Data processing method and device, equipment, medium and program product

Abstract

The invention provides a data processing method and device, equipment, a storage medium and a program product, and can be applied to the technical field of artificial intelligence. The data processing method comprises the steps of obtaining multiple pieces of first data of a target object; wherein different first data correspond to different information dimensions; the target objects are classified according to the multiple pieces of first data, and type information of the target objects is obtained; based on the type information and the first data, multiple pieces of second data of the target object are obtained; wherein a corresponding relation exists between the second data and the first data, and the granularity of the second data is smaller than that of the first data; determining first information and second information of the target object based on the multiple pieces of first data and the multiple pieces of second data; wherein the first information is used for representing the overall performance of the target object, and the second information is used for representing the specific performance of the target object on at least one information dimension; and determining the credit rating of the target object according to the first information and the second information.

Description

Technical Field

[0001] The present disclosure relates to the field of artificial intelligence technology, and more specifically to a data processing method, device, equipment, medium and program product. Background Art

[0002] With the development of information technology, artificial intelligence is being used more and more widely in statistics and analysis. For example, by collecting and analyzing relevant data of the target object, the analysis result of the target object can be obtained, and the analysis result can reflect the status of the target object in at least one specific aspect.

[0003] However, existing data processing and analysis methods usually use the same analysis rules for different target objects in the same analysis scenario and analyze the target objects based on data of a single granularity, resulting in low flexibility and accuracy of the analysis results, making it difficult to reflect the true status of the target object in this specific aspect. Summary of the invention

[0004] In view of the above problems, the present disclosure provides a data processing method, apparatus, device, medium and program product for improving data processing accuracy.

[0005] According to a first aspect of the present disclosure, a data processing method is provided, including: obtaining multiple first data of a target object; wherein different first data correspond to different information dimensions; classifying the target object according to the multiple first data to obtain type information of the target object; based on the type information and the first data, obtaining multiple second data of the target object; wherein there is a corresponding relationship between the second data and the first data, and the granularity of the second data is smaller than the granularity of the first data; determining first information and second information of the target object based on the multiple first data and the multiple second data, respectively; wherein the first information is used to characterize the overall performance of the target object, and the second information is used to characterize the specific performance of the target object in at least one information dimension; determining a credit rating of the target object based on the first information and the second information.

[0006] According to an embodiment of the present disclosure, each first data also includes multiple sub-data, and the target object is classified according to the multiple first data to obtain type information of the target object, including: determining performance information of the first data according to the sub-data of the first data; the performance information is used to reflect the performance level of the target object in this dimension; classifying the target object based on the performance information corresponding to the multiple first data to determine the type information of the target object; wherein the type information includes the type of the target object and the current development stage of the target object.

[0007] According to an embodiment of the present disclosure, sub-data in the first data includes a first form and a second form, and the performance information of the first data is determined based on the sub-data of the first data, including: standardizing the sub-data of different forms in the first data based on different methods, and mapping the sub-data to the same level; and determining the performance information of the first data based on the standardized sub-data.

[0008] According to an embodiment of the present disclosure, multiple second data of a target object are obtained based on type information and first data, including: based on performance information of the first data, determining multiple candidate second data from a second data candidate library corresponding to the first data; and determining and obtaining the second data of the target object from the multiple candidate second data according to the type information.

[0009] According to an embodiment of the present disclosure, first information and second information of a target object are determined based on first data and second data, respectively, including: determining a first rule and a second rule according to type information; wherein the first rule is used to calculate the first information of the target object, and the second rule is used to calculate the second information of the target object; calculating the first data based on the first rule to obtain the first information; and calculating the second data based on the second rule to obtain the second information.

[0010] According to an embodiment of the present disclosure, first data is calculated based on a first rule to obtain first information, including: assigning corresponding weights to multiple first data based on the first rule; and summing up performance information of multiple first data based on the weights to obtain the first information.

[0011] According to an embodiment of the present disclosure, the credit risk of a target object is determined based on first information and second information, including: assigning corresponding weights to the first information and the second information based on type information; summing the first information and the second information based on the weights to obtain third information; and obtaining a credit rating corresponding to the third information by mapping the third information to a main scale.

[0012] A second aspect of the present disclosure provides a data processing device, including: a first acquisition module, used to acquire multiple first data of a target object; wherein different first data correspond to different information dimensions; a classification module, used to classify the target object according to the multiple first data to obtain type information of the target object; a second acquisition module, used to acquire multiple second data of the target object based on the type information and the first data; wherein there is a corresponding relationship between the second data and the first data, and the granularity of the second data is smaller than the granularity of the first data; a first determination module, used to determine the first information and the second information of the target object based on the multiple first data and the multiple second data, respectively; wherein the first information is used to characterize the overall performance of the target object, and the second information is used to characterize the specific performance of the target object in at least one information dimension; a second determination module, used to determine the credit rating of the target object based on the first information and the second information.

[0013] A third aspect of the present disclosure provides an electronic device, comprising: one or more processors; and a memory for storing one or more computer programs, wherein the one or more processors execute the one or more computer programs to implement the steps of the above method.

[0014] The fourth aspect of the present disclosure further provides a computer-readable storage medium having a computer program or instructions stored thereon, which implements the steps of the above method when the above computer program or instructions are executed by a processor.

[0015] The fifth aspect of the present disclosure further provides a computer program product, including a computer program or instructions, which implement the steps of the above method when the above computer program or instructions are executed by a processor. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The above contents and other objects, features and advantages of the present disclosure will become more apparent through the following description of the embodiments of the present disclosure with reference to the accompanying drawings, in which:

[0017] Figure 1 The application scenario diagram of the data processing method, apparatus, device, medium and program product according to the embodiments of the present disclosure is schematically shown;

[0018] Figure 2 A flowchart schematically shows a data processing method according to an embodiment of the present disclosure;

[0019] Figure 3 A flowchart schematically shows how to classify a target object according to a plurality of first data to obtain type information of the target object according to an embodiment of the present disclosure;

[0020] Figure 4 A flowchart of determining the representation information of the first data according to the sub-data of the first data according to an embodiment of the present disclosure is schematically shown;

[0021] Figure 5 A flowchart of acquiring multiple second data of a target object based on type information and first data according to an embodiment of the present disclosure is schematically shown;

[0022] Figure 6 Schematically illustrates a flow chart of determining first information and second information of a target object based on first data and second data respectively according to an embodiment of the present disclosure;

[0023] Figure 7 A flowchart of determining a credit rating of a target object according to first information and second information according to an embodiment of the present disclosure is schematically shown;

[0024] Figure 8 A structural block diagram schematically shows a data processing device according to an embodiment of the present disclosure; and

[0025] Fig. 9 A block diagram of an electronic device suitable for implementing a data processing method according to an embodiment of the present disclosure is schematically shown. DETAILED DESCRIPTION

[0026] Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. However, it should be understood that these descriptions are exemplary only and are not intended to limit the scope of the present disclosure. In the following detailed description, for ease of explanation, many specific details are set forth to provide a comprehensive understanding of the embodiments of the present disclosure. However, it is apparent that one or more embodiments may also be implemented without these specific details. In addition, in the following description, descriptions of known structures and technologies are omitted to avoid unnecessary confusion of the concepts of the present disclosure.

[0027] The terms used herein are only for describing specific embodiments and are not intended to limit the present disclosure. The terms "comprise", "include", etc. used herein indicate the existence of the features, steps, operations and / or components, but do not exclude the existence or addition of one or more other features, steps, operations or components.

[0028] All terms (including technical and scientific terms) used herein have the meanings commonly understood by those skilled in the art unless otherwise defined. It should be noted that the terms used herein should be interpreted as having a meaning consistent with the context of this specification and should not be interpreted in an idealized or overly rigid manner.

[0029] When using expressions such as "at least one of A, B, and C, etc.", they should generally be interpreted according to the meaning of the expression commonly understood by those skilled in the art (for example, "a system having at least one of A, B, and C" should include but is not limited to a system having A alone, B alone, C alone, A and B, A and C, B and C, and / or A, B, C, etc.).

[0030] It should be noted that in the embodiments of the present application, certain software, components, models and other existing solutions in the industry may be mentioned, and they should be regarded as exemplary. Their purpose is only to illustrate the feasibility of implementing the technical solution of the present application, but it does not mean that the applicant has or will necessarily use the solution.

[0031] In the technical solution of the present disclosure, the user information (including but not limited to user personal information, user image information, user device information, such as location information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved are all information and data authorized by the user or fully authorized by all parties, and the collection, storage, use, processing, transmission, provision, disclosure and application of the relevant data comply with relevant laws, regulations and standards, take necessary confidentiality measures, do not violate public order and good morals, and provide corresponding operation entrances for users to choose to authorize or refuse.

[0032] In the scenario of using personal information for automated decision-making, the methods, devices, and systems provided by the embodiments of the present disclosure provide users with corresponding operation portals for users to choose to agree or reject the automated decision-making results; if the user chooses to reject, the expert decision-making process will be entered. The expression "automated decision-making" here refers to the activity of automatically analyzing and evaluating a person's behavioral habits, interests and hobbies, or economic, health, credit status, etc. through computer programs, and making decisions. The expression "expert decision-making" here refers to the activity of making decisions by people who specialize in a certain field, have specialized experience, knowledge and skills, and have reached a certain level of professionalism.

[0033] An embodiment of the present disclosure provides a data processing method, including: acquiring multiple first data of a target object; wherein different first data correspond to different information dimensions; classifying the target object according to the multiple first data to obtain type information of the target object; based on the type information and the first data, acquiring multiple second data of the target object; wherein there is a corresponding relationship between the second data and the first data, and the granularity of the second data is smaller than the granularity of the first data; determining first information and second information of the target object based on the multiple first data and the multiple second data, respectively; wherein the first information is used to characterize the overall performance of the target object, and the second information is used to characterize the specific performance of the target object in at least one information dimension; determining the credit rating of the target object based on the first information and the second information.

[0034] Figure 1 The application scenario diagram of the data processing method, apparatus, device, medium and program product according to the embodiments of the present disclosure is schematically shown.

[0035] like Figure 1 As shown, the application scenario 100 according to this embodiment may include a first terminal device 101, a second terminal device 102, a third terminal device 103, a network 104, and a server 105. The network 104 is used to provide a medium for a communication link between the first terminal device 101, the second terminal device 102, the third terminal device 103, and the server 105. The network 104 may include various connection types, such as wired, wireless communication links, or optical fiber cables, etc.

[0036] The user can use the first terminal device 101, the second terminal device 102, and the third terminal device 103 to interact with the server 105 through the network 104 to receive or send messages, etc. Various communication client applications can be installed on the first terminal device 101, the second terminal device 102, and the third terminal device 103, such as shopping applications, web browser applications, search applications, instant messaging tools, email clients, social platform software, etc. (only for example).

[0037] The first terminal device 101, the second terminal device 102, and the third terminal device 103 may be various electronic devices having display screens and supporting web browsing, including but not limited to smart phones, tablet computers, laptop computers, desktop computers, and the like.

[0038] The server 105 may be a server that provides various services, such as a background management server (only as an example) that provides support for websites browsed by users using the first terminal device 101, the second terminal device 102, and the third terminal device 103. The background management server may analyze and process the received data such as user requests, and feed back the processing results (such as web pages, information, or data obtained or generated according to user requests) to the terminal device.

[0039] It should be noted that the data processing method provided in the embodiment of the present disclosure can generally be executed by the server 105. Accordingly, the data processing device provided in the embodiment of the present disclosure can generally be set in the server 105. The data processing method provided in the embodiment of the present disclosure can also be executed by a server or server cluster that is different from the server 105 and can communicate with the first terminal device 101, the second terminal device 102, the third terminal device 103 and / or the server 105. Accordingly, the data processing device provided in the embodiment of the present disclosure can also be set in a server or server cluster that is different from the server 105 and can communicate with the first terminal device 101, the second terminal device 102, the third terminal device 103 and / or the server 105.

[0040] It should be understood that Figure 1 The number of terminal devices, networks and servers in the embodiment is only for illustration. Any number of terminal devices, networks and servers may be provided according to implementation requirements.

[0041] The following will be based on Figure 1 The scene described by Figure 2~Figure 7 The data processing method of the disclosed embodiment is described in detail.

[0042] Figure 2 The flowchart of the data processing method according to the embodiment of the present disclosure is schematically shown.

[0043] like Figure 2 As shown, the data processing method of this embodiment includes operations S210 to S250.

[0044] In operation S210, a plurality of first data of a target object is obtained, wherein different first data correspond to different information dimensions.

[0045] In some embodiments, the first data is usually key area information with broad representativeness and importance, which is used to reflect the basic information or characteristics of the target object. Taking the target object as an enterprise as an example, the first data may include enterprise scale, industry attributes, years of operation, etc. These indicators can provide a macro framework for the credit status analysis of the target object.

[0046] In operation S220, the target object is classified according to the plurality of first data to obtain type information of the target object.

[0047] In some embodiments, the target object is divided into corresponding quadrants according to the analysis results of the plurality of first data to obtain the type information of the target object.

[0048] In the specific implementation process, the type information at least includes the type of the target object and the current development stage. Taking the target object as an enterprise as an example, the enterprise type can be, for example, a manufacturing enterprise, a service enterprise, etc., and the enterprise development stage can include, for example, the start-up period, the growth period, the mature period, the decline period, etc. The credit rating standards corresponding to enterprises of different types and different development stages may be different. By dividing the type information of the target object, it is helpful to quickly determine the credit risk corresponding to the target object in the future and improve the accuracy of the credit risk rating.

[0049] In addition, the type information of the target object can also be adjusted based on the actual application scenario. For example, the type information can also reflect the industry risk and operating risk, debt repayment ability and profitability of the target object, which is not limited in this disclosure.

[0050] In operation S230, based on the type information and the first data, a plurality of second data of the target object is acquired; wherein a corresponding relationship exists between the second data and the first data, and a granularity of the second data is smaller than a granularity of the first data.

[0051] In some embodiments, based on the type information and the first data, multiple second data for evaluating the target object are determined and obtained. There is a corresponding relationship between the second data and the first data, and the second data is a further refinement and concretization of the first data. Each first data has at least one corresponding second data. For example, under the first data of "operating and competitive factors", second data such as "market share", "customer satisfaction", and "new product research and development capabilities" may be included; under the first data of "innovation construction investment", second data such as "number of first-level laboratories" and "original value of enterprise technology development equipment" may be included.

[0052] Based on the type information and the first data, selecting the second data that is closely related to the target object from the second data candidate library corresponding to the first data helps to improve the accuracy of data processing. For example, when the enterprise is a mature manufacturing enterprise and the score of the first data "innovation construction investment" is high, the "number of first-level laboratories" can be selected as the second data of the enterprise. When the score of the first data "innovation construction investment" is low, the "original value of enterprise technology development equipment" can be selected as the second data of the enterprise. By screening the second data, we can effectively reduce the attention to unimportant or redundant information, making the rating results more accurate.

[0053] In operation S240, first information and second information of the target object are determined based on the plurality of first data and the plurality of second data, respectively.

[0054] In some embodiments, the first data and the second data are calculated based on different calculation rules to obtain the first information and the second information. The first information is calculated from the first data and is used to characterize the overall performance of the target object on multiple first data; the second information is calculated from the second data and is used to characterize the specific performance of the target object on at least one first data (i.e., one information dimension).

[0055] The first data and the second data reflect different information granularities. The first data is relatively macro and abstract, while the second data is a further refinement and concretization of these macro data. By calculating them separately, they can more comprehensively reflect the performance of the company in different aspects and at different granularities. Moreover, since indicators at different levels often have different characteristics and importance, different calculation rules can more accurately measure the actual performance of these indicators, thereby improving the accuracy and reliability of the rating.

[0056] In operation S250, a credit rating of the target object is determined based on the first information and the second information.

[0057] In some embodiments, when conducting credit rating for enterprises with different types of information, the weights of their first information and second information are different. The present disclosure determines the credit rating of an enterprise through the first information and the second information, which can effectively improve the comprehensiveness and objectivity of the credit rating results, reduce the evaluation errors caused by one-sidedness, and more accurately locate the credit rating of the target object by integrating the information of the two dimensions, avoiding the confusion of enterprises with similar conditions, and effectively improving the accuracy of data processing.

[0058] The data processing method provided by the embodiment of the present disclosure first determines the type information of the enterprise based on the first data, and then further determines the second data based on the type information and the first data. This progressive approach builds a systematic evaluation framework, making the evaluation process more orderly and efficient. In addition, screening the second data based on the type information and the first data can effectively reduce the impact of redundant information or unimportant information on the rating results, making data processing more efficient and accurate. The first data provides a macro perspective, while the second data goes deep into the specific details. The combination of the two can more accurately portray the characteristics and performance of the enterprise, thereby effectively improving the accuracy and comprehensiveness of data processing.

[0059] It should be noted that before the embodiment of the present disclosure performs operation S210 to obtain multiple first data of the target object and operation S230 to obtain multiple second data of the target object, the user's consent or authorization may be obtained. For example, before operation S210 and before operation S230, a request to obtain user information may be issued to the user. When the user agrees or authorizes that the user information can be obtained, the above operation S210 and operation S230 are performed.

[0060] In the embodiment of the present disclosure, when executing operation S250, a corresponding operation entry may be provided for the user to choose to agree or reject the automated decision result. That is, before determining the credit rating of the target object according to the first information and the second information, an instruction of the user to agree or reject the above processing / decision input through the corresponding operation entry may be obtained. If the user agrees to the above processing / decision, the credit risk of the target object is determined according to the first information and the second information, that is, step S250 is executed. If the user refuses to perform the above processing / decision, the expert decision process is entered.

[0061] Figure 3 The flowchart schematically shows a method of classifying a target object according to a plurality of first data to obtain type information of the target object according to an embodiment of the present disclosure.

[0062] like Figure 3 As shown, in this embodiment, the target object is classified according to a plurality of first data to obtain the type information of the target object, including operations S310 to S320.

[0063] In operation S310, performance information of the first data is determined according to the sub-data of the first data; the performance information is used to reflect the performance level of the target object in the dimension.

[0064] In some embodiments, each first data includes multiple sub-data in different forms, such as qualitative data and quantitative data. Among them, qualitative data is information that is difficult to measure with specific values, such as the business environment and innovation ability of the enterprise, while quantitative data can be directly measured by specific values, such as the company's financial status, operating results, etc. (such as sales, profits, debt-to-asset ratio, return on net assets, etc.). The performance level of the enterprise on the current first data is determined by qualitative data and quantitative data.

[0065] In operation S320, the target object is classified based on the performance information corresponding to the plurality of first data, and type information of the target object is determined; wherein the type information includes the type of the target object and the current development stage of the target object.

[0066] In some embodiments, according to the performance information of each first data, the target object is placed in a quadrant diagram consisting of the object type and the current development stage, and the type information of the target object is obtained. The type information corresponds to the preset evaluation criteria and is used to select the evaluation criteria suitable for the target object. Among them, this quadrant diagram can be designed according to the actual situation. Taking the target object as an enterprise as an example, the enterprise type can be divided into high-tech enterprises, traditional manufacturing enterprises, etc., and the development stage can be divided into start-up period, growth period, mature period, etc. The type information of the target object is determined according to the position of the target object in the quadrant diagram.

[0067] In a specific implementation process, the type information of the target object can be determined according to the comparison result of each first data performance information by comparing the performance information with the standard value. The standard value is information used to define the range to which the first data belongs. By comparing the performance information with the standard value, the comparison result of the performance information and the standard value is obtained, and the range to which the first data corresponding to the performance information belongs is determined based on the comparison result.

[0068] The data processing method provided by the embodiment of the present disclosure determines the performance information of the first data based on multiple sub-data, thereby realizing the quantification of the performance level of the target object in each dimension, facilitating the division of the quadrant to which the target object belongs based on the performance information of each first data of the target object, and determining the type information of the enterprise, which is helpful for the subsequent selection of rating standards suitable for the enterprise, thereby improving the accuracy of the data processing results.

[0069] Figure 4 The flowchart of determining the representation information of the first data according to the sub-data of the first data according to an embodiment of the present disclosure is schematically shown.

[0070] like Figure 4 As shown, the method of determining the representation information of the first data according to the sub-data of the first data in this embodiment includes operations S410 to S420.

[0071] In operation S410, sub-data of different forms in the first data are standardized based on different methods to map the sub-data to the same level.

[0072] In operation S420, representation information of the first data is determined based on the normalized sub-data.

[0073] In some embodiments, the sub-data of the first data includes a first form (such as qualitative data) and a second form (such as quantitative data). Sub-data of different forms may have different units or dimensions, and the sub-data can be standardized in different ways to eliminate the dimensional differences between different data, so that the processed sub-data are at the same level, which is helpful for comprehensive analysis and comparison, thereby accurately revealing the relationship and rules between the data and improving the accuracy and objectivity of the first performance information.

[0074] In a specific implementation process, for example, a first score corresponding to the qualitative data can be determined based on a first method, a second score corresponding to the quantitative data can be determined based on a second method, and the performance information of the first data can be determined based on the first score and the second score. Determining the first score corresponding to the qualitative data based on the first method can, for example, include: determining the level of the qualitative data (such as excellent, good, average, poor), wherein each level corresponds to a corresponding score, assigning a weight to each qualitative data according to the importance of each qualitative data, multiplying the score of the qualitative data with the corresponding weight and summing them, and obtaining the first score of the qualitative data. Determining the second score of the quantitative data based on the second method can, for example, include: performing standardization processing on the quantitative data to eliminate the problems that different units and steel beams of different quantitative data may give different problems, so as to facilitate subsequent calculations, wherein the standardization method can, for example, include Z-score standardization, Min-Max standardization, etc. According to the importance of each quantitative data, a corresponding weight is assigned to the quantitative data, and the standardized quantitative data value is multiplied and summed with the corresponding weight to obtain the second score of the quantitative data. Calculating the performance information of the first data based on the first score and the second score can, for example, include: performing a weighted summation of the first score and the second score to obtain the first information of the first data. Among them, the first score and the second score represent the performance levels of qualitative data and quantitative data respectively. The higher the score, the better the performance, and the lower the score, the worse the performance.

[0075] The data processing method provided by the present disclosure standardizes sub-data in different forms and converts them into quantifiable standards, so that the performance information finally obtained is more objective and helps to intuitively understand the performance level of each first data.

[0076] Figure 5 The flowchart of acquiring multiple second data of a target object based on type information and first data according to an embodiment of the present disclosure is schematically shown.

[0077] like Figure 5 As shown, in this embodiment, based on the type information and the first data, acquiring multiple second data of the target object includes operations S510 to S520.

[0078] In operation S510 , based on the performance information of the first data, a plurality of candidate second data are determined from a second data candidate library corresponding to the first data.

[0079] In operation S520, second data of a target object is determined and acquired from a plurality of candidate second data according to the type information.

[0080] In some embodiments, a second data candidate library is established for each first data. These second data are further refinements and specificities of the first data, and can more comprehensively reflect the performance of the target object in this field. For example, under the first data of "profitability", the second data candidate library may include gross profit margin, net profit margin, return on total assets, etc. Based on the first information of the first data, multiple candidate second data that are most relevant or most representative to the actual situation of the target object are screened out from the second data candidate library.

[0081] Furthermore, according to the type information of the target object, combined with the actual situation of the target object and the rating requirements, the second data of the target object is further determined and obtained from the screened candidate second data, and the second data will be used for subsequent rating work.

[0082] The data processing method provided by the embodiment of the present disclosure can ensure that the rating process is more comprehensive and accurate by selecting the indicators most relevant to the target object from multiple candidate second data, and avoid interference of irrelevant or unimportant indicators on the accuracy of the rating results. Constructing a candidate library of second data corresponding to the first data provides a rich selection space for data processing and credit rating, which can be flexibly adjusted and expanded according to different rating requirements and the actual situation of the target object. Using different second data for target objects of different types and different development stages helps to improve the flexibility and accuracy of corporate ratings.

[0083] Figure 6 A flowchart for determining first information and second information of a target object based on first data and second data respectively according to an embodiment of the present disclosure is schematically shown.

[0084] like Figure 6 As shown, in this embodiment, determining the first information and the second information of the target object based on the first data and the second data respectively includes operations S610 to S630.

[0085] In operation S610, a first rule and a second rule are determined according to the type information; wherein the first rule is used to calculate first information of the target object, and the second rule is used to calculate second information of the target object.

[0086] In some embodiments, since the evaluation dimensions of the first data and the second data are different, and they have different characteristics and importance, different calculation rules need to be used to calculate the first data and the second data respectively. Taking the target object as an enterprise as an example, since enterprises of different industries and types have differences in operating characteristics, risk exposure, credit management, etc., and different development stages of enterprises will also affect their credit status, therefore, the first rule and the second rule applicable to the enterprise can be selected according to the enterprise type and enterprise development stage (i.e. type information), and the rating framework and calculation rules can be tailored for different enterprises, so as to more accurately reflect the actual credit status of the enterprise and improve the accuracy of data processing results.

[0087] In operation S620, the first data is calculated based on the first rule to obtain first information.

[0088] In some embodiments, operation S620 may include, for example: assigning corresponding weights to multiple first data based on the first rule; summing the scores of multiple first data based on the weights to obtain first information. The weights reflect the importance of different indicators in assessing corporate credit. For example, financial status is critical to all types of companies, so a higher weight may be assigned. For different types of companies, the importance of innovation capabilities may vary. For example, for the technology industry, the importance of innovation capabilities is higher, while for traditional manufacturing industries, the importance of innovation capabilities may be lower. Based on the assigned weights, the first information of each first data is weighted and summed to obtain the first information. Among them, the first information reflects the credit status of the target object in multiple aspects at the current granularity.

[0089] In operation S630, the second data is calculated based on the second rule to obtain second information.

[0090] In some embodiments, specific implementation details of operation S630 are similar to those of operation S620, and are not further described in this disclosure.

[0091] Figure 7 A flowchart of determining a credit rating of a target object according to first information and second information according to an embodiment of the present disclosure is schematically shown.

[0092] like Figure 7 As shown, determining the credit rating of the target object according to the first information and the second information in this embodiment includes operations S710 to S730.

[0093] In operation S710, corresponding weights are assigned to first information and second information based on type information.

[0094] In operation S720, the first information and the second information are summed based on the weight to obtain third information.

[0095] In operation S730, the credit rating corresponding to the third information is obtained by performing main scale mapping on the third information.

[0096] In some embodiments, the corresponding weights are analyzed for the first information and the second information according to the type information, and the first information and the second information are weighted and summed using the assigned weights to obtain the third information, so as to integrate the information of the target object in different dimensions into a comprehensive score, thereby comprehensively evaluating the credit status of the target object. And based on the pre-set main scale, the third information is matched with different credit levels to intuitively obtain the credit rating of the target object. Among them, the main scale can be set according to historical data, industry standards and expert opinions.

[0097] The data processing method provided by the embodiments of the present disclosure comprehensively considers information from multiple dimensions and meets the changing credit risk assessment needs of target objects of different types and at different development stages based on weight allocation and weighted summation, effectively improves the flexibility and accuracy of credit ratings, and simplifies complex measurement results into intuitive credit rating results through primary scale mapping, which helps to further understand and apply them.

[0098] Based on the above credit risk determination method, the present disclosure also provides a data processing device. Figure 8 The device is described in detail.

[0099] Figure 8 The structure block diagram of a data processing device according to an embodiment of the present disclosure is schematically shown.

[0100] like Figure 8 As shown, the data processing device 800 of this embodiment includes a first acquisition module 810 , a classification module 820 , a second acquisition module 830 , a first determination module 840 and a second determination module 850 .

[0101] The first acquisition module 810 is used to acquire a plurality of first data of the target object, wherein different first data correspond to different information dimensions. In one embodiment, the first acquisition module 810 can be used to perform the operation S210 described above, which will not be described in detail here.

[0102] The classification module 820 is used to classify the target object according to the plurality of first data to obtain type information of the target object. In one embodiment, the classification module 820 can be used to perform the operation S220 described above, which will not be described in detail here.

[0103] The second acquisition module 830 is used to acquire multiple second data of the target object based on the type information and the first data; wherein there is a corresponding relationship between the second data and the first data, and the granularity of the second data is smaller than the granularity of the first data. In one embodiment, the second acquisition module 830 can be used to perform the operation S230 described above, which will not be repeated here.

[0104] The first determination module 840 is used to determine the first information and the second information of the target object based on the plurality of first data and the plurality of second data, respectively; wherein the first information is used to characterize the overall performance of the target object, and the second information is used to characterize the specific performance of the target object in at least one information dimension. In one embodiment, the first determination module 840 can be used to perform the operation S240 described above, which will not be described in detail here.

[0105] The second determination module 850 is used to determine the credit rating of the target object according to the first information and the second information. In one embodiment, the second determination module 850 can be used to perform the operation S250 described above, which will not be described in detail here.

[0106] According to an embodiment of the present disclosure, any multiple modules of the first acquisition module 810, the classification module 820, the second acquisition module 830, the first determination module 840, and the second determination module 850 can be combined into one module for implementation, or any one of the modules can be split into multiple modules. Alternatively, at least part of the functions of one or more of these modules can be combined with at least part of the functions of other modules and implemented in one module. According to an embodiment of the present disclosure, at least one of the first acquisition module 810, the classification module 820, the second acquisition module 830, the first determination module 840, and the second determination module 850 can be at least partially implemented as a hardware circuit, such as a field programmable gate array (FPGA), a programmable logic array (PLA), a system on a chip, a system on a substrate, a system on a package, an application specific integrated circuit (ASIC), or can be implemented by hardware or firmware such as any other reasonable way of integrating or packaging the circuit, or implemented in any one of the three implementation methods of software, hardware, and firmware, or in any appropriate combination of any of them. Alternatively, at least one of the first acquisition module 810, the classification module 820, the second acquisition module 830, the first determination module 840 and the second determination module 850 may be at least partially implemented as a computer program module, which may perform corresponding functions when executed.

[0107] Fig. 9 A block diagram of an electronic device suitable for implementing a data processing method according to an embodiment of the present disclosure is schematically shown.

[0108] like Fig. 9As shown, the electronic device 900 according to an embodiment of the present disclosure includes a processor 901, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 902 or a program loaded from a storage part 908 to a random access memory (RAM) 903. The processor 901 may include, for example, a general-purpose microprocessor (e.g., a CPU), an instruction set processor and / or a related chipset and / or a dedicated microprocessor (e.g., an application-specific integrated circuit (ASIC)), etc. The processor 901 may also include an onboard memory for caching purposes. The processor 901 may include a single processing unit or multiple processing units for performing different actions of the method flow according to an embodiment of the present disclosure.

[0109] In RAM 903, various programs and data required for the operation of electronic device 900 are stored. Processor 901, ROM 902 and RAM 903 are connected to each other via bus 904. Processor 901 performs various operations of the method flow according to the embodiment of the present disclosure by executing the program in ROM 902 and / or RAM 903. It should be noted that the program can also be stored in one or more memories other than ROM 902 and RAM 903. Processor 901 can also perform various operations of the method flow according to the embodiment of the present disclosure by executing the program stored in the one or more memories.

[0110] According to an embodiment of the present disclosure, the electronic device 900 may further include an input / output (I / O) interface 905, which is also connected to the bus 904. The electronic device 900 may further include one or more of the following components connected to the input / output (I / O) interface 905: an input portion 906 including a keyboard, a mouse, etc.; an output portion 907 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage portion 908 including a hard disk, etc.; and a communication portion 909 including a network interface card such as a LAN card, a modem, etc. The communication portion 909 performs communication processing via a network such as the Internet. A drive 910 is also connected to the input / output (I / O) interface 905 as needed. A removable medium 911, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 910 as needed, so that a computer program read therefrom is installed into the storage portion 908 as needed.

[0111] The present disclosure also provides a computer-readable storage medium, which may be included in the device / apparatus / system described in the above embodiments; or may exist independently without being assembled into the device / apparatus / system. The above computer-readable storage medium carries one or more programs, and when the above one or more programs are executed, the method according to the embodiment of the present disclosure is implemented.

[0112] According to an embodiment of the present disclosure, a computer-readable storage medium may be a non-volatile computer-readable storage medium, for example, may include but is not limited to: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program, which may be used by or in combination with an instruction execution system, an apparatus or a device. For example, according to an embodiment of the present disclosure, a computer-readable storage medium may include the ROM 902 and / or RAM 903 described above and / or one or more memories other than ROM 902 and RAM 903.

[0113] The embodiment of the present disclosure also includes a computer program product, which includes a computer program, and the computer program contains program code for executing the method shown in the flowchart. When the computer program product is run in a computer system, the program code is used to enable the computer system to implement the data processing method provided by the embodiment of the present disclosure.

[0114] The above functions defined in the system / device of the embodiment of the present disclosure are performed when the computer program is executed by the processor 901. According to the embodiment of the present disclosure, the system, device, module, unit, etc. described above can be implemented by a computer program module.

[0115] In one embodiment, the computer program may be based on a tangible storage medium such as an optical storage device, a magnetic storage device, etc. In another embodiment, the computer program may also be transmitted and distributed in the form of a signal on a network medium, and downloaded and installed through the communication part 909, and / or installed from a removable medium 911. The program code contained in the computer program may be transmitted using any appropriate network medium, including but not limited to: wireless, wired, etc., or any suitable combination of the above.

[0116] In such an embodiment, the computer program can be downloaded and installed from the network through the communication part 909, and / or installed from the removable medium 911. When the computer program is executed by the processor 901, the above functions defined in the system of the embodiment of the present disclosure are performed. According to the embodiment of the present disclosure, the system, device, means, module, unit, etc. described above can be implemented by a computer program module.

[0117] According to an embodiment of the present disclosure, the program code for executing the computer program provided by the embodiment of the present disclosure can be written in any combination of one or more programming languages. Specifically, these computing programs can be implemented using high-level process and / or object-oriented programming languages, and / or assembly / machine languages. Programming languages ​​include, but are not limited to, Java, C++, python, "C" language or similar programming languages. The program code can be executed entirely on the user computing device, partially on the user device, partially on the remote computing device, or entirely on the remote computing device or server. In the case of a remote computing device, the remote computing device can be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computing device (for example, using an Internet service provider to connect through the Internet).

[0118] The flow charts and block diagrams in the accompanying drawings illustrate the possible architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each box in the flow chart or block diagram can represent a module, a program segment, or a part of a code, and the above-mentioned module, program segment, or a part of a code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flow chart, and the combination of the boxes in the block diagram or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0119] It will be appreciated by those skilled in the art that the features described in the various embodiments of the present disclosure may be combined and / or combined in a variety of ways, even if such combinations or combinations are not explicitly described in the present disclosure. In particular, without departing from the spirit and teachings of the present disclosure, the features described in the various embodiments of the present disclosure may be combined and / or combined in a variety of ways. All of these combinations and / or combinations fall within the scope of the present disclosure.

[0120] The embodiments of the present disclosure are described above. However, these embodiments are only for illustrative purposes and are not intended to limit the scope of the present disclosure. Although the embodiments are described above, this does not mean that the measures in the various embodiments cannot be used in combination to advantage. Without departing from the scope of the present disclosure, those skilled in the art may make a variety of substitutions and modifications, which should all fall within the scope of the present disclosure.

Claims

1. A data processing method, characterized in that: The method comprises: Acquire multiple first data of the target object; wherein different first data correspond to different information dimensions; Classify the target object according to the plurality of first data to obtain type information of the target object; Based on the type information and the first data, a plurality of second data of the target object is acquired; wherein there is a corresponding relationship between the second data and the first data, and the granularity of the second data is smaller than the granularity of the first data; Determine first information and second information of the target object based on the plurality of first data and the plurality of second data respectively; wherein the first information is used to characterize the overall performance of the target object, and the second information is used to characterize the specific performance of the target object in at least one information dimension; A credit rating of the target object is determined based on the first information and the second information.

2. The data processing method according to claim 1, characterized in that: Each of the first data further includes a plurality of sub-data, and the target object is classified according to the plurality of first data to obtain the type information of the target object, including: Determine performance information of the first data according to the sub-data of the first data; the performance information is used to reflect the performance level of the target object in this dimension; The target object is classified based on the performance information corresponding to the multiple first data to determine the type information of the target object; wherein the type information includes the type of the target object and the current development stage of the target object.

3. The data processing method according to claim 2, characterized in that: The sub-data in the first data includes a first form and a second form, and determining the representation information of the first data according to the sub-data of the first data includes: Standardizing sub-data of different forms in the first data based on different methods, and mapping the sub-data to the same level; The performance information of the first data is determined based on the standardized sub-data.

4. The data processing method according to claim 1, characterized in that: The acquiring a plurality of second data of the target object based on the type information and the first data includes: Based on the performance information of the first data, determining a plurality of candidate second data from a second data candidate library corresponding to the first data; The second data of the target object is determined and acquired from the plurality of candidate second data according to the type information.

5. The data processing method according to claim 1, characterized in that: The determining the first information and the second information of the target object based on the first data and the second data respectively includes: Determine a first rule and a second rule according to the type information; wherein the first rule is used to calculate the first information of the target object, and the second rule is used to calculate the second information of the target object; Calculate the first data based on the first rule to obtain first information; The second data is calculated based on the second rule to obtain second information.

6. The data processing method according to claim 5, characterized in that: The calculating the first data based on the first rule to obtain the first information includes: assigning corresponding weights to the plurality of first data respectively based on the first rule; The performance information of the plurality of first data is summed based on the weight to obtain first information.

7. The data processing method according to claim 1, characterized in that: The determining the credit rating of the target object according to the first information and the second information includes: assigning corresponding weights to the first information and the second information based on the type information; Summing the first information and the second information based on the weight to obtain third information; By performing main scale mapping on the third information, the credit rating corresponding to the third information is obtained.

8. A data processing device, characterized in that: The device comprises: A first acquisition module is used to acquire multiple first data of a target object; wherein different first data correspond to different information dimensions; A classification module, used for classifying the target object according to the plurality of first data to obtain type information of the target object; A second acquisition module is used to acquire a plurality of second data of the target object based on the type information and the first data; wherein there is a corresponding relationship between the second data and the first data, and the granularity of the second data is smaller than the granularity of the first data; A first determination module, configured to determine first information and second information of the target object based on the plurality of first data and the plurality of second data, respectively; wherein the first information is used to characterize the overall performance of the target object, and the second information is used to characterize the specific performance of the target object in at least one information dimension; The second determination module is used to determine the credit rating of the target object according to the first information and the second information.

9. An electronic device, comprising: one or more processors; a memory for storing one or more computer programs, It is characterized in that the one or more processors execute the one or more computer programs to implement the steps of the method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program or instruction stored thereon, characterized in that: When the computer program or instruction is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

11. A computer program product comprising a computer program or instructions, characterized in that When the computer program or instruction is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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