens 5 is located at the lower rear of the transmission grating 4, and the CMOS image sensor 6 is connected to the PC host computer 8. Among them, the axis a is the optical axis of the optical lens 1, optical slit 2, optical lens 3, and transmission grating 4, and the axis b is the optical axis of the optical lens 5. The axis a and the axis b are in the same plane and are parallel. The axis b is below the axis a, and the distance between the two is Δd = f·tanθ.

[0058] After the initial water level is set, the radar altimeter 7 can automatically and continuously monitor the water level of the water body.

[0059] The PC host computer 8 is built-in with a power supply to supply power to the CMOS image sensor 6 and the radar altimeter 7. It can control the water quality data acquisition module and the water level data acquisition module to perform synchronous monitoring, collect and store water quality and water level data. By analyzing and processing the collected data, using the hyperspectral data to invert the water quality parameters, and combining the water level data to compensate the accuracy of the water quality inversion model, the accuracy of the monitoring results is improved.

[0060] The housing 9 is made of black resin material, which fixes each component to ensure its stable position without deviation. At the same time, it effectively blocks the interference of external light sources on the water quality data acquisition module and ensures the accuracy of the monitoring data.

[0061] Figure 3 It is a schematic diagram of the characteristic spectra of the mercury lamp standard light sources of 577nm and 579nm of the images obtained by the hyperspectral camera system in the present invention. From this figure, it can be seen that the hyperspectral camera system in the present invention has high sensitivity, and the spectral resolution is better than 2nm of the existing equipment, and it can finely capture spectral information, providing strong support for the accurate inversion of water quality parameters.

[0062] In this embodiment, the device is applied to the Xishan Reservoir in Dalian to implement the method of simultaneous monitoring of water quality and water level. By establishing a multiple regression model, accurate prediction of the contents of seven substances including total phosphorus, total nitrogen, ammonia nitrogen, dissolved oxygen, turbidity, suspended solids, and COD (chemical oxygen demand) is realized. The influence of water level change on the accuracy of the water quality inversion model is introduced, and the model parameters or inversion results are dynamically corrected according to the water level change, effectively improving the accuracy of model prediction. The steps are as Figure 4 shown, specifically as follows:

[0063] Step S1, Multi-dimensional data collection: According to the sampling point selection criteria, 20 sampling points are selected in the Xishan Reservoir, covering typical water level states such as the dry season, normal season, and flood season to fully cover different water level conditions. 100 groups of hyperspectral data are collected at each sampling point, and the corresponding water level data and water quality true value data are obtained synchronously, and the time line is extended as much as possible to cover a wider water level range. During specific operations, the monitoring device is fixed on the tripod to ensure that the optical lens 1 of the hyperspectral camera system and the probe of the radar altimeter 7 are both perpendicular to the water surface for data collection. At the same time, water samples are collected at each sampling point, and the contents of total phosphorus, total nitrogen, ammonia nitrogen, dissolved oxygen, turbidity, suspended solids, and COD (chemical oxygen demand) in the samples are measured using relevant physiological and chemical instruments respectively as the water quality true value data.

[0064] Step S2, Spectral data preprocessing: The hyperspectral data of the Xishan Reservoir in Dalian collected are preprocessed as follows:

[0065] Radiometric correction: By eliminating environmental interference and equipment noise, it is ensured that the spectral data truly reflect the optical characteristics of the water body, providing a standardized input for water quality parameter inversion. After the original spectral data are corrected for dark current and radiometric calibration, the radiance L corr is calculated as:

[0066] Feature band screening: Aims to reduce the data dimension, improve the model operation efficiency and generalization ability. A two-stage optimization strategy is adopted. In the first stage, preliminary dimension reduction is carried out based on partial least squares (PLS), and the variable importance in projection (VIP) index of each wavelength variable is calculated: The bands with VIP>1.2 are selected to enter the second-stage optimization; in the second stage, the improved successive projections algorithm (SPA) is used for secondary screening, and the objective function is: Finally, 5-8 feature bands are retained through iterative calculation, reducing the spectral data volume by more than 70% while retaining more than 95% of the effective information.

[0067] Step S3, Establish a water quality inversion model and a water level dynamic compensation mechanism, as follows:

[0068] Data standardization processing: To eliminate the dimensional difference between the spectral reflectance and water level data and improve the model convergence speed and stability, the data are standardized. The formula is:

[0069] Establish a multiple regression model: The standardized feature band reflectance and standardized water level values are jointly modeled, introducing the water level main effect term and the reflectance-water level interaction term to quantify the direct and non-linear effects of water level on water quality parameters. The model expression is:

[0070] Establish a water level dynamic compensation mechanism: When the real-time water level H t and the reference water level μ H the absolute value of the difference |ΔH| exceeds the threshold ξ H or the absolute value of the water level change rate exceeds the threshold ξ v introduce a compensation mechanism that combines non-linear static compensation and dynamic rate adjustment: The compensation term constructed by the exponential function approximates linear compensation at small water level deviations and exhibits an asymptotic saturation characteristic at large deviations. At the same time, the transient response compensation during the water level rise and fall process is achieved through the dynamic compensation term. Dynamically adjust the model parameters or inversion results based on the water level change characteristics to reduce the negative impact of water level changes on the inversion accuracy of water quality parameters and ensure that the model can maintain a high prediction accuracy under different water level conditions. The compensation formula is:

[0071] In this embodiment, the threshold ξ H is 0.1m; the threshold ξ v is 0.005m / s.

[0072] Evaluation of the accuracy of the hyperspectral water quality inversion model and the effect of water level dynamic compensation: After the hyperspectral water quality inversion model and the water level dynamic compensation mechanism are established, the coefficient of determination R 2 and the root mean square error (RMSE) are selected to evaluate the performance of the inversion model. The changes before and after the introduction of water level dynamic compensation for both are used to evaluate the effect of the water level dynamic compensation mechanism. In the example of the Xishan Reservoir in Dalian, the performance of the inversion models for seven substances, namely total phosphorus, total nitrogen, ammonia nitrogen, dissolved oxygen, turbidity, suspended solids, and COD (chemical oxygen demand), and the effect of water level dynamic compensation are evaluated. The obtained R 2 and RMSE are shown in Table 1 and Table 2.

[0073] Table 1 Prediction accuracy of the hyperspectral water quality inversion model without water level dynamic compensation

[0074] Water quality parameters Total phosphorus Total nitrogen Ammonia nitrogen Dissolved oxygen Turbidity Suspended solids COD <![CDATA[R 2 > 0.752 0.764 0.781 0.840 0.827 0.812 0.796 RMSE 0.313 0.290 0.276 0.202 0.204 0.193 0.261

[0075] Table 2 Prediction accuracy of the hyperspectral water quality inversion model with water level dynamic compensation

[0076] Water quality parameters Total phosphorus Total nitrogen Ammonia nitrogen Dissolved oxygen Turbidity Suspended solids COD <![CDATA[R 2 > 0.778 0.793 0.802 0.853 0.842 0.837 0.823 RMSE 0.297 0.282 0.256 0.190 0.177 0.182 0.244

[0077] By comparing the evaluation indicators in Table 1 and Table 2, it can be clearly seen that the prediction accuracy of the hyperspectral water quality inversion model with water level dynamic compensation provided by the present invention is generally better than that of the hyperspectral water quality inversion model without water level dynamic compensation. Specifically, R 2The values are all within the range of 0.75 - 0.85, indicating that the hyperspectral water quality inversion model constructed based on the multiple regression model has good ability in explaining the changes of water quality parameters. While the RMSE values are all within the range of 0.15 - 0.35, indicating that the hyperspectral water quality inversion model constructed based on the multiple regression model also has good performance in terms of the amount of error. Further, from the two evaluation indexes of R 2 and RMSE, the hyperspectral water quality inversion model with water level dynamic compensation has obvious improvement in the prediction process of each water quality parameter, which shows that the method provided by the present invention can more accurately predict water quality parameters and reduce the prediction error at the same time.

[0078] Step S4, device installation and monitoring implementation, is as follows: at the selected monitoring point location, use measuring tools such as a meter stick to measure the distance between the water surface and the bottom of the water; fix the device in a bracket manner so that the optical lens 1 of the hyperspectral camera system and the probe of the radar altimeter 7 are both perpendicular to the water surface, use the radar altimeter 7 to measure the height of the device from the water surface, and add it to the current water level to obtain the distance of the device from the bottom of the water; set parameters such as the current distance of the device from the bottom of the water, the required water quality parameters, and the monitoring interval time on the PC host computer 8, and start the monitoring program to realize the simultaneous monitoring of water quality and water level in the Xishan Reservoir in Dalian.

[0079] The above embodiments only represent the implementation modes of the present invention, but should not be construed as limiting the scope of the patent of the present invention. It should be noted that for those skilled in the art, without departing from the concept of the present invention, several deformations and improvements can still be made, and these all belong to the protection scope of the present invention.

Claims

1. A water quality and water level simultaneous monitoring device, characterized in that, The water quality and water level simultaneous monitoring device comprises a housing (9) and a water quality data acquisition module, a water level data acquisition module, a power module, and a PC host computer (8) located in the housing (9). The power module is used to supply power to each component. The water quality data acquisition module and the water level data acquisition module are both connected to the PC host computer (8). The main body of the water quality data acquisition module is a set of hyperspectral camera systems, including an optical lens (1), an optical slit (2), an optical lens (3), a transmission grating (4), an optical lens (5), and a CMOS image sensor (6). The water level data acquisition module is mainly a radar position meter (7); The PC host computer (8) integrates control and data processing functions, accurately controls the CMOS image sensor (6) and the radar position meter (7), and stores and efficiently processes various types of data from the water quality data acquisition module and the water level data acquisition module.

2. The water quality and water level simultaneous monitoring device according to claim 1, characterized in that, The water quality data acquisition module is specifically as follows: The optical lens (1) is located at the front end of the hyperspectral camera system, and is arranged on an axis a in sequence with the optical slit (2), the optical lens (3) and the transmission grating (4); the optical slit (2) is located at the focal position of the optical lens (1) and the optical lens (3); the optical lens (5) and the CMOS image sensor (6) are arranged on an axis b in sequence, and the optical lens (5) is located at the rear and lower part of the transmission grating (4); the CMOS image sensor (6) is connected to a PC host computer (8), and converts light into a digital signal through the CMOS image sensor (6), so as to achieve accurate measurement and recording of light intensity and wavelength information; the axis a is the optical axis of the optical lens (1), the optical slit (2), the optical lens (3) and the transmission grating (4), and the axis b is the optical axis of the optical lens (5), and the axis a and the axis b are on the same plane and are parallel.

3. The water quality and water level simultaneous monitoring device according to claim 2, characterized in that, The axis b is below the axis a, and the distance between the two is determined by the diffraction angle of the transmission grating (4) and the focal length of the optical lens (5), Δd=f·tanθ, where f is the focal length of the optical lens (5), and tanθ is the diffraction angle of the transmission grating (4).

4. The water quality and water level simultaneous monitoring device according to claim 1, characterized in that, The slit width of the optical slit (2) is between 10um and 200um.

5. The water quality and water level simultaneous monitoring device according to claim 2, characterized in that, The radar position meter (7) in the water level data acquisition module is a millimeter-level liquid level radar, which is mainly used to provide accurate liquid level information of the water area monitoring point. Its radar probe is parallel to the axis a and perpendicular to the water surface during monitoring, thereby realizing non-contact liquid level monitoring.

6. The water quality and water level simultaneous monitoring device according to claim 2, characterized in that, The power module can be equipped with a built-in battery or an external power adapter, and is used to supply power to the CMOS image sensor (6), the radar position meter (7) and the PC host computer (8) in the water quality data acquisition module.

7. The water quality and water level simultaneous monitoring device according to claim 1, characterized in that The spectral range of the water quality data acquisition module is 400-1000nm.

8. A method for simultaneously monitoring water quality and water level, characterized in that, Based on the water quality and water level simultaneous device according to any one of claims 1 to 7, specifically The following steps are involved: The first step is multi-dimensional data collection; the details are as follows: 1.1) Select sampling points: Select 5 or more sampling points with water level differences in the target water area. The water level difference between adjacent points should satisfy: ΔH≥0.1m, covering typical water level states in the dry season, normal water season, and flood season; 1.2) Install the device on a stable platform to ensure that the optical lens (1) of the hyperspectral camera system and the probe of the radar altimeter (7) are both perpendicular to the water surface; 1.3) At the sampling points selected in 1.1), synchronously collect hyperspectral data, water level data, and water quality true value data obtained through laboratory analysis to provide comprehensive and accurate basic data for subsequent model establishment and data analysis; The second step is spectral data preprocessing, which is specifically as follows: 2.1) Radiometric correction: By eliminating environmental interference and equipment noise, ensure that the spectral data truly reflects the optical characteristics of the water body and provides a standardized input for water quality parameter inversion; 2.2) Feature band screening: Adopt a two-stage optimization strategy; (1) Based on partial least squares (PLS), perform preliminary dimensionality reduction, calculate the variable importance in projection (VIP) index for each wavelength variable, and select bands with VIP>1.2 to enter the second-stage optimization; (2) The improved successive projections algorithm (SPA) is used for secondary screening, and the objective function is as follows: In the formula, X unsel is the matrix of unselected bands, X sel is the matrix of selected bands, η is the regression coefficient, and N is the total number of bands satisfying VIP > 1.2; Through iterative calculations, finally retain 5 - 8 feature bands. While reducing the spectral data volume by more than 70%, retain more than 95% of the effective information to achieve efficient compression and feature extraction of spectral data; The third step is to establish a water quality inversion model and a water level dynamic compensation mechanism; specifically as follows: 3.1) Data standardization processing, with the formula: where R(λ k ) is the original reflectance value of the k-th characteristic band; μ R is the arithmetic mean of all characteristic bands; σ R is the standard deviation of the reflectance of all characteristic bands; H is the original water level value directly output by the radar altimeter (7); μ H is the arithmetic mean of all water level data and serves as the reference water level; σ H is the standard deviation of all water level data; R′(λ k ) is the normalized reflectance of the k-th characteristic band, and H′ is the normalized water level value; 3.2) Establish a multiple regression model: Jointly model the standardized reflectance of the feature bands and the water level value after the data standardization processing in 3.1), introduce the main water level effect term and the reflectance-water level interaction term to quantify the direct and non-linear effects of the water level on water quality parameters; The expression of the multiple regression model is: where Y is the predicted value of the target water quality parameter; β0 is the model intercept term; β k is the main effect coefficient of the reflectance of the k-th characteristic band, reflecting the independent influence of this band on the water quality parameter; γ is the main effect coefficient of the water level, reflecting the independent influence of the water level on the water quality parameter; δ k is the interaction coefficient between the reflectance and the water level, characterizing the modulation effect of the water level change on the spectral-water quality relationship; ε is the random error term; n is the total number of characteristic bands; 3.3) Establish a water level dynamic compensation mechanism: When the real-time water level H t and the reference water level μ H the absolute value of the difference |ΔH| exceeds the threshold ξ H or the absolute value of the water level change rate exceeds the threshold ξ v a compensation mechanism combining non-linear static compensation and dynamic rate adjustment is introduced: the compensation term constructed by the exponential function provides approximate linear compensation for small water level deviations and exhibits an asymptotic saturation characteristic for large deviations, while the transient response compensation during the water level rise and fall process is achieved through the dynamic compensation term; the model parameters or inversion results are dynamically adjusted based on the water level change characteristics to reduce the negative impact of water level changes on the inversion accuracy of water quality parameters and ensure that the model can maintain a high prediction accuracy under different water level conditions; The compensation formula is as follows: In the formula, Y′ is the predicted value of the water quality parameter after introducing the water level dynamic compensation, Y is the original predicted value without introducing the water level dynamic compensation, α is the amplitude sensitivity coefficient, τ is the non-linear attenuation factor, and ΔH = H t -u H is the deviation between the real-time water level and the reference water level, H t is the real-time monitored water level value, χ is the rate sensitivity coefficient, and is the rate of change of the water level; 3.4) Evaluate the accuracy of the hyperspectral water quality inversion model and the effect of the water level dynamic compensation; After introducing the water level dynamic compensation mechanism, the determination coefficient R 2 and the root mean square error RMSE are selected to comprehensively evaluate the accuracy of the inversion model and the compensation effect, quantify the improvement range of the model accuracy, and verify the effectiveness and stability of the compensation mechanism; The fourth step is device installation and monitoring implementation; specifically as follows: 4.1) At the selected monitoring point location, measure the distance from the water surface to the bottom of the water to provide accurate spatial position information for subsequent monitoring; 4.2) Fix the device to ensure that the optical lens (1) of the hyperspectral camera system and the probe of the radar altimeter (7) are both perpendicular to the water surface. Use the radar altimeter (7) to measure the height of the device from the water surface, and add it to the current water level to obtain the distance from the device to the bottom of the water; 4.3) Set the current distance from the device to the bottom of the water, the required water quality parameters, and the monitoring interval time parameters on the PC host computer (8), and start the monitoring program to achieve simultaneous monitoring of water quality and water level.

9. A method for simultaneously monitoring water quality and water level according to claim 8, characterized in that, In the second step described above: The 2.1) is specifically as follows: After the original spectral data is corrected for dark current and radiometrically calibrated, the radiance L corr is calculated as: In the formula, where: L raw is the original digital quantization value, L dark is the dark current value measured under the condition of no illumination, G is the gain coefficient calibrated in the laboratory, and T int is the integration time compensation factor; In the above (2.2), the calculation formula of VIP is as follows: In the formula, p is the total number of wavelengths, ω jk is the weight coefficient of the j-th wavelength in the k-th principal component, SS k is the explained variance of the k-th principal component, SS total is the total variance, and m is the total number of samples.

10. A method for simultaneously monitoring water quality and water level according to claim 8, characterized in that, In the third step described above: In the above (3.3): threshold ξ H ranges from 0.05 m to 0.5 m; threshold ξ v ranges from 0.001 m / s to 0.5 m / s; In the above 3.4), the coefficient of determination R 2 is closer to 1, the better the model fitting effect; In the formula, is the measured value, Y i is the predicted value, is the measured mean value, m is the total number of samples; the root mean square error formula is: In the formula, is the measured value, Y i is the predicted value, m is the total number of samples.

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