River water environment pollutant real-time monitoring method and system

Abstract

The invention discloses a river water environment pollutant real-time monitoring method and system, and relates to the field of river water environment monitoring. The method comprises the following steps: acquiring time series data of the pollutant concentration of the river running water environment in a monitored area at a fixed sampling frequency, constructing a time series data sequence of the concentration of the ith pollutant, constructing a gray prediction model by using the data sequence, and calculating a predicted value of the concentration of the ith pollutant at the next sampling moment through the prediction model. The evaluation value of the pollution degree of the river running water environment in the monitored area at the next sampling moment is calculated according to the obtained prediction value, and the pollutant concentration parameter at the future moment is predicted by using the gray prediction model, so that the change trend of the pollutant type and concentration in the to-be-detected river area can be reflected. The problem that in an existing monitoring mode, due to the dynamic change of the water environment, the timeliness of the sampling result is poor is effectively solved, and water environment pollution early warning can be conducted according to the change condition of the water environment.

Description

Technical Field

[0001] The present invention relates to the field of river water environment monitoring, and in particular to a real-time monitoring method and system for river water environment pollutants. Background Art

[0002] As an important part of surface water resources, rivers are the main source of water for residents' daily life and industrial production. With the continuous acceleration of industrialization, the problem of river water pollution is becoming more and more serious, and it is also receiving more and more attention from the society. River water pollution will not only have an adverse impact on the sustainable development of society, but also affect human life and health. Therefore, it is necessary to monitor river water pollution.

[0003] At present, there are two main methods for monitoring pollutants in river water environment. One is to set up monitoring points in key river areas and collect water quality parameters by installing various pollutant sensors. The other is to adopt manual sampling, in which technical personnel go to the river site to collect samples, and then conduct chemical analysis to obtain relevant water quality parameters; and through qualitative and quantitative analysis of water quality, understand the types and concentrations of pollutants.

[0004] Due to the fluidity of rivers, their water environment is always in a dynamic change process, and the types and concentrations of pollutants at intelligent monitoring points or manual sampling points often change with the changes in the water environment. Therefore, the monitoring data obtained by the existing methods can only reflect the pollutant concentration at the sampling time, and cannot accurately reflect the changing trend of the water environment in the river area to be detected, and the timeliness of the monitoring effect is poor. To this end, we propose a real-time monitoring method and system for river water environment pollutants. Summary of the invention

[0005] The main purpose of the present invention is to provide a method and system for real-time monitoring of pollutants in river water environment, which can effectively solve the problems in the background technology.

[0006] To achieve the above object, the technical solution adopted by the present invention is:

[0007] A method for real-time monitoring of pollutants in river water environment, comprising:

[0008] Obtain the time series data of pollutant concentrations in river water environment in the monitoring area at a fixed sampling frequency, and construct the time series data sequence of the concentration of the i-th type of pollutant in, is the nth sampling value of the concentration of the i-th type of pollutant;

[0009] The pollutants include at least one of hydrogen ion concentration index, suspended solids, total phosphorus, sulfide, cyanide, total nitrogen, and ammonia nitrogen;

[0010] Using data series Construct a grey prediction model and calculate the predicted value of the concentration of the i-th type of pollutant at the next sampling time through the prediction model

[0011] The construction process of the grey prediction model includes the following steps:

[0012] The time series data of the concentration of the i-th type of pollutant Accumulate and generate a sequence in,

[0013] Based on the generated data series and data series Generate the nearest mean sequence of the concentration of the i-th type of pollutant in,

[0014] The mean form of the grey prediction model is determined as: In the formula, a is the development coefficient; b is the grey action;

[0015] The least square method is used to obtain the values ​​of parameters a and b, and the time response sequence of the grey prediction model is obtained:

[0016] The predicted value of the concentration of the i-th type of pollutant at the next sampling time is obtained by a cumulative reduction of the time corresponding series in,

[0017] Based on the predicted value obtained Calculate the assessment value Pi of the river water environment pollution level in the monitoring area at the next sampling time (k+1) , the calculation formula is:

[0018]

[0019] In the formula, is the weight of the i-th type of pollutant, and m is the type of pollutant; It is expressed as the maximum value in the time series data of the concentration of the i-th type of pollutant; It is expressed as the minimum value in the time series data of the concentration of the i-th type of pollutant; It is expressed as the normalized value of the predicted value of the concentration of the i-th type of pollutant; the normalized processing formula of the predicted value of the concentration of the i-th type of pollutant is:

[0020]

[0021] when hour, Among them, x imaxis the maximum concentration of the i-th type of pollutant that can be allowed in the water body;

[0022] The method further comprises the following steps:

[0023] Calculate the pollution level assessment value Pi of the river water environment in the monitoring area at the tth sampling (t) ;

[0024] Using the obtained pollution level assessment value Pi (t) Determine the pollution level of the river water environment in the monitoring area at the tth sampling;

[0025] The pollution degree classification process includes the following steps:

[0026] Using the pollution level assessment value Pi (t) Create a sample set, denoted as {Pi (1) ,Pi (2) , ..., Pi (n)};

[0027] Get the mean and standard deviation in the sample set, and use the mean and standard deviation to standardize the data in the sample set. The standardization formula is: In this formula, z is the standard parameter, σ is the variance of the sample data, and μ is the mean of the sample data;

[0028] After standardization is completed, the standard parameters are used using the function The numerical interval is adjusted to [0,1], and the pollution degree assessment value is classified using the function value of f(k). The classification mechanism is:

[0029] When f(k) min ≤f(k)<f(k) 1 When the pollution level is assessed, it is classified as level one;

[0030] When f(k) 1 ≤f(k)<f(k) 2 When the pollution level is assessed, it is classified as Level 2;

[0031] When f(k) 2 ≤f(k)<f(k) 3 When the pollution level is assessed, it is classified as level three;

[0032] Similarly, when f(k) r ≤f(k)<f(k) max When the pollution level is assessed, it is classified as level r;

[0033] Among them, f(k) min ,f(k) maxare the minimum and maximum values ​​of the function value of f(k) respectively; f(k) 1 ,f(k) 2 , ..., f(k) r are the intermediate values ​​of the f(k) function, and f(k) min <f(k) 1 <f(k) 2 <, ..., <f(k) r <f(k) max ; r is a positive integer;

[0034] The classification results of the pollution degree assessment values ​​are used to classify the pollution degree levels of the river water environment in the monitoring area. The classification mechanism is as follows:

[0035] When the pollution degree assessment value is classified as Level 1, the pollution degree level is Level 1;

[0036] When the pollution degree assessment value is classified as Level 2, the pollution degree level is Level 2;

[0037] By analogy, when the pollution level assessment value is classified as level r, the pollution level is level r.

[0038] A real-time monitoring system for river water environment pollutants, comprising a pollutant concentration data acquisition module, a grey prediction model construction module, a data processing module, and a pollution degree assessment module;

[0039] The pollutant concentration data acquisition module is used to acquire the time series data of the pollutant concentration of the river water environment in the monitoring area at a fixed sampling frequency, and construct a time series data sequence of the concentration of the i-th type of pollutants, wherein the pollutants include at least one of hydrogen ion concentration index, suspended solids, total phosphorus, sulfide, cyanide, total nitrogen, and ammonia nitrogen;

[0040] The grey prediction model building module is used to build a grey prediction model for data sequence, and the prediction value of the concentration of the i-th type of pollutant at the next sampling time is calculated by the prediction model.

[0041] The data processing module is used to normalize the pollutant concentration parameters, wherein the normalization formula for the pollutant concentration prediction value is:

[0042] The pollution degree assessment module is used to obtain the predicted value Calculate the assessment value Pi of the river water environment pollution level in the monitoring area at the next sampling time (k+1) , the calculation formula is:

[0043]

[0044] In the formula, is the weight of the i-th type of pollutant, and m is the type of pollutant; It is expressed as the maximum value in the time series data of the concentration of the i-th type of pollutant; It is expressed as the minimum value in the time series data of the concentration of the i-th type of pollutant; It is expressed as the normalized value of the predicted value of the concentration of the i-th type of pollutant;

[0045] The system also includes a memory, a processor, and a computer program stored on the memory and executable on the processor.

[0046] The present invention has the following beneficial effects:

[0047] Compared with the existing technology, the time series data of the pollutant concentration of the river water environment in the monitoring area is obtained at a fixed sampling frequency, the time series data sequence of the concentration of the i-th type of pollutant is constructed, and the gray prediction model is constructed by using the data sequence. The predicted value of the concentration of the i-th type of pollutant at the next sampling moment is calculated by the prediction model, and the river water environment pollution degree assessment value in the monitoring area at the next sampling moment is calculated according to the obtained prediction value. The pollutant concentration parameters at future moments are predicted by the gray prediction model, which can reflect the changing trend of the types and concentrations of pollutants in the river area to be detected, effectively solve the problem of poor timeliness of sampling results caused by dynamic changes in the water environment in the existing monitoring method, and is conducive to early warning of water environment pollution according to changes in the water environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 A flow chart of a method for real-time monitoring of pollutants in river water environment according to the present invention;

[0049] Figure 2 The present invention is a structural block diagram of a real-time monitoring system for river water environment pollutants. DETAILED DESCRIPTION

[0050] The present invention will be further described below in conjunction with specific implementation methods, wherein the accompanying drawings are only used for exemplary descriptions and represent only schematic diagrams rather than actual drawings, and should not be understood as limiting the present invention. In order to better illustrate the specific implementation methods of the present invention, some parts of the accompanying drawings may be omitted, enlarged or reduced, and do not represent the size of the actual product.

[0051] Example 1

[0052] The specific implementation process of the technical solution of the present invention includes the following steps:

[0053] Step 1: Obtain the time series data of pollutant concentrations in river water environment within the monitoring area at a fixed sampling frequency;

[0054] The pollutants include at least one of hydrogen ion concentration index, suspended solids, total phosphorus, sulfide, cyanide, total nitrogen, and ammonia nitrogen;

[0055] It should be noted that the accuracy of water environment pollution data collection is directly related to the accuracy of monitoring results. Therefore, it is necessary to set a reasonable sampling period and sampling frequency. The general sampling period can be set to 5 to 60 minutes, and the sampling frequency can be set according to the actual situation, which can be set to 0.5 to 1 minute / time. In addition, it is necessary to arrange the monitoring points reasonably. Usually, a measuring line can be set up in the upper, middle and lower reaches of the river, and 3 to 5 measuring points can be set up on each measuring line. The distance between the measuring points is between 100 and 200 meters. 1 to 2 measuring points are set up at each river branch, and the measuring points are installed Corresponding pollutant parameter collection sensors are installed. In this embodiment, the collection of seven pollution parameters such as hydrogen ion concentration index, suspended solids, total phosphorus, sulfide, cyanide, total nitrogen, and ammonia nitrogen in the river water environment is taken as an example for explanation. In the actual implementation process, a pollutant parameter collector such as model IFFA-A44G can be installed at the measuring point, and the sensor parameters can be set, including pulse signal emission frequency, scanning period, scanning range, etc., and the corresponding parameter data can be obtained through signal scanning; in addition, GIS technology can also be used to obtain the spatial geographic information of the measuring point to locate the location of the water environment pollution point.

[0056] Step 2: Construct a time series data sequence of the concentration of the i-th type of pollutant in, is the nth sampling value of the concentration of the i-th type pollutant.

[0057] Step 3: Utilize the data series Construct a grey prediction model and calculate the predicted value of the concentration of the i-th type of pollutant at the next sampling time through the prediction model

[0058] The construction process of the grey prediction model includes the following steps:

[0059] Step 31: Convert the time series data of the concentration of the i-th type of pollutant Accumulate and generate a sequence in,

[0060] Step 32: Based on the generated data sequence and data series Generate the nearest mean sequence of the concentration of the i-th type of pollutant in,

[0061] Step 33: Determine the mean form of the grey prediction model: In the formula, a is the development coefficient; b is the grey action;

[0062] Step 34: Use the least squares method to obtain the values ​​of parameters a and b, and obtain the time response sequence of the grey prediction model:

[0063] Step 35: Obtain the predicted value of the concentration of the i-th type of pollutant at the next sampling time by a cumulative reduction of the time corresponding series in,

[0064] Step 4: Based on the obtained prediction value Calculate the assessment value Pi of the river water environment pollution level in the monitoring area at the next sampling time (k+1) , the calculation formula is:

[0065]

[0066] In the formula, is the weight of the i-th type of pollutant, and m is the type of pollutant; It is expressed as the maximum value in the time series data of the concentration of the i-th type of pollutant; It is expressed as the minimum value in the time series data of the concentration of the i-th type of pollutant; It is expressed as the normalized value of the predicted value of the concentration of the i-th type of pollutant;

[0067] Among them, the normalization formula is:

[0068] It should be noted that when the maximum value in the time series data of the concentration of the i-th type of pollutant is Not greater than the maximum concentration x allowed in water imax When the pollutant is present, it will not have an adverse effect on the water ecosystem or human health, so it has no negative impact on the degree of water pollution. The maximum allowable concentration threshold is determined based on environmental science research and aims to protect water quality and human health. For specific parameter values, please refer to the "New Pollutant Ecological Environmental Monitoring Standard System Table (2024 Edition)" issued by the Ministry of Ecology and Environment.

[0069] Step 5: Calculate the pollution level assessment value Pi of the river water environment in the monitoring area at the tth sampling time (t) ; The calculation method is the same as the water environment pollution degree assessment value Pi (k+1) , I will not go into details here.

[0070] Step 6: Use the obtained pollution level assessment value Pi (t) Determine the pollution level of the river water environment in the monitoring area at the tth sampling.

[0071] The pollution degree classification process includes the following steps:

[0072] Step 61: Use the pollution level assessment value Pi (t) Create a sample set, denoted as {Pi (1) ,Pi (2) , ..., Pi (n)};

[0073] Step 62: Obtain the mean and standard deviation of the sample set, and use the mean and standard deviation to standardize the data in the sample set. The standardization formula is: In this formula, z is the standard parameter, σ is the variance of the sample data, and μ is the mean of the sample data;

[0074] Step 63: After completing the standardization, use the standard parameters to The numerical interval is adjusted to [0,1], and the pollution degree assessment value is classified using the function value of f(k). The classification mechanism is:

[0075] When f(k) min ≤f(k)<f(k) 1 When the pollution level is assessed, it is classified as level one;

[0076] When f(k) 1 ≤f(k)<f(k) 2 When the pollution level is assessed, it is classified as Level 2;

[0077] When f(k) 2 ≤f(k)<f(k) 3 When the pollution level is assessed, it is classified as level three;

[0078] Similarly, when f(k) r ≤f(k)<f(k) max When the pollution level is assessed, it is classified as level r;

[0079] Among them, f(k) min ,f(k) max are the minimum and maximum values ​​of the function value of f(k) respectively; f(k) 1 ,f(k) 2 , ..., f(k) r are the intermediate values ​​of the f(k) function, and f(k) min <f(k) 1 <f(k) 2 <, ..., <f(k) r <f(k) max ; r is a positive integer;

[0080] Step 64: Classify the pollution level of the river water environment in the monitoring area using the classification results of the pollution level assessment values. The classification mechanism is:

[0081] When the pollution degree assessment value is classified as Level 1, the pollution degree level is Level 1;

[0082] When the pollution degree assessment value is classified as Level 2, the pollution degree level is Level 2;

[0083] By analogy, when the pollution level assessment value is classified as level r, the pollution level is level r.

[0084] By adopting the above technical solution: using the gray prediction model to predict the pollutant concentration parameters at future times, it is possible to reflect the changing trends of the types and concentrations of pollutants in the river area to be tested, and effectively solve the problem of poor timeliness of sampling results caused by the dynamic changes of the water environment in the existing monitoring methods, which is conducive to early warning of water environment pollution based on changes in the water environment.

[0085] Example 2

[0086] The technical solution of the present invention can also be used to evaluate the degree of pollution of river water environment in the monitoring area at any time. The specific implementation process includes the following steps:

[0087] Step 1: Obtain time series data of pollutant concentrations in river water environment within the monitoring area at a fixed sampling frequency.

[0088] Step 2: Construct a time series data sequence of the concentration of the i-th type of pollutant in, is the nth sampling value of the concentration of the i-th type pollutant.

[0089] Step 3: Based on the acquired time series data of pollutant concentration, calculate the pollution level assessment value Pi of the river water environment in the monitoring area at any time t (t) , the calculation formula is:

[0090]

[0091] In the formula, is the weight of the i-th type of pollutant, and m is the type of pollutant; It is expressed as the maximum value in the time series data of the concentration of the i-th type of pollutant; It is expressed as the minimum value in the time series data of the concentration of the i-th type of pollutant; It is expressed as the normalized value of the predicted value of the concentration of the i-th type of pollutant; the normalization processing formula is:

[0092] The above shows and describes the basic principles and main features of the present invention and the advantages of the present invention. It should be understood by those skilled in the art that the present invention is not limited to the above embodiments. The above embodiments and descriptions are only for explaining the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention to be protected. The scope of protection of the present invention is defined by the attached claims and their equivalents.

Claims

1. A real-time monitoring method for river water environment pollutants, characterized in that: include: Obtain the time series data of pollutant concentrations in river water environment in the monitoring area at a fixed sampling frequency, and construct the time series data sequence of the concentration of the i-th type of pollutant in, is the nth sampling value of the concentration of the i-th type of pollutant; Using data series Construct a grey prediction model and calculate the predicted value of the concentration of the i-th type of pollutant at the next sampling time through the prediction model Based on the predicted value obtained Calculate the assessment value Pi of the river water environment pollution level in the monitoring area at the next sampling time (k+1) , the calculation formula is: In the formula, is the weight of the i-th type of pollutant, and m is the type of pollutant; It is expressed as the maximum value in the time series data of the concentration of the i-th type of pollutant; It is expressed as the minimum value in the time series data of the concentration of the i-th type of pollutant; It is expressed as the normalized value of the predicted concentration of the i-th type of pollutant.

2. A method for real-time monitoring of river water environment pollutants according to claim 1, characterized in that: The method further comprises the following steps: Calculate the pollution level assessment value Pi of the river water environment in the monitoring area at the tth sampling (t) , t∈n; Using the obtained pollution level assessment value Pi (t) Determine the pollution level of the river water environment in the monitoring area at the tth sampling.

3. A method for real-time monitoring of river water environment pollutants according to claim 1, characterized in that: The pollutants include at least one of hydrogen ion concentration index, suspended solids, total phosphorus, sulfide, cyanide, total nitrogen, and ammonia nitrogen.

4. A method for real-time monitoring of river water environment pollutants according to claim 1, characterized in that: The construction process of the grey prediction model includes the following steps: The time series data of the concentration of the i-th type of pollutant Accumulate and generate a sequence in, Based on the generated data series and data series Generate the nearest mean sequence of the concentration of the i-th type of pollutant in, The mean form of the grey prediction model is determined as: In the formula, a is the development coefficient; b is the grey action; The least square method is used to obtain the values ​​of parameters a and b, and the time response sequence of the grey prediction model is obtained: The predicted value of the concentration of the i-th type of pollutant at the next sampling time is obtained by a cumulative reduction of the time corresponding series in, 5. A method for real-time monitoring of river water environmental pollutants according to claim 2, characterized in that: The pollution degree classification process includes the following steps: Using the pollution level assessment value Pi (t) Create a sample set, denoted as {Pi (1) ,Pi (2) , ..., Pi (n) }; Get the mean and standard deviation in the sample set, and use the mean and standard deviation to standardize the data in the sample set. The standardization formula is: In this formula, z is the standard parameter, σ is the variance of the sample data, and μ is the mean of the sample data; After standardization is completed, the standard parameters are used using the function The numerical interval is adjusted to [0,1], and the pollution degree assessment value is classified using the function value of f(k). The classification mechanism is: When f(k) min When ≤f(k)<f(k)1, the pollution degree assessment value is classified as level one; When f(k)1≤f(k)<f(k)2, the pollution degree assessment value is classified as level 2; When f(k)2≤f(k)<f(k)3, the pollution degree assessment value is classified as level three; Similarly, when f(k) r ≤f(k)<f(k) max When the pollution level is assessed, it is classified as level r; Among them, f(k) min ,f(k) max are the minimum and maximum values ​​of the function value of the f(k) function; f(k)1, f(k)2, ..., f(k) r are the intermediate values ​​of the f(k) function, and f(k) min <f(k)1<f(k)2<,...,<f(k) r <f(k) max ; r is a positive integer; The classification results of the pollution degree assessment values ​​are used to classify the pollution degree levels of the river water environment in the monitoring area. The classification mechanism is as follows: When the pollution degree assessment value is classified as Level 1, the pollution degree level is Level 1; When the pollution degree assessment value is classified as Level 2, the pollution degree level is Level 2; By analogy, when the pollution degree assessment value is classified as level r, the pollution degree level is the rth level.

6. A method for real-time monitoring of river water environmental pollutants according to claim 1, characterized in that: when hour, Among them, x imax It is the maximum concentration of the i-th type pollutant that can be allowed in the water body.

7. A method for real-time monitoring of river water environmental pollutants according to claim 1, characterized in that: The normalized processing formula for the predicted value of the concentration of the i-th type of pollutant is:

8. A real-time monitoring system for river water environment pollutants, characterized in that: It includes pollutant concentration data acquisition module, grey prediction model construction module, data processing module and pollution degree assessment module; The pollutant concentration data acquisition module is used to acquire the time series data of the pollutant concentration of the river water environment in the monitoring area at a fixed sampling frequency, and construct the time series data sequence of the i-th type of pollutant concentration in, is the nth sampling value of the concentration of the i-th type of pollutant, wherein the pollutant includes at least one of hydrogen ion concentration index, suspended solids, total phosphorus, sulfide, cyanide, total nitrogen, and ammonia nitrogen; The grey prediction model building module is used for data series Construct a grey prediction model and calculate the predicted value of the concentration of the i-th type of pollutant at the next sampling time through the prediction model The data processing module is used to normalize the pollutant concentration parameters, wherein the normalization formula for the pollutant concentration prediction value is: The pollution degree assessment module is used to obtain the predicted value Calculate the assessment value Pi of the river water environment pollution level in the monitoring area at the next sampling time (k+1) , the calculation formula is: In the formula, is the weight of the i-th type of pollutant, and m is the type of pollutant; It is expressed as the maximum value in the time series data of the concentration of the i-th type of pollutant; It is expressed as the minimum value in the time series data of the concentration of the i-th type of pollutant; It is expressed as the normalized value of the predicted concentration of the i-th type of pollutant.

9. A real-time monitoring system for river water environment pollutants according to claim 8, characterized in that: The system further comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the steps of the method according to any one of claims 1 to 7 are implemented.

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