Enterprise credit information processing method and system, storage medium and equipment

Abstract

The invention provides an enterprise credit information processing method and system, a storage medium and equipment, and the method comprises the steps: obtaining the current enterprise state information which at least comprises enterprise summarization information, operation condition information, financial condition information, credit record information, industry environment information and other sub-information; sequentially performing weighting processing on the sub-information according to a preset weight interval to obtain a weighting ratio of each piece of sub-information, constructing a risk prediction model, performing weighting processing on the five groups of sub-information to obtain a corresponding weighting ratio, and performing single risk prediction model training on the weighting ratio to obtain five groups of initial risk parameters; and then carrying out risk prediction model training in a combination mode on the obtained five groups of initial risk parameters to obtain comprehensive risk parameters, and finally outputting the comprehensive risk parameters as reference data of enterprise levels as the prediction levels of enterprise credit, thereby greatly improving the data accuracy.

Description

Technical Field

[0001] The present invention relates to the field of enterprise information processing technology, and in particular to an enterprise credit information processing method, system, storage medium and device. Background Art

[0002] Corporate credit refers to the ability and willingness of an enterprise to fulfill its commitments and repay debts in economic activities. It is a comprehensive reputation evaluation formed by the enterprise in market transactions, financing activities, and business cooperation.

[0003] Corporate credit information can be mainly divided into five categories: corporate overview, organizational structure, business operations, financial status and market performance. Each major category also includes statistics of multiple subcategories of information. Therefore, it is usually more troublesome and labor-intensive to organize and evaluate corporate credit information.

[0004] Currently, the assessment of corporate credit information is also done through feedback from the five categories of corporate credit information mentioned above. After analyzing each one, the analysis results are directly derived. Among them, the analysis method is usually subjective judgment by analysts and there is no relatively accurate assessment system, which leads to large differences in the assessment and analysis results. In addition, the current corporate credit information assessment is limited to the assessment of a single type of information. It is impossible to combine information with pre-estimation operations to assess the potential risks of corporate credit information in advance, resulting in poor quality of information assessment and analysis. Summary of the Invention

[0005] Based on this, the purpose of the present invention is to provide a method, system, storage medium and device for processing enterprise credit information, so as to fundamentally solve the problem that the current enterprise credit information evaluation is limited to the evaluation of a single type of information, and it is impossible to combine information and make predictions to evaluate the potential risks of enterprise credit information in advance, resulting in poor quality of information evaluation and analysis.

[0006] According to an embodiment of the present invention, a method for processing enterprise credit information includes: Obtaining current enterprise status information, which includes at least enterprise summary information, operating status information, financial status information, credit record information, and industry environment information; The sub-information is weighted in sequence according to a preset weight range to obtain a weighted proportion of each sub-information and construct a risk estimation model; Each of the weighted proportions is added to the risk estimation model for risk estimation training to obtain multiple sets of initial risk parameters, and then the multiple sets of initial risk estimation parameters are added to the risk estimation model again for combined training to obtain multiple sets of comprehensive risk parameters, and the estimated level of the current enterprise credit is determined based on the multiple sets of comprehensive risk parameters.

[0007] Furthermore, the step of weighting the sub-information in sequence according to the preset weight interval to obtain the weighted proportion of each sub-information, and constructing the risk estimation model based on the weighted proportion of each sub-information includes: The self-information is weighted in sequence according to the preset weight ranges, wherein the preset weight ranges include a weight range of 5%-15% for the enterprise summary information, a weight range of 20%-30% for the operating status information, a weight range of 30%-45% for the financial status information, a weight range of 10%-20% for the credit record information, and a weight range of 8%-15% for the industry environment information; Determine the preset interval standard of each sub-information within the corresponding preset weight interval, assign a proportion corresponding to the preset interval standard, obtain the weighted proportion of each sub-information, and construct a risk estimation model.

[0008] Furthermore, the risk prediction model includes at least a sub-information risk prediction model for evaluating a single sub-information, and a comprehensive information risk prediction model for combined evaluation of multiple sub-information.

[0009] Furthermore, the step of adding each weighted proportion to the risk estimation model to perform risk estimation training to obtain multiple sets of initial risk parameters includes: The sub-information risk prediction model is used to perform separate model training on each of the sub-information in sequence, and five calibrated and adjusted initial risk parameters are obtained respectively.

[0010] Furthermore, the step of adding the multiple groups of initial risk estimation parameters into the risk estimation model again for combined training to obtain multiple groups of comprehensive risk parameters includes: The five groups of initial risk estimation parameters are added to the comprehensive information risk estimation model for combination training, wherein the combination training includes at least a two-to-two combination, a three-to-three combination, a four-to-four combination, and a training method of five groups of the five groups of initial risk estimation parameters, so as to respectively obtain the first comprehensive risk parameters of ten groups of two-to-two combinations, the second comprehensive risk parameters of ten groups of three-to-three combinations, the third comprehensive risk parameters of five groups of four-to-four combinations, and the fourth comprehensive risk parameter of one group of five-to-five combinations.

[0011] Furthermore, the step of determining the estimated level of the current enterprise credit based on the multiple sets of comprehensive risk parameters includes: performing weighted assignments on the first comprehensive risk parameter, the second comprehensive risk parameter, the third comprehensive risk parameter, and the fourth comprehensive risk parameter in sequence, and assigning a value of 20% to the first comprehensive risk parameter, a value of 20% to the second comprehensive risk parameter, a value of 40% to the third comprehensive risk parameter, and a value of 30% to the fourth comprehensive risk parameter; Determining whether each group of risk parameters in the first comprehensive risk parameter, the second comprehensive risk parameter, the third comprehensive risk parameter, and the fourth comprehensive risk parameter meets a preset weighted value assignment standard; If it meets the requirements, each group of risk parameters will be assigned a value in turn, and the sum of the assigned values ​​of each group of risk parameters will be obtained to determine the estimated level of the current corporate credit. According to an embodiment of the present invention, an enterprise credit information processing system includes: An information acquisition module is used to obtain current enterprise status information, which includes at least enterprise summary information, operating status information, financial status information, credit record information, and industry environment information; A model processing module is used to perform weighted processing on the sub-information in sequence according to a preset weight range to obtain a weighted proportion of each sub-information and construct a risk estimation model; The grade estimation module is used to add each of the weighted proportions to the risk estimation model for risk estimation training to obtain multiple sets of initial risk parameters, and then add the multiple sets of initial risk estimation parameters to the risk estimation model again for combined training to obtain multiple sets of comprehensive risk parameters, and determine the estimated grade of the current enterprise credit based on the multiple sets of comprehensive risk parameters.

[0012] The present invention also provides a computer-readable storage medium having a computer program stored thereon, which implements the above-mentioned enterprise credit information processing method when executed by a processor.

[0013] The present invention also proposes an enterprise credit information processing device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, to implement the above-mentioned enterprise credit information processing method.

[0014] Compared with the existing technology: the enterprise credit information processing method in the above embodiment of the present invention obtains the corresponding weighted proportions after weighted processing of the five groups of sub-information, and performs a single risk estimation model training on the weighted proportions to obtain five groups of initial risk parameters. Thereafter, the five groups of initial risk parameters are subjected to combined risk estimation model training to obtain comprehensive risk parameters, and finally the estimated level of enterprise credit is output as reference data for the enterprise level, which greatly improves the data accuracy and solves the problem that the current enterprise credit information assessment is limited to the assessment of a single type of information and cannot perform information combination estimation operations to assess the potential risks of enterprise credit information in advance, resulting in poor quality of information assessment and analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 Flowchart of the enterprise credit information processing method in the first embodiment of the present invention; Figure 2 Flowchart of the enterprise credit information processing method in the second embodiment of the present invention; Figure 3 This is a schematic diagram of the structure of the enterprise credit information processing system in the third embodiment of the present invention; Figure 4 Schematic diagram of the structure of the enterprise credit information processing device in the fourth embodiment of the present invention.

[0016] The following specific embodiments will further illustrate the present invention in conjunction with the above-mentioned drawings. DETAILED DESCRIPTION

[0017] To facilitate understanding of the present invention, the present invention will be described more fully below with reference to the accompanying drawings. The drawings illustrate several embodiments of the present invention. However, the present invention may be implemented in many different forms and is not limited to the embodiments described herein. Rather, these embodiments are provided to provide a more thorough and comprehensive understanding of the present invention.

[0018] It should be noted that when an element is referred to as being "fixed to" another element, it may be directly on the other element or there may be an intermediate element. When an element is referred to as being "connected to" another element, it may be directly connected to the other element or there may be an intermediate element. The terms "vertical," "horizontal," "left," "right," and similar expressions used herein are for illustrative purposes only.

[0019] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one skilled in the art to which this invention pertains. The terms used in this specification of the present invention are for the purpose of describing specific embodiments only and are not intended to limit the present invention. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0020] Example 1 See also Figure 1 , shown is the enterprise credit information processing method in the first embodiment of the present invention, and the method shown specifically includes steps S01 to S03.

[0021] Step S01, obtaining current enterprise status information, which includes at least enterprise summary information, operating status information, financial status information, credit record information, and industry environment information.

[0022] Step S02: weighting the sub-information in sequence according to the preset weight interval to obtain the weighted proportion of each sub-information and construct a risk estimation model.

[0023] During specific implementation, based on step S01 and step S02, the current enterprise status information is obtained. The enterprise status information can be obtained through the Internet and enterprise background research, wherein the enterprise status information at least includes enterprise summary information, operating status information, financial status information, credit record information, and industry environment information and other sub-information, and the above sub-information is also the key display information reflecting the current enterprise credit.

[0024] The company status information includes a company overview, such as company name, registered address, founding date, business scope, and registered capital. This information helps understand the company's basic background and scale. The organizational structure includes understanding the company's equity structure, management composition, and departmental structure to assess its governance structure and decision-making efficiency.

[0025] Operating information includes: Market share: Assessing a company's market position and competitiveness within its industry; Customer base: Understanding a company's key customer groups and sales channels to assess its market stability and growth potential; and Supply Chain Management: Examining a company's supplier relationships, inventory management, and logistics capabilities to assess operational efficiency and cost control.

[0026] The financial status information includes, first, financial statements: the balance sheet reflects the financial status of the company on a specific date, including assets, liabilities and owner's equity. The income statement: shows the operating results of the company in a certain period, including revenue, costs and profits. The cash flow statement: records the inflow and outflow of cash and cash equivalents of the company in a certain period. Second, financial indicators: including debt-paying ability indicators: such as current ratio, quick ratio, debt-to-asset ratio, etc., which are used to evaluate the short-term and long-term debt-paying ability of the company. Operating capacity indicators: such as accounts receivable turnover rate, inventory turnover rate, etc., reflect the management efficiency of the company's assets. Profitability indicators: such as gross profit margin, net profit margin, return on equity, etc., measure the profitability and operating efficiency of the company.

[0027] Credit records include bank credit records, including a company's bank loan history, repayment status, and credit limit utilization, which serve as a crucial basis for assessing a company's creditworthiness. Commercial credit records, such as transaction records with other companies, contract fulfillment, and accounts payable payments, reflect a company's creditworthiness in commercial activities. Legal proceedings and enforcement records: Investigate whether a company has a negative record of legal proceedings, enforcement cases, or other similar cases to assess its legal risks and compliance.

[0028] Industry environment information also includes industry development trends: analyzing the development prospects, policy environment, and competitive landscape of the industry to assess the company's growth potential and market risks. Macroeconomic environment: considering the impact of macroeconomic factors such as economic growth rate, inflation rate, interest rate, etc. on the company's operations and financial status. The sub-information is weighted in turn according to the importance of the current information and the preset weight range, wherein the definition range of the preset weight range is understandable to those skilled in the art, and is also the weight range ratio derived by those skilled in the art that can more accurately reflect the credit of the enterprise, including a weight range of 5%-15% for enterprise summary information, a weight range of 20%-30% for operating status information, a weight range of 30%-45% for financial status information, a weight range of 10%-20% for credit record information, and a weight range of 8%-15% for industry environment information, and a risk prediction model is constructed. In some optional embodiments, the risk prediction model can be, but is not limited to, linear regression, logistic regression, decision tree, support vector machine, random forest, etc., to train and evaluate the possible risk problems of the above-mentioned sub-information when they are in different weight ranges.

[0029] In step S03, each weighted proportion is added to the risk estimation model for risk estimation training to obtain multiple sets of initial risk parameters. The multiple sets of initial risk estimation parameters are then added to the risk estimation model again for combined training to obtain multiple sets of comprehensive risk parameters. The estimated level of the current enterprise credit is determined based on the multiple sets of comprehensive risk parameters.

[0030] In the specific implementation, the weighted proportion of each sub-information obtained in step S02 is added to the risk estimation model for model training to output five sets of initial risk parameters. For example, it can be understood that when the weighted proportion of financial status information is low, the initial risk parameters fed back by the training are higher, indicating that there are certain problems with the enterprise, but it is not used as the final output result. The present application is to add the obtained five sets of initial risk parameters to the risk estimation model again for combined training. For example, when the proportion of enterprise financial status information is low but the proportion of operating status information is high, it can be understood within the enterprise that the current financial status of the enterprise is improved through operating status, so that the comprehensive risk parameters obtained by training are balanced and free from potential risks. The purpose of this combined training is to be able to accurately give the estimated risk of the current enterprise credit through combined analysis at multiple levels, and finally output it as the estimated level of enterprise credit as reference data for enterprise level, so that subsequent experts can combine the reference data to more accurately derive the current enterprise credit level.

[0031] In summary, the enterprise credit information processing method in the above embodiment of the present invention obtains the corresponding weighted proportions after weighted processing of the five groups of sub-information, and performs a single risk estimation model training on the weighted proportions to obtain five groups of initial risk parameters. Thereafter, the five groups of initial risk parameters are subjected to combined risk estimation model training to obtain comprehensive risk parameters, and finally the estimated level of enterprise credit is output as reference data for the enterprise level, which greatly improves the data accuracy and solves the problem that the current enterprise credit information assessment is limited to the assessment of a single type of information and cannot perform information combination estimation operations to assess the potential risks of enterprise credit information in advance, resulting in poor quality of information assessment and analysis.

[0032] Example 2 See also Figure 2 , shown is the enterprise credit information processing method in the second embodiment of the present invention, and the method shown specifically includes steps S11 to S17.

[0033] Step S11, obtaining current enterprise status information, which includes at least enterprise summary information, operating status information, financial status information, credit record information, and industry environment information.

[0034] Step S12: weighting the sub-information in sequence according to the preset weight interval to obtain the weighted proportion of each sub-information and construct a risk estimation model.

[0035] In step S13, each weighted proportion is added to the risk estimation model to perform risk estimation training to obtain five sets of initial risk parameters.

[0036] Step S14, adding the five groups of initial risk estimation parameters into the comprehensive information risk estimation model for combination training, wherein the combination training includes at least two-to-two combinations, three-to-three combinations, four-to-four combinations, and five groups of training methods for the five groups of initial risk estimation parameters, so as to respectively obtain the first comprehensive risk parameters of ten groups of two-to-two combinations, the second comprehensive risk parameters of ten groups of three-to-three combinations, the third comprehensive risk parameters of five groups of four-to-four combinations, and the fourth comprehensive risk parameter of one group of five-to-five combinations.

[0037] Step S15, weightedly assign values ​​to the first comprehensive risk parameter, the second comprehensive risk parameter, the third comprehensive risk parameter and the fourth comprehensive risk parameter in sequence, and assign a value of 20% to the first comprehensive risk parameter, a value of 20% to the second comprehensive risk parameter, a value of 40% to the third comprehensive risk parameter, and a value of 30% to the fourth comprehensive risk parameter in sequence.

[0038] Step S16, determining whether each group of risk parameters in the first comprehensive risk parameter, the second comprehensive risk parameter, the third comprehensive risk parameter and the fourth comprehensive risk parameter meets the preset weighted assignment standard. If so, executing step S17.

[0039] Step S17: assign values ​​to each group of risk parameters in turn, and obtain the sum of the assigned values ​​of each group of risk parameters to determine as the estimated level of the current enterprise credit.

[0040] In a specific implementation, five groups of initial risk estimation parameters are arranged and combined, such as a two-to-two combination, a three-to-three combination, a four-to-four combination, and a five-to-five combination, by a decoder or a generator, and are imported into a comprehensive information risk estimation model to output ten groups of first comprehensive risk parameters for the two-to-two combination, ten groups of second comprehensive risk parameters for the three-to-three combination, five groups of third comprehensive risk parameters for the four-to-four combination, and one group of fourth comprehensive risk parameters for the five-to-five combination, and the first comprehensive risk parameter is assigned 20%, the second comprehensive risk parameter is assigned 20%, the third comprehensive risk parameter is assigned 40%, and the fourth comprehensive risk parameter is assigned 40%. The value is assigned to 30%, wherein, based on the assignment conditions of the ten groups of first comprehensive risk parameters and the ten groups of second comprehensive risk parameters, the ten groups of first comprehensive risk parameters are analyzed in turn and divided into three risk levels, namely, higher risk, medium risk and no risk, and each risk level has an evaluation interval, which is set by the operator according to the enterprise situation and will not be elaborated here. The corresponding higher risk weighted assignment is 2%, the medium risk weighted assignment is 1%, and the no risk weighted assignment is 0, which is also the preset weighted assignment standard mentioned in this application, and the total weighted assignment of the first comprehensive risk parameter is 20%. It can be understood that Each first comprehensive risk parameter has a 2% variable influence to further ensure the accuracy of subsequent evaluations, and the weighted assignment of the second comprehensive risk parameter is the same as the first comprehensive risk parameter, and the third comprehensive risk parameter is the same, but the variable intervals of the assignment are different, namely, the higher risk weighted assignment is 8%, the medium risk weighted assignment is 4%, and the no risk weighted assignment is 0, and the fourth comprehensive risk weighted assignment is 30% for high risk, 20% for medium risk, and 10% for no risk. For the weighted assignment of the fourth comprehensive risk parameter, it should be noted that this application does not use subjective The overall comprehensive data of the five-to-five combination is not used as a key reference, but is analyzed in combination with other comprehensive risk parameters, which greatly ensures the uniformity of the data analysis results and improves the accuracy of the data. Finally, the weighted percentages obtained from the first to fourth comprehensive risk parameters are added together to determine the estimated level of the current corporate credit. The level definition can be to establish a risk level for every 10% or a level for every 20%. The evaluator can determine the estimated level of the current corporate credit based on the range of the sum of the weighted percentages obtained from the current first to fourth comprehensive risk parameters. This level further improves the accuracy of data analysis compared to Example 1.

[0041] Example 3 Another aspect of the present invention is to provide a corporate credit information processing system. Figure 3 , which shows an enterprise credit information processing system in a third embodiment of the present invention, the system includes: The information acquisition module 11 is used to obtain the current enterprise status information, which includes at least enterprise summary information, operating status information, financial status information, credit record information, and industry environment information; The model processing module 12 is used to perform weighted processing on the sub-information in sequence according to the preset weight interval to obtain the weighted proportion of each sub-information and construct a risk estimation model; The grade estimation module 13 is used to add each weighted proportion into the risk estimation model for risk estimation training to obtain multiple sets of initial risk parameters, and then add the multiple sets of initial risk estimation parameters into the risk estimation model again for combined training to obtain multiple sets of comprehensive risk parameters, and determine the estimated grade of the current enterprise credit based on the multiple sets of comprehensive risk parameters.

[0042] Furthermore, in some optional embodiments of the present invention, the level estimation module 13 further includes: A data combination unit is used to add five groups of initial risk estimation parameters into the comprehensive information risk estimation model for combination training, wherein the combination training includes at least two-to-two combinations, three-to-three combinations, four-to-four combinations, and five groups of training methods for the five groups of initial risk estimation parameters, so as to respectively obtain the first comprehensive risk parameters of ten groups of two-to-two combinations, the second comprehensive risk parameters of ten groups of three-to-three combinations, the third comprehensive risk parameters of five groups of four-to-four combinations, and the fourth comprehensive risk parameter of one group of five-to-five combinations.

[0043] The weighted assignment unit performs weighted assignment on the first comprehensive risk parameter, the second comprehensive risk parameter, the third comprehensive risk parameter and the fourth comprehensive risk parameter in sequence, and assigns 20% to the first comprehensive risk parameter, 20% to the second comprehensive risk parameter, 40% to the third comprehensive risk parameter, and 30% to the fourth comprehensive risk parameter in sequence.

[0044] The judgment unit judges whether each group of risk parameters in the first comprehensive risk parameter, the second comprehensive risk parameter, the third comprehensive risk parameter and the fourth comprehensive risk parameter meets the preset weighted assignment standard, and executes the first execution unit if it meets the standard.

[0045] The first execution unit assigns values ​​to each group of risk parameters in turn, and obtains the sum of the values ​​assigned to each group of risk parameters to determine as the estimated level of the current enterprise credit.

[0046] Example 4 Another aspect of the present invention also provides an enterprise credit information processing device, see Figure 4, shown is an enterprise credit information processing device in the fourth embodiment of the present invention, including a memory 20, a processor 10, and a computer program 30 stored in the memory and executable on the processor. When the processor 10 executes the computer program 30, the enterprise credit information processing method as described above is implemented.

[0047] Specifically, the enterprise credit information processing device may be a processor 10, which in some embodiments may be a central processing unit (CPU), a controller, a microcontroller, a microprocessor, or other data processing chip, for running program codes or processing data stored in the memory 20, such as executing access restriction programs.

[0048] The memory 20 includes at least one type of readable storage medium, including flash memory, a hard disk, a multimedia card, a card-type memory (e.g., SD or DX memory), a magnetic storage device, a magnetic disk, an optical disk, and the like. In some embodiments, the memory 20 may be an internal storage unit of the planar design review system, such as the hard disk of the enterprise credit information processing system. In other embodiments, the memory 20 may also be an external storage system of the enterprise credit information processing system, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) card, a flash memory card, and the like. Furthermore, the memory 20 may include both an internal storage unit and an external storage system of the enterprise credit information processing system. The memory 20 can be used not only to store application software installed in the enterprise credit information processing system and various data, but also to temporarily store data that has been output or is about to be output.

[0049] It should be pointed out that Figure 4 The structure shown does not constitute a limitation on the enterprise credit information processing system. In other embodiments, the enterprise credit information processing system may include fewer or more components than shown in the figure, or combine certain components, or arrange the components differently.

[0050] An embodiment of the present invention further provides a computer-readable storage medium having a computer program stored thereon, which implements the above-mentioned enterprise credit information processing method when executed by a processor.

[0051] Those skilled in the art will appreciate that the logic and / or steps represented in the flowcharts or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing the logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, system, or device (e.g., a computer-based system, a system including a processor, or other system that can fetch and execute instructions from an instruction execution system, system, or device). For purposes of this specification, a "computer-readable medium" can be any system that can contain, store, communicate, propagate, or transport a program for use by, or in conjunction with, an instruction execution system, system, or device.

[0052] More specific examples (a non-exhaustive list) of computer-readable media include the following: an electrical connection with one or more wires (electronic systems), a portable computer disk cartridge (magnetic systems), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic system, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering, or processing in another suitable manner as necessary, and then stored in a computer memory.

[0053] It should be understood that various components of the present invention may be implemented using hardware, software, firmware, or a combination thereof. In the aforementioned embodiments, multiple steps or methods may be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented using hardware, as in another embodiment, any one or a combination of the following technologies known in the art may be used: a discrete logic circuit having logic gate circuits for implementing logic functions on data signals, an application-specific integrated circuit having suitable combinational logic gate circuits, a programmable gate array (PGA), a field-programmable gate array (FPGA), etc.

[0054] Throughout this specification, reference to terms such as "one embodiment," "some embodiments," "examples," "specific examples," or "some examples" means that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

[0055] The above-described embodiments merely illustrate several implementations of the present invention, and while their descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that a person skilled in the art would be able to make numerous variations and improvements without departing from the spirit of the present invention, all of which fall within the scope of protection of the present invention. Therefore, the scope of protection of the present invention shall be determined by the appended claims.

Claims

1. A method for processing enterprise credit information, characterized in that: The method comprises: Obtaining current enterprise status information, which includes at least enterprise summary information, operating status information, financial status information, credit record information, and industry environment information; The sub-information is weighted in sequence according to a preset weight range to obtain a weighted proportion of each sub-information and construct a risk estimation model; Each of the weighted proportions is added to the risk estimation model for risk estimation training to obtain multiple sets of initial risk parameters, and then the multiple sets of initial risk estimation parameters are added to the risk estimation model again for combined training to obtain multiple sets of comprehensive risk parameters, and the estimated level of the current enterprise credit is determined based on the multiple sets of comprehensive risk parameters.

2. A method for processing enterprise credit information according to claim 1, characterized in that: The step of weighting the sub-information in sequence according to the preset weight interval to obtain the weighted proportion of each sub-information, and constructing a risk estimation model based on the weighted proportion of each sub-information includes: The self-information is weighted in sequence according to the preset weight ranges, wherein the preset weight ranges include a weight range of 5%-15% for the enterprise summary information, a weight range of 20%-30% for the operating status information, a weight range of 30%-45% for the financial status information, a weight range of 10%-20% for the credit record information, and a weight range of 8%-15% for the industry environment information; Determine the preset interval standard of each sub-information within the corresponding preset weight interval, assign a proportion corresponding to the preset interval standard, obtain the weighted proportion of each sub-information, and construct a risk estimation model.

3. The enterprise credit information processing method according to claim 2, characterized in that: The risk prediction model includes at least a sub-information risk prediction model for evaluating a single sub-information and a comprehensive information risk prediction model for evaluating a plurality of sub-information.

4. A method for processing enterprise credit information according to claim 3, characterized in that: The step of adding each weighted proportion to the risk estimation model to perform risk estimation training to obtain multiple sets of initial risk parameters includes: The sub-information risk prediction model is used to sequentially perform separate model training on the single sub-information to obtain five calibrated and adjusted initial risk parameters.

5. The method for processing enterprise credit information according to claim 4, characterized in that: The step of adding the multiple groups of initial risk estimation parameters into the risk estimation model again for combined training to obtain multiple groups of comprehensive risk parameters includes: The five groups of initial risk estimation parameters are added to the comprehensive information risk estimation model for combination training, wherein the combination training includes at least a two-to-two combination, a three-to-three combination, a four-to-four combination, and a training method of five groups of the five groups of initial risk estimation parameters, so as to respectively obtain the first comprehensive risk parameters of ten groups of two-to-two combinations, the second comprehensive risk parameters of ten groups of three-to-three combinations, the third comprehensive risk parameters of five groups of four-to-four combinations, and the fourth comprehensive risk parameter of one group of five-to-five combinations.

6. The enterprise credit information processing method according to claim 1, characterized in that: The step of determining the estimated level of current corporate credit based on multiple sets of comprehensive risk parameters includes: performing weighted assignments on the first comprehensive risk parameter, the second comprehensive risk parameter, the third comprehensive risk parameter, and the fourth comprehensive risk parameter in sequence, and assigning a value of 20% to the first comprehensive risk parameter, a value of 20% to the second comprehensive risk parameter, a value of 40% to the third comprehensive risk parameter, and a value of 30% to the fourth comprehensive risk parameter; Determining whether each group of risk parameters in the first comprehensive risk parameter, the second comprehensive risk parameter, the third comprehensive risk parameter, and the fourth comprehensive risk parameter meets a preset weighted value assignment standard; If it meets the requirements, each group of risk parameters will be assigned a value in turn, and the sum of the assigned values ​​of each group of risk parameters will be obtained to determine the estimated level of the current corporate credit.

7. An enterprise credit information processing system for implementing the method for improving urea mileage as claimed in any one of claims 1 to 6, characterized in that: The system comprises: An information acquisition module is used to obtain current enterprise status information, which includes at least enterprise summary information, operating status information, financial status information, credit record information, and industry environment information; A model processing module is used to perform weighted processing on the sub-information in sequence according to a preset weight range to obtain a weighted proportion of each sub-information and construct a risk estimation model; The grade estimation module is used to add each of the weighted proportions to the risk estimation model for risk estimation training to obtain multiple sets of initial risk parameters, and then add the multiple sets of initial risk estimation parameters to the risk estimation model again for combined training to obtain multiple sets of comprehensive risk parameters, and determine the estimated grade of the current enterprise credit based on the multiple sets of comprehensive risk parameters.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the enterprise credit information processing method according to any one of claims 1 to 6 is implemented.

9. An enterprise credit information processing device, characterized in that: The method comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the method for processing enterprise credit information according to any one of claims 1 to 6 is implemented.

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