CN116362567B - Urban industrial heat source enterprise emergency response remote sensing evaluation method - Google Patents
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Abstract
The invention relates to the technical field of monitoring and evaluating production activity level changes of industrial heat source enterprises, in particular to a remote sensing evaluation method for emergency response of urban industrial heat source enterprises, which comprises the following steps: s1, establishing an area industrial heat source enterprise list according to medium-high resolution satellite remote sensing heat abnormal point data and map POI data; s2, acquiring daily surface temperature and vegetation index products according to the medium-resolution satellite remote sensing multispectral data, calculating the background value of the surface temperature of the enterprise, and acquiring the heat radiation power of the industrial heat source enterprise; s3, constructing an enterprise production activity level index according to the early warning starting condition of the urban heavy pollution weather; s4, classifying and evaluating the implementation condition of emergency response measures of enterprises according to the emergency response list of the heavily polluted weather. The invention dynamically grasps the heat radiation and energy consumption of industrial heat source enterprises, and provides important support for the management and accurate management and control of emergency response in heavy polluted weather for two high enterprises.
Description
Technical Field
The invention relates to the technical field of monitoring and evaluating production activity level changes of industrial heat source enterprises, in particular to a remote sensing evaluation method for emergency response of urban industrial heat source enterprises.
Background
In recent years, the problem of atmospheric pollution in autumn and winter in key areas of China is more remarkable, and in order to 'peak clipping' for heavy pollution weather, emergency treatment plans for the heavy pollution weather are formulated in a plurality of cities, so that the smooth realization of the target of eliminating the heavy pollution weather is ensured. When the urban air quality index AQI exceeds 200, in order to effectively control heavy pollution weather and reduce pollutant emission, an environmental management department can timely start heavy pollution weather emergency response work according to air quality conditions, and issue emergency management and control measures such as production stopping, production limiting and the like for industrial enterprises in a management and control list, particularly, high-energy-consumption industrial heat source enterprises such as steel, cement, coking and the like are pollution emission 'large households', and are important objects of heavy pollution emergency response management and control.
In order to timely monitor industrial enterprise emergency response in a list, traditional heavy pollution weather emergency response management and control measures mainly rely on monitoring law enforcement personnel to check a resident field, checking production record accounts, checking electricity consumption, emission data and other fields to evaluate whether the enterprise emergency response measures are implemented, so that a large amount of manpower and material resources are required, monitoring and monitoring objects are incomplete, and law enforcement efficiency is low; on the other hand, the on-site verification of enterprises mainly depends on prior experience, main willingness and the like of verification staff, has insufficient objectivity, and is difficult to find illegal and illegal enterprises. Therefore, the traditional monitoring, supervising and evaluating method has the defects of incomplete coverage, insufficient objectivity and the like, so that the supervising and efficacy of enterprises is seriously affected, a large amount of monitoring, enforcing, manpower and material resources are consumed, meanwhile, the checking frequency is limited, the objectivity is insufficient, the enterprise coverage is not complete, dynamic monitoring, enforcing of emergency response of all enterprises are often difficult to realize, and the extensive supervising and evaluating mode is not suitable for the current refined managing and controlling requirements and is not beneficial to eliminating the realization of heavy pollution weather targets.
The satellite remote sensing is used as an emerging technology, provides an important technical means for monitoring and supervising production activity changes of industrial heat source enterprises, has the characteristics of macroscopicity, dynamics, objectivity, accuracy and the like, has the advantages of comprehensive coverage and multiple monitoring on the space range compared with the traditional ground monitoring means in the aspect of information acquisition, continuously acquires the production heat radiation energy space-time change of enterprises in a large area through a multispectral and thermal infrared satellite monitoring technology, and can effectively support emergency response supervision of urban heavy pollution weather industrial heat source enterprises. At present, the thermal infrared monitoring of the satellite remote sensing data (such as MODIS, VIIRS and the like) of the main atmospheric environment at home and abroad can achieve the maximum 2 times per day, the spatial resolution is up to 375 meters, and the requirements of dynamic monitoring and supervision of the production activity change of enterprises evaluating the industrial heat sources in urban areas can be met.
Therefore, in order to solve the problems, the application provides an emergency response remote sensing evaluation method for urban industrial heat source enterprises, which is used for identifying urban industrial heat source enterprises by fusing multisource medium-high resolution satellite remote sensing data, calculating the heat radiation energy of enterprises, and constructing an enterprise production activity level index model to objectively and quantitatively represent the implementation condition of emergency response measures of the urban industrial heat source enterprises.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provides an emergency response remote sensing evaluation method for urban industrial heat source enterprises, which is used for identifying urban industrial heat source enterprises by fusing multi-source medium-high resolution satellite remote sensing data, calculating the heat radiation energy produced by the enterprises and constructing an enterprise production activity level index model to objectively and quantitatively represent the implementation condition of emergency response measures of the urban industrial heat source enterprises.
In order to achieve the above purpose, the invention provides a remote sensing evaluation method for emergency response of urban industrial heat source enterprises, which comprises the following steps:
s1, establishing an area industrial heat source enterprise list according to medium-high resolution satellite remote sensing heat abnormal point data and map POI data;
s2, acquiring daily surface temperature and vegetation index products according to the medium-resolution satellite remote sensing multispectral data, calculating the background value of the surface temperature of the enterprise, and acquiring the heat radiation power of the industrial heat source enterprise;
s3, constructing an enterprise production activity level index according to the early warning starting condition of the urban heavy pollution weather;
s4, classifying and evaluating the implementation condition of emergency response measures of enterprises according to the emergency response list of heavy pollution weather.
Step S1 further comprises:
s1.1, dividing the urban area into grids of 1km multiplied by 1km at equal intervals according to the administrative division boundary range of the urban area;
s1.2, acquiring information of thermal anomaly points detected by a VIIRS sensor on a last year NPP and NOAA20 satellite, and extracting coordinate information of the thermal anomaly points;
s1.3, carrying out space superposition on the thermal anomaly point information and 3km grids, counting the number of thermal anomaly points of each grid, and screening grids with the number of thermal anomaly points exceeding 10 as suspected industrial heat source grids;
s1.4, overlapping a suspected industrial heat source grid with a high-resolution satellite image of one year, and identifying the area exceeding 100m by adopting a visual interpretation method 2 The boundary of an industrial heat source enterprise is outlined according to the distribution of the thermal anomaly points in the grid, and the high resolution is required to be better than 2m;
s1.5, according to the enterprise boundary of the industrial heat source, the map POI data of one year is overlapped, and the industrial heat source enterprise name is obtained.
Step S2 further comprises:
s2.1, extracting surface reflectivity data of 2.1 mu m middle infrared, 0.86 mu m near infrared and 0.65 mu m red wave bands with 1km resolution synthesized in 8 days from an MOD09 surface reflectivity product, extracting a multiband surface reflectivity data set of an urban area according to an urban administrative division boundary, and performing projection conversion;
s2.2, calculating a normalized vegetation index (NDVI) and a normalized building index (NDBI) of the urban area according to the surface reflectivity data set, wherein the calculation formula is as follows:
wherein ρ is 0.65 、ρ 0.86 And ρ 2.1 The earth surface reflectivity of the wave bands at the infrared position in the red wave band of 0.65 mu m, the infrared wave band of 0.86 mu m and the middle infrared wave band of 2.1 mu m respectively;
NDVI is a normalized vegetation index indicating surface vegetation coverage;
NDBI is normalized building index, and represents the coverage condition of the surface building;
s2.3, extracting a ground surface temperature LST and an emissivity data set with 1km resolution of a city area per day from an MOD11 ground surface temperature product, and performing projection conversion;
s2.4, establishing a buffer area with the radius of 15km for any enterprise according to the industrial heat source enterprise boundary extracted in the S1.4, and extracting normalized vegetation indexes NDVI, normalized building indexes NDBI and surface temperature LST of industrial enterprise pixels and peripheral pixels in the buffer area;
s2.5, extracting 3 surface parameters in the buffer area according to the S2.4, and establishing a multiple regression model of the background surface temperature of the industrial enterprise by taking the peripheral pixel parameters as training samples, wherein the formula is as follows: lst=a 0 +a 1 NDVI+a 2 NDBI；
Wherein LST, NDVI and NDBI are respectively the surface temperature, normalized vegetation index and normalized building index, a0, a1 and a2 are respectively regression coefficients, and the industrial enterprise pixels and the peripheral pixels are used as training samples to carry out fitting calculation by adopting a least square method to obtain the composite material;
s2.6, calculating the production heat radiation energy of the daily industrial enterprise according to the industrial enterprise background surface temperature multiple regression model constructed in the S2.5, namely, the difference between the actual temperature and the heat radiation energy at the background temperature, wherein the calculation formula is as follows:
e is the heat radiation power produced by an industrial enterprise, and the unit is W; sigma is the Stefan constant, i.e. 5.67×10 -8 The unit is W/(m) 2 ·K4)；∈ i The i pixel emissivity of the industrial enterprise; LST (least squares) o,i The temperature is actually observed for the ith pixel of an industrial enterprise, and the unit is K; LST (least squares) b,i The ith pixel background temperature of the industrial enterprise is calculated and obtained by a multiple regression model of the background surface temperature of the industrial enterprise according to the NDVI and NDBI parameter values of the pixels, wherein the unit is K; a is that i The unit is m for the ith pixel area of an industrial enterprise 2 。
Step S3 further comprises:
s3.1, collecting satellite remote sensing monitoring data of heat radiation energy produced by enterprises day by day within 30 days and early warning starting conditions of urban heavy pollution weather by taking an evaluation day as a reference;
s3.2, calculating average heat radiation energy and standard deviation thereof in normal production state when any enterprise does not start early warning by combining with urban heavy pollution weather early warning time information, wherein the calculation formula is as follows:
wherein, avgE j Average production of heat radiation energy for the jth enterprise of the city in W; e (E) j (k is the production heat radiation energy in the kth normal production state of the jth enterprise, the unit is W, N is the normal production days, stdE j The standard deviation of heat radiation is produced for enterprises, and the unit is W;
s3.3, calculating production activity level index EPALI of all industrial heat source enterprises in the urban area, wherein the calculation formula is as follows:
wherein, EPALI j Producing an activity level index for a j-th enterprise; e (E) j (t) is the j-th enterprise production heat radiation energy of the evaluation day t, and the unit is W; avgE j Average production of heat radiation energy for the jth enterprise of the city in W; stdE (StdE) j The standard deviation of heat radiation is produced for enterprises, and the unit is W.
Step S4 further comprises:
s4.1, searching early warning and production stopping measures of all industrial heat source enterprises according to the industrial heat source enterprise list names of the S1.5 by combining with the urban heavy pollution weather emergency response list;
s4.2, aiming at industrial heat source enterprises for which early warning measures are stopped, evaluating the implementation of emergency response measures of the enterprises according to the heat radiation energy produced by the enterprises on evaluation days; when the production heat radiation energy of the enterprise is less than or equal to 0, judging that the enterprise realizes the production stopping measures according to the requirements, otherwise judging that the production stopping measures are not realized according to the requirements;
s4.3, aiming at an industrial heat source enterprise with the early warning measure as the yield limitation, calculating a yield limitation proportion coefficient according to the requirement of the yield limitation measure, evaluating the implementation of emergency response measures of the enterprise according to the production activity level index EPALI of the enterprise on the evaluation day, and judging that the enterprise implements the yield limitation measure according to the requirement when the production activity level index of the enterprise is smaller than the yield limitation proportion coefficient, otherwise judging that the yield limitation measure is not implemented according to the requirement;
s4.4, aiming at an industrial heat source enterprise with the early warning measures of autonomous emission reduction, evaluating the implementation of emergency response measures of the enterprise according to the evaluation of the EPALI, and judging that the enterprise implements the autonomous emission reduction measures according to the requirements when the production activity level index of the enterprise is smaller than 1, or judging that the enterprise does not implement the autonomous emission reduction measures according to the requirements;
s4.5, respectively counting enterprises of the urban industrial heat source enterprises which start emergency response measures according to requirements and enterprises which do not realize the emergency response measures according to requirements, and calculating the proportion of the industrial heat source enterprises which do not realize the emergency response measures so as to evaluate the implementation condition of the emergency response of the urban heavy pollution weather.
Compared with the prior art, the invention has the following beneficial effects:
the urban industrial heat source enterprise emergency response remote sensing evaluation method is constructed, the urban industrial heat source enterprises are identified by fusing multi-source medium-high resolution satellite remote sensing data, the enterprise production heat radiation energy is calculated, the enterprise production activity level index model is constructed, the urban industrial heat source enterprise base number can be found out, the enterprise production activity level change can be comprehensively, objectively and dynamically evaluated, accurate support is provided for urban heavy pollution weather emergency response, and therefore the limitation that monitoring supervision is incomplete and objectivity is insufficient to cause low supervision efficiency in the traditional prediction method is overcome, and the implementation condition of emergency response measures of the urban industrial heat source enterprises is objectively and quantitatively represented.
Drawings
FIG. 1 is a schematic flow chart of the method of the present invention.
Detailed Description
The invention will now be further described with reference to the accompanying drawings.
Referring to fig. 1, the invention provides a remote sensing evaluation method for emergency response of an urban industrial heat source enterprise, which comprises the following steps:
s1, establishing an area industrial heat source enterprise list according to medium-high resolution satellite remote sensing heat abnormal point data and map POI data;
s2, acquiring daily surface temperature and vegetation index products according to the medium-resolution satellite remote sensing multispectral data, calculating the background value of the surface temperature of the enterprise, and acquiring the heat radiation power of the industrial heat source enterprise;
s3, constructing an enterprise production activity level index according to the early warning starting condition of the urban heavy pollution weather;
s4, classifying and evaluating the implementation condition of emergency response measures of enterprises according to the emergency response list of heavy pollution weather.
Step S1 further comprises:
s1.1, dividing the urban area into grids of 1km multiplied by 1km at equal intervals according to the administrative division boundary range of the urban area;
s1.2, acquiring information of thermal anomaly points detected by a VIIRS sensor on a last year NPP and NOAA20 satellite, and extracting coordinate information of the thermal anomaly points;
s1.3, carrying out space superposition on the thermal anomaly point information and 3km grids, counting the number of thermal anomaly points of each grid, and screening grids with the number of thermal anomaly points exceeding 10 as suspected industrial heat source grids;
s1.4, overlapping a suspected industrial heat source grid with a high-resolution satellite image of one year, and identifying the area exceeding 100m by adopting a visual interpretation method 2 The boundary of an industrial heat source enterprise is outlined according to the distribution of the thermal anomaly points in the grid, and the high resolution is required to be better than 2m;
s1.5, according to the enterprise boundary of the industrial heat source, the map POI data of one year is overlapped, and the industrial heat source enterprise name is obtained.
Step S2 further comprises:
s2.1, extracting surface reflectivity data of 2.1 mu m middle infrared, 0.86 mu m near infrared and 0.65 mu m red wave bands with 1km resolution synthesized in 8 days from an MOD09 surface reflectivity product, extracting a multiband surface reflectivity data set of an urban area according to an urban administrative division boundary, and performing projection conversion;
s2.2, calculating a normalized vegetation index (NDVI) and a normalized building index (NDBI) of the urban area according to the surface reflectivity data set, wherein the calculation formula is as follows:
wherein ρ is 0.65 、ρ 0.86 And ρ 2.1 The earth surface reflectivity of the wave bands at the infrared position in the red wave band of 0.65 mu m, the infrared wave band of 0.86 mu m and the middle infrared wave band of 2.1 mu m respectively;
NDVI is a normalized vegetation index indicating surface vegetation coverage;
NDBI is normalized building index, and represents the coverage condition of the surface building;
s2.3, extracting a ground surface temperature LST and an emissivity data set with 1km resolution of a city area per day from an MOD11 ground surface temperature product, and performing projection conversion;
s2.4, establishing a buffer area with the radius of 15km for any enterprise according to the industrial heat source enterprise boundary extracted in the S1.4, and extracting normalized vegetation indexes NDVI, normalized building indexes NDBI and surface temperature LST of industrial enterprise pixels and peripheral pixels in the buffer area;
s2.5, extracting 3 surface parameters in the buffer area according to the S2.4, and establishing a multiple regression model of the background surface temperature of the industrial enterprise by taking the peripheral pixel parameters as training samples, wherein the formula is as follows: lst=a 0 +a 1 NDVI+a 2 NDBI
Wherein LST, NDVI and NDBI are respectively the surface temperature, normalized vegetation index and normalized building index, a0, a1 and a2 are respectively regression coefficients, and the industrial enterprise pixels and the peripheral pixels are used as training samples to carry out fitting calculation by adopting a least square method to obtain the composite material;
s2.6, calculating the production heat radiation energy of the daily industrial enterprise according to the industrial enterprise background surface temperature multiple regression model constructed in the S2.5, namely, the difference between the actual temperature and the heat radiation energy at the background temperature, wherein the calculation formula is as follows:
e is the heat radiation power produced by an industrial enterprise, and the unit is W; sigma is the Stefan constant, i.e. 5.67×10 -8 The unit is W/(m) 2 ·K4)；∈ i The i pixel emissivity of the industrial enterprise; LST (least squares) o,i The temperature is actually observed for the ith pixel of an industrial enterprise, and the unit is K; LST (least squares) b,i The ith pixel background temperature of the industrial enterprise is calculated and obtained by a multiple regression model of the background surface temperature of the industrial enterprise according to the NDVI and NDBI parameter values of the pixels, wherein the unit is K; a is that i The unit is m for the ith pixel area of an industrial enterprise 2 。
Step S3 further comprises:
s3.1, collecting satellite remote sensing monitoring data of heat radiation energy produced by enterprises day by day within 30 days and early warning starting conditions of urban heavy pollution weather by taking an evaluation day as a reference;
s3.2, calculating average heat radiation energy and standard deviation thereof in normal production state when any enterprise does not start early warning by combining with urban heavy pollution weather early warning time information, wherein the calculation formula is as follows:
wherein, avgE j Average production of heat radiation energy for the jth enterprise of the city in W; e (E) j (k is the production heat radiation energy in the kth normal production state of the jth enterprise, the unit is W, N is the normal production days, stdE j The standard deviation of heat radiation is produced for enterprises, and the unit is W;
s3.3, calculating production activity level index EPALI of all industrial heat source enterprises in the urban area, wherein the calculation formula is as follows:
wherein, EPALI j Producing an activity level index for a j-th enterprise; e (E) j (t) is the j-th enterprise production heat radiation energy of the evaluation day t, and the unit is W; avgE j Average production of heat radiation energy for the jth enterprise of the city in W; stdE (StdE) j The standard deviation of heat radiation is produced for enterprises, and the unit is W.
Step S4 further comprises:
s4.1, searching early warning and production stopping measures of all industrial heat source enterprises according to the industrial heat source enterprise list names of the S1.5 by combining with the urban heavy pollution weather emergency response list;
s4.2, aiming at industrial heat source enterprises for which early warning measures are stopped, evaluating the implementation of emergency response measures of the enterprises according to the heat radiation energy produced by the enterprises on evaluation days; when the production heat radiation energy of the enterprise is less than or equal to 0, judging that the enterprise realizes the production stopping measures according to the requirements, otherwise judging that the production stopping measures are not realized according to the requirements;
s4.3, aiming at an industrial heat source enterprise with the early warning measure as the yield limitation, calculating a yield limitation proportion coefficient according to the requirement of the yield limitation measure, evaluating the implementation of emergency response measures of the enterprise according to the production activity level index EPALI of the enterprise on the evaluation day, and judging that the enterprise implements the yield limitation measure according to the requirement when the production activity level index of the enterprise is smaller than the yield limitation proportion coefficient, otherwise judging that the yield limitation measure is not implemented according to the requirement;
s4.4, aiming at an industrial heat source enterprise with the early warning measures of autonomous emission reduction, evaluating the implementation of emergency response measures of the enterprise according to the evaluation of the EPALI, and judging that the enterprise implements the autonomous emission reduction measures according to the requirements when the production activity level index of the enterprise is smaller than 1, or judging that the enterprise does not implement the autonomous emission reduction measures according to the requirements;
s4.5, respectively counting enterprises of the urban industrial heat source enterprises which start emergency response measures according to requirements and enterprises which do not realize the emergency response measures according to requirements, and calculating the proportion of the industrial heat source enterprises which do not realize the emergency response measures so as to evaluate the implementation condition of the emergency response of the urban heavy pollution weather.
The above is only a preferred embodiment of the present invention, only for helping to understand the method and the core idea of the present application, and the protection scope of the present invention is not limited to the above examples, and all technical solutions belonging to the concept of the present invention belong to the protection scope of the present invention. It should be noted that modifications and adaptations to the present invention may occur to one skilled in the art without departing from the principles of the present invention and are intended to be within the scope of the present invention.
The method solves the problems of incomplete monitoring and supervision, insufficient objectivity and low efficiency in the prior art on the whole, comprehensively, dynamically and objectively reflects the quantitative index of the regional enterprise production activity level change and also reflects the implementation condition of emergency response measures of urban heavy pollution weather by using the urban industrial heat source enterprise emergency response remote sensing evaluation method based on satellite remote sensing.
According to the method, the urban industrial heat source enterprises are identified by integrating the multi-source medium-high resolution satellite remote sensing data, the heat radiation energy produced by the enterprises is calculated, and the enterprise production activity level index model is constructed, so that the urban industrial heat source enterprise base number can be found out, the enterprise production activity level change can be comprehensively, objectively and dynamically evaluated, and accurate support is provided for emergency response of urban heavy pollution weather, so that the limitation of low supervision efficiency caused by incomplete supervision and insufficient objectivity in the traditional prediction method is overcome.
The invention objectively and quantitatively characterizes the implementation condition of emergency response measures of urban industrial heat source enterprises, and provides a new effective technical means for supporting emergency supervision and accurate management and control of atmospheric pollution sources.
Claims (1)
1. The remote sensing evaluation method for the emergency response of the urban industrial heat source enterprise is characterized by comprising the following steps of:
s1, establishing an area industrial heat source enterprise list according to medium-high resolution satellite remote sensing heat abnormal point data and map POI data;
s2, acquiring daily surface temperature and vegetation index products according to the medium-resolution satellite remote sensing multispectral data, calculating the background value of the surface temperature of the enterprise, and acquiring the heat radiation power of the industrial heat source enterprise;
s3, constructing an enterprise production activity level index according to the early warning starting condition of the urban heavy pollution weather;
s4, classifying and evaluating the implementation condition of emergency response measures of enterprises according to the emergency response list of heavy pollution weather;
step S1 further comprises:
s1.1, dividing the urban area into grids of 1km multiplied by 1km at equal intervals according to the administrative division boundary range of the urban area; s1.2, acquiring information of thermal anomaly points detected by a VIIRS sensor on a last year NPP and NOAA20 satellite, and extracting coordinate information of the thermal anomaly points;
s1.3, carrying out space superposition on the thermal anomaly point information and 3km grids, counting the number of thermal anomaly points of each grid, and screening grids with the number of thermal anomaly points exceeding 10 as suspected industrial heat source grids;
s1.4, overlapping the suspected industrial heat source grid with a one-year high-resolution satellite image, and identifying the area exceeding 100m by adopting a visual interpretation method 2 The boundary of an industrial heat source enterprise is outlined according to the distribution of the thermal anomaly points in the grid, and the high resolution is required to be better than 2m;
s1.5, according to the enterprise boundary of the industrial heat source, overlapping map POI data of one year to obtain the enterprise name of the industrial heat source;
step S2 further comprises:
s2.1, extracting surface reflectivity data of 2.1 mu m middle infrared, 0.86 mu m near infrared and 0.65 mu m red wave bands with 1km resolution synthesized in 8 days from an MOD09 surface reflectivity product, extracting a multiband surface reflectivity data set of an urban area according to an urban administrative division boundary, and performing projection conversion; s2.2, calculating a normalized vegetation index (NDVI) and a normalized building index (NDBI) of the urban area according to the surface reflectivity data set, wherein the calculation formula is as follows:
wherein said ρ is 0.65 、ρ 0.86 And ρ 2.1 The earth surface reflectivity of the wave bands at the infrared position in the red wave band of 0.65 mu m, the infrared wave band of 0.86 mu m and the middle infrared wave band of 2.1 mu m respectively;
the NDVI is a normalized vegetation index and indicates the coverage condition of the vegetation on the surface;
the NDBI is a normalized building index and represents the coverage condition of the surface building;
s2.3, extracting a ground surface temperature LST and an emissivity data set with 1km resolution of a city area per day from an MOD11 ground surface temperature product, and performing projection conversion;
s2.4, establishing a buffer area with the radius of 15km for any enterprise according to the industrial heat source enterprise boundary extracted in the S1.4, and extracting normalized vegetation indexes NDVI, normalized building indexes NDBI and surface temperature LST of industrial enterprise pixels and peripheral pixels in the buffer area;
s2.5, extracting 3 surface parameters in a buffer area according to the S2.4, and establishing a multiple regression model of the background surface temperature of the industrial enterprise by taking the peripheral pixel parameters as training samples, wherein the formula is as follows:
LST＝a 0 +a 1 NDVI+a 2 NDBI
the LST, the NDVI and the NDBI are respectively the surface temperature, the normalized vegetation index and the normalized building index, the a0, the a1 and the a2 are respectively regression coefficients, and the industrial enterprise pixels and the peripheral pixels are used as training samples to be obtained by adopting a least square method for fitting calculation;
s2.6, calculating the production heat radiation energy of the daily industrial enterprise according to the industrial enterprise background surface temperature multiple regression model constructed in the S2.5, namely, the difference between the actual temperature and the heat radiation energy at the background temperature, wherein the calculation formula is as follows:
wherein E is the heat radiation power produced by an industrial enterprise, and the unit is W; sigma is the Stefan constant, i.e. 5.67×10 -8 The unit is W/(m) 2 ·K4)；∈ i The i pixel emissivity of the industrial enterprise; LST (least squares) o,i The temperature is actually observed for the ith pixel of an industrial enterprise, and the unit is K; LST (least squares) b,i The ith pixel background temperature of the industrial enterprise is calculated and obtained by a multiple regression model of the background surface temperature of the industrial enterprise according to the NDVI and NDBI parameter values of the pixels, wherein the unit is K; a is that i The unit is m for the ith pixel area of an industrial enterprise 2 ；
Step S3 further comprises:
s3.1, collecting satellite remote sensing monitoring data of heat radiation energy produced by enterprises day by day within 30 days and early warning starting conditions of urban heavy pollution weather by taking an evaluation day as a reference;
s3.2, calculating average heat radiation energy and standard deviation thereof in normal production state when any enterprise does not start early warning by combining with urban heavy pollution weather early warning time information, wherein the calculation formula is as follows:
wherein, avgE j Average production of heat radiation energy for the jth enterprise of the city in W; e (E) j (k) The unit of the production heat radiation energy is W, which is the production heat radiation energy of the jth enterprise in the kth normal production state; n is the number of normal production days; stdE (StdE) j The standard deviation of heat radiation is produced for enterprises, and the unit is W;
s3.3, calculating production activity level index EPALI of all industrial heat source enterprises in the urban area, wherein the calculation formula is as follows:
wherein, EPALI j Producing an activity level index for a j-th enterprise; e (E) j (t) is the j-th enterprise production heat radiation energy of the evaluation day t, and the unit is W; avgE j Average production of heat radiation energy for the jth enterprise of the city in W; stdE (StdE) j The standard deviation of heat radiation is produced for enterprises, and the unit is W;
step S4 further comprises:
s4.1, searching early warning and production stopping measures of all industrial heat source enterprises according to the industrial heat source enterprise list names of the S1.5 by combining with an urban heavy pollution weather emergency response list;
s4.2, aiming at industrial heat source enterprises for which early warning measures are stopped, evaluating the implementation of emergency response measures of the enterprises according to the heat radiation energy produced by the enterprises on evaluation days; when the production heat radiation energy of the enterprise is less than or equal to 0, judging that the enterprise realizes the production stopping measures according to the requirements, otherwise judging that the production stopping measures are not realized according to the requirements;
s4.3, aiming at an industrial heat source enterprise with the early warning measure as the yield limitation, calculating a yield limitation proportion coefficient according to the requirement of the yield limitation measure, evaluating the implementation of emergency response measures of the enterprise according to the production activity level index EPALI of the enterprise on the evaluation day, and judging that the enterprise implements the yield limitation measure according to the requirement when the production activity level index of the enterprise is smaller than the yield limitation proportion coefficient, otherwise judging that the yield limitation measure is not implemented according to the requirement;
s4.4, aiming at an industrial heat source enterprise with the early warning measures of autonomous emission reduction, evaluating the implementation of emergency response measures of the enterprise according to the evaluation of the EPALI, and judging that the enterprise implements the autonomous emission reduction measures according to the requirements when the production activity level index of the enterprise is smaller than 1, or judging that the enterprise does not implement the autonomous emission reduction measures according to the requirements;
s4.5, respectively counting enterprises of the urban industrial heat source enterprises which start emergency response measures according to requirements and enterprises which do not realize the emergency response measures according to requirements, and calculating the proportion of the industrial heat source enterprises which do not realize the emergency response measures so as to evaluate the implementation condition of the emergency response of the urban heavy pollution weather.
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