Loan risk assessment method and device, storage medium and electronic equipment

Abstract

The invention discloses a loan risk assessment method and device, a storage medium and electronic equipment. Relates to the technical field of data processing, the field of financial science and technology and other related technical fields. The method comprises the following steps: acquiring a target surface temperature remote sensing image of an area where a target enterprise is located and first meteorological data of the area where the target enterprise is located in different time periods; according to the target surface temperature remote sensing image and the first meteorological data, determining a product temperature difference value of an area where the target enterprise is located; according to the product temperature difference value and historical carbon emission data of the target enterprise, determining predicted carbon emission of the target enterprise; and determining a loan risk level of the target enterprise according to the predicted carbon emission. According to the method and the device, the problem that in the related technology, loan risk assessment is carried out according to enterprise carbon emission data collected by CEMS equipment, but the equipment is relatively high in installation cost and limited in installation number, so that enterprise carbon emission data collection is inaccurate, and the assessment accuracy of enterprise loan risks is relatively low is solved.

Description

Technical Field

[0001] The present application relates to the fields of data processing technology, financial technology and other related technical fields, and specifically, to a loan risk assessment method and device, storage medium and electronic device. Background Art

[0002] When high-carbon emission enterprises apply for loans, they will bring certain risks to financial institutions. Therefore, financial institutions need to pay close attention to the carbon emission data of enterprises, evaluate enterprises based on carbon emission data, and then quantify loan risks. However, traditional methods usually use online continuous emission monitoring systems (CEMS) to monitor carbon emission data in real time, but it is unrealistic to install CEMS equipment in all enterprises in the short term. At the same time, the installation cost is high, many units are involved, and it is extremely difficult to promote. This leads to inaccurate collection of carbon emission data of enterprises, which in turn makes it impossible for financial institutions to accurately assess the loan risks of enterprises.

[0003] Regarding the related technology, loan risk assessment is carried out based on the carbon emission data of enterprises collected by CEMS equipment. However, the installation cost of this equipment is high and the number of installations is limited, resulting in inaccurate collection of enterprise carbon emission data, which in turn leads to a relatively low accuracy rate in assessing enterprise loan risks. No effective solution has been proposed so far. Summary of the invention

[0004] The main purpose of this application is to provide a loan risk assessment method and device, storage medium and electronic device to solve the problem in the related art that loan risk assessment is performed based on corporate carbon emission data collected by CEMS equipment, but the equipment has a high installation cost and a limited number of installations, resulting in inaccurate collection of corporate carbon emission data, which in turn leads to a relatively low accuracy rate in assessing corporate loan risks.

[0005] In order to achieve the above-mentioned purpose, according to one aspect of the present application, a method for assessing loan risk is provided. The method comprises: obtaining a target surface temperature remote sensing image of the area where the target enterprise is located in different time periods and first meteorological data of the area where the target enterprise is located; determining the accumulated temperature difference of the area where the target enterprise is located based on the target surface temperature remote sensing image and the first meteorological data; determining the predicted carbon emissions of the target enterprise based on the accumulated temperature difference and the historical carbon emission data of the target enterprise; determining the loan risk level of the target enterprise based on the predicted carbon emissions, wherein the loan risk level is used to indicate whether to process a target loan business for the target enterprise.

[0006] Furthermore, determining the accumulated temperature difference of the area where the target enterprise is located based on the target surface temperature remote sensing image and the first meteorological data includes: obtaining the number of valid pixels and the total number of pixels of the target surface temperature remote sensing image; calculating based on the number of valid pixels and the total number of pixels to obtain the pixel ratio value corresponding to the target surface temperature remote sensing image; performing image filtering on the pixel ratio value corresponding to the target surface temperature remote sensing image based on a preset filtering threshold to obtain a filtered target surface temperature remote sensing image; determining the accumulated temperature difference of the area where the target enterprise is located based on the first meteorological data and the filtered target surface temperature remote sensing image.

[0007] Furthermore, determining the accumulated temperature difference of the area where the target enterprise is located based on the first meteorological data and the filtered target surface temperature remote sensing image includes: performing feature extraction on the filtered target surface temperature remote sensing image to obtain a first characteristic thermogram corresponding to the target enterprise; performing mask processing on the first meteorological data to obtain a mask image corresponding to the first meteorological data; performing feature extraction on the mask image to obtain a second characteristic thermogram corresponding to the target enterprise; determining the accumulated temperature difference of the area where the target enterprise is located based on the first characteristic thermogram and the second characteristic thermogram.

[0008] Furthermore, determining the accumulated temperature difference of the area where the target enterprise is located based on the first characteristic thermodynamic map and the second characteristic thermodynamic map includes: calculating the first characteristic thermodynamic map to obtain a first accumulated temperature value corresponding to the target enterprise; calculating the second characteristic thermodynamic map to obtain a second accumulated temperature value corresponding to the target enterprise; and calculating based on the first accumulated temperature value and the second accumulated temperature value to obtain the accumulated temperature difference of the area where the target enterprise is located.

[0009] Furthermore, determining the predicted carbon emissions of the target enterprise based on the accumulated temperature difference and the historical carbon emission data of the target enterprise includes: calculating the correlation coefficient between the accumulated temperature difference and the historical carbon emission data of the target enterprise to obtain a target correlation coefficient; determining a target fitting function based on the target correlation coefficient; and determining the predicted carbon emissions of the target enterprise based on the target fitting function.

[0010] Furthermore, determining the loan risk level of the target enterprise based on the predicted carbon emissions includes: judging whether the predicted carbon emissions are less than or equal to a preset value to obtain a judgment result; if the judgment result indicates that the predicted carbon emissions are less than or equal to the preset value, determining the loan risk level to be a first risk level; if the judgment result indicates that the predicted carbon emissions are greater than the preset value, determining the loan risk level to be a second risk level, wherein the second risk level is higher than the first risk level.

[0011] Furthermore, after determining that the loan risk level is the first risk level, the method also includes: when the loan risk level is the first risk level, obtaining historical loan behavior data of the target enterprise; based on the historical loan behavior data, determining the loan information of the target enterprise, wherein the loan information at least includes: the issuance method of the target loan business and the repayment method of the target enterprise; and based on the loan information, processing the target loan business for the target enterprise.

[0012] Furthermore, obtaining a target surface temperature remote sensing image of the area where the target enterprise is located in different time periods and first meteorological data of the area where the target enterprise is located includes: obtaining an initial surface temperature remote sensing image of the area where the target enterprise is located in different time periods and initial meteorological data of the area where the target enterprise is located; filtering and processing missing values ​​of the initial meteorological data to obtain the first meteorological data; obtaining second meteorological data of the area where the target enterprise is located, wherein the second meteorological data and the first meteorological data have different data sources; performing radiation calibration, atmospheric correction and cloud detection on the initial surface temperature remote sensing image to obtain a processed initial surface temperature remote sensing image; and fusing the processed initial surface temperature remote sensing image and the second meteorological data according to a spatiotemporal fusion algorithm to obtain the target surface temperature remote sensing image.

[0013] In order to achieve the above-mentioned purpose, according to another aspect of the present application, a loan risk assessment device is provided. The device includes: a first acquisition unit, which is used to acquire a target surface temperature remote sensing image of the area where the target enterprise is located and first meteorological data of the area where the target enterprise is located in different time periods; a first determination unit, which is used to determine the accumulated temperature difference of the area where the target enterprise is located based on the target surface temperature remote sensing image and the first meteorological data; a second determination unit, which is used to determine the predicted carbon emissions of the target enterprise based on the accumulated temperature difference and the historical carbon emissions data of the target enterprise; a third determination unit, which is used to determine the loan risk level of the target enterprise based on the predicted carbon emissions, wherein the loan risk level is used to indicate whether to process a target loan business for the target enterprise.

[0014] Furthermore, the first determination unit includes: a first acquisition module, used to obtain the number of valid pixels and the total number of pixels of the target surface temperature remote sensing image; a first calculation module, used to calculate based on the number of valid pixels and the total number of pixels to obtain the pixel ratio value corresponding to the target surface temperature remote sensing image; a filtering module, used to perform image filtering on the pixel ratio value corresponding to the target surface temperature remote sensing image according to a preset filtering threshold to obtain a filtered target surface temperature remote sensing image; a first determination module, used to determine the accumulated temperature difference in the area where the target enterprise is located based on the first meteorological data and the filtered target surface temperature remote sensing image.

[0015] Furthermore, the first determination module includes: a first feature extraction submodule, used to perform feature extraction on the filtered target surface temperature remote sensing image to obtain a first feature heat map corresponding to the target enterprise; a processing submodule, used to perform mask processing on the first meteorological data to obtain a mask image corresponding to the first meteorological data; a second feature extraction submodule, used to perform feature extraction on the mask image to obtain a second feature heat map corresponding to the target enterprise; a determination submodule, used to determine the accumulated temperature difference in the area where the target enterprise is located based on the first feature heat map and the second feature heat map.

[0016] Furthermore, the determination submodule includes: a first calculation submodule, used to calculate the first characteristic thermodynamic map to obtain a first accumulated temperature value corresponding to the target enterprise; a second calculation submodule, used to calculate the second characteristic thermodynamic map to obtain a second accumulated temperature value corresponding to the target enterprise; and a third calculation submodule, used to calculate based on the first accumulated temperature value and the second accumulated temperature value to obtain the accumulated temperature difference of the area where the target enterprise is located.

[0017] Furthermore, the second determination unit includes: a second calculation module, used to calculate the correlation coefficient between the accumulated temperature difference and the historical carbon emission data of the target enterprise to obtain a target correlation coefficient; a second determination module, used to determine a target fitting function based on the target correlation coefficient; and a third determination module, used to determine the predicted carbon emissions of the target enterprise based on the target fitting function.

[0018] Furthermore, the third determination unit includes: a judgment module, used to judge whether the predicted carbon emissions are less than or equal to a preset value, and obtain a judgment result; a fourth determination module, used to determine that the loan risk level is a first risk level if the judgment result indicates that the predicted carbon emissions are less than or equal to the preset value; and a fifth determination module, used to determine that the loan risk level is a second risk level if the judgment result indicates that the predicted carbon emissions are greater than the preset value, wherein the second risk level is higher than the first risk level.

[0019] Furthermore, the device also includes: a second acquisition unit, used to obtain historical loan behavior data of the target enterprise after determining that the loan risk level is the first risk level, when the loan risk level is the first risk level; a fourth determination unit, used to determine the loan information of the target enterprise based on the historical loan behavior data, wherein the loan information at least includes: the issuance method of the target loan business and the repayment method of the target enterprise; a processing unit, used to process the target loan business for the target enterprise based on the loan information.

[0020] Furthermore, the first acquisition unit includes: a second acquisition module, used to acquire the initial surface temperature remote sensing image of the area where the target enterprise is located and the initial meteorological data of the area where the target enterprise is located in different time periods; a first processing module, used to filter and process the missing values ​​of the initial meteorological data to obtain the first meteorological data; a third acquisition module, used to acquire the second meteorological data of the area where the target enterprise is located, wherein the data source of the second meteorological data is different from that of the first meteorological data; a second processing module, used to perform radiation calibration, atmospheric correction and cloud detection on the initial surface temperature remote sensing image to obtain the processed initial surface temperature remote sensing image; a fusion module, used to fuse the processed initial surface temperature remote sensing image and the second meteorological data according to a spatiotemporal fusion algorithm to obtain the target surface temperature remote sensing image.

[0021] In order to achieve the above-mentioned purpose, according to one aspect of the present application, a computer-readable storage medium is provided, wherein the storage medium stores a program, wherein when the program is running, the device where the storage medium is located is controlled to execute any one of the loan risk assessment methods described above.

[0022] In order to achieve the above-mentioned purpose, according to another aspect of the present application, an electronic device is also provided, the electronic device includes one or more processors and a memory, and the memory is used to store the loan risk assessment method implemented by one or more processors as described above.

[0023] Through this application, the following steps are adopted: obtaining the target surface temperature remote sensing image and the first meteorological data of the target enterprise in the area where the target enterprise is located in different time periods; determining the accumulated temperature difference of the target enterprise in the area according to the target surface temperature remote sensing image and the first meteorological data; determining the predicted carbon emissions of the target enterprise according to the accumulated temperature difference and the historical carbon emission data of the target enterprise; determining the loan risk level of the target enterprise according to the predicted carbon emissions, wherein the loan risk level is used to indicate whether to handle the target loan business for the target enterprise. Through this application, the problem of loan risk assessment based on the carbon emission data of the enterprise collected by the CEMS device in the related technology is solved, but the installation cost of the device is high and the installation quantity is limited, resulting in inaccurate collection of carbon emission data of the enterprise, and then resulting in a relatively low accuracy rate of assessment of the enterprise loan risk. In this scheme, first, the target surface temperature remote sensing image and the first meteorological data of the target enterprise in the area where the target enterprise is located in different time periods are obtained, and the target surface temperature remote sensing image and the first meteorological data are used to calculate the accumulated temperature difference (i.e., the accumulated temperature difference) of the target enterprise in the area where the target enterprise is located in different time periods, so as to assess the impact of climate change on the carbon emissions of the enterprise. Then, based on the accumulated temperature difference and the historical carbon emission data of the target enterprise, the predicted carbon emissions of the target enterprise are determined by using relevant models or statistical analysis methods. Finally, the loan risk level of the target enterprise is determined based on the predicted carbon emissions, and whether to process the loan business for the target enterprise is decided based on the loan risk level. The predicted carbon emissions of the target enterprise are obtained by processing and predicting the target surface temperature remote sensing image, the first meteorological data and the historical carbon emission data of the target enterprise, and the loan risk of the target enterprise is evaluated by predicting the carbon emissions, which avoids the problems of incomplete data collection and inaccurate data caused by collecting corporate carbon emission data through CEMS equipment in the traditional method, thereby achieving the effect of improving the accuracy of the assessment of corporate loan risks. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] The drawings constituting a part of the present application are used to provide a further understanding of the present application. The illustrative embodiments and descriptions of the present application are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0025] Figure 1 is a flow chart of a loan risk assessment method provided according to an embodiment of the present application;

[0026] Figure 2 is a schematic diagram of a loan risk assessment method provided according to an embodiment of the present application;

[0027] Figure 3 is a schematic diagram of a loan risk assessment device provided according to an embodiment of the present application;

[0028] Figure 4is a schematic diagram of an electronic device provided according to an embodiment of the present application. DETAILED DESCRIPTION

[0029] It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0030] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present application.

[0031] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequential order. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present application described here. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0032] It should be noted that the relevant information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for display, data for analysis, etc.) involved in this disclosure are all information and data authorized by the user or fully authorized by all parties. For example, an interface is set between this system and the relevant user or organization. Before obtaining relevant information, it is necessary to send an acquisition request to the aforementioned user or organization through the interface, and obtain relevant information after receiving the consent information fed back by the aforementioned user or organization.

[0033] The present invention is described below in conjunction with preferred implementation steps. Figure 1 is a flow chart of a loan risk assessment method provided in accordance with an embodiment of the present application, such as Figure 1 As shown, the method comprises the following steps:

[0034] Step S101, obtaining a target surface temperature remote sensing image of the area where the target enterprise is located and first meteorological data of the area where the target enterprise is located in different time periods.

[0035] Optionally, a remote sensing image of the target surface temperature is obtained by satellite remote sensing. The first meteorological data is obtained from a meteorological station, meteorological department or other meteorological data provider in the area where the target enterprise is located. The first meteorological data includes but is not limited to: meteorological element data such as temperature, humidity, wind speed, precipitation, etc. in the area where the target enterprise is located.

[0036] Step S102, determining the accumulated temperature difference of the area where the target enterprise is located based on the target surface temperature remote sensing image and the first meteorological data.

[0037] Optionally, the target surface temperature remote sensing image and the first meteorological data are processed and calculated to obtain the accumulated temperature difference of the area where the target enterprise is located.

[0038] Step S103, determining the predicted carbon emissions of the target enterprise based on the accumulated temperature difference and the historical carbon emissions data of the target enterprise.

[0039] Optionally, the accumulated temperature difference of the target enterprise's region and the historical carbon emission data of the target enterprise are integrated to ensure the consistency of the data in time and space. The accumulated temperature difference and historical carbon emission data are analyzed using statistical analysis methods to obtain the predicted carbon emissions of the target enterprise in the future.

[0040] Step S104, determining the loan risk level of the target enterprise based on the predicted carbon emissions, wherein the loan risk level is used to indicate whether to process a target loan business for the target enterprise.

[0041] Optionally, based on the predicted carbon emissions, a loan risk assessment is conducted on the target enterprise to determine its loan risk level. And based on the loan risk level of the target enterprise, a decision is made as to whether to process a loan business for the target enterprise.

[0042] In summary, the target surface temperature remote sensing image and the first meteorological data of the target enterprise in different time periods are obtained, and the target surface temperature remote sensing image and the first meteorological data are used to calculate the accumulated temperature difference (i.e., the accumulated temperature difference) of the target enterprise in different time periods to evaluate the impact of climate change on the carbon emissions of the enterprise. Then, based on the accumulated temperature difference and the historical carbon emission data of the target enterprise, the predicted carbon emissions of the target enterprise are determined by using relevant models or statistical analysis methods. Finally, the loan risk level of the target enterprise is determined based on the predicted carbon emissions, and it is decided whether to apply for a loan business for the target enterprise based on the loan risk level. The predicted carbon emissions of the target enterprise are obtained by processing and predicting the target surface temperature remote sensing image, the first meteorological data and the historical carbon emission data of the target enterprise, and the loan risk of the target enterprise is evaluated by predicting the carbon emissions, which avoids the problems of incomplete data collection and inaccurate data caused by collecting carbon emission data of the enterprise through CEMS equipment in the traditional method, thereby achieving the effect of improving the accuracy of the assessment of the loan risk of the enterprise.

[0043] Optionally, in the loan risk assessment method provided in the embodiment of the present application, determining the accumulated temperature difference of the area where the target enterprise is located based on the target surface temperature remote sensing image and the first meteorological data includes: obtaining the number of valid pixels and the total number of pixels of the target surface temperature remote sensing image; calculating based on the number of valid pixels and the total number of pixels to obtain the pixel ratio value corresponding to the target surface temperature remote sensing image; performing image filtering on the pixel ratio value corresponding to the target surface temperature remote sensing image based on a preset filtering threshold to obtain a filtered target surface temperature remote sensing image; determining the accumulated temperature difference of the area where the target enterprise is located based on the first meteorological data and the filtered target surface temperature remote sensing image.

[0044] In an optional embodiment, the number of effective pixels and the total number of pixels of the target surface temperature remote sensing image are obtained. In the remote sensing image, not all pixels represent surface temperature information. Effective pixels refer to those pixels that actually reflect the surface temperature characteristics, such as land, water, etc. Effective pixels can exclude invalid information such as clouds and shadows.

[0045] Then, the pixel ratio value is calculated by the following formula:

[0046] Pixel ratio value = (number of effective pixels / total number of pixels)

[0047] The target land surface temperature remote sensing image is filtered according to the pixel ratio value. A pixel ratio filtering threshold is set (the filtering threshold is usually 0.6). If the pixel ratio value is lower than the preset filtering threshold, it is considered that the target land surface temperature remote sensing image is greatly affected by clouds, shadows, etc., and needs to be further processed or excluded, thereby obtaining a filtered target land surface temperature remote sensing image containing only high-quality surface pixels.

[0048] Finally, the accumulated temperature difference of the area where the target enterprise is located is determined based on the first meteorological data and the filtered target surface temperature remote sensing image.

[0049] By calculating the ratio of the number of effective pixels to the total number of pixels in the target surface temperature remote sensing image, the target surface temperature remote sensing image can be quickly filtered according to the preset filtering threshold, thereby accurately obtaining high-quality surface temperature information.

[0050] Optionally, in the loan risk assessment method provided in the embodiment of the present application, determining the accumulated temperature difference in the area where the target enterprise is located based on the first meteorological data and the filtered target surface temperature remote sensing image includes: performing feature extraction on the filtered target surface temperature remote sensing image to obtain a first characteristic heat map corresponding to the target enterprise; performing mask processing on the first meteorological data to obtain a mask image corresponding to the first meteorological data; performing feature extraction on the mask image to obtain a second characteristic heat map corresponding to the target enterprise; and determining the accumulated temperature difference in the area where the target enterprise is located based on the first characteristic heat map and the second characteristic heat map.

[0051] In an optional embodiment, the filtered target surface temperature remote sensing image is processed for missing pixel completion, and image processing techniques (such as edge detection, texture analysis, temperature gradient calculation, etc.) are used to extract features from the filtered target surface temperature remote sensing image to generate a first feature heat map, which highlights the distribution of surface temperature in the area where the target enterprise is located. Then, according to the quality and integrity of the first meteorological data, mask conditions (such as setting missing data thresholds, etc.) are set to remove invalid or irrelevant information (such as clouds, missing data, etc.) in the first meteorological data to obtain a mask image corresponding to the first meteorological data, and the same image processing technology as the filtered target surface temperature remote sensing image is applied to generate a second feature heat map, which displays the key features in the first meteorological data. Finally, the accumulated temperature difference is calculated through the first feature heat map (surface temperature features of the target enterprise's area) and the second feature heat map (first meteorological data features of the target enterprise).

[0052] By processing and extracting features from the filtered target surface temperature remote sensing image using image processing technology, the first characteristic heat map corresponding to the target enterprise can be accurately obtained; at the same time, masking and feature extraction are performed on the first meteorological data to quickly remove invalid or irrelevant information, thereby accurately obtaining the second characteristic heat map corresponding to the target enterprise.

[0053] Optionally, in the loan risk assessment method provided in the embodiment of the present application, determining the accumulated temperature difference of the area where the target enterprise is located based on the first characteristic heat map and the second characteristic heat map includes: calculating the first characteristic heat map to obtain a first accumulated temperature value corresponding to the target enterprise; calculating the second characteristic heat map to obtain a second accumulated temperature value corresponding to the target enterprise; calculating based on the first accumulated temperature value and the second accumulated temperature value to obtain the accumulated temperature difference of the area where the target enterprise is located.

[0054] In an optional embodiment, a reference temperature (such as 10°C) is usually selected as the starting point for the accumulated temperature calculation, and the daily average temperature exceeding the reference temperature is accumulated to obtain the accumulated temperature value. The surface temperature data of the target enterprise is extracted from the first characteristic thermodynamic map, and for each time period (such as daily), the surface temperature difference exceeding the reference temperature is accumulated to obtain the first accumulated temperature value. Then, the relevant meteorological temperature data is extracted from the second characteristic thermodynamic map, and the meteorological temperature difference exceeding the reference temperature is accumulated to obtain the second accumulated temperature value. By calculating the difference between the first accumulated temperature value and the second accumulated temperature value, the accumulated temperature difference of the area where the target enterprise is located is obtained.

[0055] Through the first characteristic thermodynamic map and the second characteristic thermodynamic map corresponding to the area where the target enterprise is located, the first accumulated temperature value and the second accumulated temperature value can be quickly calculated, and based on the first accumulated temperature value and the second accumulated temperature value, the accumulated temperature difference of the area where the target enterprise is located can be accurately obtained.

[0056] Optionally, in the loan risk assessment method provided in the embodiment of the present application, determining the predicted carbon emissions of the target enterprise based on the accumulated temperature difference and the historical carbon emissions data of the target enterprise includes: calculating the correlation coefficient between the accumulated temperature difference and the historical carbon emissions data of the target enterprise to obtain a target correlation coefficient; determining a target fitting function based on the target correlation coefficient; and determining the predicted carbon emissions of the target enterprise based on the target fitting function.

[0057] In an optional embodiment, first, the accumulated temperature difference of the area where the target enterprise is located and the historical carbon emission data of the target enterprise are cleaned and formatted to remove outliers, missing values, etc. in the data, and format the data into a form suitable for analysis. Then, statistical software or programming language is used to calculate the correlation coefficient between the accumulated temperature difference and the historical carbon emission data (for example: Pearson correlation coefficient, Spearman rank correlation coefficient, etc.), and according to the calculated target correlation coefficient, the relationship strength between the accumulated temperature difference and the carbon emissions is analyzed to obtain a suitable target fitting function (for example: linear regression, polynomial regression, nonlinear regression, etc.). Finally, the carbon emissions of the target enterprise are predicted by the target fitting function to obtain the predicted carbon emissions.

[0058] By calculating the correlation coefficient between the accumulated temperature difference and the historical carbon emission data, the strength of the relationship between the two can be analyzed, the target fitting function can be quickly obtained, and then the carbon emissions of the target enterprise can be accurately predicted based on the target fitting function.

[0059] Optionally, in the loan risk assessment method provided in the embodiment of the present application, determining the loan risk level of the target enterprise based on the predicted carbon emissions includes: judging whether the predicted carbon emissions are less than or equal to a preset value to obtain a judgment result; if the judgment result indicates that the predicted carbon emissions are less than or equal to the preset value, then determining the loan risk level to be a first risk level; if the judgment result indicates that the predicted carbon emissions are greater than the preset value, then determining the loan risk level to be a second risk level, wherein the second risk level is higher than the first risk level.

[0060] In an optional embodiment, a preset value is determined and used as a threshold value of carbon emissions to evaluate the loan risk level of the target enterprise. The predicted carbon emissions are compared with the preset value. If the predicted carbon emissions are less than or equal to the preset value, it means that the carbon emissions of the target enterprise are low and the loan risk is low, so the loan risk level of the target enterprise is determined to be the first risk level; if the predicted carbon emissions are greater than the preset value, it means that the carbon emissions of the target enterprise are high and the loan risk is high, so the loan risk level of the target enterprise is determined to be the second risk level.

[0061] By judging the relationship between the predicted carbon emissions of the target enterprise and the preset value, the loan risk level of the target enterprise can be determined quickly and accurately.

[0062] Optionally, in the loan risk assessment method provided in the embodiment of the present application, after determining that the loan risk level is the first risk level, the method also includes: when the loan risk level is the first risk level, obtaining historical loan behavior data of the target enterprise; based on the historical loan behavior data, determining the loan information of the target enterprise, wherein the loan information includes at least: the issuance method of the target loan business and the repayment method of the target enterprise; and based on the loan information, processing the target loan business for the target enterprise.

[0063] In an optional embodiment, if the loan risk level is the first risk level, the historical loan data of the target enterprise is collected, including but not limited to: the historical loan amount, loan term, loan interest rate and repayment record of the target enterprise. And based on the historical loan behavior data, the credit history of the target enterprise is analyzed, including but not limited to: repayment timeliness, loan default, etc. The loan information of the target enterprise is determined through the analysis results, including but not limited to: the issuance method of the target loan business (for example: one-time issuance, installment issuance, etc.), the repayment method of the target loan business (for example: equal principal and interest, equal principal, one-time principal and interest repayment, etc.) and other terms (for example: penalty interest, early repayment, loan extension, etc.). Through the above loan information, the target loan business is processed for the target enterprise.

[0064] Through the historical loan behavior data of the target enterprise, the credit history of the target enterprise can be accurately analyzed, and the loan information of the target enterprise can be quickly determined based on the analysis results, so as to accurately handle the target loan business for the target enterprise.

[0065] Optionally, in the loan risk assessment method provided in the embodiment of the present application, obtaining a target surface temperature remote sensing image of the area where the target enterprise is located in different time periods and first meteorological data of the area where the target enterprise is located includes: obtaining an initial surface temperature remote sensing image of the area where the target enterprise is located in different time periods and initial meteorological data of the area where the target enterprise is located; filtering and processing missing values ​​of the initial meteorological data to obtain first meteorological data; obtaining second meteorological data of the area where the target enterprise is located, wherein the second meteorological data and the first meteorological data have different data sources; performing radiation calibration, atmospheric correction and cloud detection on the initial surface temperature remote sensing image to obtain a processed initial surface temperature remote sensing image; and performing data fusion on the processed initial surface temperature remote sensing image and the second meteorological data according to a spatiotemporal fusion algorithm to obtain a target surface temperature remote sensing image.

[0066] In an optional embodiment, first, the target surface temperature remote sensing image and initial meteorological data of the target enterprise's area in different time periods are collected, and the initial meteorological data are filtered and processed for missing values ​​to remove irrelevant or erroneous information to obtain the first meteorological data. Then, the initial surface temperature remote sensing image is subjected to radiation calibration, atmospheric correction, cloud detection and other operations to obtain the processed initial surface temperature remote sensing image, and the second meteorological data (MODIS data) of the target enterprise's area is obtained. Finally, the spatial (geographic) and temporal (series) dimensions of the data are considered using a spatiotemporal fusion algorithm, and the processed initial surface temperature remote sensing image is fused with the second meteorological data to obtain the target surface temperature remote sensing image.

[0067] It should be noted that the purpose of radiation calibration is to correct the sensor data and convert it into actual physical temperature units; the purpose of atmospheric correction is to eliminate the influence of the earth's atmosphere on the image data and improve the accuracy of surface temperature readings; the purpose of cloud detection is to identify areas in the image that are obscured by clouds and improve the accuracy of temperature measurement data.

[0068] By preprocessing the initial surface temperature remote sensing images and initial meteorological data of the target enterprise in different time periods, the processed initial surface temperature remote sensing images and the first meteorological data can be quickly obtained. At the same time, the processed initial surface temperature remote sensing images are fused with MODIS data using a spatiotemporal fusion algorithm to accurately obtain the target surface temperature remote sensing image.

[0069] It should be noted that Figure 2 Schematic diagram of a loan risk assessment method provided according to an embodiment of the present application. Figure 2 As shown, first, the target surface temperature remote sensing image of the area where the target enterprise is located in different time periods and the first meteorological data of the area where the target enterprise is located are obtained, and the ratio of the number of effective pixels to the total number of pixels of the target surface temperature remote sensing image is calculated to obtain the pixel ratio value corresponding to the target surface temperature remote sensing image, and the pixel ratio value corresponding to the target surface temperature remote sensing image is image filtered by a preset filtering threshold to obtain the filtered target surface temperature remote sensing image.

[0070] Then, feature extraction is performed on the filtered target surface temperature remote sensing image to obtain the first feature thermogram corresponding to the target enterprise, and mask processing and feature extraction are performed on the first meteorological data to obtain the second feature thermogram corresponding to the target enterprise. The first feature thermogram and the second feature thermogram are calculated to obtain the first accumulated temperature value and the second accumulated temperature value corresponding to the target enterprise, respectively. The difference between the first accumulated temperature value and the second accumulated temperature value is calculated to obtain the accumulated temperature difference in the area where the target enterprise is located.

[0071] Finally, according to the accumulated temperature difference and the historical carbon emission data of the target enterprise, the target fitting function is determined, and the predicted carbon emissions of the target enterprise are determined through the target fitting function. And it is judged whether the predicted carbon emissions are less than or equal to the preset value: if the judgment result indicates that the predicted carbon emissions are less than or equal to the preset value, it means that the carbon emissions of the target enterprise are low and the loan risk is small, so the loan risk level of the target enterprise is determined to be the first risk level; if the judgment result indicates that the predicted carbon emissions are greater than the preset value, it means that the carbon emissions of the target enterprise are high, so the loan risk level of the target enterprise is determined to be the second risk level.

[0072] It should be noted that when the loan risk level is the first risk level, the historical loan behavior data of the target enterprise is obtained, and through the historical loan behavior data, the loan information such as the loan issuance method and repayment method of the target enterprise when handling the target loan business is determined, and based on the above loan information, the target loan business is handled for the target enterprise.

[0073] The loan risk assessment method provided by the embodiment of the present application is to obtain the target surface temperature remote sensing image and the first meteorological data of the target enterprise in the area where the target enterprise is located in different time periods; determine the accumulated temperature difference of the target enterprise in the area according to the target surface temperature remote sensing image and the first meteorological data; determine the predicted carbon emissions of the target enterprise according to the accumulated temperature difference and the historical carbon emission data of the target enterprise; determine the loan risk level of the target enterprise according to the predicted carbon emissions, wherein the loan risk level is used to indicate whether to handle the target loan business for the target enterprise, which solves the problem that the loan risk assessment is performed based on the carbon emission data of the enterprise collected by the CEMS device in the related technology, but the installation cost of the device is high and the installation quantity is limited, resulting in inaccurate collection of carbon emission data of the enterprise, and then resulting in a relatively low accuracy rate of assessment of the loan risk of the enterprise. In summary, in this scheme, first, the target surface temperature remote sensing image and the first meteorological data of the target enterprise in different time periods are obtained, and the target surface temperature remote sensing image and the first meteorological data are used to calculate the accumulated temperature difference (i.e., the accumulated temperature difference) of the target enterprise in the area where the target enterprise is located in different time periods, so as to assess the impact of climate change on the carbon emissions of the enterprise. Then, based on the accumulated temperature difference and the historical carbon emission data of the target enterprise, the predicted carbon emissions of the target enterprise are determined by using relevant models or statistical analysis methods. Finally, the loan risk level of the target enterprise is determined based on the predicted carbon emissions, and whether to process the loan business for the target enterprise is decided based on the loan risk level. The predicted carbon emissions of the target enterprise are obtained by processing and predicting the target surface temperature remote sensing image, the first meteorological data and the historical carbon emission data of the target enterprise, and the loan risk of the target enterprise is evaluated by predicting the carbon emissions, which avoids the problems of incomplete data collection and inaccurate data caused by collecting corporate carbon emission data through CEMS equipment in the traditional method, thereby achieving the effect of improving the accuracy of the assessment of corporate loan risks.

[0074] It should be noted that the steps shown in the flowcharts of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and that, although a logical order is shown in the flowcharts, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0075] The embodiment of the present application also provides a loan risk assessment device. It should be noted that the loan risk assessment device of the embodiment of the present application can be used to execute the loan risk assessment method provided in the embodiment of the present application. The loan risk assessment device provided in the embodiment of the present application is introduced below.

[0076] Figure 3 Schematic diagram of a loan risk assessment device according to an embodiment of the present application. Figure 3 As shown, the device includes: a first obtaining unit 301, a first determining unit 302, a second determining unit 303, and a third determining unit 304.

[0077] The first acquisition unit 301 is used to acquire a target surface temperature remote sensing image of the area where the target enterprise is located and first meteorological data of the area where the target enterprise is located in different time periods;

[0078] The first determination unit 302 is used to determine the accumulated temperature difference of the area where the target enterprise is located according to the target surface temperature remote sensing image and the first meteorological data;

[0079] The second determination unit 303 is used to determine the predicted carbon emissions of the target enterprise based on the accumulated temperature difference and the historical carbon emissions data of the target enterprise;

[0080] The third determination unit 304 is used to determine the loan risk level of the target enterprise according to the predicted carbon emissions, wherein the loan risk level is used to indicate whether to process a target loan business for the target enterprise.

[0081] The loan risk assessment device provided in the embodiment of the present application comprises a first acquisition unit 301 that acquires a target surface temperature remote sensing image of the area where the target enterprise is located and first meteorological data of the area where the target enterprise is located in different time periods; a first determination unit 302 determines the accumulated temperature difference of the area where the target enterprise is located based on the target surface temperature remote sensing image and the first meteorological data; a second determination unit 303 determines the predicted carbon emissions of the target enterprise based on the accumulated temperature difference and the historical carbon emission data of the target enterprise; a third determination unit 304 determines the loan risk level of the target enterprise based on the predicted carbon emissions, wherein the loan risk level is used to indicate whether a target loan business is processed for the target enterprise, thereby solving the problem in the related art of conducting loan risk assessment based on the carbon emission data of the enterprise collected by the CEMS device, but the installation cost of the device is high and the number of installations is limited, resulting in inaccurate collection of the carbon emission data of the enterprise, and thus resulting in a relatively low accuracy rate in assessing the loan risk of the enterprise. In this scheme, first, the target surface temperature remote sensing image and the first meteorological data of the target enterprise in different time periods are obtained, and the target surface temperature remote sensing image and the first meteorological data are used to calculate the accumulated temperature difference (i.e., the accumulated temperature difference) of the target enterprise in different time periods to evaluate the impact of climate change on the carbon emissions of the enterprise. Then, based on the accumulated temperature difference and the historical carbon emission data of the target enterprise, the predicted carbon emissions of the target enterprise are determined by using relevant models or statistical analysis methods. Finally, the loan risk level of the target enterprise is determined based on the predicted carbon emissions, and it is decided whether to apply for a loan business for the target enterprise based on the loan risk level. The predicted carbon emissions of the target enterprise are obtained by processing and predicting the target surface temperature remote sensing image, the first meteorological data and the historical carbon emission data of the target enterprise, and the loan risk of the target enterprise is evaluated by predicting the carbon emissions, which avoids the problems of incomplete data collection and inaccurate data caused by collecting carbon emission data of enterprises through CEMS equipment in the traditional method, thereby achieving the effect of improving the accuracy of the assessment of enterprise loan risks.

[0082] Optionally, in the loan risk assessment device provided in the embodiment of the present application, the first determination unit includes: a first acquisition module, used to obtain the number of valid pixels and the total number of pixels of the target surface temperature remote sensing image; a first calculation module, used to calculate based on the number of valid pixels and the total number of pixels to obtain the pixel ratio value corresponding to the target surface temperature remote sensing image; a filtering module, used to perform image filtering on the pixel ratio value corresponding to the target surface temperature remote sensing image according to a preset filtering threshold to obtain a filtered target surface temperature remote sensing image; a first determination module, used to determine the accumulated temperature difference in the area where the target enterprise is located based on the first meteorological data and the filtered target surface temperature remote sensing image.

[0083] Optionally, in the loan risk assessment device provided in the embodiment of the present application, the first determination module includes: a first feature extraction submodule, used to perform feature extraction on the filtered target surface temperature remote sensing image to obtain a first feature heat map corresponding to the target enterprise; a processing submodule, used to perform mask processing on the first meteorological data to obtain a mask image corresponding to the first meteorological data; a second feature extraction submodule, used to perform feature extraction on the mask image to obtain a second feature heat map corresponding to the target enterprise; a determination submodule, used to determine the accumulated temperature difference in the area where the target enterprise is located based on the first feature heat map and the second feature heat map.

[0084] Optionally, in the loan risk assessment device provided in the embodiment of the present application, the determination submodule includes: a first calculation submodule, used to calculate the first characteristic heat map to obtain a first accumulated temperature value corresponding to the target enterprise; a second calculation submodule, used to calculate the second characteristic heat map to obtain a second accumulated temperature value corresponding to the target enterprise; and a third calculation submodule, used to calculate based on the first accumulated temperature value and the second accumulated temperature value to obtain the accumulated temperature difference of the area where the target enterprise is located.

[0085] Optionally, in the loan risk assessment device provided in the embodiment of the present application, the second determination unit includes: a second calculation module, used to calculate the correlation coefficient between the accumulated temperature difference and the historical carbon emission data of the target enterprise to obtain a target correlation coefficient; a second determination module, used to determine a target fitting function based on the target correlation coefficient; and a third determination module, used to determine the predicted carbon emissions of the target enterprise based on the target fitting function.

[0086] Optionally, in the loan risk assessment device provided in the embodiment of the present application, the third determination unit includes: a judgment module, used to judge whether the predicted carbon emissions are less than or equal to a preset value to obtain a judgment result; a fourth determination module, used to determine that the loan risk level is a first risk level if the judgment result indicates that the predicted carbon emissions are less than or equal to the preset value; and a fifth determination module, used to determine that the loan risk level is a second risk level if the judgment result indicates that the predicted carbon emissions are greater than the preset value, wherein the second risk level is higher than the first risk level.

[0087] Optionally, in the loan risk assessment device provided in the embodiment of the present application, the device also includes: a second acquisition unit, used to obtain historical loan behavior data of the target enterprise after determining that the loan risk level is the first risk level, when the loan risk level is the first risk level; a fourth determination unit, used to determine the loan information of the target enterprise based on the historical loan behavior data, wherein the loan information includes at least: the issuance method of the target loan business and the repayment method of the target enterprise; a processing unit, used to process the target loan business for the target enterprise based on the loan information.

[0088] Optionally, in the loan risk assessment device provided in the embodiment of the present application, the first acquisition unit includes: a second acquisition module, used to acquire the initial surface temperature remote sensing image of the area where the target enterprise is located and the initial meteorological data of the area where the target enterprise is located in different time periods; a first processing module, used to filter and process the missing values ​​of the initial meteorological data to obtain the first meteorological data; a third acquisition module, used to acquire the second meteorological data of the area where the target enterprise is located, wherein the second meteorological data and the first meteorological data have different data sources; the second processing module, used to perform radiation calibration, atmospheric correction and cloud detection on the initial surface temperature remote sensing image to obtain the processed initial surface temperature remote sensing image; a fusion module, used to fuse the processed initial surface temperature remote sensing image and the second meteorological data according to a spatiotemporal fusion algorithm to obtain the target surface temperature remote sensing image.

[0089] The loan risk assessment device includes a processor and a memory. The first acquisition unit 301, the first determination unit 302, the second determination unit 303, the third determination unit 304, etc. are all stored in the memory as program units. The processor executes the program units stored in the memory to achieve accurate assessment of the loan risk.

[0090] The processor includes a kernel, which calls the corresponding program unit from the memory. One or more kernels can be set, and accurate assessment of loan risks can be achieved by adjusting kernel parameters.

[0091] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM, and the memory includes at least one memory chip.

[0092] An embodiment of the present invention provides a computer-readable storage medium having a program stored thereon, and the program, when executed by a processor, implements a method for evaluating loan risk.

[0093] An embodiment of the present invention provides a processor, which is used to run a program, wherein the program executes a loan risk assessment method when running.

[0094] like Figure 4As shown, an embodiment of the present invention provides an electronic device, the device includes a processor, a memory, and a program stored in the memory and executable on the processor, and the processor implements the following steps when executing the program: obtaining a target surface temperature remote sensing image of the area where the target enterprise is located in different time periods and first meteorological data of the area where the target enterprise is located; determining the accumulated temperature difference of the area where the target enterprise is located based on the target surface temperature remote sensing image and the first meteorological data; determining the predicted carbon emissions of the target enterprise based on the accumulated temperature difference and the historical carbon emissions data of the target enterprise; determining the loan risk level of the target enterprise based on the predicted carbon emissions, wherein the loan risk level is used to indicate whether to process a target loan business for the target enterprise.

[0095] Optionally, determining the accumulated temperature difference of the area where the target enterprise is located based on the target surface temperature remote sensing image and the first meteorological data includes: obtaining the number of valid pixels and the total number of pixels of the target surface temperature remote sensing image; calculating based on the number of valid pixels and the total number of pixels to obtain the pixel ratio value corresponding to the target surface temperature remote sensing image; performing image filtering on the pixel ratio value corresponding to the target surface temperature remote sensing image based on a preset filtering threshold to obtain a filtered target surface temperature remote sensing image; determining the accumulated temperature difference of the area where the target enterprise is located based on the first meteorological data and the filtered target surface temperature remote sensing image.

[0096] Optionally, determining the accumulated temperature difference of the area where the target enterprise is located based on the first meteorological data and the filtered target surface temperature remote sensing image includes: performing feature extraction on the filtered target surface temperature remote sensing image to obtain a first characteristic heat map corresponding to the target enterprise; performing mask processing on the first meteorological data to obtain a mask image corresponding to the first meteorological data; performing feature extraction on the mask image to obtain a second characteristic heat map corresponding to the target enterprise; and determining the accumulated temperature difference of the area where the target enterprise is located based on the first characteristic heat map and the second characteristic heat map.

[0097] Optionally, determining the accumulated temperature difference of the area where the target enterprise is located based on the first characteristic thermogram and the second characteristic thermogram includes: calculating the first characteristic thermogram to obtain a first accumulated temperature value corresponding to the target enterprise; calculating the second characteristic thermogram to obtain a second accumulated temperature value corresponding to the target enterprise; calculating based on the first accumulated temperature value and the second accumulated temperature value to obtain the accumulated temperature difference of the area where the target enterprise is located.

[0098] Optionally, determining the predicted carbon emissions of the target enterprise based on the accumulated temperature difference and the historical carbon emissions data of the target enterprise includes: calculating the correlation coefficient between the accumulated temperature difference and the historical carbon emissions data of the target enterprise to obtain a target correlation coefficient; determining a target fitting function based on the target correlation coefficient; and determining the predicted carbon emissions of the target enterprise based on the target fitting function.

[0099] Optionally, determining the loan risk level of the target enterprise based on the predicted carbon emissions includes: judging whether the predicted carbon emissions are less than or equal to a preset value to obtain a judgment result; if the judgment result indicates that the predicted carbon emissions are less than or equal to the preset value, determining the loan risk level to be a first risk level; if the judgment result indicates that the predicted carbon emissions are greater than the preset value, determining the loan risk level to be a second risk level, wherein the second risk level is higher than the first risk level.

[0100] Optionally, after determining that the loan risk level is the first risk level, the method also includes: when the loan risk level is the first risk level, obtaining historical loan behavior data of the target enterprise; determining the loan information of the target enterprise based on the historical loan behavior data, wherein the loan information at least includes: the issuance method of the target loan business and the repayment method of the target enterprise; and processing the target loan business for the target enterprise based on the loan information.

[0101] Optionally, obtaining a target surface temperature remote sensing image of the area where the target enterprise is located in different time periods and first meteorological data of the area where the target enterprise is located includes: obtaining an initial surface temperature remote sensing image of the area where the target enterprise is located in different time periods and initial meteorological data of the area where the target enterprise is located; filtering and processing missing values ​​of the initial meteorological data to obtain first meteorological data; obtaining second meteorological data of the area where the target enterprise is located, wherein the second meteorological data and the first meteorological data have different data sources; performing radiation calibration, atmospheric correction and cloud detection on the initial surface temperature remote sensing image to obtain a processed initial surface temperature remote sensing image; and fusing the processed initial surface temperature remote sensing image and the second meteorological data according to a spatiotemporal fusion algorithm to obtain a target surface temperature remote sensing image.

[0102] The devices in this article can be servers, PCs, PADs, mobile phones, etc.

[0103] The present application also provides a computer program product, which, when executed on a data processing device, is suitable for executing a program that initializes the following method steps: obtaining a target surface temperature remote sensing image of the area where the target enterprise is located and first meteorological data of the area where the target enterprise is located in different time periods; determining the accumulated temperature difference of the area where the target enterprise is located based on the target surface temperature remote sensing image and the first meteorological data; determining the predicted carbon emissions of the target enterprise based on the accumulated temperature difference and the historical carbon emissions data of the target enterprise; determining the loan risk level of the target enterprise based on the predicted carbon emissions, wherein the loan risk level is used to indicate whether to process a target loan business for the target enterprise.

[0104] Optionally, determining the accumulated temperature difference of the area where the target enterprise is located based on the target surface temperature remote sensing image and the first meteorological data includes: obtaining the number of valid pixels and the total number of pixels of the target surface temperature remote sensing image; calculating based on the number of valid pixels and the total number of pixels to obtain the pixel ratio value corresponding to the target surface temperature remote sensing image; performing image filtering on the pixel ratio value corresponding to the target surface temperature remote sensing image based on a preset filtering threshold to obtain a filtered target surface temperature remote sensing image; determining the accumulated temperature difference of the area where the target enterprise is located based on the first meteorological data and the filtered target surface temperature remote sensing image.

[0105] Optionally, determining the accumulated temperature difference of the area where the target enterprise is located based on the first meteorological data and the filtered target surface temperature remote sensing image includes: performing feature extraction on the filtered target surface temperature remote sensing image to obtain a first characteristic heat map corresponding to the target enterprise; performing mask processing on the first meteorological data to obtain a mask image corresponding to the first meteorological data; performing feature extraction on the mask image to obtain a second characteristic heat map corresponding to the target enterprise; and determining the accumulated temperature difference of the area where the target enterprise is located based on the first characteristic heat map and the second characteristic heat map.

[0106] Optionally, determining the accumulated temperature difference of the area where the target enterprise is located based on the first characteristic thermogram and the second characteristic thermogram includes: calculating the first characteristic thermogram to obtain a first accumulated temperature value corresponding to the target enterprise; calculating the second characteristic thermogram to obtain a second accumulated temperature value corresponding to the target enterprise; calculating based on the first accumulated temperature value and the second accumulated temperature value to obtain the accumulated temperature difference of the area where the target enterprise is located.

[0107] Optionally, determining the predicted carbon emissions of the target enterprise based on the accumulated temperature difference and the historical carbon emissions data of the target enterprise includes: calculating the correlation coefficient between the accumulated temperature difference and the historical carbon emissions data of the target enterprise to obtain a target correlation coefficient; determining a target fitting function based on the target correlation coefficient; and determining the predicted carbon emissions of the target enterprise based on the target fitting function.

[0108] Optionally, determining the loan risk level of the target enterprise based on the predicted carbon emissions includes: judging whether the predicted carbon emissions are less than or equal to a preset value to obtain a judgment result; if the judgment result indicates that the predicted carbon emissions are less than or equal to the preset value, determining the loan risk level to be a first risk level; if the judgment result indicates that the predicted carbon emissions are greater than the preset value, determining the loan risk level to be a second risk level, wherein the second risk level is higher than the first risk level.

[0109] Optionally, after determining that the loan risk level is the first risk level, the method also includes: when the loan risk level is the first risk level, obtaining historical loan behavior data of the target enterprise; determining the loan information of the target enterprise based on the historical loan behavior data, wherein the loan information at least includes: the issuance method of the target loan business and the repayment method of the target enterprise; and processing the target loan business for the target enterprise based on the loan information.

[0110] Optionally, obtaining a target surface temperature remote sensing image of the area where the target enterprise is located in different time periods and first meteorological data of the area where the target enterprise is located includes: obtaining an initial surface temperature remote sensing image of the area where the target enterprise is located in different time periods and initial meteorological data of the area where the target enterprise is located; filtering and processing missing values ​​of the initial meteorological data to obtain first meteorological data; obtaining second meteorological data of the area where the target enterprise is located, wherein the second meteorological data and the first meteorological data have different data sources; performing radiation calibration, atmospheric correction and cloud detection on the initial surface temperature remote sensing image to obtain a processed initial surface temperature remote sensing image; and fusing the processed initial surface temperature remote sensing image and the second meteorological data according to a spatiotemporal fusion algorithm to obtain a target surface temperature remote sensing image.

[0111] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0112] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0113] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0114] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0115] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0116] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0117] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0118] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity 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, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0119] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0120] The above are only embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included within the scope of the claims of the present application.

Claims

1. A method for assessing loan risk, characterized in that: include: Acquire a target surface temperature remote sensing image of the area where the target enterprise is located and first meteorological data of the area where the target enterprise is located in different time periods; Determine the accumulated temperature difference of the area where the target enterprise is located based on the target surface temperature remote sensing image and the first meteorological data; Determine the predicted carbon emissions of the target enterprise based on the accumulated temperature difference and the historical carbon emissions data of the target enterprise; The loan risk level of the target enterprise is determined according to the predicted carbon emissions, wherein the loan risk level is used to indicate whether to process a target loan business for the target enterprise.

2. The method according to claim 1, characterized in that Determining the accumulated temperature difference of the area where the target enterprise is located according to the target surface temperature remote sensing image and the first meteorological data includes: Obtaining the number of valid pixels and the total number of pixels of the target surface temperature remote sensing image; Calculating according to the number of effective pixels and the total number of pixels to obtain a pixel ratio value corresponding to the target surface temperature remote sensing image; According to a preset filtering threshold, image filtering is performed on the pixel ratio values ​​corresponding to the target surface temperature remote sensing image to obtain a filtered target surface temperature remote sensing image; The accumulated temperature difference of the area where the target enterprise is located is determined based on the first meteorological data and the filtered target surface temperature remote sensing image.

3. The method according to claim 2, characterized in that Determining the accumulated temperature difference of the area where the target enterprise is located according to the first meteorological data and the filtered target surface temperature remote sensing image includes: Performing feature extraction on the filtered target surface temperature remote sensing image to obtain a first feature thermal map corresponding to the target enterprise; Performing mask processing on the first meteorological data to obtain a mask image corresponding to the first meteorological data; Extracting features from the mask image to obtain a second feature heat map corresponding to the target enterprise; The accumulated temperature difference of the area where the target enterprise is located is determined based on the first characteristic thermodynamic map and the second characteristic thermodynamic map.

4. The method according to claim 3, characterized in that Determining the accumulated temperature difference of the area where the target enterprise is located according to the first characteristic thermodynamic map and the second characteristic thermodynamic map includes: Calculating the first characteristic thermodynamic map to obtain a first accumulated temperature value corresponding to the target enterprise; Calculating the second characteristic thermodynamic map to obtain a second accumulated temperature value corresponding to the target enterprise; The accumulated temperature difference of the area where the target enterprise is located is obtained by calculation based on the first accumulated temperature value and the second accumulated temperature value.

5. The method according to claim 1, characterized in that Determining the predicted carbon emissions of the target enterprise based on the accumulated temperature difference and the historical carbon emissions data of the target enterprise includes: Calculating the correlation coefficient between the accumulated temperature difference and the historical carbon emission data of the target enterprise to obtain a target correlation coefficient; Determining a target fitting function according to the target correlation coefficient; The predicted carbon emissions of the target enterprise are determined based on the target fitting function.

6. The method according to claim 1, characterized in that Determining the loan risk level of the target enterprise based on the predicted carbon emissions includes: Determine whether the predicted carbon emissions are less than or equal to a preset value, and obtain a determination result; If the judgment result indicates that the predicted carbon emissions are less than or equal to the preset value, the loan risk level is determined to be the first risk level; If the judgment result indicates that the predicted carbon emissions are greater than the preset value, the loan risk level is determined to be a second risk level, wherein the second risk level is higher than the first risk level.

7. The method according to claim 6, characterized in that After determining that the loan risk level is the first risk level, the method further includes: When the loan risk level is the first risk level, obtaining historical loan behavior data of the target enterprise; Determining the loan information of the target enterprise based on the historical loan behavior data, wherein the loan information at least includes: the issuance method of the target loan business and the repayment method of the target enterprise; Based on the loan information, the target loan business is processed for the target enterprise.

8. The method according to claim 1, characterized in that Acquiring a target surface temperature remote sensing image of a region where a target enterprise is located and first meteorological data of the region where the target enterprise is located in different time periods includes: Acquire initial surface temperature remote sensing images of the area where the target enterprise is located and initial meteorological data of the area where the target enterprise is located in different time periods; Filtering and processing missing values ​​on the initial meteorological data to obtain the first meteorological data; Acquire second meteorological data of the area where the target enterprise is located, wherein the second meteorological data and the first meteorological data have different data sources; Performing radiation calibration, atmospheric correction and cloud detection on the initial land surface temperature remote sensing image to obtain a processed initial land surface temperature remote sensing image; The processed initial land surface temperature remote sensing image and the second meteorological data are fused according to a spatiotemporal fusion algorithm to obtain the target land surface temperature remote sensing image.

9. A loan risk assessment device, characterized in that: include: A first acquisition unit is used to acquire a target surface temperature remote sensing image of the area where the target enterprise is located and first meteorological data of the area where the target enterprise is located in different time periods; A first determining unit, configured to determine an accumulated temperature difference value of an area where the target enterprise is located based on the target surface temperature remote sensing image and the first meteorological data; A second determination unit is used to determine the predicted carbon emissions of the target enterprise based on the accumulated temperature difference and the historical carbon emissions data of the target enterprise; The third determination unit is used to determine the loan risk level of the target enterprise according to the predicted carbon emissions, wherein the loan risk level is used to indicate whether to process a target loan business for the target enterprise.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored program, wherein when the program is executed, the storage medium is controlled to execute the loan risk assessment method according to any one of claims 1 to 8 on a device.

11. An electronic device, characterized in that: The invention comprises one or more processors and a memory, wherein the memory is used to store one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the loan risk assessment method described in any one of claims 1 to 8.

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