CN110309562B - Method and device for analyzing artificial thermal warming effect and storage medium - Google Patents
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Abstract
The invention discloses a method, a device and a storage medium for analyzing artificial heat warming effect, wherein the method comprises the following steps: acquiring DMSP/OLS data and NDVI data, generating an HSI index, and constructing artificial heat emission space data; coupling the artificial heat emission space data with the WRF model, updating average artificial heat emission amount of different land types in the WRF model, calculating emission amount difference between different artificial heat emission conditions and the unmanned heat emission conditions, and representing the emission amount difference as a corresponding artificial heat warming effect; carrying out density segmentation processing on the artificial heat warming effect, analyzing the artificial heat warming effect by using a local autoregressive related algorithm to obtain corresponding space aggregation characteristics, and counting the intensity of the artificial heat warming effect corresponding to different building type areas; and setting the height and the density of the building as independent variables, and constructing an artificial thermal warming effect calculation method by taking the intensity of the artificial thermal warming effect as a dependent variable. The invention can rapidly and accurately obtain the artificial thermal warming effect and realize scientific simulation of the artificial thermal warming effect.
Description
Technical Field
The invention relates to the technical field of environmental monitoring, in particular to a method and a device for analyzing artificial heat warming effect and a storage medium.
Background
Man-made heat emission refers to a large amount of waste heat discharged to the surrounding environment by underutilization of energy in production and life, and is one of important input variables of urban energy systems. Production and living activities in cities, including industry, transportation, resident life and the like, all need to discharge a large amount of waste heat, so that the urban surface energy balance is influenced, and the urban heat island effect is aggravated, therefore, the rapid and effective artificial heat warming effect quantitative expression becomes one of important prerequisites for improving the urban ecological quality.
The existing artificial heat warming effect evaluation method mainly comprises an energy inventory method, a remote sensing inversion method and a numerical simulation method, but in the research and practice process of the prior art, the inventor of the invention finds that the energy inventory method usually takes a city as a minimum unit, can not quantitatively express the artificial heat warming effect in the city, and has the defect of low resolution; the remote sensing inversion method relates to more empirical equations, the addition of empirical parameters often reduces the simulation precision, and the acquisition of most variables needs to be replaced by difference values or single point values, so that the artificial thermal simulation of the whole area can be determined by only one value, and the inversion result precision is greatly reduced; the numerical simulation method has high requirement on operation resources, long operation period and difficult mastering, and in addition, the artificial heat emission in the WRF model is a default fixed value and cannot reflect the real situation of artificial heat emission. Therefore, how to realize the rapid scientific simulation of the artificial heat warming effect is one of the problems needing to be solved firstly in the urban thermal environment research, and the method has important theoretical and practical significance for urban ecology and urban energy regulation and control.
Disclosure of Invention
The technical problem to be solved by the embodiments of the present invention is to provide an analysis method, an analysis device, and a storage medium for an artificial thermal warming effect, which can rapidly and accurately obtain the artificial thermal warming effect.
In order to solve the above problems, an embodiment of the present invention provides a method for analyzing an artificial thermal warming effect, including the following steps:
preprocessing DMSP/OLS data and NDVI data obtained by downloading to generate an HSI index, and constructing an artificial heat emission regression model to obtain artificial heat emission space data;
coupling the artificial heat emission space data with a WRF model, updating average artificial heat emission amount of different land types in the WRF model, calculating emission amount difference between different artificial heat emission conditions and the unmanned heat emission conditions, and representing the emission amount difference as a corresponding artificial heat temperature increase effect;
after density segmentation processing is carried out on the artificial heat warming effect, the artificial heat warming effect is analyzed by utilizing a local autoregressive related algorithm to obtain corresponding space aggregation characteristics, and the artificial heat warming effect strength corresponding to different building type areas is counted by taking different building attribute data as bottom layer data;
setting the height and density of the building as independent variables, and taking the artificial heat temperature effect strength as a dependent variable, and constructing a corresponding artificial heat temperature effect strength calculation method to obtain an analysis result of the corresponding artificial heat temperature effect strength.
Further, the method for analyzing the artificial thermal warming effect further comprises the following steps:
and (3) obtaining the industrial energy consumption, the domestic energy consumption and the holding capacity of the civil motor vehicle by counting energy consumption chapters in the yearbook, and calculating the industrial artificial heat emission, the domestic artificial heat emission, the traffic artificial heat emission and the total artificial heat emission.
Further, the method for analyzing the artificial thermal warming effect further comprises the following steps:
and verifying the precision of the artificial heat warming effect intensity calculation method by constructing an artificial heat emission inversion model.
Further, the accuracy of the artificial heat warming effect intensity calculation method is verified by constructing an artificial heat emission inversion model, specifically:
calculating a square value of a correlation coefficient according to the artificial heat temperature effect intensity calculation method to obtain precision information of an artificial heat temperature inversion equation;
selecting a preset condition area, constructing an artificial heat temperature rising inversion model according to the artificial heat temperature rising effect strength calculation method, and calculating to obtain an artificial heat temperature rising effect simulation value;
and comparing the artificial thermal warming effect simulation value with the obtained artificial thermal warming effect value, calculating a correlation coefficient value between the simulation result and the real result, and verifying the precision of the model.
Further, the pretreatment specifically comprises:
and carrying out homogenization treatment on the annual average value of the DMSP/OLS data, and taking the maximum value of each standard grid as the monthly average value in the NDVI data.
Further, the constructing of the artificial heat emission regression model includes:
constructing a regression equation according to the total artificial heat emission and the HSI index, wherein the regression equation is
AHR＝42.13(HSI)2-15.42(HSI)+1.537，
Wherein AHR is total artificial heat emission amount, and HSI is human population index.
Further, the density segmentation specifically includes:
and dividing the artificial heat emission warming effect according to the grade gradients of the average value-2 multiplied by standard deviation, the average value-1 multiplied by standard deviation, the average value-0.5 multiplied by standard deviation, the average value +0.5 multiplied by standard deviation, the average value +1 multiplied by standard deviation and the average value +2 multiplied by standard deviation by taking the average value of the artificial heat emission warming effect as a reference.
Further, the local autoregressive correlation algorithm includes:
the spatial aggregation characteristics of the artificial thermal warming effect are quantitatively analyzed by utilizing a Moire index calculation formula
Wherein, IiIs the Moire index, xiIs the artificial thermal warming effect value of grid i,
is the average value of the artificial thermal warming effect, WijRepresenting the weight relationship between grid i and grid j.
An embodiment of the present invention further provides an apparatus for analyzing artificial thermal warming effect, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor executes the computer program to implement the method for analyzing artificial thermal warming effect as described above.
An embodiment of the present invention further provides a computer-readable storage medium, which includes a stored computer program, wherein when the computer program runs, the apparatus where the computer-readable storage medium is located is controlled to execute the method for analyzing the artificial thermal warming effect as described above.
The embodiment of the invention has the following beneficial effects:
the embodiment of the invention provides an analysis method, a device and a storage medium for an artificial heat temperature rise effect, wherein the method comprises the following steps: preprocessing DMSP/OLS data and NDVI data obtained by downloading to generate an HSI index, and constructing an artificial heat emission regression model to obtain artificial heat emission space data; coupling the artificial heat emission space data with a WRF model, updating average artificial heat emission amount of different land types in the WRF model, calculating emission amount difference between different artificial heat emission conditions and the unmanned heat emission conditions, and representing the emission amount difference as a corresponding artificial heat temperature increase effect; after density segmentation processing is carried out on the artificial heat warming effect, the artificial heat warming effect is analyzed by utilizing a local autoregressive related algorithm to obtain corresponding space aggregation characteristics, and the artificial heat warming effect strength corresponding to different building type areas is counted by taking different building attribute data as bottom layer data; setting the height and density of the building as independent variables, and taking the artificial heat temperature effect strength as a dependent variable, and constructing a corresponding artificial heat temperature effect strength calculation method to obtain an analysis result of the corresponding artificial heat temperature effect strength. The invention can combine the height and density information of the building, quickly and accurately acquire the artificial heat warming effect under different artificial heat emission situations, and realize the scientific simulation of the artificial heat warming effect.
Drawings
FIG. 1 is a schematic flow chart illustrating a method for analyzing an artificial thermal warming effect according to an embodiment of the present invention;
FIG. 2 is a schematic illustration of artificial hot space discharge data provided by an embodiment of the present invention;
FIG. 3 is a graph of the intensity of the artificial thermal heating effect in different areas of a building according to an embodiment of the present invention;
FIG. 4 is a graph of space concentration characteristics for an artificial thermal warming effect provided by an embodiment of the present invention;
fig. 5 is a schematic flow chart of another method for analyzing artificial thermal warming effect according to an embodiment of the present 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.
Firstly, the application scenarios that the invention can provide, such as fast and high-precision acquisition of the artificial heat warming effect, are introduced.
The first embodiment of the present invention:
please refer to fig. 1-5.
As shown in fig. 1, the method for analyzing the artificial thermal warming effect provided in this embodiment includes the following steps:
s101, preprocessing DMSP/OLS data and NDVI data obtained by downloading to generate an HSI index, and constructing an artificial heat emission regression model to obtain artificial heat emission space data;
specifically, for step S101, year-average DMSP/OLS data is downloaded from the national geographic information center of the united states, and NDVI monthly mean of MODIS is downloaded from the geographic data spatial data cloud; meanwhile, the DMSP/OLS data obtained by downloading is subjected to homogenization treatment according to a formula
Calculating, taking the maximum value of each grid for the NDVI monthly mean value of the MODIS, wherein the grid is the minimum unit of the remote sensing data, the grid in the embodiment is a grid of 250m × 250m, and the NDVI is calculated according to a formulamax(i,j)＝{NDVI1(i,j)，NDVI2(i,j)，NDVI3(i,j)，……NDVI12(i,j)And calculating; building a human living index HSI by utilizing the industrial artificial heat emission and the living artificial heat emission obtained by calculation according to a formula
Calculating; constructing a regression equation AHR (42.13) (HSI) by using the HSI index and the total amount of artificial heat emission obtained by calculation215.42(HSI) +1.537, wherein AHR is human heat emission and HSI is human population index, and the results obtained based on regression equation are shown in FIG. 2.
S102, coupling the artificial heat emission space data with a WRF model, updating average artificial heat emission amount of different land types in the WRF model, calculating emission amount difference between the artificial heat emission space data and the WRF model under different artificial heat emission conditions and during unmanned heat emission, and representing the emission amount difference as a corresponding artificial heat warming effect;
specifically, in step S102, the average artificial heat emission amounts of the low-density residential area, the high-density residential area, and the commercial area in fig. 2 are counted, and according to the counted values, the artificial heat emission amounts of the low-density residential area, the high-density residential area, and the commercial area of the city canopy module (UCM) in the WRF model are corrected, and the artificial heat emission-free (hereinafter, collectively referred to as test a), the normal artificial heat emission (hereinafter, collectively referred to as test b), and the doubled artificial heat emission scenario (hereinafter, collectively referred to as test c) are set; calculating the difference between the test b and the test a, and representing the warming effect of the normal artificial heat discharge; the difference between test c and test a was calculated and the two-fold artificial heat emission warming effect was characterized.
It should be noted that, in the embodiment, the WRF model is used to set three artificial heat emission scenarios, that is, the scenarios of no artificial heat emission, normal artificial heat emission and double artificial heat emission, and differences of simulation results are used to quantitatively characterize the artificial heat warming effect under different artificial heat emission intensities, thereby overcoming the dilemma that the conventional artificial heat estimation method cannot obtain temperature indexes.
Meanwhile, before the WRF model operates, a set of artificial heat emission space data set is manufactured by utilizing DMSP/OLS and NDVI data, and the data set is coupled with the WRF model, so that the simulation precision of the artificial heat warming effect of the WRF model is improved, and the difficulty that the artificial heat emission in the WRF model is constant is overcome.
S103, after density segmentation processing is carried out on the artificial heat warming effect, the artificial heat warming effect is analyzed by utilizing a local autoregressive related algorithm to obtain corresponding space aggregation characteristics, and the artificial heat warming effect strength corresponding to different building type areas is counted by taking different building attribute data as bottom layer data;
specifically, for step S103, the average value and the standard deviation of the artificial thermal discharge warming effect are counted, and density division is performed, where the density division is based on the average value of the artificial thermal discharge warming effect, and is performed according to the following steps: the grading gradient of the average value-2 multiplied by the standard deviation, the average value-1 multiplied by the standard deviation, the average value-0.5 multiplied by the standard deviation, the average value +0.5 multiplied by the standard deviation, the average value +2 multiplied by the standard deviation is used for dividing the artificial heat warming effect; similarly, counting the average value and variance of the two times of artificial heat emission warming effect, and performing density segmentation; and (3) performing space aggregation characteristic analysis on the density segmentation result under different artificial heat emission intensities by adopting a local autoregressive correlation calculation method.
It should be noted that, in this embodiment, spatial autocorrelation (which refers to potential interdependencies between observation data of some variables in the same distribution area) analysis has a function of identifying spatial clustering degree, and it can be known from a spatial distribution rule of geographic elements in reality: the artificial thermal warming objects with close spatial distance have similar artificial thermal warming effect grade and spatial heterogeneity characteristics. And (3) quantitatively analyzing the aggregation characteristics of the artificial thermal warming effect by using a Moire index according to a local autocorrelation algorithm. Wherein, the Moire index calculation formula is as follows:
wherein Ii is the Moran index, xi is the artificial heat temperature effect value of the grid i, which is the average value of the artificial heat temperature effect, WijRepresenting the weight relation between the grid i and the grid j; when the Moire index calculation result is greater than zero, the spatial positive correlation is represented, the correlation is more obvious when the Moire index calculation result is greater than zero, and when the Moire index calculation result is less than zero, the spatial negative correlation is represented, and the spatial difference is greater when the Moire index calculation result is smaller; when the Moire index calculation result is greater than zero, the spatial positive correlation is represented, and the higher the value of the spatial positive correlation isThe larger the correlation, the more obvious the correlation, and when the Moire index calculation result is less than zero, the spatial negative correlation is represented, and the smaller the value of the correlation, the larger the spatial difference.
It should be noted that, in this embodiment, the artificial heat warming effect value is combined with the building attribute data, and the artificial heat warming effects in different building attribute areas are counted, and the building attributes are classified according to the difference of building height and building density, and are totally divided into 9 categories, which are: a bottom layer-low density (L-L), a bottom layer-medium density (L-M), a bottom layer-high density (L-H), a middle layer-low density (M-L), a middle layer-medium density (M-M), a middle layer-high density (M-H), a top layer-low density (H-L), a top layer-medium density (H-M), and a top layer-high density (H-H); and a density segmentation method is applied, and the strength of the artificial thermal warming effect is defined by taking the average value and one-time standard deviation of the artificial thermal warming effect as basic indexes; and (4) combining the Moran index to carry out space aggregation characteristic inversion of the artificial thermal warming effect, thereby improving the accuracy of extraction of the artificial thermal warming effect.
S104, setting the height and the density of the building as independent variables, and taking the artificial heat temperature effect strength as a dependent variable, and constructing a corresponding artificial heat temperature effect strength calculation method to obtain an analysis result of the corresponding artificial heat temperature effect strength.
Specifically, in step S104, as shown in fig. 3 and 4, the building density and the building height are used as independent variables, the artificial thermal warming effect intensities under different artificial heat emission intensities are used as dependent variables, and the T in the bottom layer-high density normal artificial heat emission situation is obtained respectivelyahr＝-0.645×BD-0.001×BH+0.46，R20.249; high-rise low-density normal artificial heat emission situation Tahr＝0.689×BD-0.011×BH+0.305，R20.550; bottom-high density double man-made heat emission situation Tahr＝-1.1×BD-0.014×BH+0.844，R20.305; t in high-rise-low density double man-made heat emission situationahr＝1.541×BD-0.015×BH+0.465，R20.679, wherein TahrFor artificial heat-increasing strength, BD is building density, BH is building height, R2Is an evaluation index; the result is that the bottom-high density and the top-low density regions do notAnd the same calculation method of artificial heat temperature-increasing effect strength under the artificial heat emission strength.
In a preferred embodiment, the method for analyzing the artificial thermal warming effect further includes:
and (3) obtaining the industrial energy consumption, the domestic energy consumption and the holding capacity of the civil motor vehicle by counting energy consumption chapters in the yearbook, and calculating the industrial artificial heat emission, the domestic artificial heat emission, the traffic artificial heat emission and the total artificial heat emission.
Specifically, the industrial energy consumption, the domestic energy consumption and the civil motor vehicle remaining amount are obtained by counting energy consumption sections in the yearbook; calculating to obtain the industrial artificial heat emission amount, namely according to a formula E1＝C1×εcPerforming a calculation of where E1For industrial artificial heat emission, C1Total amount of coal consumed for industrial production, epsiloncThe calorific value of standard coal; calculating to obtain the artificial heat emission of life, namely according to a formula E2＝C2×εcPerforming a calculation of where E2Discharge of heat by living people, C2The standard coal total consumed in life, epsiloncThe calorific value of standard coal; calculating to obtain the artificial heat emission of the traffic, namely according to a formula E3＝N×D×β×εgPerforming a calculation of where E3For the man-made heat emission of traffic, N is the reserved quantity of the civil vehicle, D is the total mileage of the vehicle, beta is the gasoline consumption per kilometer, epsilongIs the calorific value of gasoline; calculating total consumption of artificial heat, i.e. Etotal＝E1+E2+E3。
In a preferred embodiment, the method for analyzing the artificial thermal warming effect further includes:
and verifying the precision of the artificial heat warming effect intensity calculation method by constructing an artificial heat emission inversion model.
In a preferred embodiment, the verifying the accuracy of the calculation method of the artificial heat warming effect strength by constructing an artificial heat emission inversion model specifically includes:
calculating a square value of a correlation coefficient according to the artificial heat temperature effect intensity calculation method to obtain precision information of an artificial heat temperature inversion equation;
selecting a preset condition area, constructing an artificial heat temperature rising inversion model according to the artificial heat temperature rising effect strength calculation method, and calculating to obtain an artificial heat temperature rising effect simulation value;
and comparing the artificial thermal warming effect simulation value with the obtained artificial thermal warming effect value, calculating a correlation coefficient value between the simulation result and the real result, and verifying the precision of the model.
Specifically, a square value of a correlation coefficient is obtained through calculation, precision information of an artificial heat inversion equation is obtained, a bottom-high-density region and a high-low-density region outside a Guangzhou city core region are selected, a multivariate relation between artificial heat temperature, building height and building density is established through a multivariate regression equation method according to an obtained artificial heat inversion model, an artificial heat temperature effect value is obtained through calculation, the obtained artificial heat temperature effect value is compared, and a correlation coefficient value R between a simulation result and a real result is calculated2As an evaluation index, the accuracy of the model is verified.
In a preferred embodiment, the pretreatment specifically comprises:
and carrying out homogenization treatment on the annual average value of the DMSP/OLS data, and taking the maximum value of each standard grid as the monthly average value in the NDVI data.
In a preferred embodiment, the constructing the artificial heat emission regression model includes:
constructing a regression equation according to the total artificial heat emission and the HSI index, wherein the regression equation is
AHR＝42.13(HSI)2-15.42(HSI)+1.537，
Wherein AHR is total artificial heat emission amount, and HSI is human population index.
In a preferred embodiment, the density segmentation specifically includes:
and dividing the artificial heat emission warming effect according to the grade gradients of the average value-2 multiplied by standard deviation, the average value-1 multiplied by standard deviation, the average value-0.5 multiplied by standard deviation, the average value +0.5 multiplied by standard deviation, the average value +1 multiplied by standard deviation and the average value +2 multiplied by standard deviation by taking the average value of the artificial heat emission warming effect as a reference.
In a preferred embodiment, the local autoregressive correlation algorithm comprises:
the spatial aggregation characteristics of the artificial thermal warming effect are quantitatively analyzed by utilizing a Moire index calculation formula
Wherein, IiIs the Moire index, xiIs the artificial thermal warming effect value of grid i,
is the average value of the artificial thermal warming effect, WijRepresenting the weight relationship between grid i and grid j.
In a specific embodiment, as shown in fig. 5, another method for analyzing the artificial thermal warming effect is provided, which includes the following steps: generating an HSI index by utilizing DMSP/OLS and NDVI data, constructing an artificial heat emission space data set to be coupled with a WRF model, updating different types of artificial heat emission peak values of the WRF model, setting two artificial heat emission scenes and quantitatively expressing an artificial heat emission warming effect; performing density segmentation on the artificial thermal warming effect, and inverting the space aggregation characteristic of the artificial thermal warming effect by utilizing a Moran' sI index; taking different building attribute areas as bottom data, and counting the artificial heat warming effect intensity in the areas with different building types; building height and building density are used as independent variables, and artificial heat temperature increasing effect strength is used as a dependent variable, so that an artificial heat estimation method is constructed; calculating to obtain a square value of the correlation coefficient to obtain precision information of the artificial heat inversion equation; and calculating to obtain an artificial heat temperature effect value according to the obtained artificial heat inversion model, comparing the obtained artificial heat temperature effect value, calculating a correlation coefficient value between a simulation result and a real result, and verifying the precision of the model.
The method for analyzing the artificial thermal warming effect provided by the embodiment comprises the following steps: preprocessing DMSP/OLS data and NDVI data obtained by downloading to generate an HSI index, and constructing an artificial heat emission regression model to obtain artificial heat emission space data; coupling the artificial heat emission space data with a WRF model, updating average artificial heat emission amount of different land types in the WRF model, calculating emission amount difference between different artificial heat emission conditions and the unmanned heat emission conditions, and representing the emission amount difference as a corresponding artificial heat temperature increase effect; after density segmentation processing is carried out on the artificial heat warming effect, the artificial heat warming effect is analyzed by utilizing a local autoregressive related algorithm to obtain corresponding space aggregation characteristics, and the artificial heat warming effect strength corresponding to different building type areas is counted by taking different building attribute data as bottom layer data; setting the height and density of the building as independent variables, and taking the artificial heat temperature effect strength as a dependent variable, and constructing a corresponding artificial heat temperature effect strength calculation method to obtain an analysis result of the corresponding artificial heat temperature effect strength. The invention can combine the height and density information of the building, quickly and accurately acquire the artificial heat warming effect under different artificial heat emission situations, and realize the scientific simulation of the artificial heat warming effect.
An embodiment of the present invention further provides an apparatus for analyzing artificial thermal warming effect, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor executes the computer program to implement the method for analyzing artificial thermal warming effect as described above.
An embodiment of the present invention further provides a computer-readable storage medium, which includes a stored computer program, wherein when the computer program runs, the apparatus where the computer-readable storage medium is located is controlled to execute the method for analyzing the artificial thermal warming effect as described above.
The foregoing is directed to the preferred embodiment of the present invention, and it is understood that various changes and modifications may be made by one skilled in the art without departing from the spirit of the invention, and it is intended that such changes and modifications be considered as within the scope of the invention.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by a computer program, which can be stored in a computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. The storage medium may be a magnetic disk, an optical disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), or the like.
Claims (8)
1. An analysis method for artificial thermal warming effect is characterized by comprising the following steps:
preprocessing DMSP/OLS data and NDVI data obtained by downloading to generate an HSI index, and constructing an artificial heat emission regression model to obtain artificial heat emission space data; wherein, the establishing of the artificial heat emission regression model comprises the following steps: constructing a regression equation according to the total artificial heat emission and the HSI index, wherein the regression equation is that AHR is 42.13(HSI)2-15.42(HSI) +1.537, where AHR is total anthropogenic fever emissions and HSI is human population index;
coupling the artificial heat emission space data with a WRF model, updating average artificial heat emission amount of different land types in the WRF model, calculating emission amount difference between different artificial heat emission conditions and the unmanned heat emission conditions, and representing the emission amount difference as a corresponding artificial heat temperature increase effect;
after density segmentation processing is carried out on the artificial heat warming effect, the artificial heat warming effect is analyzed by utilizing a local autoregressive related algorithm to obtain corresponding space aggregation characteristics, and the artificial heat warming effect strength corresponding to different building type areas is counted by taking different building attribute data as bottom layer data; specifically, the local autoregressive correlation algorithm includes: the spatial aggregation characteristics of the artificial thermal warming effect are quantitatively analyzed by utilizing a Moire index calculation formula
Wherein, IiIs the Moire index, xiIs the artificial thermal warming effect value of grid i,
is the average value of the artificial thermal warming effect, WijRepresenting the weight relation between the grid i and the grid j;
setting the height and density of the building as independent variables, and taking the artificial heat temperature effect strength as a dependent variable, and constructing a corresponding artificial heat temperature effect strength calculation method to obtain an analysis result of the corresponding artificial heat temperature effect strength.
2. The method of analyzing an artificial thermal warming effect according to claim 1, further comprising:
and (3) obtaining the industrial energy consumption, the domestic energy consumption and the holding capacity of the civil motor vehicle by counting energy consumption chapters in the yearbook, and calculating the industrial artificial heat emission, the domestic artificial heat emission, the traffic artificial heat emission and the total artificial heat emission.
3. The method of analyzing an artificial thermal warming effect according to claim 1, further comprising:
and verifying the precision of the artificial heat warming effect intensity calculation method by constructing an artificial heat emission inversion model.
4. The method for analyzing the artificial thermal warming effect according to claim 3, wherein the accuracy of the method for calculating the intensity of the artificial thermal warming effect is verified by constructing an artificial heat emission inversion model, specifically:
calculating a square value of a correlation coefficient according to the artificial heat temperature effect intensity calculation method to obtain precision information of an artificial heat temperature inversion equation;
selecting a preset condition area, constructing an artificial heat temperature rising inversion model according to the artificial heat temperature rising effect strength calculation method, and calculating to obtain an artificial heat temperature rising effect simulation value;
and comparing the artificial thermal warming effect simulation value with the obtained artificial thermal warming effect value, calculating a correlation coefficient value between the simulation result and the real result, and verifying the precision of the model.
5. The method for analyzing the effect of artificial heat temperature increase according to claim 1, wherein the pretreatment is specifically:
and carrying out homogenization treatment on the annual average value of the DMSP/OLS data, and taking the maximum value of each standard grid as the monthly average value in the NDVI data.
6. The method for analyzing the artificial thermal warming effect according to claim 1, wherein the density segmentation is specifically:
and dividing the artificial heat emission warming effect according to the grade gradients of the average value-2 multiplied by standard deviation, the average value-1 multiplied by standard deviation, the average value-0.5 multiplied by standard deviation, the average value +0.5 multiplied by standard deviation, the average value +1 multiplied by standard deviation and the average value +2 multiplied by standard deviation by taking the average value of the artificial heat emission warming effect as a reference.
7. An apparatus for analyzing artificial thermal warming effect, comprising a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, the processor implementing the method of analyzing artificial thermal warming effect of claims 1-6 when executing the computer program.
8. A computer-readable storage medium, comprising a stored computer program, wherein the computer program, when executed, controls an apparatus in which the computer-readable storage medium is located to perform the method for analyzing artificial thermal warming effects according to claims 1-6.
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