CN112381369A - Water body pollution tracing and risk prediction evaluation method based on online spectrum identification - Google Patents

Abstract

The invention discloses a water pollution source traceability and risk prediction assessment method based on online spectrum identification, which belongs to the technical field of surface water pollutant monitoring, source traceability and risk assessment. Background value of water quality of rivers and lakes; use immersion UV water quality online analyzer to monitor water quality in real time and feed back water quality data; build a cloud computing platform to collect real-time water quality monitoring data of rivers and lakes fed back by water quality analyzers and perform real-time comparison of water quality data; For the results, if it is determined that there is a pollutant leakage, a high-precision water quality traceability model based on the Kalman filter method is used to trace the source of the pollutant; according to the traceability result, the pollution risk assessment can be quickly performed. The system constructed by the invention includes the functions of real-time online monitoring and cloud computing, and realizes the traceability and risk assessment goals that integrate the characteristics of data platformization, accurate identification, high-speed calculation, and simplified operation.

Description

An evaluation method for water pollution source traceability and risk prediction based on online spectral identification

technical field

The invention belongs to the technical field of surface water pollutant monitoring, traceability and risk assessment, and in particular relates to a water body pollution traceability and risk prediction assessment method based on online spectrum identification.

Background technique

In recent years, sudden water pollution incidents have been increasing year by year. Sudden water pollution incidents are uncertain and hazard urgent. They rapidly affect the water supply system in a short period of time, leading to water outages. Such mechanisms seriously affect the urban ecosystem, which in turn causes complex social problems and becomes the primary threat factor affecting the safety of drinking water sources. In order to minimize the adverse effects of water pollution accidents, in addition to strengthening real-time monitoring of water quality, it is necessary to establish a system technology that can identify water pollution events, trace pollutants, and risk assessment and forecast online.

Existing monitoring and traceability technologies or systems for water pollution have a single function, which is limited to single-function water quality monitoring or traceability work. Time-effectiveness, and at the same time, intelligence is relatively one-sided and not systematic.

SUMMARY OF THE INVENTION

Purpose of the invention: Aiming at the water pollution problem and its important timeliness, the purpose of the present invention is to provide a water pollution source traceability and risk prediction assessment method based on online spectral identification.

Technical scheme: in order to realize the above-mentioned purpose, the present invention provides the following technical scheme:

The water pollution source traceability and risk prediction assessment method based on online spectral identification includes the following steps:

1) Investigate possible pollutants and sources in the target waters;

2) Place the immersion UV water quality online analyzer in the target water area to be monitored for a period of time to obtain the background value of the water quality of the local river and lake;

3) Build a cloud computing platform;

4) Real-time online monitoring of the immersion UV water quality online analyzer is arranged in the water area affected by potential pollution sources. The UV online water quality analyzer contains independent communication equipment, and the water quality data is uploaded to the cloud computing platform application server in real time for data storage and processing;

5) According to the water quality data uploaded in real time, the online identification and calculation of water pollution is carried out through the water pollution identification calculation program embedded in the cloud computing platform, and the corresponding pollutant leakage is determined in real time under the background concentration of local river and lake water quality; water pollution identification calculation The program performs data screening and filtering according to the background value of the water body and the established control indicators of related substances, and determines the types of substances with excessive concentrations according to the spectral range corresponding to the peak of the spectral data;

6) When the immersion UV water quality online analyzer detects that the concentration of abnormal substances exceeds the standard, the traceability program embedded in the cloud computing platform is used to locate the pollution source and identify the release history;

7) According to the traceability results, the risk early-warning program embedded in the cloud computing platform is used to quickly conduct pollution risk assessment in the vicinity of the monitoring waters for a period of time in the future; The parameters are derived from the calculation results of the traceability parameters, and carry out risk prediction, early warning and assessment for the future diffusion of pollutants in the downstream of the target waters.

Further, in step 1), the pollutants and sources that may appear in the survey target water area are the pollution sources with potential pollution near the survey target water area, and a pollution fingerprint is made to facilitate comparison and query to determine the source of pollutants.

Further, described step 2) is specifically that immersion type UV water quality on-line analyzer observes target entity water quality by time interval, and the time interval is 1h; During the observation period, two kinds of contrast situations are included: different weather conditions, different production states of high and low peaks of the factory; According to the repeated observation results, the average value is taken as the water quality body value of the period; the pollutants that can be monitored should have the property of absorbing light at 200nm-800nm, and the monitoring indicators include COD, permanganate, nitrate nitrogen, Metal ions, chlorophyll.

Further, in the described step 3), the cloud computing platform includes an application server and a communication server; the application server is connected with the communication server, and the application server is used to record the water quality data uploaded in real time and process related business logic , wherein the business logic includes a water pollution identification calculation program, a traceability program, and a risk warning program. The communication server is used for communication with the mobile terminal, the monitoring terminal and the application terminal, wherein the monitoring terminal is an immersion UV water quality online analyzer.

Further, in the described step 6), the traceability program is based on the uncertainty optimization model of the Kalman filter method, and is coupled by the Kalman filter optimization module and the two-dimensional material transport module, and the Kalman filter optimization The sampling algorithm of the module adopts the Monte Carlo random sampling method, and the material transport module is based on the two-dimensional convection-diffusion equation and the finite volume difference method.

Further, it is characterized in that: the two-dimensional material transport model in the traceability program is based on the basic governing equation of the following formula (1):

In the formula: H is the water depth (m); C is the concentration of the substance at the constant depth (kg/m ³ ); t is the time (s); u and v are the speeds in the x and y directions (m/s), respectively; K _xx , K _xy , K _yx and K _yy are two-dimensional diffusion coefficient tensors (m ² /s).

Further, the formula (1) is divided into the following equations of convection term and diffusion term:

Convective term:

Diffusion term:

Using the finite volume method, the above convection term and diffusion term are differentially calculated to simulate the pollutant concentration field.

Further, in the described traceability program, the Kalman filter optimization module process comprises two steps of prediction and update:

表示集合均值，以K′表示两者之差，如公式(4)：First, use K to represent the state variable set (concentration of pollutants) of the prediction model, and the set individual is obtained by the Monte Carlo random sampling algorithm,

represents the set mean, and K' represents the difference between the two, as in formula (4):

In the above formula (5), I _N is the average operator, N is the number of state variable sets; P represents the error covariance matrix of K, and the superscript f and a represent the predicted value and the analysis value respectively, then P ^f and P ^a respectively Represents the predicted and analyzed error covariance matrix of the state variable;

P ^f =K ^f' (K ^f' ) ^T (6);

P ^a =K ^a' (K ^a' ) ^T (7);

上标T表示矩阵转置，K^f和K^f分别为状态变量的预测值和分析值。in,

The superscript T represents the matrix transpose, and K ^f and K ^f are the predicted and analyzed values of the state variables, respectively.

Further, in the described source tracing program, the Kalman filter optimization module process includes two steps of prediction and update, and specifically includes the following steps:

1) The prediction step is represented by the following formula

表示t时刻第i个集合成员的状态变量的更新值；

表示t+1时刻第i个集合成员状态变量的预测值；

表示模型算子，即二维物质输运计算模型；e_1i表示模型误差向量，误差服从e_1i～N(0，X)，本模型X取1％；In the formula: i represents the member serial number of the state variable set. Since only one state variable is contained in the pollutant concentration, i is only 1;

Represents the updated value of the state variable of the ith set member at time t;

Represents the predicted value of the state variable of the i-th set member at time t+1;

Represents the model operator, that is, the two-dimensional material transport calculation model; e _1i represents the model error vector, the error obeys e _1i ～N(0, X), and X is 1% in this model;

2) The update step is represented by the following formula

[A]

为t+1时刻第i个集合成员的仪器观测的真实状态；Among them, S _{i, t+1} represents the vector of the observed state variable of the i-th set member at time t+1; Q is the transformation matrix, which is the unit matrix in this model; e _2i represents the observation error, and the error obeys e _2i ～N(0 , Y), according to the observation instrument and observation conditions, this model Y is taken as 1%;

is the true state of the instrumental observation of the i-th ensemble member at time t+1;

E is the observation error matrix, as follows:

E=(ε ₁ , ε ₂ ,...,ε _N )∈R ^m×N (10);

where m is the number of observations, N is the number of state variable sets, and ε is the state variable observation error;

_Re is the observation error covariance matrix, as follows:

_Re = E'(E') ^T (11);

in,

[B]

Among them, G _t+1 represents the gain matrix at time t+1, which can be expressed by the following formula:

In the formula, Y _t+1 is the matrix composed of the variance Y of all state variables in the set at time t+1;

为：Updated state error covariance matrix

for:

In the formula, I is the identity matrix.

Aiming at the water pollution problem and its important timeliness, build a cloud computing platform. Based on the powerful database and computing power of the cloud computing platform, develop and embed the online identification, traceability and early warning technology of pollutants exceeding the standard, so as to detect and automate the phenomenon of excessive pollution in time. Traceability and early warning are also helpful for relevant departments to take necessary control measures and governance plans, and provide necessary technical support for the accountability of pollution incidents.

Beneficial effect: Compared with the prior art, the present invention builds a cloud computing platform with powerful database and computing power, and embeds the self-developed online identification, traceability and risk prediction evaluation calculation module of pollutants in the platform. It can meet the acquisition of real-time monitoring water quality data under the condition of pollutants exceeding the standard, and can automatically identify the type and concentration of pollutants exceeding the standard, and automatically perform traceability and risk early warning calculations, which can be used as technical support for emergency decision-making by relevant departments. The design of the system is highly intelligent and integrated, which can ensure the timeliness of dealing with emergencies. At the same time, the system is highly malleable based on the cloud computing platform, and can update hardware devices and expand computing templates at any time, which will meet the system requirements for the development of hardware technology and artificial intelligence for a long period of time in the future.

Description of drawings

Figure 1 is a schematic diagram of the structure of a water pollution traceability and risk prediction assessment method based on online spectral identification;

Figure 2 is a schematic diagram of the workflow of the water pollution source traceability and risk prediction assessment method based on online spectral identification.

Detailed ways

The present invention will be further described below with reference to the accompanying drawings and specific embodiments.

As shown in Figure 1-2, the water pollution source traceability and risk prediction assessment method based on online spectral identification includes the following steps:

1) Investigate possible pollutants and sources in the target waters;

Investigate potentially polluting sources such as factories, sewage pipelines, and gaps between farmland and rivers near the target waters, and make pollution fingerprints to facilitate comparison and query to determine the possible sources of pollutants;

2) Place the immersion UV water quality online analyzer in the target water area to be monitored for a period of time to obtain the background value of the water quality of the local river and lake;

The immersion UV water quality online analyzer needs to observe the water quality of the target entity in different time periods, and the time interval is 1h; during the observation period, the following two comparison situations should be paid attention to: different weather conditions in sunny and rainy conditions, and different production states of high and low peaks of the factory; according to the repeated observation results, When segmenting, the average value is taken as the water quality body value of this period;

The pollutants that can be monitored should have the property of absorbing 200nm-800nm light, and the monitoring indicators include dissolved COD, total organic carbon, nitrate nitrogen, chlorophyll, and heavy metal ions;

3) Build a cloud computing platform;

The cloud computing platform includes an application server and a communication server; the application server is connected to the communication server, and the application server is used to record the real-time uploaded water quality data and process related business logic, wherein the business logic includes a water pollution identification calculation program, a traceability program, Risk warning program, the communication server is used for communication with the mobile terminal, the monitoring terminal and the application terminal, wherein the monitoring terminal is an immersion UV water quality online analyzer;

4) Real-time online monitoring of immersion UV water quality online analyzers in water areas that may be affected by nearby factories, sewage treatment plants, sewage pipelines or some other potential pollution sources. The water quality data is uploaded to the cloud computing platform application server in real time within the signal area for data storage and processing;

5) According to the water quality data uploaded in real time, the online identification and calculation of water pollution is carried out through the water pollution identification calculation program embedded in the cloud computing platform, and the corresponding pollutant leakage is determined in real time under the background concentration of local river and lake water quality; water pollution identification calculation The program performs data screening and filtering according to the background value of the water body and the established control indicators of related substances, and determines the types of substances with excessive concentrations according to the spectral range corresponding to the peak of the spectral data;

6) When the immersion UV water quality online analyzer detects that the concentration of abnormal substances exceeds the standard, the traceability program embedded in the cloud computing platform is used to locate the pollution source and identify the release history;

The traceability program is an uncertainty optimization model based on the Kalman filter method, which is coupled by a Kalman filter optimization module and a two-dimensional material transport module. The sampling algorithm of the Kalman filter optimization module adopts the Monte Carlo random sampling method. The Mass Transport Module is based on the two-dimensional convective-diffusion equation and finite volume difference method.

The two-dimensional material transport model in the traceability program is based on the following basic governing equations:

In the formula: H is the water depth (m); C is the concentration of the substance at the constant depth (kg/m ³ ); t is the time (s); u and v are the speeds in the x and y directions (m/s), respectively; K _xx , K _xy , K _yx and K _yy are two-dimensional diffusion coefficient tensors (m ² /s).

For equation (1), it is divided into the following convection term and diffusion term equation:

Convective term:

Diffusion term:

Using the finite volume method, the above convection term and diffusion term are differentially calculated to simulate the pollutant concentration field. The Kalman filter optimization module process described in the traceability program mainly includes two steps of prediction and update:

表示集合均值，以K′表示两者之差，如式(4)。First, use K to represent the state variable set (concentration of pollutants) of the prediction model, and the set individual is obtained by the Monte Carlo random sampling algorithm,

represents the mean value of the set, and K' represents the difference between the two, as in formula (4).

In the above formula (5), I _N is the average operator, and N is the number of state variable sets, which is taken as 20 in this model.

P represents the error covariance matrix of K, the superscripts f and a represent the predicted value and the analyzed value, respectively, then P ^f and P ^a represent the predicted value and the analyzed value of the state variable, respectively, the error covariance matrix.

P ^f =K ^f' (K ^f' ) ^T (6);

P ^a =K ^a' (K ^a' ) ^T (7);

上标T表示矩阵转置，K^f和K^f分别为状态变量的预测值和分析值。in,

The superscript T represents the matrix transpose, and K ^f and K ^f are the predicted and analyzed values of the state variables, respectively.

(1) The prediction step is represented by the following equation

表示t时刻第i个集合成员的状态变量的更新值；

表示t+1时刻第i个集合成员状态变量的预测值；

表示模型算子，即二维物质输运计算模型；e_1i表示模型误差向量，误差服从e_1i～N(0，X)，本模型X取1％。In the formula: i represents the member serial number of the state variable set. Since only one state variable is contained in the pollutant concentration, i is only 1;

Represents the updated value of the state variable of the ith set member at time t;

Represents the predicted value of the state variable of the i-th set member at time t+1;

Represents the model operator, that is, the two-dimensional material transport calculation model; e _1i represents the model error vector, the error obeys e _1i ～N(0, X), and this model X takes 1%.

(2) Update steps

[A]

为t+1时刻第i个集合成员的仪器观测的真实状态。Among them, S _{i, t+1} represents the vector of the observed state variable of the i-th set member at time t+1; Q is the transformation matrix, which is the unit matrix in this model; e _2i represents the observation error, and the error obeys e _2i ～N(0 , Y), according to the observation instrument and observation conditions, this model Y is taken as 1%;

is the true state of the instrumental observation of the ith ensemble member at time t+1.

E is the observation error matrix, as follows:

E=(ε ₁ , ε ₂ ,...,ε _N )∈R ^m×N (10);

where m is the number of observations, N is the number of state variable sets, and ε is the state variable observation error.

_Re is the observation error covariance matrix, as follows:

_Re = E'(E') ^T (11);

in,

[B]

Among them, G _t+1 represents the gain matrix at time t+1, which can be expressed by the following formula:

In the formula, Y _t+1 is the matrix composed of the variance Y of all state variables in the set at time t+1.

为：Updated state error covariance matrix

for:

In the formula, I is the identity matrix.

7) According to the traceability results, the risk early-warning program embedded in the cloud computing platform can quickly conduct a pollution risk assessment near the monitoring waters for a period of time in the future. Based on the two-dimensional material transport forward model in the traceability program in step 6), the pollution source item parameters in the model are derived from the calculation results of the traceability parameters, and the risk prediction, early warning and assessment of the future pollutant diffusion in the downstream of the target water area are performed.

Compared with the prior art, the present invention has the following advantages: by constructing a cloud computing platform with powerful database and computing power, and embedding in the platform a self-developed online identification, traceability and risk prediction evaluation calculation module for pollutants exceeding the standard, normal Under certain circumstances, it can meet the acquisition of real-time monitoring of water quality data. When the pollutants exceed the standard, it can identify the types and concentrations of pollutants that exceed the standard, and automatically perform traceability and risk early warning calculations, which can be used as technical support for emergency decision-making by relevant departments. The design of the system is highly intelligent and integrated, which can ensure the timeliness of dealing with emergencies. At the same time, the system is highly malleable based on the cloud computing platform, and can update hardware devices and expand computing templates at any time, which will meet the system requirements for the development of hardware technology and artificial intelligence for a long period of time in the future.

Claims (9)

1. The water body pollution tracing and risk prediction evaluation method based on online spectrum identification is characterized by comprising the following steps: the method comprises the following steps:

1) investigating pollutants and sources appearing in a target water area;

2) placing an immersion type UV water quality on-line analyzer in a target water area to be monitored for a period of time to obtain a background value of the water quality of the local river and lake water body;

3) constructing a cloud computing platform;

4) arranging an immersion UV water quality online analyzer in a water area affected by a potential pollution source for real-time online monitoring, and uploading water quality data to a cloud computing platform application server in real time for data storage and processing;

5) according to the water quality data uploaded in real time, performing online water pollution identification calculation through a water pollution identification calculation program embedded in a cloud computing platform, and judging whether corresponding pollutant leakage conditions exist under the background concentration of the water quality of the local rivers and lakes in real time; the water pollution identification calculation program carries out data screening and filtering, and the species of substances with overproof concentration are determined according to the spectral range corresponding to the peak value of the spectral data;

6) when the concentration of the heterogeneous substances exceeds the standard, the immersed UV water quality online analyzer carries out pollution source positioning and release history identification by adopting a tracing program embedded in a cloud computing platform;

7) and according to the tracing result, carrying out pollution risk assessment in a future period of time near the monitoring water area through a risk early warning program embedded in the cloud computing platform.

2. The water body pollution tracing and risk prediction assessment method based on online spectrum identification as claimed in claim 1, wherein: in the step 1), the pollutants and sources appearing in the investigation target water area are pollution sources with potential pollution near the investigation target water area, and a pollution fingerprint is manufactured to facilitate comparison, inquiry and judgment of the pollutant sources.

3. The water body pollution tracing and risk prediction assessment method based on online spectrum identification as claimed in claim 1, wherein: the step 2) is to observe the water quality of the target entity at different time intervals by using an immersion type UV water quality on-line analyzer, wherein the time interval is 1 h; the observation period included two comparative cases: weather conditions in sunny days and rainy days and different production states in high and low peaks of factories; and taking an average value in a segmentation process as a water quality body value of the time period according to repeated observation results.

4. The water body pollution tracing and risk prediction assessment method based on online spectrum identification as claimed in claim 1, wherein: in the step 3), the cloud computing platform comprises an application server and a communication server; the application server is connected with the communication server and used for recording water quality data uploaded in real time and processing relevant business logic, wherein the business logic comprises a water pollution identification calculation program, a source tracing program and a risk early warning program, the communication server is used for communicating with the mobile terminal, the monitoring terminal and the application terminal, and the monitoring terminal is an immersion type UV water quality online analyzer.

5. The water body pollution tracing and risk prediction assessment method based on online spectrum identification as claimed in claim 1, wherein: in the step 6), the tracing program is an uncertainty optimization model based on a Kalman filtering method, and is coupled by a Kalman filtering optimization module and a two-dimensional material transportation module, wherein a sampling algorithm of the Kalman filtering optimization module adopts a Monte Carlo random sampling method, and the material transportation module is based on a two-dimensional convection diffusion equation and a finite volume difference method.

6. The water body pollution tracing and risk prediction assessment method based on online spectrum identification as claimed in claim 5, wherein: the two-dimensional material transportation model in the tracing program is based on a basic control equation of the following formula (1):

in the formula: h is water depth; c is the concentration of the substance at equal depth; t is time; u and v are the velocities in the x and y directions, respectively; k_xx，K_xy，K_yxAnd K_yyIs two-dimensionally diffusedThe coefficient tensor.

7. The water body pollution tracing and risk prediction assessment method based on online spectrum identification as claimed in claim 6, wherein: the equation (1) is divided into the following convection term and diffusion term equations:

convection term:

diffusion term:

and carrying out differential calculation on the convection term and the diffusion term by using a finite volume method to simulate a pollutant concentration field.

8. The water body pollution tracing and risk prediction assessment method based on online spectrum identification as claimed in claim 7, wherein: the Kalman filtering optimization module process in the tracing program comprises two steps of prediction and updating:

firstly, expressing a state variable set of a prediction model by K, obtaining set individuals by a Monte Carlo random sampling algorithm,

represents the mean value of the set, and K' represents the difference between the two as shown in formula (4):

in the above formula (5)_NThe average operator is N, and the number of the state variable sets is N; p denotes the error covariance matrix of K, and the superscripts f and a denote the predicted value and score, respectivelyAnalysis value, then P^fAnd P^aRespectively representing the error covariance matrixes of the predicted values and the analysis values of the state variables;

P^f＝K^f′(K^f′)^T (6)；

P^a＝K^a′(K^a′)^T (7)；

wherein,

t denotes the matrix transposition, K^fAnd K^fRespectively, predicted values and analyzed values of the state variables.

9. The water body pollution tracing and risk prediction assessment method based on online spectrum identification as claimed in claim 7, wherein: the Kalman filtering optimization module process in the tracing program comprises two steps of prediction and update, and specifically comprises the following steps:

1) the prediction step is represented by the following formula

In the formula: i denotes the state variable set member number,

an updated value of a state variable representing the ith set member at time t;

representing the predicted value of the ith set member state variable at the moment of t + 1;

representing a model operator; e.g. of the type_1iRepresenting the model error vector, error obedient e_1i～N(0,X)；

2) The update step is represented by the following equation

Wherein S is_i,t+1A vector representing the observation state variable of the ith set member at the moment t + 1; q is a conversion matrix; e.g. of the type_2iIndicating observation error, error obedience e_2i～N(0,Y)；

The real state of the instrument observation of the ith set member at the time t + 1;

e is an observation error matrix, which is as follows:

E＝(ε₁,ε₂,…,ε_N)∈R^m×N (10)；

in the formula, m is the number of observed values, N is the number of state variable sets, and epsilon is a state variable observation error;

R_eto observe the error covariance matrix, the following equation:

R_e＝E′(E′)^T (11)；

wherein,

wherein G is_t+1The gain matrix representing time t +1 is expressed by:

in the formula, Y_t+1A matrix formed by the variances Y of the state variables in all the sets at the moment of t + 1;

updated state error covariance matrix

Comprises the following steps:

in the formula, I is an identity matrix.

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