A data model for emergency prediction of sudden water pollution in water delivery channels

Abstract

The present invention discloses an emergency prediction data model for sudden water pollution in a water delivery channel, comprising the following steps: using hydrodynamic software to simulate and determine the impact of pollution accidents, determining the factors affecting the peak concentration of pollutants at water quality control points, establishing emergency control technology, constructing a pollution concentration prediction model, formulating response strategies in the case of unknown pollution sources, and selecting the application of control technology. The main factors affecting the peak concentration of pollutants at water quality control points are obtained by simulating sudden water pollution events with hydrodynamic simulation software, and the prediction data model is constructed using the least squares method. According to the characteristics of sudden water pollution, different emergency control technologies are used to improve the problem of slow prediction of traditional prediction models, and provide more time for emergency control to improve prediction efficiency. In the case of abnormal monitoring indicators of the monitoring section, the accident location and the leakage amount are unknown, the sudden water pollution event can be evaluated in a short time.

Description

Technical Field

[0001] The present invention relates to the field of water pollution emergency management, and in particular to an emergency prediction data model for sudden water pollution in a water delivery channel. Background Art

[0002] The water quality of water diversion is related to the health and safety of many water supply users along the route. When sudden pollution occurs along the channel, because there is no fixed discharge method and discharge route for pollutants, it occurs suddenly and discharges pollutants in large quantities instantly or in a short period of time, causing serious pollution and damage to the water quality. It is necessary to carry out corresponding emergency management of the sudden pollution of the river. Initially, emergency management mainly relied on post-event response, including the search and cleanup of pollution sources. With the improvement of environmental awareness and the development of science and technology, a more systematic emergency response system has begun to be established, including the establishment of emergency plans and professional handling teams in advance. Today, the management of sudden water pollution has made significant progress, a monitoring and early warning system has been established, standardized plans have been formulated, and cross-departmental and cross-regional collaboration has been achieved.

[0003] In recent years, although the emergency prediction technology for sudden water pollution in water transfer channels has been continuously improved, there are still many technical defects, including at least the following aspects: 1. The traditional water quality prediction model is slow when applied to the simulation and prediction of sudden water pollution accidents, and cannot cope with the high randomness and suddenness of sudden water pollution accidents; 2. For sudden water pollution incidents in rivers, most of the studies are based on the conditions where the location and leakage of the pollution accident are known. For abnormal monitoring indicators of the monitoring section, the accident location and the leakage amount are unknown, it is impossible to quickly deal with sudden water pollution incidents. Summary of the invention

[0004] The technical problem to be solved by the present invention is to provide an emergency prediction data model for sudden water pollution in water delivery channels in view of various deficiencies in the prior art.

[0005] In order to solve the above technical problems, the technical solutions adopted by the present invention are as follows.

[0006] A data model for emergency prediction of sudden water pollution in a water transmission channel comprises the following steps: S1, using hydrodynamic software simulation to determine the impact of pollution accidents; S2, determining the factors affecting the peak concentration of pollutants at water quality control points; S3, establishing emergency control technology; S4, constructing a pollution concentration prediction model; S5, formulating response strategies in the case of unknown pollution sources; S6, selecting the application of control technology.

[0007] As a preferred technical solution of the present invention, step S1 imports the plan view of the river into the hydrodynamic simulation software MIKE11, simplifies the plan view of the river, inputs all the river section information including the cross-sectional shape rules, hydraulic parameters, the river to which it belongs and its location, and sets appropriate boundary conditions for the water transfer channel; defines the key parameters of the water quality model including the types of various components in the water body, the initial state, the diffusion and attenuation rate of the substances, and sets the simulation results that need additional output; simulates sudden water pollution incidents in the software, and records the highest values ​​of pollutant concentrations at different location nodes to evaluate the impact range of the pollution accident.

[0008] As a preferred technical solution of the present invention, step S2 identifies the main factors affecting the peak concentration of pollutants at the water quality control point based on the results of the simulation software, which are the quality of the pollutants, the distance from the incident location to the water quality control point, the average flow velocity of the channel pool, and the longitudinal dispersion coefficient.

[0009] As a preferred technical solution of the present invention, step S3 obtains three emergency control technologies according to the characteristics of the pollutants and the pollution situation:

[0010] (1) Flow control: by adjusting the water flow rate, the diffusion, degradation and dispersion speed of pollutants are affected, thereby controlling the time it takes for pollutants to reach the downstream;

[0011] (2) Control by closing gates: When a pollution accident is serious, the spread of pollutants is limited by closing upstream and downstream gates until other measures are taken to reduce the pollution concentration to a safe standard and then restore water flow;

[0012] (3) Water discharge control: For heavy pollutants that are difficult to quickly handle, numerical simulation of sudden water pollution accidents is used to predict when the pollutants will be transferred to the discharge gate, and to determine when the water quality concentration at the discharge gate begins to exceed the standard and how long the excess will last. The discharge gate is opened to discharge the water that exceeds the standard, thereby reducing the time of water supply interruption.

[0013] As a preferred technical solution of the present invention, step S4 uses the least squares method to construct a prediction data model to predict the maximum concentration of pollutants at the control point based on the four main factors affecting the peak concentration of pollutants at the water quality control point; the specific construction process is as follows:

[0014] The pollutant concentration prediction model under the "pollution location is known" condition is:

[0015] f(C max , m, x, u, Ed) = 0

[0016] Among them, C max represents the maximum value of the pollutant concentration. According to the principle of dimensional analysis, by considering the dimension of the physical quantity, C maxIt is expressed as the pollutant mass m, the distance from the incident location to the water quality control point x, the average flow velocity of the channel pool u, and the longitudinal dispersion coefficient Ed.

[0017] Exponential product of four physical quantities C max =km a x b u c Ed d

[0018] Through dimensional harmony, we get C max =kmx -3-d u -d Ed d , where k and d are coefficients to be determined;

[0019] The least squares method is used to determine the unknown coefficients in the formula. The optimal coefficient value is obtained by minimizing the difference between the predicted value and the actual observed value, so that the model prediction is closest to the actual situation. i and the model predicted value b' i The formulas for estimating k and d are as follows:

[0020] d m =(n∑ n i＝1 (y i -b' i )a' i -∑ n i＝1 a' i ∑ n (i＝1) (y i -b' i ) / n∑ n (i＝1) a' i 2 -(∑ n i＝1 a' i ) 2

[0021] k m =10^[∑ n (i＝1) (y i -b' i )-d m* ∑ n i＝1 a' i ] / n

[0022] Where n represents the number of samples, a' i are the measured values ​​of m, x, u and Ed, b' i is the corresponding model prediction value, d mand k m are the estimated values ​​of d and k obtained by the least squares method.

[0023] As a preferred technical solution of the present invention, another construction method of step S4 is: the movement and distribution of pollutants in the water body are defined by partial differential equations, while considering fluid movement, diffusion, natural attenuation, and the source of pollutants. The specific construction process is as follows:

[0024] The rate of change of pollutant concentration C with time t and position x: dC / dt+u*dC / dx=D*d^2C / dx^2-λC+S(m,x,u,Ed,t) where C is the pollutant concentration at position x and time t, u is the flow velocity, D is the effective diffusion coefficient, which is a function of the longitudinal dispersion coefficient Ed, λ is a constant representing the natural attenuation rate, including degradation and adsorption processes, S is the source term, which represents the input of pollutant mass m, which changes with time and depends on the position, flow velocity and longitudinal dispersion coefficient of the pollution source; the numerical method is used to solve the above partial differential equations to obtain the simulation results of the pollutant concentration changing with time and space. After model verification, pollution control strategies can be formulated based on the model results.

[0025] As a preferred technical solution of the present invention, in step S5, when the location of the pollution source is unknown, it is simulated to assume that the pollution occurs at the starting point of each channel section, and the effects of different control measures are tested, so that the reservoir inlet just reaches the national Class III water standard after control measures are taken under different pollution conditions, and the pollutant concentrations at different monitoring sections are recorded, and a pollution concentration threshold table for selecting control technology according to the concentration at the monitoring point is formulated.

[0026] As a preferred technical solution of the present invention, step S6 takes corresponding control measures according to the pollution amount and location for known pollution source locations, and uses a prediction model to quickly determine whether the water quality at the entrance of the downstream reservoir meets the standards; for pollution source locations that are unknown, a control technology is selected based on the concentration at the monitoring point and a preset pollution concentration threshold table.

[0027] The beneficial effects of adopting the above technical solution are: the present invention provides an emergency prediction data model for sudden water pollution in water transfer channels with rapid prediction and rapid regulation. The main factors affecting the peak concentration of pollutants at water quality control points are obtained by simulating sudden water pollution events through hydrodynamic simulation software, and the prediction data model is constructed using the least squares method. According to the characteristics of sudden water pollution, different emergency regulation technologies are adopted to improve the problem of slow prediction of traditional prediction models, and provide more time for emergency regulation to improve prediction efficiency. For abnormal monitoring indicators of the monitoring section, the accident location and leakage volume are unknown. The sudden water pollution event can be evaluated in a short time, and the polluted section can be controlled according to the characteristics of the pollution accident by using hydraulic structures. The response speed of emergency personnel can be significantly improved, and the processing capacity of emergency business can be comprehensively improved.

[0028] The following embodiments describe in detail the technical advantages and beneficial effects of various technical details of the present invention. DETAILED DESCRIPTION

[0029] The following examples describe the present invention in detail. In the description of the following examples, specific details such as specific system structures, technologies, etc. are proposed for illustration rather than limitation, so as to provide a thorough understanding of the present application embodiments. However, it should be clear to those skilled in the art that the present application can also be implemented in other embodiments without these specific details. In other cases, the detailed description of well-known systems, devices, circuits and methods is omitted to prevent unnecessary details from hindering the description of the present application.

[0030] It should be understood that when used in the present specification and the appended claims, the term "comprising" indicates the presence of the described features, integrals, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or their collections. It should also be understood that the term "and / or" used in the present specification and the appended claims refers to any combination of one or more of the associated listed items and all possible combinations, and includes these combinations.

[0031] As used in the specification of this application and the appended claims, the term "if" may be interpreted as "when" or "uponce" or "in response to determining" or "in response to detecting" depending on the context. Similarly, the phrase "if it is determined" or "if [the described condition or event] is detected" may be interpreted as meaning "uponce it is determined" or "in response to determining" or "uponce [the described condition or event] is detected" or "in response to detecting [the described condition or event]" depending on the context. In addition, in the description of the specification of this application and the appended claims, the terms "first", "second", "third", etc. are only used to distinguish the description and cannot be understood as indicating or implying relative importance.

[0032] References to "one embodiment" or "some embodiments" etc. described in the specification of this application mean that one or more embodiments of the present application include specific features, structures or characteristics described in conjunction with the embodiment. Therefore, the statements "in one embodiment", "in some embodiments", "in some other embodiments", "in some other embodiments", etc. that appear in different places in this specification do not necessarily refer to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized in other ways. The terms "including", "comprising", "having" and their variations all mean "including but not limited to", unless otherwise specifically emphasized in other ways.

[0033] Example 1

[0034] The data model for emergency prediction of sudden water pollution in water transfer channels includes the following steps: S1. Determine the impact of pollution accidents by simulating with hydrodynamic software; S2. Determine the factors affecting the peak concentration of pollutants at water quality control points; S3. Establish emergency control technology; S4. Construct a pollution concentration prediction model; S5. Formulate response strategies in the case of unknown pollution sources; S6. Select the application of control technology.

[0035] In step S1, the plan of the river is imported into the hydrodynamic simulation software MIKE11, the plan of the river is simplified, all the cross-sectional information of the river including the cross-sectional shape rules, hydraulic parameters, the river to which it belongs and its location is input, and appropriate boundary conditions are set for the water transfer channel; key parameters of the water quality model including the types of various components in the water body, the initial state, the diffusion and attenuation rate of substances are defined, and simulation results that require additional output are set; sudden water pollution incidents are simulated in the software, and the highest values ​​of pollutant concentrations at different location nodes are recorded to assess the impact range of the pollution accident.

[0036] In order to explore the reduction rules of different types of pollutants, combined with the actual engineering characteristics, the following working conditions are designed:

[0037] Working condition 1: At the Qingshui River pile number 0+300 meters, when all pumping stations in the water supply channel were delivering water at full capacity, a pollution accident occurred with a pollutant leakage of 600kg of seven pollutants including lead, chromium, cadmium, sulfide, mercury and soluble iron.

[0038] Working condition 2: At pile number 0+300m of Luxin Canal, with the three units of the upstream Qingshui River test station in operation and all gates of Renzhuang sluice gate open, the water inflow (30.9m3 / s) caused a pollution accident with a leakage of 600kg of seven pollutants including lead, chromium, cadmium, sulfide, mercury and soluble iron.

[0039] In order to explore the impact of pollutant leakage and water flow on the reduction rules of different types of pollutants, the following working conditions are designed in combination with actual project characteristics:

[0040] Working condition 3: At the clear water pile number 0+300m, 5+076m, 17+775m, 33+067m, the water delivery under the condition of three units of the first-level pumping station, second-level pumping station and third-level pumping station of the clear water river are turned on at the same time (42~43m3 / s), the water delivery under the condition of two units of the first-level pumping station, second-level pumping station and third-level pumping station of the clear water river are turned on at the same time (28~28.6m3 / s), and the water level of the clear water river is the normal water delivery level and the first-level pumping station, second-level pumping station and third-level pumping station of the clear water river are turned on at the same time. The water delivery under the condition of one unit (14~14.3m3 / s), the pollutant leakage is 60kg, 120kg, and 300kg. The pollutant type selects the common soluble pollutant type: lead, sulfide.

[0041] Working condition 4: At the accident pile number sections 0+300, 4+100 and 12+393 of the Luxin Canal, when the three units of the Qingshui River Test Station were in operation and the gates of the Renzhuang Gate of the Luxin Canal were all opened (30.9m3 / s), and one unit of the Qingshui River Test Station was running with a water flow of 10.3m3 / s, a pollution accident with a pollutant leakage of 30kg, 60kg and 120kg occurred. The pollutant type selected was the common soluble pollutant type: lead, sulfide.

[0042] Based on the results of the simulation software, step S2 identified the main factors affecting the peak concentration of pollutants at the water quality control point as pollutant mass, the distance from the incident location to the water quality control point, the average flow rate of the channel pool, and the longitudinal dispersion coefficient.

[0043] Analysis of factors affecting the maximum concentration of pollutants:

[0044] ① Pollutant leakage

[0045] The amount of pollutant leakage is the most important factor affecting pollutant concentration. The higher the leakage, the higher the peak concentration along the channel and the longer the water quality exceeds the standard at key nodes downstream.

[0046] The amount of pollutant leakage is an important factor affecting the downstream pollutant concentration. In actual projects, the amount of pollutant leakage at different pile numbers can be considered as the basis for selecting different control technologies.

[0047] ②Water volume

[0048] The inflow flow has a certain impact on the peak concentration of pollutants in the downstream, but the influence trend of the inflow on the peak concentration of pollutants in the Qingshui River section and the Luxin River section is different. In the Qingshui River section, the smaller the flow, the lower the peak concentration of pollutants at the downstream key nodes, but the longer the impact time on the downstream key nodes. For the Luxin Canal section, the smaller the inflow flow, the greater the peak concentration of pollutants in the downstream of the Luxin Canal and the longer the impact time.

[0049] Since the Qingshui River section uses the original river channel and builds a new 3-level pumping station on the Qingshui River to pump water upstream and transport water to Henan, reducing the amount of water inflow will not affect the total water volume in the river channel. However, the Luxin Canal uses the original river channel to flow to the regulating reservoir. Reducing the amount of water inflow will reduce the amount of water in the river channel and increase the concentration of pollutants. Therefore, for the Qingshui River section, the smaller the inflow flow, the smaller the peak concentration of pollutants at the test gate, but the impact time is longer, and the pollution group takes longer to reach the downstream key receptors, that is, more emergency response time.

[0050] For the Qingshui River section, when sudden water pollution occurs, the number of pump station units can be reduced to increase emergency response time and reduce the peak concentration of pollution clusters at key points downstream. For the Luxin Canal section, it is not recommended to reduce flow control.

[0051] ③ Location of pollutants

[0052] The location of the pollution accident is also one of the factors affecting the maximum concentration of pollutants. The closer the location of the accident is to the water inlet of the downstream reservoir, the higher the peak concentration at the water inlet of the downstream reservoir and the shorter the impact time. The farther away from the accident site, the lower the peak concentration of pollutants, but the decline rate of the peak concentration of pollutants is relatively slow as the distance from the accident site increases. Under working condition 3, a 60kg lead accident occurred at 0+300 meters of Qingshui River. The peak concentration of pollutants at pile number 5+340 was 0.15mg / L, at pile number 10+340, the peak concentration was 0.123mg / L, and at Zhaolou Gate (pile number 15+340) the peak concentration of pollutants was 0.12mg / L. From pile number 5+340 to 10+340, the peak concentration of pollutants decreased by 0.027mg / L in 5 kilometers, a decrease of 18%; from pile number 10+340 to 15+340, the peak concentration of pollutants decreased by 0.003mg / L in 5 kilometers, a decrease of 2.5%.

[0053] ④ Pollutant type

[0054] The type of pollutant determines the attenuation efficiency of the pollutant. In the Qingshui River section and the Luxin Canal section, the concentration change trends of the above pollutants are consistent. Under the same working conditions, the peak concentrations and concentration change trends of different pollutants at the test gate and Houchenlou Gate are basically the same.

[0055] Analysis of factors affecting pollutant transmission speed:

[0056] The upstream water volume is an important factor affecting the pollutant transmission rate, and the influence trend of the Qingshui River section is consistent with that of the Luxin Canal. In the Qingshui River section and the Luxin Canal section, the greater the upstream water volume, the shorter the arrival time of the peak concentration at the downstream reservoir inlet, and the faster the pollutant transmission rate. In the Qingshui River section, the water volume under the simultaneous operation of three units of the Yuanqiao Pumping Station (43m3 / s), the water volume under the simultaneous operation of two units (28.6m3 / s), and the water volume under the operation of only one unit (14.3m3 / s), the arrival time of the peak concentration at the downstream test reservoir inlet section was 34h49min, 47h47min, and 87h20min, respectively. The greater the upstream water volume, the faster the pollutant transmission rate.

[0057] When flow control measures are taken to reduce flow, the spread of the pollution group will be slowed down, thereby increasing the time for emergency treatment, but at the same time lengthening the control time of pollutants.

[0058] In step S3, three emergency control technologies are obtained according to the characteristics of pollutants and pollution conditions:

[0059] (1) Flow control: By adjusting the water flow rate, the diffusion, degradation and dispersion speed of pollutants are affected, thereby controlling the time it takes for pollutants to reach the downstream.

[0060] (2) Control by closure of gates: When a pollution accident is serious, the spread of pollutants is limited by closing the upstream and downstream gates until other measures are taken to reduce the pollution concentration to a safe standard and then restore the water flow; this method is applicable to more serious or major sudden water pollution incidents.

[0061] (3) Water discharge control: For heavy pollutants that are difficult to quickly handle, numerical simulation of sudden water pollution accidents is used to predict when the pollutants will be transferred to the water discharge gate, and to determine when the water quality concentration at the water discharge gate begins to exceed the standard and the duration of the excess. The water that exceeds the standard is discharged by opening the water discharge gate to reduce the time of water supply interruption. This is suitable for pollutants that are difficult to handle, such as mercury pollutants, and when the range of pollutants is too large.

[0062] Step S4 combines the above-mentioned sudden water pollution scenario simulation results and calculates the peak concentration (C max ) characteristic parameters, the peak concentration (C max ) Rapid prediction method of characteristic parameters.

[0063] If a sudden water pollution accident occurs in a channel or pond, the factors affecting the peak concentration of pollutants at the water quality control point include the mass of pollutants m, the distance x from the accident location to the water quality control point, the average flow velocity u of the channel or pond, and the longitudinal dispersion coefficient E. d These four are:

[0064] f(C max , m, x, u, Ed) = 0 (1)

[0065] According to the principle of dimensional analysis, it can be expressed as the exponential product of the other four physical quantities:

[0066] C max =km a x b u c Ed d (2)

[0067] From dimensional harmony we can get:

[0068] C max =kmx -3-d u -d Ed d (3)

[0069] Only two coefficients, k and d, need to be determined, which can be calculated using the least squares method.

[0070] ① Qingshui River pollutant concentration peak prediction formula

[0071] Select 33 groups of Qingshui river section data to calculate the optimal coefficient k m =0.6694, d m =-2.6644.

[0072] C max =0.6694mx -0.3356 u 2.6644 Ed -2.6644 (4)

[0073] Where: C max is the peak concentration of pollutants at the water quality control point, in mg / L; m is the total amount of pollutants, in g; x is the distance from the incident location to the water quality control point, in m; Ed is the diffusion coefficient, in m 2 / s; u is the flow velocity, the unit is m / s.

[0074] ②Prediction formula for peak concentration of pollutants in Luxin Canal

[0075] Select 21 groups of data from the Luxin Canal section to calculate the optimal coefficient k m =0.5723, d m =-2.6785

[0076] C max =0.5723x -0.3215 u 2.6785 Ed -2.6785 (5)

[0077] If sudden water pollution occurs with known pollutant leakage amount and leakage location, the concentration peak value can be quickly predicted for the key section.

[0078] If the data of the detection section is abnormal, the maximum pollution concentration of the polluted channel section and the pile number section where the maximum pollution concentration is located will be measured by a mobile detection vehicle, and the location of the pollution source will be identified to calculate the amount of pollutant leakage.

[0079] In step S5, when the location of the pollution source is unknown, it is simulated that the pollution occurs at the starting point of each channel section, and the effects of different control measures are tested, so that the reservoir inlet just reaches the national Class III water standard after control measures are taken under different pollution conditions, and the pollutant concentrations at different monitoring sections are recorded, and a pollution concentration threshold table is formulated for selecting control technology according to the concentration at the monitoring point.

[0080] In the "unknown pollution location" condition, the control technology is adopted only based on the pollution concentration of each detection section. For safety reasons, it can be assumed that the pollutant concentration detected in the detection section is the pollutant concentration after the pollution occurs at the starting point of the channel section and is fully diffused and decomposed.

[0081] The control effects when different control technologies are adopted are simulated so that the water inlet of the reservoir can just reach the national Class III water standard after control measures are taken under different pollution conditions. The pollutant concentrations at different monitoring sections are recorded to form a pollution concentration threshold table for different control technologies.

[0082] Table 1 Concentration thresholds for flow control at risk points in the Qingshuihe Sluice-Zhaolou Sluice section

[0083]

[0084] Table 2 Concentration thresholds for flow control at risk points in the Zhaolou Sluice-Trial Sluice section

[0085]

[0086]

[0087] Table 3. Concentration thresholds for each risk point section of Qingshuihe Sluice-Zhaolou Sluice section

[0088]

[0089] Table 4 Concentration thresholds for each risk point section of Zhaolou Sluice-Trial Sluice Section closure control

[0090]

[0091] Table 5 Concentration thresholds for each risk point section of the Renzhuang Sluice-Baigou River inverted siphon section closure control

[0092]

[0093]

[0094] Table 6 Concentration thresholds for each risk point section of the Baigou River inverted siphon-Houchenlou sluice section closed

[0095]

[0096] Table 7 Concentration thresholds for each risk point section of water discharge control in the Qingshuihe Sluice-Zhaolou Sluice section

[0097]

[0098] Table 8 Concentration thresholds for each risk point section of Zhaolou Sluice-Trial Sluice section water discharge control

[0099]

[0100]

[0101] Table 9 Concentration thresholds for each risk point section of water discharge control in the Renzhuang Sluice-Baigou River inverted siphon section

[0102]

[0103] Table 10 Concentration thresholds for each risk point section of Baigou River inverted siphon-Houchenlou control sluice section water discharge regulation

[0104]

[0105] For pollution sources with known locations, appropriate control measures are taken according to the amount and location of pollution, and a prediction model is used to quickly determine whether the water quality at the entrance of the downstream reservoir meets the standards. For pollution sources with unknown locations, control technology is selected based on the concentration at the monitoring point and the preset pollution concentration threshold table.

[0106] Example 2

[0107] A traffic accident occurred at Yuanqiao Village Bridge (Qingshui River 4+100), resulting in the leakage of 30kg of lead pollutants.

[0108] According to the Qingshui River pollutant concentration peak prediction formula:

[0109] C max =0.6694mx -0.3356 u 2.6644 Ed -2.6644

[0110] When no measures are taken, calculations show that the maximum concentration of pollutants at the test reservoir inlet Qingshui River 46+950 is 0.061 mg / l, exceeding the Class III water standard in the "Surface Water Environmental Quality Standard" (GB 3838-83).

[0111] When flow control is adopted (the number of operating units of Yuanqiao Station, Zhaolou Station and Test Station pump stations is reduced from three to one), calculation shows that the maximum pollutant concentration at the test reservoir inlet (Qingshui River 46+950) is 0.047 mg / l, which meets the Class III water standard in the "Surface Water Environmental Quality Standard" (GB3838-83).

[0112] Therefore, it is decided to adopt traffic control.

[0113] Example 3

[0114] The monitoring section at Zhaoxi Village Bridge (Luxinyunhe 8+943) found that the mercury concentration exceeded the standard, with the highest concentration reaching 0.006 mg / l.

[0115] The mercury concentration in this section has exceeded 0.00017 mg / l, and water discharge control is adopted within the range of Table 9 (Table of concentration thresholds for sections at risk points in water discharge control in the Renzhuang Sluice-Baigou River inverted siphon section).

[0116] Example 4

[0117] Another way to construct a pollution concentration prediction model is to define the movement and distribution of pollutants in water bodies through partial differential equations, while taking into account fluid movement, diffusion, natural attenuation, and pollutant sources. The specific construction process is as follows:

[0118] The rate of change of pollutant concentration C with time t and location x:

[0119] dC / dt+u*dC / dx=D*d^2C / dx^2-λC+S(m,x,u,Ed,t)

[0120] Where: C is the pollutant concentration at position x and time t, u is the flow velocity, D is the effective diffusion coefficient, which is a function of the longitudinal dispersion coefficient Ed, λ is a constant representing the natural attenuation rate, including degradation and adsorption processes, S is the source term, which represents the input of pollutant mass m, which changes with time and depends on the location, flow velocity and longitudinal dispersion coefficient of the pollution source; the numerical method is used to solve the above partial differential equations to obtain the simulation results of the pollutant concentration changing with time and space. After the model is verified, the pollution control strategy can be formulated according to the model results.

[0121] In each embodiment, the hardware implementation of the technology can directly adopt existing intelligent devices, including but not limited to industrial computers, PCs, smart phones, handheld stand-alone machines, floor-standing stand-alone machines, etc. Its input device preferably adopts a screen keyboard, its data storage and calculation module adopts existing memory, calculator, controller, its internal communication module adopts existing communication ports and protocols, and its remote communication adopts existing GPRS networks, World Wide Internet, etc. It can be clearly understood by technicians in the relevant field that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example. In actual applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the device is divided into different functional units or modules to complete all or part of the functions described above. Each functional unit and module in the embodiment can be integrated in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of each functional unit and module are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the above-mentioned method embodiment, and will not be repeated here. In the embodiments provided by the present invention, it should be understood that the disclosed device / terminal equipment and method can be implemented in other ways. For example, the device / terminal equipment embodiments described above are only schematic, for example, the division of modules or units is only a logical function division, and there may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point, the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms. The unit described as a separate component may or may not be physically separated, and the component displayed as a unit may or may not be a physical unit, that is, it may be located in one place, or it may be distributed on multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the scheme of this embodiment. The functional units in various embodiments of the present invention can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit.

[0122] The embodiments described above are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included in the protection scope of the present invention.

Claims

1. A method for predicting sudden water pollution in a water channel, characterized by: The method comprises the following steps: S1. Use hydrodynamic software to simulate and determine the impact of pollution accidents; S2. Determine the factors affecting the peak concentration of pollutants at water quality control points; S3. Establish emergency control technology; S4. Construct a pollution concentration prediction model; S5. Develop response strategies in the event of unknown pollution sources; S6. Application and selection of control technology; Step S4 uses the least squares method to construct a prediction data model, and predicts the maximum concentration of pollutants at the control point based on the four main factors that affect the peak concentration of pollutants at the water quality control point. The specific construction process is as follows: The pollutant concentration prediction model under the "pollution location is known" condition is: f(C max ,m,x,u,Ed)=0 Among them, C max represents the maximum value of the pollutant concentration. According to the principle of dimensional analysis, by considering the dimension of the physical quantity, C max It is expressed as the pollutant mass m, the distance from the incident location to the water quality control point x, the average flow velocity of the channel pool u, and the longitudinal dispersion coefficient Ed. According to the principle of dimensional analysis, it is expressed as the exponential product of four physical quantities C max =km a x b u c Ed d Through dimensional harmony, we get C max =kmx -3-d u -d Ed d , where k and d are coefficients to be determined; The least squares method determines the unknown coefficients in the formula, and obtains the optimal coefficient value by minimizing the difference between the predicted value and the actual observed value, so that the model prediction is closest to the actual situation, using the actual observed data y i and the model predicted value b' i The formulas for estimating k and d are as follows: d m =(n∑ n i＝1 (y i -b' i )a' i -∑ n i＝1 a' i ∑ n (i＝1) (y i -b' i ) / n∑ n (i＝1) a' i 2 -(∑ n i＝1 a' i ) 2 k m =10^[∑ n (i＝1) (y i -b' i )-d m* ∑ n i＝1 a' i ] / n Where n represents the number of samples, a' i are the measured values ​​of m, x, u and Ed, b' i is the corresponding model prediction value, d m and k m are the estimated values ​​of d and k obtained by the least squares method; In step S5, when the location of the pollution source is unknown, it is simulated that the pollution occurs at the starting point of each channel section, and the effects of different control measures are tested, so that the reservoir inlet just reaches the national Class III water standard after control measures are taken under different pollution conditions, and the pollutant concentrations at different monitoring sections are recorded, and a pollution concentration threshold table is formulated for selecting control technology according to the concentration at the monitoring point.

2. The method for predicting sudden water pollution in a water delivery channel according to claim 1 is characterized in that: Step S1 imports the plan of the river into the hydrodynamic simulation software MIKE11, simplifies the plan of the river, inputs all the river section information including the cross-sectional shape rules, hydraulic parameters, the river to which it belongs and its location, and sets appropriate boundary conditions for the water transfer channel; defines the key parameters of the water quality model including the types of various components in the water body, the initial state, the diffusion and attenuation rate of the substance, and sets the simulation results that need additional output; simulates sudden water pollution incidents in the software, and records the highest values ​​of pollutant concentrations at different location nodes to evaluate the impact range of the pollution accident.

3. The method for predicting sudden water pollution in a water delivery channel according to claim 1 is characterized by: Based on the results of the simulation software, step S2 identifies the main factors affecting the peak concentration of pollutants at the water quality control point as the quality of the pollutants, the distance from the incident location to the water quality control point, the average flow rate of the channel pool, and the longitudinal dispersion coefficient.

4. The method for predicting sudden water pollution in water delivery channels according to claim 1 is characterized by: Step S3 obtains three emergency control technologies according to the characteristics of the pollutants and the pollution situation: (1) Flow control: by adjusting the water flow rate, the diffusion, degradation and dispersion speed of pollutants are affected, and the time it takes for pollutants to reach the downstream is controlled; (2) Control by closing gates: When a pollution accident is serious, the spread of pollutants is limited by closing upstream and downstream gates until other measures are taken to reduce the pollution concentration to a safe standard and then restore water flow; (3) Water discharge control: For heavy pollutants that are difficult to quickly handle, numerical simulation of sudden water pollution accidents is used to predict when the pollutants will be transferred to the discharge gate, and to determine when the water quality concentration at the discharge gate begins to exceed the standard and how long the concentration exceeds the standard. The discharge gate is opened to remove the water exceeding the standard to reduce the time of water supply interruption.

5. The method for predicting sudden water pollution in water delivery channels according to claim 1 is characterized by: In step S6, for the known pollution source location, corresponding control measures are taken according to the pollution amount and location, and the prediction model is used to quickly determine whether the water quality at the downstream reservoir entrance meets the standard; for the unknown pollution source location, the control technology is selected according to the monitoring point concentration and the preset pollution concentration threshold table.

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