Method and device for determining credit rating of object, processor and electronic equipment

Abstract

The invention discloses a method and device for determining the credit rating of an object, a processor and electronic equipment, and relates to the field of artificial intelligence. The method comprises the steps that an attribute information set of an object is acquired, the attribute information set comprises at least one piece of attribute information, and the attribute information is used for representing attributes associated with the credit rating of the object; determining target attribute information from the attribute information of the attribute information set; the target attribute information is input into a target prediction model in a target prediction model set for prediction, the initial credit rating of the object is obtained, the target prediction model is obtained by training the initial prediction model based on a sample attribute information set of a sample object, the sample attribute information set comprises at least sample attribute information, and the sample attribute information comprises at least one piece of sample attribute information; the sample attribute information is used for representing attributes associated with the credit rating of the sample object; a target credit rating associated with the initial credit rating is determined. According to the invention, the technical problem of low determination precision of the credit rating is solved.

Description

Technical Field

[0001] The present invention relates to the field of artificial intelligence, and in particular to a method, device, processor and electronic device for determining the credit rating of an object. Background Art

[0002] In the process of applying for a credit card, whether to approve the user's credit card application and the allocated credit card limit are determined based on manual judgment, which is highly subjective and lacks theoretical support.

[0003] In order to avoid the above defects, the constructed model is used to predict the user's credit rating, and the user's credit card application and the allocated credit card limit are directly determined based on the predicted rating. However, the prediction results of this prediction method are not comprehensive, which leads to the technical problem of low accuracy in determining the user's credit rating.

[0004] With respect to the above-mentioned technical problem of low accuracy in determining credit ratings, no effective solution has been proposed so far. Summary of the invention

[0005] The embodiments of the present invention provide a method, device, processor and electronic device for determining the credit rating of an object, so as to at least solve the technical problem of low accuracy in determining the credit rating.

[0006] According to one aspect of an embodiment of the present invention, there is provided a method for determining a credit rating of an object, the method comprising: obtaining an attribute information set of the object, wherein the attribute information set comprises at least one attribute information, and the attribute information is used to represent an attribute associated with the credit rating of the object; determining target attribute information from the attribute information of the attribute information set, wherein the degree of association between the target attribute information and the credit rating is greater than the degree of association between the remaining attribute information in the attribute information set except the target attribute information and the credit rating; inputting the target attribute information into a target prediction model in a target prediction model set for prediction to obtain an initial credit rating of the object, wherein the target prediction model is obtained by training an initial prediction model based on a sample attribute information set of a sample object, and the sample attribute information set comprises at least one sample attribute information, and the sample attribute information is used to represent an attribute associated with the credit rating of the sample object; determining a target credit rating associated with the initial credit rating, wherein the initial credit rating is integrated into the target credit rating.

[0007] Optionally, the target prediction model set includes a first target prediction model, a second target prediction model and a third target prediction model, the first target prediction model is a prediction model constructed for the overdue number of credit cards held by the object, the second target prediction model is a prediction model constructed for the overdue time of the credit cards, and the third target prediction model is a prediction model constructed for the overdue amount of the credit cards. The target attribute information is input into the target prediction model in the target prediction model set for prediction to obtain the initial credit rating of the object, including: inputting the target attribute information into the first target prediction model for prediction to obtain the first initial credit rating; inputting the target attribute information into the second target prediction model for prediction to obtain the second initial credit rating; inputting the target attribute information into the third target prediction model for prediction to obtain the third initial credit rating.

[0008] Optionally, determining a target credit rating associated with the initial credit rating includes: determining a weight of the initial credit rating; and determining the target credit rating based on the weight and the initial credit rating.

[0009] Optionally, the weights include a first weight for a first initial credit rating, a second weight for a second initial credit rating, and a third weight for a third initial credit rating, and based on the weights and the initial credit ratings, determining the target credit rating includes: determining the target credit rating based on the first weight, the second weight, the third weight and the initial credit ratings.

[0010] Optionally, based on the first weight, the second weight, the third weight and the initial credit rating, determining the target credit rating includes: determining a first product between the first weight and the first initial credit rating, a second product between the second weight and the second initial credit rating, and a third product between the third weight and the third initial credit rating; and determining the sum of the first product, the second product and the third product as the target credit rating.

[0011] Optionally, target attribute information is determined from the attribute information of the attribute information set, including: determining the degree of association between the attribute information of the attribute information set and the credit rating; determining the target association degree from the association degrees, wherein the target association degree is greater than the remaining association degrees in the association degrees except the target association degree; and determining the attribute information corresponding to the target association degree as the target attribute information.

[0012] According to one aspect of an embodiment of the present invention, there is provided a device for determining a credit rating of an object, the device may include: an acquisition unit, for acquiring an attribute information set of the object, wherein the attribute information set includes at least one attribute information, and the attribute information is used to represent an attribute associated with the credit rating of the object; a first determination unit, for determining target attribute information from the attribute information of the attribute information set, wherein the degree of association between the target attribute information and the credit rating is greater than the degree of association between the remaining attribute information in the attribute information set except the target attribute information and the credit rating; a prediction unit, for inputting the target attribute information into a target prediction model in a target prediction model set for prediction, to obtain an initial credit rating of the object, wherein the target prediction model is obtained by training an initial prediction model based on a sample attribute information set of a sample object, and the sample attribute information set includes at least one sample attribute information, and the sample attribute information is used to represent an attribute associated with the credit rating of the sample object; a second determination unit, for determining a target credit rating associated with the initial credit rating, wherein the initial credit rating is integrated into the target credit rating.

[0013] According to another aspect of an embodiment of the present invention, a processor is further provided, wherein the processor is used to run a program, wherein when the program is run by the processor, the method for determining the credit rating of an object in an embodiment of the present invention is executed.

[0014] According to another aspect of an embodiment of the present invention, there is also provided an electronic device, comprising: a memory storing an executable program; and a processor for running the program, wherein when the program is running, the method for determining the credit rating of an object in each embodiment of the present invention is executed.

[0015] According to another aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium includes a stored program, wherein when the program is executed, the device where the computer-readable storage medium is located is controlled to execute the method for determining the credit rating of an object in the present invention.

[0016] According to another aspect of an embodiment of the present invention, a computer program product is provided. The computer program product includes a computer program. When the computer program is executed by a processor, the method for determining the credit rating of an object in an embodiment of the present invention is implemented.

[0017] According to another aspect of an embodiment of the present invention, a computer program product is also provided, including a non-volatile computer-readable storage medium, wherein the non-volatile computer-readable storage medium is used to store a computer program, and when the computer program is executed by a processor, the method for determining the credit rating of an object in an embodiment of the present invention is implemented.

[0018] According to another aspect of an embodiment of the present invention, an embodiment of the present application further provides a computer program, which, when executed by a processor, implements the method for determining the credit rating of an object in the above-mentioned embodiment of the present invention.

[0019] In the embodiment of the present invention, when determining the credit rating of an object, an attribute information set of the object can be obtained. From the attribute information of the acquired attribute information set, target attribute information can be determined, and the determined target attribute information is input into the target prediction model in the target prediction model set for prediction, so that the initial credit rating of the object can be obtained, and the target credit rating associated with the initial credit rating can be determined, thereby achieving the purpose of ensuring the comprehensiveness of the prediction result, thereby solving the technical problem of low accuracy in determining the user's credit rating, and further achieving the technical effect of improving the accuracy in determining the user's credit rating. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] 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:

[0021] Figure 1 is a hardware structure block diagram of a computer terminal for implementing a method for determining a credit rating of an object according to an embodiment of the present invention;

[0022] Figure 2 is a flow chart of a method for determining a credit rating of an object according to an embodiment of the present invention;

[0023] Figure 3 is a flow chart of a method for analyzing user credit risk according to an embodiment of the present invention;

[0024] Figure 4 is a schematic diagram of a device for determining a credit rating of an object according to an embodiment of the present invention;

[0025] Figure 5 It is a structural block diagram of an electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0026] 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 embodiments of a part of the present invention, not all 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.

[0027] 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.

[0028] According to an embodiment of the present invention, a method for determining the credit rating of an object 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 may be executed in an order different from that shown here.

[0029] The method for determining the credit rating of an object provided in the embodiment of the present application can be executed in a mobile terminal, a computer terminal or a similar computing device. Figure 1 1 is a hardware structure block diagram of a computer terminal for implementing a method for determining a credit rating of an object according to an embodiment of the present invention. Figure 1 As shown, the computer terminal 10 (or mobile device) may include one or more (102a, 102b, ..., 102n are used to illustrate) processors 102 (the processor 102 may include but is not limited to a processing device such as a microprocessor MCU or a programmable logic device FPGA), a memory 104 for storing data, and a transmission device 106 for communication functions. 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 BUS bus), a network interface, a power supply and / or a camera. It can be understood by those skilled in the art that Figure 1 The structure shown is only for illustration and does not limit the structure of the above electronic device. Figure 1 More or fewer components as shown, or with Figure 1 Different configurations are shown.

[0030] It should be noted that the one or more processors 102 and / or other data processing circuits described above may generally be referred to herein as "data processing circuits". The data processing circuits may be embodied in whole or in part as software, hardware, firmware, or any other combination thereof. In addition, the data processing circuit may be a single independent processing module, or may be incorporated in whole or in part into any of the other components in the computer terminal 10 (or mobile device). As described in the embodiments of the present application, the data processing circuit acts as a processor control (e.g., selection of a variable resistor terminal path connected to an interface).

[0031] The memory 104 can be used to store software programs and modules of application software, such as program instructions / data storage devices corresponding to the method for determining the credit rating of an object in the embodiment of the present application. The processor 102 executes various functional applications and data processing by running the software programs and modules stored in the memory 104, that is, the method for determining the credit rating of the object described above is implemented. The memory 104 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 104 may further include a memory remotely arranged relative to the processor 102, and these remote memories may be connected to the computer terminal 10 via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0032] The transmission device 106 can be used to receive or send data via a network. The specific example of the above network may include a wireless network provided by a communication provider of the computer terminal 10. In one example, the transmission device 106 includes a network adapter (Network Interface Controller, referred to as NIC), which can be connected to other network devices through a base station so as to communicate with the Internet. In one example, the transmission device 106 can be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0033] The display may be, for example, a touch screen liquid crystal display (LCD), which enables a user to interact with a user interface of the computer terminal 10 (or mobile device).

[0034] Under the above operating environment, this application provides Figure 2 The method for determining the credit rating of the object shown. Figure 2 1 is a flow chart of a method for determining a credit rating of an object according to an embodiment of the present invention. The method may include the following steps:

[0035] Step S201, obtaining an attribute information set of an object.

[0036] In the technical solution provided in the above step S201 of the present invention, the above attribute information set may include at least one attribute information. The above attribute information may be used to represent the attribute associated with the credit rating of the object. For example, the above object may be a current user who needs to apply for a credit card, and the attribute associated with the credit rating of the object may include: basic information, asset status, historical credit status and family situation of the current user, etc. The basic information may include: age, gender, occupation, years of work and income, etc., which are only given as examples and are not specifically limited.

[0037] In this embodiment, the attribute information set of the object is obtained. Optionally, this embodiment filters the background data according to the needs of the object to obtain the attribute information set of the object, that is, if the need of the object is to apply for a credit card, the background data is filtered to obtain the attribute information set of the object. Alternatively, if the need of the object is to apply for a credit card, the attribute information set of the object is directly downloaded from the server.

[0038] Step S202: determine target attribute information from the attribute information in the attribute information set.

[0039] In the technical solution provided in step S202 of the present invention, the degree of association between the target attribute information and the credit rating may be greater than the degree of association between the remaining attribute information in the attribute information set except the target attribute information and the credit rating. For example, the degree of association may also be referred to as the degree of influence, which is only used as an example and is not specifically limited.

[0040] In this embodiment, after acquiring the attribute information set of the object, the target attribute information is determined from the attribute information of the attribute information set. Optionally, based on acquiring the attribute information set of the object, the embodiment can determine the degree of association between the attribute information of the attribute information set and the credit rating. The attribute information corresponding to the maximum degree of association among the degrees of association between the attribute information of the attribute information set and the credit rating is determined as the target attribute information.

[0041] Optionally, based on the acquired attribute information set of the object, the degree of correlation between the attribute information of the attribute information set and the credit rating can be determined. For example, the degree of correlation between the attribute information of the attribute information set and the credit rating can be calculated using a statistical method, and the calculated degree of correlation is used as the degree of correlation between the attribute information of the attribute information set and the credit rating. The statistical method can be, but is not limited to, a correlation coefficient, a chi-square test, etc., which are only used as examples here and are not specifically limited.

[0042] Step S203, input the target attribute information into the target prediction model in the target prediction model set for prediction to obtain the initial credit rating of the object.

[0043] In the technical solution provided in step S203 of the present invention, the target prediction model can be obtained by training the initial prediction model based on the sample attribute information set of the sample object. The sample attribute information set can include at least one sample attribute information, and the sample attribute information can be used to represent the attribute associated with the credit rating of the sample object.

[0044] In this embodiment, the above-mentioned initial prediction model can be constructed based on a multi-layer perceptron (Multi-LayerPerceptron, abbreviated as MLP), and the above-mentioned sample object can be a historical user who has applied for a credit card. The attributes associated with the credit rating of the sample object may include: basic information of the historical user, asset status, historical credit status and family situation, etc. The basic information may include: age, gender, occupation, years of work and income, etc., which are only given as examples here and are not specifically limited.

[0045] In this embodiment, the initial credit rating may be expressed in at least one of the following forms or a combination thereof: English letters and Arabic numerals, etc. For example, if the initial credit rating is expressed in English letters, an initial credit rating of A is higher than an initial credit rating of B, and an initial credit rating of B is higher than an initial credit rating of C; if the initial credit rating is expressed in Arabic numerals, an initial credit rating of 10 is higher than an initial credit rating of 9, and an initial credit rating of 9 is higher than an initial credit rating of 8. This is only an example and is not specifically limited.

[0046] In this embodiment, after the target attribute information is determined from the attribute information in the attribute information set, the target attribute information is input into the target prediction model in the target prediction model set for prediction to obtain the initial credit rating of the object. Optionally, this embodiment inputs the determined target attribute information into the target prediction model in the target prediction model set for prediction based on the determination of the target attribute information, and uses the output layer of the target prediction model to output the predicted initial credit rating.

[0047] Step S204: determining a target credit rating associated with the initial credit rating.

[0048] In the technical solution provided in the above step S204 of the present invention, the initial credit rating is integrated into the above target credit rating.

[0049] In this embodiment, after inputting the target attribute information into the target prediction model in the target prediction model set for prediction and obtaining the initial credit rating of the object, the target credit rating associated with the initial credit rating is determined. Optionally, on the basis of obtaining the initial credit rating of the object, this embodiment performs data fusion on each initial credit rating obtained by using the target prediction model in the target prediction model set to obtain the target credit rating.

[0050] In the above steps S201 to S204 of the present application, when determining the credit rating of an object, an attribute information set of the object can be obtained. From the attribute information of the acquired attribute information set, the target attribute information can be determined, and the determined target attribute information is input into the target prediction model in the target prediction model set for prediction, so that the initial credit rating of the object can be obtained, and the target credit rating associated with the initial credit rating can be determined, thereby achieving the purpose of ensuring the comprehensiveness of the prediction results, thereby solving the technical problem of low accuracy in determining the user's credit rating, and further achieving the technical effect of improving the accuracy of determining the user's credit rating.

[0051] The above method of this embodiment is further introduced below.

[0052] As an optional implementation method, step S203, inputs the target attribute information into the target prediction model in the target prediction model set for prediction to obtain the initial credit rating of the object, including: inputting the target attribute information into the first target prediction model for prediction to obtain the first initial credit rating; inputting the target attribute information into the second target prediction model for prediction to obtain the second initial credit rating; inputting the target attribute information into the third target prediction model for prediction to obtain the third initial credit rating.

[0053] In this embodiment, the target prediction model set may include a first target prediction model, a second target prediction model and a third target prediction model. The first target prediction model may be a prediction model constructed for the overdue number of credit cards held by the subject, the second target prediction model may be a prediction model constructed for the overdue time of the credit card, and the third target prediction model may be a prediction model constructed for the overdue amount of the credit card.

[0054] In this embodiment, after the target attribute information is determined from the attribute information of the attribute information set, the target attribute information is input into the first target prediction model for prediction to obtain the first initial credit rating. Optionally, in this embodiment, based on the determination of the target attribute information, when the target prediction model is the first target prediction model, the determined target attribute information is input into the first target prediction model for prediction, and the output layer of the first target prediction model is used to output the predicted first initial credit rating.

[0055] In this embodiment, after the target attribute information is determined from the attribute information of the attribute information set, the target attribute information is input into the second target prediction model for prediction to obtain the second initial credit rating. Optionally, in this embodiment, based on the determination of the target attribute information, when the target prediction model is the second target prediction model, the determined target attribute information is input into the second target prediction model for prediction, and the output layer of the second target prediction model is used to output the predicted second initial credit rating.

[0056] In this embodiment, after the target attribute information is determined from the attribute information of the attribute information set, the target attribute information is input into the third target prediction model for prediction to obtain the third initial credit rating. Optionally, in this embodiment, based on the determination of the target attribute information, when the target prediction model is the third target prediction model, the determined target attribute information is input into the third target prediction model for prediction, and the predicted third initial credit rating is output using the output layer of the third target prediction model.

[0057] As an optional implementation manner, step S204, determining a target credit rating associated with the initial credit rating, includes: determining a weight of the initial credit rating; and determining the target credit rating based on the weight and the initial credit rating.

[0058] In this embodiment, after the target attribute information is input into the target prediction model in the target prediction model set for prediction and the initial credit rating of the object is obtained, the weight of the initial credit rating is determined. Optionally, based on the initial credit rating of the object, this embodiment can determine the first weight of the first initial credit rating, the second weight of the second initial credit rating, and the third weight of the third initial credit rating from the weight allocation table. The weight allocation table may include a mapping relationship between different initial information levels and different weights.

[0059] In this embodiment, after determining the weight of the initial credit rating, the target credit rating is determined based on the weight and the initial credit rating. Optionally, this embodiment sets the determined weight to the initial credit rating based on the determined weight of the initial credit rating to obtain the target credit rating.

[0060] As an optional implementation manner, determining the target credit rating based on the weight and the initial credit rating includes: determining the target credit rating based on the first weight, the second weight, the third weight and the initial credit rating.

[0061] In this embodiment, the weights may include a first weight for the first initial credit rating, a second weight for the second initial credit rating, and a third weight for the third initial credit rating.

[0062] In this embodiment, after determining the weight of the initial credit rating, the target credit rating is determined based on the first weight, the second weight, the third weight and the initial credit rating. Optionally, this embodiment sets the determined first weight for the first initial credit rating, sets the determined second weight for the second initial credit rating, and sets the determined third weight for the third initial credit rating on the basis of determining the weight of the initial credit rating, so as to obtain the target credit rating.

[0063] As an optional implementation method, the target credit rating is determined based on the first weight, the second weight, the third weight and the initial credit rating, including: determining a first product between the first weight and the first initial credit rating, a second product between the second weight and the second initial credit rating, and a third product between the third weight and the third initial credit rating; and determining the sum of the first product, the second product and the third product as the target credit rating.

[0064] In this embodiment, after determining the weight of the initial credit rating, a first product between the first weight and the first initial credit rating, a second product between the second weight and the second initial credit rating, and a third product between the third weight and the third initial credit rating are determined. Optionally, based on determining the weight of the initial credit rating, this embodiment multiplies the first weight and the first initial credit rating to obtain a first product between the first weight and the first initial credit rating, multiplies the second weight and the second initial credit rating to obtain a second product between the second weight and the second initial credit rating, and multiplies the third weight and the third initial credit rating to obtain a third product between the third weight and the third initial credit rating.

[0065] In this embodiment, after determining a first product between the first weight and the first initial credit rating, a second product between the second weight and the second initial credit rating, and a third product between the third weight and the third initial credit rating, the sum of the first product, the second product, and the third product is determined as the target credit rating. Optionally, this embodiment, on the basis of determining the first product, the second product, and the third product, performs a sum operation on the first product, the second product, and the third product to obtain the sum of the first product, the second product, and the third product, and determines the obtained sum as the target credit rating.

[0066] As an optional implementation method, step S202, determining target attribute information from the attribute information of the attribute information set, includes: determining the degree of association between the attribute information of the attribute information set and the credit rating; determining the target association degree from the association degree; and determining the attribute information corresponding to the target association degree as the target attribute information.

[0067] In this embodiment, the target association degree may be greater than the remaining association degrees in the association degrees except for the target association degree.

[0068] In this embodiment, after obtaining the attribute information set of the object, the degree of correlation between the attribute information of the attribute information set and the credit rating is determined. Optionally, based on the obtained attribute information set, this embodiment can calculate the degree of correlation between the attribute information of the attribute information set and the credit rating using a statistical method, and use the calculated degree of correlation as the degree of correlation between the attribute information of the attribute information set and the credit rating. The statistical method can be, but is not limited to, a correlation coefficient, a chi-square test, etc., which are only used as examples here and are not specifically limited.

[0069] In this embodiment, after determining the degree of association between the attribute information of the attribute information set and the credit rating, a target degree of association is determined from the degree of association. Optionally, based on the determined degree of association, this embodiment sequentially compares the degrees of association between the attribute information of the attribute information set and the credit rating until the target degree of association is found, that is, the maximum degree of association is found.

[0070] In this embodiment, after determining the target association degree from the association degrees, the attribute information corresponding to the target association degree is determined as the target attribute information. Optionally, this embodiment directly determines the attribute information corresponding to the target association degree as the target attribute information based on determining the target association degree, that is, determines the attribute information corresponding to the maximum association degree as the target attribute information.

[0071] In the embodiment of the present invention, when determining the credit rating of an object, an attribute information set of the object can be obtained. From the attribute information of the acquired attribute information set, target attribute information can be determined, and the determined target attribute information is input into the target prediction model in the target prediction model set for prediction, so that the initial credit rating of the object can be obtained, and the target credit rating associated with the initial credit rating can be determined, thereby achieving the purpose of ensuring the comprehensiveness of the prediction result, thereby solving the technical problem of low accuracy in determining the user's credit rating, and further achieving the technical effect of improving the accuracy in determining the user's credit rating.

[0072] The technical solution of the embodiment of the present invention is illustrated below in conjunction with preferred implementation modes.

[0073] In the process of applying for a credit card, whether to approve the user's credit card application and the allocated credit card limit are determined based on manual judgment, which is highly subjective and lacks theoretical support.

[0074] In order to avoid the above defects, the constructed model is used to predict the user's credit rating, and the user's credit card application and the allocated credit card limit are directly determined based on the predicted rating. However, the prediction results of this prediction method are not comprehensive, which leads to the technical problem of low accuracy in determining the user's credit rating.

[0075] In order to solve the above technical problems, an embodiment of the present invention proposes a method for determining the credit rating of an object. From the attribute information of the acquired attribute information set, target attribute information can be determined, and the determined target attribute information is input into the target prediction model in the target prediction model set for prediction. The initial credit rating of the object can be obtained, and the target credit rating associated with the initial credit rating can be determined. The purpose of ensuring the comprehensiveness of the prediction results is achieved, thereby solving the technical problem of low accuracy in determining the user's credit rating, and further achieving the technical effect of improving the accuracy of determining the user's credit rating.

[0076] In this embodiment, the user credit risk analysis method in this application is executed to obtain the user's credit rating. For example, Figure 3 is a flow chart of a method for analyzing user credit risk according to an embodiment of the present invention. Figure 3 As shown, the method may include the following steps:

[0077] Step S301, using an improved particle swarm optimization algorithm, optimizes the initial weights and biases of each node in the first multi-layer perceptron (Multi-Layer Perceptron, MLP for short).

[0078] In the technical solution provided in the above step S301 of the present invention, the above first multi-layer perceptron is applicable to the credit risk analysis model of the number of overdue cards of users.

[0079] After optimizing the initial weights and biases of each node in the first MLP, proceed to step S304, substitute the optimized initial weights and biases into the first multilayer perceptron to obtain the first target multilayer perceptron, and use the first target multilayer perceptron to train the user overdue card number credit risk analysis model.

[0080] After training the user's overdue credit card number credit risk analysis model, proceed to step S307, input the user's characteristic parameters into the trained user's overdue credit card number credit risk analysis model for risk prediction, and obtain a first initial credit rating.

[0081] Step S302, using an improved particle swarm optimization algorithm to optimize the initial weights and biases of each node in the second multilayer perceptron.

[0082] In the technical solution provided in the above step S302 of the present invention, the above second multi-layer perceptron is applicable to the user overdue days credit risk analysis model.

[0083] After optimizing the initial weights and biases of each node in the second MLP, proceed to step S305, substitute the optimized initial weights and biases into the second multilayer perceptron to obtain a second target multilayer perceptron, and use the second target multilayer perceptron to train a user overdue days credit risk analysis model.

[0084] After training the user overdue days credit risk analysis model, proceed to step S308, input the user's characteristic parameters into the trained user overdue days credit risk analysis model for risk prediction, and obtain a second initial credit rating.

[0085] Step S303, using an improved particle swarm optimization algorithm to optimize the initial weights and biases of each node in the third multilayer perceptron.

[0086] In the technical solution provided in the above step S303 of the present invention, the above third multi-layer perceptron is applicable to the user overdue amount credit risk analysis model.

[0087] In this embodiment, the above-mentioned optimization process may include the following steps:

[0088] Step 1: Initialize the parameters. According to the number of optimized parameters and the parameter range, the total number of particles, particle dimensions, the search range of each particle parameter and the search speed range can be determined. The maximum and minimum values ​​of the inertia weight coefficient are w and w respectively. max =0.95, w min=0.45, learning factors c1=2.05, c2=2.05, annealing process attenuation parameter lamda=0.98, and maximum number of iterations k max and error threshold;

[0089] Step 2: Randomly generate the initial velocity and initial position of each particle, calculate the fitness value of each particle, initialize the individual optimal solution of the particle and the global optimal solution of the population, whose global optimal fitness value is Gvalue, and set the initial temperature value of simulated annealing to T = Gvalue / ln(5);

[0090] Step 3: Update the inertia weight coefficient w according to the following formula, and update the particle's speed and position. Where D can represent the dimension, and the flight speed of the i-th particle can be expressed as V i =(V i1 , V i2 ,…V iD ), the position of the i-th particle can be represented by X i =(X i1 , X i2 , …X iD ), the individual optimal position can be expressed by PBest i =(p i1 , p i2 ,…p iD ), the global optimal position of the entire particle population so far can be expressed as GBest = (g1, g2, ... g D ) to indicate that It can be used to represent the velocity of the d-th dimension of the i-th particle in the k-th iteration. and Similarly, and Can be used to represent random numbers between 0 and 1;

[0091] w=1.4×(w max -w min )+0.5×tanh(-2+4×(k max -k) / k max )(w max -w min ) (1)

[0092]

[0093] Step 4: Calculate the fitness of each particle after moving and the individual optimal solution of each particle after moving, integrate the idea of ​​simulated annealing, and use the Metropolis criterion to determine whether the new solution is used as the individual optimal solution of the particle. If so, perform the annealing operation;

[0094] Step 5: Update the global optimal solution of the particle;

[0095] Step 6: Continue to execute the above steps until the termination condition is reached;

[0096] After optimizing the initial weights and biases of each node in the third MLP, proceed to step S306, substitute the optimized initial weights and biases into the third multilayer perceptron to obtain a third target multilayer perceptron, and use the third target multilayer perceptron to train a user overdue amount credit risk analysis model.

[0097] In the technical solution provided in the above step S306 of the present invention, using the third target multilayer perceptron, training the user overdue amount credit risk analysis model can be completed by executing the following steps: preprocessing the data to remove outliers; determining the network structure, for example, in the network structure, the input layer may include 10 input nodes and 1 bias node, and the output layer may include 4 nodes, determining the appropriate number of hidden layers, and the number of neurons in each hidden layer, and setting each initial value, the initial weight and bias size of each neuron can be optimized by the above-mentioned improved particle swarm optimization algorithm to obtain the optimal initial value; for forward propagation, each neuron calculates the weighted input and applies the activation function, where the tanh function is used as the activation function; calculating the loss function, wherein the loss function can be calculated using the mean square error; for back propagation, calculating the gradient, updating the weights and biases; and repeatedly training the network model and evaluating the trained network model until the prediction result output by the network model meets expectations.

[0098] After training the user overdue amount credit risk analysis model, proceed to step S309, input the user's characteristic parameters into the trained user overdue amount credit risk analysis model for risk prediction, and obtain a third initial credit rating.

[0099] In the technical solution provided in the above step S309 of the present invention, based on the user sample data obtained, the user sample data is divided according to the target division ratio (for example, but not limited to: 7:3), and a training set and a test set can be obtained. Among them, the input of the three models can be the basic feature data of the user, and the basic feature data can include personal basic information, personal asset status, personal historical credit status and personal family situation, etc., wherein the personal basic information can include: age, gender, occupation, years of work and income, etc.; the above basic feature data are classified, the classified basic feature data are combined, the combined basic feature data are preprocessed, and the preprocessed basic feature data are denoised; using methods such as principal component analysis, from the basic feature data after denoising, the feature data with the highest degree of correlation with the credit rating can be determined as the input of the model, the feature data with the highest degree of correlation with the credit rating is normalized, and the existing null values ​​are filled.

[0100] In this embodiment, the output of the above three models is the credit rating of the user. For the credit risk analysis model of the number of overdue cards of the user, the initial credit rating of the user is determined according to the number of overdue cards of the user. For example, if the number of overdue cards is 0, the initial credit rating of the user is A, if the number of overdue cards is 1, the initial credit rating of the user is B, if the number of overdue cards is 2, the initial credit rating of the user is C, and if the number of overdue cards is 3 or more, the initial credit rating of the user is D. Similarly, the credit risk analysis model of the number of overdue days of the user determines the initial credit rating of the user according to the number of overdue days, and the credit risk analysis model of the overdue amount of the user determines the initial credit rating of the user according to the size of the overdue amount. The initial credit rating of the user under different analysis conditions is respectively used as the output of the above three models.

[0101] After obtaining the first initial credit rating, the second initial credit rating and the third initial credit rating, the process proceeds to step S310 to perform weighted fusion on the first initial credit rating, the second initial credit rating and the third initial credit rating to obtain a target credit rating.

[0102] In this embodiment, when executing an automated test script, a set of automated test scripts to be executed can be obtained, and the relationship between the automated test scripts in the automated test script set and the normal automated test scripts and abnormal automated test scripts can be determined. According to the determined above relationship, a set of automated test script tasks to be executed can be constructed, and the automated test scripts in the constructed automated test script task set can be executed to obtain test results, thereby achieving the purpose of avoiding the time-consuming execution of automated test scripts, thereby solving the technical problem of low efficiency of automated testing, and further achieving the technical effect of improving the efficiency of automated testing.

[0103] According to an embodiment of the present invention, a device for determining the credit rating of an object is also provided. It should be noted that the device for determining the credit rating of an object can be used to execute a method for determining the credit rating of an object in an embodiment.

[0104] Figure 4 Schematic diagram of a device for determining a credit rating of an object according to an embodiment of the present invention. Figure 4 As shown, the device 400 for determining the credit rating of the object may include: an acquisition unit 401 , a first determination unit 402 , a prediction unit 403 and a second determination unit 404 .

[0105] The acquisition unit 401 is used to acquire an attribute information set of an object, wherein the attribute information set includes at least one attribute information, and the attribute information is used to represent an attribute associated with the credit rating of the object.

[0106] The first determining unit 402 is used to determine target attribute information from the attribute information in the attribute information set, wherein the degree of association between the target attribute information and the credit rating is greater than the degree of association between the remaining attribute information in the attribute information set except the target attribute information and the credit rating.

[0107] The prediction unit 403 is used to input the target attribute information into the target prediction model in the target prediction model set for prediction to obtain the initial credit rating of the object, wherein the target prediction model is obtained by training the initial prediction model based on the sample attribute information set of the sample object, and the sample attribute information set includes at least one sample attribute information, and the sample attribute information is used to represent the attributes associated with the credit rating of the sample object.

[0108] The second determining unit 404 is configured to determine a target credit rating associated with the initial credit rating, wherein the initial credit rating is integrated into the target credit rating.

[0109] Optionally, the target prediction model set includes a first target prediction model, a second target prediction model and a third target prediction model, the first target prediction model is a prediction model constructed for the overdue number of credit cards held by the object, the second target prediction model is a prediction model constructed for the overdue time of the credit card, and the third target prediction model is a prediction model constructed for the overdue amount of the credit card. The prediction unit 403 may include: a first prediction module, used to input the target attribute information into the first target prediction model for prediction, and obtain a first initial credit rating; a second prediction module, used to input the target attribute information into the second target prediction model for prediction, and obtain a second initial credit rating; a third prediction module, used to input the target attribute information into the third target prediction model for prediction, and obtain a third initial credit rating.

[0110] Optionally, the second determining unit 404 may include: a first determining module, configured to determine a weight of an initial credit rating; and a second determining module, configured to determine a target credit rating based on the weight and the initial credit rating.

[0111] Optionally, the weights include a first weight for a first initial credit rating, a second weight for a second initial credit rating, and a third weight for a third initial credit rating, and the second determination module may include: a determination submodule for determining a target credit rating based on the first weight, the second weight, the third weight and the initial credit rating.

[0112] Optionally, the determination submodule can complete the determination of the target credit rating based on the first weight, the second weight, the third weight and the initial credit rating by performing the following steps: determining a first product between the first weight and the first initial credit rating, a second product between the second weight and the second initial credit rating, and a third product between the third weight and the third initial credit rating; and determining the sum of the first product, the second product and the third product as the target credit rating.

[0113] Optionally, the first determination unit 402 may include: a third determination module, used to determine the degree of association between the attribute information of the attribute information set and the credit rating; a fourth determination module, used to determine a target association degree from the association degrees, wherein the target association degree is greater than the remaining association degrees in the association degrees except the target association degree; and a fifth determination module, used to determine the attribute information corresponding to the target association degree as the target attribute information.

[0114] In this embodiment, the following units are deployed in the device for determining the credit rating of the object: an acquisition unit, which is used to acquire an attribute information set of the object, wherein the attribute information set includes at least one attribute information, and the attribute information is used to represent the attribute associated with the credit rating of the object; a first determination unit, which is used to determine the target attribute information from the attribute information of the attribute information set, wherein the degree of association between the target attribute information and the credit rating is greater than the degree of association between the remaining attribute information in the attribute information set except the target attribute information and the credit rating; a prediction unit, which is used to input the target attribute information into the target prediction model in the target prediction model set for prediction, so as to obtain the initial credit rating of the object, wherein the target prediction model is obtained by training the initial prediction model based on the sample attribute information set of the sample object, and the sample attribute information set includes at least one sample attribute information, and the sample attribute information is used to represent the attribute associated with the credit rating of the sample object; a second determination unit, which is used to determine the target credit rating associated with the initial credit rating, wherein the initial credit rating is integrated into the target credit rating, thereby achieving the purpose of ensuring the comprehensiveness of the prediction result, thereby solving the technical problem of low determination accuracy of the user's credit rating, and further achieving the technical effect of improving the determination accuracy of the user's credit rating.

[0115] It should be noted that the acquisition unit 401, the first determination unit 402, the prediction unit 403 and the second determination unit 404 correspond to steps S201 to S204 in the embodiment, and the four units are the same as the examples and application scenarios implemented by the corresponding steps, but are not limited to the contents disclosed in the above embodiment. It should be noted that the above modules or units can be hardware components or software components stored in a memory (e.g., memory 104) and processed by one or more processors (e.g., processors 102a, 102b, ..., 102n), and the above modules can also be part of the device and can be run in the computer terminal 10 provided in the first embodiment.

[0116] An embodiment of the present application may provide an electronic device, Figure 5 is a structural block diagram of an electronic device according to an embodiment of the present application. Figure 5 As shown, the electronic device 500 may include: one or more ( Figure 5 (only one is shown) processor 502, memory 504, storage controller 506, and peripheral interface 508, wherein the peripheral interface 508 is connected to the RF module 5101, the audio module 5102 and the display 5103.

[0117] Among them, the memory can be used to store software programs and modules, such as program instructions / modules corresponding to the methods and devices in the embodiments of the present application, and the processor executes various functional applications and data processing by running the software programs and modules stored in the memory, that is, realizing the above-mentioned method. The memory may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory may further include a memory remotely arranged relative to the processor, and these remote memories may be connected to the terminal via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0118] The processor can call the information and application stored in the memory through the transmission device to perform the following steps: obtain an attribute information set of the object, wherein the attribute information set includes at least one attribute information, and the attribute information is used to represent the attribute associated with the credit rating of the object; determine the target attribute information from the attribute information in the attribute information set, wherein the degree of association between the target attribute information and the credit rating is greater than the degree of association between the remaining attribute information in the attribute information set except the target attribute information and the credit rating; input the target attribute information into the target prediction model in the target prediction model set for prediction to obtain the initial credit rating of the object, wherein the target prediction model is obtained by training the initial prediction model based on the sample attribute information set of the sample object, and the sample attribute information set includes at least one sample attribute information, and the sample attribute information is used to represent the attribute associated with the credit rating of the sample object; determine the target credit rating associated with the initial credit rating, wherein the initial credit rating is integrated into the target credit rating.

[0119] The processor can also call the information and application programs stored in the memory through the transmission device to perform the following steps: input the target attribute information into the first target prediction model for prediction to obtain a first initial credit rating; input the target attribute information into the second target prediction model for prediction to obtain a second initial credit rating; input the target attribute information into the third target prediction model for prediction to obtain a third initial credit rating.

[0120] The processor may also call the information and application program stored in the memory through the transmission device to perform the following steps: determining the weight of the initial credit rating; and determining the target credit rating based on the weight and the initial credit rating.

[0121] The processor can also call the information and application stored in the memory through the transmission device to perform the following steps: determine a first product between the first weight and the first initial credit level, a second product between the second weight and the second initial credit level, and a third product between the third weight and the third initial credit level; and determine the sum of the first product, the second product and the third product as the target credit level.

[0122] The processor may also call the information and application program stored in the memory through the transmission device to perform the following steps: determining the target credit rating based on the first weight, the second weight, the third weight and the initial credit rating.

[0123] The processor can also call the information and application stored in the memory through the transmission device to perform the following steps: determine the degree of association between the attribute information of the attribute information set and the credit rating; determine the target association degree from the association degree, wherein the target association degree is greater than the remaining association degrees in the association degree except the target association degree; determine the attribute information corresponding to the target association degree as the target attribute information.

[0124] In the embodiment of the present invention, when determining the credit rating of an object, an attribute information set of the object can be obtained. From the attribute information of the acquired attribute information set, target attribute information can be determined, and the determined target attribute information is input into the target prediction model in the target prediction model set for prediction, so that the initial credit rating of the object can be obtained, and the target credit rating associated with the initial credit rating can be determined, thereby achieving the purpose of ensuring the comprehensiveness of the prediction result, thereby solving the technical problem of low accuracy in determining the user's credit rating, and further achieving the technical effect of improving the accuracy in determining the user's credit rating.

[0125] Figure 5 is a structural block diagram of an electronic device according to an embodiment of the present application. A person skilled in the art can understand that Figure 5 The structure shown is for illustration only, and the electronic device may also be a smart phone (such as an Android phone, an iOS phone, etc.), a tablet computer, a PDA, a mobile Internet device (Mobile Internet Devices, MID), a PAD, or other terminal devices. Figure 5 The structure of the electronic device is not limited. Figure 5 More or fewer components (such as network interfaces, display devices, etc.) shown in, or having Figure 5 Different configurations are shown.

[0126] A person of ordinary skill in the art can understand that all or part of the steps in the various methods of the above embodiments can be completed by instructing the hardware related to the terminal device through a program, and the program can be stored in a computer-readable storage medium, and the storage medium may include: a flash drive, a read-only memory (ROM), a random access memory (RAM), a disk or an optical disk, etc.

[0127] According to an embodiment of the present invention, a processor is further provided. The processor is used to run a program, wherein the program executes the method for reproducing abnormal information in the embodiment when the program is run by the processor.

[0128] According to an embodiment of the present invention, there is also provided an electronic device, comprising: a memory storing an executable program; and a processor for running the program, wherein the method for determining the credit rating of an object in the embodiment is executed when the program is running.

[0129] According to another aspect of an embodiment of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium includes a stored program, wherein when the program is executed, the device where the computer-readable storage medium is located is controlled to execute the method for determining the credit rating of an object in the embodiment.

[0130] According to 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 method for determining the credit rating of an object in the embodiment is implemented.

[0131] According to an embodiment of the present invention, a computer program product is also provided, including a non-volatile computer-readable storage medium, the non-volatile computer-readable storage medium is used to store a computer program, and when the computer program is executed by a processor, the method for determining the credit rating of an object in the embodiment is implemented.

[0132] According to an embodiment of the present invention, a computer program is also provided. When the computer program is executed by a processor, the method for determining the credit rating of an object in the embodiment is implemented.

[0133] 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.

[0134] 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.

[0135] 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 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.

[0136] 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 over multiple units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0137] 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.

[0138] 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, or the part that contributes to the relevant technology, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server or network device, etc.) to perform all or part of the steps of the methods of various embodiments of the present invention. The aforementioned storage medium includes: U disk, ROM, RAM, mobile hard disk, disk or optical disk and other media that can store program codes.

[0139] The above are only preferred embodiments 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 principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A method for determining the credit rating of an object, characterized in that: include: Acquire an attribute information set of an object, wherein the attribute information set includes at least one attribute information, and the attribute information is used to represent an attribute associated with the credit rating of the object; Determining target attribute information from the attribute information in the attribute information set, wherein the degree of association between the target attribute information and the credit rating is greater than the degree of association between the remaining attribute information in the attribute information set except the target attribute information and the credit rating; Inputting the target attribute information into a target prediction model in a target prediction model set for prediction, and obtaining an initial credit rating of the object, wherein the target prediction model is obtained by training an initial prediction model based on a sample attribute information set of a sample object, and the sample attribute information set includes at least one sample attribute information, and the sample attribute information is used to represent an attribute associated with the credit rating of the sample object; A target credit rating associated with the initial credit rating is determined, wherein the initial credit rating is integrated into the target credit rating.

2. The method according to claim 1, characterized in that: The target prediction model set includes a first target prediction model, a second target prediction model and a third target prediction model. The first target prediction model is a prediction model constructed for the overdue number of credit cards held by the object, the second target prediction model is a prediction model constructed for the overdue time of the credit card, and the third target prediction model is a prediction model constructed for the overdue amount of the credit card. The target attribute information is input into the target prediction model in the target prediction model set for prediction to obtain an initial credit rating, including: Inputting the target attribute information into a first target prediction model for prediction to obtain a first initial credit rating; Inputting the target attribute information into a second target prediction model for prediction to obtain a second initial credit rating; The target attribute information is input into a third target prediction model for prediction to obtain a third initial credit rating.

3. The method according to claim 2, characterized in that Determining a target credit rating associated with the initial credit rating includes: determining a weight for said initial credit rating; The target credit rating is determined based on the weight and the initial credit rating.

4. The method according to claim 3, characterized in that: The weights include a first weight of the first initial credit rating, a second weight of the second initial credit rating, and a third weight of the third initial credit rating, and determining the target credit rating based on the weights and the initial credit ratings includes: The target credit rating is determined based on the first weight, the second weight, the third weight, and the initial credit rating.

5. The method according to claim 4, characterized in that Determining the target credit rating based on the first weight, the second weight, the third weight, and the initial credit rating includes: determining a first product between the first weight and the first initial credit rating, a second product between the second weight and the second initial credit rating, and a third product between the third weight and the third initial credit rating; The sum of the first product, the second product and the third product is determined as the target credit rating.

6. The method according to claim 1, characterized in that Determining target attribute information from the attribute information of the attribute information set includes: Determining the degree of association between the attribute information of the attribute information set and the credit rating; Determining a target correlation degree from the correlation degrees, wherein the target correlation degree is greater than the remaining correlation degrees among the correlation degrees except the target correlation degree; The attribute information corresponding to the target association degree is determined as the target attribute information.

7. A device for determining the credit rating of an object, characterized in that: include: An acquiring unit, configured to acquire an attribute information set of an object, wherein the attribute information set includes at least one attribute information, and the attribute information is used to represent an attribute associated with the credit rating of the object; A first determining unit, configured to determine target attribute information from the attribute information in the attribute information set, wherein the degree of association between the target attribute information and the credit rating is greater than the degree of association between the remaining attribute information in the attribute information set except the target attribute information and the credit rating; A prediction unit, used for inputting the target attribute information into a target prediction model in a target prediction model set for prediction, so as to obtain an initial credit rating of the object, wherein the target prediction model is obtained by training an initial prediction model based on a sample attribute information set of a sample object, the sample attribute information set including at least one sample attribute information, and the sample attribute information is used to represent an attribute associated with the credit rating of the sample object; The second determining unit is configured to determine a target credit rating associated with the initial credit rating, wherein the initial credit rating is integrated into the target credit rating.

8. A processor, characterized in that: The processor is used to run a program, wherein the program, when run by the processor, executes the method for determining the credit rating of the object as claimed in any one of claims 1 to 6.

9. An electronic device, characterized in that: include: A memory storing an executable program; A processor is used to run the program, wherein the program, when running, executes the method for determining the credit rating of the object as described in any one of claims 1 to 6.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored executable program, wherein when the executable program is executed, the device where the storage medium is located is controlled to execute the method for determining the credit rating of an object as claimed in any one of claims 1 to 6.

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