CN116862264A - Rating method, rating system, electronic equipment and computer readable storage medium - Google Patents
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- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
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- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0637—Strategic management or analysis, e.g. setting a goal or target of an organisation; Planning actions based on goals; Analysis or evaluation of effectiveness of goals
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- G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
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- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/30—Computing systems specially adapted for manufacturing
Abstract
The application discloses a rating method, a rating system, electronic equipment and a computer readable storage medium, wherein target data for rating a target object are obtained, and the target data comprise identity information, running information, request information and associated information of the target object; generating target index data of the target data; transmitting the target index data to a pre-trained evaluation model; obtaining a rating result of the target object output by the evaluation model; the target index data comprises derived indexes and derived indexes; the derived index comprises an index in which an atomic index is subjected to dimension limitation; the derived indexes comprise indexes synthesized by target logic operation on the basis of one or more derived indexes; the atomic index includes a metric value. The method generates the corresponding target index data based on the target data, processes the target index data by means of the pre-trained evaluation model to obtain the rating result, and has the advantages of good accuracy, high degree of automation and good applicability.
Description
Technical Field
The present application relates to the field of data processing technology, and more particularly, to a rating method, a rating system, an electronic device, and a computer readable storage medium.
Background
Currently, when a product, a user or an enterprise needs to be rated, the product, the user or the enterprise often depends on manual rating, however, the manual rating is limited by fluctuation and error, the product cannot be rated in batches, quickly and accurately, the user needs are difficult to meet, and the applicability is poor.
In view of the above, how to improve the applicability of the rating method is a problem to be solved by those skilled in the art.
Disclosure of Invention
The application aims to provide a rating method which can solve the technical problem of how to improve the applicability of the rating method to a certain extent. The application also provides a rating system, electronic equipment and a computer readable storage medium.
In a first aspect, the present application provides a rating method comprising:
acquiring target data for rating a target object, wherein the target data comprises identity information, running information, request information and associated information of the target object;
generating target index data of the target data;
transmitting the target index data to a pre-trained evaluation model;
obtaining a rating result of the target object output by the rating model;
wherein the target index data comprises derived indexes and derived indexes; the derived indexes comprise indexes of which the atomic indexes are subjected to dimension limitation; the derived indexes comprise indexes synthesized by target logic operation on the basis of one or more derived indexes; the atomic index includes a metric value.
Preferably, before the target index data is transmitted to the pre-trained evaluation model, the method further includes:
acquiring a training sample for training the evaluation model;
selecting one index data from the training samples as training index data;
marking the training index data to obtain marking index data;
transmitting the marking index data to an initial evaluation model, so that the evaluation model calculates WOE and IV values of the marking index data, iterating an algorithm model until the IV values corresponding to the marking index data are within a target range, taking the WOE values as weight values of the marking index data, then enabling the evaluation model to calculate a grading result of the marking index data, and iterating the algorithm model until a loss function of the evaluation model is minimum;
and if the training is continued, returning to the step of selecting one index data from the training samples as training index data, and if the training is ended, obtaining the trained evaluation model.
Preferably, the evaluation model calculates a rating result through a rating formula;
the rating formula includes:
S＝ln(WOE)*A+B；
wherein S represents a rating result; a represents the index score increase rate of the marking index data; b represents a base score of the marking index data; ln represents the natural logarithm.
Preferably, the acquiring the target data for rating the target object includes:
acquiring original data for rating the target object;
and processing the original data to obtain the target data.
Preferably, the processing the raw data to obtain the target data includes:
and carrying out data cleaning on the original data to obtain the structured target data.
Preferably, the processing the raw data to obtain the target data includes:
and carrying out data complement on the original data to obtain the target data.
Preferably, the processing the raw data to obtain the target data includes:
and performing quality inspection on the original data, and taking the original data as the target data if a quality inspection result meets the quality requirement of the target object.
In a second aspect, the present application provides a rating system comprising:
the system comprises an acquisition module, a processing module and a processing module, wherein the acquisition module is used for acquiring target data for rating a target object, and the target data comprises identity information, running information, request information and associated information of the target object;
the index calculation module is used for generating target index data of the target data;
the model calculation module is used for transmitting the target index data to a pre-trained evaluation model;
the result acquisition module is used for acquiring a rating result of the target object output by the rating model;
wherein the target index data comprises derived indexes and derived indexes; the derived indexes comprise indexes of which the atomic indexes are subjected to dimension limitation; the derived indexes comprise indexes synthesized by target logic operation on the basis of one or more derived indexes; the atomic index includes a metric value.
In a third aspect, the application provides an electronic device comprising a processor, a memory, a communication interface, and one or more programs stored in the memory and configured to be executed by the processor, the programs comprising instructions for performing the steps of the method as described in any of the above.
In a fourth aspect, the present application provides a computer readable storage medium having a computer program stored therein which, when run on a user equipment, performs a method as described in any of the above.
The application provides a rating method, which is used for obtaining target data for rating a target object, wherein the target data comprises identity information, running information, request information and associated information of the target object; generating target index data of the target data; transmitting the target index data to a pre-trained evaluation model; obtaining a rating result of the target object output by the evaluation model; the target index data comprises derived indexes and derived indexes; the derived index comprises an index in which an atomic index is subjected to dimension limitation; the derived indexes comprise indexes synthesized by target logic operation on the basis of one or more derived indexes; the atomic index includes a metric value. The method and the system can generate corresponding derivative indexes and derivative indexes based on the identity information, the running information, the request information and the associated information of the target object, so that the characteristics of the target object can be accurately reflected by indexes with different dimensions, and the target index data can be processed by means of a pre-trained evaluation model to obtain a rating result, so that the accuracy is good, the degree of automation is high, and the applicability is good. The application provides a rating system, electronic equipment and a computer readable storage medium, which also solve the corresponding technical problems.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings that are required to be used in the embodiments or the description of the prior art will be briefly described below, and it is obvious that the drawings in the following description are only embodiments of the present application, and that other drawings can be obtained according to the provided drawings without inventive effort for a person skilled in the art.
FIG. 1 is a first schematic diagram of a rating method according to an embodiment of the present application;
FIG. 2 is a training flow chart of an assessment model in the rating method of the present application;
FIG. 3 is a schematic diagram of a rating system according to an embodiment of the present application;
FIG. 4 is a schematic diagram of a database according to the present application;
fig. 5 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Detailed Description
The following description of the embodiments of the present application will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present application, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
The term "and/or" in the present application is merely an association relation describing the association object, and indicates that three kinds of relations may exist, for example, a and/or B may indicate: a exists alone, A and B exist together, and B exists alone. In this context, the character "/" indicates that the front and rear associated objects are an "or" relationship.
The term "plurality" as used in the embodiments of the present application means two or more. The first, second, etc. descriptions in the embodiments of the present application are only used for illustrating and distinguishing the description objects, and no order is used, nor is the number of the devices in the embodiments of the present application limited, and no limitation on the embodiments of the present application should be construed. The "connection" in the embodiment of the present application refers to various connection manners such as direct connection or indirect connection, so as to implement communication between devices, which is not limited in the embodiment of the present application.
Referring to fig. 1, fig. 1 is a first schematic diagram of a rating method according to an embodiment of the application.
The grading method provided by the embodiment of the application can comprise the following steps:
step S101: and acquiring target data for rating the target object, wherein the target data comprises identity information, running information, request information and association information of the target object.
In practical application, the target data for rating the target object may be obtained first, for example, the target object may be collected to obtain corresponding target data, and the target data may include identity information, running information, request information, association information and the like of the target object, where the running information of the target object refers to corresponding information generated in the running process of the target object, the request information refers to information of the target object for corresponding request, and the association information refers to information given to the target object by other objects associated with the target object.
For easy understanding, if the target object is a computer, the identity information may be the memory, capacity, electric quantity, volume, etc. of the computer; the operation information can be the power consumption, the heat generation amount, the reaction time length and the like of the computer in the operation process; the request information can be request type, request content, request data amount and the like sent by the computer to the outside; the associated information can be the data size, the sending duration, the sending channel and the like sent by other computers or mobile phones and other devices. Of course, the types of the target object and the target data may be determined according to actual needs, for example, the target object may be a server, a product, an enterprise, etc., and the present application is not limited herein specifically.
In a specific application scenario, in order to ensure the accuracy of the rating, there may be a quality requirement on the data used for rating, and for this reason, the data may need to be processed to meet the requirement, that is, in the process of obtaining the target data used for rating the target object, the original data used for rating the target object may be obtained; and processing the original data to obtain target data. For example, the method can clean the original data, filter and convert the dirty data, and perform standardized processing on the data to obtain the structured target data. For example, the original data can be subjected to data complement to obtain the target data. And performing quality inspection on the original data, and taking the original data as target data if the quality inspection result meets the quality requirement of the target object.
Step S102: generating target index data of target data, wherein the target index data comprises derived indexes and derived indexes; the derived index comprises an index in which an atomic index is subjected to dimension limitation; the derived indexes comprise indexes synthesized by target logic operation on the basis of one or more derived indexes; the atomic index includes a metric value.
In practical application, in order to accurately process the target data, after the target data for rating the target object is obtained, the target data may be processed according to the association between the target data, so as to obtain target index data reflecting the characteristics of the target object from different dimensions.
It is to be understood that the target index data in the present application includes derived indexes and derived indexes; the derived index refers to an index for performing dimension limitation on the atomic index, for example, the derived index is the power consumption of the computer in different time periods; the derived indexes are synthesized indexes through target logic operation on the basis of one or more derived indexes, for example, the derived indexes are the change rate of the power consumption of a computer along with the running time and the like, and the type, the number and the like of the target logic operation can be determined according to actual needs; the atomic index is a measurement value of the target object, and may be a measurement value based on a business process, for example, the atomic index is power consumption of a computer.
Step S103: and transmitting the target index data to a pre-trained evaluation model.
Step S104: and obtaining a rating result of the target object output by the evaluation model.
In practical application, in order to automatically obtain the rating result of the target object, after generating the target index data of the target data, the target index data may be transmitted to a pre-trained evaluation model, and the rating result of the target object output by the evaluation model is obtained, where the rating result output by the evaluation model may be a specific score value, and then the rating result may be further classified into general, good, excellent and the like according to the score value, and of course, the evaluation model may also directly output the general, good, excellent and the like rating result.
It should be noted that, the evaluation model in the present application refers to a model obtained by training by a machine learning method, and the training process can be determined according to an application scenario. In addition, after the rating result of the target object is obtained, the rating result may be further processed optionally, for example, the rating result may be sent to the target object itself or sent to an operator of the target object, which is not limited herein.
The application provides a rating method, which is used for obtaining target data for rating a target object, wherein the target data comprises identity information, running information, request information and associated information of the target object; generating target index data of the target data; transmitting the target index data to a pre-trained evaluation model; obtaining a rating result of the target object output by the evaluation model; the target index data comprises derived indexes and derived indexes; the derived index comprises an index in which an atomic index is subjected to dimension limitation; the derived indexes comprise indexes synthesized by target logic operation on the basis of one or more derived indexes; the atomic index includes a metric value. The method and the system can generate corresponding derivative indexes and derivative indexes based on the identity information, the running information, the request information and the associated information of the target object, so that the characteristics of the target object can be accurately reflected by indexes with different dimensions, and the target index data can be processed by means of a pre-trained evaluation model to obtain a rating result, so that the accuracy is good, the degree of automation is high, and the applicability is good.
Referring to fig. 2, fig. 2 is a training flowchart of an evaluation model in the rating method of the present application.
Based on the above embodiment, in the rating method provided by the present application, before transmitting the target index data to the pre-trained evaluation model, the evaluation model may be trained, including the following steps:
step S201: a training sample is obtained for training the assessment model.
Step S202: and selecting one index data from the training samples as training index data.
Step S203: and marking the training index data to obtain marking index data.
Step S204: and transmitting the marking index data to an initial evaluation model so that the evaluation model calculates WOE and IV values of the marking index data, iterating the algorithm model until the IV values corresponding to the marking index data are within a target range, taking the WOE values as weight values of the marking index data, then enabling the evaluation model to calculate a rating result of the marking index data, and iterating the algorithm model until a loss function of the evaluation model is minimum.
In practical application, training index data can be marked as two types of bad and good, and the target range can be 0.30-0.49, and in addition, the WOE and IV value calculation method adopts the existing WOE and IV calculation mode, and the application is not limited in detail here.
Step S205: judging whether training is continued, if so, returning to the step S202; if the training is finished, step S206 is performed.
Step S206: and obtaining a trained evaluation model.
In practical applications, the condition for continuing training may be that the training frequency does not reach the preset frequency, or that the evaluation model does not converge, which is not limited in detail herein. In a specific application scene, the evaluation model can calculate a rating result through a rating formula; the rating formula includes:
S＝ln(WOE)*A+B；
wherein S represents a rating result; a represents the index score increase rate of the marker index data; b represents the basic score of the marking index data; ln represents the natural logarithm.
Referring to fig. 3, fig. 3 is a schematic structural diagram of a rating system according to an embodiment of the application.
The rating system provided by the embodiment of the application can comprise:
the acquisition module 101 is configured to acquire target data for rating a target object, where the target data includes identity information, operation information, request information, and association information of the target object;
an index calculation module 102, configured to generate target index data of the target data;
the model calculation module 103 is used for transmitting the target index data to a pre-trained evaluation model;
the result obtaining module 104 is configured to obtain a rating result of the target object output by the evaluation model;
the target index data comprises derived indexes and derived indexes; the derived index comprises an index in which an atomic index is subjected to dimension limitation; the derived indexes comprise indexes synthesized by target logic operation on the basis of one or more derived indexes; the atomic index includes a metric value.
The rating system provided by the embodiment of the application can further comprise:
the acquisition module is used for acquiring a training sample for training the evaluation model before the model calculation module transmits the target index data to the pre-trained evaluation model;
the selecting module is used for selecting one index data from the training samples as training index data;
the marking module is used for marking the training index data to obtain marking index data;
the transmission module is used for transmitting the marking index data to an initial evaluation model so that the evaluation model calculates WOE and IV values of the marking index data, and iterates the algorithm model until the IV values corresponding to the marking index data are within a target range, and then the evaluation model calculates a rating result of the marking index data by taking the WOE values as weight values of the marking index data, and iterates the algorithm model until a loss function of the evaluation model is minimum; if training is continued, returning to execute the step of selecting one index data from the training samples as training index data, and if training is finished, obtaining a trained evaluation model.
According to the rating system provided by the embodiment of the application, the rating result is calculated by the evaluation model through the rating formula;
the rating formula includes:
S＝ln(WOE)*A+B；
wherein S represents a rating result; a represents the index score increase rate of the marker index data; b represents the basic score of the marking index data; ln represents the natural logarithm.
The rating system provided by the embodiment of the application, the acquisition module may include:
an acquisition unit configured to acquire raw data for rating a target object;
and the processing unit is used for processing the original data to obtain target data.
The rating system provided by the embodiment of the application, the processing unit may be specifically configured to: and performing data cleaning on the original data to obtain structured target data.
The rating system provided by the embodiment of the application, the processing unit may be specifically configured to: and carrying out data complement on the original data to obtain target data.
The rating system provided by the embodiment of the application, the processing unit may be specifically configured to: and performing quality inspection on the original data, and taking the original data as target data if the quality inspection result meets the quality requirement of the target object.
In order to facilitate understanding of the rating scheme provided by the present application, the scheme of the present application will be described with reference to a pre-credit rating process of tax data of an enterprise, referring to fig. 4, fig. 4 is a schematic diagram of a functional architecture of a database provided by the present application, where, as shown in fig. 4, the functional architecture includes: data source, data access, data ETL and data application; the data source may include a source for acquiring data, mainly tax data of an enterprise. The data access may include: real-time data acquisition and timing batch acquisition. The data ETL may include: data manipulation and data storage, wherein data manipulation includes, but is not limited to: data cleaning, data complementation, data quality inspection, data standard establishment, service rule configuration, target binding, real-time alarm, quality report output and the like; the data storage may specifically include: file storage, relational database, HDFS data, mongoDB data. The data application may specifically include: wind control model training, BI report, credit reporting and label system. The operation and maintenance management comprises the following steps: network security management, cluster resource management, component management, log monitoring, failure alarms, such as network security and access rights management, expansion and contraction capacity management of server hardware resources, component service management, service log monitoring, service abnormality alarms, timing task failure alarms and the like. The whole process can be as follows:
the method comprises the steps that a terminal collects enterprise tax data to serve as sample data, the sample data is subjected to data cleaning, complementation and quality inspection, an index result is calculated through an index system, and then the index result is used as training data to be input into a machine learning algorithm model to be trained, so that a credit evaluation model is obtained; terminals include, but are not limited to: personal computers, servers, data centers, tablet computers, smart phones, etc.; the enterprise tax data comprises enterprise identity information, such as enterprise basic information data, enterprise contact person information data and the like, enterprise operation information, such as enterprise illegal violation data, enterprise check case data, enterprise income tax data, enterprise collection information data, enterprise tax due data, enterprise asset liability information data, enterprise profit information data and the like, enterprise request information, such as enterprise reporting information data and the like, and enterprise association information, such as enterprise investor information data, enterprise transaction object information data and the like; the enterprise tax data can be stored by a database, wherein related enterprise basic information, contact person information, investor information, transaction object information, illegal violation information, inspection case information, declaration information, income tax information, collection information, tax due information, liability production information and profit information are all stored in the enterprise tax information database, and the storage of the above data can be used for repeated model training;
the method comprises the steps that a calculation initiator sends unique identification information of an enterprise to be subjected to credit evaluation before credit, and the unique identification information of the enterprise can be uniform social credit codes and the like;
the terminal receives the unique identification information of the enterprise, collects tax data of the enterprise, and cleans, compiles and checks the data;
the index system receives the calculation request and calculates an index result of the enterprise to be evaluated;
the terminal inputs the index result data to the intelligent credit evaluation model to execute calculation to obtain a calculation result, and the credit rating of the enterprise to be evaluated is determined according to the calculation result, wherein the credit rating comprises but is not limited to: typically, good, excellent, and returns the rating results to the computing initiator. The whole scheme is automatic, and has the advantages of low cost and high efficiency.
It will be appreciated that the apparatus, in order to achieve the above-described functions, comprises corresponding hardware and/or software modules for performing the respective functions. The present application can be implemented in hardware or a combination of hardware and computer software, in conjunction with the example algorithm steps described in connection with the embodiments disclosed herein. Whether a function is implemented as hardware or computer software driven hardware depends upon the particular application and design constraints imposed on the solution. Those skilled in the art may implement the described functionality using different approaches for each particular application in conjunction with the embodiments, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
The present embodiment may divide the functional modules of the electronic device according to the above method example, for example, each functional module may be divided corresponding to each function, or two or more functions may be integrated into one processing module. The integrated modules described above may be implemented in hardware. It should be noted that, in this embodiment, the division of the modules is schematic, only one logic function is divided, and another division manner may be implemented in actual implementation.
It should be noted that, all relevant contents of each step related to the above method embodiment may be cited to the functional description of the corresponding functional module, which is not described herein.
In case an integrated unit is employed, the user equipment may comprise a processing module and a storage module. The processing module may be configured to control and manage actions of the user equipment, for example, may be configured to support the electronic device to execute the steps executed by the communication unit and the acquisition unit. The memory module may be used to support the electronic device to execute stored program code, data, etc.
Wherein the processing module may be a processor or a controller. Which may implement or perform the various exemplary logic blocks, modules and circuits described in connection with this disclosure. A processor may also be a combination that performs computing functions, e.g., including one or more microprocessors, digital signal processing (digital signal processing, DSP) and microprocessor combinations, and the like. The memory module may be a memory. The communication module can be a radio frequency circuit, a Bluetooth chip, a Wi-Fi chip and other equipment which interact with other electronic equipment.
It should be understood that the connection relationship between the modules illustrated in the embodiment of the present application is only illustrative, and does not limit the structure of the ue. In other embodiments of the present application, the ue may also use different interfacing manners in the foregoing embodiments, or a combination of multiple interfacing manners.
Referring to fig. 5, fig. 5 is an electronic device 40 provided by an embodiment of the present application, where the electronic device 40 includes a processor 401, a memory 402, a communication interface 403, and a display screen 404, where the processor 401, the memory 402, and the communication interface 403 are connected to each other through a bus, and the display screen supplies power to the electronic device, and the electronic device may further include:
memory 402 includes, but is not limited to, random access memory (random access memory, RAM), read-only memory (ROM), erasable programmable read-only memory (erasable programmable read only memory, EPROM), or portable read-only memory (compact disc read-only memory, CD-ROM), with memory 402 for associated computer programs and data. The communication interface 403 is used to receive and transmit data.
The processor 401 may be one or more central processing units (central processing unit, CPU), and in the case where the processor 401 is one CPU, the CPU may be a single-core CPU or a multi-core CPU.
The processor 401 may include one or more processing units, such as: the processing units may include an application processor (application processor, AP), a modem processor, a graphics processor (graphics processing unit, GPU), an image signal processor (image signal processor, ISP), a controller, a video codec, a digital signal processor (digital signal processor, DSP), a baseband processor, and/or a neural network processor (neural-network processing unit, NPU), etc. Wherein the different processing units may be separate components or may be integrated in one or more processors. In some embodiments, the user equipment may also include one or more processing units. The controller can generate operation control signals according to the instruction operation codes and the time sequence signals to finish the control of instruction fetching and instruction execution. In other embodiments, memory may also be provided in the processing unit for storing instructions and data. The memory in the processing unit may be a cache memory, for example. The memory may hold instructions or data that the processing unit has just used or recycled. If the processing unit needs to reuse the instruction or data, it can be called directly from the memory. In this way, repeated accesses are avoided, and the latency of the processing unit is reduced, thereby improving the efficiency of the user equipment in processing data or executing instructions.
In some embodiments, the processor 401 may include one or more interfaces. The interfaces may include inter-integrated circuit (inter-integrated circuit, I2C) interfaces, inter-integrated circuit audio (inter-integrated circuit sound, I2S) interfaces, pulse code modulation (pulse code modulation, PCM) interfaces, universal asynchronous receiver transmitter (universal asynchronous receiver/transmitter, UART) interfaces, mobile industry processor interfaces (mobile industry processor interface, MIPI), general-purpose input/output (GPIO) interfaces, SIM card interfaces, and/or USB interfaces, among others. The USB interface is an interface conforming to the USB standard specification, and specifically may be a Mini USB interface, a Micro USB interface, a USB Type C interface, or the like. The USB interface can be used for connecting a charger to charge the user equipment and can also be used for transmitting data between the user equipment and the peripheral equipment. The USB interface can also be used for connecting with a headset, and playing audio through the headset.
If the electronic device 40 is a user device or a terminal device, such as a smart phone, a computer device, or a server, the processor 401 in the electronic device 40 is configured to read the computer program code stored in the memory 402, and perform the following operations:
collecting enterprise tax data as sample data, calculating an index result through an index system after the sample data is subjected to data cleaning, complementation and quality inspection, and inputting the index result as training data into a machine learning algorithm model for training operation to obtain a credit evaluation model;
receiving unique identification information of an object to be evaluated, which is sent by a computing initiator, acquiring tax data of the object to be evaluated by using a tax data acquisition system, synchronizing the tax data to an index system after cleaning, complement and quality inspection are carried out, and computing a corresponding index result;
and inputting the index result as input data into a pre-trained credit evaluation model to execute calculation to obtain a calculation result, determining the credit rating of the object to be evaluated according to the calculation result, and returning the rating result to a calculation initiator.
All relevant contents of each scenario related to the above method embodiment may be cited to the description of the corresponding method embodiment, and are not repeated herein.
Embodiments of the present application also provide a computer readable storage medium having a computer program stored therein, which when run on a network device, implements the method flow shown in fig. 1.
Embodiments of the present application also provide a computer program product, which when run on a terminal, implements the method flow shown in fig. 1.
Embodiments of the present application also provide an electronic device comprising a processor, a memory, a communication interface, and one or more programs stored in the memory and configured to be executed by the processor, the programs comprising instructions for performing the steps in the method of the embodiment shown in fig. 1.
The foregoing description of the embodiments of the present application has been presented primarily in terms of a method-side implementation. It will be appreciated that the electronic device, in order to achieve the above-described functions, includes corresponding hardware structures and/or software templates for performing the respective functions. Those of skill in the art will readily appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as hardware or combinations of hardware and computer software. Whether a function is implemented as hardware or computer software driven hardware depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
The embodiment of the application can divide the functional units of the electronic device according to the method example, for example, each functional unit can be divided corresponding to each function, and two or more functions can be integrated in one processing unit. The integrated units may be implemented in hardware or in software functional units. It should be noted that, in the embodiment of the present application, the division of the units is schematic, which is merely a logic function division, and other division manners may be implemented in actual practice.
It should be noted that, for simplicity of description, the foregoing method embodiments are all described as a series of acts, but it should be understood by those skilled in the art that the present application is not limited by the order of acts described, as some steps may be performed in other orders or concurrently in accordance with the present application. Further, those skilled in the art will also appreciate that the embodiments described in the specification are presently preferred, and that the acts and templates referred to are not necessarily essential to the application.
In the foregoing embodiments, the descriptions of the embodiments are emphasized, and for parts of one embodiment that are not described in detail, reference may be made to related descriptions of other embodiments.
In the several embodiments provided by the present application, it should be understood that the disclosed apparatus may be implemented in other manners. For example, the above-described apparatus embodiments are merely illustrative, such as the above-described division of units, merely a division of logic functions, and there may be additional manners of dividing in actual implementation, such as multiple units or components may be combined or integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, or may be in electrical or other forms.
The units described above as separate components may or may not be physically separate, and components shown as units may or may not be physical units, may be located in one place, or may be distributed over a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in the embodiments of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.
The integrated units described above, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable memory. Based on such understanding, the technical solution of the present application may be embodied in essence or a part contributing to the prior art or all or part of the technical solution in the form of a software product stored in a memory, comprising several instructions for causing a computer device (which may be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the above-mentioned method of the various embodiments of the present application. And the aforementioned memory includes: a U-disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a removable hard disk, a magnetic disk, or an optical disk, or other various media capable of storing program codes.
Those of ordinary skill in the art will appreciate that all or a portion of the steps in the various methods of the above embodiments may be implemented by a program that instructs associated hardware, and the program may be stored in a computer readable memory, which may include: flash disk, read-Only Memory (ROM), random access Memory (Random Access Memory, RAM), magnetic disk or optical disk.
It is further noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the application. Thus, the present application is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (10)
1. A method of rating, comprising:
acquiring target data for rating a target object, wherein the target data comprises identity information, running information, request information and associated information of the target object;
generating target index data of the target data;
transmitting the target index data to a pre-trained evaluation model;
obtaining a rating result of the target object output by the rating model;
wherein the target index data comprises derived indexes and derived indexes; the derived indexes comprise indexes of which the atomic indexes are subjected to dimension limitation; the derived indexes comprise indexes synthesized by target logic operation on the basis of one or more derived indexes; the atomic index includes a metric value.
2. The method of claim 1, wherein prior to transmitting the target metric data to a pre-trained assessment model, further comprising:
acquiring a training sample for training the evaluation model;
selecting one index data from the training samples as training index data;
marking the training index data to obtain marking index data;
transmitting the marking index data to an initial evaluation model, so that the evaluation model calculates WOE and IV values of the marking index data, iterating an algorithm model until the IV values corresponding to the marking index data are within a target range, taking the WOE values as weight values of the marking index data, then enabling the evaluation model to calculate a grading result of the marking index data, and iterating the algorithm model until a loss function of the evaluation model is minimum;
and if the training is continued, returning to the step of selecting one index data from the training samples as training index data, and if the training is ended, obtaining the trained evaluation model.
3. The method of claim 2, wherein the assessment model calculates a rating result by a rating formula;
the rating formula includes:
S＝ln(WOE)*A+B；
wherein S represents a rating result; a represents the index score increase rate of the marking index data; b represents a base score of the marking index data; ln represents the natural logarithm.
4. A method according to any one of claims 1 to 3, wherein the obtaining target data for rating a target object comprises:
acquiring original data for rating the target object;
and processing the original data to obtain the target data.
5. The method of claim 4, wherein processing the raw data to obtain the target data comprises:
and carrying out data cleaning on the original data to obtain the structured target data.
6. The method of claim 4, wherein processing the raw data to obtain the target data comprises:
and carrying out data complement on the original data to obtain the target data.
7. The method of claim 4, wherein processing the raw data to obtain the target data comprises:
and performing quality inspection on the original data, and taking the original data as the target data if a quality inspection result meets the quality requirement of the target object.
8. A rating system, comprising:
the system comprises an acquisition module, a processing module and a processing module, wherein the acquisition module is used for acquiring target data for rating a target object, and the target data comprises identity information, running information, request information and associated information of the target object;
the index calculation module is used for generating target index data of the target data;
the model calculation module is used for transmitting the target index data to a pre-trained evaluation model;
the result acquisition module is used for acquiring a rating result of the target object output by the rating model;
wherein the target index data comprises derived indexes and derived indexes; the derived indexes comprise indexes of which the atomic indexes are subjected to dimension limitation; the derived indexes comprise indexes synthesized by target logic operation on the basis of one or more derived indexes; the atomic index includes a metric value.
9. An electronic device comprising a processor, a memory, a communication interface, and one or more programs stored in the memory and configured to be executed by the processor, the programs comprising instructions for performing the steps of the method of any of claims 1-7.
10. A computer readable storage medium, characterized in that the computer readable storage medium has stored therein a computer program which, when run on a user equipment, performs the method according to any of claims 1-7.
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