CN116977144A - Surface runoff pollution load calculation method, device, equipment and storage medium - Google Patents
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Abstract
The invention relates to the technical field of pollution load measurement and calculation, and discloses a method, a device, equipment and a storage medium for calculating surface runoff pollution load, wherein the calculation method calculates the surface runoff pollution load of a preset area by using a first calculation model representing the association relation between total surface runoff pollution load, rainfall information and functional area underlying surface information, and considers the environmental influence of surface runoff pollution in the actual migration process when calculating the surface runoff pollution load, and inputs the rainfall information and the functional area underlying surface information affecting the pollution load migration process in the target rainfall process of the preset area into the first calculation model to obtain the surface runoff pollution load of the preset area, thereby effectively improving the accuracy of the calculation result of the surface runoff pollution load, and solving the problem that the calculation result is inaccurate in the scheme for calculating the surface runoff pollution load based on a statistical model method in the related technology.
Description
Technical Field
The invention relates to the technical field of pollution load measurement and calculation, in particular to a method, a device, equipment and a storage medium for calculating surface runoff pollution load.
Background
In the prior art, a statistical model method is generally adopted to calculate the pollution load of the surface runoff flowing into the water body. When the method calculates the pollution load of the surface runoff, the runoff water sample is collected from a preset position, the pollution load of the surface runoff flowing into the water body is calculated based on the collected runoff water sample, and the environmental influence of the surface runoff in the process of migrating into the water body is not considered in the calculation process. However, the urban rainfall surface runoff pollution has the characteristics of complex sources, uncertain occurrence, more influence of environmental elements on emission and migration, and the like, and if the influence of the actual migration process of pollutants on the regional surface is not considered in the process of calculating the surface runoff, the calculation result of the surface runoff pollution load is not accurate enough.
Disclosure of Invention
In view of the above, the invention provides a method, a device, equipment and a storage medium for calculating the pollution load of the surface runoff, which are used for solving the problem that the calculation result is inaccurate in the existing scheme for calculating the pollution load of the surface runoff based on a statistical model method.
In a first aspect, the present invention provides a method for calculating a surface runoff pollution load, including:
acquiring first rainfall information and first functional area underlying surface information in a target rainfall process in a preset area, wherein the functional area underlying surface information is used for representing the population density in the preset area and the distribution information of human activity points; inputting the first rainfall information and the first functional area underlying surface information into a first calculation model, so that the first calculation model outputs the total load of the surface runoff pollution in the target rainfall process, and the first calculation model is used for representing the association relation between the total load of the surface runoff pollution, the rainfall information and the functional area underlying surface information.
According to the method for calculating the surface runoff pollution load, provided by the invention, the first calculation model for representing the association relation between the total surface runoff pollution load, rainfall information and functional area underlying surface information is utilized to calculate the surface runoff pollution load of the preset area, the environmental influence of the surface runoff pollution in the actual migration process is considered when the surface runoff pollution load is calculated, the rainfall information influencing the pollution load migration process in the target rainfall process of the preset area and the functional area underlying surface information are input into the first calculation model, the surface runoff pollution load of the preset area is obtained, the accuracy of the surface runoff pollution load calculation result is effectively improved, and the problem that the calculation result is inaccurate in the scheme for calculating the surface runoff pollution load based on the statistical model method in the related art is solved.
In an alternative embodiment, the method further comprises: acquiring second rainfall information and second functional area underlying surface information in a target rainfall process in a preset area; inputting second rainfall information and second functional area underlying surface information into a second calculation model, so that the second calculation model outputs transferable parameters of surface runoff pollution in a target rainfall process, and the second calculation model is used for representing association relations between the transferable parameters of the surface runoff pollution, the rainfall information and the functional area underlying surface information; determining the transferable load of the surface runoff pollution in the target rainfall process based on the total load of the surface runoff pollution in the target rainfall process and the transferable parameter of the surface runoff pollution in the target rainfall process.
According to the method provided by the alternative embodiment, the migration parameters of the surface runoff pollution of the target rainfall in the preset area are calculated based on the association relation between the migration parameters representing the surface runoff pollution, the rainfall information and the underlying surface information of the functional area, and the migration load of the surface runoff pollution in the target rainfall process is calculated based on the migration parameters and the total load of the surface runoff pollution, so that the accurate calculation of the migration load is realized.
In an alternative embodiment, the first calculation model is calculated by: acquiring third rainfall information of multiple rainfall processes in a preset area, total surface runoff pollution load of the multiple rainfall processes and underlying surface information of a third functional area of the multiple rainfall processes; and constructing a first calculation model based on third rainfall information of the multiple rainfall processes, total surface runoff pollution load of the multiple rainfall processes and underlying surface information of a third functional area of the multiple rainfall processes.
The method in the alternative embodiment is provided, the first calculation model is constructed based on the third rainfall information of the multiple rainfall processes in the preset area, the total surface runoff pollution load of the multiple rainfall processes and the underlying surface information of the third functional area of the multiple rainfall processes, the environmental influence of the pollution load in the actual migration process is considered when the model is constructed, and the constructed first calculation model can accurately calculate the surface runoff pollution load of the preset area in the rainfall processes.
In an alternative embodiment, the third rainfall information includes rainfall information, rainfall duration and rainfall pre-dry period duration, the third functional area under-pad information includes a type of a functional area, a cleaning frequency of the functional area under-pad and an area of the functional area under-pad, and the step of constructing the first calculation model based on the third rainfall information of the multiple rainfall processes, the total surface runoff pollution load of the multiple rainfall processes and the third functional area under-pad information of the multiple rainfall processes includes:
constructing a first calculation model based on a first relation, third rainfall information of multiple rainfall processes, total surface runoff pollution load of multiple rainfall processes and underlying surface information of a third functional area of the multiple rainfall processes, wherein the first relation is as follows:
wherein y is a(j) Representing the total pollution load of the surface runoff output pollutant j corresponding to any rainfall in a preset area; k represents the number of different types of underlying surfaces in a preset area; p represents the rainfall corresponding to the rainfall of the field, and x1 is an index corresponding to the rainfall; λ1 (i,j) The ith underlying surface being the jthRainfall coefficient of the pollutant; t represents the rainfall duration of the scene rainfall, x2 is an index corresponding to the rainfall duration, and lambda 2 (i,j) The rainfall duration coefficient of the jth pollutant of the ith underlying surface; d represents the length of the period before rainfall corresponding to the field rainfall, lambda 3 (i,j) The coefficient of the dry period before rain of the jth pollutant of the ith underlying surface; lambda 4 (i,j) Load correction coefficients for the jth contaminant of the ith underlying surface; s is S i Represents the i-th underlying surface area; l (L) i Represents the daily manual cleaning frequency of the ith underlying surface, lambda 5 (i,j) Is the cleaning frequency coefficient of the ith underlying surface.
According to the method provided by the alternative embodiment, environmental influence of pollution load in an actual migration process is considered when the first calculation model is built, the first calculation model is built based on the first relational expression, and the built first calculation model can accurately calculate the surface runoff pollution load of the preset area in a rainfall process.
In an alternative embodiment, the second calculation model is calculated by: acquiring fourth rainfall information of a plurality of rainfall processes in a preset area, transferable parameters of surface runoff pollution of the plurality of rainfall processes and underlying surface information of a fourth functional area of the plurality of rainfall processes; and constructing a second calculation model based on fourth rainfall information of the multiple rainfall processes, transferable parameters of surface runoff pollution of the multiple rainfall processes and underlying surface information of a fourth functional area of the multiple rainfall processes.
According to the method provided by the alternative embodiment, the second calculation model is constructed based on the fourth rainfall information of the multiple rainfall processes in the preset area, the transferable parameters of the surface runoff pollution of the multiple rainfall processes and the underlying surface information of the fourth functional area of the multiple rainfall processes, and the second calculation model can accurately calculate the transferable coefficients of the surface runoff pollution of the preset area in the rainfall processes.
In an alternative embodiment, the fourth rainfall information includes rainfall information and rainfall duration, the fourth functional area under-pad information includes a type of a functional area, an area of the functional area under-pad, and the second calculation model is constructed based on the fourth rainfall information of the multiple rainfall processes, the transferable parameters of the surface runoff pollution of the multiple rainfall processes, and the fourth functional area under-pad information of the multiple rainfall processes, including:
constructing a second calculation model based on a second relation, fourth rainfall information of multiple rainfall processes, transferable parameters of surface runoff pollution of multiple rainfall processes and underlying surface information of a fourth functional area of the multiple rainfall processes, wherein the second relation is as follows:
wherein, gamma j Representing the migration coefficient of the pollutant j in any rainfall of the preset area; k represents the number of different types of underlying surfaces in a preset area; p is the rainfall in the rainfall process; t represents the rainfall duration of the scene rainfall process; x3 is an index of rainfall intensity; lambda 6 (i,j) Is the rainfall intensity coefficient of the j pollutant of the i-th underlying surface; lambda 7 (i,j) Is the migration correction coefficient of the jth pollutant of the ith underlying surface; s is S i Represents the i-th underlying surface area; a represents the total area of the preset area.
According to the method provided by the alternative embodiment, the second calculation model is constructed based on the second relation, and the obtained second calculation model can accurately calculate the surface runoff pollution mobility coefficient of the preset area in the rainfall process.
In an alternative embodiment, the first calculation model includes a first calculation sub-model, a second calculation sub-model and a third calculation sub-model, where the first calculation sub-model is used to calculate a total load of surface runoff pollution corresponding to a rainfall process of a small rain in a preset area, the second calculation sub-model is used to calculate a total load of surface runoff pollution corresponding to a rainfall process of a heavy rain in the preset area, and the third calculation sub-model is used to calculate a total load of surface runoff pollution corresponding to a rainfall process of a large rain in the preset area; inputting the first rainfall information and the first functional area underlying surface information into a first calculation model, so that the first calculation model outputs the total load of the surface runoff pollution in the target rainfall process, and the method comprises the following steps: determining a rainfall level of a target rainfall process based on rainfall information, wherein the rainfall level comprises light rain, medium rain and heavy rain; when the rainfall level of the target rainfall process is light rain, inputting first rainfall information and first functional area underlying surface information into a first calculation sub-model, so that the first calculation sub-model outputs the total load of surface runoff pollution in the target rainfall process; when the rainfall level of the target rainfall process is middle rain, the first rainfall information and the first functional area underlying surface information are input into a second calculation sub-model, so that the second calculation sub-model outputs the total load of surface runoff pollution in the target rainfall process; when the rainfall level of the target rainfall process is heavy rain, the first rainfall information and the first functional area underlying surface information are input into a third calculation sub-model, so that the third calculation sub-model outputs the total load of surface runoff pollution in the target rainfall process.
According to the method provided by the alternative embodiment, the pollution load can be influenced by the runoff amount in the actual migration process, the total load of the surface runoff pollution in the target rainfall process is calculated by using the corresponding calculation model based on the rainfall level of the target rainfall process, the influence of different rainfall on the migration of the pollution load is considered when the total load of the surface runoff pollution is calculated, and the accurate calculation of the total load of the surface runoff pollution is realized.
In an alternative embodiment, the second calculation model includes a fourth calculation sub-model, a fifth calculation sub-model, and a sixth calculation sub-model, and the step of inputting the second rainfall information and the second functional area underlying surface information into the second calculation model so that the second calculation model outputs the mobilizable parameters of the surface runoff pollution in the target rainfall process includes: when the rainfall level of the target rainfall process is light rain, inputting second rainfall information and second functional area underlying surface information into a fourth calculation sub-model, so that the fourth calculation sub-model outputs transferable parameters of surface runoff pollution in the target rainfall process; when the rainfall level of the target rainfall process is middle rain, inputting second rainfall information and second functional area underlying surface information into a fifth calculation sub-model, so that the fifth calculation sub-model outputs transferable parameters of surface runoff pollution in the target rainfall process; and when the rainfall level of the target rainfall process is heavy rain, inputting the second rainfall information and the second functional area underlying surface information into a sixth calculation sub-model, so that the sixth calculation sub-model outputs the transferable parameters of the surface runoff pollution in the target rainfall process.
According to the method provided by the alternative embodiment, the migration coefficient of the surface runoff pollution in the target rainfall process is calculated by utilizing the corresponding calculation model based on the rainfall level of the target rainfall process, so that the accurate calculation of the migration coefficient is realized.
In an alternative embodiment, the first calculation sub-model, the second calculation sub-model and the third calculation sub-model are calculated by: acquiring rainfall information of multiple small rains in a preset area, rainfall information of multiple rains in multiple rains, rainfall information of multiple big rains, first water quantity information and first water quality information acquired by at least one preset monitoring point in the rainfall process of each small rain, second water quantity information and second water quality information acquired by at least one preset monitoring point in the rainfall process of each rain, and third water quantity information and third water quality information acquired by at least one preset monitoring point in the rainfall process of each big rain; calculating the total load of the surface runoff pollution of the corresponding small rain in the rainfall process based on the first water quantity information and the first water quality information; determining a first calculation sub-model based on the total load of surface runoff pollution of each small rain in the rainfall process, rainfall information of each small rain and underlying surface information of a functional area corresponding to each small rain; calculating the total load of the surface runoff pollution of the medium rain in the rainfall process based on the second water quantity information and the second water quality information; determining a second calculation sub-model based on the total load of the surface runoff pollution of the rain in each field in the rainfall process, the rainfall information of the rain in each field and the underlying surface information of the functional area corresponding to the rain in each field; calculating the total load of the surface runoff pollution of the heavy rain in the rainfall process based on the third water quantity information and the third water quality information; and determining a third calculation sub-model based on the total load of the surface runoff pollution of each heavy rain in the rainfall process, the rainfall information of each heavy rain and the underlying surface information of the functional area corresponding to each heavy rain.
In an alternative embodiment, the fourth, fifth and sixth computational sub-models are obtained by: acquiring rainfall information of multiple small rains in a preset area, rainfall information of multiple big rains, total concentration of a runoff water sample in a rainfall process of each small rain, movable concentration of a runoff water sample in a rainfall process of each small rain, total concentration of a runoff water sample in a rainfall process of each rain, movable concentration of a runoff water sample in a rainfall process of each rain, total concentration of a runoff water sample in a rainfall process of each big rain and movable concentration of a runoff water sample in a rainfall process of each big rain; determining corresponding migration parameters of surface runoff pollution based on the total concentration and the migration concentration of the runoff water sample in the rainfall process of each small rain; determining a fourth calculation sub-model based on the transferable parameters of the surface runoff pollution of each small rain, the rainfall information of each small rain and the underlying surface information of the functional area of each small rain; determining corresponding migration parameters of surface runoff pollution based on the total concentration and the migration concentration of the runoff water sample in the rainfall process of each field of rain; determining a fifth calculation sub-model based on the mobilizable parameters of the surface runoff pollution of the rains in each field, the rainfall information of the rains in each field and the underlying surface information of the functional area of the rains in each field; determining corresponding migration parameters of surface runoff pollution based on the total concentration and the migration concentration of the runoff water sample in the rainfall process of each heavy rain; and determining a sixth calculation sub-model based on the transferable parameters of the surface runoff pollution of each heavy rain, the rainfall information of each heavy rain and the underlying surface information of the functional area of each heavy rain.
In a second aspect, the present invention provides a surface runoff pollution load calculation device, comprising: the first acquisition module is used for acquiring first rainfall information and first functional area underlying surface information in a target rainfall process in a preset area, wherein the functional area underlying surface information is used for representing the population density in the preset area and the distribution information of human activity points; the first calculation module is used for inputting the first rainfall information and the first functional area underlying surface information into the first calculation model, so that the first calculation model outputs the total load of the surface runoff pollution in the target rainfall process, and the first calculation model is used for representing the association relation between the total load of the surface runoff pollution, the rainfall information and the functional area underlying surface information.
In an alternative embodiment, the apparatus further comprises: the second acquisition module is used for acquiring second rainfall information and second functional area underlying surface information in the target rainfall process in the preset area; the second calculation module is used for inputting second rainfall information and second functional area underlying surface information into a second calculation model, so that the second calculation model outputs the transferable parameters of the surface runoff pollution in the target rainfall process, and the second calculation model is used for representing the association relationship between the transferable parameters of the surface runoff pollution, the rainfall information and the functional area underlying surface information; the first determining module is used for determining the transferable load of the surface runoff pollution in the target rainfall process based on the total load of the surface runoff pollution in the target rainfall process and the transferable parameter of the surface runoff pollution in the target rainfall process.
In an alternative embodiment, the first calculation model is calculated by: acquiring third rainfall information of multiple rainfall processes in a preset area, total surface runoff pollution load of the multiple rainfall processes and underlying surface information of a third functional area of the multiple rainfall processes; and constructing a first calculation model based on third rainfall information of the multiple rainfall processes, total surface runoff pollution load of the multiple rainfall processes and underlying surface information of a third functional area of the multiple rainfall processes.
In a third aspect, the present invention provides a computer device comprising: the system comprises a memory and a processor, wherein the memory and the processor are in communication connection, the memory stores computer instructions, and the processor executes the computer instructions, so that the surface runoff pollution load calculation method of the first aspect or any corresponding implementation mode is executed.
In a fourth aspect, the present invention provides a computer readable storage medium having stored thereon computer instructions for causing a computer to perform the surface runoff pollution loading method of the first aspect or any one of its corresponding embodiments.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are needed in the description of the embodiments or the prior art will be briefly described, and it is obvious that the drawings in the description below are some embodiments of the present invention, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic flow diagram of a surface runoff pollution load method according to an embodiment of the present invention;
FIG. 2 is a schematic flow diagram of another surface runoff pollution load method according to an embodiment of the present invention;
FIG. 3 is a flow diagram of yet another method of surface runoff pollution loading in accordance with embodiments of the present invention;
FIG. 4 is a block diagram of a surface runoff pollution load device according to an embodiment of the present invention;
fig. 5 is a schematic diagram of a hardware structure of a computer device according to an embodiment of the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments of the present invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The urban rainfall surface runoff pollution has the characteristics of complex sources, uncertain occurrence, more influence of environmental elements on emission and migration and the like, so that the pollution load calculation is complex. Rainfall runoff from production to entering the receiving water body needs to undergo a complex converging process, including: rainfall accumulates on the ground to form runoff, the runoff migrates on different underlying surfaces of the ground, the runoff flows into a rainwater drainage system and migrates therein, sediment pollutants in the ground surface and the drainage system can be washed when the runoff flow is large, sediment in the runoff flow can be settled in the migration process when the runoff flow is small, and the like.
In the related art, the surface runoff pollution load is generally calculated by using a statistical model method, the actual migration process of pollutants on the surface is not considered in the method for calculating the surface runoff pollution, however, the surface runoff pollution is more influenced by the environment, and if the actual migration process of the pollutants is not considered, the calculation result of the surface runoff pollution load is not accurate enough.
In order to solve the problems in the related art, the embodiment of the invention provides a method for calculating the surface runoff pollution load, which can be applied to a processor to realize the calculation of the surface runoff pollution load. According to the method, the environmental influence of the surface runoff pollution in the actual migration process is considered when the surface runoff pollution load is calculated, rainfall information influencing the pollution load migration process in the target rainfall process of the preset area and the underlying surface information of the functional area are input into the first calculation model, the surface runoff pollution load of the preset area is obtained, and the accuracy of the surface runoff pollution load calculation result is effectively improved.
In accordance with an embodiment of the present application, there is provided an embodiment of a method for calculating a surface runoff pollution load, it being noted that the steps shown in the flowcharts of the figures may be performed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowcharts, in some cases the steps shown or described may be performed in an order different from that shown or described herein.
In this embodiment, a method for calculating a surface runoff pollution load is provided, which may be used in the above processor, and fig. 1 is a flowchart of a method for calculating a surface runoff pollution load according to an embodiment of the present application, as shown in fig. 1, where the flowchart includes the following steps:
step S101, first rainfall information and first functional area underlying surface information in a target rainfall process in a preset area are obtained, and the functional area underlying surface information is used for representing distribution information of population density and human activity characteristics in the preset area.
The preset area may be, for example, any area where the calculation of the surface runoff pollution load is to be performed. The target rainfall process can be any rainfall process in a preset area; in the embodiment of the application, the preset area can be a city along a certain river basin or a catchment area in the city along the line; based on the arrangement characteristics and the underlying surface characteristics of the urban rainwater pipe network, the sub-catchment areas of the urban water body drainage basin range can be divided, and at least one sub-catchment area in the urban is determined; the first rainfall information may include, but is not limited to, rainfall information of a target rainfall process, rainfall duration information, and rainfall pre-dry period duration information; the first functional area under-pad information may include, but is not limited to, a type of functional area, an area of the functional area under-pad, and sweeping frequency information. In the embodiment of the application, the underlying surface can be divided into 5 classes according to functions based on population density of the underlying surface in the preset area and distribution of human activity characteristics: the preset area can comprise the five functional areas of the under-pad surface of the traffic area, the living area, the business area, the industrial area and the greening area. In the embodiment of the present application, the first rainfall information and the underlying surface information of the first functional area in a rainfall process in a preset area may be shown in the following table 1. In table 1, P represents the rainfall, T represents the rainfall duration, D represents the duration of the previous dry period of the rainfall, S represents the area of the underlying surface of the functional area, and L represents the frequency of daily manual cleaning of the underlying surface of the functional area.
TABLE 1
| Functional area under-pad type | P(mm) | T(min) | D(d) | S(hm 2 ) | L |
| Industrial area | 16.2 | 53 | 8 | 1.32 | 0.5 |
| Residential area | 16.2 | 53 | 8 | 8.93 | 1 |
| Commercial district | 16.2 | 53 | 8 | 1.09 | 2 |
| Traffic zone | 16.2 | 53 | 8 | 2.51 | 2 |
| Greenbelt | 16.2 | 53 | 8 | 3.72 | 0 |
Step S102, inputting first rainfall information and first functional area underlying surface information into a first calculation model, so that the first calculation model outputs total load of surface runoff pollution in a target rainfall process, and the first calculation model is used for representing association relations between the total load of surface runoff pollution, the rainfall information and the functional area underlying surface information.
Illustratively, the total load of surface runoff pollution is used to characterize the severity of surface runoff pollution during regional rainfall. The first calculation model represents the association relation between the total surface runoff pollution load, rainfall information and functional area underlying surface information, and the first rainfall information and the first functional area underlying surface information are input into the first calculation model, so that the total surface runoff pollution load corresponding to the target rainfall process can be calculated.
According to the method for calculating the surface runoff pollution load, the first calculation model for representing the association relation between the total surface runoff pollution load, rainfall information and functional area underlying surface information is utilized to calculate the surface runoff pollution load of the preset area, the environmental influence of the surface runoff pollution in the actual migration process is considered when the surface runoff pollution load is calculated, the rainfall information affecting the pollution load migration process in the target rainfall process of the preset area and the functional area underlying surface information are input into the first calculation model, the surface runoff pollution load of the preset area is obtained, the accuracy of the surface runoff pollution load calculation result of the preset area is effectively improved, and the problem that the calculation result is inaccurate in the scheme for calculating the surface runoff pollution load based on the statistical model method in the related art is solved.
In this embodiment, a method for calculating a surface runoff pollution load is provided, which may be used in the above processor, and fig. 2 is a flowchart of a surface runoff pollution load method according to an embodiment of the present application, as shown in fig. 2, where the flowchart includes the following steps:
step S201, first rainfall information and first functional area underlying surface information in a target rainfall process in a preset area are obtained, and the functional area underlying surface information is used for representing distribution information of population density and human activity characteristics in the preset area. Please refer to step S101 in the embodiment shown in fig. 1 in detail, which is not described herein.
Step S202, inputting first rainfall information and first functional area underlying surface information into a first calculation model, so that the first calculation model outputs total load of surface runoff pollution in a target rainfall process, and the first calculation model is used for representing association relations between the total load of surface runoff pollution, the rainfall information and the functional area underlying surface information. Please refer to step S102 in the embodiment shown in fig. 1 in detail, which is not described herein.
Step S203, second rainfall information and second functional area underlying surface information in the target rainfall process in the preset area are obtained.
Illustratively, in an embodiment of the present application, the second rainfall information may include, but is not limited to, rainfall information of the target rainfall process and rainfall duration information. The second functional area under-pad information may include, but is not limited to, a functional area under-pad type in a preset area and corresponding area information.
Step S204, inputting the second rainfall information and the second functional area underlying surface information into a second calculation model, so that the second calculation model outputs the transferable parameters of the surface runoff pollution in the target rainfall process, and the second calculation model is used for representing the association relationship between the transferable parameters of the surface runoff pollution, the rainfall information and the functional area underlying surface information.
Illustratively, in an embodiment of the present application, the migratable coefficient may be a ratio of a migratable concentration to a total concentration of a runoff water sample collected during rainfall. The total concentration of the runoff water sample refers to the total concentration of pollutants after the runoff water sample is uniformly mixed, and the mobilizable concentration of the runoff water sample can be the concentration of the pollutants in the overlying water sample after the polluted water sample is settled and stabilized.
Step S205, determining the transferable load of the surface runoff pollution in the target rainfall process based on the total load of the surface runoff pollution in the target rainfall process and the transferable parameter of the surface runoff pollution in the target rainfall process.
Illustratively, in embodiments of the present application, the transferable load of surface runoff contamination during a target rainfall may be determined based on the product of the transferable parameter of surface runoff contamination and the total load of surface runoff contamination.
In some alternative embodiments, the first calculation model is calculated by:
step a1, obtaining third rainfall information of multiple rainfall processes in a preset area, total surface runoff pollution load of the multiple rainfall processes and underlying surface information of a third functional area of the multiple rainfall processes.
Illustratively, in the embodiment of the present application, the multiple rainfall processes may include, but are not limited to, 15 rainfall processes in the preset area, and the third rainfall information may include, but is not limited to, rainfall information, rainfall duration information, and pre-rainfall dry period information corresponding to each rainfall in the 15 rainfall processes; the third functional area under-pad information includes the type of the functional area in the preset area, the cleaning frequency of the functional area under-pad and the area of the functional area under-pad.
And a step a2, constructing a first calculation model based on third rainfall information of the multiple rainfall processes, total surface runoff pollution load of the multiple rainfall processes and underlying surface information of a third functional area of the multiple rainfall processes.
The first calculation model is constructed based on the third rainfall information of the multiple rainfall processes and the association relation between the underlying surface information of the third functional area and the corresponding surface runoff pollution load.
In some optional embodiments, the third rainfall information includes rainfall information, rainfall duration, and rainfall pre-dry period duration, and the third functional area under-pad information includes a type of the functional area, a cleaning frequency of the functional area under-pad, and an area of the functional area under-pad, where the step a2 includes:
step a21, constructing a first calculation model based on a first relation, third rainfall information of multiple rainfall processes, total surface runoff pollution load of multiple rainfall processes and underlying surface information of a third functional area of the multiple rainfall processes, wherein the first relation is as follows:
wherein y is a(j) Representing the total pollution load of the surface runoff output pollutant j corresponding to any rainfall in a preset area; k represents the number of different types of underlying surfaces in a preset area; p represents the rainfall corresponding to the rainfall of the field, and x1 is an index corresponding to the rainfall; λ1 (i,j) A rainfall coefficient for the jth pollutant of the ith underlying surface; t represents the rainfall duration of the scene rainfall, x2 is an index corresponding to the rainfall duration, and lambda 2 (i,j) The rainfall duration coefficient of the jth pollutant of the ith underlying surface; d represents the length of the period before rainfall corresponding to the field rainfall, lambda 3 (i,j) The coefficient of the dry period before rain of the jth pollutant of the ith underlying surface; lambda 4 (i,j) Load correction coefficients for the jth contaminant of the ith underlying surface; s is S i Represents the i-th underlying surface area; l (L) i Represents the daily manual cleaning frequency of the ith underlying surface, lambda 5 (i,j) Is the cleaning frequency coefficient of the ith underlying surface.
According to the method provided by the alternative embodiment, surface runoffs are formed on the surface during rainfall, the runoff amount of the formed surface runoffs is influenced by the rainfall amount, the migration process of the surface runoffs is influenced by the amount of the runoff amount during actual migration of pollution loads, the total load of the surface runoff pollution during target rainfall is calculated by using a corresponding calculation model, the influence of different rainfall levels on the generation and migration of the rainfall runoffs is considered during calculation of the total load of the runoff pollution, and the accurate calculation of the total load of the surface runoff pollution is realized.
In some alternative embodiments, the second calculation model is calculated by:
step b1, fourth rainfall information of multiple rainfall processes in a preset area, transferable parameters of surface runoff pollution of the multiple rainfall processes and underlying surface information of a fourth functional area of the multiple rainfall processes are obtained.
Illustratively, in the embodiment of the present application, the multiple rainfall processes may include, but are not limited to, 15 rainfall processes in the preset area, and the fourth rainfall information may include, but is not limited to, rainfall information corresponding to each rainfall in the 15 rainfall processes, and rainfall duration information; the fourth functional area under-pad information includes a type of the functional area in the preset area and an area of the functional area under-pad.
And b2, constructing a second calculation model based on fourth rainfall information of the multiple rainfall processes, transferable parameters of surface runoff pollution of the multiple rainfall processes and underlying surface information of a fourth functional area of the multiple rainfall processes.
Illustratively, a second computational model is constructed based on the fourth rainfall information of the multiple rainfall processes and the association between the underlying surface information of the fourth functional area and the mobilizable parameters of the corresponding surface runoff pollution load.
In an alternative embodiment, the fourth rainfall information includes rainfall information and rainfall duration, the fourth functional area under-pad information includes a type of the functional area and an area of the functional area under-pad, and the step b2 includes:
step b21, constructing a second calculation model based on a second relation, fourth rainfall information of the multiple rainfall processes, transferable parameters of surface runoff pollution of the multiple rainfall processes and underlying surface information of a fourth functional area of the multiple rainfall processes, wherein the second relation is as follows:
wherein, gamma j Representing the migration coefficient of the pollutant j in any rainfall of the preset area; k represents the number of different types of underlying surfaces in a preset area; p is the rainfall in the rainfall process; t represents the rainfall duration of the scene rainfall process; x3 is an index of rainfall intensity; lambda 6 (i,j) Is the rainfall intensity coefficient of the j pollutant of the i-th underlying surface; lambda 7 (i,j) Is the migration correction coefficient of the jth pollutant of the ith underlying surface; s is S i Represents the i-th underlying surface area; a represents the total area of the preset area.
According to the method for calculating the surface runoff pollution load, the first calculation model for representing the association relation between the total surface runoff pollution load, rainfall information and functional area underlying surface information is utilized to calculate the surface runoff pollution load of the preset area, the environmental influence of the surface runoff pollution in the actual migration process is considered when the surface runoff pollution load is calculated, the rainfall information affecting the pollution load migration process in the target rainfall process of the preset area and the functional area underlying surface information are input into the first calculation model, the surface runoff pollution load of the preset area is obtained, the accuracy of the surface runoff pollution load calculation result of the preset area is effectively improved, and the problem that the calculation result is inaccurate in the scheme for calculating the surface runoff pollution load based on the statistical model method in the related art is solved.
In this embodiment, a method for calculating a surface runoff pollution load is provided, which may be used in the above processor, and fig. 3 is a flowchart of a method for calculating a surface pollution load according to an embodiment of the present invention, as shown in fig. 3, where the flowchart includes the following steps:
Step S301, first rainfall information and first functional area underlying surface information in a target rainfall process in a preset area are obtained, and the functional area underlying surface information is used for representing distribution information of population density and human activity characteristics in the preset area. Please refer to step S201 in the embodiment shown in fig. 2 in detail, which is not described herein.
Step S302, inputting first rainfall information and first functional area underlying surface information into a first calculation model, so that the first calculation model outputs total load of surface runoff pollution in a target rainfall process, and the first calculation model is used for representing association relations between the total load of surface runoff pollution, the rainfall information and the functional area underlying surface information. Please refer to step S202 in the embodiment shown in fig. 2, which is not described herein.
In some alternative embodiments, the first calculation model includes a first calculation sub-model for calculating a total amount of surface runoff pollution corresponding to a rainfall process of a small rain in the preset area, a second calculation sub-model for calculating a total amount of surface runoff pollution corresponding to a rainfall process of a heavy rain in the preset area, and a third calculation sub-model for calculating a total amount of surface runoff pollution corresponding to a rainfall process of a large rain in the preset area; the step S302 includes:
In step S3021, a rainfall level of the target rainfall process is determined based on the rainfall information, and the rainfall level includes light rain, medium rain, and heavy rain.
In the embodiment of the application, three rainfall levels of light rain (less than 10 mm), medium rain (10-25 mm) and heavy rain (more than or equal to 25 mm) are divided according to the annual rainfall monitoring statistical data of the weather station of the city where the preset area is located and in combination with rainfall. The rainfall amount of the target rainfall process can be determined based on the first rainfall information of the target rainfall process, and the rainfall grade of the target rainfall process is determined based on the rainfall amount of the target rainfall process and the rainfall grade information.
In step S3022, when the rainfall level of the target rainfall process is light rain, the first rainfall information and the first functional area underlying surface information are input into the first calculation sub-model, so that the first calculation sub-model outputs the total load of the surface runoff pollution in the target rainfall process.
Illustratively, in an embodiment of the present application, the first computation sub-model may be represented by the following formula (3).
Wherein Y' a(j) Representing the total pollution load of the surface runoff output pollutant j corresponding to the rainfall process of any small rain in a preset area; k represents the number of different types of underlying surfaces in a preset area; λ1' (i,j) The rainfall coefficient of the j pollutant on the i-th underlying surface in the rainfall process of any small rain; t represents the rainfall duration of the scene rainfall, x2 is an index corresponding to the rainfall duration, and λ2' (i,j) The rainfall duration coefficient of the j pollutant on the i-th underlying surface in the rainfall process of the small field rain; d represents the length of the period before rainfall corresponding to the field rainfall, λ3' (i,j) The coefficient of the pre-rain dry period of the jth pollutant on the ith underlying surface in the rainfall process of the small rain; λ4' (i,j) The load correction coefficient of the jth pollutant on the ith underlying surface in the rainfall process of the scene light rain is used for correcting the load correction coefficient of the jth pollutant on the ith underlying surface; s is S i Represents the i-th underlying surface area; l (L) i Represents the daily manual cleaning frequency of the ith underlying surface, λ5' (i,j) The cleaning frequency coefficient of the ith underlying surface in the rainfall process of the small rain.
In step S3023, when the rainfall level of the target rainfall process is middle rain, the first rainfall information and the first functional area underlying surface information are input into the second calculation sub-model, so that the second calculation sub-model outputs the total load of the surface runoff pollution in the target rainfall process.
Illustratively, in an embodiment of the present application, the second computation sub-model may be represented by the following formula (4).
Wherein Y' a(j) Representing the total pollution load of the surface runoff output pollutant j corresponding to the rainfall process of any scene in the preset area; k represents the number of different types of underlying surfaces in a preset area; λ2' (i,j) The rainfall coefficient of the j pollutant on the i-th underlying surface in the rainfall process of any field; t represents the rainfall duration of the scene rainfall, x2 is an index corresponding to the rainfall duration, and λ2 (i,j) The rainfall duration coefficient of the j pollutant on the i-th underlying surface in the rainfall process of the rains in the scene; d represents the length of the pre-rainfall period corresponding to the scene rainfall, λ3 (i,j) The coefficient of the pre-rain dry period of the jth pollutant of the ith underlying surface in the rainfall process of the rainy field; λ4' (i,j) ) The load correction coefficient of the jth pollutant on the ith underlying surface in the rainfall process of the rainy scene; s is S i Represents the i-th underlying surface area; l (L) i Represents the daily manual cleaning frequency of the ith underlying surface, λ5 (i,j) The cleaning frequency coefficient of the ith underlying surface in the rainfall process of the rain in the field.
In step S3024, when the rainfall level of the target rainfall process is heavy rain, the first rainfall information and the first functional area underlying surface information are input into the third calculation sub-model, so that the third calculation sub-model outputs the total load of the surface runoff pollution in the target rainfall process.
Illustratively, in an embodiment of the present application, the third calculation sub-model may be represented by the following formula (5).
Wherein Y ', is' a(j) Representing the total pollution load of the surface runoff output pollutant j corresponding to the rainfall process of any heavy rain in a preset area; k represents the number of different types of underlying surfaces in a preset area; λ1'. (i,j) The rainfall coefficient of the j pollutant on the i-th underlying surface in the rainfall process of any heavy rain; t represents the rainfall duration of the scene rainfall, x2 is an index corresponding to the rainfall duration, and λ2 '' (i,j) The rainfall duration coefficient of the j pollutant on the i-th underlying surface in the rainfall process of the scene heavy rain; d represents the length of the rainfall front dry period corresponding to the field rainfall, and lambda 3 '' (i,j) The coefficient of the pre-rain dry period of the jth pollutant on the ith underlying surface in the rainfall process of the scene heavy rain; λ4'. (i,j) The load correction coefficient of the jth pollutant on the ith underlying surface in the rainfall process of the scene heavy rain is used for correcting the load correction coefficient of the jth pollutant on the ith underlying surface; s is S i Represents the i-th underlying surface area; l (L) i Represents the daily manual cleaning frequency of the ith underlying surface, [ lambda ] 5 '' (i,j) The cleaning frequency coefficient of the ith underlying surface in the rainfall process of the heavy rain.
Step S303, second rainfall information and second functional area underlying surface information in the target rainfall process in the preset area are obtained. Please refer to step S203 in the embodiment shown in fig. 2 in detail, which is not described herein.
Step S304, inputting the second rainfall information and the second functional area underlying surface information into a second calculation model, so that the second calculation model outputs the transferable parameters of the surface runoff pollution in the target rainfall process, and the second calculation model is used for representing the association relationship between the transferable parameters of the surface runoff pollution, the rainfall information and the functional area underlying surface information. Please refer to step S204 in the embodiment shown in fig. 2 in detail, which is not described herein.
In some possible embodiments, the second computing model includes a fourth computing sub-model, a fifth computing sub-model, and a sixth computing sub-model, where step S304 includes:
step S3041, when the rainfall level of the target rainfall process is small rain, inputting the second rainfall information and the second functional area underlying surface information into a fourth calculation sub-model, so that the fourth calculation sub-model outputs the transferable parameters of the surface runoff pollution in the target rainfall process.
Illustratively, in an embodiment of the present application, the fourth computation sub-model may be represented by the following formula (6).
Wherein Γ' j Representing the migration coefficient of the pollutant j in any small rain of the preset area; k represents the number of different types of underlying surfaces in a preset area; p is the rainfall in the rainfall process; t represents the rainfall duration of the scene rainfall process; x3 is an index of rainfall intensity; λ6' (i,j) Is the rainfall intensity coefficient of the jth pollutant on the ith underlying surface in the scene rainfall process; λ7' (i,j) Is the migration correction coefficient of the jth pollutant on the ith underlying surface in the field rainfall process; s is S i Represents the i-th underlying surface area; a represents the total area of the preset area.
Step S3042, when the rainfall level of the target rainfall process is middle rain, the second rainfall information and the second functional area underlying surface information are input into the fifth calculation sub-model, so that the fifth calculation sub-model outputs the transferable parameters of the surface runoff pollution in the target rainfall process.
Illustratively, in an embodiment of the present application, the fifth calculation sub-model may be represented by the following formula (7).
Wherein Γ' j Showing the migration coefficient of the j pollutants in the rain in any field time of the preset area; k represents the number of different types of underlying surfaces in a preset area; p is the rainfall in the rainfall process; t represents the rainfall duration of the scene rainfall process; x3 is an index of rainfall intensity; λ6' (i,j) Is the rainfall intensity coefficient of the jth pollutant on the ith underlying surface in the scene rainfall process; λ7' (i,j) Is the migration correction coefficient of the jth pollutant on the ith underlying surface in the field rainfall process; s is S i Represents the i-th underlying surface area; a represents the total area of the preset area.
And S3043, inputting the second rainfall information and the second functional area underlying surface information into a sixth calculation sub-model when the rainfall level of the target rainfall process is heavy rain, so that the sixth calculation sub-model outputs the transferable parameters of the surface runoff pollution in the target rainfall process.
Illustratively, in an embodiment of the present application, the sixth computational submodel may be represented by the following formula (8).
Wherein Γ'. j Representing the migration coefficient of the pollutant j in any large field rain in the preset area; k represents the number of different types of underlying surfaces in a preset area; p is the rainfall in the rainfall process; t represents the rainfall duration of the scene rainfall process; x3 is an index of rainfall intensity; λ6'. (i,j) Is the rainfall intensity coefficient of the jth pollutant on the ith underlying surface in the scene rainfall process; λ7'. (i,j) Is the migration correction coefficient of the jth pollutant on the ith underlying surface in the field rainfall process; s is S i Represents the i-th underlying surface area; a represents the total area of the preset area.
Step S305, determining the transferable load of the surface runoff pollution in the target rainfall process based on the total load of the surface runoff pollution in the target rainfall process and the transferable parameter of the surface runoff pollution in the target rainfall process.
In some possible embodiments, the first computation sub-model, the second computation sub-model, and the third computation sub-model are computed by:
step c1, acquiring rainfall information of multiple small rains in a preset area, rainfall information of multiple medium rains, rainfall information of multiple large rains, first water quantity information and first water quality information acquired by at least one preset monitoring point in the rainfall process of each small rain, second water quantity information and second water quality information acquired by at least one preset monitoring point in the rainfall process of each large rain, and third water quantity information and third water quality information acquired by at least one preset monitoring point in the rainfall process of each large rain.
The preset monitoring point can be any monitoring point in the preset area for collecting runoff pollution water samples in the rainfall process. In the embodiment of the application, the sub-catchment area can be divided according to the water collecting range of the rainwater pipe network, and the rainwater drainage port of the directly discharged water body in the sub-catchment area is selected as an output end monitoring point; it should be noted that all the rainwater drains in the selected sub-catchment area should be clear without draining in order to avoid the interference of point source pollution caused by misconnection. According to the characteristics of the underlying surface of the urban area and the arrangement of a rainwater pipe network, a water collecting area A, B, C is divided as a monitoring area. Under different rainfall conditions, according to the selected output end monitoring point position, collecting effluent water sample and monitoring water quantity and quality, wherein the water quality parameters comprise total concentration (C a ) And a migratable concentration (C) b ). The method can select 15 rains, 15 rains and 15 rains with heavy rain based on rainfall level, collect runoff water samples from the rainwater drainage positions of three catchment areas, wherein the sampling interval of the runoff water samples is 5min from 0 to 30min, the sampling interval is 10min from 30 to 60min, the sampling interval is 20min after 60min, and synchronously record the flow rate during each sampling; the measured water quality index may include: SS, COD, NH 3 -N, TP; the monitoring concentration of the water sample is divided into total concentration (C a ) And after settling stabilizationConcentration (C) capable of migration b ) By carrying out a sedimentation experiment on the water sample, obtaining the corresponding relation between the sedimentation time and the concentration of the overlying water sample, wherein all the water samples reach sedimentation balance within 12 hours, and the concentration of the overlying water at the moment is C b 。
And c2, calculating the total load of the surface runoff pollution of the corresponding heavy rain in the rainfall process based on the first water quantity information and the first water quality information.
And c3, determining a first calculation sub-model based on the total load of the surface runoff pollution of each small rain in the rainfall process, the rainfall information of each small rain and the underlying surface information of the functional area corresponding to each small rain.
And c4, calculating the total load of the surface runoff pollution corresponding to the medium rain in the rainfall process based on the second water quantity information and the second water quality information.
Step c5, determining a second calculation sub-model based on the total load of the surface runoff pollution of the rain in each field in the rainfall process, the rainfall information of the rain in each field and the underlying surface information of the functional area corresponding to the rain in each field;
step c6, calculating the total load of the surface runoff pollution of the heavy rain in the rainfall process based on the third water quantity information and the third water quality information;
and c7, determining a third calculation sub-model based on the total load of the surface runoff pollution of each heavy rain in the rainfall process, the rainfall information of each heavy rain and the underlying surface information of the functional area corresponding to each heavy rain.
Illustratively, in an embodiment of the present application, the water is based on the amount of water and the water quality (C a ) Information, calculating to obtain the sub-rainfall surface runoff pollution load (y) of the sub-catchment area a ) The method comprises the steps of carrying out a first treatment on the surface of the Under the single rainfall condition, the rainfall surface runoff pollution output load (y) of the sub-catchment area a ) Can be calculated as shown in the following formula (9).
Wherein y is a(j) Indicating the total load of surface runoff output j pollutants in the single sub-catchment area during the rainfall,unit mg; m represents the number of rainwater drainage ports at the output end in a single sub-catchment area; c (C) t The instantaneous concentration of the effluent pollutant at the output end at the moment t is expressed in mg/L; q (Q) t The instantaneous output end outflow flow at the moment t is expressed as a unit L/s; t represents the total outflow time of the output end of the secondary field rain, and the unit is s; n represents the total number of samples of a single output port in the rainfall of the field; c (C) a(i,j) And C a(i+1,j) Represents the total concentration of j contaminants in the i and i+1 samples, respectively, in mg/L, and max (i+1) =n; q (Q) i And Q i+1 Respectively representing the outflow flow rate in units of L/s when the ith and the (i+1) th samples are collected; Δt represents the sampling interval of the ith and the (i+1) th samples in s.
Taking rainfall (16.2 mm) in a certain period as an example, runoff water sample information collected at a No. 1 outlet of a catchment area A is shown in a table 2, and pollutants shown in the table are TP.
TABLE 2
| Number of samples | T(min) | Q t (L/s) | C a (mg/L) |
| 1 | 0 | 4.8 | 0.23 |
| 2 | 5 | 4.3 | 0.21 |
| 3 | 10 | 5.8 | 0.21 |
| 4 | 15 | 6.9 | 0.11 |
| 5 | 20 | 9.8 | 0.16 |
| 6 | 25 | 8.6 | 0.11 |
| 7 | 30 | 7.6 | 0.08 |
| 8 | 40 | 5.1 | 0.07 |
| 9 | 50 | 2.5 | 0.08 |
| 10 | 60 | 0.8 | 0.07 |
The total pollution load of TP at outlet No. 1 under the rainfall condition can be calculated as follows according to equation 9: y is a(TP) = 2395.88 (mg). Calculating TP total pollution load of other 3 discharge ports of the catchment area A under the rainfall condition by using the same method, and then y of 4 discharge ports in the catchment area A a Value and total y of catchment area A a The values are shown in table 3:
TABLE 3 Table 3
| Discharge outlet | 1 | 2 | 3 | 4 | Catchment area A total |
| y a(TP) (mg) | 2395.88 | 2339.91 | 1575.77 | 1664.9 | 7976.46 |
In the embodiment of the application, the rainfall (P), rainfall duration (T) and rainfall pre-period length (D) of 15 rainfall sites under different rainfall conditions are screened, and three sub-catchments are adoptedThe area of the pad surface under 15 functional areas (S) and the cleaning frequency (L) of the pad surface under the functional areas are used for carrying out the Pearson correlation coefficient (R) 2 ) Calculation, calculation results are shown in the following table 4:
TABLE 4 Table 4
In this embodiment, pearson correlation coefficient calculation is performed on the rainfall (P), rainfall duration (T), and rainfall front dry period length (D) of 15 rainfall under each rain type, the area (S) of the mat surface under the function area of the three sub-catchment areas, and the cleaning frequency (L) of the mat surface under the function area. According to R in the table 2 As a result, a redundancy factor with higher correlation was not found, and therefore, the above-mentioned 5 kinds of environmental factors are all regarded as critical environmental factors.
In the embodiment of the application, the secondary rainfall surface runoff pollution output load (y) of the catchment area is constructed a ) And calculating a model A between the model A and the key environmental influence factors, and substituting data under different rainfall conditions into the model A for fitting. The model a can be represented by a first relational expression as expressed by the above expression (1). It should be noted that, in the model a, each variable only participates in the calculation, and when substituting into the data fitting, the same variable of different data sets needs to keep consistent units.
Taking rainfall in a certain period (16.2 mm) as an example, the P value, the T value and the D value in the catchment area A, and the S value and the L value of the underlying surface of different functional areas are shown in the table 5:
TABLE 5
| Functional area under-pad type | P(mm) | T(min) | D(d) | S(hm 2 ) | L |
| Industrial area | 16.2 | 53 | 8 | 1.32 | 0.5 |
| Residential area | 16.2 | 53 | 8 | 8.93 | 1 |
| Commercial district | 16.2 | 53 | 8 | 1.09 | 2 |
| Traffic zone | 16.2 | 53 | 8 | 2.51 | 2 |
| Greenbelt | 16.2 | 53 | 8 | 3.72 | 0 |
Based on calculated y a The values and the environmental factor data in table 5 are regarded as 1 group of variable data under the medium rain condition, 15 rainfall data (namely 45 groups of variable data) of three sub-catchments under the medium rain condition are all substituted into the model a, the rainfall data is read by using the Python language, an exponential equation is constructed by using the SciPy library, and the equation is fitted by using the rainfall data, so that the parameters in table 6 can be obtained by fitting:
TABLE 6
In the embodiment of the application, the runoff pollution load calculation model of three sub-catchment areas in the city in the middle rain process can be determined based on the environmental parameter values and the first relational expression in the table 6.
In some possible embodiments, the fourth, fifth, and sixth computational sub-models are obtained by:
step d1, acquiring rainfall information of multiple small rains, rainfall information of multiple medium rains, rainfall information of multiple large rains, total concentration of a runoff water sample in a rainfall process of each small rain, movable concentration of a runoff water sample in a rainfall process of each small rain, total concentration of a runoff water sample in a rainfall process of each small rain, movable concentration of a runoff water sample in a rainfall process of each rain, total concentration of a runoff water sample in a rainfall process of each large rain and movable concentration of a runoff water sample in a rainfall process of each large rain in a preset area.
Illustratively, the concentration of the run-off water sample is divided into a total concentration (C a ) And the migratable concentration after sedimentation stabilization (C b ) By carrying out a sedimentation experiment on the water sample, obtaining the corresponding relation between the sedimentation time and the concentration of the overlying water sample, wherein all the water samples reach sedimentation balance within 12 hours, and the concentration of the overlying water at the moment is C b 。
And d2, determining the corresponding migration parameters of the surface runoff pollution based on the total concentration and the migration concentration of the runoff water sample in the rainfall process of each small rain.
And d3, determining a fourth calculation sub-model based on the transferable parameters of the surface runoff pollution of each small rain, the rainfall information of each small rain and the underlying surface information of the functional area of each small rain.
And d4, determining the corresponding mobilizable parameters of the surface runoff pollution based on the total load and the mobilizable load of the surface runoff pollution in the rainfall process of each field.
And d5, determining a fifth calculation sub-model based on the transferable parameters of the surface runoff pollution of the rain in each field, the rainfall information of the rain in each field and the underlying surface information of the functional area of the rain in each field.
And d6, determining the corresponding mobilizable parameters of the surface runoff pollution based on the total load and the mobilizable load of the surface runoff pollution of each heavy rain in the rainfall process.
And d7, determining a sixth calculation sub-model based on the transferable parameters of the surface runoff pollution of each heavy rain, the rainfall information of each heavy rain and the underlying surface information of the functional area of each heavy rain.
Illustratively, in embodiments of the application, the concentration (C a ) Concentration (C) of migration b ) The contaminant mobility coefficient (γ) is calculated. Firstly, obtaining C of all effluent water samples in three sub-catchment areas b /C a Value and then divide each C in the same sub-catchment area b /C a Arithmetic average is carried out on the values to obtain the average mobility coefficient of the effluent water sample of the sub-catchment areaThe specific calculation method of gamma is shown in the following formula 10:
wherein, gamma j The migration coefficient of j pollutants in the water sample flowing out of the single sub-catchment area in the field rainfall is represented, and the migration coefficient is dimensionless; z represents the total number of effluent water samples collected in the field rainfall in a single sub-catchment area, and the units are each; c (C) b(i,j) And C a(i,j) The migratable concentration and the total concentration of the j contaminant in mg/L in the ith water sample are shown, respectively.
Taking rainfall of a certain period (16.2 mm) as an example, the Total Phosphorus (TP) C in each water sample at the outlet 1 of the water collecting area A b /C a The values are shown in table 7:
TABLE 7
| n | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 |
| C b | 0.14 | 0.18 | 0.15 | 0.08 | 0.06 | 0.07 | 0.05 | 0.06 | 0.07 | 0.06 |
| C a | 0.23 | 0.21 | 0.21 | 0.11 | 0.16 | 0.11 | 0.08 | 0.07 | 0.08 | 0.07 |
| C b /C a | 0.61 | 0.86 | 0.71 | 0.73 | 0.38 | 0.64 | 0.62 | 0.86 | 0.88 | 0.86 |
All C of 4 discharge ports in the catchment area A under the rainfall condition of the field b /C a Substituting the value into formula (10), and calculating to obtain gamma TP 0.72.
And constructing a calculation model B between the movable coefficient (gamma) and the key factors, substituting numerical values under different rainfall conditions into the model B for fitting, wherein the model B can be a second calculation model shown in the formula (2). It should be noted that, in the model B, each variable only participates in the calculation, and when substituting into the data fitting, the same variable of different data sets needs to keep consistent units.
Taking rainfall of a certain period (16.2 mm) as an example, the gamma obtained by calculation TP The values and the environmental factor data in table 5 above can be regarded as 1 set of variable data under the moderate rain condition, and 15 rainfall data (i.e. 45 sets of variable data) of the three sub-catchments under the moderate rain condition are all substituted into the model B, so that the parameters in table 8 can be obtained by fitting:
TABLE 8
In the related technology, a statistical model method is utilized to calculate the pollution load of the surface runoff, and generally, according to the synchronous monitoring data of the water quality and the water quantity of the surface runoff or the receiving water body, the correlation between the pollution load and related influence factors is established by analyzing a large amount of actual measurement data. The method is simple and convenient to calculate and easy to operate, but has higher accuracy requirement on measured data, and has the following problems: (1) The method based on the analysis of the surface runoff water quality and the water quantity has the problem of systematic deficiency, is biased to consider the surface confluence flushing of a local area, and ignores the influence of sediment of a drainage pipe network and the actual water inlet proportion; (2) The systematic problem can be avoided when the pollution load is estimated based on the water quality and the water quantity of the receiving water body, but the surface runoff pollution load is difficult to distinguish from other non-point source pollution loads. According to the method provided by the embodiment of the application, the runoff water sample is obtained through the rainwater drainage in the water collecting area in the preset area, and the surface runoff pollution load is calculated based on the obtained runoff water sample, so that the influence of sediment of the drainage pipe network and the actual water inlet proportion can be effectively considered, and meanwhile, the surface runoff pollution load can be distinguished from other non-point source pollution loads.
According to the method for calculating the surface runoff pollution load, the first calculation model for representing the association relation between the total surface runoff pollution load, rainfall information and functional area underlying surface information is utilized to calculate the surface runoff pollution load of the preset area, the environmental influence of the surface runoff pollution in the actual migration process is considered when the surface runoff pollution load is calculated, the rainfall information affecting the pollution load migration process in the target rainfall process of the preset area and the functional area underlying surface information are input into the first calculation model, the surface runoff pollution load of the preset area is obtained, the accuracy of the surface runoff pollution load calculation result of the preset area is effectively improved, and the problem that the calculation result is inaccurate in the scheme for calculating the surface runoff pollution load based on the statistical model method in the related art is solved.
The following describes a method for calculating the pollution load of surface runoff according to the present invention by means of a specific embodiment.
Taking the medium rain condition as an example, according to each parameter coefficient of the model A under the medium rain condition in the table 6, the total pollution load of TP output by the surface runoff of the preset area under the medium rain condition of a single field can be calculated by the following formula (11):
Taking medium rain conditions as an example, according to each parameter coefficient of the model B in the table 8, the migration coefficient of TP in single-rainfall surface runoff in the preset area under the medium rain conditions can be calculated by the following formula (12):
taking a rainfall of 13.4mm as an example, the application area (total area a=1392 hm 2 ) Total pollution load of TP under the condition of rainfall of the field (Y a ) And a migration coefficient (Γ), rainfall information and application area underlying information are shown in table 9:
TABLE 9
| Lower pad surface | P(mm) | T(min) | D(d) | S(hm 2 ) | L |
| Industrial area | 13.4 | 46 | 6 | 42 | 0.5 |
| Residential area | 13.4 | 46 | 6 | 847 | 1 |
| Commercial district | 13.4 | 46 | 6 | 79 | 2 |
| Traffic zone | 13.4 | 46 | 6 | 151 | 2 |
| Greenbelt | 13.4 | 46 | 6 | 273 | 0 |
Because the rainfall is between 10 and 25mm, the field rainfall is of the medium rain type. Substituting the parameter data in Table 9 into the data (11) can calculate the total load Y' of the runoff pollution a(TP) =0.544; substituting the parameter data in Table 9 into the data (12) can calculate the transferable parameter Γ' of the runoff pollution load TP ＝0.72。
According to the total load (Y) of rainfall surface runoff pollution a ) And a migration coefficient (Γ) to determine a rainfall surface runoff pollution migration load (Y) b ) The specific calculation mode is shown as the following formula (13):
Y b(j) ＝Y a(j) ×Γ j (13)
thus, under the rainfall conditions of table 9, the TP mobilizable load output by the application area via rainfall surface runoff is calculated as follows (14):
Y″ b(TP) ＝Y″ a(TP) ×Γ″ TP ＝0.544×0.72＝0.392(t) (14)
according to specific parameter coefficients of the model A and the model B under three rainfall conditions of light rain, medium rain and heavy rain, the total load (Y) of the output pollution of the surface runoff of the rainfall in any field of the whole year of the target area can be calculated by combining the annual calendar rainfall information of the target area a ) And a movable load (Y) b ) And then the total load (Y) of annual surface runoff output pollution of the target area can be calculated A ) And a movable load (Y) B ) The specific calculation modes can be shown in the following formulas (15) and (16).
Wherein Y is A(j) Representing the total load of j pollutants output by annual rainfall surface runoff in the water basin range of the target city; l represents the total rainfall occasions of the target city in the target year; y is Y a(i,j) And (5) representing the total load of the j pollutants output by the surface runoff in the ith rainfall in the water basin range of the target city.
Wherein Y is B(j) Representing the movable load of j pollutants output by annual rainfall surface runoff in the river basin range of the target city water body; y is Y b(i,j) And (5) representing the movable load of the j pollutants output by the surface runoff in the ith rainfall in the water basin range of the target city.
The embodiment also provides a device for calculating the surface runoff pollution load, which is used for realizing the embodiment and the preferred implementation manner, and is not described in detail. As used below, the term "module" may be a combination of software and/or hardware that implements a predetermined function. While the means described in the following embodiments are preferably implemented in software, implementation in hardware, or a combination of software and hardware, is also possible and contemplated.
The present embodiment provides a surface runoff pollution load calculation device, as shown in fig. 4, including:
the first obtaining module 401 is configured to obtain first rainfall information and first functional area underlying surface information in a target rainfall process in a preset area, where the functional area underlying surface information is used to represent distribution information of population density and human activity points in the preset area;
the first calculation module 402 is configured to input first rainfall information and first functional area underlying surface information into a first calculation model, so that the first calculation model outputs a total load of surface runoff pollution in a target rainfall process, and the first calculation model is used for representing an association relationship between the total load of surface runoff pollution, the rainfall information and the functional area underlying surface information.
In some alternative embodiments, the apparatus further comprises:
the second acquisition module is used for acquiring second rainfall information and second functional area underlying surface information in the target rainfall process in the preset area;
the second calculation module is used for inputting second rainfall information and second functional area underlying surface information into a second calculation model, so that the second calculation model outputs the transferable parameters of the surface runoff pollution in the target rainfall process, and the second calculation model is used for representing the association relationship between the transferable parameters of the surface runoff pollution, the rainfall information and the functional area underlying surface information;
The first determining module is used for determining the transferable load of the surface runoff pollution in the target rainfall process based on the total load of the surface runoff pollution in the target rainfall process and the transferable parameter of the surface runoff pollution in the target rainfall process.
In some alternative embodiments, the first computational model is calculated by:
acquiring third rainfall information of multiple rainfall processes in a preset area, total surface runoff pollution load of the multiple rainfall processes and underlying surface information of a third functional area of the multiple rainfall processes;
and constructing a first calculation model based on third rainfall information of the multiple rainfall processes, total surface runoff pollution load of the multiple rainfall processes and underlying surface information of a third functional area of the multiple rainfall processes.
In some alternative embodiments, the third rainfall information includes rainfall information, rainfall duration, and rainfall pre-dry period duration, the third functional area under-pad information includes a type of the functional area, a cleaning frequency of the functional area under-pad, and an area of the functional area under-pad, and the first calculating module 402 includes:
the first construction unit is used for constructing a first calculation model based on a first relation, third rainfall information of multiple rainfall processes, total surface runoff pollution load of multiple rainfall processes and underlying surface information of a third functional area of the multiple rainfall processes, wherein the first relation is as follows:
Wherein y is a(j) Representing the total pollution load of the surface runoff output pollutant j corresponding to any rainfall in a preset area; k represents the number of different types of underlying surfaces in a preset area; p represents the rainfall corresponding to the rainfall of the field, and x1 is an index corresponding to the rainfall; λ1 (i,j) A rainfall coefficient for the jth pollutant of the ith underlying surface; t represents the rainfall duration of the scene rainfall, x2 is an index corresponding to the rainfall duration, and lambda 2 (i,j) The rainfall duration coefficient of the jth pollutant of the ith underlying surface; d represents the length of the period before rainfall corresponding to the field rainfall, lambda 3 (i,j) The coefficient of the dry period before rain of the jth pollutant of the ith underlying surface; lambda 4 (i,j) Load correction coefficients for the jth contaminant of the ith underlying surface; s is S i Represents the i-th underlying surface area; l (L) i Represents the daily manual cleaning frequency of the ith underlying surface, lambda 5 (i,j) Is the cleaning frequency coefficient of the ith underlying surface.
In some alternative embodiments, the second computational model is calculated by:
acquiring fourth rainfall information of a plurality of rainfall processes in a preset area, transferable parameters of surface runoff pollution of the plurality of rainfall processes and underlying surface information of a fourth functional area of the plurality of rainfall processes;
and constructing a second calculation model based on fourth rainfall information of the multiple rainfall processes, transferable parameters of surface runoff pollution of the multiple rainfall processes and underlying surface information of a fourth functional area of the multiple rainfall processes.
In some alternative embodiments, the fourth rainfall information includes rainfall information and a rainfall duration, the fourth functional area under-pad information includes a type of a functional area and an area of the functional area under-pad, and the constructing the second calculation model based on the fourth rainfall information of the multiple rainfall processes, the mobilizable parameters of the surface runoff pollution of the multiple rainfall processes, and the fourth functional area under-pad information of the multiple rainfall processes includes:
constructing a second calculation model based on a second relation, fourth rainfall information of multiple rainfall processes, transferable parameters of surface runoff pollution of multiple rainfall processes and underlying surface information of a fourth functional area of the multiple rainfall processes, wherein the second relation is as follows:
wherein, gamma j Representing the migration coefficient of the pollutant j in any rainfall of the preset area; k represents the number of different types of underlying surfaces in a preset area; p is the rainfall in the rainfall process; t represents the rainfall duration of the scene rainfall process; x3 is an index of rainfall intensity; lambda 6 (i,j) Is the rainfall intensity coefficient of the j pollutant of the i-th underlying surface; lambda 7 (i,j) Is the migration correction coefficient of the jth pollutant of the ith underlying surface; s is S i Represents the i-th underlying surface area; a represents the total area of the preset area.
In some alternative embodiments, the first calculation model includes a first calculation sub-model for calculating a total amount of surface runoff pollution corresponding to a rainfall process of the light rain in the preset area, a second calculation sub-model for calculating a total amount of surface runoff pollution corresponding to a rainfall process of the rain in the preset area, and a third calculation sub-model for calculating a total amount of surface runoff pollution corresponding to a rainfall process greater than the preset area; a first computing module 402 comprising:
a first determining unit configured to determine a rainfall level of a target rainfall process based on rainfall information, the rainfall level including light rain, medium rain, and heavy rain;
the first calculation unit is used for inputting the first rainfall information and the first functional area underlying surface information into the first calculation sub-model when the rainfall level of the target rainfall process is small rain, so that the first calculation sub-model outputs the total load of the surface runoff pollution in the target rainfall process;
the second calculation unit is used for inputting the first rainfall information and the first functional area underlying surface information into the second calculation sub-model when the rainfall level of the target rainfall process is middle rainfall, so that the second calculation sub-model outputs the total load of the surface runoff pollution in the target rainfall process;
And the third calculation unit is used for inputting the first rainfall information and the first functional area underlying surface information into the third calculation sub-model when the rainfall level of the target rainfall process is heavy rain, so that the third calculation sub-model outputs the total load of the surface runoff pollution in the target rainfall process.
In some alternative embodiments, the second computing model includes a fourth computing sub-model, a fifth computing sub-model, and a sixth computing sub-model, the second computing module including:
the fourth calculation unit is used for inputting the second rainfall information and the second functional area underlying surface information into the fourth calculation sub-model when the rainfall level of the target rainfall process is small rain, so that the fourth calculation sub-model outputs the transferable parameters of the surface runoff pollution in the target rainfall process;
the fifth calculation unit is used for inputting the second rainfall information and the second functional area underlying surface information into the fifth calculation sub-model when the rainfall level of the target rainfall process is middle rainfall, so that the fifth calculation sub-model outputs the transferable parameters of the surface runoff pollution in the target rainfall process;
and the sixth calculation unit is used for inputting the second rainfall information and the second functional area underlying surface information into the sixth calculation sub-model when the rainfall level of the target rainfall process is heavy rain, so that the sixth calculation sub-model outputs the transferable parameters of the surface runoff pollution in the target rainfall process.
In some alternative embodiments, the first, second, and third computational sub-models are calculated by:
acquiring rainfall information of multiple small rains in a preset area, rainfall information of multiple rains in multiple rains, rainfall information of multiple big rains, first water quantity information and first water quality information acquired by at least one preset monitoring point in the rainfall process of each small rain, second water quantity information and second water quality information acquired by at least one preset monitoring point in the rainfall process of each rain, and third water quantity information and third water quality information acquired by at least one preset monitoring point in the rainfall process of each big rain; calculating the total load of the surface runoff pollution of the corresponding small rain in the rainfall process based on the first water quantity information and the first water quality information; determining a first calculation sub-model based on the total load of surface runoff pollution of each small rain in the rainfall process, rainfall information of each small rain and underlying surface information of a functional area corresponding to each small rain; calculating the total load of the surface runoff pollution of the medium rain in the rainfall process based on the second water quantity information and the second water quality information; determining a second calculation sub-model based on the total load of the surface runoff pollution of the rain in each field in the rainfall process, the rainfall information of the rain in each field and the underlying surface information of the functional area corresponding to the rain in each field; calculating the total load of the surface runoff pollution of the heavy rain in the rainfall process based on the third water quantity information and the third water quality information; and determining a third calculation sub-model based on the total load of the surface runoff pollution of each heavy rain in the rainfall process, the rainfall information of each heavy rain and the underlying surface information of the functional area corresponding to each heavy rain.
In some alternative embodiments, the fourth, fifth, and sixth computational sub-models are obtained by:
acquiring rainfall information of multiple small rains in a preset area, rainfall information of multiple big rains, total concentration of a runoff water sample in a rainfall process of each small rain, movable concentration of a runoff water sample in a rainfall process of each small rain, total concentration of a runoff water sample in a rainfall process of each rain, movable concentration of a runoff water sample in a rainfall process of each rain, total concentration of a runoff water sample in a rainfall process of each big rain and movable concentration of a runoff water sample in a rainfall process of each big rain; determining corresponding migration parameters of surface runoff pollution based on the total concentration and the migration concentration of the runoff water sample in the rainfall process of each small rain; determining a fourth calculation sub-model based on the transferable parameters of the surface runoff pollution of each small rain, the rainfall information of each small rain and the underlying surface information of the functional area of each small rain; determining corresponding migration parameters of surface runoff pollution based on the total concentration and the migration concentration of the runoff water sample in the rainfall process of each field of rain; determining a fifth calculation sub-model based on the mobilizable parameters of the surface runoff pollution of the rains in each field, the rainfall information of the rains in each field and the underlying surface information of the functional area of the rains in each field; determining corresponding migration parameters of surface runoff pollution based on the total concentration and the migration concentration of the runoff water sample in the rainfall process of each heavy rain; and determining a sixth calculation sub-model based on the transferable parameters of the surface runoff pollution of each heavy rain, the rainfall information of each heavy rain and the underlying surface information of the functional area of each heavy rain.
Further functional descriptions of the above respective modules and units are the same as those of the above corresponding embodiments, and are not repeated here.
The surface runoff pollution load calculation device in this embodiment is presented in the form of a functional unit, where the unit refers to an ASIC (Application Specific Integrated Circuit ) circuit, a processor and a memory executing one or more software or fixed programs, and/or other devices that can provide the above functions.
The embodiment of the invention also provides computer equipment, which is provided with the surface runoff pollution load calculating device shown in the figure 4.
Referring to fig. 5, fig. 5 is a schematic structural diagram of a computer device according to an alternative embodiment of the present invention, as shown in fig. 5, the computer device includes: one or more processors 10, memory 20, and interfaces for connecting the various components, including high-speed interfaces and low-speed interfaces. The various components are communicatively coupled to each other using different buses and may be mounted on a common motherboard or in other manners as desired. The processor may process instructions executing within the computer device, including instructions stored in or on memory to display graphical information of the GUI on an external input/output device, such as a display device coupled to the interface. In some alternative embodiments, multiple processors and/or multiple buses may be used, if desired, along with multiple memories and multiple memories. Also, multiple computer devices may be connected, each providing a portion of the necessary operations (e.g., as a server array, a set of blade servers, or a multiprocessor system). One processor 10 is illustrated in fig. 5.
The processor 10 may be a central processor, a network processor, or a combination thereof. The processor 10 may further include a hardware chip, among others. The hardware chip may be an application specific integrated circuit, a programmable logic device, or a combination thereof. The programmable logic device may be a complex programmable logic device, a field programmable gate array, a general-purpose array logic, or any combination thereof.
Wherein the memory 20 stores instructions executable by the at least one processor 10 to cause the at least one processor 10 to perform the methods shown in implementing the above embodiments.
The memory 20 may include a storage program area that may store an operating system, at least one application program required for functions, and a storage data area; the storage data area may store data created according to the use of the computer device, etc. In addition, the memory 20 may include high-speed random access memory, and may also include non-transitory memory, such as at least one magnetic disk storage device, flash memory device, or other non-transitory solid-state storage device. In some alternative embodiments, memory 20 may optionally include memory located remotely from processor 10, which may be connected to the computer device via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
Memory 20 may include volatile memory, such as random access memory; the memory may also include non-volatile memory, such as flash memory, hard disk, or solid state disk; the memory 20 may also comprise a combination of the above types of memories.
The computer device also includes a communication interface 30 for the computer device to communicate with other devices or communication networks.
The embodiments of the present invention also provide a computer readable storage medium, and the method according to the embodiments of the present invention described above may be implemented in hardware, firmware, or as recordable storage medium, or as a second computer code stored in a remote storage medium or a non-transitory machine-readable storage medium and to be stored in a local storage medium downloaded through a network, so that the method described herein may be stored on such software process on a storage medium using a general purpose computer, a special purpose processor, or programmable or dedicated hardware. The storage medium can be a magnetic disk, an optical disk, a read-only memory, a random access memory, a flash memory, a hard disk, a solid state disk or the like; further, the storage medium may also comprise a combination of memories of the kind described above. It will be appreciated that a computer, processor, microprocessor controller or programmable hardware includes a storage element that can store or receive software or computer code that, when accessed and executed by the computer, processor or hardware, implements the methods illustrated by the above embodiments.
Although embodiments of the present invention have been described in connection with the accompanying drawings, various modifications and variations may be made by those skilled in the art without departing from the spirit and scope of the invention, and such modifications and variations fall within the scope of the invention as defined by the appended claims.
Claims (15)
1. A method for calculating a surface runoff pollution load, the method comprising:
acquiring first rainfall information and first functional area underlying surface information in a target rainfall process in a preset area, wherein the functional area underlying surface information is used for representing distribution information of population density and human activity characteristics in the preset area;
inputting the first rainfall information and the first functional area underlying surface information into a first calculation model, so that the first calculation model outputs the total load of the surface runoff pollution in the target rainfall process, and the first calculation model is used for representing the association relation between the total load of the surface runoff pollution, the rainfall information and the functional area underlying surface information.
2. The method according to claim 1, wherein the method further comprises:
acquiring second rainfall information and second functional area underlying surface information in a target rainfall process in a preset area;
Inputting the second rainfall information and the second functional area underlying surface information into a second calculation model, so that the second calculation model outputs the transferable parameters of the surface runoff pollution in the target rainfall process, and the second calculation model is used for representing the association relationship between the transferable parameters of the surface runoff pollution, the rainfall information and the functional area underlying surface information;
determining the transferable load of the surface runoff pollution in the target rainfall process based on the total load of the surface runoff pollution in the target rainfall process and the transferable parameter of the surface runoff pollution in the target rainfall process.
3. The method of claim 1, wherein the first computational model is calculated by:
acquiring third rainfall information of multiple rainfall processes in a preset area, total surface runoff pollution load of the multiple rainfall processes and underlying surface information of a third functional area of the multiple rainfall processes;
and constructing a first calculation model based on the third rainfall information of the multiple rainfall processes, the total surface runoff pollution load of the multiple rainfall processes and the underlying surface information of the third functional area of the multiple rainfall processes.
4. The method of claim 3, wherein the third rainfall information includes rainfall information, a duration of rainfall, and a duration of a dry period before rainfall, the third functional area under-pad information includes a type of a functional area, a cleaning frequency of the functional area under-pad, and an area of the functional area under-pad, and the step of constructing the first calculation model based on the third rainfall information of the multiple rainfall processes, a total surface runoff pollution load of the multiple rainfall processes, and the third functional area under-pad information of the multiple rainfall processes includes:
constructing a first calculation model based on a first relation, third rainfall information of the multiple rainfall processes, total surface runoff pollution load of the multiple rainfall processes and underlying surface information of a third functional area of the multiple rainfall processes, wherein the first relation is as follows:
wherein y is a(j) Representing the total pollution load of the surface runoff output pollutant j corresponding to any rainfall in a preset area; k represents the number of different types of underlying surfaces in a preset area; p represents the rainfall corresponding to the rainfall of the field, and x1 is an index corresponding to the rainfall; λ1 (i,j) A rainfall coefficient for the jth pollutant of the ith underlying surface; t represents the rainfall duration of the scene rainfall, x2 is an index corresponding to the rainfall duration, and lambda 2 (i,j) The rainfall duration coefficient of the jth pollutant of the ith underlying surface; d represents the fieldThe length of the dry period before rainfall corresponding to secondary rainfall, lambda 3 (i,j) The coefficient of the dry period before rain of the jth pollutant of the ith underlying surface; lambda 4 (i,j) Load correction coefficients for the jth contaminant of the ith underlying surface; s is S i Represents the i-th underlying surface area; l (L) i Represents the daily manual cleaning frequency of the ith underlying surface, lambda 5 (i,j) Is the cleaning frequency coefficient of the ith underlying surface.
5. The method of claim 2, wherein the second calculation model is calculated by:
acquiring fourth rainfall information of a plurality of rainfall processes in a preset area, transferable parameters of surface runoff pollution of the plurality of rainfall processes and underlying surface information of a fourth functional area of the plurality of rainfall processes;
and constructing a second calculation model based on the fourth rainfall information of the multiple rainfall processes, the transferable parameters of the surface runoff pollution of the multiple rainfall processes and the underlying surface information of the fourth functional area of the multiple rainfall processes.
6. The method of claim 5, wherein the fourth rainfall information comprises rainfall information and a rainfall duration, the fourth functional area under-pad information comprises a type of functional area, an area of functional area under-pad, and constructing a second calculation model based on the fourth rainfall information of the multiple rainfall processes, the mobilizable parameters of surface runoff pollution of the multiple rainfall processes, and the fourth functional area under-pad information of the multiple rainfall processes comprises:
Constructing a second calculation model based on a second relation, fourth rainfall information of the multiple rainfall processes, transferable parameters of surface runoff pollution of the multiple rainfall processes and underlying surface information of a fourth functional area of the multiple rainfall processes, wherein the second relation is as follows:
wherein, gamma j Representing the migration coefficient of the pollutant j in any rainfall of the preset area; k represents the number of different types of underlying surfaces in a preset area; p is the rainfall in the rainfall process; t represents the rainfall duration of the scene rainfall process; x3 is an index of rainfall intensity; lambda 6 (i,j) Is the rainfall intensity coefficient of the j pollutant of the i-th underlying surface; lambda 7 (i,j) Is the migration correction coefficient of the jth pollutant of the ith underlying surface; s is S i Represents the i-th underlying surface area; a represents the total area of the preset area.
7. The method of claim 4, wherein the first computing model comprises a first computing sub-model for computing a total amount of load of surface runoff pollution corresponding to a rainfall process of a heavy rain in a preset area, a second computing sub-model for computing a total amount of load of surface runoff pollution corresponding to a rainfall process of a heavy rain in a preset area, and a third computing sub-model for computing a total amount of load of surface runoff pollution corresponding to a rainfall process of a heavy rain in a preset area; inputting the first rainfall information and the first functional area underlying surface information into a first calculation model, so that the first calculation model outputs the total load of surface runoff pollution in the target rainfall process, and the method comprises the following steps:
Determining a rainfall level of the target rainfall process based on the rainfall information, wherein the rainfall level comprises light rain, medium rain and heavy rain;
when the rainfall level of the target rainfall process is light rain, inputting the first rainfall information and the first functional area underlying surface information into a first calculation sub-model, so that the first calculation sub-model outputs the total load of surface runoff pollution in the target rainfall process;
when the rainfall level of the target rainfall process is middle rain, inputting the first rainfall information and the first functional area underlying surface information into a second calculation sub-model, so that the second calculation sub-model outputs the total load of surface runoff pollution in the target rainfall process;
and when the rainfall level of the target rainfall process is heavy rain, inputting the first rainfall information and the first functional area underlying surface information into a third calculation sub-model, so that the third calculation sub-model outputs the total load of the surface runoff pollution in the target rainfall process.
8. The method of claim 6, wherein the second computing model includes a fourth computing sub-model, a fifth computing sub-model, and a sixth computing sub-model, the step of inputting the second rainfall information and second functional area underlying information into the second computing model such that the second computing model outputs the mobilizable parameters of surface runoff pollution during the target rainfall process, comprising:
When the rainfall level of the target rainfall process is light rain, the second rainfall information and the second functional area underlying surface information are input into a fourth calculation sub-model, so that the fourth calculation sub-model outputs the transferable parameters of the surface runoff pollution in the target rainfall process;
when the rainfall level of the target rainfall process is middle rain, inputting the second rainfall information and the second functional area underlying surface information into a fifth calculation sub-model, so that the fifth calculation sub-model outputs the transferable parameters of the surface runoff pollution in the target rainfall process;
and when the rainfall level of the target rainfall process is heavy rain, inputting the second rainfall information and the second functional area underlying surface information into a sixth calculation sub-model, so that the sixth calculation sub-model outputs the transferable parameters of the surface runoff pollution in the target rainfall process.
9. The method of claim 7, wherein the first, second, and third computational sub-models are calculated by:
acquiring rainfall information of multiple small rains in a preset area, rainfall information of multiple rains in multiple rains, rainfall information of multiple big rains, first water quantity information and first water quality information acquired by at least one preset monitoring point in the rainfall process of each small rain, second water quantity information and second water quality information acquired by at least one preset monitoring point in the rainfall process of each rain, and third water quantity information and third water quality information acquired by at least one preset monitoring point in the rainfall process of each big rain;
Calculating the total load of the surface runoff pollution of the corresponding small rain in the rainfall process based on the first water quantity information and the first water quality information;
determining a first calculation sub-model based on the total load of surface runoff pollution of each small rain in the rainfall process, rainfall information of each small rain and underlying surface information of a functional area corresponding to each small rain;
calculating the total load of the surface runoff pollution of the medium rain in the rainfall process based on the second water quantity information and the second water quality information;
determining a second calculation sub-model based on the total load of the surface runoff pollution of the rain in each field in the rainfall process, the rainfall information of the rain in each field and the underlying surface information of the functional area corresponding to the rain in each field;
calculating the total load of the surface runoff pollution of the heavy rain in the rainfall process based on the third water quantity information and the third water quality information;
and determining a third calculation sub-model based on the total load of the surface runoff pollution of each heavy rain in the rainfall process, the rainfall information of each heavy rain and the underlying surface information of the functional area corresponding to each heavy rain.
10. The method of claim 8, wherein the fourth, fifth, and sixth computational sub-models are obtained by:
Acquiring rainfall information of multiple small rains in a preset area, rainfall information of multiple big rains, total concentration of a runoff water sample in a rainfall process of each small rain, movable concentration of a runoff water sample in a rainfall process of each small rain, total concentration of a runoff water sample in a rainfall process of each rain, movable concentration of a runoff water sample in a rainfall process of each rain, total concentration of a runoff water sample in a rainfall process of each big rain and movable concentration of a runoff water sample in a rainfall process of each big rain;
determining corresponding migration parameters of surface runoff pollution based on the total concentration and the migration concentration of the runoff water sample in the rainfall process of each small rain;
determining a fourth calculation sub-model based on the transferable parameters of the surface runoff pollution of each small rain, the rainfall information of each small rain and the underlying surface information of the functional area of each small rain;
determining corresponding migration parameters of surface runoff pollution based on the total concentration and the migration concentration of the runoff water sample in the rainfall process of each field of rain;
determining a fifth calculation sub-model based on the mobilizable parameters of the surface runoff pollution of the rains in each field, the rainfall information of the rains in each field and the underlying surface information of the functional area of the rains in each field;
Determining corresponding migration parameters of surface runoff pollution based on the total concentration and the migration concentration of the runoff water sample in the rainfall process of each heavy rain;
and determining a sixth calculation sub-model based on the transferable parameters of the surface runoff pollution of each heavy rain, the rainfall information of each heavy rain and the underlying surface information of the functional area of each heavy rain.
11. A surface runoff pollution load computing device, the device comprising:
the first acquisition module is used for acquiring first rainfall information and first functional area underlying surface information in a target rainfall process in a preset area, wherein the functional area underlying surface information is used for representing the population density in the preset area and the distribution information of human activity points;
the first calculation module is used for inputting the first rainfall information and the first functional area underlying surface information into a first calculation model, so that the first calculation model outputs the total load of the surface runoff pollution in the target rainfall process, and the first calculation model is used for representing the association relation between the total load of the surface runoff pollution, the rainfall information and the functional area underlying surface information.
12. The apparatus of claim 11, wherein the apparatus further comprises:
The second acquisition module is used for acquiring second rainfall information and second functional area underlying surface information in the target rainfall process in the preset area;
the second calculation module is used for inputting the second rainfall information and the second functional area underlying surface information into a second calculation model, so that the second calculation model outputs the transferable parameters of the surface runoff pollution in the target rainfall process, and the second calculation model is used for representing the association relationship between the transferable parameters of the surface runoff pollution, the rainfall information and the functional area underlying surface information;
the first determining module is used for determining the transferable load of the surface runoff pollution in the target rainfall process based on the total load of the surface runoff pollution in the target rainfall process and the transferable parameter of the surface runoff pollution in the target rainfall process.
13. The apparatus of claim 11, wherein the first computational model is calculated by:
acquiring third rainfall information of multiple rainfall processes in a preset area, total surface runoff pollution load of the multiple rainfall processes and underlying surface information of a third functional area of the multiple rainfall processes;
And constructing a first calculation model based on the third rainfall information of the multiple rainfall processes, the total surface runoff pollution load of the multiple rainfall processes and the underlying surface information of the third functional area of the multiple rainfall processes.
14. A computer device, comprising:
a memory and a processor communicatively connected to each other, the memory having stored therein computer instructions, the processor executing the computer instructions to perform the surface runoff pollution load calculation method of any one of claims 1 to 10.
15. A computer-readable storage medium having stored thereon computer instructions for causing a computer to execute the surface runoff pollution load calculation method according to any one of claims 1 to 10.
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