Loan evaluation method and device, electronic equipment and computer program product

Abstract

The invention discloses a loan evaluation method and device, electronic equipment and a computer program product, and relates to the field of artificial intelligence, the field of financial science and technology or other related technical fields, and the evaluation method comprises the steps: receiving a loan request of a user side, and determining a to-be-evaluated target forest region based on the loan request; acquiring a remote sensing image set of the target forest region; respectively inputting the first remote sensing image and the second remote sensing image into an image recognition model, and outputting a first moment carbon content corresponding to the first moment and a second moment carbon content corresponding to the second moment of the target forest region; and calculating the carbon content of the target forest region based on the carbon content at the first moment and the carbon content at the second moment, and evaluating the loan request based on the carbon content. According to the method and the device, the technical problem of relatively low evaluation efficiency of a loan evaluation mode for estimating the value of the forest region through a manual visiting and measuring and calculating mode in related technologies is solved.

Description

Technical Field

[0001] The present invention relates to the field of artificial intelligence, financial technology or other related technical fields, and in particular to a loan assessment method and device, electronic equipment and computer program product. Background Art

[0002] As the main business of financial institutions, loan business is divided into many types according to user needs, such as housing loans, agricultural loans, forestry loans, etc. Among them, agricultural and forestry loans, as the current popular loan project, have promoted the development of agriculture and forestry to a certain extent. When applying for forestry loans, users can use forest areas as collateral for loans. When financial institutions apply for forestry loans, they need to conduct a value assessment of the mortgaged forest areas based on the actual situation of the forest areas, so as to achieve pre-loan risk assessment, which can reduce the bad debt rate of financial institutions.

[0003] Since the mortgaged forest areas are generally large in area and widely distributed, the relevant technologies use manual visits or mathematical models to estimate the value of the forest areas. However, both of the above methods have low evaluation efficiency and inaccurate evaluation, which brings certain challenges to pre-loan evaluation.

[0004] To address the above-mentioned problems, no effective solution has been proposed yet. Summary of the invention

[0005] The embodiments of the present invention provide a loan assessment method and device, electronic device and computer program product, so as to at least solve the technical problem of low assessment efficiency of the loan assessment method in the related art that estimates the value of forest areas by manual visits and measurements.

[0006] According to one aspect of an embodiment of the present invention, a loan assessment method is provided, comprising: receiving a loan request from a user terminal, and determining a target forest area to be assessed based on the loan request, wherein the loan request includes at least: forest area information of the target forest area and a loan amount; obtaining a remote sensing image set of the target forest area, the remote sensing image set including a first remote sensing image at a first moment and a second remote sensing image at a second moment, wherein the first moment is earlier than the second moment; inputting the first remote sensing image and the second remote sensing image into an image recognition model respectively, and outputting a first moment carbon content corresponding to the first moment and a second moment carbon content corresponding to the second moment of the target forest area; calculating the carbon content rate of the target forest area based on the carbon content at the first moment and the carbon content at the second moment, and assessing the loan request based on the carbon content rate.

[0007] Optionally, the steps of inputting the first remote sensing image and the second remote sensing image into an image recognition model respectively, and outputting the first moment carbon content corresponding to the first moment and the second moment carbon content corresponding to the second moment include: inputting the first remote sensing image and the second remote sensing image into an image recognition model respectively, performing frame difference processing on the first remote sensing image and the second remote sensing image through the image recognition model to obtain a difference matrix; obtaining change information of the target forest area between the first moment and the second moment based on the difference matrix; obtaining the first moment carbon content corresponding to the first moment based on the image recognition model; obtaining the second moment carbon content corresponding to the second moment based on the first moment carbon content and the change information, and outputting the first moment carbon content corresponding to the first moment and the second moment carbon content corresponding to the second moment.

[0008] Optionally, the step of obtaining the first moment carbon content corresponding to the first moment based on the image recognition model includes: selecting the remote sensing band to be analyzed; plotting the remote sensing band values ​​of the pixel points of the first remote sensing image on different remote sensing bands into a curve based on the image recognition model to obtain the spectral curve of the pixel points; based on the spectral curve, obtaining the spectral characteristics of the target forest area; and extracting the first moment carbon content corresponding to the first moment based on the spectral characteristics.

[0009] Optionally, the step of calculating the carbon content of the target forest area based on the carbon content at the first moment and the carbon content at the second moment includes: calculating the difference between the carbon content at the second moment and the carbon content at the first moment to obtain the carbon content difference; calculating the difference between the second moment and the first moment to obtain the moment difference; calculating the ratio of the carbon content difference and the moment difference to obtain the carbon content of the target forest area.

[0010] Optionally, after acquiring the remote sensing image set of the target forest area, the method further includes: preprocessing the remote sensing images to obtain preprocessed remote sensing images, wherein the preprocessing includes at least: radiation correction and atmospheric correction.

[0011] Optionally, the step of evaluating the loan request based on the carbon content also includes: collecting market carbon information, determining the value data of the target forest area based on the market carbon information and the carbon content; comparing the value data of the target forest area with the loan amount in the loan request to obtain a comparison result; if the comparison result indicates that the value data of the target forest area is greater than or equal to the loan amount, approving the loan request; or, if the comparison result indicates that the value data of the target forest area is less than the loan amount, rejecting the loan request.

[0012] Optionally, the image recognition model is pre-constructed, and the steps of constructing the image recognition model include: obtaining remote sensing images of forest areas within a historical time period, and obtaining the carbon content of the forest areas within the historical time period based on ground carbon content monitoring equipment; preprocessing and feature extraction of the remote sensing images to obtain preprocessed remote sensing images and spectral features; constructing a sample set based on the preprocessed remote sensing images, the spectral features and the carbon content of the forest areas; splitting the sample set based on a preset ratio to obtain a training set and a test set; training an initial neural network model based on the training set to obtain an initial image recognition model; testing the initial image recognition model based on the test set to obtain a test result, and when the test result indicates that the initial image recognition model passes the test, obtaining the final image recognition model.

[0013] According to another aspect of an embodiment of the present invention, a loan evaluation device is also provided, comprising: a receiving unit, used to receive a loan request from a user terminal, and determine a target forest area to be evaluated based on the loan request, wherein the loan request includes at least: forest area information of the target forest area and a loan amount; an acquisition unit, used to acquire a remote sensing image set of the target forest area, wherein the remote sensing image set includes a first remote sensing image at a first moment and a second remote sensing image at a second moment, wherein the first moment is earlier than the second moment; an output unit, used to input the first remote sensing image and the second remote sensing image into an image recognition model respectively, and output a first moment carbon content corresponding to the first moment and a second moment carbon content corresponding to the second moment of the target forest area; an evaluation unit, used to calculate the carbon content of the target forest area based on the carbon content at the first moment and the carbon content at the second moment, and evaluate the loan request based on the carbon content.

[0014] Optionally, the output unit includes: a first input module, used to input the first remote sensing image and the second remote sensing image into an image recognition model respectively, and perform frame difference processing on the first remote sensing image and the second remote sensing image through the image recognition model to obtain a difference matrix; a first acquisition module, used to obtain change information of the target forest area between the first moment and the second moment based on the difference matrix; a second acquisition module, used to obtain the first moment carbon content corresponding to the first moment based on the image recognition model; a first output module, used to obtain the second moment carbon content corresponding to the second moment based on the first moment carbon content and the change information, and output the first moment carbon content corresponding to the first moment and the second moment carbon content corresponding to the second moment.

[0015] Optionally, the second output module includes: a first selection module, used to select the remote sensing band to be analyzed; a first drawing module, used to draw the remote sensing band values ​​of the pixel points of the first remote sensing image into curves on different remote sensing bands based on the image recognition model, to obtain the spectral curve of the pixel points; a third acquisition module, based on the spectral curve, to obtain the spectral characteristics of the target forest area; a first extraction module, used to extract the carbon content at the first moment corresponding to the first moment based on the spectral characteristics.

[0016] Optionally, the evaluation unit includes: a first calculation module, used to calculate the difference between the carbon content at the second moment and the carbon content at the first moment, to obtain the carbon content difference; a second calculation module, used to calculate the difference between the second moment and the first moment, to obtain the moment difference; a third calculation module, used to calculate the ratio of the carbon content difference and the moment difference, to obtain the carbon content rate of the target forest area.

[0017] Optionally, the loan assessment device further comprises: a first preprocessing module, configured to preprocess the remote sensing image to obtain a preprocessed remote sensing image, wherein the preprocessing at least comprises: radiation correction and atmospheric correction.

[0018] Optionally, the evaluation unit also includes: a first collection module, used to collect market carbon information, and determine the value data of the target forest area based on the market carbon information and the carbon content; a first comparison module, used to compare the value data of the target forest area with the loan amount in the loan request to obtain a comparison result; a first approval module, used to approve the loan request if the comparison result indicates that the value data of the target forest area is greater than or equal to the loan amount; a first rejection module, used to reject the loan request if the comparison result indicates that the value data of the target forest area is less than the loan amount.

[0019] Optionally, the loan assessment device also includes: a fourth acquisition module, used to acquire remote sensing images of forest areas within a historical time period, and acquire the carbon content of forest areas within a historical time period based on ground carbon content monitoring equipment; a first processing module, used to preprocess and extract features of the remote sensing images to obtain preprocessed remote sensing images and spectral features; a first construction module, used to construct a sample set based on the preprocessed remote sensing images, the spectral features and the carbon content of the forest areas; a first splitting module, used to split the sample set based on a preset ratio to obtain a training set and a test set; a first training module, used to train an initial neural network model based on the training set to obtain an initial image recognition model; a first testing module, used to test the initial image recognition model based on the test set to obtain a test result, and when the test result indicates that the initial image recognition model passes the test, obtain the final image recognition model.

[0020] According to another aspect of an embodiment of the present invention, there is also provided an electronic device, comprising 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 any one of the above-mentioned loan assessment methods.

[0021] According to another aspect of an embodiment of the present invention, a computer program product is further provided. The computer program product includes a computer program. When the computer program is executed by a processor, any one of the above loan assessment methods is implemented.

[0022] In the present disclosure, the following steps are taken: first, a loan request from a user is received, and based on the loan request, a target forest area to be evaluated is determined, wherein the loan request includes at least: forest area information of the target forest area and a loan amount; then a remote sensing image set of the target forest area is obtained, wherein the remote sensing image set includes a first remote sensing image at a first moment and a second remote sensing image at a second moment, wherein the first moment is earlier than the second moment; then, the first remote sensing image and the second remote sensing image are respectively input into an image recognition model, and a first moment carbon content corresponding to the first moment and a second moment carbon content corresponding to the second moment of the target forest area are output; finally, the carbon content of the target forest area is calculated based on the carbon content at the first moment and the carbon content at the second moment, and the loan request is evaluated based on the carbon content.

[0023] In the present disclosure, remote sensing images of a target forest area at different times are identified and analyzed through an image recognition model to obtain the carbon content of the target forest area at different times, and then the carbon content of the target forest area is calculated. The value of the target forest area is assessed through the carbon content of the forest area, and then the risk assessment of the loan request is performed. The carbon content can characterize the ability of the forest area to absorb carbon dioxide, and then reflect the biomass of the forest area. The value of the forest area can be automatically assessed through remote sensing technology and image recognition technology, without the need for manual visits, thereby improving the efficiency of loan assessment. At the same time, the value assessment is performed with an exact carbon content rate to improve the accuracy of the assessment, thereby solving the technical problem of low assessment efficiency in the related art of loan assessment method that estimates the value of the forest area through manual visits and measurements. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of this application. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

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

[0026] Figure 2 is a schematic diagram of an optional loan evaluation process according to an embodiment of the present invention;

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

[0028] Figure 4 The figure is a hardware structure block diagram of an electronic device (or mobile device) for a loan evaluation method according to an embodiment of the present invention. DETAILED DESCRIPTION

[0029] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0030] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0031] It should be noted that the loan assessment method and device in the present disclosure can be used in the field of artificial intelligence to automatically conduct risk assessment of loan requests based on remote sensing images and image recognition models, and can also be used in any field other than the field of artificial intelligence to automatically conduct risk assessment of loan requests based on remote sensing images and image recognition models. The present disclosure does not limit the application field of the loan assessment method and device.

[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 used for analysis, stored data, displayed data, etc.) involved in this disclosure are information and data authorized by the user or fully authorized by all parties, and the collection, storage, use, processing, transmission, provision, disclosure and application of the relevant data comply with the relevant laws, regulations and standards of the relevant regions, take necessary confidentiality measures, do not violate public order and good customs, and provide corresponding operation entrances for users to choose to authorize or refuse. For example, an interface is set up between this system and relevant users or organizations. 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] It should be noted that in the present disclosure, when collecting and analyzing customer information, corresponding operation entrances are provided for users to choose to agree or reject the automated decision results; if the user chooses to reject, the expert decision process is entered.

[0034] The following embodiments of the present invention can be applied to various loan assessment systems / applications / devices. The present invention combines wireless remote sensing technology, automatically assesses the carbon content of the target forest area through an image recognition model, and determines the value of the target forest area according to the carbon content of the target forest area, thereby performing risk assessment on the user's loan request and improving the efficiency and accuracy of loan risk assessment.

[0035] The present invention is described in detail below in conjunction with various embodiments.

[0036] Embodiment 1

[0037] According to an embodiment of the present invention, an embodiment of a loan assessment method is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0038] Figure 1 is a flow chart of an optional loan assessment method according to an embodiment of the present invention. Figure 1 As shown, the method comprises the following steps:

[0039] Step S101, receiving a loan request from a user, and determining a target forest area to be evaluated based on the loan request, wherein the loan request at least includes: forest area information of the target forest area and a loan amount;

[0040] Step S102, obtaining a remote sensing image set of the target forest area, wherein the remote sensing image set includes a first remote sensing image at a first moment and a second remote sensing image at a second moment, wherein the first moment is earlier than the second moment;

[0041] Step S103, inputting the first remote sensing image and the second remote sensing image into the image recognition model respectively, and outputting the carbon content of the target forest area at the first moment corresponding to the first moment and the carbon content of the target forest area at the second moment corresponding to the second moment;

[0042] Step S104, calculating the carbon content of the target forest area based on the carbon content at the first moment and the carbon content at the second moment, and evaluating the loan request based on the carbon content.

[0043] Through the above steps, a loan request from a user is first received, and a target forest area to be evaluated is determined based on the loan request, wherein the loan request at least includes: forest area information of the target forest area and a loan amount, and then a remote sensing image set of the target forest area is obtained, wherein the remote sensing image set includes a first remote sensing image at a first moment and a second remote sensing image at a second moment, wherein the first moment is earlier than the second moment, and then the first remote sensing image and the second remote sensing image are respectively input into an image recognition model, and a first moment carbon content corresponding to the first moment and a second moment carbon content corresponding to the second moment of the target forest area are output, and finally the carbon content of the target forest area is calculated based on the carbon content at the first moment and the carbon content at the second moment, and the loan request is evaluated based on the carbon content.

[0044] In this embodiment, remote sensing images of the target forest area at different times are identified and analyzed through an image recognition model to obtain the carbon content of the target forest area at different times, and then the carbon content of the target forest area is calculated. The value of the target forest area is assessed by the carbon content of the forest area, and then the risk assessment of the loan request is performed. The carbon content can characterize the ability of the forest area to absorb carbon dioxide, and then reflect the biomass of the target forest area. The value of the forest area can be automatically assessed through remote sensing technology and image recognition technology, without the need for manual visits, thereby improving the efficiency of loan assessment. At the same time, the value assessment is performed with an exact carbon content rate to improve the accuracy of the assessment, thereby solving the technical problem of low assessment efficiency in the related technology of loan assessment method that estimates the value of the forest area through manual visits and measurements.

[0045] The embodiment of the present invention is described in detail below in combination with the above steps.

[0046] It should be noted that loan business is one of the main businesses of financial institutions. With the rapid development of information technology and e-commerce industries, the development of agriculture and forestry has also been promoted. The loan business based on forestry has also gradually grown. Users of forestry loans often use forest areas as collateral for loans. Due to the special nature of forest areas such as large areas and complex biological species, pre-loan assessments of forest areas pose huge challenges. If the assessment is inaccurate, the bad debt rate of financial institutions will increase.

[0047] It should be noted that the carbon content of a forest area is a key parameter, which can be used to characterize the ability of a forest area to absorb carbon dioxide. The larger the biomass of a forest, the more carbon it usually fixes, which means that the forest has a greater potential to absorb carbon dioxide. The different components of a forest area (such as trees, shrubs, herbs and mosses) all contribute to carbon absorption. Therefore, the carbon content of a forest area can reflect the carbon fixation capacity of the entire ecosystem. The stronger the carbon fixation capacity, the higher the value of the forest area. In addition, fast-growing forests usually have a higher carbon content, because plants need more carbon dioxide to build biomass during their growth process. Therefore, the carbon content can also be used to evaluate the growth status of organisms in the forest area, which is conducive to evaluating the value of the forest area.

[0048] It should be noted that the embodiment of the present invention uses a pre-trained image recognition model to input remote sensing images of forest areas taken at different times into the image recognition model, and can output the carbon content of the target forest area. Finally, the value data of the target forest area is calculated based on the market value of carbon, and the risk of the loan request is evaluated by the calculated value data. The embodiment of the present invention uses the carbon content as an indicator of value assessment, which can improve the precision and accuracy of value assessment and reduce the bad debt rate of financial institutions. At the same time, the automation of forest area value assessment and loan risk assessment can be achieved through image recognition models and remote sensing technology.

[0049] Step S101, receiving a loan request from a user, and determining a target forest area to be evaluated based on the loan request.

[0050] It should be noted that the user submits a loan request based on the forest area to be mortgaged, the loan amount and user-related information. After the business end of the financial institution receives the loan request from the user, it needs to first evaluate the loan request, which is actually a value evaluation of the target forest area to be evaluated submitted by the user, to determine whether the value of the target forest area is greater than the loan amount requested by the user, and then approve or reject the user's loan request, thereby enhancing the accuracy of the pre-loan evaluation and reducing the bad debt rate of financial institutions.

[0051] Step S102, obtaining a remote sensing image set of the target forest area, wherein the remote sensing image set includes a first remote sensing image at a first moment and a second remote sensing image at a second moment.

[0052] It should be noted that the carbon content of the target forest area can be used to characterize the target forest area's ability to absorb carbon dioxide and fix carbon. The value data of the target forest area can be determined in combination with the market value of carbon. Therefore, the embodiment of the present invention uses the carbon content of the target forest area as an indicator for value assessment. The carbon content of the forest area indicates the difference in carbon content of the target forest area over a period of time. In order to calculate the carbon content of the target forest area, the embodiment of the present invention combines wireless remote sensing technology to collect remote sensing images at different times to obtain a first remote sensing image at a first moment and a second remote sensing image at a second moment. There is an early and late relationship between the first moment and the second moment, and the first moment is earlier than the second moment.

[0053] Optionally, after acquiring the remote sensing image set of the target forest area, the method further includes: preprocessing the remote sensing image to obtain a preprocessed remote sensing image, wherein the preprocessing includes at least: radiation correction and atmospheric correction.

[0054] It should be noted that after acquiring remote sensing images of the target forest area at different times, the remote sensing images need to be preprocessed first to remove noise in the images. Specific preprocessing operations may include: radiation correction, eliminating the errors of the sensor itself and the impact of external factors such as changes in the atmosphere and solar radiation on the image, to ensure that the image data reflects the true situation of the surface; atmospheric correction, correcting the scattering and absorption of radiation by the atmosphere, converting the image data into surface reflectivity or radiation brightness, so that images acquired at different times and with different sensors can be compared; geometric correction, adjusting the geometric distortion of the image, such as distortion caused by terrain, changes in the sensor's field of view, etc., to ensure that the image matches the actual geographical location; image enhancement, by adjusting the image's contrast, brightness, color, etc., to make specific features in the image more obvious, so as to facilitate identification and analysis; cloud removal, identifying and removing clouds and cloud shadows in the image, because clouds will affect the observation of surface features; image segmentation, dividing the image into multiple regions or objects, each region containing pixels with similar attributes. Image fusion combines image data from different sensors or different bands to obtain richer surface information; noise removal uses filters to remove random noise in the image and improve the signal-to-noise ratio of the image. Preprocessing operations help improve the usability of remote sensing images and the accuracy of analysis results.

[0055] Step S103, inputting the first remote sensing image and the second remote sensing image into the image recognition model respectively, and outputting the carbon content of the target forest area at the first moment corresponding to the first moment and the carbon content of the target forest area at the second moment corresponding to the second moment.

[0056] It should be noted that the embodiment of the present invention automatically processes remote sensing images through image recognition and directly outputs the carbon content of the target forest area. The image recognition model can identify the spectral features of the remote sensing image, determine the carbon content at the target moment, and determine the carbon content at other moments related to the target moment based on the image changes at different moments. Since the process of spectral feature recognition is relatively complex and cumbersome, and the calculation time is long, the embodiment of the present invention can identify the spectral features of the remote sensing image at a certain moment, determine the carbon content of the forest area at that moment, and then obtain the change situation based on the difference between the remote sensing images at that moment and other moments, and then determine the carbon content of the forest area at other moments based on the change situation. This can save computing resources, improve the computing speed, and improve the efficiency of the entire evaluation process.

[0057] Optionally, the image recognition model is pre-built, and the steps of building the image recognition model include: obtaining remote sensing images of forest areas within a historical time period, and obtaining the carbon content of the forest areas within the historical time period based on ground carbon content monitoring equipment; preprocessing and feature extraction of remote sensing images to obtain preprocessed remote sensing images and spectral features; constructing a sample set based on the preprocessed remote sensing images, spectral features and carbon content of the forest areas; splitting the sample set based on a preset ratio to obtain a training set and a test set; training an initial neural network model based on the training set to obtain an initial image recognition model; testing the initial image recognition model based on the test set to obtain test results, and when the test results indicate that the initial image recognition model passes the test, obtaining a final image recognition model.

[0058] It should be noted that the image recognition model is obtained by training the neural network model based on a large amount of remote sensing images and forest area information. The image recognition model can extract spectral features based on remote sensing images, and then calculate the carbon content of the forest area based on the spectral features. At the same time, the convolutional neural network can also be used to perform convolution processing on remote sensing images at different times to obtain the changes in images at different times, and then calculate the carbon content at other times based on the carbon content of the forest area at a certain moment and the changes with other times, and finally output the carbon content of the target forest area at different times.

[0059] Specifically, when constructing an image recognition model, it is first necessary to collect a large amount of historical data, including remote sensing images of forest areas at various times during the historical period, and the carbon content of the forest areas collected by ground carbon content detection equipment at various times. The remote sensing images are then preprocessed and feature extracted to obtain preprocessed remote sensing images and spectral features of remote sensing images. A sample set is constructed based on the remote sensing images, spectral features and carbon content at each time after preprocessing, and the sample set is split according to a preset ratio to obtain a training set and a test set. The training set is used for iterative training of the model, and the test set is used to test whether the model meets the conditions and to optimize the model.

[0060] Optionally, the steps of inputting the first remote sensing image and the second remote sensing image into the image recognition model respectively, and outputting the carbon content at the first moment corresponding to the first moment and the carbon content at the second moment corresponding to the second moment include: inputting the first remote sensing image and the second remote sensing image into the image recognition model respectively, performing frame difference processing on the first remote sensing image and the second remote sensing image through the image recognition model to obtain a difference matrix; obtaining change information of the target forest area between the first moment and the second moment based on the difference matrix; obtaining the first moment carbon content corresponding to the first moment based on the image recognition model; obtaining the second moment carbon content corresponding to the second moment based on the first moment carbon content and the change information, and outputting the first moment carbon content corresponding to the first moment and the second moment carbon content corresponding to the second moment.

[0061] It should be noted that when the carbon content corresponding to the first moment and the carbon content corresponding to the second moment are output through the image recognition model, the image recognition model is first used to perform frame difference processing on the first remote sensing image corresponding to the first moment and the second remote sensing image corresponding to the second moment to obtain a difference matrix, and the obtained difference matrix is ​​input into the convolutional neural network in the image recognition model to perform alternating convolution and pooling processing on the difference matrix to obtain change information of the target forest area between the first moment and the second moment, and then feature analysis is performed on the remote sensing image at the first moment according to the image recognition model, and the first moment carbon content of the first moment is determined based on the remote sensing image, and then the second moment carbon content corresponding to the second moment is determined in combination with the change information of the first moment and the second moment, and finally the first moment carbon content corresponding to the first moment and the second moment carbon content corresponding to the second moment of the target forest area are output through the image recognition model.

[0062] In another optional embodiment, frame difference processing is performed on the first remote sensing image and the second remote sensing image through an image recognition model, and the specific steps of obtaining a differential matrix include: respectively obtaining a first pixel matrix corresponding to the first remote sensing image and a second pixel matrix corresponding to the second remote sensing image, and performing a subtraction operation on the first pixel matrix and the second pixel matrix to obtain a differential matrix.

[0063] Optionally, the step of obtaining the carbon content at the first moment corresponding to the first moment based on the image recognition model includes: selecting the remote sensing band to be analyzed; plotting the remote sensing band values ​​of the pixel points of the first remote sensing image on different remote sensing bands into a curve based on the image recognition model to obtain a spectral curve of the pixel points; based on the spectral curve, obtaining the spectral characteristics of the target forest area; and extracting the carbon content at the first moment corresponding to the first moment based on the spectral characteristics.

[0064] It should be noted that when obtaining the carbon content at the first moment corresponding to the first moment, the remote sensing band to be analyzed is first selected, and the selected remote sensing band to be analyzed is a characteristic band related to the carbon content. Then, based on the image recognition model, the remote sensing band values ​​of the pixel points of the first remote sensing image are plotted into a spectral curve on different pre-selected remote sensing bands, and spectral features are extracted from the spectral curve. These spectral features are features related to the carbon content, such as: normalized vegetation index, photosynthetically active radiation, light energy utilization rate, etc. The carbon content at the first moment corresponding to the target forest area at the first moment is calculated based on the above spectral features.

[0065] Step S104, calculating the carbon content of the target forest area based on the carbon content at the first moment and the carbon content at the second moment, and evaluating the loan request based on the carbon content.

[0066] It should be noted that the carbon content refers to the difference in carbon content of the target forest area at different times, which can indicate the ability of the target forest area to absorb carbon dioxide and fix carbon, thereby reflecting the value of the target forest area. By calculating the carbon content of the target forest area between the first moment and the second moment, the value of the target forest area can be evaluated. The value data obtained based on the value evaluation can be used to conduct a risk assessment of the user-side loan request.

[0067] Optionally, the step of calculating the carbon content of the target forest area based on the carbon content at the first moment and the carbon content at the second moment includes: calculating the difference between the carbon content at the second moment and the carbon content at the first moment to obtain the carbon content difference; calculating the difference between the second moment and the first moment to obtain the moment difference; calculating the ratio of the carbon content difference and the moment difference to obtain the carbon content of the target forest area.

[0068] It should be noted that when calculating the carbon content of the target forest area, the difference between the carbon content at the second moment and the carbon content at the first moment is first calculated, then the difference between the second moment and the first moment is calculated, and then the ratio of the carbon content difference and the moment difference is calculated to obtain the carbon content rate of the target forest area.

[0069] Optionally, the step of evaluating the loan request based on the carbon content also includes: collecting market carbon information, determining the value data of the target forest area based on the market carbon information and the carbon content; comparing the value data of the target forest area with the loan amount in the loan request to obtain a comparison result; if the comparison result indicates that the value data of the target forest area is greater than or equal to the loan amount, approving the loan request; or, if the comparison result indicates that the value data of the target forest area is less than the loan amount, rejecting the loan request.

[0070] It should be noted that after calculating the carbon content of the target forest area, the carbon content can be used to evaluate the loan request. First, market carbon information is collected, and the value data of the target forest area is calculated based on the current market value of carbon. The value data is compared with the loan amount of the loan request to conduct a risk assessment of the loan request from the user side. If the value data of the target forest area is less than the loan amount, it indicates that there is a risk in the loan request and the loan request from the user side needs to be rejected. If the value data of the target forest area is greater than or equal to the loan amount, then the loan request from the user side can be approved through pre-loan risk assessment, which can reduce the bad debt rate of financial institutions.

[0071] Another optional specific implementation is described in detail below.

[0072] Figure 2 is a schematic diagram of an optional loan evaluation process according to an embodiment of the present invention, such as Figure 2 As shown, the process of risk assessment for loan requests based on targeted forest areas includes:

[0073] Step 1: receiving a loan request;

[0074] The user end submits a loan request based on the forest area to be mortgaged, the loan amount and user-related information. The business end of the financial institution receives the loan request from the user end and can determine the target forest area to be evaluated and the loan amount required by the user based on the loan request.

[0075] Step 2: Obtain remote sensing images of the target forest area at different times;

[0076] The embodiment of the present invention combines wireless remote sensing technology to collect remote sensing images at different times to obtain a first remote sensing image at a first time and a second remote sensing image at a second time. There is a morning and evening relationship between the first time and the second time, and the first time is earlier than the second time.

[0077] Step 3: Input the remote sensing images into the image recognition model respectively, and output the carbon content of the target forest area at the first moment and the carbon content at the second moment;

[0078] The remote sensing image is automatically processed through image recognition, and the carbon content of the target forest area is directly output. The image recognition model can identify the spectral features of the remote sensing image, determine the carbon content at the target moment, and determine the carbon content at other moments related to the target moment based on the image changes at different moments. Since the spectral feature recognition process is relatively complex and cumbersome, and the calculation time is long, the embodiment of the present invention can identify the spectral features of the remote sensing image at a certain moment, determine the carbon content of the forest area at that moment, and then obtain the change situation from the difference between the remote sensing images at that moment and other moments, and then determine the carbon content of the forest area at other moments according to the change situation, which can save resources, improve the calculation rate, and improve the efficiency of the entire evaluation process.

[0079] Step 4, calculating the carbon content of the target forest area based on the carbon content at the first moment and the carbon content at the second moment;

[0080] When calculating the carbon content of the target forest area, first calculate the difference between the carbon content at the second moment and the carbon content at the first moment, then calculate the difference between the second moment and the first moment, and then calculate the ratio of the carbon content difference and the moment difference to obtain the carbon content rate of the target forest area.

[0081] Step 5: evaluate the value of the target forest area based on the carbon content of the target forest area to obtain the value data of the target forest area;

[0082] Step six: determine whether to approve the user's loan request based on the value data.

[0083] After calculating the carbon content of the target forest area, the carbon content can be used to evaluate the loan request. First, market carbon information is collected, and the value data of the target forest area is calculated based on the current market value of carbon. The value data is compared with the loan amount of the loan request to conduct a risk assessment on the loan request from the user side. If the value data of the target forest area is less than the loan amount, it indicates that there is a risk in the loan request and the loan request from the user side needs to be rejected. If the value data of the target forest area is greater than or equal to the loan amount, the loan request from the user side is passed. Through pre-loan risk assessment, the bad debt rate of financial institutions can be reduced.

[0084] In the embodiment of the present invention, wireless remote sensing technology is combined to automatically evaluate the carbon content of the target forest area through an image recognition model, and the value of the target forest area is determined based on the carbon content of the target forest area, thereby performing a risk assessment on the user's loan request, thereby improving the efficiency and accuracy of the loan risk assessment.

[0085] The following is a detailed description in conjunction with another embodiment.

[0086] Embodiment 2

[0087] A loan assessment device provided in this embodiment includes multiple implementation units, each of which corresponds to each implementation step in the above-mentioned embodiment 1.

[0088] Figure 3 is a schematic diagram of an optional loan assessment device according to an embodiment of the present invention, such as Figure 3 As shown, the loan evaluation device may include: a receiving unit 31, an acquisition unit 32, an output unit 33, and an evaluation unit 34, wherein:

[0089] The receiving unit 31 is used to receive a loan request from a user terminal, and determine a target forest area to be evaluated based on the loan request, wherein the loan request at least includes: forest area information of the target forest area and a loan amount;

[0090] An acquisition unit 32 is used to acquire a remote sensing image set of a target forest area, wherein the remote sensing image set includes a first remote sensing image at a first moment and a second remote sensing image at a second moment, wherein the first moment is earlier than the second moment;

[0091] An output unit 33 is used to input the first remote sensing image and the second remote sensing image into the image recognition model respectively, and output the carbon content at the first moment corresponding to the target forest area at the first moment and the carbon content at the second moment corresponding to the target forest area at the second moment;

[0092] The evaluation unit 34 is used to calculate the carbon content of the target forest area based on the carbon content at the first moment and the carbon content at the second moment, and evaluate the loan request based on the carbon content.

[0093] The above-mentioned loan evaluation device receives a loan request from a user terminal through a receiving unit 31, and determines a target forest area to be evaluated based on the loan request, wherein the loan request at least includes: forest area information of the target forest area and a loan amount; obtains a remote sensing image set of the target forest area through an acquisition unit 32, wherein the remote sensing image set includes a first remote sensing image at a first moment and a second remote sensing image at a second moment, wherein the first moment is earlier than the second moment; inputs the first remote sensing image and the second remote sensing image into an image recognition model through an output unit 33, respectively, and outputs a first moment carbon content corresponding to the first moment and a second moment carbon content corresponding to the second moment of the target forest area; calculates the carbon content of the target forest area based on the carbon content at the first moment and the carbon content at the second moment through an evaluation unit 34, and evaluates the loan request based on the carbon content.

[0094] In this embodiment, remote sensing images of the target forest area at different times are identified and analyzed through an image recognition model to obtain the carbon content of the target forest area at different times, and then the carbon content of the target forest area is calculated. The value of the target forest area is assessed by the carbon content of the forest area, and then the risk assessment of the loan request is performed. The carbon content can characterize the ability of the forest area to absorb carbon dioxide, and then reflect the biomass of the target forest area. The value of the forest area can be automatically assessed through remote sensing technology and image recognition technology, without the need for manual visits, thereby improving the efficiency of loan assessment. At the same time, the value assessment is performed with an exact carbon content rate to improve the accuracy of the assessment, thereby solving the technical problem of low assessment efficiency in the related technology of loan assessment method that estimates the value of the forest area through manual visits and measurements.

[0095] Optionally, the output 33 unit includes: a first input module, used to input the first remote sensing image and the second remote sensing image into the image recognition model respectively, and perform frame difference processing on the first remote sensing image and the second remote sensing image through the image recognition model to obtain a differential matrix; a first acquisition module, used to obtain change information of the target forest area between the first moment and the second moment based on the differential matrix; a second acquisition module, used to obtain the first moment carbon content corresponding to the first moment based on the image recognition model; a first output module, used to obtain the second moment carbon content corresponding to the second moment based on the first moment carbon content and the change information, and output the first moment carbon content corresponding to the first moment and the second moment carbon content corresponding to the second moment.

[0096] Optionally, the second output module includes: a first selection module, used to select the remote sensing band to be analyzed; a first drawing module, used to draw the remote sensing band values ​​of the pixel points of the first remote sensing image into curves on different remote sensing bands based on an image recognition model to obtain a spectral curve of the pixel points; a third acquisition module, based on the spectral curve, to obtain the spectral characteristics of the target forest area; a first extraction module, used to extract the carbon content at the first moment corresponding to the first moment based on the spectral characteristics.

[0097] Optionally, the evaluation unit 34 includes: a first calculation module, used to calculate the difference between the carbon content at the second moment and the carbon content at the first moment, to obtain the carbon content difference; a second calculation module, used to calculate the difference between the second moment and the first moment, to obtain the moment difference; a third calculation module, used to calculate the ratio of the carbon content difference and the moment difference, to obtain the carbon content rate of the target forest area.

[0098] Optionally, the loan assessment device further comprises: a first preprocessing module, configured to preprocess the remote sensing image to obtain a preprocessed remote sensing image, wherein the preprocessing at least comprises: radiation correction and atmospheric correction.

[0099] Optionally, the evaluation unit 34 also includes: a first collection module, used to collect market carbon information, and determine the value data of the target forest area based on the market carbon information and the carbon content; a first comparison module, used to compare the value data of the target forest area with the loan amount in the loan request to obtain a comparison result; a first approval module, used to approve the loan request when the comparison result indicates that the value data of the target forest area is greater than or equal to the loan amount; a first rejection module, used to reject the loan request when the comparison result indicates that the value data of the target forest area is less than the loan amount.

[0100] Optionally, the loan assessment device also includes: a fourth acquisition module, used to acquire remote sensing images of forest areas within a historical time period, and acquire the carbon content of forest areas within a historical time period based on ground carbon content monitoring equipment; a first processing module, used to preprocess and extract features of remote sensing images to obtain preprocessed remote sensing images and spectral features; a first construction module, used to construct a sample set based on the preprocessed remote sensing images, spectral features and carbon content of forest areas; a first splitting module, used to split the sample set based on a preset ratio to obtain a training set and a test set; a first training module, used to train an initial neural network model based on the training set to obtain an initial image recognition model; a first testing module, used to test the initial image recognition model based on the test set to obtain a test result, and when the test result indicates that the initial image recognition model passes the test, obtain the final image recognition model.

[0101] The loan assessment device may further include a processor and a memory. The receiving unit 31, the acquiring unit 32, the output unit 33, the assessment unit 34, etc. are all stored in the memory as program units, and the processor executes the program units stored in the memory to implement corresponding functions.

[0102] The processor includes a kernel, which retrieves the corresponding program unit from the memory. One or more kernels can be set, and risk assessment of loan requests based on forest areas can be performed by adjusting kernel parameters.

[0103] The above-mentioned 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 memory (fl ash RAM), and the memory includes at least one storage chip.

[0104] According to another aspect of an embodiment of the present invention, a computer-readable storage medium is provided. The computer-readable storage medium includes a stored computer program. When the computer program is executed, the device where the computer-readable storage medium is located is controlled to execute the above-mentioned loan assessment method.

[0105] According to another aspect of an embodiment of the present invention, there is also provided an electronic device, comprising 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 one or more processors, the one or more processors implement the above-mentioned loan assessment method.

[0106] According to another aspect of an embodiment of the present invention, a computer program product is further provided. The computer program product includes a computer program. When the computer program is executed by a processor, the above-mentioned loan assessment method is implemented.

[0107] 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: receiving a loan request from a user end, and determining a target forest area to be evaluated based on the loan request, wherein the loan request includes at least: forest area information of the target forest area and a loan amount; obtaining a remote sensing image set of the target forest area, wherein the remote sensing image set includes a first remote sensing image at a first moment and a second remote sensing image at a second moment, wherein the first moment is earlier than the second moment; inputting the first remote sensing image and the second remote sensing image into an image recognition model respectively, and outputting a first moment carbon content corresponding to the first moment and a second moment carbon content corresponding to the second moment of the target forest area; calculating the carbon content of the target forest area based on the carbon content at the first moment and the carbon content at the second moment, and evaluating the loan request based on the carbon content.

[0108] The present application also provides a computer program product, which, when executed on a data processing device, is also suitable for executing a program initialized with the following method steps: inputting the first remote sensing image and the second remote sensing image into the image recognition model respectively, and outputting the first moment carbon content corresponding to the first moment and the second moment carbon content corresponding to the second moment, the steps comprising: inputting the first remote sensing image and the second remote sensing image into the image recognition model respectively, performing frame difference processing on the first remote sensing image and the second remote sensing image through the image recognition model to obtain a difference matrix; obtaining change information of the target forest area between the first moment and the second moment based on the difference matrix; obtaining the first moment carbon content corresponding to the first moment based on the image recognition model; obtaining the second moment carbon content corresponding to the second moment based on the first moment carbon content and the change information, and outputting the first moment carbon content corresponding to the first moment and the second moment carbon content corresponding to the second moment.

[0109] The present application also provides a computer program product, which, when executed on a data processing device, is also suitable for executing a program that initializes the following method steps: the step of obtaining the carbon content at the first moment corresponding to the first moment based on the image recognition model includes: selecting a remote sensing band to be analyzed; based on the image recognition model, plotting the remote sensing band values ​​of the pixel points of the first remote sensing image into a curve on different remote sensing bands to obtain a spectral curve of the pixel points; based on the spectral curve, obtaining the spectral characteristics of the target forest area; based on the spectral characteristics, extracting the carbon content at the first moment corresponding to the first moment.

[0110] The present application also provides a computer program product, which, when executed on a data processing device, is also suitable for executing a program that initializes the following method steps: the steps of calculating the carbon content of a target forest area based on the carbon content at a first moment and the carbon content at a second moment include: calculating the difference between the carbon content at the second moment and the carbon content at the first moment to obtain the carbon content difference; calculating the difference between the second moment and the first moment to obtain the moment difference; calculating the ratio of the carbon content difference to the moment difference to obtain the carbon content of the target forest area.

[0111] The present application also provides a computer program product, which, when executed on a data processing device, is also suitable for executing a program that initializes the following method steps: after obtaining a set of remote sensing images of a target forest area, it also includes: preprocessing the remote sensing images to obtain preprocessed remote sensing images, wherein the preprocessing includes at least: radiation correction and atmospheric correction.

[0112] The present application also provides a computer program product, which, when executed on a data processing device, is also suitable for executing a program that initializes the following method steps: the step of evaluating the loan request based on the carbon content also includes: collecting market carbon information, and determining the value data of the target forest area based on the market carbon information and the carbon content; comparing the value data of the target forest area with the loan amount in the loan request to obtain a comparison result; if the comparison result indicates that the value data of the target forest area is greater than or equal to the loan amount, approving the loan request; or, if the comparison result indicates that the value data of the target forest area is less than the loan amount, rejecting the loan request.

[0113] The present application also provides a computer program product, which, when executed on a data processing device, is also suitable for executing a program initialized with the following method steps: an image recognition model is pre-constructed, and the steps of constructing the image recognition model include: obtaining remote sensing images of forest areas within a historical time period, and obtaining the carbon content of the forest areas within the historical time period based on ground carbon content monitoring equipment; preprocessing and feature extraction of remote sensing images to obtain preprocessed remote sensing images and spectral features; constructing a sample set based on the preprocessed remote sensing images, spectral features and carbon content of the forest areas; splitting the sample set based on a preset ratio to obtain a training set and a test set; training an initial neural network model based on the training set to obtain an initial image recognition model; testing the initial image recognition model based on the test set to obtain a test result, and when the test result indicates that the initial image recognition model passes the test, obtaining a final image recognition model.

[0114] Figure 4 FIG. 1 is a hardware structure block diagram of an electronic device (or mobile device) according to a loan evaluation method according to an embodiment of the present invention. Figure 4 As shown, the electronic device may include one or more ( Figure 4 402a, 402b, ..., 402n are used to illustrate) processor 402 (processor 402 may include but is not limited to a processing device such as a microprocessor MCU or a programmable logic device FPGA), memory 404 for storing data. In addition, it may also include: a display, an input / output interface (I / O interface), a universal serial bus (USB) port (which may be included as one of the ports of the I / O interface), a network interface, a keyboard, a power supply and / or a camera. It can be understood by those skilled in the art that Figure 4 The structure shown is only for illustration and does not limit the structure of the above electronic device. Figure 4 More or fewer components as shown, or with Figure 4 Different configurations are shown.

[0115] The serial numbers of the above embodiments of the present invention are only for description and do not represent the advantages or disadvantages of the embodiments.

[0116] In the above embodiments of the present invention, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0117] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only schematic. For example, the division of the units can be a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0118] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0119] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0120] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, magnetic disk or optical disk and other media that can store program codes.

[0121] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A loan assessment method, characterized in that: include: Receive a loan request from a user, and determine a target forest area to be evaluated based on the loan request, wherein the loan request at least includes: forest area information of the target forest area and a loan amount; Acquire a remote sensing image set of the target forest area, wherein the remote sensing image set includes a first remote sensing image at a first moment and a second remote sensing image at a second moment, the first moment being earlier than the second moment; Inputting the first remote sensing image and the second remote sensing image into an image recognition model respectively, and outputting the carbon content of the target forest area at the first moment corresponding to the first moment and the carbon content of the target forest area at the second moment corresponding to the second moment; The carbon content of the target forest area is calculated based on the carbon content at the first moment and the carbon content at the second moment, and the loan request is evaluated based on the carbon content.

2. The method according to claim 1, characterized in that The steps of inputting the first remote sensing image and the second remote sensing image into an image recognition model respectively, and outputting the first moment carbon content corresponding to the first moment and the second moment carbon content corresponding to the second moment include: Inputting the first remote sensing image and the second remote sensing image into an image recognition model respectively, performing frame difference processing on the first remote sensing image and the second remote sensing image through the image recognition model to obtain a difference matrix; Acquire change information of the target forest area between the first moment and the second moment based on the difference matrix; Acquire the carbon content at the first moment corresponding to the first moment based on the image recognition model; The second-moment carbon content corresponding to the second moment is acquired based on the first-moment carbon content and the change information, and the first-moment carbon content corresponding to the first moment and the second-moment carbon content corresponding to the second moment are output.

3. The method according to claim 2, characterized in that The step of acquiring the carbon content at the first moment corresponding to the first moment based on the image recognition model comprises: Select the remote sensing band to be analyzed; Based on the image recognition model, the remote sensing band values ​​of the pixel points of the first remote sensing image are plotted into curves on different remote sensing bands to obtain a spectral curve of the pixel points; Based on the spectral curve, obtaining spectral characteristics of the target forest area; The carbon content at a first moment corresponding to the first moment is extracted based on the spectral feature.

4. The method according to claim 1, characterized in that: The step of calculating the carbon content of the target forest area based on the carbon content at the first moment and the carbon content at the second moment comprises: Calculating the difference between the carbon content at the second moment and the carbon content at the first moment to obtain a carbon content difference; Calculating the difference between the second moment and the first moment to obtain a moment difference; The ratio of the carbon content difference and the time difference is calculated to obtain the carbon content rate of the target forest area.

5. The method according to claim 1, characterized in that After obtaining the remote sensing image set of the target forest area, the method further includes: The remote sensing image is preprocessed to obtain a preprocessed remote sensing image, wherein the preprocessing at least includes: radiation correction and atmospheric correction.

6. The method according to claim 1, characterized in that The step of evaluating the loan request based on the carbon content further comprises: Collecting market carbon information, and determining value data of the target forest area based on the market carbon information and the carbon content rate; Comparing the value data of the target forest area with the loan amount in the loan request to obtain a comparison result; If the comparison result indicates that the value data of the target forest area is greater than or equal to the loan amount, approve the loan request; or When the comparison result indicates that the value data of the target forest area is less than the loan amount, the loan request is rejected.

7. The method according to claim 1, characterized in that The image recognition model is pre-constructed, and the steps of constructing the image recognition model include: Obtain remote sensing images of forest areas within a historical period, and obtain the carbon content of forest areas within a historical period based on ground carbon content monitoring equipment; Preprocessing and feature extraction are performed on the remote sensing image to obtain a preprocessed remote sensing image and spectral features; Constructing a sample set based on the preprocessed remote sensing image, the spectral characteristics and the carbon content of the forest area; Splitting the sample set based on a preset ratio to obtain a training set and a test set; Training the initial neural network model based on the training set to obtain an initial image recognition model; The initial image recognition model is tested based on the test set to obtain a test result, and when the test result indicates that the initial image recognition model passes the test, the final image recognition model is obtained.

8. A loan evaluation device, characterized in that: include: A receiving unit, configured to receive a loan request from a user terminal, and determine a target forest area to be evaluated based on the loan request, wherein the loan request at least includes: forest area information of the target forest area and a loan amount; an acquisition unit, configured to acquire a remote sensing image set of the target forest area, wherein the remote sensing image set includes a first remote sensing image at a first moment and a second remote sensing image at a second moment, the first moment being earlier than the second moment; An output unit, used to input the first remote sensing image and the second remote sensing image into an image recognition model respectively, and output a first moment carbon content of the target forest area corresponding to the first moment and a second moment carbon content corresponding to the second moment; An evaluation unit is used to calculate the carbon content of the target forest area based on the carbon content at the first moment and the carbon content at the second moment, and evaluate the loan request based on the carbon content.

9. 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 assessment method according to any one of claims 1 to 7.

10. A computer program product, characterized in that The computer program product comprises a computer program, wherein the computer program implements the loan assessment method according to any one of claims 1 to 7 when executed by a processor.

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