CN117106849B - Urban air microorganism ecological distribution monitoring method - Google Patents
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Abstract
The invention relates to the technical field of air microorganism ecological distribution monitoring, and discloses a city air microorganism ecological distribution monitoring method, which comprises the following steps: collecting historical urban air microorganism distribution concentration data and constructing a static urban air microorganism grid refinement level calculation model; constructing an urban air microorganism ecological distribution influence index system; constructing an urban air microorganism grid refinement level self-adaptive regulation model to obtain an urban air microorganism grid map; constructing a dynamic diffusion model of urban air microorganisms to obtain a minimized urban grid air microorganism concentration difference value; and carrying out quick optimization solving on the urban air microorganism dynamic diffusion model to obtain an air microorganism direction diffusion probability vector, and adjusting and calculating the air microorganism concentration in grids of different areas according to the air microorganism direction diffusion probability vector obtained by optimization so as to obtain the air microorganism ecological distribution of the whole urban area.
Description
Technical Field
The invention relates to the technical field of air microorganism ecological distribution monitoring, in particular to a city air microorganism ecological distribution monitoring method.
Background
Although the national measures for treating air pollution are continuously increased, the current environmental air quality situation is still severe. Therefore, the health influence and health risk of environmental air pollution exposure need to be accurately evaluated, and targeted intervention measures are adopted to better protect the health of people. The exposure level of the environmental air pollution of the crowd is accurately estimated, and the spatial distribution characteristics of the microorganism concentration in the environmental air are needed to be mastered at first. The existing method for acquiring the concentration of pollutants in the ambient air mainly adopts a direct monitoring method, has the advantages of directly and accurately acquiring the concentration value of the pollutants in the ambient air at a monitoring point, has the defects of consuming manpower and material resources, and is often unable to accurately acquire the concentration of the pollutants in the air at the position of a research crowd because the quantity and coverage range of the monitoring point positions limited by expenses are limited. Aiming at the problem, the invention provides a method for monitoring urban air microorganism ecological distribution, which realizes quick and accurate monitoring of the urban air microorganism ecological distribution by self-adaptive grid division and quick model optimization.
Disclosure of Invention
In view of the above, the invention provides a method for monitoring urban air microorganism ecological distribution, which aims at: 1) The method is characterized in that an urban air microorganism grid refinement level self-adaptive adjustment model is built, each feature vector is formed into one point in the urban air microorganism grid refinement level self-adaptive adjustment model through a graph model technology, membership degree relation among feature vectors is formed as edges in the urban air microorganism grid refinement level self-adaptive adjustment model, static grid refinement levels and urban air microorganism ecological distribution influence indexes are used as inputs, dynamically adjusted grid refinement levels are output, self-adaptive grid division of urban air microorganism distribution is achieved, and urban air microorganism ecological distribution can be described in a finer and self-adaptive mode; 2) Constructing an air microorganism diffusion matrix, determining an urban air microorganism optimization objective function, determining an urban air microorganism diffusion probability vector by minimizing an urban grid air microorganism concentration difference value, realizing formal description of an urban air microorganism dynamic diffusion process, integrating the dynamic migration process of air microorganisms into a distribution monitoring process, and more accurately realizing accurate monitoring of air microorganism distribution; 3) The local search directions of the multiple processors are calculated in parallel, and the local search directions calculated by the processors are integrated globally to ensure that each processor has the latest global search direction information, so that the urban air microorganism dynamic diffusion model is rapidly optimized and solved to obtain an air microorganism direction diffusion probability vector, the rapid optimization model is achieved, and the purpose of improving the distribution monitoring efficiency is achieved.
In order to achieve the above purpose, the invention provides a method for monitoring urban air microorganism ecological distribution, which comprises the following steps:
s1: collecting historical urban air microorganism distribution concentration data, constructing a static urban air microorganism grid refinement level calculation model, calculating a static grid refinement level at the current moment according to the historical microorganism distribution concentration by the calculation model, and gridding an air microorganism area to be monitored according to the calculated static grid refinement level;
s2: constructing an urban air microorganism ecological distribution influence index system, wherein the index system comprises temperature, humidity, weather and wind data;
s3: constructing an adaptive regulation model of the grid refinement level of the urban air microorganisms, wherein the model takes a static grid refinement level and an influence index system of urban air microorganism ecological distribution as input and takes a grid refinement level after dynamic regulation as output;
s4: further gridding the air microorganism area to be monitored according to the calculated dynamic grid refinement level to dynamically adjust the area grid so as to obtain an urban air microorganism grid map;
s5: constructing a dynamic diffusion model of urban air microorganisms, wherein the dynamic diffusion model takes the directional diffusion probability of the air microorganisms in grids as an independent variable, takes the minimum urban grid air microorganism concentration difference value as a target, and takes the adjacency relationship among regional grids as a limiting condition;
S6: and carrying out quick optimization solving on the urban air microorganism dynamic diffusion model to obtain an air microorganism direction diffusion probability vector, adjusting and calculating air microorganism concentrations in different grids according to the air microorganism direction diffusion probability vector obtained by optimization, and obtaining microorganism ecological distribution of the whole urban area by integrating the air microorganism concentrations in different grid areas, wherein a biosensor is a specific implementation mode for detecting the air microorganism concentrations in the grid areas.
As a further improvement of the present invention:
optionally, the step S1 constructs a static urban air microorganism grid refinement level calculation model, including:
the grid refinement level calculation formula is:
；
wherein:representing the maximum air microorganism concentration value in the selected set;
m represents the grid air microorganism concentration value of the ith row and the jth columnAir microorganism concentration value of adjacent upper, lower, left and right grids->，/>，/>，/>Difference of differenceThe average value;
a grid static refinement level representing an ith row and jth column grid;
when (when)At the time, the air microorganism concentration value representing the mesh and the surrounding mesh was 75ug/m 3 The grids are fused into a large grid;
when (when)When the concentration of microorganisms in the air of the grid is higher than 115ug/m 3 And the average difference of the air microorganism concentration of the grid and the surrounding grids is more than 5ug/m 3 And is not higher than 10ug/m 3 Indicating that the values of the air microorganism concentration of the grids are in an unhealthy range, and the grids need to be subdivided;
when (when)When the concentration of microorganisms in the air of the grid is higher than 115ug/m 3 And the average difference of the air microorganism concentration of the grid and the surrounding grids is more than 10ug/m 3 Indicating that the grid air microorganism concentration value is in a severely unhealthy condition, the grid needs to be subdivided into minimum grids;
when (when)When the grid state is maintained unchanged. In the embodiment of the invention, a biosensor is used as an air microorganism concentration monitoring device, a city is divided into a plurality of urban air microorganism grids, the urban air microorganism concentration monitoring device is used for only acquiring the fuzzy urban air microorganism concentration distribution due to the fact that the grid area of the divided urban air microorganism grids is larger due to the limited acquisition precision of the urban air microorganism concentration monitoring device, and the urban air microorganism concentration monitoring device is based on urban air on the basis of the data monitored by the urban air microorganism concentration monitoring deviceThe microbial concentration neighborhood distribution of the microbial grids performs fine-grained grid division on the urban area to obtain more accurate grid air microbial concentration, and further obtain more accurate urban air microbial concentration distribution.
Optionally, constructing an urban air microorganism ecological distribution influence index system in the step S2, which comprises the following steps:
the method comprises the steps of constructing an urban air microorganism ecological distribution influence index system, wherein the urban air microorganism ecological distribution influence index system comprises temperature characteristics, humidity characteristics, weather characteristics and wind power characteristic data, the temperature characteristics represent temperature values in grids, the humidity characteristics represent humidity values in the grids, the weather characteristics represent weather conditions in the grids, the weather conditions in sunny days, cloudy days, rainy days, middle rain days and snow days are respectively set to be 1, 2, 3, 4 and 5, and the wind power characteristics represent wind power values in the grids.
Optionally, constructing an adaptive adjustment model of the urban air microorganism grid refinement level in the step S3, which comprises the following steps:
the urban air microorganism grid refinement level self-adaptive regulation model is a graph model, and each characteristic vectorThe method comprises the steps that a point in an urban air microorganism grid refinement level self-adaptive adjustment model is used, membership degree relation among feature vectors is an edge in the urban air microorganism grid refinement level self-adaptive adjustment model, input of the urban air microorganism grid refinement level self-adaptive adjustment model is a static grid refinement level and an urban air microorganism ecological distribution influence index, and output of the urban air microorganism grid refinement level self-adaptive adjustment model is a grid refinement level after dynamic adjustment;
The dynamic adjustment grid refinement level flow comprises the following steps:
s31: the self-adaptive regulation model of the urban air microorganism grid refinement level calculates the probability of the level of each dimension feature in the index features, and the probability of the input feature belonging to the level r is as follows:
wherein: />Representing the%>Dimension feature relative level->Membership of->；
Representing the +.>Standard deviation of dimensional characteristics;
representing the%>A mean value of the dimensional features;
representation level->Is>A mean value of the dimensional features;
e represents a natural index;
representing the probability that the input feature belongs to level r;
s32: and selecting the level with the highest probability as the grid refinement level after the current grid is dynamically adjusted.
Optionally, in the step S4, the area grid is dynamically adjusted by further gridding the air microorganism area to be monitored according to the calculated dynamic grid refinement level, so as to obtain an urban air microorganism grid map, which includes:
calculating grid side length according to the grid refinement level, determining the size of each grid, dividing the urban area into different grid units according to the grid side length obtained by dynamic calculation, wherein the specific calculation formula is as follows:
wherein: />Representing the original grid side length;
Representing a grid refinement level;
representing the edge length of the adaptively processed grid.
Optionally, constructing a dynamic diffusion model of urban air microorganisms in the step S5, including:
s51: constructing an air microorganism diffusion matrix, wherein the specific calculation formula is as follows:
wherein: />Representing the number of grids within the monitored area;
a probability vector representing the diffusion of airborne microorganisms by grid c into between its adjacent n adjacent grids;
representing a grid index;
representing adjacent grid indexes;
s52: determining an urban air microorganism optimizing target function according to the constructed air microorganism diffusion matrix, wherein the optimizing target determines an urban air microorganism diffusion probability vector by minimizing the urban grid air microorganism concentration difference value, and a specific calculation formula is as follows:
wherein: />Representing the air microorganism concentration value in the grid C;
indicating the concentration of airborne microorganisms that actually diffuse out of the grid C. According to the embodiment of the invention, the grid to be refined is refined according to the environmental characteristics, the refined grid is subjected to microbial diffusion by combining with the diffusion probability of microorganisms in the air, so that finer and more accurate urban air microorganism concentration distribution gridding representation under the environmental influence is obtained, and the ecological distribution monitoring result of the urban air microorganisms is obtained by dynamically modeling the air microorganism diffusion process according to the refined grid.
Optionally, in the step S6, a rapid optimization solution is performed on the urban air microorganism dynamic diffusion model to obtain an air microorganism direction diffusion probability vector, which includes:
the solving to obtain the airborne microorganism direction diffusion probability vector comprises the following steps:
s61: selecting an initial urban air microorganism diffusion probability vectorCalculating initial residual errorSetting search direction +.>Iteration count k=0;
s62: iteratively updating according to the steps (1) to (5);
in the step (1), each processor calculates the respective local search direction in parallel, and the calculation formula is as follows:
wherein:
coefficients representing the calculated search direction for determining the search direction in the next iteration;
indicating the search direction of the current round,
the residual error of the current round is represented,
representing the residual error of the previous round;
step (2) the local search direction calculated by each processorPerforming global communication and calculating a mean search direction to ensure that each processor has the latest global search direction information;
step (3), calculating the iteration step length of the current round according to the calculated average value searching direction, wherein the calculation formula is as follows:
wherein:
representing a symmetric positive coefficient matrix;
representation matrix- >And search direction->For calculating a step size and updating a residual error;
updating urban air microorganism diffusion probability vectors and residual errors according to the calculated iteration step length:
；
step (5) checking whether the residual size meets a threshold condition or reaches the maximum iteration number, if so, stopping iteration, otherwise, returning to the step (1) to perform iterative calculation;
according to the air microorganism direction diffusion probability vector obtained by optimization, the air microorganism concentration in grids of different areas is adjusted and calculated, and the microorganism ecological distribution of the whole urban area is obtained by integrating the air microorganism concentration of different grid areas, comprising the following steps:
and adjusting and calculating air microorganism concentration values of different grids according to the calculated optimal urban air microorganism diffusion probability vector, wherein the calculation formula is as follows:
wherein: />Representing the current airborne microorganism concentration of grid C;
representing the current airborne microorganism concentration of grid j;
representing the number of grids adjacent to grid C;
representing the probability of airborne microorganism diffusion of grid c to its neighboring grid i;
representing the probability of airborne microorganism diffusion of adjacent grid j to grid C;
representing the air microorganism concentration after grid C update.
In order to solve the above-mentioned problems, the present invention also provides an electronic apparatus including:
a memory storing at least one instruction;
the communication interface is used for realizing the communication of the electronic equipment;
and the processor executes the instructions stored in the memory to realize the urban air microorganism ecological distribution monitoring method.
In order to solve the above problems, the present invention also provides a computer-readable storage medium having at least one instruction stored therein, the at least one instruction being executed by a processor in an electronic device to implement the urban air microorganism ecological distribution monitoring method described above.
Compared with the prior art, the invention provides a method for monitoring urban air microorganism ecological distribution, which has the following advantages:
firstly, the scheme provides an urban air microorganism grid refinement level self-adaptive adjustment model, which comprises the following steps ofUrban air microorganism grid refinement level self-adaptive regulation model is a graph model, and each feature vectorThe method comprises the steps that a point in an urban air microorganism grid refinement level self-adaptive adjustment model is used, membership degree relation among feature vectors is an edge in the urban air microorganism grid refinement level self-adaptive adjustment model, input of the urban air microorganism grid refinement level self-adaptive adjustment model is a static grid refinement level and an urban air microorganism ecological distribution influence index, and output of the urban air microorganism grid refinement level self-adaptive adjustment model is a grid refinement level after dynamic adjustment;
The dynamic grid refinement level adjustment process comprises the steps that firstly, the probability of the level of each dimension feature in index features is calculated through an urban air microorganism grid refinement level self-adaptive adjustment model, and then the probability of the input feature belonging to the level r is as follows:
wherein: />Representing the%>Dimension feature relative level->Membership of->，/>Representing the +.>Standard deviation of dimensional characteristics->Representing the%>Mean value of dimensional characteristics>Representation level->Is>Mean value of dimensional features, e represents natural index, +.>Representing the probability that the input features belong to the level r, and selecting the level with the maximum probability as a grid refinement level after the current grid is dynamically adjusted; then, carrying out gridding treatment on the urban area according to the calculated dynamic grid refinement level to obtain an urban air microorganism grid graph, calculating the grid side length according to the grid refinement level, and determining the size of each grid, wherein a specific calculation formula is as follows:
wherein: />Representing the original grid side length,/->Representing grid refinement level,/->Representing the edge length of the adaptively processed grid.
Meanwhile, the scheme provides a dynamic diffusion method of urban air microorganisms, which constructs an air microorganism diffusion matrix, and the specific calculation formula is as follows:
Wherein: />Representing the number of meshes in the monitored area, +.>A probability vector representing the diffusion of the air microorganisms of grid c between its adjacent n adjacent grids,/->Representing the grid index->Representing adjacent grid indexes, determining an urban air microorganism optimizing target function according to the constructed air microorganism diffusion matrix, and determining an urban air microorganism diffusion probability vector by minimizing an urban grid air microorganism concentration difference value by the optimizing target, wherein a specific calculation formula is as follows:
wherein: />Represents the air microorganism concentration value in grid C, < + >>The method for obtaining the air microorganism direction diffusion probability vector by expressing the concentration value of the air microorganism actually diffused out of the grid C and then carrying out quick optimization solution on the urban air microorganism dynamic diffusion model comprises the following steps: the solution is carried out to obtain an airborne microorganism direction diffusion probability vector, and an initial urban airborne microorganism diffusion probability vector is selected>Calculating an initial residual +.>Setting search direction +.>Iteration count k=0; iterative updating according to the steps (1) to (5): step by stepIn the step (1), each processor calculates the respective local search direction in parallel, and the calculation formula is as follows:
wherein->Coefficients representing calculated search direction for determining search direction in the next iteration, +. >Indicates the search direction of the current round, +.>Residual representing current round, ++>Representing the residual error of the previous round, step (2) calculating the local search direction +.>Performing global communication and calculating a mean search direction to ensure that each processor has the latest global search direction information; step (3), calculating the iteration step length of the current round according to the calculated average value searching direction, wherein the calculation formula is as follows:
wherein: />Coefficient matrix representing symmetrical positive definite, +.>Representation matrix->And search direction->For calculating the product ofStep size and updating residual errors; updating urban air microorganism diffusion probability vectors and residual errors according to the calculated iteration step length:
，/>the method comprises the steps of carrying out a first treatment on the surface of the Step (5) checking whether the residual size meets a threshold condition or reaches the maximum iteration number, if so, stopping iteration, otherwise, returning to the step (1) to perform iterative calculation; returning the solution vector obtained by the final solution +.>As an optimal urban air microorganism diffusion probability vector; finally, according to the optimized air microorganism direction diffusion probability vector, the air microorganism concentration in different grids is adjusted and calculated, and the calculation formula is as follows:
wherein: / >Represents the current airborne microorganism concentration of grid C, < >>Represents the current airborne microorganism concentration of grid j, < >>Representing the number of grids adjacent to grid C,representing the probability of airborne microorganism diffusion of grid c to its neighboring grid i, < >>Represents the probability of airborne microorganism diffusion of the adjacent grid j to grid C,/>And (5) representing the air microorganism concentration after updating the grid C, and further obtaining the air microorganism ecological distribution. According to the scheme, the grid to be refined is refined according to the environmental characteristics, the refined grid is subjected to microbial diffusion by combining with the diffusion probability of microorganisms in the air, so that finer and more accurate urban air microorganism concentration distribution gridding representation under the environmental influence is obtained, the diffused refined grid microorganism concentration is obtained through calculation according to the dynamic diffusion process of the air microorganisms by the refined grid, and further, the ecological distribution monitoring result of the urban air microorganisms is obtained.
Drawings
FIG. 1 is a schematic flow chart of a method for monitoring ecological distribution of microorganisms in urban air according to an embodiment of the invention;
fig. 2 is a schematic structural diagram of an electronic device for implementing a method for monitoring urban air microorganism ecological distribution according to an embodiment of the invention.
In fig. 2: 1 an electronic device, 10 a processor, 11 a memory, 12 a program, 13 a communication interface;
the achievement of the objects, functional features and advantages of the present invention will be further described with reference to the accompanying drawings, in conjunction with the embodiments.
Detailed Description
It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
The embodiment of the application provides a method for monitoring urban air microorganism ecological distribution. The main implementation body of the urban air microorganism ecological distribution monitoring method comprises, but is not limited to, at least one of a server, a terminal and the like which can be configured to execute the electronic equipment of the method provided by the embodiment of the application. In other words, the urban air microorganism ecological distribution monitoring method may be performed by software or hardware installed in a terminal device or a server device, and the software may be a blockchain platform. The service end includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, and the like.
Example 1
S1: and collecting historical urban air microorganism distribution concentration data, and constructing a static urban air microorganism grid refinement level calculation model, wherein the calculation model calculates grid refinement level at the current moment according to the historical microorganism distribution concentration.
In the step S1, a static city air microorganism grid refinement level calculation model is constructed, and the method comprises the following steps:
the grid refinement level calculation formula is:
；
wherein:representing the maximum air microorganism concentration value in the selected set;
m represents the grid air microorganism concentration value of the ith row and the jth columnAir microorganism concentration value of adjacent upper, lower, left and right grids->，/>，/>，/>The average value of the difference values;
a grid static refinement level representing an ith row and jth column grid;
when (when)At the time, the air microorganism concentration value representing the mesh and the surrounding mesh was 75ug/m 3 The grids are fused into a large grid;
when the concentration of microorganisms in the air of the grid is higher than 115ug/m 3 And the average difference of the air microorganism concentration of the grid and the surrounding grids is more than 5ug/m 3 And is not higher than 10ug/m 3 Indicating that the values of the air microorganism concentration of the grids are in an unhealthy range, and the grids need to be subdivided;
when (when)When the concentration of microorganisms in the air of the grid is higher than 115ug/m 3 And the average difference of the air microorganism concentration of the grid and the surrounding grids is more than 10ug/m 3 Indicating that the grid air microorganism concentration value is in a severely unhealthy condition, the grid needs to be subdivided into minimum grids;
when (when) When the grid state is maintained unchanged. In the embodiment of the invention, a biosensor is used as an air microorganism concentration monitoring device, a city is divided into a plurality of city air microorganism grids, and because the city air microorganism concentration monitoring device has limited collection precision, the grid area of the divided city air microorganism grids is larger, and only a relatively fuzzy city air microorganism concentration distribution can be collected by utilizing the city air microorganism concentration monitoring device.
S2: and constructing an index system for influence of urban air microorganism ecological distribution, wherein the index system comprises temperature, humidity, weather and wind power data.
In the step S2, an urban air microorganism ecological distribution influence index system is constructed, which comprises the following steps:
the method comprises the steps of constructing an urban air microorganism ecological distribution influence index system, wherein the urban air microorganism ecological distribution influence index system comprises temperature characteristics, humidity characteristics, weather characteristics and wind power characteristic data, the temperature characteristics represent temperature values in grids, the humidity characteristics represent humidity values in the grids, the weather characteristics represent weather conditions in the grids, the weather conditions in sunny days, cloudy days, rainy days, middle rain days and snow days are respectively set to be 1, 2, 3, 4 and 5, and the wind power characteristics represent wind power values in the grids.
S3: an adaptive regulation model of the grid refinement level of the urban air microorganisms is constructed, wherein the model takes a static grid refinement level and an urban air microorganism ecological distribution influence index system as input, and takes a grid refinement level after dynamic regulation as output.
And in the step S3, constructing an urban air microorganism grid refinement level self-adaptive regulation model, which comprises the following steps:
the urban air microorganism grid refinement level self-adaptive regulation model is a graph model, and each characteristic vectorThe method comprises the steps that a point in an urban air microorganism grid refinement level self-adaptive adjustment model is used, membership degree relation among feature vectors is an edge in the urban air microorganism grid refinement level self-adaptive adjustment model, input of the urban air microorganism grid refinement level self-adaptive adjustment model is a static grid refinement level and an urban air microorganism ecological distribution influence index, and output of the urban air microorganism grid refinement level self-adaptive adjustment model is a grid refinement level after dynamic adjustment;
the dynamic adjustment grid refinement level flow comprises the following steps:
s31: the self-adaptive regulation model of the urban air microorganism grid refinement level calculates the probability of the level of each dimension feature in the index features, and the probability of the input feature belonging to the level r is as follows:
Wherein: />Representing the%>Dimension feature relative level->Membership of->；
Representing the +.>Standard deviation of dimensional characteristics;
representing the%>A mean value of the dimensional features;
representation level->Is>A mean value of the dimensional features; />
e represents a natural index;
representing the probability that the input feature belongs to level r;
s32: and selecting the level with the highest probability as the grid refinement level after the current grid is dynamically adjusted.
S4: and (5) carrying out gridding treatment on the urban area according to the calculated dynamic grid refinement level to obtain an urban air microorganism grid map.
And in the step S4, the area grid is dynamically adjusted by further gridding the air microorganism area to be monitored according to the calculated dynamic grid refinement level, so as to obtain an urban air microorganism grid map, which comprises the following steps:
calculating the side length of the grids according to the grid refinement level, and determining the size of each grid, wherein the specific calculation formula is as follows:
wherein: />Representing the original grid side length;
representing a grid refinement level;
representing the edge length of the adaptively processed grid.
S5: and constructing a dynamic diffusion model of the urban air microorganisms, wherein the dynamic diffusion model takes the directional diffusion probability of the air microorganisms in the grids as an independent variable, and aims at minimizing the difference value of the concentration of the urban grid air microorganisms.
And in the step S5, constructing a dynamic diffusion model of urban air microorganisms, which comprises the following steps:
s51: constructing an air microorganism diffusion matrix, wherein the specific calculation formula is as follows:
wherein: />Representing the number of grids within the monitored area;
a probability vector representing the diffusion of airborne microorganisms by grid c into between its adjacent n adjacent grids;
representing a grid index;
representing adjacent grid indexes;
s52: determining an urban air microorganism optimizing target function according to the constructed air microorganism diffusion matrix, wherein the optimizing target determines an urban air microorganism diffusion probability vector by minimizing the urban grid air microorganism concentration difference value, and a specific calculation formula is as follows:
wherein: />Representing the air microorganism concentration value in the grid C;
indicating the concentration of airborne microorganisms that actually diffuse out of the grid C. According to the embodiment of the invention, the grid to be refined is refined according to the environmental characteristics, the refined grid is subjected to microbial diffusion by combining with the diffusion probability of microorganisms in the air, so that finer and more accurate urban air microorganism concentration distribution gridding representation under the environmental influence is obtained, and the ecological distribution monitoring result of the urban air microorganisms is obtained by dynamically modeling the air microorganism diffusion process according to the refined grid.
S6: and carrying out quick optimization solving on the urban air microorganism dynamic diffusion model to obtain an air microorganism direction diffusion probability vector, adjusting and calculating the air microorganism concentration in different grids according to the air microorganism direction diffusion probability vector obtained by optimization, and obtaining the microorganism ecological distribution of the whole urban area by integrating the air microorganism concentration in different grid areas.
In the step S6, the urban air microorganism dynamic diffusion model is rapidly optimized and solved to obtain an air microorganism direction diffusion probability vector, which comprises the following steps:
the solving to obtain the airborne microorganism direction diffusion probability vector comprises the following steps:
s61: selecting an initial urban air microorganism diffusion probability vectorCalculating initial residual errorSetting search direction +.>Iteration count k=0;
s62: iteratively updating according to the following steps (1) to (5);
in the step (1), each processor calculates the respective local search direction in parallel, and the calculation formula is as follows:
wherein: />Coefficients representing the calculated search direction for determining the search direction in the next iteration;
indicating the search direction of the current round,
the residual error of the current round is represented,
representing the residual error of the previous round;
step (2) the local search direction calculated by each processor Performing global communication and calculating a mean search direction to ensure that each processor has the latest global search direction information;
step (3), calculating the iteration step length of the current round according to the calculated average value searching direction, wherein the calculation formula is as follows:
wherein: />Representing a symmetric positive coefficient matrix; />
Representation matrix->And search direction->For calculating a step size and updating a residual error;
updating urban air microorganism diffusion probability vectors and residual errors according to the calculated iteration step length:
；
step (5) checking whether the residual size meets a threshold condition or reaches the maximum iteration number, if so, stopping iteration, otherwise, returning to the step (1) to perform iterative calculation;
s63: returning the solution vector obtained by final solutionAs the optimal urban air microorganism diffusion probability vector.
And S6, adjusting and calculating the air microorganism concentration in different grids according to the air microorganism direction diffusion probability vector obtained by optimization, and obtaining the microorganism ecological distribution of the whole urban area by integrating the air microorganism concentration in different grid areas, wherein the method comprises the following steps:
and adjusting and calculating air microorganism concentration values of different grids according to the calculated optimal urban air microorganism diffusion probability vector, wherein the calculation formula is as follows:
Wherein: />Representing the current airborne microorganism concentration of grid C;
representing the current airborne microorganism concentration of grid j;
representing the number of grids adjacent to grid C;
representing the probability of airborne microorganism diffusion of grid c to its neighboring grid i;
representing the probability of airborne microorganism diffusion of adjacent grid j to grid C;
representing the air microorganism concentration after grid C update.
Example 2
Fig. 2 is a schematic structural diagram of an electronic device for implementing the urban air microorganism ecological distribution monitoring method according to an embodiment of the invention.
The electronic device 1 may comprise a processor 10, a memory 11, a communication interface 13 and a bus, and may further comprise a computer program, such as program 12, stored in the memory 11 and executable on the processor 10.
The memory 11 includes at least one type of readable storage medium, including flash memory, a mobile hard disk, a multimedia card, a card memory (e.g., SD or DX memory, etc.), a magnetic memory, a magnetic disk, an optical disk, etc. The memory 11 may in some embodiments be an internal storage unit of the electronic device 1, such as a removable hard disk of the electronic device 1. The memory 11 may in other embodiments also be an external storage device of the electronic device 1, such as a plug-in mobile hard disk, a Smart Media Card (SMC), a secure digital (SecureDigital, SD) Card, a Flash memory Card (Flash Card) or the like, which are provided on the electronic device 1. Further, the memory 11 may also include both an internal storage unit and an external storage device of the electronic device 1. The memory 11 may be used not only for storing application software installed in the electronic device 1 and various types of data, such as codes of the program 12, but also for temporarily storing data that has been output or is to be output.
The processor 10 may be comprised of integrated circuits in some embodiments, for example, a single packaged integrated circuit, or may be comprised of multiple integrated circuits packaged with the same or different functions, including one or more central processing units (Central Processing unit, CPU), microprocessors, digital processing chips, graphics processors, combinations of various control chips, and the like. The processor 10 is a Control Unit (Control Unit) of the electronic device, connects the respective components of the entire electronic device using various interfaces and lines, executes or executes programs or modules (programs 12 for implementing urban air microorganism ecological distribution monitoring, etc.) stored in the memory 11, and invokes data stored in the memory 11 to perform various functions of the electronic device 1 and process data.
The communication interface 13 may comprise a wired interface and/or a wireless interface (e.g. WI-FI interface, bluetooth interface, etc.), typically used to establish a communication connection between the electronic device 1 and other electronic devices and to enable connection communication between internal components of the electronic device.
The bus may be a peripheral component interconnect standard (peripheral component interconnect, PCI) bus or an extended industry standard architecture (extended industry standard architecture, EISA) bus, among others. The bus may be classified as an address bus, a data bus, a control bus, etc. The bus is arranged to enable a connection communication between the memory 11 and at least one processor 10 etc.
Fig. 2 shows only an electronic device with components, it being understood by a person skilled in the art that the structure shown in fig. 2 does not constitute a limitation of the electronic device 1, and may comprise fewer or more components than shown, or may combine certain components, or may be arranged in different components.
For example, although not shown, the electronic device 1 may further include a power source (such as a battery) for supplying power to each component, and preferably, the power source may be logically connected to the at least one processor 10 through a power management device, so that functions of charge management, discharge management, power consumption management, and the like are implemented through the power management device. The power supply may also include one or more of any of a direct current or alternating current power supply, recharging device, power failure detection circuit, power converter or inverter, power status indicator, etc. The electronic device 1 may further include various sensors, bluetooth modules, wi-Fi modules, etc., which will not be described herein.
The electronic device 1 may optionally further comprise a user interface, which may be a Display, an input unit, such as a Keyboard (Keyboard), or a standard wired interface, a wireless interface. Alternatively, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode) touch, or the like. The display may also be referred to as a display screen or display unit, as appropriate, for displaying information processed in the electronic device 1 and for displaying a visual user interface.
It should be understood that the embodiments described are for illustrative purposes only and are not limited to this configuration in the scope of the patent application.
The program 12 stored in the memory 11 of the electronic device 1 is a combination of instructions that, when executed in the processor 10, may implement:
collecting historical urban air microorganism distribution concentration data, constructing a static urban air microorganism grid refinement level calculation model, calculating a static grid refinement level at the current moment according to the historical microorganism distribution concentration by the calculation model, and gridding an air microorganism area to be monitored according to the calculated static grid refinement level;
constructing an urban air microorganism ecological distribution influence index system, wherein the index system comprises temperature, humidity, weather and wind data;
constructing an adaptive regulation model of the grid refinement level of the urban air microorganisms, wherein the model takes a static grid refinement level and an influence index system of urban air microorganism ecological distribution as input and takes a grid refinement level after dynamic regulation as output; performing further gridding treatment on the air microorganism area to be monitored according to the calculated dynamic grid refinement level to obtain an urban air microorganism grid chart;
Constructing a dynamic diffusion model of urban air microorganisms, wherein the dynamic diffusion model takes the directional diffusion probability of the air microorganisms in grids as an independent variable, takes the minimum urban grid air microorganism concentration difference value as a target, and takes the adjacency relationship among regional grids as a limiting condition;
and carrying out quick optimization solving on the urban air microorganism dynamic diffusion model to obtain an air microorganism direction diffusion probability vector, adjusting and calculating the air microorganism concentration in different grids according to the air microorganism direction diffusion probability vector obtained by optimization, and obtaining the microorganism ecological distribution of the whole urban area by integrating the air microorganism concentration in different grid areas.
Specifically, the specific implementation method of the above instruction by the processor 10 may refer to descriptions of related steps in the corresponding embodiments of fig. 1 to 2, which are not repeated herein.
It should be noted that, the foregoing reference numerals of the embodiments of the present invention are merely for describing the embodiments, and do not represent the advantages and disadvantages of the embodiments. And the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, apparatus, article, or method that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, apparatus, article, or method. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, apparatus, article or method that comprises the element.
From the above description of the embodiments, it will be clear to those skilled in the art that the above-described embodiment method may be implemented by means of software plus a necessary general hardware platform, but of course may also be implemented by means of hardware, but in many cases the former is a preferred embodiment. Based on such understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art in the form of a software product stored in a storage medium (e.g. ROM/RAM, magnetic disk, optical disk) as described above, comprising instructions for causing a terminal device (which may be a mobile phone, a computer, a server, or a network device, etc.) to perform the method according to the embodiments of the present invention.
The foregoing description is only of the preferred embodiments of the present invention, and is not intended to limit the scope of the invention, but rather is intended to cover any equivalents of the structures or equivalent processes disclosed herein or in the alternative, which may be employed directly or indirectly in other related arts.
Claims (1)
1. A method for monitoring urban air microorganism ecological distribution, which is characterized by comprising the following steps:
S1: collecting historical urban air microorganism distribution concentration data, constructing a static urban air microorganism grid refinement level calculation model, calculating a static grid refinement level at the current moment according to the historical microorganism distribution concentration by the calculation model, and gridding an air microorganism area to be monitored according to the calculated static grid refinement level;
s2: constructing an urban air microorganism ecological distribution influence index system, wherein the index system comprises temperature, humidity, weather and wind data;
s3: constructing an adaptive regulation model of the grid refinement level of the urban air microorganisms, wherein the model takes a static grid refinement level and an influence index system of urban air microorganism ecological distribution as input and takes a grid refinement level after dynamic regulation as output;
s4: further gridding the air microorganism area to be monitored according to the calculated dynamic grid refinement level to dynamically adjust the area grid so as to obtain an urban air microorganism grid map;
s5: constructing a dynamic diffusion model of urban air microorganisms, wherein the dynamic diffusion model takes the directional diffusion probability of the air microorganisms in grids as an independent variable, takes the minimum urban grid air microorganism concentration difference value as a target, and takes the adjacency relationship among regional grids as a limiting condition;
S6: carrying out rapid optimization solving on an urban air microorganism dynamic diffusion model to obtain an air microorganism direction diffusion probability vector, adjusting and calculating air microorganism concentration in grids of different areas according to the air microorganism direction diffusion probability vector obtained by optimization, and obtaining microorganism ecological distribution of the whole urban area by integrating the air microorganism concentration of different grid areas, wherein a biosensor is a specific implementation mode for detecting the air microorganism concentration of the grid areas;
in the step S1, a static city air microorganism grid refinement level calculation model is constructed, and the method comprises the following steps:
the grid refinement level calculation formula is:
wherein:
representing the maximum air microorganism concentration value in the selected set;
m represents the grid air microorganism concentration value of the ith row and the jth columnAir microorganism concentration value of adjacent upper, lower, left and right grids->，/>，/>，/>The average value of the difference values;
a grid static refinement level representing an ith row and jth column grid;
when (when)At the time, the air microorganism concentration value representing the mesh and the surrounding mesh was 75ug/m 3 The grids are fused into a large grid;
when (when)When the concentration of microorganisms in the air of the grid is higher than 115ug/m 3 And the average difference of the air microorganism concentration of the grid and the surrounding grids is more than 5ug/m 3 And is not higher than 10ug/m 3 Indicating that the values of the air microorganism concentration of the grids are in an unhealthy range, and the grids need to be subdivided;
when (when)When the concentration of microorganisms in the air of the grid is higher than 115ug/m 3 And the average difference of the air microorganism concentration of the grid and the surrounding grids is more than 10ug/m 3 Indicating that the grid air microorganism concentration value is in a severely unhealthy condition, the grid needs to be subdivided into minimum grids;
when (when)When the grid state is maintained unchanged;
in the step S2, an urban air microorganism ecological distribution influence index system is constructed, which comprises the following steps:
constructing an urban air microorganism ecological distribution influence index system, wherein the urban air microorganism ecological distribution influence index system comprises temperature characteristics, humidity characteristics, weather characteristics and wind power characteristic data, wherein the temperature characteristics represent temperature values in grids, the humidity characteristics represent humidity values in the grids, the weather characteristics represent weather conditions in the grids, the sunny, cloudy, rainy, middle rain and snow are respectively set to be 1, 2, 3, 4 and 5, and the wind power characteristics represent wind power values in the grids;
and in the step S3, constructing an urban air microorganism grid refinement level self-adaptive regulation model, which comprises the following steps:
the urban air microorganism grid refinement level self-adaptive regulation model is a graph model, and each characteristic vector The method comprises the steps that a point in an urban air microorganism grid refinement level self-adaptive adjustment model is used, membership degree relation among feature vectors is an edge in the urban air microorganism grid refinement level self-adaptive adjustment model, input of the urban air microorganism grid refinement level self-adaptive adjustment model is a static grid refinement level and an urban air microorganism ecological distribution influence index, and output of the urban air microorganism grid refinement level self-adaptive adjustment model is a grid refinement level after dynamic adjustment;
the dynamic adjustment grid refinement level flow comprises the following steps:
s31: the self-adaptive regulation model of the urban air microorganism grid refinement level calculates the probability of the level of each dimension feature in the index features, and the probability of the input feature belonging to the level r is as follows:
wherein:
representing the%>Dimension feature relative level->Membership of->；
Representing the +.>Standard deviation of dimensional characteristics;
representing the%>A mean value of the dimensional features;
representation level->Is>A mean value of the dimensional features;
e represents a natural index;
representing the probability that the input feature belongs to level r;
s32: selecting the level with the highest probability as the grid refinement level after the current grid is dynamically adjusted;
and in the step S4, the area grid is dynamically adjusted by further gridding the air microorganism area to be monitored according to the calculated dynamic grid refinement level, so as to obtain an urban air microorganism grid map, which comprises the following steps:
Calculating grid side length according to the grid refinement level, determining the size of each grid, dividing the urban area into different grid units according to the grid side length obtained by dynamic calculation, wherein the specific calculation formula is as follows:
wherein:
representing the original grid side length;
representing a grid refinement level;
representing the grid side length after the self-adaptive processing;
and in the step S5, constructing a dynamic diffusion model of urban air microorganisms, which comprises the following steps:
s51: constructing an air microorganism diffusion matrix, wherein the specific calculation formula is as follows:
wherein:
representing the number of grids within the monitored area;
a probability vector representing the diffusion of airborne microorganisms by grid c into between its adjacent n adjacent grids;
representing a grid index;
representing adjacent grid indexes;
s52: determining an urban air microorganism optimizing target function according to the constructed air microorganism diffusion matrix, wherein the optimizing target determines an urban air microorganism diffusion probability vector by minimizing the urban grid air microorganism concentration difference value, and a specific calculation formula is as follows:
wherein:
representing the air microorganism concentration value in the grid C;
an airborne microorganism concentration value representing the actual diffusion out of the grid C;
in the step S6, the urban air microorganism dynamic diffusion model is rapidly optimized and solved to obtain an air microorganism direction diffusion probability vector, which comprises the following steps:
The solving to obtain the airborne microorganism direction diffusion probability vector comprises the following steps:
s61: selecting an initial urban air microorganism diffusion probability vectorCalculating an initial residual +.>Setting search direction +.>Iteration meterThe number k=0;
s62: iteratively updating according to the steps (1) to (5);
in the step (1), each processor calculates the respective local search direction in parallel, and the calculation formula is as follows:
wherein:
coefficients representing the calculated search direction for determining the search direction in the next iteration;
indicating the search direction of the current round,
the residual error of the current round is represented,
representing the residual error of the previous round;
step (2) the local search direction calculated by each processorPerforming global communication and calculating a mean search direction to ensure that each processor has the latest global search direction information;
step (3), calculating the iteration step length of the current round according to the calculated average value searching direction, wherein the calculation formula is as follows:
wherein:
representing a symmetric positive coefficient matrix;
representation matrix->And search direction->For calculating a step size and updating a residual error;
updating urban air microorganism diffusion probability vectors and residual errors according to the calculated iteration step length:
Step (5) checking whether the residual size meets a threshold condition or reaches the maximum iteration number, if so, stopping iteration, otherwise, returning to the step (1) to perform iterative calculation;
s63: returning the solution vector obtained by final solutionAs an optimal urban air microorganism diffusion probability vector;
and S6, adjusting and calculating the air microorganism concentration in grids of different areas according to the air microorganism direction diffusion probability vector obtained by optimization, and obtaining the microorganism ecological distribution of the whole urban area by integrating the air microorganism concentration of different grid areas, wherein the method comprises the following steps:
and adjusting and calculating air microorganism concentration values of different grids according to the calculated optimal urban air microorganism diffusion probability vector, wherein the calculation formula is as follows:
wherein:
representing the current airborne microorganism concentration of grid C;
representing the current airborne microorganism concentration of grid j;
representing the number of grids adjacent to grid C;
representing the probability of airborne microorganism diffusion of grid c to its neighboring grid i;
representing the probability of airborne microorganism diffusion of adjacent grid j to grid C;
representing the air microorganism concentration after grid C update.
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