Device and method for monitoring algae content in reservoir

Abstract

The present invention relates to the field of water quality monitoring. Disclosed are a device and method for monitoring algae content in a reservoir, which are used for solving the problem that monitoring data is affected by the environment when the environment is not conducive to data monitoring. The method comprises: acquiring light data of collection points in real time; carrying out calculation to obtain a light non-uniformity coefficient of the collection points; performing light correction on the basis of the light non-uniformity coefficient; monitoring algae data collected at the light-corrected collection points; carrying out calculation to obtain a biological influence coefficient, a physical influence coefficient, and a chemical influence coefficient; comprehensively evaluating to obtain an algae content index; on the basis of the algae content index, issuing an algae content excess warning; collecting the algae content index within a detection time period; using an ARIMA model time series analysis method to predict an algae future growth trend; and, on the basis of the growth trend, issuing an algae growth speed excess warning, thereby effectively improving the accuracy of monitoring algae data.

Description

A reservoir algae content monitoring device and method TECHNICAL FIELD

[0001] The present application relates to the field of water quality monitoring, more particularly to a reservoir algae content monitoring device and method. BACKGROUND

[0002] The main reasons for algae growth in reservoirs include the presence of sufficient nutrients such as nitrogen and phosphorus from agricultural runoff, domestic sewage and industrial discharges, which provide the necessary nutrients for algae growth. In addition, the surface layer of the reservoir usually has sufficient sunlight, especially in sunny days and shallow water areas, and the sufficient light promotes the photosynthesis of algae. Suitable water temperature also provides ideal conditions for the rapid reproduction of algae. The neutral to weakly alkaline pH value of the water body, appropriate dissolved oxygen concentration and static or slow flowing water environment further support the growth of algae.

[0003] Reservoirs are often important sources of drinking water, and monitoring the algae content can ensure that the water quality meets safety standards and prevent excessive algae reproduction from polluting the water quality. By monitoring the algae content, the water quality can be assessed in a timely manner, potential water quality problems can be identified, and appropriate management measures can be taken.

[0004] Existing methods for monitoring the algae content of reservoirs usually use optical methods, such as measuring the absorption spectrum or reflection spectrum of water samples using a spectrometer to infer the type and quantity of algae in the reservoir. However, this detection method can be affected by the measurement environment, which can affect the accuracy of the monitoring data.

[0005] To solve the above problems, the present application provides a solution.

[0006] SUMMARY

[0007] In order to overcome the above-mentioned defects of the prior art, the present application provides a reservoir algae content monitoring device and method to solve the problems in the background art.

[0008] To achieve the above-mentioned purpose, the present application provides the following technical solutions:

[0009] A reservoir algae content monitoring method, comprising the following steps:

[0010] M area collection points are set in the reservoir, the collection points are evenly divided into n parts, denoted as sub-collection points, real-time light data of the sub-collection points are obtained, and the light inhomogeneity coefficient of the collection points is calculated, and the light is corrected according to the light inhomogeneity coefficient;

[0011] The concentration of chlorophyll-a is collected using a spectrometer on the collection points after light correction, and the biological influence coefficient is calculated according to the collected parameters, and the calculation formula is Wherein BG represents the biological influence coefficient, M is the number of collection points, CA i represents the concentration of chlorophyll-a at the i-th collection point;

[0012] Real-time monitoring of each measurement point water temperature data, light intensity and water transparency, and the calculation of physical influence coefficient;

[0013] Setting detection instrument at the measurement point, real-time detection of nutrient salt concentration in the measurement point, normalizing the real-time measured nutrient salt concentration, and calculating the chemical influence coefficient according to the normalized nutrient salt concentration;

[0014] According to the biological influence coefficient, the physical influence coefficient and the chemical influence coefficient, the algae content index is calculated, and the algae content index is used for algae content overproof warning, reminding the relevant personnel to take corresponding measures;

[0015] Collecting the algae content index in the detection period, using ARIMA model time series analysis method to predict the future growth trend of algae, and according to the growth trend, the algae grows too fast, reminding the relevant personnel to take corresponding measures.

[0016] Preferably, the light uneven coefficient calculation step is:

[0017] The light data of the obtained sub-collection points is preprocessed, and the preprocessing includes removing outliers, smoothing data and background correction operation;

[0018] For each measurement point, the light data of the sub-measurement points is calculated by mean value, and the average light intensity of the measurement point is calculated The calculation formula is Wherein represents the average light intensity, n is the number of sub-measurement points, I i represents the light intensity of the i-th sub-measurement point;

[0019] For each measurement point, according to the average light intensity, the square of the difference between the light intensity of each sub-measurement point and the average value is calculated, and then the square root of the average value of the square of the difference is obtained to obtain the light intensity standard deviation, which is used to measure the change degree of the light intensity of the measurement point, and the calculation formula is Wherein σ represents the light intensity standard deviation, I i represents the light intensity of the i-th sub-measurement point, represents the average light intensity;

[0020] The light intensity standard deviation of each measurement point is weighted and averaged, and the light uneven coefficient of the water surface is calculated, and the calculation formula is Wherein UL represents the light uneven index, M is the number of collection points, σi The standard deviation of light intensity at the i-th sampling point.

[0021] Preferably, the step of correcting the light according to the light unevenness coefficient is setting a light unevenness coefficient threshold value, a standard correction intensity, and calculating an actual light correction intensity according to the real-time light unevenness index, with the calculation formula being wherein LC represents the actual light correction intensity, UL represents the light unevenness index, UL' represents the light unevenness coefficient threshold value, and L 标准 represents the standard correction intensity, and correcting the light according to the actual light correction intensity.

[0022] Preferably, the step of collecting the concentration of chlorophyll-a using a spectrometer is:

[0023] Collecting the spectral data of the water sample at different monitoring points using a spectrometer, and recording the light intensity at different wavelengths;

[0024] Pretreating the spectral data, which includes removing noise, background correction, and smoothing processing, etc., and the background correction formula is I 矫正 (λ) = I(λ) - I 背景 (λ), wherein I(λ) represents the original spectral data, I 背景 (λ) represents the background spectral data, and the characteristic absorption wavelength of chlorophyll-a is selected;

[0025] Calculating the spectral absorption value at the characteristic wavelength, with the calculation formula being wherein A(λ) represents the spectral absorption value, I0(λ) represents the reference light intensity, and I 矫正 (λ) represents the corrected light intensity;

[0026] Calculating the concentration of chlorophyll-a according to the spectral absorption value, with the calculation formula being CA = k·A(665), wherein CA represents the concentration of chlorophyll-a, k is the calibration coefficient, and A(665) represents the absorbance at 665 nm.

[0027] Preferably, the step of calculating the physical influence coefficient is:

[0028] Installing corresponding sensors at each measurement point for measuring the water temperature, light intensity, and water transparency, respectively;

[0029] Obtaining real-time water temperature data, and calculating the water temperature influence degree according to the real-time water temperature data;

[0030] Obtaining real-time light intensity data, and calculating the light influence degree according to the real-time light intensity data;

[0031] acquire real-time water transparency data, and calculate a transparency influence degree according to the real-time water transparency data;

[0032] a physical influence coefficient is comprehensively evaluated according to the water temperature influence degree, the light influence degree and the transparency influence degree.

[0033] Preferably, the step of performing an algae content over-standard early warning according to the algae content index comprises: comparing the algae content index with a preset threshold value; if the algae content index is less than the preset threshold value, determining that the current reservoir has a small amount of algae content, and then not performing the algae content over-standard early warning; and if the algae content index is greater than the preset threshold value, determining that the current reservoir has a large amount of algae content, and then performing the algae content over-standard early warning.

[0034] Preferably, the step of collecting the algae content index in the detection time period and predicting a future growth trend of the algae by using an ARIMA model time series analysis method comprises:

[0035] collecting the algae content index data in the monitoring time period, and pre-processing the collected data;

[0036] determining the characteristics of the time series data, identifying the ARIMA model order by using autocorrelation and partial autocorrelation analysis, and selecting the ARIMA model order (p, d, q);

[0037] fitting the ARIMA model by using the historical data, and performing model diagnosis to check whether the model residual error meets the white noise assumption;

[0038] predicting the algae content index at a future time point by using the fitted ARIMA model, and the expression is where Y t+h is a predicted value at a time point t+h, is a model predicted value at a time point t+h, e t+h is a residual error at a time point t+h.

[0039] Preferably, the step of performing an algae growth too fast early warning according to the growth trend comprises: comparing the growth trend with a preset threshold value; if the growth trend is less than the preset threshold value, not performing the algae growth too fast early warning; and if the growth trend is greater than the preset threshold value, determining that the current algae content has a relatively fast growth speed, and then performing the algae growth too fast early warning.

[0040] Preferably, a device for implementing the reservoir algae content monitoring method according to any one of claims 1-8 is provided, and the device comprises:

[0041] a light data acquisition module, configured to acquire light data of the monitoring point, calculate a light unevenness coefficient, and transmit the light unevenness coefficient to the light correction module;

[0042] The light correction module is used for receiving the light unevenness coefficient transmitted by the light data acquisition module and correcting the light according to the light unevenness coefficient.

[0043] The algal data monitoring module is used for collecting relevant data of the collection point after light correction, calculating biological influence coefficient, physical influence coefficient and chemical influence coefficient, and comprehensively evaluating algal content index.

[0044] The algal content early warning module is used for early warning processing in the case of high algal content.

[0045] The algal growth trend early warning module is used for budgeting the future growth trend of algae according to the algal content index, and early warning processing in the case of high growth trend.

[0046] The technical effects and advantages of the present application are as follows:

[0047] The light data of the collection point is acquired in real time, the light unevenness coefficient of the collection point is calculated, the light is corrected according to the light unevenness coefficient, the algal data of the collection point after light correction is collected, the biological influence coefficient, the physical influence coefficient and the chemical influence coefficient are calculated, the algal content index is comprehensively evaluated, the algal content is early warned according to the algal content index, the algal content index in the detection period is collected, the ARIMA model time series analysis method is used to predict the future growth trend of algae, and the algal growth is early warned according to the growth trend, so that the accuracy of monitoring algal data is effectively improved. BRIEF DESCRIPTION OF DRAWINGS

[0048] Fig. 1 is a whole flow chart of the present application. DETAILED DESCRIPTION

[0049] The technical solutions in the present application will be described clearly and completely in combination with the drawings in the present application, and the forms of each structure described in the following embodiments are only examples, and the water reservoir algal content monitoring device and method involved in the present application are not limited to each structure described in the following embodiments. All other embodiments obtained by those skilled in the art without making creative efforts belong to the scope of protection of the present application.

[0050] The present application provides a water reservoir algal content monitoring method, comprising the following steps:

[0051] M area-equal collection points are arranged in the water reservoir, the collection points are evenly divided into n parts, denoted as sub-collection points, light data of the sub-collection points is acquired in real time, light unevenness coefficient of the collection points is calculated, and the light is corrected according to the light unevenness coefficient.

[0052] The concentration of chlorophyll-a is collected by using a spectrometer on the collected points after light correction, and the biological influence coefficient is calculated according to the collected parameters, and the calculation formula is Wherein BG represents the biological influence coefficient, M is the number of collected points, CA i represents the concentration of chlorophyll-a at the ith collected point;

[0053] Real-time monitoring of water temperature data, light intensity and water transparency at each measurement point, and calculating the physical influence coefficient;

[0054] Setting a detection instrument at the measurement point to detect the nutrient salt concentration in the measurement point in real time. Nutrient salt usually includes elements such as nitrogen and phosphorus, and its concentration can be measured by chemical analysis instruments such as ion chromatograph and spectrometer. The real-time measured nutrient salt concentration is normalized, and the expression is Wherein C' represents the normalized nutrient salt concentration, C is the real-time measured nutrient salt concentration, C min and C max represents the minimum and maximum values of the nutrient salt concentration, and the chemical influence coefficient is calculated according to the nutrient salt concentration, and the expression is CM=f(C'), wherein CM represents the chemical influence coefficient;

[0055] The algae content index is calculated according to the biological influence coefficient, the physical influence coefficient and the chemical influence coefficient, and the calculation formula is AC = a1 x BG + a2 x PS + a3 x CM, wherein AC represents the algae content index, BG represents the biological influence coefficient, the biological influence coefficient comprehensively considers various environmental parameters such as the concentration of chlorophyll-a, the concentration of nitrogen and phosphorus nutrients, water temperature and the like, and these factors directly affect the growth and reproduction of algae. Therefore, when these environmental conditions become more favorable for the growth of algae, the biological influence coefficient will rise, reflecting that the actual content of algae in the water body is also increasing. This relationship shows that the biological influence coefficient is an effective comprehensive index, which can be used to predict and evaluate the current situation and future growth trend of algae in the water body, and provide a scientific basis for the environmental management of the reservoir and the prevention of algae outbreak, PS represents the physical influence coefficient, the physical influence coefficient comprehensively considers the physical properties of the water body such as water temperature, light intensity, water flow velocity and water transparency and the like, and these factors have an important influence on the growth environment of algae. Therefore, when these physical conditions become more favorable for the growth of algae, the physical influence coefficient will rise, reflecting that the actual content of algae in the water body is also increasing. This relationship shows that the physical influence coefficient is an effective comprehensive index, which can be used to predict and evaluate the current situation and future growth trend of algae in the water body, and provide a scientific basis for the environmental management of the reservoir and the prevention of algae outbreak, CM represents the chemical influence coefficient, the chemical influence coefficient comprehensively considers the chemical properties of the water body such as the concentration of nitrogen and phosphorus, dissolved oxygen, pH value and the like, and these chemical factors have an important influence on the growth and reproduction of algae. Therefore, when these chemical conditions become more favorable for the growth of algae, the chemical influence coefficient will rise, reflecting that the actual content of algae in the water body is also increasing. This relationship shows that the chemical influence coefficient is an effective comprehensive index, which can be used to predict and evaluate the current situation and future growth trend of algae in the water body, a1, a2, a3 represent the weight coefficients of the biological influence coefficient, the physical influence coefficient and the chemical influence coefficient, and the specific values of a1, a2 and a3 are not calculated in this embodiment, and the algae content index is used for algae content over-standard early warning, reminding the relevant personnel to take corresponding measures;

[0056] The algae content index in the detection period is collected, the ARIMA model time series analysis method is used to predict the future growth trend of algae, and the algae growth too fast early warning is carried out according to the growth trend, reminding the relevant personnel to take corresponding measures, the ARIMA model time series analysis is a classic time series analysis method, which is used for modeling and prediction of time series data. It combines autoregressive model (AR), difference (I) and moving average model (MA) to process time series data with trend and seasonality.

[0057] Monitoring the algae content in reservoirs can help assess the ecological health of water bodies. Algae, as an important part of the bottom layer of the biological chain, their number and species change directly affect the stability and function of the entire aquatic ecosystem. Through monitoring, abnormal growth or changes in species can be detected in a timely manner, which helps prevent and control ecological problems such as eutrophication and blue-green algae blooms, and protect the stability and health of the ecological environment of the reservoir. Algae content monitoring is one of the important indicators for evaluating the water quality of reservoirs. The growth of algae is affected by factors such as nutrient salts, temperature, and light in the water body, and its changes reflect the trend of water quality changes in the reservoir. By monitoring the algae content and its growth trend, water management strategies can be optimized, and water resource utilization methods can be adjusted to ensure that the water quality meets the needs of domestic, industrial, and agricultural water use.

[0058] Regular monitoring of algae content can establish an effective early warning system. When algae content abnormally increases or harmful algae such as blue-green algae blooms appear, timely warnings can be issued, and appropriate emergency measures can be taken to avoid potential harm to the ecological environment of the reservoir and the surrounding community. Algae content monitoring data is an important basis for water ecology research and ecosystem management. Through long-term monitoring data, the variation of algae in different seasons and different environmental conditions can be analyzed, and the relationship between algae and water quality, ecosystem health can be further explored.

[0059] In this embodiment, it needs to be specifically pointed out that the light unevenness coefficient calculation step is:

[0060] The light data of the obtained sub-acquisition points is preprocessed, and the preprocessing includes operations such as removing outliers, smoothing data, and background correction to ensure data quality and stability;

[0061] For each measurement point, the light data of the sub-measurement points is calculated by mean value, and the average light intensity of the measurement point is calculated The calculation formula is Wherein represents the average light intensity, n is the number of sub-measurement points, I i represents the light intensity of the i-th sub-measurement point;

[0062] For each measurement point, according to the average light intensity, the square of the difference between the light intensity of each sub-measurement point and the average value is calculated, and then the square root of the average value of these square differences is obtained to obtain the light intensity standard deviation, which is used to measure the degree of change of the light intensity in the position or wave band. The calculation formula is Wherein σ represents the light intensity standard deviation, I i represents the light intensity of the i-th sub-measurement point, and I represents the average light intensity;

[0063] The light intensity standard deviation of each measurement point is weighted and averaged to calculate the light unevenness coefficient of the water surface, and the calculation formula is Where UL represents the light unevenness index, M is the number of collection points, and σ i represents the light intensity standard deviation of the i-th collection point.

[0064] In this embodiment, it needs to be specifically pointed out that the light correction step according to the light unevenness coefficient is to set a light unevenness coefficient threshold and a standard correction intensity, calculate the actual light correction intensity according to the real-time light unevenness index, and the calculation formula is Where LC represents the actual light correction intensity, UL represents the light unevenness index, UL' represents the light unevenness coefficient threshold, and L 标准 represents the standard correction intensity, and the light is corrected according to the actual light correction intensity.

[0065] In this embodiment, it needs to be specifically pointed out that the light correction step according to the actual light correction intensity is:

[0066] Adjust the measured spectral data through a correction curve or a mathematical model to make it closer to the expected theoretical model or standard spectrum, the correction curve is a tool commonly used in laboratory analysis to establish the relationship between the measured value and the concentration of the measured substance. It is usually a curve or a straight line that describes the functional relationship between the signal measured by the measuring instrument (such as spectral intensity, voltage, etc.) and the known concentration or standard substance concentration;

[0067] Improve the signal-to-noise ratio of spectral data through filtering techniques or mathematical processing methods to remove noise or interference signals, thereby improving the clarity and reliability of the data, the filtering technique is a signal processing method used to change the frequency characteristics or waveform shape of the signal. In practical applications, filtering techniques are mainly used to remove noise in signals, adjust the frequency response of signals, or extract information in a specific frequency range from complex signals. These techniques are widely used in many fields, including communication, audio processing, image processing, biomedical engineering, and control engineering;

[0068] Quality control is required at each stage of data processing to ensure that the collected spectral data is of good quality, which may include the exclusion of abnormal data points, the comparison of repeated measurements, the verification of correction parameters, etc.

[0069] In this embodiment, it needs to be specifically pointed out that the step of collecting the concentration of chlorophyll-a using a spectrometer is:

[0070] Use the spectrometer to collect spectral data of water samples at different monitoring points and record the light intensity at different wavelengths;

[0071] The spectral data is pre-processed, which includes removing noise, background correction and smoothing, etc. The formula for background correction of the collected spectral data is I 矫正 (λ) = I(λ) - I 背景 (λ), wherein I(λ) represents the original spectral data, I 背景 (λ) represents the background spectral data.

[0072] The characteristic wavelength related to algae is determined, and the characteristic absorption wavelength (665 nm) of chlorophyll-a and other related wavelengths are usually selected;

[0073] The spectral absorption value at the characteristic wavelength is calculated, which is used to reflect the concentration of algae, and the calculation formula is wherein A(λ) represents the spectral absorption value, I0(λ) represents the reference light intensity, I 矫正 (λ) represents the corrected light intensity.

[0074] The concentration of chlorophyll-a is calculated according to the spectral absorption value, which is an important indicator of algal biomass, and the calculation formula is CA=k·A(665), wherein CA represents the concentration of chlorophyll-a, k is a calibration coefficient determined according to laboratory calibration, and A(665) represents the absorbance at 665 nm.

[0075] In this embodiment, the physical influence coefficient calculation step is specifically described as follows:

[0076] A corresponding sensor is installed at each measurement point for measuring water temperature, light intensity and water transparency, respectively;

[0077] Real-time water temperature data is obtained, usually in units of Celsius (℃), and the water temperature influence degree is calculated according to the real-time water temperature data, and the calculation formula is wherein I 温度 represents the water temperature influence degree, W is the real-time water temperature data, W max and W min are the maximum and minimum values of water temperature, which are the maximum and minimum values during the monitoring period;

[0078] Real-time light intensity data is obtained, usually in units of light flux or illuminance, and the light influence degree is calculated according to the real-time light intensity data, and the calculation formula is wherein I 光照 represents the light influence degree, G is the real-time light data, G max and G min are the maximum and minimum values of light, which are the maximum and minimum values during the monitoring period;

[0079] The real-time water transparency data is acquired, usually in units of transparency or transmittance, and a transparency influence degree is calculated according to the real-time water transparency data, and the calculation formula is Wherein I 透明度 is expressed as the transparency influence degree, D is the real-time water transparency data, D max and D min are the maximum and minimum values of the transparency, and the maximum and minimum values of the transparency are the maximum and minimum values during the monitoring period;

[0080] A physical influence coefficient is comprehensively evaluated according to the water temperature influence degree, the light influence degree and the transparency influence degree, and the calculation formula is Wherein PS is expressed as the physical influence coefficient, I 温度 is expressed as the water temperature influence degree, I 光照 is expressed as the light influence degree, I 透明度 is expressed as the transparency influence degree, respectively represent the weights of the water temperature influence degree, the light influence degree and the transparency influence degree, and

[0081] In this embodiment, it needs to be specifically pointed out that the step of performing early warning of excessive algae content according to the algae content index is that the algae content index is compared with a preset threshold value, if the algae content index is less than the preset threshold value, it is determined that the current reservoir has a small amount of algae content, and then early warning of excessive algae content is not performed, and if the algae content index is greater than the preset threshold value, it is determined that the current reservoir has a large amount of algae content, and then early warning of excessive algae content is performed.

[0082] In this embodiment, it needs to be specifically pointed out that the step of collecting the algae content index in the detection time period and using an ARIMA model time series analysis method to predict the future growth trend of the algae is as follows:

[0083] The algae content index data in a certain time period is collected to ensure the continuity and integrity of the data, and the collected data is preprocessed, including processing missing values, abnormal values and stationarity test, etc.

[0084] The characteristics of the time series data are determined, such as whether there is a trend and seasonality, and autocorrelation function (ACF) and partial autocorrelation function (PACF) analysis are used to identify appropriate ARIMA model order;

[0085] An appropriate ARIMA model order (p, d, q) is selected, wherein p is the autoregressive (AR) order, indicating the order of dependence on past observations, d is the integral (I) order, indicating the number of differences that need to be performed to make the time series a stationary sequence, and q is the moving average (MA) order, indicating the order of dependence on past prediction errors;

[0086] ARIMA model is fitted using historical data, and model diagnosis is performed to check whether the model residual meets the white noise assumption;

[0087] The fitted ARIMA model is used to predict the algae content index at future time points, and the expression is Where Y t+h is the predicted value at time point t+h, is the model prediction value at time point t+h, e t+h is the residual at time point t+h.

[0088] In this embodiment, it needs to be specifically pointed out that the algae growth too fast early warning step according to the growth trend is to compare the growth trend with the preset threshold, if the growth trend is less than the preset threshold, no algae growth too fast early warning is performed, if the growth trend is greater than the preset threshold, it is determined that the current algae content growth speed is fast, and the algae growth too fast early warning is performed.

[0089] In this embodiment, it needs to be specifically pointed out that a reservoir algae content monitoring device, the device comprises:

[0090] Light data acquisition module, for acquiring light data of monitoring point, calculating light unevenness coefficient, and transmitting light unevenness coefficient to light correction module;

[0091] Light correction module, for receiving light unevenness coefficient transmitted by light data acquisition module, and correcting light according to light unevenness coefficient;

[0092] Algae data monitoring module, for collecting relevant data of the collection point after light correction, and calculating biological influence coefficient, physical influence coefficient and chemical influence coefficient, and comprehensively evaluating to obtain algae content index, and transmitting algae content index to algae content early warning module;

[0093] Algae content early warning module, for early warning processing in the case of high algae content;

[0094] Algae growth trend early warning module, for budgeting future growth trend of algae according to algae content index, and early warning processing in the case of high growth trend.

[0095] Finally: the above only for the preferred embodiments of the present application, and does not limit the present application, any modification, equivalent replacement, improvement, etc. within the spirit and principles of the present application, should be included in the protection scope of the present application.

[0096] The above merely provides the specific implementation of the present application, but the protection scope of the present application is not limited to this. Any person skilled in the art can easily think of the changes or replacements within the technical range disclosed by the present application, which should be covered in the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A method for monitoring algae content in a reservoir, characterized in that, Includes the following steps: M collection points with the same area are set up in the reservoir. The collection points are divided into n parts, which are called sub-collection points. The light data of the sub-collection points are acquired in real time, and the light non-uniformity coefficient of the collection points is calculated. The light is then corrected according to the light non-uniformity coefficient. The concentration of chlorophyll-a was collected at the light-corrected sampling points using a spectrometer. The bioinfluence coefficient was calculated based on the collected parameters, using the following formula: Where BG represents the biological impact coefficient, M is the number of collection points, and CA i This represents the concentration of chlorophyll-a at the i-th sampling point; Real-time monitoring of water temperature, light intensity, and water transparency at each measurement point, and calculation of the physical influence coefficient; Detection instruments are set up at the measurement points to detect the nutrient concentration in real time. The real-time measured nutrient concentration is normalized, and the chemical influence coefficient is calculated based on the normalized nutrient concentration. The algae content index is calculated by combining the biological impact coefficient, physical impact coefficient, and chemical impact coefficient. The algae content index is used to issue an early warning of excessive algae content and remind relevant personnel to take appropriate measures. Collect algae content indices during the detection period, use ARIMA model time series analysis to predict future algae growth trends, and issue early warnings for excessively rapid algae growth based on these trends, reminding relevant personnel to take appropriate measures.

2. The method for monitoring algae content in a reservoir according to claim 1, characterized in that: The steps for calculating the light non-uniformity coefficient are as follows: The acquired light data from the sub-collection points is preprocessed, including outlier removal, data smoothing, and background correction. For each measurement point, the average light intensity of the measurement point is calculated by averaging the light data of the sub-measurement point. Its calculation formula is: in n represents the average light intensity. I represents the number of sub-measurement points. i Let the light intensity at the i-th sub-measurement point be denoted as ; For each measurement point, based on the average light intensity, the square of the difference between the light intensity at each sub-measurement point and the average value is calculated. Then, the square root of the average of these squared differences is taken to obtain the standard deviation of the light intensity, which measures the degree of variation in light intensity at the measurement point. The calculation formula is as follows: Where σ represents the standard deviation of light intensity, I i Let represent the light intensity at the i-th sub-measurement point. Expressed as average light intensity; The light intensity non-uniformity coefficient of the water surface is calculated by weighted averaging the standard deviation of light intensity at each measurement point. The formula is as follows: Where UL represents the light non-uniformity index, M is the number of sampling points, and σ i It is represented as the standard deviation of the light intensity at the i-th sampling point.

3. The method for monitoring algae content in a reservoir according to claim 1, characterized in that: The step of correcting light based on the light non-uniformity coefficient involves setting a threshold for the light non-uniformity coefficient and a standard correction intensity. The actual light correction intensity is then calculated based on the real-time light non-uniformity index, using the following formula: Where LC represents the actual light correction intensity, UL represents the light non-uniformity index, UL' represents the light non-uniformity coefficient threshold, and L... 标准 This is expressed as the standard correction intensity, and the light is corrected according to the actual light correction intensity.

4. The method for monitoring algae content in a reservoir according to claim 1, characterized in that: The steps for collecting chlorophyll-a concentration using a spectrometer are as follows: Spectral data of water samples were collected at different monitoring points using a spectrometer, and the light intensity at different wavelengths was recorded. The spectral data undergoes preprocessing, including noise removal, background correction, and smoothing. The background correction formula is I. 矫正 (λ)=I(λ)-I 背景 (λ), where I(λ) represents the original spectral data, I 背景 (λ) represents the background spectral data, selecting the characteristic absorption wavelength of chlorophyll-a; The formula for calculating the spectral absorbance at the characteristic wavelength is as follows: Where A(λ) I0(λ) represents the spectral absorbance value, and I0(λ) represents the reference illumination intensity. 矫正 (λ) represents the corrected light intensity; The concentration of chlorophyll-a is calculated based on the spectral absorbance value. The formula is CA=k·A(665), where CA represents the concentration of chlorophyll-a, k is the calibration coefficient, and A(665) represents the absorbance at 665nm.

5. The method for monitoring algae content in a reservoir according to claim 1, characterized in that: The steps for calculating the physical influence coefficient are as follows: A corresponding sensor is installed at each measurement point to measure water temperature, light intensity, and water transparency, respectively. Acquire real-time water temperature data and calculate the water temperature impact based on the real-time water temperature data; Acquire real-time light intensity data and calculate the light impact degree based on the real-time light intensity data; Acquire real-time water transparency data and calculate the transparency impact based on the real-time water transparency data; The physical influence coefficient is obtained by comprehensively evaluating the influence of water temperature, light intensity, and transparency.

6. The method for monitoring algae content in a reservoir according to claim 1, characterized in that: The step of issuing an early warning for excessive algae content based on the algae content index is as follows: the algae content index is compared with a preset threshold. If the algae content index is less than the preset threshold, it is determined that the current algae content in the reservoir is low, and no early warning for excessive algae content is issued. If the algae content index is greater than the preset threshold, it is determined that the current algae content in the reservoir is high, and an early warning for excessive algae content is issued.

7. The reservoir algae content monitoring device according to claim 1, characterized in that: The steps for predicting the future growth trend of algae using ARIMA model time series analysis during the collection and detection period are as follows: Collect algae content index data during the monitoring period and preprocess the collected data; Determine the characteristics of time series data and use autocorrelation plots and partial autocorrelation plots to identify ARIMA. Model order: Select ARIMA model order (p, d, q); Historical data was used to fit the ARIMA model, and model diagnostics were performed to check whether the model residuals met the white noise assumption. The algae abundance index at future time points is predicted using the fitted ARIMA model, and its expression is: Where Y t+h It is the predicted value at time point t+h. The model prediction value at time point t+h, e t+h It is the residual at time point t+h.

8. The reservoir algae content monitoring device according to claim 1, characterized in that: The step of issuing an early warning for excessively rapid algae growth based on the growth trend is as follows: the growth trend is compared with a preset threshold. If the growth trend is less than the preset threshold, no early warning for excessively rapid algae growth is issued. If the growth trend is greater than the preset threshold, it is determined that the current algae content is growing too fast, and an early warning for excessively rapid algae growth is issued.

9. An apparatus for implementing the method for monitoring algae content in a reservoir according to any one of claims 1-8, the apparatus comprising: The light data acquisition module is used to collect light data from monitoring points, calculate the light non-uniformity coefficient, and transmit the light non-uniformity coefficient to the light correction module; The light correction module is used to receive the light non-uniformity coefficient transmitted by the light data acquisition module and correct the light according to the light non-uniformity coefficient; The algae data monitoring module is used to collect relevant data from the collection points where light correction is performed, calculate the biological influence coefficient, physical influence coefficient and chemical influence coefficient, and comprehensively evaluate the algae content index, which is then transmitted to the algae content early warning module. The algae content early warning module is used to issue early warnings when the algae content is high. The algae growth trend early warning module is used to estimate the future growth trend of algae based on the algae content index, and to issue an early warning when the growth trend is high.

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