Drinking water quality on-line monitoring and risk assessment algorithm

Abstract

The invention relates to the technical field of drinking water quality monitoring, in particular to a drinking water quality online monitoring and risk assessment algorithm, which comprises the following steps of: acquiring specific water source acquisition position information in a drinking water production process, setting a monitoring range by taking an acquisition point as a circle center, and arranging a plurality of monitoring points, the method comprises the following steps: monitoring and analyzing air and soil pollution components of each monitoring point to obtain a corresponding pollution influence trend index, comprehensively analyzing to obtain a first water quality risk assessment value, monitoring and analyzing water quality state parameters of each link of drinking water production, obtaining change values of the water quality state parameters, and comprehensively analyzing to obtain a second water quality risk assessment value; and then comprehensively analyzing the obtained risk assessment value, calculating a water quality comprehensive risk assessment value, and judging the water quality risk, thereby realizing real-time monitoring and dynamic assessment of the water quality of the drinking water, facilitating timely discovery of potential pollution risks, and improving timeliness of water quality safety monitoring.

Description

Technical Field

[0001] The present invention relates to the technical field of drinking water quality monitoring, and in particular to an algorithm for online monitoring and risk assessment of drinking water quality. Background Art

[0002] The safety of drinking water quality is crucial to human health. With the acceleration of urbanization and the continuous advancement of industrialization, water pollution is becoming increasingly serious, and the need for water quality monitoring and risk assessment is becoming more urgent. Traditional water quality monitoring methods often use laboratory analysis methods, relying on manual sampling, testing and data analysis. This method has the following main problems:

[0003] (1) Lag: Since sample collection, transmission and analysis take a lot of time, there is usually a large lag, which cannot reflect water quality changes in real time. Sudden pollution incidents can only be detected after sample analysis is completed, which often affects the entire water treatment process and delays emergency response.

[0004] (2) Singleness: Traditional water quality monitoring focuses on physical and chemical parameters, but lacks monitoring of possible pollution sources in the environment, such as pollutants in the air and soil, and cannot fully assess the potential impact of the external environment on water sources;

[0005] (3) Lack of dynamic assessment: Water quality monitoring is generally based on single or periodic sampling, lacking dynamic trend monitoring of water quality, making it difficult to provide accurate risk warnings based on changes in pollution sources and dynamic changes in water quality during the production process;

[0006] In order to solve the above defects, a technical solution is now provided. Summary of the invention

[0007] The purpose of the present invention is to solve the problem that the traditional water quality monitoring method has the hysteresis and singleness, and it is difficult to carry out real-time monitoring and risk assessment according to the changes in pollution sources and the changes in water quality status during the production process, and propose an online monitoring and risk assessment algorithm for drinking water quality.

[0008] The purpose of the present invention can be achieved through the following technical solutions:

[0009] An online monitoring and risk assessment algorithm for drinking water quality includes the following steps:

[0010] Step 1: Obtain the specific water source collection location information in the drinking water production process, and use the water source collection location information as the center of the circle and set a radius to draw a circular area with the radius. The area of ​​the circle is the monitoring range. At the same time, the monitoring points are arranged in the monitoring range to obtain each monitoring point. The air pollution component parameters and soil pollution component parameters of each monitoring point are tested and analyzed to obtain the first pollution impact trend index and the second pollution impact trend index, and a comprehensive analysis is performed to obtain the first water quality risk assessment value SZP1;

[0011] Step 2: Monitor and analyze the turbidity value, pH value, dissolved oxygen content, chlorine content and nitrate concentration in the water quality parameters of each link in the drinking water production process, obtain the turbidity change value, pH change value, dissolved oxygen content change value, chlorine content change value and nitrate concentration change value of each link, and conduct a comprehensive analysis to obtain the second water quality risk assessment value SZP2;

[0012] Step 3: Extract the values ​​of the first risk assessment value SZP1 and the second risk assessment value SZP2 of drinking water quality and normalize them according to the formula: ZH SZP =log(SZP1×β1+SZP2×β2), and the comprehensive water quality risk assessment value ZH is obtained. SZP , where β1 and β2 represent weight coefficients respectively;

[0013] Step 4: Based on the comprehensive water quality risk assessment value, the water quality risk is judged and analyzed to obtain the risk type signal.

[0014] Furthermore, monitoring points are arranged in the monitoring range. The specific process is as follows:

[0015] a1: With a set distance of m units as the interval, gradually expand outward from the center to form several concentric circles. The radius of each concentric circle increases proportionally with the previous circle, and monitoring points are arranged on these concentric circles according to the principle of equidistant to ensure that the monitoring points are evenly distributed on each concentric circle, thereby obtaining each monitoring point R(i, j), where i represents the number of the concentric circle, and i=1, 2, 3...n1, n1 represents the total number of concentric circle numbers, j represents the number of the monitoring point, and j=1, 2, 3...n2, n2 represents the total number of monitoring point numbers.

[0016] Furthermore, the process of solving the first pollution impact trend index is as follows:

[0017] a2: By extracting the concentration of volatile organic compounds in the air pollution component parameters of each monitoring point over a period of time Nitrogen oxide concentration Sulfide concentration and particle concentration According to the formula:

[0018] , and obtain the first pollution impact trend index δ1;

[0019] Wherein, t represents the number of each monitoring time point within a period of time, and t=1, 2, 3…z, and z represents the total number of the monitoring time point numbers within a period of time;

[0020] and They represent the average volatile organic compound concentration, average nitrogen oxide concentration, average sulfide concentration and average particulate matter concentration on the i-th concentric circle at the t-th monitoring time point within a period of time;

[0021] K t VOC , K t NOX , K t SOX and K t PMX They represent the concentration changes of volatile organic compounds, nitrogen oxides, sulfides and particulate matter respectively;

[0022] K * VOC , K * NOX , K * SOX and K * PMX They represent the reference volatile organic compound concentration change value, the reference nitrogen oxide concentration change value, the reference sulfide concentration change value and the reference particulate matter concentration change value respectively;

[0023] k1, k2, k3, k4, k5, k6, k7 and k8 represent the set correction coefficients respectively;

[0024] η1, η2, η3 and η4 represent weight coefficients of the volatile organic compound concentration change deviation value, the nitrogen oxide concentration change deviation value, the sulfide concentration change deviation value and the particulate matter concentration change deviation value, respectively.

[0025] Furthermore, the solution process for the second pollution impact trend index is as follows:

[0026] a2: By extracting the heavy metal content in the soil pollution component parameters of each monitoring point over a period of time pH and organic pollutant content According to the formula:

[0027] , and obtain the second pollution impact trend index δ2;

[0028] in, and They represent the average heavy metal content, average pH value and average organic pollutant content on the i-th concentric circle at the t-th monitoring time point within a period of time;

[0029] T t ZJS , T t PHD and T t YJW They represent the change values ​​of heavy metal content, pH value and organic pollutant content respectively;

[0030] T * ZJS , T * PHD and T * YJW They represent the reference heavy metal content change value, reference pH change value and reference organic pollutant content change value respectively;

[0031] f1, f2, f3, f4, f5 and f6 represent the set correction coefficients respectively;

[0032] λ1, λ2 and λ3 represent the deviation value of heavy metal content change, pH change deviation value and organic pollutant content change deviation value respectively.

[0033] Furthermore, the solution process for the first water quality risk assessment value is as follows:

[0034] a3: Extract the values ​​of the first pollution impact trend index δ1 and the second pollution impact trend index δ2 for normalization, according to the formula: A first water quality risk assessment value SZP1 is obtained, wherein μ1 and μ2 represent weight coefficients of the first pollution impact trend index and the second pollution impact trend index, respectively.

[0035] Furthermore, the solution process for the turbidity change value, acid-base change value, dissolved oxygen content change value and chlorine content change value of each link is as follows:

[0036] b1: By obtaining the turbidity value of the water quality state parameters in each link of the drinking water production process within a period of time, the turbidity value Qzd of each link within a period of time is obtained h t , h represents the number of each link, and h = 1, 2, 3...v, v represents the total number of each link, and the turbidity value corresponding to the first monitoring time point is extracted from the turbidity values ​​of each link within a period of time, marked as the reference turbidity value Qzd h 1. The specific calculation process is:

[0037] Get the turbidity change value ζ1 of each link h ;

[0038] Among them, Qzd h t-1 It is expressed as the turbidity value of the t-1th monitoring time point corresponding to each link within a period of time, Q & represents the normal turbidity value corresponding to each link stored in the system repository, y1, y2 and y3 represent the correction factors of the initial turbidity change value, the target turbidity change value and the normal turbidity change value respectively;

[0039] The same analysis process as above can be used to obtain the acid-base change value ζ2 of each link h , dissolved oxygen content change value ζ3 h 、Chlorine content change value ζ4 h and nitrate concentration change value ζ5 h .

[0040] Furthermore, the solution process for the second water quality risk assessment value is as follows:

[0041] b2: Extract the turbidity change value ζ1 of each link h 、Acid-base change value ζ2 h , dissolved oxygen content change value ζ3 h 、Chlorine content change value ζ4 h and nitrate concentration change value ζ5 h The value of is normalized according to the formula:

[0042] , and obtain the second water quality risk assessment value SZP2, where ζ1 h* ,ζ2 h* , ζ3 h* ,ζ4 h* and ζ5 h* They respectively represent the reference turbidity change value, reference acid-base change value, reference dissolved oxygen content change value, reference chlorine content change value and reference nitrate concentration change value, and μ3, μ4, μ5, μ6 and μ7 respectively represent the weight coefficients of turbidity change value, acid-base change value, dissolved oxygen content change value, chlorine content change value and nitrate concentration change value.

[0043] Furthermore, the water quality risk is determined and analyzed. The specific process is as follows:

[0044] d1: Set three risk gradient comparison intervals for the comprehensive water quality risk assessment value, match and analyze the comprehensive water quality risk assessment value with the three risk gradient comparison intervals, and obtain the risk type signal.

[0045] Compared with the known prior art, the technical solution provided by the present invention has the following beneficial effects:

[0046] 1. The present invention defines the monitoring range with the water source collection point as the center, arranges monitoring points within the monitoring range, collects parameters of air and soil pollution components, analyzes their impact trends on water quality, and obtains the first risk assessment value of water quality, thereby achieving rapid response to pollution changes, overcoming the lag of traditional methods, significantly improving the timeliness of water quality safety monitoring, and ensuring that sudden pollution incidents can be discovered and handled in a timely manner.

[0047] 2. The present invention obtains a second risk assessment value of water quality by performing trend analysis on the turbidity value, pH value, dissolved oxygen content, chlorine content and nitrate concentration in the water quality status parameters of each link in the drinking water production process, and then performs a comprehensive analysis on the obtained first and second risk assessment values ​​of water quality, so as to finely quantify the comprehensive risk assessment value of water quality and ensure the accuracy of risk grading judgment, thereby realizing a comprehensive assessment of water quality risks in multiple dimensions, enhancing the accuracy of the assessment, avoiding the limitations of single parameter monitoring, and at the same time being able to provide early warning of potential water quality risks and provide a scientific basis for management decisions. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention, and for ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0049] Figure 1 It is the overall algorithm block diagram of the present invention. DETAILED DESCRIPTION

[0050] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0051] like Figure 1 As shown, an algorithm for online monitoring and risk assessment of drinking water quality includes the following steps:

[0052] Step 1: Solve the first risk assessment value of drinking water quality. The specific solution process is as follows:

[0053] a1: By obtaining the specific water source collection location information of the drinking water plant during the production process, and taking the water source collection location information as the center of the circle, and setting a radius, a circular area is drawn with the radius, and the area of ​​the circle is the monitoring range. At the same time, with a set distance of m units as the interval, it is gradually expanded outward from the center of the circle to form a number of concentric circles. The radius of each concentric circle increases in proportion to the previous circle, and monitoring points are arranged on these concentric circles according to the equidistant principle to ensure that the monitoring points are evenly distributed on each concentric circle, thereby obtaining each monitoring point R (i, j), where i represents the number of the concentric circle, and i = 1, 2, 3 ... n1, n1 represents the total number of concentric circle numbers, j represents the number of the monitoring point, and j = 1, 2, 3 ... n2, n2 represents the total number of monitoring point numbers. For example, monitoring point R (1, 1) represents the first monitoring point arranged on the first concentric circle. This numbering method can accurately determine the location and distribution of each monitoring point, ensuring comprehensive monitoring and management of the entire monitoring range;

[0054] The specific setting of the unit level of m is: by obtaining the pollution source information within the monitoring range, the pollution source information includes the pollution quantity value, pollution emission value and pollution distance value, and calibrating them as Wr1, Wr2 and Wr3 respectively, according to the formula: The pollution value θ is obtained, where e represents a natural constant, and p1, p2, and p3 represent the set weight coefficients respectively;

[0055] It should be noted that the pollution quantity value indicates the total number of pollution sources within the monitoring range, that is, the total number of all possible pollution sources that can be determined within the monitoring range. The number of pollution sources directly affects the water quality. A larger number of pollution sources means a greater potential pollution risk. The pollution discharge value indicates the total amount of sewage discharged by the pollution sources within the monitoring range. This parameter is mainly used to measure the total amount of pollutants discharged by the pollution sources into the environment within a certain period of time. The greater the sewage discharge, the more significant the impact on water quality. The pollution distance value indicates the distance between the pollution source and the water source collection location. This parameter reflects the geographical relationship between the pollution source and the water source. The closer the distance, the greater the possibility that the water source will be directly affected by the pollutants.

[0056] Matching the pollution value with a plurality of preset value ranges, each value range corresponding to a specific m-unit magnitude, and determining the final m-unit magnitude according to the matching result;

[0057] a2: Obtain the pollution component status parameters of each monitoring point. The pollution component status parameters include air pollution component parameters and soil pollution component parameters, and analyze their pollution component status. The specific analysis process is as follows:

[0058] Different types of sensors are installed at each monitoring point to monitor the air and soil conditions within the monitoring range. The sensors will collect relevant data in the air and soil in real time, thereby obtaining the air pollution component parameters and soil pollution component parameters of each monitoring point.

[0059] By extracting the concentration of volatile organic compounds, nitrogen oxides, sulfides and particulate matter from the air pollution component parameters of each monitoring point over a period of time, and calibrating them as and According to the formula:

[0060] , and obtain the first pollution impact trend index δ1;

[0061] Wherein, t represents the number of each monitoring time point within a period of time, and t = 1, 2, 3...z, z represents the total number of monitoring time point numbers within a period of time, and They represent the average concentration of volatile organic compounds, average concentration of nitrogen oxides, average concentration of sulfides and average concentration of particulate matter on the i-th concentric circle at the t-th monitoring time point in a period of time, respectively. t VOC , K t NOX , K t SOX and K t PMX They represent the concentration changes of volatile organic compounds, nitrogen oxides, sulfides and particulate matter, respectively. * VOC , K * NOX , K * SOX and K * PMX denote reference volatile organic compound concentration change values, reference nitrogen oxide concentration change values, reference sulfide concentration change values ​​and reference particulate matter concentration change values, respectively; k1, k2, k3, k4, k5, k6, k7 and k8 denote set correction coefficients, respectively; η1, η2, η3 and η4 denote weight coefficients of volatile organic compound concentration change deviation values, nitrogen oxide concentration change deviation values, sulfide concentration change deviation values ​​and particulate matter concentration change deviation values, respectively; and η1>η2>η3>η4;

[0062] It represents the difference between the average volatile organic compound concentration on the i-th concentric circle at the t-th monitoring time point within a period of time and the average volatile organic compound concentration on the first concentric circle. It represents the difference between the average nitrogen oxide concentration on the ith concentric circle and the average nitrogen oxide concentration on the first concentric circle at the tth monitoring time point within a period of time. It represents the difference between the average sulfide concentration on the i-th concentric circle and the average sulfide concentration on the first concentric circle at the t-th monitoring time point within a period of time. It represents the difference between the average particle concentration on the ith concentric circle and the average particle concentration on the first concentric circle at the tth monitoring time point within a period of time;

[0063] It represents the difference between the average concentration of volatile organic compounds on the i-th concentric circle at the t-th monitoring time point within a period of time and the average concentration of volatile organic compounds on the i-1-th concentric circle. It represents the difference between the average nitrogen oxide concentration on the i-th concentric circle and the average nitrogen oxide concentration on the i-1-th concentric circle at the t-th monitoring time point within a period of time. It represents the difference between the average sulfide concentration on the i-th concentric circle at the t-th monitoring time point within a period of time and the average sulfide concentration on the i-1-th concentric circle. It represents the difference between the average particle concentration on the i-th concentric circle and the average particle concentration on the i-1-th concentric circle at the t-th monitoring time point within a period of time;

[0064] By extracting the heavy metal content, pH value and organic pollutant content in the soil pollution component parameters of each monitoring point over a period of time, and calibrating them respectively as and According to the formula:

[0065] , and obtain the second pollution impact trend index δ2;

[0066] in, and They represent the average heavy metal content, average pH value and average organic pollutant content on the i-th concentric circle at the t-th monitoring time point within a period of time, respectively. t ZJS , T t PHD and T t YJW Respectively represent the change value of heavy metal content, pH change value and organic pollutant content change value, T * ZJS , T * PHD and T * YJWThey represent the reference heavy metal content change value, reference pH change value and reference organic pollutant content change value respectively, f1, f2, f3, f4, f5 and f6 represent the set correction coefficients respectively, λ1, λ2 and λ3 represent the heavy metal content change deviation value, pH change deviation value and organic pollutant content change deviation value respectively;

[0067] It represents the difference between the average heavy metal content on the ith concentric circle and the average heavy metal content on the first concentric circle at the tth monitoring time point within a period of time. It represents the difference between the average pH value on the ith concentric circle and the average pH value on the first concentric circle at the tth monitoring time point within a period of time. It represents the difference between the average organic pollutant content on the ith concentric circle and the average organic pollutant content on the first concentric circle at the tth monitoring time point within a period of time;

[0068] It represents the difference between the average heavy metal content on the i-th concentric circle and the average heavy metal content on the i-1-th concentric circle at the t-th monitoring time point within a period of time. It represents the difference between the average pH value on the i-th concentric circle and the average pH value on the i-1-th concentric circle at the t-th monitoring time point within a period of time. It represents the difference between the average organic pollutant content on the i-th concentric circle and the average organic pollutant content on the i-1-th concentric circle at the t-th monitoring time point within a period of time;

[0069] a3: Extract the values ​​of the first pollution impact trend index δ1 and the second pollution impact trend index δ2 for normalization, according to the formula: A first water quality risk assessment value SZP1 is obtained, wherein μ1 and μ2 represent weight coefficients of the first pollution impact trend index and the second pollution impact trend index, respectively, and μ1>μ2;

[0070] Step 2: Solve the second risk assessment value of drinking water quality. The specific solution process is as follows:

[0071] b1: Install online sensors at each stage of the drinking water production process to monitor the water quality parameters at each stage of the drinking water production process;

[0072] By obtaining the turbidity value of the water quality state parameters in each link within a period of time, the turbidity value of each link within a period of time is obtained and marked as Qzd h t, h represents the number of each link, and h = 1, 2, 3...v, v represents the total number of each link, and the turbidity value corresponding to the first monitoring time point is extracted from the turbidity values ​​of each link within a period of time, and marked as the reference turbidity value Qzd h 1. The specific calculation process is:

[0073] Get the turbidity change value ζ1 of each link h ;

[0074] Among them, Qzd h t-1 It is expressed as the turbidity value of the t-1th monitoring time point corresponding to each link within a period of time, Q & represents the normal turbidity value corresponding to each link stored in the system repository (i.e., the turbidity reference value under standard conditions), y1, y2, and y3 represent the correction factors of the initial turbidity change value, the target turbidity change value, and the normal turbidity change value, respectively, wherein the initial turbidity change value is the turbidity change value of the turbidity value at each monitoring time point compared with the reference turbidity value, the target turbidity change value is the turbidity change value of the turbidity value at each monitoring time point compared with the turbidity value at the previous monitoring time point, and the normal turbidity change value is the turbidity change value of the turbidity value at each monitoring time point compared with its corresponding normal turbidity value;

[0075] By obtaining the pH value of the water quality state parameters in each link within a period of time, the pH value of each link within a period of time is obtained and marked as Sjz h t , and extract the pH value corresponding to the first monitoring time point from the pH values ​​of each link within a period of time, and mark it as the baseline pH value Sjz h 1. The specific calculation process is:

[0076] Get the acid-base change value ζ2 of each link h ;

[0077] Among them, Sjz h t-1 It is expressed as the pH value of the t-1th monitoring time point corresponding to each link in a period of time, S & represents the normal acid-base value corresponding to each link stored in the system repository (i.e., the acid-base reference value under standard conditions), y4, y5, and y6 represent the correction factors of the initial acid-base change value, the target acid-base change value, and the normal acid-base change value, respectively, wherein the initial acid-base change value is the acid-base change value of the acid-base value at each monitoring time point compared with the benchmark acid-base value, the target acid-base change value is the acid-base change value of the acid-base value at each monitoring time point compared with the acid-base value at the previous monitoring time point, and the normal acid-base change value is the acid-base change value of the acid-base value at each monitoring time point compared with its corresponding normal acid-base value;

[0078] By obtaining the dissolved oxygen content in the water quality state parameters of each link within a period of time, the dissolved oxygen content of each link within a period of time is obtained and marked as Yhn h t The dissolved oxygen content corresponding to the first monitoring time point is extracted from the dissolved oxygen content of each link within a period of time, and marked as the benchmark dissolved oxygen content Yhn h 1. The specific calculation process is:

[0079] Get the dissolved oxygen content change value ζ3 of each link h ;

[0080] Among them, Yhn h t-1 It is expressed as the dissolved oxygen content at the t-1th monitoring time point corresponding to each link within a period of time, Y & It represents the normal dissolved oxygen content corresponding to each link stored in the system repository (i.e. the reference value of dissolved oxygen content under standard conditions), y7, y8 and y9 represent the correction factors of the initial dissolved oxygen content change value, the target dissolved oxygen content change value and the normal dissolved oxygen content change value, respectively, wherein the initial dissolved oxygen content change value is the dissolved oxygen content change value of each monitoring time point compared with the benchmark dissolved oxygen content, the target dissolved oxygen content change value is the dissolved oxygen content change value of each monitoring time point compared with the dissolved oxygen content of the previous monitoring time point, and the normal dissolved oxygen content change value is the dissolved oxygen content change value of each monitoring time point compared with the corresponding normal dissolved oxygen content;

[0081] By obtaining the chlorine content in the water quality state parameters of each link within a period of time, the chlorine content of each link within a period of time is obtained and marked as Lhz h t The chlorine content corresponding to the first monitoring time point is extracted from the chlorine content of each link within a period of time, and marked as the baseline chlorine content Lhz h 1. The specific calculation process is:

[0082] Obtain the chlorine content change value ζ4 of each link h ;

[0083] Among them, Lhz h t-1 It is expressed as the chlorine content at the t-1th monitoring time point corresponding to each link within a period of time, L &represents the normal chlorine content corresponding to each link stored in the system repository (i.e., the reference value of chlorine content under standard conditions), y10, y11, and y12 represent the correction factors of the initial chlorine content change value, the target chlorine content change value, and the normal chlorine content change value, respectively, wherein the initial chlorine content change value is the chlorine content change value of the chlorine content at each monitoring time point compared with the benchmark chlorine content, the target chlorine content change value is the chlorine content change value of the chlorine content at each monitoring time point compared with the chlorine content at the previous monitoring time point, and the normal chlorine content change value is the chlorine content change value of the chlorine content at each monitoring time point compared with the corresponding normal chlorine content;

[0084] By obtaining the nitrate concentration in the water quality state parameters of each link within a period of time, the nitrate concentration of each link within a period of time is obtained and marked as Nszht. The nitrate concentration corresponding to the first monitoring time point is extracted from the nitrate concentration of each link within a period of time and marked as the benchmark nitrate concentration Nszh1. The specific calculation process is:

[0085]

[0086] The nitrate concentration change value ζ5h of each link is obtained;

[0087] Wherein, Nszht-1 represents the nitrate concentration at the t-1th monitoring time point corresponding to each link within a period of time, N& represents the normal nitrate concentration corresponding to each link stored in the system repository (i.e., the reference value of nitrate concentration under standard conditions), y13, y14 and y15 represent the correction factors of the initial nitrate concentration change value, the target nitrate concentration change value and the normal nitrate concentration change value, respectively, wherein the initial nitrate concentration change value is the nitrate concentration change value of the nitrate concentration at each monitoring time point compared with the benchmark nitrate concentration, the target nitrate concentration change value is the nitrate concentration change value of the nitrate concentration at each monitoring time point compared with the nitrate concentration at the previous monitoring time point, and the normal nitrate concentration change value is the nitrate concentration change value of the nitrate concentration at each monitoring time point compared with its corresponding normal nitrate concentration;

[0088] b2: Extract the turbidity change value ζ1 of each link h 、Acid-base change value ζ2 h , dissolved oxygen content change value ζ3 h 、Chlorine content change value ζ4 h and nitrate concentration change value ζ5 h The value of is normalized according to the formula:

[0089] , and obtain the second water quality risk assessment value SZP2, where ζ1 h* ,ζ2 h* , ζ3h* ,ζ4 h* and ζ5 h* They represent reference turbidity change value, reference acid-base change value, reference dissolved oxygen content change value, reference chlorine content change value and reference nitrate concentration change value respectively, μ3, μ4, μ5, μ6 and μ7 represent weight coefficients of turbidity change value, acid-base change value, dissolved oxygen content change value, chlorine content change value and nitrate concentration change value respectively, and μ3>μ4>μ5>μ6>μ7;

[0090] Step 3: Solve the comprehensive risk assessment value of drinking water quality. The specific solution process is as follows:

[0091] c1: extract the values ​​of the first risk assessment value SZP1 and the second risk assessment value SZP2 of drinking water quality and perform normalization according to the formula: ZH SZP =log(SZP1×β1+SZP2×β2), and the comprehensive water quality risk assessment value ZH is obtained. SZP , where β1 and β2 represent the weight coefficients of the first water quality risk assessment value and the second water quality risk assessment value, respectively, and their specific values ​​are 0.85 and 0.97;

[0092] Step 4: Based on the comprehensive risk assessment value of drinking water quality, the water quality risk is determined and analyzed. The specific analysis process is as follows:

[0093] d1: setting three risk gradient comparison intervals of the comprehensive risk assessment value of water quality, namely the first gradient risk interval fxng1, the second gradient risk interval fxng2 and the third gradient risk interval fxng3, and fxng1=Ψfxng2=2Ψfxng3, wherein fxng1>fxng2>fxng3, Ψ represents the multiple of the gradient, and the specific value of Ψ is set by those skilled in the art in the specific drinking water quality monitoring risk example;

[0094] When the comprehensive risk assessment value of water quality is in the preset first gradient risk interval fxng1, a Class A risk signal is generated; when the comprehensive risk assessment value of water quality is in the preset second gradient risk interval fxng2, a Class B risk signal is generated; when the comprehensive risk assessment value of water quality is in the preset third gradient risk interval fxng3, a Class C risk signal is generated;

[0095] The generated A-level risk signal, B-level risk signal and C-level risk signal constitute a risk type signal;

[0096] Step 5: Execute corresponding warning display operations based on risk type signals;

[0097] In a specific embodiment, if the risk type signal is a Class A risk signal, a red warning sign is displayed within the set E1 time period, and a red light alarm is issued through an audible and visual alarm device to remind the operator to immediately conduct an on-site inspection and start a backup water source to ensure water quality safety;

[0098] If the risk type signal is a Class B risk signal, a yellow warning sign will be displayed during the set E2 time period, and a yellow light alarm will be issued through the sound and light alarm device to remind operators to pay attention to the water quality;

[0099] If the risk type signal is a Class C risk signal, a blue warning sign will be displayed during the set E3 time period, and a blue light alarm will be emitted through the sound and light alarm device to remind operators that there is a potential risk and routine inspections are required.

[0100] The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to only specific implementation methods. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can understand and use the present invention well. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. An online monitoring and risk assessment algorithm for drinking water quality, characterized in that: The following steps are involved: Step 1: Obtain the water source collection location information in the drinking water production process, and construct a circular area monitoring range based on it, set up monitoring points in the monitoring range, obtain each monitoring point, detect and analyze the air pollution component parameters and soil pollution component parameters of each monitoring point, and obtain the first pollution impact trend index and the second pollution impact trend index, thereby calculating the first water quality risk assessment value, where: The first pollution impact trend index is obtained based on the change values ​​of organic compound concentration, nitrogen oxide concentration, sulfide concentration and particulate matter concentration; The second pollution impact trend index is obtained based on the change values ​​of heavy metal content, pH value and organic pollutant content; Step 2: Monitor and analyze the turbidity value, pH value, dissolved oxygen content, chlorine content and nitrate concentration in the water quality parameters of each link in the drinking water production process, obtain the turbidity change value, pH change value, dissolved oxygen content change value, chlorine content change value and nitrate concentration change value of each link, and conduct a comprehensive analysis to obtain the second risk assessment value of water quality; Step 3: Obtain a comprehensive water quality risk assessment value based on the first water quality risk assessment value and the second water quality risk assessment value; Step 4: Based on the comprehensive water quality risk assessment value, determine and analyze the water quality risk to obtain the risk type signal.

2. The online monitoring and risk assessment algorithm for drinking water quality according to claim 1 is characterized in that: The monitoring points are arranged in the monitoring range. The specific process is as follows: a1: With a set distance of m units as the interval, gradually expand outward from the center to form several concentric circles, the radius of each concentric circle increases proportionally with the previous circle, and monitoring points are arranged on these concentric circles according to the principle of equidistant, thereby obtaining each monitoring point R(i, j), where i represents the number of the concentric circle, and i=1, 2, 3...n1, n1 represents the total number of concentric circle numbers, j represents the number of the monitoring point, and j=1, 2, 3...n2, n2 represents the total number of monitoring point numbers.

3. The online monitoring and risk assessment algorithm for drinking water quality according to claim 1 is characterized in that: The solution process for the first pollution impact trend index is as follows: a2: By extracting the concentration of volatile organic compounds in the air pollution component parameters of each monitoring point over a period of time Nitrogen oxide concentration Sulfide concentration and particle concentration According to the formula: Get the first pollution impact trend index δ1; Wherein, t represents the number of each monitoring time point within a period of time, and t=1, 2, 3…z, and z represents the total number of the monitoring time point numbers within a period of time; and They represent the average volatile organic compound concentration, average nitrogen oxide concentration, average sulfide concentration and average particulate matter concentration on the i-th concentric circle at the t-th monitoring time point within a period of time; K t VOC , K t NOX , K t SOX and K t PMX They represent the concentration changes of volatile organic compounds, nitrogen oxides, sulfides and particulate matter respectively; K * VOC , K * NOX , K * SOX and K * PMX They represent the reference volatile organic compound concentration change value, the reference nitrogen oxide concentration change value, the reference sulfide concentration change value and the reference particulate matter concentration change value respectively; k1, k2, k3, k4, k5, k6, k7 and k8 represent the set correction coefficients respectively; η1, η2, η3 and η4 represent weight coefficients of the volatile organic compound concentration change deviation value, the nitrogen oxide concentration change deviation value, the sulfide concentration change deviation value and the particulate matter concentration change deviation value, respectively.

4. The online monitoring and risk assessment algorithm for drinking water quality according to claim 1 is characterized in that: The solution process for the second pollution impact trend index is as follows: a2: By extracting the heavy metal content in the soil pollution component parameters of each monitoring point over a period of time pH and organic pollutant content According to the formula: The second pollution impact trend index δ2 is obtained; in, and They represent the average heavy metal content, average pH value and average organic pollutant content on the i-th concentric circle at the t-th monitoring time point within a period of time; T t ZJS , T t PHD and T t YJW They represent the change values ​​of heavy metal content, pH value and organic pollutant content respectively; T * ZJS , T * PHD and T * YJW They represent the reference heavy metal content change value, reference pH change value and reference organic pollutant content change value respectively; f1, f2, f3, f4, f5 and f6 represent the set correction coefficients respectively; λ1, λ2 and λ3 represent the deviation value of heavy metal content change, pH change deviation value and organic pollutant content change deviation value respectively.

5. The online monitoring and risk assessment algorithm for drinking water quality according to claim 1 is characterized in that: The solution process for the first water quality risk assessment value is as follows: a3: Extract the values ​​of the first pollution impact trend index δ1 and the second pollution impact trend index δ2 for normalization, according to the formula: A first water quality risk assessment value SZP1 is obtained, wherein μ1 and μ2 represent weight coefficients of the first pollution impact trend index and the second pollution impact trend index, respectively.

6. The online monitoring and risk assessment algorithm for drinking water quality according to claim 1 is characterized in that: The solution process for the turbidity change value, acid-base change value, dissolved oxygen content change value and chlorine content change value of each link is as follows: b1: By obtaining the turbidity value of the water quality state parameters in each link of the drinking water production process within a period of time, the turbidity value Qzd of each link within a period of time is obtained h t , h represents the number of each link, and h = 1, 2, 3...v, v represents the total number of each link, and the turbidity value corresponding to the first monitoring time point is extracted from the turbidity values ​​of each link within a period of time, marked as the reference turbidity value Qzd h 1. The specific calculation process is: Get the turbidity change value ζ1 of each link h ; Among them, Qzd h t-1 It is expressed as the turbidity value of the t-1th monitoring time point corresponding to each link within a period of time, Q & represents the normal turbidity value corresponding to each link stored in the system repository, y1, y2 and y3 represent the correction factors of the initial turbidity change value, the target turbidity change value and the normal turbidity change value respectively; The same analysis process as above can be used to obtain the acid-base change value ζ2 of each link h , dissolved oxygen content change value ζ3 h 、Chlorine content change value ζ4 h and nitrate concentration change value ζ5 h .

7. The drinking water quality online monitoring and risk assessment algorithm according to claim 1 is characterized in that: The solution process for the second water quality risk assessment value is as follows: b2: Extract the turbidity change value ζ1 of each link h 、Acid-base change value ζ2 h , dissolved oxygen content change value ζ3 h 、Chlorine content change value ζ4 h and nitrate concentration change value ζ5 h The value of is normalized according to the formula: The second water quality risk assessment value SZP2 is obtained, where ζ1 h* ,ζ2 h* , ζ3 h* ,ζ4 h* and ζ5 h* They respectively represent the reference turbidity change value, reference acid-base change value, reference dissolved oxygen content change value, reference chlorine content change value and reference nitrate concentration change value, and μ3, μ4, μ5, μ6 and μ7 respectively represent the weight coefficients of turbidity change value, acid-base change value, dissolved oxygen content change value, chlorine content change value and nitrate concentration change value.

8. The online monitoring and risk assessment algorithm for drinking water quality according to claim 1 is characterized in that: The specific process of water quality risk determination and analysis is as follows: d1: Set three risk gradient comparison intervals for the comprehensive water quality risk assessment value, match and analyze the comprehensive water quality risk assessment value with the three risk gradient comparison intervals, and obtain the risk type signal.

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