Loan Process Control Method, Device, Medium and Equipment Based on Loan Collateral

Abstract

An embodiment of the present application discloses a loan process control method, device, medium and equipment based on a loan collateral. Among them, the method includes: obtaining a loan application; the loan application includes credit service area information and loan collateral area data; determining a target RGB image in a pre-established credit service database according to the credit service area information; determining the available credit area data according to the target RGB image, the loan collateral classification image corresponding to the target RGB image in the credit service database, and the imaging ratio of the loan collateral classification image; determining the approval result of the loan application according to the available credit area data and the loan collateral area data. According to this technical solution, the risk of a loan application can be judged through the target RGB image in the credit service database and the loan collateral classification image corresponding to the target RGB image, thereby ensuring the reliability of the loan.

Description

Technical Field

[0001] The embodiments of the present application relate to the field of financial technology, and in particular, to a loan process control method, device, medium, and equipment based on loan collateral. Background Art

[0002] Bank credit is an economic activity in which a bank temporarily lends part of its deposits to enterprises and institutions and recovers them within a specified time with a certain amount of interest charged. Bank credit refers to currency lending with a bank as an intermediary and interest as a return.

[0003] In the process of bank credit approval, first, the borrower submits materials such as a loan application to the bank, and then the bank credit staff investigates the actual economic situation of the borrower and reviews and verifies the materials such as the loan application. Finally, the approval result of the loan application is made based on information such as the investigation report and the review result.

[0004] In the prior art, when investigating the actual economic situation of the borrower, it takes a lot of time and labor costs, especially for on-site investigations in large-scale crop planting areas. In addition, the borrower may create a false site, making the information obtained from the on-site investigation not conform to the actual situation. Therefore, an accurate, fast, and reliable credit process control method is needed to solve the above problems. Summary of the Invention

[0005] The embodiments of the present application provide a loan process control method, device, medium, and equipment based on loan collateral, which can judge the risk of a loan application through the target RGB image in the credit service database and the loan collateral classification image corresponding to the target RGB image, thereby ensuring the reliability of the loan and facilitating the realization of a fast and accurate loan process.

[0006] In a first aspect, the embodiments of the present application provide a loan process control method based on loan collateral, and the method includes:

[0007] Obtain a loan application; the loan application includes credit service area information and loan collateral area data;

[0008] Determine a target RGB image in a pre-established credit service database according to the credit service area information;

[0009] Determine the available credit area data according to the target RGB image, the loan collateral classification image corresponding to the target RGB image in the credit service database, and the imaging ratio of the loan collateral classification image;

[0010] Determine the approval result of the loan application according to the available credit area data and the loan collateral area data.

[0011] Second aspect, an embodiment of the present application provides a loan process control device based on loan collateral, and the device includes:

[0012] A loan application acquisition module, configured to acquire a loan application; the loan application includes credit service area information and loan collateral area data;

[0013] A target RGB image determination module, configured to determine a target RGB image in a pre-established credit service database according to the credit service area information;

[0014] An available credit area data determination module, configured to determine available credit area data according to the target RGB image, the loan collateral classification image corresponding to the target RGB image in the credit service database, and the imaging ratio of the loan collateral classification image;

[0015] An approval result determination module, configured to determine the approval result of the loan application according to the available credit area data and the loan collateral area data.

[0016] Third aspect, an embodiment of the present application provides a computer-readable storage medium, on which a computer program is stored, and when the program is executed by a processor, it implements the loan process control method based on loan collateral as described in the embodiment of the present application.

[0017] Fourth aspect, an embodiment of the present application provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, it implements the loan process control method based on loan collateral as described in the embodiment of the present application.

[0018] The technical solution provided by the embodiment of the present application determines a target RGB image in a pre-established credit service database according to the credit service area information in the loan application. Then, according to the target RGB image, the loan collateral classification image corresponding to the target RGB image in the credit service database, and the imaging ratio of the loan collateral classification image, available credit area data is determined. Finally, according to the available credit area data and the loan collateral area data, the approval result of the loan application is determined. This solution can perform risk judgment on the loan application through the target RGB image in the credit service database and the loan collateral classification image corresponding to the target RGB image, thereby ensuring the reliability of the loan and facilitating the realization of a fast and accurate loan process. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 is a flowchart of the loan process control method based on loan collateral provided by Embodiment 1 of the present application;

[0020] Figure 2Schematic diagram of a loan process control device based on a loan collateral provided in the second embodiment of the present invention;

[0021] Figure 3 Schematic diagram of an electronic device provided in the fourth embodiment of the present application. Detailed implementation manners

[0022] The present application will be further described in detail below with reference to the drawings and embodiments. It can be understood that the specific embodiments described herein are only used to explain the present application, rather than limiting the present application. Additionally, it should be noted that for the sake of description, only parts related to the present application rather than all structures are shown in the drawings.

[0023] Before discussing the exemplary embodiments in more detail, it should be mentioned that some exemplary embodiments are described as processes or methods depicted as flowcharts. Although the flowcharts describe the steps as sequential processes, many of the steps can be implemented in parallel, concurrently, or simultaneously. In addition, the order of the steps can be rearranged. The process can be terminated when its operations are completed, but it can also have additional steps not included in the drawings. The process can correspond to a method, function, procedure, subroutine, subprogram, etc.

[0024] Embodiment 1

[0025] Figure 1 It is a flowchart of a loan process control method based on a loan collateral provided in the first embodiment of the present application. This embodiment is applicable to any loan process control scenario based on a loan collateral. This method can be executed by the loan process control device provided in the embodiments of the present application. The device can be implemented in software and / or hardware and can be integrated into an electronic device.

[0026] As Figure 1 shown, the loan process control method based on a loan collateral includes:

[0027] S110, obtaining a loan application; the loan application includes credit service area information and loan collateral area data.

[0028] This solution can be executed by a credit system. The borrower can input loan-related information in the credit system in advance to generate a loan application. At the same time, the borrower can also upload materials such as identity certificates, social relationship certificates, and picture certificates in the credit system. The loan application may include credit service area information and the area data of the borrower's collateral. Among them, the credit service area information may be the area information that can provide credit services for the borrower, such as the borrower's household registration location information. The area data of the borrower's collateral may be the area data of the collateral provided by the borrower, such as the cultivated area of the borrower's farmland, the area of the mine operated by the borrower, and the floor area of the house where the borrower lives, etc. The area data of the borrower's collateral can be used as an evaluation index for the borrower's economic repayment ability.

[0029] S120. Determine a target RGB image in a pre-established credit service database according to the credit service area information.

[0030] The credit system can obtain the credit service area information from the borrower's loan application. According to the credit service area information, a target RGB image can be determined in the credit service database. The target RGB image may be all or part of the image of the credit service area, which can be a visible light image, a regional map, or a remote sensing image. The credit service database may include RGB images of each partition of the credit service area. The credit system can locate the specific partition of the credit service area according to the credit service area information in the loan application.

[0031] In this solution, optionally, the establishment process of the credit service database includes:

[0032] Obtain remote sensing image data of the credit service area;

[0033] Extract data from the remote sensing image data to generate RGB images and NDVI images;

[0034] Classify the NDVI image using an artificial intelligence classification algorithm and save the classified images of the borrower's collateral;

[0035] Save the classified images of the borrower's collateral, the RGB images, and the corresponding relationship between the classified images of the borrower's collateral and the RGB images to the credit service database.

[0036] In this solution, the credit system can pre-acquire remote sensing image data of the credit service area. For example, a bank can periodically collect remote sensing image data or download the remote sensing image data of the credit service area that meets the requirements through the network. After obtaining the remote sensing image data, the credit system can preprocess the remote sensing image data, and the preprocessing includes steps such as cropping, splicing, and / or projection transformation to make the remote sensing image data meet the actual requirements.

[0037] According to the electromagnetic spectrum, the remote sensing image data can include multiple band data. For example, the remote sensing image data usually includes seven different bands from band 1 to band 7. Specifically, band 1 is the blue band (Blue, B) with a wavelength of 0.45 - 0.52 microns. This band is located at the part with the smallest attenuation coefficient of water bodies, has the greatest penetration ability for water bodies, is used to distinguish water depth, study underwater topography and water turbidity in shallow seas, etc., and is used for water system and shallow sea area mapping; band 2 is the green band (Green, G) with a wavelength of 0.52 - 0.60 microns. This band is near the reflection peak of green plants, is sensitive to the reflection of healthy and lush plants, can identify plant categories and evaluate plant productivity, has a certain penetration ability for water bodies, and can reflect features such as underwater topography, sandbars, and coastal sandbars; band 3 is the red band (Red, R) with a wavelength of 0.63 - 0.69 microns. This band is located in the main absorption band of chlorophyll, can be used to distinguish plant types, coverage, and judge plant growth conditions, etc. In addition, this band can provide rich plant information for features such as bare ground, vegetation, lithology, strata, structure, landform, and hydrology; band 4 is the near-infrared band (Near Infrared, NIR) with a wavelength of 0.76 - 0.90 microns. This band is located in the high reflection area of plants, reflects a large amount of plant information, and is mostly used for plant identification and classification. At the same time, it is also located in the strong absorption area of water bodies, and is used to outline the water body boundary and identify geological structures and landforms related to water; band 5 is the mid-infrared band (Mid Infrared, MIR) with a wavelength of 1.55 - 1.75 microns. This band is located between two water body absorption bands, is sensitive to the moisture content of plants and soil, thus improving the ability to distinguish crops, has a large amount of information, and a high application rate; band 6 is the thermal infrared band (Thermal Infrared, TIR) with a wavelength of 10.40 - 12.50 microns. This band is sensitive to the heat radiation of ground objects, can distinguish the growth trend of agricultural and forestry coverage, surface humidity difference, water bodies and rocks according to the difference in radiation response, and monitor heat characteristics related to human activities for thermal mapping; band 7 is the far-infrared band (Far Infrared, FIR) with a wavelength of 2.08 - 2.35 microns, which is an additional band specifically for geological surveys. It is in the strong absorption band of water, and water bodies appear black. It can be used to distinguish main rock types, the thermal erosion degree of rocks, and detect clay minerals related to metasomatic rocks.

[0038] The credit system can extract data from the obtained remote sensing image data to generate RGB images and NDVI images. The NDVI image can be an image generated based on the Normalized Difference Vegetation Index (NDVI). The calculation formula for the Normalized Difference Vegetation Index is: NDVI = (NIR - R) / (NIR + R), where NIR represents the NIR band data and R represents the R band data.

[0039] Specifically, the extracting data from the remote sensing image data to generate RGB images and NDVI images includes:

[0040] Extracting the R band data and NIR band data from the remote sensing image data to generate an NDVI image; and, extracting the R band data, G band data, and B band data from the remote sensing image data to generate an RGB image.

[0041] The credit system can generate an NDVI image based on the R band data and NIR band data in the remote sensing image data using the Normalized Difference Vegetation Index calculation formula. It is easy to understand that the credit system can generate an RGB image based on the R band data, G band data, and B band data in the remote sensing image data.

[0042] After generating the RGB image and NDVI image, the credit system can use an artificial intelligence classification algorithm to identify the loan collateral in the NDVI image, and then classify the NDVI image to obtain a loan collateral classification image and a non-loan collateral image. Specifically, the artificial intelligence classification algorithm can identify targets such as mountains, rivers, farmland, and houses in the NDVI image, and classify the NDVI image according to the recognition results of the targets, and save the loan collateral classification image according to the classification results. Assuming that the loan collateral is farmland, the artificial intelligence classification algorithm can identify the farmland area in the NDVI image, mark the farmland area, and save the farmland classification image. More specifically, under the condition that the resolution of the remote sensing image data permits, the artificial intelligence classification algorithm can also achieve more detailed classification, such as identifying specifically what crops are planted in the farmland area in the NDVI image. The credit system can save the loan collateral classification image, the RGB image, and the corresponding relationship between the loan collateral classification image and the RGB image to the credit service database.

[0043] In this solution, the RGB image and the NDVI image can be saved in the credit service database. Among them, the RGB image has good visibility and is convenient for personnel such as bank account managers to use; the NDVI image has good discrimination and is conducive to achieving accurate target classification. Therefore, the credit service database established by this solution has a wide range of application scenarios and brings convenience to credit work.

[0044] S130. Determine the credit available area data based on the target RGB image, the classified image of the loan collateral corresponding to the target RGB image in the credit service database, and the imaging ratio of the classified image of the loan collateral.

[0045] The credit system can determine the classified image of the loan collateral corresponding to the target RGB image in the credit service database according to the determined target RGB image and the corresponding relationship between the classified image of the loan collateral and the RGB image. Further, the credit system can determine the credit available area data of the loan collateral based on the classified image of the loan collateral and the imaging ratio of the classified image of the loan collateral.

[0046] S140. Determine the approval result of the loan application based on the credit available area data and the area data of the loan collateral.

[0047] It can be understood that the credit system can determine whether there is a risk for the bank in this loan application based on the credit available area data and the area data of the loan collateral in the borrower's loan application, and then decide whether to approve the loan application.

[0048] Specifically, the determining the approval result of the loan application based on the credit available area data and the area data of the loan collateral includes:

[0049] If the area data of the loan collateral exceeds the credit available area data, the loan application is not approved;

[0050] If the area data of the loan collateral does not exceed the credit available area data, the loan application is approved.

[0051] The credit system can also set an area threshold. When the area data of the loan collateral exceeds the credit available area data and the exceeded area data exceeds the area threshold, it indicates that the risk of approving this loan application exceeds the bank's control range, so the loan application is not approved. When the area data of the loan collateral exceeds the credit available area data but the exceeded area data does not exceed the area threshold, it indicates that the risk of approving this loan application is within the bank's control range, so the loan application can be approved or more processes are needed to further evaluate this loan application.

[0052] This solution can strictly control the approval result of the loan application and ensure the reliability of lending.

[0053] In a feasible solution, optionally, the loan application further includes credit service plan information;

[0054] Correspondingly, after obtaining the loan application, the method further includes:

[0055] According to the credit service plan, determine a target RGB image in a pre-established credit service database, and circle the area related to the credit service plan in the target RGB image to generate a circled image;

[0056] According to the circled image, the lending collateral classification image corresponding to the RGB image, and the imaging ratio of the lending collateral classification image, determine the credit available area data of the circled area in the circled image.

[0057] In view of influencing factors such as different regions, different populations, and different social situations, the bank's credit department can formulate flexible credit service plans to meet the needs of different borrowers. Therefore, the loan application may also include credit service plan information. The credit system can determine a target RGB image in a pre-established credit service database according to the credit service plan, and circle the area related to the credit service plan in the target RGB image to generate a circled image.

[0058] It is easy to understand that the credit system can determine the credit available area data of the circled area in the circled image according to the circled image, the lending collateral classification image corresponding to the RGB image, and the imaging ratio of the lending collateral classification image, and then flexibly determine the credit available area data.

[0059] This solution can generate a circled image for different credit service plans and determine the credit available area data based on the circled image. This solution can widely handle credit scenarios in various situations and has flexibility.

[0060] In a preferred solution, optionally, after determining the credit available area data, the method further includes:

[0061] According to the credit available area data, the lending collateral area data, and the historical credit information obtained in the credit system, determine the approval result of the loan application.

[0062] To achieve more reliable credit services, the credit system can also retrieve historical credit information as a reference for determining the approval result. The credit system can comprehensively determine the approval result of the loan application according to the credit available area data, the lending collateral area data, and the historical credit information. For example, if the historical credit information shows that the borrower has multiple overdue repayments or multiple outstanding loans, the credit system can evaluate that the lending risk of this borrower is relatively high and reject the loan application.

[0063] This solution can refer to historical credit information to provide a judgment basis for the approval of this loan application and can achieve more reliable loan process control.

[0064] Based on the above solution, optionally, the historical credit information includes the data of the approved area of the credit service solution;

[0065] Correspondingly, determining the approval result of the loan application according to the available credit area data, the area data of the loan collateral, and the historical credit information obtained in the credit system includes:

[0066] Determine the unapproved area data of the credit service solution according to the available credit area data and the approved area data of the credit service solution;

[0067] Determine the approval result of the loan application according to the unapproved area data and the area data of the loan collateral.

[0068] In actual credit scenarios, there are various situations. For example, the borrower divides the same loan collateral and uses part of the loan collateral for one or more loans. Another example is that the bank's support for a certain credit service solution is limited and only allows a certain number of loan applications. The historical credit information can include the data of the approved area of the credit service solution. The credit system can determine the unapproved area data of the credit service solution according to the available credit area data and the approved area data of the credit service solution. Further, the credit system can determine the approval result of the loan application according to the unapproved area data and the area data of the loan collateral. Suppose the loan collateral is agricultural land. Under a certain credit service solution, a borrower has 40 mu of agricultural land. Among them, 30 mu of agricultural land has been used as loan collateral to apply for a loan from the bank. Now, the borrower applies to use 20 mu of agricultural land as loan collateral to apply for a loan from the bank. The credit system can determine the unapproved agricultural land area of 10 mu according to the borrower's available credit agricultural land area of 40 mu and the approved agricultural land area of 30 mu. According to the unapproved agricultural land area of 10 mu and the agricultural land area of 20 mu in the loan application, judge the credit risk, and then determine the approval result of the loan application. Another example is that a bank launches a certain credit service solution and provides a total of 200 mu of agricultural land mortgage loans to borrowers. Among them, the credit service solution has provided 180 mu of agricultural land mortgage loans to borrowers. A borrower applies to use 10 mu of agricultural land as loan collateral to apply for a loan from this bank. The credit system can determine the unapproved agricultural land area of 20 mu according to the available credit agricultural land area of 200 mu and the approved agricultural land area of 180 mu of this credit service solution of the bank. According to the unapproved agricultural land area of 20 mu and the agricultural land area of 10 mu in the loan application, determine whether to lend to this borrower.

[0069] This solution can have wide applicability to various credit scenarios.

[0070] The technical solution provided by the embodiments of the present application determines a target RGB image in a pre-established credit service database according to the credit service area information in the loan application. Then, according to the target RGB image, the loan collateral classification image corresponding to the target RGB image in the credit service database, and the imaging ratio of the loan collateral classification image, the available credit area data is determined. Finally, according to the available credit area data and the loan collateral area data, the approval result of the loan application is determined. This solution can judge the risk of the loan application through the target RGB image in the credit service database and the loan collateral classification image corresponding to the target RGB image, thereby ensuring the reliability of the loan and facilitating the realization of a fast and accurate loan process.

[0071] Embodiment 2

[0072] Figure 2 FIG. is a schematic structural diagram of a loan process control device based on loan collateral provided by Embodiment 2 of the present invention. This device can execute the loan process control method based on loan collateral provided by any embodiment of the present invention, and has corresponding functional modules and beneficial effects for executing the method. As Figure 2 shown, the device may include:

[0073] A loan application acquisition module 210, configured to acquire a loan application; the loan application includes credit service area information and loan collateral area data;

[0074] A target RGB image determination module 220, configured to determine a target RGB image in a pre-established credit service database according to the credit service area information;

[0075] An available credit area data determination module 230, configured to determine available credit area data according to the target RGB image, the loan collateral classification image corresponding to the target RGB image in the credit service database, and the imaging ratio of the loan collateral classification image;

[0076] An approval result determination module 240, configured to determine the approval result of the loan application according to the available credit area data and the loan collateral area data.

[0077] In this solution, optionally, the device further includes a credit service database construction module, and the credit service database construction module includes:

[0078] A remote sensing image data acquisition sub-module, configured to acquire remote sensing image data of the credit service area;

[0079] An image generation sub-module, configured to perform data extraction on the remote sensing image data to generate an RGB image and an NDVI image;

[0080] The loan collateral classification image generation module is used to classify the NDVI image by using an artificial intelligence classification algorithm and save the loan collateral classification image;

[0081] The credit service database construction sub-module is used to save the loan collateral classification image, the RGB image, and the corresponding relationship between the loan collateral classification image and the RGB image to the credit service database.

[0082] On the basis of the above solution, optionally, the image generation sub-module is specifically used to extract the R-band data and NIR-band data in the remote sensing image data to generate an NDVI image; and extract the R-band data, G-band data, and B-band data in the remote sensing image data to generate an RGB image.

[0083] In a feasible solution, optionally, the loan application further includes credit service plan information;

[0084] Correspondingly, the target RGB image determination module 220 is further used for:

[0085] Determine a target RGB image in the pre-established credit service database according to the credit service plan, and circle the area related to the credit service plan in the target RGB image to generate a circled image;

[0086] Determine the available credit area data of the circled area in the circled image according to the circled image, the loan collateral classification image corresponding to the RGB image, and the imaging ratio of the loan collateral classification image.

[0087] In this embodiment, optionally, the approval result determination module 140 is specifically used for:

[0088] If the loan collateral area data exceeds the available credit area data, the loan application is not approved;

[0089] If the loan collateral area data does not exceed the available credit area data, the loan application is approved.

[0090] On the basis of the above solution, optionally, the approval result determination module 140 is further used for:

[0091] Determine the approval result of the loan application according to the available credit area data, the loan collateral area data, and the historical credit information obtained in the credit system.

[0092] In a feasible solution, optionally, the historical credit information includes the already-approved area data of the credit service plan;

[0093] Correspondingly, the approval result determination module 140 is specifically configured to:

[0094] Determine the ungranted area data of the credit service plan according to the grantable area data and the granted area data of the credit service plan;

[0095] Determine the approval result of the loan application according to the ungranted area data and the area data of the loan collateral.

[0096] The above product can execute the loan process control method based on loan collateral provided by the embodiments of the present application, and has the corresponding function modules and beneficial effects of the execution method.

[0097] Embodiment III

[0098] Embodiment III of the present invention provides a computer-readable storage medium, on which a computer program is stored, and when the program is executed by a processor, it implements the loan process control method based on loan collateral provided by all the inventive embodiments of the present application:

[0099] Obtain a loan application; the loan application includes credit service area information and area data of the loan collateral;

[0100] Determine a target RGB image in a pre-established credit service database according to the credit service area information;

[0101] Determine the grantable area data according to the target RGB image, the loan collateral classification image corresponding to the target RGB image in the credit service database, and the imaging ratio of the loan collateral classification image;

[0102] Determine the approval result of the loan application according to the grantable area data and the area data of the loan collateral.

[0103] Any combination of one or more computer-readable media may be employed. The computer-readable media may be a computer-readable signal medium or a computer-readable storage medium. A computer-readable storage medium may be, for example - but not limited to - an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the computer-readable storage medium include: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In this document, a computer-readable storage medium may be any tangible medium that contains or stores a program which can be used by or in connection with an instruction execution system, apparatus, or device.

[0104] A computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, in which computer-readable program code is carried. Such a propagated data signal may take many forms, including - but not limited to - an electromagnetic signal, an optical signal, or any suitable combination of the foregoing. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device.

[0105] The program code contained on the computer-readable medium may be transmitted using any appropriate medium, including - but not limited to - wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

[0106] The computer program code for performing the operations of the present invention may be written in one or more programming languages or combinations thereof, including object-oriented programming languages - such as Java, Smalltalk, C++ - and also including conventional procedural programming languages - such as the "C" language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer, or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network - including a local area network (LAN) or a wide area network (WAN) - or, alternatively, may be connected to an external computer (e.g., through the Internet using an Internet service provider).

[0107] Embodiment Four

[0108] Embodiment 4 of the present application provides an electronic device. Figure 3 It is a schematic structural diagram of an electronic device provided by Embodiment 4 of the present application. As Figure 3 shown, this embodiment provides an electronic device 300, which includes: one or more processors 320; a storage device 310 for storing one or more programs, and when the one or more programs are executed by the one or more processors 320, the one or more processors 320 implement the loan process control method based on loan collateral provided by the embodiments of the present application. This method includes:

[0109] Obtain a loan application; the loan application includes credit service area information and loan collateral area data;

[0110] According to the credit service area information, determine a target RGB image in a pre-established credit service database;

[0111] According to the target RGB image, the loan collateral classification image corresponding to the target RGB image in the credit service database, and the imaging ratio of the loan collateral classification image, determine the available credit area data;

[0112] According to the available credit area data and the loan collateral area data, determine the approval result of the loan application.

[0113] Of course, those skilled in the art can understand that the processor 320 also implements the technical solutions of the loan process control method based on loan collateral provided by any embodiment of the present application.

[0114] Figure 3 The displayed electronic device 300 is only an example and should not impose any limitations on the functions and usage scope of the embodiments of the present application.

[0115] As Figure 3 shown, the electronic device 300 includes a processor 320, a storage device 310, an input device 330, and an output device 340; the number of processors 320 in the electronic device can be one or more, Figure 3 taking one processor 320 as an example; the processor 320, storage device 310, input device 330, and output device 340 in the electronic device can be connected through a bus or other means, Figure 3 taking connection through a bus 350 as an example.

[0116] The storage device 310, as a computer-readable storage medium, can be used to store software programs, computer-executable programs, and module units, such as program instructions corresponding to the loan process control method based on loan collateral in the embodiments of the present application.

[0117] The storage device 310 may mainly include a program storage area and a data storage area. Among them, the program storage area may store an operating system and application programs required for at least one function; the data storage area may store data created according to the use of the terminal, etc. In addition, the storage device 310 may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one magnetic disk storage device, a flash memory device, or other non-volatile solid-state storage devices. In some instances, the storage device 310 may further include a memory remotely provided with respect to the processor 320, and these remote memories may be connected through a network. Examples of the above network include but are not limited to the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0118] The input device 330 may be used to receive input digital, character information, or voice information, and generate key signal inputs related to the user settings and function controls of the electronic device. The output device 340 may include electronic devices such as a display screen and a speaker.

[0119] The electronic device provided in the embodiment of the present application can judge the risk of a loan application through the target RGB image in the credit service database and the loan collateral classification image corresponding to the target RGB image, thereby ensuring the reliability of the loan, which is conducive to realizing a fast and accurate loan process.

[0120] The loan process control device, medium, and electronic device provided in the above embodiments can execute the loan process control method based on loan collateral provided in any embodiment of the present application, and have corresponding functional modules and beneficial effects for executing this method. Technical details not described in detail in the above embodiments can be found in the loan process control method based on loan collateral provided in any embodiment of the present application.

[0121] Note that the above is only a preferred embodiment of the present invention and the applied technical principle. Those skilled in the art will understand that the present invention is not limited to the specific embodiments described herein, and various obvious changes, re-adjustments, and substitutions can be made by those skilled in the art without departing from the protection scope of the present invention. Therefore, although the present invention has been described in detail through the above embodiments, the present invention is not limited to the above embodiments. Without departing from the concept of the present invention, more other equivalent embodiments can be included, and the scope of the present invention is determined by the scope of the appended claims.

Claims

1. A loan process control method based on loan collateral, characterized in that The method includes: Obtaining a loan application; the loan application includes a credit service area, loan collateral area data, and a credit service plan; According to the credit service plan, determining a target RGB image in a pre-established credit service database, and selecting a credit service plan-related area in the target RGB image to generate a selected image; According to the selected image, the loan collateral classification image corresponding to the target RGB image in the credit service database, and the imaging ratio of the loan collateral classification image, determining the area data that can be granted credit; the area data that can be granted credit is the area data that can be granted credit for the selected area in the selected image; According to the area data that can be granted credit and the loan collateral area data, determining the approval result of the loan application; Among them, the establishment process of the credit service database includes: Obtaining remote sensing image data of the credit service area; Performing data extraction on the remote sensing image data to generate an RGB image and an NDVI image; Using an artificial intelligence classification algorithm to classify the NDVI image and saving the loan collateral classification image; Saving the loan collateral classification image, the RGB image, and the corresponding relationship between the loan collateral classification image and the RGB image to the credit service database.

2. The method according to claim 1, wherein The performing data extraction on the remote sensing image data to generate an RGB image and an NDVI image includes: Extracting the R-band data and NIR-band data in the remote sensing image data to generate an NDVI image; and extracting the R-band data, G-band data, and B-band data in the remote sensing image data to generate an RGB image.

3. The method according to claim 1, wherein The determining the approval result of the loan application according to the area data that can be granted credit and the loan collateral area data includes: If the loan collateral area data exceeds the area data that can be granted credit, the loan application is not approved; If the loan collateral area data does not exceed the area data that can be granted credit, the loan application is approved.

4. The method according to claim 1, wherein After determining the area data that can be granted credit, the method further includes: According to the area data that can be granted credit, the loan collateral area data, and historical credit information obtained in the credit system, determining the approval result of the loan application.

5. The method according to claim 4, wherein The historical credit information includes the area data that has been granted credit for the credit service plan; Correspondingly, the determining the approval result of the loan application according to the area data that can be granted credit, the loan collateral area data, and historical credit information obtained in the credit system includes: According to the area data that can be granted credit and the area data that has been granted credit for the credit service plan, determining the area data that has not been granted credit for the credit service plan; According to the area data that has not been granted credit and the loan collateral area data, determining the approval result of the loan application.

6. A loan process control device based on loan collateral, characterized in that, The device includes: A loan application acquisition module for acquiring a loan application; the loan application includes a credit service area, loan collateral area data, and a credit service plan; A target RGB image determination module, configured to determine a target RGB image in a pre-established credit service database according to the credit service plan, and circle a credit service plan-related area in the target RGB image to generate a circled image; An available credit area data determination module, configured to determine available credit area data according to the circled image, a lending collateral classification image corresponding to the target RGB image in the credit service database, and an imaging ratio of the lending collateral classification image; the available credit area data is the available credit area data of the circled area in the circled image; An approval result determination module, configured to determine an approval result of the loan application according to the available credit area data and the lending collateral area data; Wherein, the establishment process of the credit service database includes: Obtaining remote sensing image data of a credit service area; Performing data extraction on the remote sensing image data to generate an RGB image and an NDVI image; Classifying the NDVI image by using an artificial intelligence classification algorithm, and saving the lending collateral classification image; Saving the lending collateral classification image, the RGB image, and the corresponding relationship between the lending collateral classification image and the RGB image to the credit service database.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by a processor, it implements the loan process control method based on lending collateral according to any one of claims 1-5.

8. An electronic device, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that When the processor executes the computer program, it implements the loan process control method based on lending collateral according to any one of claims 1-5.

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