CN115408646A - River pollutant monitoring method and system based on big data - Google Patents
River pollutant monitoring method and system based on big data Download PDFInfo
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Abstract
The invention provides a river pollutant monitoring method and system based on big data, the method includes monitoring each data parameter in the monitored river section in real time; constructing a distribution function model of Brownian motion of suspended particles in the monitored river channel water body in the monitored river section, and calculating the sedimentation velocity of the ith suspended particle at the time t; optimizing by adopting a genetic neural algorithm to obtain the optimal suspended particle concentration of each suspended particle; constructing a nonlinear relation model of each heavy metal to be monitored in the monitored river section and the average optimal concentration of suspended particles in the river section; and judging whether the obtained concentrations of various heavy metal elements and the optimal suspended matter meet the pollutant discharge standard, and if so, discharging the sewage in the river section. The river pollutant monitoring method based on big data provided by the invention carries out comprehensive evaluation on the water quality in the monitored river section by means of data analysis, and can quantitatively and intuitively master the overall condition of the water environment quality.
Description
Technical Field
The invention belongs to the technical field of water pollutant emission monitoring, and particularly relates to a passenger flow pollutant monitoring method and system based on big data.
Background
The problem of water environment is a phenomenon accompanying the production and living activities of human beings, and is intensified with the progress of industrialization and urbanization. The influx of pollutants caused by human activities exceeds the natural bearing capacity of the environment and the speed and capacity of ecological self-restoration, so that the balance is lost, the quality of ecological environment is influenced, and simultaneously, the pollutants also influence the human and activities. The water environment problem of rivers is closely related to the production and life of human beings, dissolved and suspended pollutants in water bodies always exist objectively for natural water bodies such as rivers, lakes and the like, and artificial activities are main factors for increasing the content of the pollutants and deteriorating other water quality evaluation standards.
In the prior art, there are also methods and systems for analyzing the water quality of rivers partially by using big data statistics, for example, a safety early warning method and system based on water environment monitoring data disclosed in chinese patent 202111532765.6, in the technical scheme, the method mainly obtains and collects water quality index data of urban rivers, and pays attention to whether the obtained index information is in accordance with the calculation of surface dissolved oxygen index, so as to perform early warning monitoring on the water quality of the monitored rivers.
Disclosure of Invention
Aiming at the defects, the invention provides a river pollutant monitoring method and system based on big data. The invention constructs the suspended particles of the suspended matters in the monitored river section into a mathematical model of the brownian motion which moves disorderly and conforms to the normal distribution, and adopts the genetic neural algorithm to optimize to obtain the optimal suspended particle concentration of each suspended particle when the settling velocity of each suspended particle reaches the steady state, thereby constructing a fitness function for checking the iterative optimization result
And limiting the threshold value of the concentration of the suspended particles, effectively screening the data of iteration entering the next generation, improving the finally obtained optimal concentration of each suspended particle, finally constructing a nonlinear relation model of the concentrations of various heavy metal elements and the average optimal concentration of the suspended particles, effectively providing an accurate calculation model of the concentrations of various heavy metal elements, and simultaneously limiting the heavy metalThe element concentration and the suspended particle concentration meet the monitoring standard that the sewage can be discharged only when the sewage is discharged in different water application scenes or discharge scenes, and the clean state of the discharged water quality is further ensured.
The invention provides the following technical scheme: the river pollutant monitoring method based on the big data comprises the following steps:
s1: monitoring various data parameters in the monitored river section in real time;
s2: according to the data obtained by the step S1, establishing a distribution function model of Brownian motion of the suspended particles in the monitored river section in the monitored river water body, and further calculating the sedimentation velocity of the ith suspended particle at the time t;
s3: optimizing by adopting a genetic neural algorithm to obtain the optimal suspended particle concentration of each suspended particle when the settling velocity of each suspended particle reaches a steady state;
s4: constructing a nonlinear relation model of the concentration of each monitored heavy metal element in the monitored river section and the average optimal concentration of the suspended particles in the river section based on the optimal concentration of each suspended particle calculated in the step S3;
s5: and judging whether the concentrations of various heavy metal elements calculated based on the nonlinear relation model in the step S4 meet the pollutant discharge standard or not, judging whether the optimal suspended matter concentrations in the river reach the pollutant discharge standard or not based on the calculation in the step S3, if so, discharging the sewage in the river reach, and otherwise, repeating the steps S1-S4.
Further, the S2 step includes the steps of:
s21: in the process of monitoring various data parameters in the monitored river section in real time in the step S1, the flow velocity v of the water molecule particles in the monitored river section at the moment t is monitored in real time w (T), T Water temperature T in monitored river section r (t) and the flow velocity of the ith suspended particle at t in the monitored river section in the x-axis
Flow velocity in y axis
And z-axis flow velocity
Calculating the mean free path of the ith suspended particle in the time slot T in the monitored river section
And mean free path lambda of water molecule particles in time slot T in the river section being monitored w (ii) a Calculating the kanehin correction coefficient of the ith suspended particle according to the Stokes law
And water molecule kangning Han correction coefficient C (d) w )；
S22: according to the calculation result of the step S21, calculating the concentration of suspended particles of the ith suspended particle in the monitored river section
i＝1,2,…,N；
S23: according to the calculation result of the step S22, a distribution function phi (p) of the Brownian motion of the ith suspended particle in the monitored river channel water body is constructed i )；
S24: according to the calculation result of the step S23 and the flow speed of the ith suspended particle at the t moment in the monitored river section in the x-axis, which is obtained by monitoring in real time in the step S21
Flow velocity in y-axis
And speed of flow in z-axis
Calculating the sedimentation velocity of the ith suspended particle at the t moment
Further, in the step S21, a mean free path of the ith suspended particle in the monitored river segment is calculated
And mean free path lambda of water molecule particles in time slot T in the river section being monitored w Respectively as follows:
wherein k is Boltzmann constant, P is atmospheric pressure,
for the diameter of the i-th suspended particle in the river section to be monitored, d w The diameter of water molecules in the monitored river section;
the flow rate of the ith suspended particle at time t;
calculating the kanehan correction coefficient of the ith suspended particle
And water molecule kangning Han correction coefficient C (d) w ) Respectively as follows:
further, in the step S22, the concentration of suspended particles in the i-th suspended particle in the monitored river section is calculated
The formula of (1) is as follows:
where ρ is w To monitor the density of water molecules within the river section,
for the diameter of the i-th suspended particle in the river section to be monitored, d w The diameter of water molecules in the monitored river section is measured.
Further, in the step S23, the distribution function of the i-th suspended particle in the monitored river water body is constructed as follows:
wherein p is i Representing the ith suspended particle in the monitored river section, N is the total number of suspended particles,
is the average value of the particle sizes of all suspended particles,
σ is the geometric standard deviation of the suspended particle distribution.
Further, the air conditioner is provided with a fan,
wherein g is gravity acceleration, and g =9.8m in general 2 /s。
Further, the S3 step includes the steps of:
s31: constructing a genetic neural network iterative optimization model for limiting the optimal suspended particle concentration of each suspended particle when the settling velocity of each suspended particle reaches a steady state:
s32: iterative optimization is carried out on the concentration of the suspended particles of each suspended particle, and a fitness function for checking the iterative optimization result is constructed
Wherein | | | purple hair 2 For the calculation of the norm in the form of the euclidean,
flow velocity vector of the ith suspended particle at the time t +1
Flow velocity vector of ith suspended particle at t moment
European norm therebetween，
Flow velocity vector of water molecule particles in monitored river section for t +1 moment
And the Euclidean norm between the flow velocity vectors of the water molecule particles in the monitored river section at the moment t;
and
respectively the flow velocity of the ith suspended particle in the x-axis
Vector of (d) and flow velocity in y-axis
Vector sum of (2) and z-axis flow velocity
The vector of (a);
s33: judging the fitness function value of the suspended particle concentration of each suspended particle after iteration
And if so, stopping iteration and taking the iteration result as the optimal suspended particle concentration of each suspended particle, otherwise, repeating the steps S31-S32.
Further, the nonlinear relation model of each monitored heavy metal in the monitored river section and the average optimal concentration of suspended particles in the river section, which is constructed in the step S4, is as follows:
wherein M is j Is the jth heavy metal element, D (M) j ) Is the concentration of the jth heavy metal element,
the optimal suspended particle concentration of the ith suspended particle calculated and obtained in the step S3, A j Multiplication coefficient for the constructed non-linear relationship between the concentration of the jth heavy metal element and the average optimum suspended particle concentration, B j Power exponent coefficient (H) of nonlinear relation between concentration of j-th heavy metal element and average optimal suspended particle concentration j An error term of a nonlinear relation between the concentration of the constructed jth heavy metal element and the average optimal suspended particle concentration is used; the heavy metal elements monitored comprise Fe, mn, ni, cr, cu or Cd.
Further, the pollutant discharge standard in the step S5 is integrated wastewater discharge standard GB8978-1996.
The invention also provides a river pollutant monitoring system based on big data by adopting the method, which comprises a parameter acquisition module, a river pollutant motion parameter calculation module and a main control module;
the parameter acquisition module is used for monitoring various data parameters in the monitored river section in real time;
the river pollutant motion parameter calculation module is used for constructing a distribution function model of Brownian motion of suspended particles in the monitored river channel water body in the monitored river section according to data obtained by real-time monitoring, and further calculating the sedimentation velocity of the ith suspended particle at the time t;
the main control module is used for obtaining the optimal suspended particle concentration of each suspended particle when the settling velocity of each suspended particle reaches a steady state by adopting genetic neural algorithm optimization, constructing a nonlinear relation model of each monitored heavy metal element concentration in the monitored river section and the average optimal suspended particle concentration in the river section based on each calculated optimal suspended particle concentration, further judging whether each calculated heavy metal element concentration based on the nonlinear relation model meets the pollutant discharge standard, and judging whether the optimal suspended particle concentration in the river section meets the pollutant discharge standard or not so as to control whether the sewage in the river section is discharged or not.
The invention has the beneficial effects that:
1. the river pollutant monitoring method based on big data provided by the invention monitors the speed of each direction in the three-dimensional coordinate system of the suspended particles in the monitored river section in real time
And
and velocity vector
And
and the flow velocity v of water molecule particles w (t) and water molecule flow velocity vector
And then the suspended particles in the monitored river section are simulated into disordered Brownian motion to further influence the turbidity in the monitored river section, most of heavy metal elements in the river are adsorbed and attached to the suspended particles, further the suspension state of the suspended particles also influences the concentration of the heavy metal elements in the monitored river section, and the mean free path of the ith suspended particles in the monitored river section is calculated
And mean free path lambda of water molecule particles in time slot T in the river section being monitored w And further calculating the kanehan correction coefficient of the ith suspended particle according to the Stokes law
And water molecule kangning Han correction coefficient C (d) w ) Further obtaining the concentration of the ith suspended particle in the monitored river section according to the calculation results of the steps S21 and S22
The distribution function phi (p) of the i-th suspended particle in the brownian motion of the monitored river water body can be constructed according to the suspended particle concentration of each suspended particle i ) And furthermore, the monitoring of the suspended particles in the monitored river section is effectively converted into a Brownian motion model, so that the difficult problems of modeling of the suspended particle concentration on the TSS concentration monitoring and the heavy metal concentration monitoring of the monitored river and the difficult problem of improving the calculation accuracy are solved.
2. The invention provides a river pollutant monitoring method based on big data, which is characterized in that when the optimal suspended particle concentration of each suspended particle is optimized by adopting a genetic neural algorithm, the two conditions that the sedimentation velocity of each suspended particle in the vertical direction is equal by limiting the Brownian motion of each suspended particle, and the sedimentation velocity of each suspended particle at each moment in a monitored time slot T is constant are further limited, the sedimentation velocity of each suspended particle is constrained to reach a steady state, the maximum suspended particle concentration of each suspended particle is further obtained by calculation, namely, when each suspended particle stops the Brownian motion and gradually subsides downwards, the suspended particle concentration of each suspended particle in the maximum monitored river section at the moment, which is limited by an objective function under the limited conditions, can be obtained by limiting the fitness function of the genetic neural network, and the suspended particle concentration of each suspended particle in the maximum monitored river section at the moment is defined by the objective function under the limited conditions, namely, the suspended particle concentration is the suspended particle concentration meeting the iterative optimization model of the genetic neural network
The optimal suspended particle concentration of each suspended particle effectively restricts the Brownian motion disorder state of each suspended particle in the genetic neural algorithm optimization process to achieve dynamic stable order, and an effective restriction model is constructed for the iterative optimization of the genetic neural algorithm.
3. The invention providesThe river pollutant monitoring method based on big data is provided, when the optimal suspended particle concentration of each suspended particle is optimized by adopting a genetic neural algorithm, the flow velocity vector of the ith suspended particle at the t +1 moment is further calculated
Flow velocity vector of ith suspended particle at t moment
Between the euclidean norm
Flow velocity vector of water molecule particles in monitored river section at t +1 moment
And the Euclidean norm between the flow velocity vectors of the water molecule particles in the monitored river section at the moment t
And the difference between the particle size of the ith suspended particle and the particle size of the ith suspended particle
Constructing fitness function for checking iterative optimization result
And the threshold value of the concentration of the suspended particles is limited to be 0.87, so that data of iteration entering the next generation are effectively screened, and the optimal concentration of the suspended particles of each finally obtained suspended particle is improved.
4. The river pollutant monitoring method based on big data provided by the invention finally constructs a nonlinear relation model of various heavy metal element concentrations and the average optimal suspended particle concentration, effectively provides an accurate calculation model of various heavy metal element concentrations, and simultaneously limits the monitoring standard that the heavy metal element concentration and the suspended particle concentration can be discharged only when the heavy metal element concentration and the suspended particle concentration meet the sewage discharge standard, thereby further ensuring the clean state of the discharged water quality.
5. The river pollutant monitoring method based on big data provided by the invention is used for comprehensively evaluating the water quality in the monitored river section by means of data analysis, computer technology and information technology and taking the theoretical thought of the system as guidance, and can quantitatively and intuitively master the overall condition of the water environment quality and the distribution characteristics of water quality indexes.
6. The river pollutant monitoring method based on big data provided by the invention estimates and researches the water ecological pressure of the natural water body through water quality evaluation and river water quality modeling, and has certain significance for mastering the total pollution condition of the water body and protecting the water environment.
Drawings
The invention will be described in more detail hereinafter on the basis of embodiments and with reference to the accompanying drawings. Wherein:
FIG. 1 is a schematic flow chart of a river pollutant monitoring method based on big data provided by the invention;
fig. 2 is a schematic structural diagram of a river pollutant monitoring system based on big data provided by the invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
As shown in fig. 1, the river pollutant monitoring method based on big data provided by the invention comprises the following steps:
s1: monitoring various data parameters in the monitored river section in real time;
s2: according to the data obtained by the step S1, the distribution function model of the Brownian motion of the suspended particles in the monitored river section in the monitored river water body is constructed, and then the sedimentation velocity of the ith suspended particle at the moment t is calculated
S3: optimizing by adopting a genetic neural algorithm to obtain the optimal suspended particle concentration of each suspended particle when the settling velocity of each suspended particle reaches a steady state;
s4: based on the optimal suspended particle concentration of each suspended particle calculated in the step S3, constructing a nonlinear relation model of the concentration of each monitored heavy metal element in the monitored river section and the average optimal suspended particle concentration in the river section; the average optimal suspended particle concentration in the river section is obtained by further calculating the optimal suspended particle concentration of each suspended particle obtained by calculation in the step S3;
s5: and judging whether the concentrations of various heavy metal elements calculated based on the nonlinear relation model in the step S4 meet the pollutant discharge standard or not, judging whether the optimal suspended matter concentrations in the river section and the river section calculated based on the step S3 meet the pollutant discharge standard or not, if so, discharging the sewage in the river section, and otherwise, repeating the steps S1-S4.
As a preferred embodiment of the present invention, the S2 step includes the steps of:
s21: in the process of monitoring various data parameters in the monitored river section in real time in the step S1, the flow velocity v of the water molecule particles in the monitored river section at the moment t is monitored in real time w (T), T Water temperature T in monitored river section r (t) and the flow velocity of the ith suspended particle in the x-axis at the time t in the monitored river section
Flow velocity in y axis
And speed of flow in z-axis
Calculating the mean free path of the ith suspended particle in the time slot T in the monitored river section
And mean free path lambda of water molecule particles in time slot T in the river section being monitored w (ii) a Calculating the kanehan correction coefficient of the ith suspended particle according to the Stokes law
And water molecule kangning Han correction coefficient C (d) w )；
S22: according to the calculation result of the step S21, calculating the concentration of the suspended particles of the ith suspended particle in the monitored river section
i＝1,2,…,N；
S23: according to the calculation result of the step S22, a distribution function phi (p) of the i-th suspended particle in the Brownian motion in the monitored river water body is constructed i )；
S24: according to the calculation result of the step S23 and the flow speed of the ith suspended particle at the t moment in the monitored river section in the x-axis monitored in real time in the step S21
Flow velocity in y axis
And speed of flow in z-axis
Calculating the sedimentation velocity of the ith suspended particle at the t moment
The particle detection sensor based on the MEMS sensor can be adopted to perform real-time monitoring on various parameters in the steps S1 and S21.
As another preferred embodiment of the present invention, in the step S21, the mean free path of the i-th suspended particle in the monitored river section is calculated
And mean free path lambda of water molecule particles in time slot T in the river section being monitored w Respectively as follows:
wherein k is Boltzmann constant, P is atmospheric pressure, and P is generally 0.1 Mpa,
for the diameter of the i-th suspended particle in the monitored section of river, d w The diameter of water molecules in the monitored river section;
the flow rate of the ith suspended particle at time t;
calculating kaning Han correction coefficient of ith suspended particle
And water molecule kangning Han correction coefficient C (d) w ) Respectively as follows:
as another preferred embodiment of the present invention, step S22In the step of calculating the concentration of suspended particles of the ith suspended particle in the monitored river section
The formula of (1) is as follows:
wherein ρ w For the density of water molecules in the river section being monitored, in general p w ＝1.0×10 3 kg/m 3 ，
For the diameter of the i-th suspended particle in the monitored section of river, d w The diameter of water molecules in the monitored river section is measured.
As another preferred embodiment of the present invention, in step S23, the distribution function of the i-th suspended particle in the monitored river water body is constructed as follows:
wherein p is i Representing the ith suspended particle in the monitored river section, N is the total number of the suspended particles,
is the average of the particle sizes of all suspended particles,
σ is the geometric standard deviation of the suspended particle distribution.
As another preferred embodiment of the present invention, the sedimentation velocity of the i-th suspended particle at time t is calculated in step S2
The calculation formula of (a) is as follows:
wherein g is gravity acceleration, and g =9.8m in general 2 /s。
As another preferred embodiment of the present invention, the S3 step includes the steps of:
s31: constructing a genetic neural network iterative optimization model for limiting the optimal suspended particle concentration of each suspended particle when the settling velocity of each suspended particle reaches a steady state:
first one of the defining conditions:
specifically, the sedimentation velocity of the 1 st suspended particle at the time t is equal to that of the ith, i +1 th to nth suspended particles, that is, the sedimentation velocity of each suspended particle in the vertical direction when performing brownian motion is equal; second limitation condition:
it is shown that the settling velocity of the individual suspended particles at the respective time within the monitored time slot T is defined to be constant, so that by means of these two defining conditions, settling in the vertical direction is shown when the individual suspended particles in the monitored river section are subjected to brownian motionThe speeds are unchanged and equal, namely the settling rate of each suspended particle reaches a steady state, the maximum suspended particle concentration of each suspended particle is obtained through calculation, namely the suspended particle concentration of each suspended particle in the maximum monitored river section at the moment limited by the target function under the limited condition is obtained through limiting the fitness function of the genetic neural network when each suspended particle stops Brownian motion and gradually settles downwards, and the iterative optimization model of the genetic neural network is satisfied
The optimum suspended particle concentration of each suspended particle of (a);
s32: iterative optimization is carried out on the concentration of the suspended particles of each suspended particle, and a fitness function for checking the iterative optimization result is constructed
Wherein | | | purple hair 2 For the calculation of the norm in the form of the euclidean,
flow velocity vector of i-th suspended particle at time t +1
Flow velocity vector of ith suspended particle at t time
The euclidean norm between the two,
flow velocity vector of water molecule particles in monitored river section for t +1 moment
And the Euclidean norm between the flow velocity vectors of the water molecule particles in the monitored river section at the time t;
and
respectively the flow velocity of the ith suspended particle in the x-axis
Vector of (d) and flow velocity in y-axis
Vector sum of (2) and z-axis flow velocity
The vector of (a);
because of d w Keeping the same, the model constructed by the steps S21 and S22 in the step S2 can be used to calculate the density of the suspended particles of the ith suspended particle
Finally fall into
By setting the fitness function of the genetic neural algorithm, when the Euclidean norm of the flow velocity vector of the ith suspended particle is larger and larger at different moments, the Euclidean norm of the flow velocity vector of the water molecule particle is smaller and smaller at different moments, and the difference between the particle diameter of the ith suspended particle and the particle diameter of the ith suspended particle is smaller and smaller
Increasing in hours; namely, it is
The size of the composite material is getting larger and larger,
the size of the material is getting smaller and smaller,
as the time goes by, the more and more hours,
gradually become larger while
The maximum suspended particle concentration of each suspended particle can be screened out through the fitness function, namely the maximum suspended particle concentration of each suspended particle is the optimal suspended particle concentration of each suspended particle;
s33: judging the fitness function value of the suspended particle concentration of each suspended particle after iteration
And if the concentration is larger than the threshold value of the suspended particle concentration by 0.87, stopping iteration and taking the iteration result as the optimal suspended particle concentration of each suspended particle, otherwise, repeating the steps S31-S32.
As another preferred embodiment of the present invention, the nonlinear relation model of each heavy metal monitored in the monitored river section and the average optimal concentration of suspended particles in the river section constructed in the step S4 is as follows:
wherein M is j Is a j heavy metal element, D (M) j ) The concentration of the jth heavy metal element,
the optimum suspended particle concentration of the ith suspended particle calculated and obtained in the step S3, A j For the constructed j heavy metal elements are concentratedMultiplication factor of the non-linear relationship between the degree and the mean optimum suspended particle concentration, B j A power exponent coefficient of a nonlinear relation between the concentration of the jth heavy metal element and the average optimal suspended particle concentration, H j An error term of a nonlinear relation between the concentration of the constructed jth heavy metal element and the average optimal suspended particle concentration is used; the heavy metal elements monitored comprise Fe, mn, ni, cr, cu or Cd.
According to MatLab simulation, the nonlinear relations between the Fe element, the Mn element, the Ni element, the Cr element, the Cu element and the Cd element constructed by the method provided by the application and the average optimal suspended particle concentration can be obtained as follows:
as a preferred real-time mode of the present invention, the pollutant discharge standard in the S5 step is "Integrated wastewater discharge Standard" (GB 8978-1996). The pollutant emission standard in the step S5 can also be set according to different emission standards of the application range of the monitored river, such as urban domestic water, industrial water, electroplating wastewater and livestock breeding water, therefore, the river pollutant monitoring method based on big data provided by the application can monitor and limit the heavy metal concentration and the emission index of suspended particulate matters of different water emission standards according to the steps provided by the method by monitoring various parameters in the monitored river section in real time, has a wide application range and has better applicability of monitoring the emission of industrial and agricultural sewage pollutants in the river.
As shown in fig. 2, the river pollutant monitoring system based on big data provided by the invention and adopting the method comprises a parameter acquisition module, a river pollutant motion parameter calculation module and a main control module;
the parameter acquisition module is used for monitoring various data parameters in the monitored river section in real time;
the river pollutant motion parameter calculation module is used for constructing a distribution function model of Brownian motion of suspended particles in the monitored river section in the monitored river channel water body according to data obtained by real-time monitoring, and further calculating the sedimentation velocity of the ith suspended particle at the time t;
and the main control module is used for optimizing by adopting a genetic neural algorithm to obtain the optimal concentration of each suspended particle when the sedimentation velocity of each suspended particle reaches a steady state, constructing a nonlinear relation model of each monitored heavy metal element concentration in the monitored river section and the average optimal suspended particle concentration in the river section based on the calculated optimal suspended particle concentration of each suspended particle, further judging whether each heavy metal element concentration calculated based on the nonlinear relation model meets the pollutant discharge standard, and judging whether the optimal suspended particle concentration in the river section meets the pollutant discharge standard so as to control whether the sewage in the river section is discharged.
The present invention also provides an electronic device comprising:
a processor;
a memory for storing processor-executable instructions;
the processor is configured to call the instructions stored in the memory to execute the intelligent hydraulic engineering management method based on ecological monitoring.
The invention also provides a computer readable storage medium, which stores computer program instructions, and is characterized in that the computer program instructions are executed by a processor to realize the intelligent hydraulic engineering management method based on ecological monitoring.
In an exemplary embodiment, the electronic device may be implemented by one or more Application Specific Integrated Circuits (ASICs), digital Signal Processors (DSPs), digital Signal Processing Devices (DSPDs), programmable Logic Devices (PLDs), field Programmable Gate Arrays (FPGAs), controllers, micro-controllers, microprocessors or other electronic components for performing the above-described methods.
In an exemplary embodiment, a non-transitory computer-readable storage medium, such as a memory, including computer program instructions executable by a processor of an electronic device to perform the above-described method is also provided.
The present disclosure may be systems, methods, and/or computer program products. The computer program product may include a computer-readable storage medium having computer-readable program instructions embodied thereon for causing a processor to implement various aspects of the present disclosure.
The computer readable storage medium may be a tangible device that can hold and store the instructions for use by the instruction execution device. The computer readable storage medium may be, for example, but not limited to, an electronic memory device, a magnetic memory device, an optical memory device, an electromagnetic memory device, a semiconductor memory device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a Static Random Access Memory (SRAM), a portable compact disc read-only memory (CD-ROM), a Digital Versatile Disc (DVD), a memory stick, a floppy disk, a mechanical coding device, such as punch cards or in-groove projection structures having instructions stored thereon, and any suitable combination of the foregoing. Computer-readable storage media as used herein is not to be interpreted as a transitory signal per se, such as a radio wave or other freely propagating electromagnetic wave, an electromagnetic wave propagating through a waveguide or other transmission medium (e.g., optical pulses through a fiber optic cable), or an electrical signal transmitted through an electrical wire.
The computer-readable program instructions described herein may be downloaded from a computer-readable storage medium to a respective computing/processing device, or to an external computer or external storage device via a network, such as the internet, a local area network, a wide area network, and/or a wireless network. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers and/or edge servers. The network adapter card or network interface in each computing/processing device receives the computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in a computer-readable storage medium in the respective computing/processing device.
The computer program instructions for carrying out operations of the present disclosure may be assembler instructions, instruction Set Architecture (ISA) instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The computer-readable program instructions may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider). In some embodiments, the electronic circuitry that can execute the computer-readable program instructions implements aspects of the present disclosure by utilizing the state information of the computer-readable program instructions to personalize the electronic circuitry, such as a programmable logic circuit, a Field Programmable Gate Array (FPGA), or a Programmable Logic Array (PLA).
Various aspects of the present disclosure are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the disclosure. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer readable program instructions.
These computer-readable program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. These computer-readable program instructions may also be stored in a computer-readable storage medium that can direct a computer, programmable data processing apparatus, and/or other devices to function in a particular manner, such that the computer-readable medium storing the instructions comprises an article of manufacture including instructions which implement the function/act specified in the flowchart and/or block diagram block or blocks.
The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other devices to produce a computer implemented process such that the instructions which execute on the computer, other programmable apparatus or other devices implement the functions/acts specified in the flowchart and/or block diagram block or blocks.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems that perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The computer program product may be embodied in hardware, software or a combination thereof. In an alternative embodiment, the computer program product is embodied in a computer storage medium, and in another alternative embodiment, the computer program product is embodied in a Software product, such as a Software Development Kit (SDK), or the like.
While specific embodiments of the disclosure have been described above, the above description is illustrative, not exhaustive, and not limited to the disclosed embodiments. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is chosen in order to best explain the principles of the embodiments, the practical application, or improvements made to the technology in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein.
Claims (10)
1. The river pollutant monitoring method based on the big data is characterized by comprising the following steps of:
s1: monitoring various data parameters in the monitored river section in real time;
s2: according to the data obtained by the step S1, establishing a distribution function model of Brownian motion of the suspended particles in the monitored river section in the monitored river water body, and further calculating the sedimentation velocity of the ith suspended particle at the time t;
s3: optimizing by adopting a genetic neural algorithm to obtain the optimal suspended particle concentration of each suspended particle when the settling velocity of each suspended particle reaches a steady state;
s4: constructing a nonlinear relation model of the concentration of each monitored heavy metal element in the monitored river section and the average optimal concentration of the suspended particles in the river section based on the optimal concentration of each suspended particle calculated in the step S3;
s5: and judging whether the concentrations of various heavy metal elements calculated based on the nonlinear relation model in the step S4 meet the pollutant discharge standard or not, judging whether the optimal suspended matter concentrations in the river reach the pollutant discharge standard or not based on the calculation in the step S3, if so, discharging the sewage in the river reach, and otherwise, repeating the steps S1-S4.
2. The big-data based river pollutant monitoring method according to claim 1, wherein the S2 step comprises the steps of:
s21: in the process of monitoring various data parameters in the monitored river section in real time in the step S1, the flow velocity v of the water molecule particles in the monitored river section at the moment t is monitored in real time w (T), T Water temperature T in monitored river section r (t) and the flow velocity of the ith suspended particle at t in the monitored river section in the x-axis
Flow velocity in y-axis
And speed of flow in z-axis
Calculating the mean free path of the ith suspended particle in the time slot T in the monitored river section
And the mean free path lambda of the water molecule particles in the time slot T in the monitored river section w (ii) a Calculating the kanehan correction coefficient of the ith suspended particle according to the Stokes law
And water molecule kangning Han correction coefficient C (d) w )；
S22: according to the calculation result of the step S21, calculating the concentration of suspended particles of the ith suspended particle in the monitored river section
S23: according to the calculation result of the step S22, a distribution function phi (p) of the Brownian motion of the ith suspended particle in the monitored river channel water body is constructed i )；
S24: according to the calculation result of the step S23 and the flow speed of the ith suspended particle at the t moment in the monitored river section in the x-axis, which is obtained by monitoring in real time in the step S21
Flow velocity in y axis
And speed of flow in z-axis
Calculating the sedimentation velocity of the ith suspended particle at the t moment
3. The river pollutant monitoring method based on big data as claimed in claim 2, wherein in the step S21, the mean free path of the ith suspended particle in the monitored river section is calculated
And mean free path lambda of water molecule particles in time slot T in the river section being monitored w Respectively as follows:
wherein k is Boltzmann constant, P is atmospheric pressure,
for the diameter of the i-th suspended particle in the river section to be monitored, d w The diameter of water molecules in the monitored river section;
the flow rate of the ith suspended particle at time t;
calculating kaning Han correction coefficient of ith suspended particle
And water molecule kangning Han correction coefficient C (d) w ) Respectively as follows:
4. the river pollutant monitoring method based on big data as claimed in claim 2, wherein in the step S22, the suspended particle concentration of the ith suspended particle in the monitored river section is calculated
The formula of (1) is as follows:
wherein ρ w To monitor the density of water molecules within the river section,
monitoring the diameter of the ith suspended particle in the river section for pants, d w Is the diameter of water molecules in the monitored river section.
5. The big data based river pollutant monitoring method according to claim 4, wherein in the step S23, the distribution function of the Brownian motion of the i-th suspended particle in the monitored river water body is constructed as follows:
wherein p is i Representing the ith suspended particle in the monitored river section, N is the total number of the suspended particles,
is the average of the particle sizes of all suspended particles,
σ is the geometric standard deviation of the suspended particle distribution.
6. The big data based river pollutant monitoring method according to claim 5, wherein the S2 step is implemented by calculating the sedimentation velocity of the ith suspended particle at the t moment
The formula (c) is as follows:
wherein g is gravity acceleration, and g =9.8m in general 2 /s。
7. The big-data based river pollutant monitoring method according to claim 2, wherein the S3 step comprises the steps of:
s31: constructing a genetic neural network iterative optimization model for limiting the optimal suspended particle concentration of each suspended particle when the settling velocity of each suspended particle reaches a steady state:
s32: iterative optimization is carried out on the concentration of the suspended particles of each suspended particle, and a fitness function for checking the iterative optimization result is constructed
Wherein | | | purple hair 2 For the calculation of the norm in the form of the euclidean,
flow velocity vector of i-th suspended particle at time t +1
Flow velocity vector of ith suspended particle at t moment
The euclidean norm between the two,
flow velocity vector of water molecule particles in monitored river section for t +1 moment
And the Euclidean norm between the flow velocity vectors of the water molecule particles in the monitored river section at the time t;
and
respectively the flow velocity of the ith suspended particle in the x-axis
Vector of (d) and flow velocity in y-axis
Vector sum of (2) and z-axis flow velocity
The vector of (a);
s33: judging the fitness function value of the suspended particle concentration of each suspended particle after iteration
And if so, stopping iteration and taking the iteration result as the optimal suspended particle concentration of each suspended particle, otherwise, repeating the steps S31-S32.
8. The big-data based river pollutant monitoring method according to claim 1, wherein the non-linear relationship model of each heavy metal monitored in the monitored river section and the average optimal concentration of suspended particles in the river section, which is constructed in the step S4, is as follows:
wherein M is j Is a j heavy metal element, D (M) j ) The concentration of the jth heavy metal element,
the optimal suspended particle concentration of the ith suspended particle calculated and obtained in the step S3, A j Multiplication coefficient for the constructed non-linear relationship between the concentration of the jth heavy metal element and the average optimum suspended particle concentration, B j A power exponent coefficient of a nonlinear relation between the concentration of the jth heavy metal element and the average optimal suspended particle concentration, H j An error term of a nonlinear relation between the concentration of the constructed jth heavy metal element and the average optimal suspended particle concentration is used; the heavy metal elements monitored comprise Fe, mn, ni, cr, cu or Cd.
9. The river pollutant monitoring method based on big data according to claim 1, characterized in that the pollutant discharge standard in the step S5 is integrated wastewater discharge standard GB8978-1996.
10. A river pollutant monitoring system based on big data using the method of any one of claims 1 to 9, comprising a parameter acquisition module, a river pollutant movement parameter calculation module, a main control module;
the parameter acquisition module is used for monitoring various data parameters in the monitored river section in real time;
the river pollutant motion parameter calculation module is used for constructing a Brownian motion distribution function model of suspended particles in the monitored river channel water body in the monitored river section according to data obtained by real-time monitoring, and further calculating the sedimentation velocity of the ith suspended particles at the t moment;
the main control module is used for obtaining the optimal concentration of each suspended particle when the sedimentation velocity of each suspended particle reaches a steady state by adopting genetic neural algorithm optimization, constructing a nonlinear relation model of each monitored heavy metal element concentration in the monitored river section and the average optimal suspended particle concentration in the river section based on the calculated optimal suspended particle concentration of each suspended particle, further judging whether each heavy metal element concentration calculated based on the nonlinear relation model meets the pollutant discharge standard, and judging whether the optimal suspended particle concentration in the river section meets the pollutant discharge standard or not so as to control whether the sewage in the river section is discharged or not.
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| CN117422271B (en) * | 2023-11-07 | 2024-05-14 | 南通恒源自控工程有限公司 | Pipe network scheduling adjustment method and system based on water quality data |
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