Satellite-based heavy industry production intensity dynamic monitoring and evaluation method and device

Abstract

The invention provides a satellite-based heavy industry production intensity dynamic monitoring evaluation method and device, and the method comprises the steps: obtaining the data of a night transit satellite, and extracting a night thermal anomaly pixel data set based on a dynamic threshold value; performing clustering analysis on the night thermal anomaly pixel data set, and determining a heavy industry high-value region based on high-resolution land cover type data or NDVI data; calculating a thermal radiation power index of a heavy industry high-value area, and constructing a data effective rate correction model based on environmental factors to correct the thermal radiation power index; and inputting the corrected thermal radiation power index into a pre-constructed heavy industry production intensity index model, and then outputting a satellite-based heavy industry production intensity dynamic monitoring evaluation result. The heavy industry production intensity dynamic monitoring and evaluation method provided by the invention has the advantages of objectivity, wide monitoring range, historical traceability, low cost and the like.

Description

Technical Field

[0001] The present application relates to the field of thermal infrared remote sensing monitoring, and more specifically, to a satellite-based method and device for dynamic monitoring and evaluation of heavy industry production intensity. Background Art

[0002] At present, industrial production is still one of the main sources of ecological and environmental pollution. Monitoring and understanding the changes in industrial production intensity can provide data support for the analysis of pollution emissions, carbon emissions, etc., and can also monitor the implementation of environmental policies, such as the implementation of emission reduction measures. Industrial production intensity monitoring is mainly achieved through field surveys and monitoring of real-time industrial electricity consumption and energy consumption data, which has high manpower and time costs and limited access. In recent years, with the ultra-low emission transformation of coal-fired power plants, non-electric industries such as steel, cement, building materials and petrochemical heavy industries have gradually become the main source of industrial pollution emissions. Such heavy industrial production is often accompanied by a large amount of energy consumption, releasing a large amount of heat energy, causing the local temperature to be significantly higher than the surrounding area. Thermal infrared remote sensing can monitor industrial heat sources by detecting temperature anomalies. At the same time, remote sensing monitoring methods have the advantages of timely feedback, low cost and wide monitoring range.

[0003] Existing related studies are mostly based on satellite data such as Landsat and VIIRS, among which Landsat has high spatial resolution but low temporal resolution, while VIIRS is more suitable for daily monitoring and large-scale monitoring. Most industrial heat source monitoring studies based on VIIRS directly use its officially released thermal anomaly point products, but its thermal anomaly products are not set for industrial heat source monitoring. Affected by the temperature, the ability to extract industrial heat sources in different seasons varies greatly; in addition, previous studies have conducted comparative analysis through industrial thermal anomaly points without considering environmental factors. For periods of time with large differences in environmental conditions, simple numerical comparisons will lead to deviations or even errors in monitoring and analysis conclusions. Therefore, it is the purpose of this invention to solve the above problems and to monitor and quantitatively analyze changes in heavy industry production intensity efficiently, conveniently, quickly and accurately based on satellite remote sensing data. Summary of the invention

[0004] In order to solve the above technical problems, the present invention proposes a satellite-based dynamic monitoring and evaluation method and device for heavy industry production intensity, which provides data support for scientific management and control, positioning of management objects and change analysis.

[0005] In a first aspect, the present invention provides a satellite-based method for dynamic monitoring and evaluation of heavy industry production intensity, the method comprising:

[0006] Obtain satellite data passing at night and extract nighttime thermal anomaly pixel dataset based on dynamic thresholds;

[0007] Performing cluster analysis on the nighttime thermal anomaly pixel dataset, and determining heavy industry high-value areas based on high-resolution land cover type data or NDVI data;

[0008] Calculating the thermal radiation power index of the heavy industrial high-value area, and constructing a data efficiency correction model based on environmental factors to correct the thermal radiation power index;

[0009] The corrected thermal radiation power index is input into the pre-built heavy industry production intensity index model to output the satellite-based dynamic monitoring and evaluation results of heavy industry production intensity.

[0010] In combination with the first aspect, in a possible implementation, the step of acquiring satellite data passing through at night and extracting a nighttime thermal anomaly pixel data set based on a dynamic threshold includes:

[0011] Obtain satellite data passing at night and extract valid pixels after preprocessing;

[0012] Set an N×N window as the background window and set a dynamic threshold based on the temperature function. u > A, and the number of effective pixels is greater than 10 or the proportion of effective pixels is greater than 20%,

[0013] The nighttime thermal anomaly pixel data is extracted using the following discriminant formula:

[0014]

[0015] Among them, A, B, C, D, and E are dynamic thresholds based on temperature, T u and T v are the brightness temperatures of the central pixels with u and v as the central wavelengths, T' u and T' v are the brightness temperatures of the bands with u and v as the central wavelengths of the effective pixels in the background window, respectively. The value range of u is 3.7-4 μm, and the value range of v is 10.5-11.5 μm. △T u-v =T u -T v , △T' u-v =T' u -T' v , MeanT' u 、Mean△T' u-v Respectively represent T' u , △T' u-v The mean value, StdT' u 、StdT' v 、Std△T' u-v Respectively represent T' u , T' v , △T'u-v The mean absolute deviation.

[0016] In combination with the first aspect, in a possible implementation manner, a method for calculating the dynamic thresholds A, B, C, D, and E is as follows:

[0017] Based on the existing heavy industrial areas as samples, the temperature t at the time of satellite transit is obtained, and the temperature of different heavy industrial areas is calculated. The values ​​of are represented by tu, fb, fc, fd, and fe respectively;

[0018] The temperature t is classified from minT to maxT according to a fixed interval step, and different temperature classifications are set as Construct the data set sample corresponding to ti:

[0019] TU i ={tu1,tu2,...,tu n} i ,

[0020] FB i ={fb1,fb2,...,fb n} i , FC i ={fc1,fc2,...,fc n} i ,

[0021] FD i ={fd1,fd2,...,fd n} i , FE i ={fe1,fe2,...,fe n} i ;

[0022] When the temperature at the time of transit is t x hour, Dynamic threshold A=A i =μ(TU i )-2σ(TU i ), B=B i =μ(FB i )-2σ(FB i ), C=C i =μ(FC i )-2σ(FC i ), D=D i =μ(FD i )-2σ(FD i ), E=E i =μ(FE i )-2σ(FE i), where μ is the mean of the data set samples and σ is the standard deviation of the data set samples.

[0023] In combination with the first aspect, in a possible implementation, the step of performing cluster analysis on the nighttime thermal anomaly pixel dataset and determining the heavy industry high value area based on the high-resolution land cover type data or NDVI data includes obtaining the range of the clustering area of ​​several pixels through cluster analysis based on the nighttime thermal anomaly pixel dataset, retrieving the high-resolution land cover type data or NDVI data, and determining the non-heavy industry high value area that meets the following conditions:

[0024] More than 90% of the pixels in the cluster area are vegetation types or the NDVI mean is greater than θ1 and the night light index LN mean is less than θ2; θ1 is the NDVI threshold and θ2 is the night light index threshold.

[0025] The area outside the non-heavy industry high value area is determined as the heavy industry high value area.

[0026] In combination with the first aspect, in a possible implementation manner, the step of calculating the thermal radiation power index of the heavy industrial high-value area includes:

[0027] Calculate the thermal radiation power index F = Aa × ε × σ × T for each pixel in the heavy industry high value area u 4 -MeanT' u 4 ) / L. Where Aa is the pixel area, ε is the pixel emissivity, σ is the Stefan Boltzmann constant, and L = 10 8 .

[0028] In combination with the first aspect, in a possible implementation manner, the step of constructing a data efficiency correction model based on environmental factors to correct the thermal radiation power index includes:

[0029] Based on the pixel size, the heavy industrial high-value area to be analyzed is divided according to the grid size, and the attribute values ​​of the thermal radiation power index and the interference pixels based on environmental factors are respectively input into the grids corresponding to the spatial positions. Repeated pixels at adjacent moments are not counted twice.

[0030] Obtain the accumulated thermal radiation power index Fs of each grid in the heavy industrial high-value area to be analyzed during the study period, the accumulated number of interference pixels N0 and the number of effective pixels N1 of each grid, and generate the corrected thermal radiation power index of each grid:

[0031]

[0032] The thermal radiation power index of the heavy industrial high-value area to be analyzed is expressed as:

[0033]

[0034] Wherein, j represents the jth grid, and the interference pixels based on environmental factors include one or more of cloud, fog, snow, and dust pixels.

[0035] In combination with the first aspect, in a possible implementation, the heavy industry production intensity index model in the step of inputting the corrected thermal radiation power index into a pre-constructed heavy industry production intensity index model and then outputting a satellite-based dynamic monitoring and evaluation result of heavy industry production intensity is expressed as follows:

[0036]

[0037] Among them, Fs_C p Fs_C represents the cumulative heavy industry heat radiation power index of the study area within the study period p, k,p It represents the accumulated heavy industry heat radiation power index of the study area in the kth study period p. When k represents the kth year and p represents the study period in the same period of previous years, FAI>1 means that the production intensity is higher than the same period in history.

[0038] In a second aspect, a satellite-based dynamic monitoring and evaluation device for heavy industry production intensity is provided, the device comprising:

[0039] The acquisition module is used to obtain satellite data passing through at night and extract the nighttime thermal anomaly pixel data set based on a dynamic threshold;

[0040] A first processing module is used to perform cluster analysis on the nighttime thermal anomaly pixel data set and determine the heavy industry high value area based on high-resolution land cover type data or NDVI data;

[0041] The second processing module is used to calculate the thermal radiation power index of the heavy industrial high-value area and construct a data efficiency correction model based on environmental factors to correct the thermal radiation power index;

[0042] The output module is used to input the corrected thermal radiation power index into the pre-built heavy industry production intensity model and then output the satellite-based dynamic monitoring and evaluation results of heavy industry production intensity.

[0043] In conjunction with the second aspect, in a possible implementation manner, the first processing module includes:

[0044] A calculation submodule, used to calculate the thermal radiation power index of the heavy industrial high-value area;

[0045] The correction submodule is used to correct the thermal radiation power index by constructing a data efficiency correction model based on environmental factors.

[0046] In a third aspect, an electronic device is provided, including:

[0047] A memory on which application programs and an operating system are stored;

[0048] The processor is used to implement the function of executing the method described in the first aspect when executing the program or instruction. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 A flowchart of a satellite-based dynamic monitoring and evaluation method for heavy industry production intensity provided in an embodiment of the present application.

[0050] Figure 2 A schematic diagram of high-value heat points in heavy industry in an embodiment of a satellite-based dynamic monitoring and evaluation method for heavy industry production intensity provided in an embodiment of the present application.

[0051] Figure 3 Schematic diagram of the location distribution of nighttime thermal anomalies and high-value heavy industrial thermal output in an embodiment of a satellite-based dynamic monitoring and evaluation method for heavy industrial production intensity provided in an embodiment of the present application.

[0052] Figure 4 A graph of changes in thermal radiation power index within a time interval in an embodiment of a satellite-based dynamic monitoring and evaluation method for heavy industrial production intensity provided in an embodiment of the present application.

[0053] Figure 5 Framework diagram of the satellite-based dynamic monitoring and evaluation device for heavy industry production intensity provided in an embodiment of the present application. DETAILED DESCRIPTION

[0054] The present application is further described in detail below in conjunction with the accompanying drawings. It is necessary to point out here that the following specific implementation methods are only used to further illustrate the present application and cannot be understood as limiting the scope of protection of the present application. Technical personnel in this field can make some non-essential improvements and adjustments to the present application based on the above application content.

[0055] like Figure 1 As shown, the present invention proposes a satellite-based dynamic monitoring and evaluation method for heavy industry production intensity, which specifically includes the following steps:

[0056] S101. Obtain satellite data passing at night, and extract a nighttime thermal anomaly pixel dataset based on a dynamic threshold.

[0057] In this embodiment, the steps of acquiring satellite data passing through at night and extracting a nighttime thermal anomaly pixel data set based on a dynamic threshold include:

[0058] Obtain satellite data passing at night and extract valid pixels after preprocessing;

[0059] Set N×N pixels as the background window, when the center pixel T u > A, and the number of effective pixels is greater than 10 or the proportion of effective pixels is greater than 20%, the nighttime thermal anomaly pixel data is extracted using the following discriminant formula:

[0060]

[0061] Among them, A, B, C, D, and E are dynamic thresholds based on temperature, T u and T v are the brightness temperatures of the central pixels with u and v as the central wavelengths, T' u and T' v are the brightness temperatures of the bands with u and v as the central wavelengths of the effective pixels in the background window, respectively. The value range of u is 3.7-4 μm, and the value range of v is 10.5-11.5 μm. △T u-v =T u -T v , △T′ u-v =T′ u -T′ v , MeanT′ u 、Mean△T' u-v Respectively represent T' u , △T' u-v The mean value, StdT' u 、StdT' v 、Std△T' u-v Respectively represent T' u , T' v , △T' u-v The mean absolute deviation of , abs() represents the absolute value function.

[0062] The calculation method of the dynamic thresholds A, B, C, D, and E is as follows:

[0063] Based on the existing heavy industrial areas as samples, the temperature t at the time of satellite transit is obtained, and the temperature of different heavy industrial areas is calculated. The values ​​of are represented by tu, fb, fc, fd, and fe respectively;

[0064] The temperature t is classified from minT to maxT according to a fixed interval step, and different temperature classifications are set as Build i The corresponding dataset sample:

[0065] TU i ={tu1,tu2,...,tu n} i ,

[0066] FBi ={fb1,fb2,...,fb n} i , FC i ={fc1,fc2,...,fc n} i ,

[0067] FD i ={fd1,fd2,...,df n ] i , FE i ={fe1,fe2,...,fe n} i ;

[0068] When the temperature at the time of transit is t x hour, Dynamic threshold A=A i =μ(TU i )-2σ(TU i ), B=B i =μ(FB i )-2σ(FB i ), C=C i =μ(FC i )-2σ(FC i ), D=D i =μ(TU i )-2σ(FD i ), E=E i =μ(FE i )-2σ(FE i ), where μ is the mean of the data set samples and σ is the standard deviation of the data set samples. Indicates rounding down.

[0069] It should be noted that the satellite data obtained in this embodiment include data such as cloud, snow, fog, and dust. Cloud, snow, fog, and dust pixels and non-land pixels (water bodies, tidal flats, etc.) are extracted and marked as invalid pixels, and the others are valid pixels. In this embodiment, cloud, snow, fog, and dust pixels are used as environmental factors as the content of the correction model below to improve the analysis efficiency. A, B, C, D, and E are functions of temperature, which are determined by sample calculation. The temperature is the average temperature of the study area at 1 a.m. When the study area is large and the temperature difference is large, the study area is decomposed into multiple small and medium scales for separate calculations.

[0070] S102. Perform cluster analysis on the nighttime thermal anomaly pixel dataset, and determine heavy industry high-value areas based on high-resolution land cover type data or NDVI data.

[0071] In this embodiment, based on the nighttime thermal anomaly data set, the range of several pixel clustering areas is obtained through cluster analysis, and the clustering areas are used as objects for discrimination. High-resolution land cover type data or NDVI data are superimposed on an annual scale. Non-heavy industrial high-value areas that meet the following conditions are determined:

[0072] More than 90% of the pixels in the cluster area are vegetation types or the NDVI mean is greater than θ1 and the night light index LN mean is less than θ2; θ1 is the NDVI threshold and θ2 is the night light index threshold.

[0073] The area outside the non-heavy industry high value area is determined as the heavy industry high value area.

[0074] S103. Calculate the thermal radiation power index of the heavy industrial high-value area, and construct a data efficiency correction model based on environmental factors to correct the thermal radiation power index.

[0075] In this embodiment, the step of calculating the thermal radiation power index of the heavy industry high value area includes:

[0076] Calculate the thermal radiation power index F = Aa × ε × σ × T for each pixel in the heavy industry high value area u 4 -MeanT' u 4 ) / L. Where Aa is the pixel area, ε is the pixel emissivity, σ is the Stefan Boltzmann constant, and L = 10 8 .

[0077] The steps of constructing a data efficiency correction model based on environmental factors to correct the thermal radiation power index include:

[0078] Based on the pixel size, the heavy industrial high-value area to be analyzed is divided according to the grid size, and the attribute values ​​of the thermal radiation power index and the interference pixels based on environmental factors are respectively input into the grid corresponding to the spatial position, and the repeated pixels at the adjacent time are not counted twice. Specifically, the study area is divided according to the unified grid size L (1km<L<10km) based on the pixel size. The attribute values ​​of the thermal radiation power index and the interference pixels (cloud, fog, snow, dust pixels) are counted into the grid corresponding to the spatial position, and the repeated pixels at the adjacent time are not counted twice.

[0079] Obtain the accumulated thermal radiation power index Fs of each grid in the heavy industrial high-value area to be analyzed during the study period, the accumulated number of interference pixels N0 of each grid, and the number of effective pixels (all pixels except interference pixels) N1, and generate the corrected thermal radiation power index of each grid:

[0080]

[0081] The thermal radiation power index of the heavy industrial high-value area to be analyzed is expressed as:

[0082]

[0083] Among them, j represents the grid identifier and indicates the j-th grid.

[0084] S104. Input the corrected thermal radiation power index into the pre-built heavy industry production intensity index model and then output the satellite-based dynamic monitoring and evaluation results of heavy industry production intensity.

[0085] In this embodiment, the constructed heavy industry production intensity index model is expressed as follows:

[0086]

[0087] Among them, Fs_C p Fs_C represents the cumulative thermal radiation power index of heavy industrial high-value areas in the study area within the study period p, k,p It represents the accumulated heat radiation power of high-value areas of heavy industry in the study area within the kth study period p. When k represents the kth year and p represents the study period of the same period in previous years, FAI>1 means that the production intensity is higher than the same period in history.

[0088] The present invention is specifically described below by listing embodiments.

[0089] Example 1.

[0090] Corresponding to step s101, extract the high value of heavy industrial heat at night. Taking January 17, 2024 as an example, obtain the brightness temperature data and surface type data of NPP-VIIRS night bands I4, I5, M13, and M15. First, extract cloud, snow, fog, dust pixels and non-land pixels (water bodies, tidal flats, etc.), mark them as invalid pixels, and the others are valid pixels.

[0091] Set 21×21 pixels as the background window, when the center pixel T u >268, and the number of effective pixels is greater than 10 or the proportion of effective pixels is greater than 20%, then the pixels that meet the following conditions are considered to be nighttime thermal anomalies:

[0092]

[0093] Where u is I4 and M13, and v is I5 and M15. Substitute them into the above formulas and calculate them respectively, and take the union of the two. Combined with the historical heavy industrial high-value area database and high-resolution satellite maps to remove non-industrial high-value points, the extraction of heavy industrial high-value points on that day is obtained, such as Figure 2 shown. Figure 2The figure on the left in the middle shows the high-value points of heavy industrial heat extracted based on the above method, and the figure on the right shows the high-value points of heavy industrial heat extracted based on the thermal anomaly products released by VIIRS. It can be seen that more high-value points of heavy industrial heat can be extracted based on this method.

[0094] Example 2.

[0095] Corresponding to step s102. Taking the Beijing-Tianjin-Hebei region from 2019 to 2022 as an example, based on the nighttime thermal anomaly data set, the range of several pixel clustering areas is obtained through cluster analysis, and the clustering areas are used as objects for discrimination. More than 90% of the pixels in the clustering area are vegetation types or the NDVI mean value is greater than 0.4 & the nighttime light index LN mean value is less than 0.7, which is judged as a non-heavy industrial high-value area, and the others are heavy industrial high-value areas. Figure 3 As shown, Figure 3 The figure on the left in the middle shows the distribution of thermal anomalies at night. Figure 3 The figure on the right shows the distribution of high heat value locations in heavy industry after screening.

[0096] Example 3.

[0097] Take the quantitative analysis of heavy industrial production intensity in a certain place in February 2022 as an example.

[0098] Corresponding to step S103, all heavy industry heat high value data from February 2019 to February 2022 are selected, and after correction by environmental factors, the heat radiation power index of the heavy industry heat high value area is calculated, such as Figure 4 The values ​​corrected by environmental factors show that during the study period, the heavy industry heat radiation power index was 1030 in 2022, the lowest level in recent years; 2020 was the second highest at 1400, mainly affected by special environment; 2019 and 2021 were roughly the same, with 2021 being slightly higher.

[0099] Corresponding to step S104, the heavy industry production intensity index is combined to evaluate the changes in the heavy industry production intensity of a certain place during the study period of 2022 compared with previous years. p is the accumulated industrial heat high value pixel radiation power index of a certain place in February 2022, K = 4, Fs_C k,p It represents the accumulated industrial thermal high-value pixel radiation power index of a certain place in February of the kth year, where k = 1, 2, 3, and 4 represent 2019, 2020, 2021, and 2022 respectively. Substituting the corresponding thermal radiation power index from 2019 to 2022 into the formula, the industrial production intensity index in 2022 is calculated. It indicates that heavy industrial production emission reduction measures were implemented in a certain place in February 2022.

[0100] like Figure 5As shown, it is a framework diagram of a satellite-based heavy industry production intensity dynamic monitoring and evaluation device 50 provided in an embodiment of the present application, and the device 50 includes:

[0101] An acquisition module 51 is used to acquire satellite data passing through at night, and extract a nighttime thermal anomaly pixel data set based on a dynamic threshold;

[0102] A first processing module 52 is used to perform cluster analysis on the nighttime thermal anomaly pixel data set and determine the heavy industry high value area based on high-resolution land cover type data or NDVI data;

[0103] The second processing module 53 is used to calculate the thermal radiation power index of the heavy industrial high-value area and correct the thermal radiation power index by constructing a data efficiency correction model based on environmental factors;

[0104] The output module 54 is used to input the corrected thermal radiation power index into a pre-built heavy industry production intensity index model and then output a satellite-based dynamic monitoring and evaluation result of heavy industry production intensity.

[0105] In conjunction with the second aspect, in a possible implementation, the first processing module 62 includes:

[0106] A calculation submodule 521 is used to calculate the thermal radiation power index of the heavy industry high value area;

[0107] The correction submodule 522 is used to correct the thermal radiation power index by using a data efficiency correction model constructed based on environmental factors.

[0108] An embodiment of the present application also provides an electronic device, which includes a memory and a processor, wherein the memory stores an application program and an operating system; the processor is used to implement the above-mentioned dynamic monitoring and evaluation steps when executing the program or instructions.

[0109] It should be noted that, in this article, the terms "comprise", "include" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the sentence "comprises one..." does not exclude the presence of other identical elements in the process, method, article or device including the element. In addition, it should be noted that the scope of the method and device in the embodiment of the present application is not limited to performing functions in the order shown or discussed, and may also include performing functions in a substantially simultaneous manner or in reverse order according to the functions involved, for example, the described method may be performed in an order different from that described, and various steps may also be added, omitted, or combined. In addition, the features described with reference to certain examples may be combined in other examples.

[0110] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a computer software product, which is stored in a storage medium (such as ROM / RAM, a disk, or an optical disk), and includes a number of instructions for a terminal (which can be a mobile phone, a computer, a server, or a network device, etc.) to execute the methods of each embodiment of the present application.

[0111] The embodiments of the present application are described above in conjunction with the accompanying drawings, but the present application is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the guidance of the present application, ordinary technicians in this field can also make many forms without departing from the purpose of the present application and the scope of protection of the claims, all of which are within the protection of the present application.

Claims

1. A satellite-based dynamic monitoring and evaluation method for heavy industry production intensity, characterized in that: The method comprises: Obtain satellite data passing at night and extract nighttime thermal anomaly pixel dataset based on dynamic thresholds; Performing cluster analysis on the nighttime thermal anomaly pixel dataset, and determining heavy industry high-value areas based on high-resolution land cover type data or NDVI data; Calculating the thermal radiation power index of the heavy industrial high-value area, and constructing a data efficiency correction model based on environmental factors to correct the thermal radiation power index; The corrected thermal radiation power index is input into the pre-built heavy industry production intensity index model to output the satellite-based dynamic monitoring and evaluation results of heavy industry production intensity.

2. The satellite-based dynamic monitoring and evaluation method for heavy industry production intensity according to claim 1 is characterized in that: The step of acquiring satellite data passing through at night and extracting a nighttime thermal anomaly pixel data set based on a dynamic threshold comprises: Obtain satellite data passing at night, and extract valid pixels and invalid pixels after preprocessing; Set N×N pixels as the background window, when the center pixel T u > A, and the number of effective pixels is greater than 10 or the proportion of effective pixels is greater than 20%, the nighttime thermal anomaly pixel data is extracted using the following discriminant formula: Among them, A, B, C, D, and E are dynamic thresholds based on temperature, T u and T v are the brightness temperatures of the central pixels with u and v as the central wavelengths, T' u and T' v are the brightness temperatures of the bands with u and v as the central wavelengths of the effective pixels in the background window, respectively. The value range of u is 3.7-4 μm, and the value range of v is 10.5-11.5 μm; △T u-v =T u -T v , △T u-v =T u -T v , MeanT u 、Mean△T' u-v Respectively represent T' u , △T' u-v The mean value, StdT' u 、StdT' v 、Std△T' u-v Respectively represent T' u , T' v , △T' u-v The mean absolute deviation.

3. The satellite-based dynamic monitoring and evaluation method for heavy industry production intensity according to claim 2 is characterized in that: The calculation method of the dynamic thresholds A, B, C, D, and E is as follows: Based on the existing heavy industrial areas as samples, the temperature t at the time of satellite transit is obtained, and the T of different heavy industrial areas is calculated. u , △T u-v , The values ​​of are represented by tu, fb, fc, fd, and fe respectively; The temperature t is classified from minT to maxT according to a fixed interval step, and different temperature levels are set to t i , Build i The corresponding dataset sample: HERE i ={ti1,tu2,...,tu n } i , FB i ={fb1,fb2,...,fb n } i ,FC i ={fc1,fc2,...,fc n } i , FD i ={fd1,fd2,...,fd n } i ,FE i ={fe1,fe2,...,fe n } i ; When the temperature is tx 时 , Dynamic threshold A=A i =μ(TU i )-2σ(TU i ), B=B i =μ(FB i )-2σ(FB i ), C=C i =μ(FC i )-2σ(FC i ), D=D i =μ(FD i )-2σ(PD i ), E=E i =μ(FE i )-2σ(FE i ); Where μ is the mean of the data set samples and σ is the standard deviation of the data set samples.

4. The satellite-based dynamic monitoring and evaluation method for heavy industry production intensity according to claim 3 is characterized in that: The step of performing cluster analysis on the nighttime thermal anomaly pixel data set and determining the heavy industry high value area based on the high-resolution land cover type data or NDVI data comprises: obtaining a cluster area of ​​a plurality of pixels through cluster analysis based on the nighttime thermal anomaly pixel data set, retrieving the high-resolution land cover type data or NDVI data, and determining the non-heavy industry high value area that meets the following conditions: More than 90% of the pixels in the cluster area are vegetation types or the average NDVI value in the cluster area is greater than θ1 and the average night light index LN value is less than θ2; θ1 is the NDVI threshold, and θ2 is the night light index threshold; The area outside the non-heavy industry high value area is determined as the heavy industry high value area.

5. The satellite-based dynamic monitoring and evaluation method for heavy industry production intensity according to claim 4 is characterized in that The steps of calculating the thermal radiation power index of the heavy industry high value area include: Calculate the thermal radiation power index of each pixel in the heavy industry high value area F = Aa × ε × σ × (T u 4 -MeanT' u 4 ) / L, where Aa is the pixel area, ε is the pixel emissivity, σ is the Stefan Boltzmann constant, and L = 10 8 .

6. The satellite-based dynamic monitoring and evaluation method for heavy industry production intensity according to claim 5 is characterized in that: The step of constructing a data efficiency correction model based on environmental factors to correct the thermal radiation power index includes: Based on the pixel size, the heavy industrial high-value area to be analyzed is divided according to the grid size, and the attribute values ​​of the thermal radiation power index and the interference pixels based on environmental factors are respectively input into the grids corresponding to the spatial positions. Repeated pixels at adjacent moments are not counted twice. Obtain the accumulated thermal radiation power index Fs, the number of interfering pixels N0, and the number of effective pixels N1 of each grid in the heavy industrial high-value area to be analyzed during the study period, and generate the corrected thermal radiation power index of each grid: The thermal radiation power index of the heavy industrial high-value area to be analyzed is expressed as: Wherein, j represents the jth grid, and the interference pixels based on environmental factors include one or more of cloud, fog, snow, and dust pixels.

7. The satellite-based dynamic monitoring and evaluation method for heavy industry production intensity according to claim 6 is characterized in that: In the step of inputting the corrected thermal radiation power index into a pre-constructed heavy industry production intensity index model and then outputting a satellite-based dynamic monitoring and evaluation result of heavy industry production intensity, the heavy industry production intensity index model is expressed as follows: Among them, Fs_C p Fs_C represents the cumulative heavy industry heat radiation power index of the study area within the study period p, k,p It represents the accumulated heavy industry heat radiation power index of the study area in the kth study period p. When k represents the kth year and p represents the study period in the same period of previous years, FAI>1 means that the production intensity is higher than the same period in history.

8. The satellite-based heavy industry production intensity dynamic monitoring and evaluation device according to any one of claims 1 to 7, characterized in that: The device comprises: The acquisition module is used to obtain satellite data passing at night and extract the nighttime thermal anomaly pixel dataset based on dynamic thresholds. A first processing module is used to perform cluster analysis on the nighttime thermal anomaly pixel data set and determine the heavy industry high value area based on high-resolution land cover type data or NDVI data; The second processing module is used to calculate the thermal radiation power index of the heavy industrial high-value area and correct the thermal radiation power by constructing a data efficiency correction model based on environmental factors; The output module is used to input the corrected thermal radiation power index into a pre-built heavy industry production intensity index model and then output the satellite-based dynamic monitoring and evaluation results of heavy industry production intensity.

9. The satellite-based dynamic monitoring and evaluation device for heavy industry production intensity according to claim 8, characterized in that: The first processing module comprises: A calculation submodule, used to calculate the thermal radiation power index of the heavy industrial high-value area; The correction submodule is used to correct the thermal radiation power index by constructing a data efficiency correction model based on environmental factors.

10. An electronic device, characterized in that: include: A memory on which application programs and an operating system are stored; A processor, used to implement the steps of the method for dynamic monitoring and evaluation of heavy industry production intensity as described in any one of claims 1 to 7 when executing the program or instructions.

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