CN108615101A - Credit information processing method and processing device - Google Patents
Credit information processing method and processing device Download PDFInfo
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Abstract
The embodiment of the present invention provides a kind of credit information processing method and processing device.This method includes：Determine that N number of evaluative dimension of target object and the dimension weight of each evaluative dimension, N are the integer more than or equal to 2；According to the data to be evaluated of N number of evaluative dimension of the target object obtained from background server, the dimension evaluation of estimate of N number of evaluative dimension of target object is generated；According to the dimension evaluation of estimate of N number of evaluative dimension of target object and the dimension weight of each evaluative dimension, the practical comprehensive evaluation value of target object is generated.The dimension evaluation of estimate that this method passes through multiple evaluative dimensions of acquisition target object, and by each dimension evaluation of estimate and corresponding dimension weight combination processing, so as to the confidence level higher of the practical comprehensive evaluation value of target object caused by making, overcomes and make the problem relatively low to the confidence level of the evaluation result of target object because evaluative dimension is single, unilateral in the prior art.
Description
Technical field
The present embodiments relate to information technology field more particularly to a kind of credit information processing method and processing devices.
Background technology
Currently, China has established the strategical planning of the establishment of social credit system, sincere from government affairs, commercial affairs sincerity, society
Meeting is sincere, four aspects of judicial public affairs letter construction promote Credit System Constructions, to realize comprehensive covering of social credibility.Each field,
Every profession and trade correlation supporting policy is also closely being put into effect and is being carried out, all kinds of participants in the market especially financial institution, (i.e. to enterprise
Target object) credit appraisal require it is higher and higher.Therefore, for the relatively accurate of the especially medium and small micro- enterprise of numerous enterprises and
Stable credit appraisal system has become the research emphasis of financial institution and related credit information service.
Existing evaluation of enterprises credit mainly passes through the letter to " financial situation " of enterprise this evaluative dimension
It ceases to carry out analyzing processing to obtain the credit appraisal result of enterprise.Thus, will result in because evaluative dimension it is single,
The unilateral and problem that keeps the credit appraisal credible result degree of target object relatively low.
Invention content
In view of this, one of the technical issues of embodiment of the present invention is solved is to provide a kind of credit information processing method
And device, to overcome in the prior art because evaluative dimension is single, unilateral make generate target object credit appraisal knot
The relatively low problem of fruit confidence level, to effectively improve the confidence level of the credit appraisal result to target object.
The first aspect of the embodiment of the present invention provides a kind of credit information processing method.This method includes：
Determine that N number of evaluative dimension of target object and the dimension weight of each evaluative dimension, N are whole more than or equal to 2
Number；
According to the data to be evaluated of N number of evaluative dimension of the target object obtained from background server, target is generated
The dimension evaluation of estimate of N number of evaluative dimension of object；
According to the dimension evaluation of estimate of N number of evaluative dimension of target object and the dimension weight of each evaluative dimension,
Generate the practical comprehensive evaluation value of target object.
Optionally, it in a specific embodiment of the invention, is evaluated according to the dimension of N number of evaluative dimension of target object
The dimension weight of value and each evaluative dimension, the practical comprehensive evaluation value for generating target object further include later：According to target
The practical comprehensive evaluation value of object obtains the practical overall merit classification results of target object.
Optionally, in a specific embodiment of the invention, according to the practical comprehensive evaluation value of target object, target pair is obtained
The practical overall merit classification results of elephant include：Classified according to the comprehensive evaluation value of pre-set target object and overall merit
Mapping table, overall merit classification corresponding with the practical comprehensive evaluation value is determined as to the practical synthesis of target object
Classification of assessment result.
Optionally, in a specific embodiment of the invention, according to the described N number of of the target object obtained from background server
The data to be evaluated of evaluative dimension, the dimension evaluation of estimate for generating N number of evaluative dimension of target object include：According to the N
The data to be evaluated of a evaluative dimension and the dimension evaluation of estimate standards of grading of pre-set target object, generate target object
The dimension evaluation of estimate of N number of evaluative dimension.
Optionally, in a specific embodiment of the invention, according to the described N number of of the target object obtained from background server
The data to be evaluated of evaluative dimension, the dimension evaluation of estimate for generating N number of evaluative dimension of target object include：
One or more evaluation index is respectively set to N number of evaluative dimension of target object；
According under N number of evaluative dimension of the target object obtained from background server whole evaluation index it is to be evaluated
Data obtain the metrics evaluation value for each evaluation index being respectively set under N number of evaluative dimension of target object；
It is raw according to the metrics evaluation value for each evaluation index being respectively set under N number of evaluative dimension of target object
At the dimension evaluation of estimate of N number of evaluative dimension of target object.
Optionally, in a specific embodiment of the invention, according to the described N number of of the target object obtained from background server
Data to be evaluated of whole evaluation indexes under evaluative dimension are obtained and are respectively set under N number of evaluative dimension of target object
Each the metrics evaluation value of evaluation index includes：
According to the standards of grading of the data to be evaluated and each evaluation index of pre-set target object, mesh is obtained
Mark the metrics evaluation value for each evaluation index being respectively set under N number of evaluative dimension of object.
Optionally, in a specific embodiment of the invention, under N number of evaluative dimension according to target object respectively
The metrics evaluation value for each evaluation index being arranged generates the dimension evaluation of estimate of N number of evaluative dimension of target object, packet
It includes：
The each evaluation index being respectively set for N number of evaluative dimension of target object accordingly weigh by one index of setting
Weight；
According to the metrics evaluation value for each evaluation index being respectively set under N number of evaluative dimension of target object, and
In conjunction with the index weights of corresponding evaluation index, the dimension evaluation of estimate of N number of evaluative dimension of target object is obtained.
Optionally, it in a specific embodiment of the invention, is respectively set according under N number of evaluative dimension of target object
Each evaluation index metrics evaluation value, and combine corresponding evaluation index index weights, obtain the described N number of of target object
The dimension evaluation of estimate of evaluative dimension, including：
Each of to each evaluative dimension of N number of evaluative dimension of target object, calculate separately under the evaluative dimension
The product of the metrics evaluation value of evaluation index and corresponding index weights point, then sum to each product, it will be obtained and true
It is set to the dimension evaluation of estimate of corresponding evaluative dimension.
Optionally, in a specific embodiment of the invention, the N is the integer more than or equal to 5.
The second aspect of the embodiment of the present invention provides a kind of credit information processing unit.The device includes：
Evaluative dimension determining module, the dimension of N number of evaluative dimension and each evaluative dimension for determining target object
Weight, N are the integer more than or equal to 2；
Dimension evaluation of estimate generation module, for N number of evaluation dimension according to the target object obtained from background server
The data to be evaluated of degree, generate the dimension evaluation of estimate of N number of evaluative dimension of target object；
Comprehensive evaluation value generation module is used for the dimension evaluation of estimate of N number of evaluative dimension according to target object, and
The dimension weight of each evaluative dimension generates the practical comprehensive evaluation value of target object.
Optionally, in a specific embodiment of the invention, which further includes：Classification of assessment result determining module, is used for
According to the practical comprehensive evaluation value of target object, the practical overall merit classification results of target object are obtained.
Optionally, in a specific embodiment of the invention, classification of assessment result determining module is specifically used for basis and sets in advance
The mapping table of the comprehensive evaluation value for the target object set and overall merit classification, will be corresponding with the practical comprehensive evaluation value
Overall merit classification be determined as the practical overall merit classification results of target object.
Optionally, in an of the invention specific embodiment, dimension evaluation of estimate generation module is specifically used for N number of being commented according to described
The data to be evaluated of valence dimension and the dimension evaluation of estimate standards of grading of pre-set target object, generate the described of target object
The dimension evaluation of estimate of N number of evaluative dimension.
Optionally, in a specific embodiment of the invention, dimension evaluation of estimate generation module includes：
One or more is respectively set for N number of evaluative dimension to target object in evaluation index setting unit
Evaluation index；
Metrics evaluation value acquiring unit, for N number of evaluation dimension according to the target object obtained from background server
The data to be evaluated of the lower whole evaluation indexes of degree are obtained each of to be respectively set under N number of evaluative dimension of target object and be commented
The metrics evaluation value of valence index；
Dimension evaluation of estimate generation unit, for each of being respectively set under N number of evaluative dimension according to target object
The metrics evaluation value of evaluation index generates the dimension evaluation of estimate of N number of evaluative dimension of target object.
Optionally, in a specific embodiment of the invention, metrics evaluation value acquiring unit is specifically used for according to described to be evaluated
The standards of grading of valence mumber evidence and each evaluation index of pre-set target object obtain N number of evaluation of target object
The metrics evaluation value for each evaluation index being respectively set under dimension.
Optionally, in a specific embodiment of the invention, dimension evaluation of estimate generation unit includes：
Subelement, each evaluation for being respectively set for N number of evaluative dimension of target object is arranged in index weights
An index weights are accordingly arranged in index；
Dimension evaluation of estimate generates subelement, every for being respectively set under N number of evaluative dimension according to target object
The metrics evaluation value of a evaluation index, and the index weights of corresponding evaluation index are combined, generate N number of evaluation of target object
The dimension evaluation of estimate of dimension.
Optionally, in a specific embodiment of the invention, dimension evaluation of estimate generates subelement and is specifically used for target object
N number of evaluative dimension each evaluative dimension, calculate separately the metrics evaluation of each evaluation index under the evaluative dimension
The product of value and corresponding index weights point, then sum to each product, will be obtained and be determined as corresponding evaluative dimension
Dimension evaluation of estimate.
By above technical scheme as it can be seen that the embodiment of the present invention is commented by obtaining the dimension of multiple evaluative dimensions of target object
Value, and by each dimension evaluation of estimate and corresponding dimension weight combination processing, so as to make the reality of generated target object
The confidence level higher of comprehensive evaluation value overcomes and makes in the prior art to target object because evaluative dimension is single, unilateral
The relatively low problem of the confidence level of evaluation result.
Description of the drawings
In order to more clearly explain the embodiment of the invention or the technical proposal in the existing technology, to embodiment or will show below
There is attached drawing needed in technology description to be briefly described, it should be apparent that, the accompanying drawings in the following description is only this
Some embodiments described in inventive embodiments can also obtain according to these attached drawings for those of ordinary skill in the art
Obtain other attached drawings.
Fig. 1 is the credit information process flow figure that the embodiment of the present invention one provides；
Fig. 2 is credit information process flow figure provided by Embodiment 2 of the present invention；
Fig. 3 is the credit information processing unit structure chart that the embodiment of the present invention three provides；
Fig. 4 is the credit information processing unit structure chart that the embodiment of the present invention four provides；
Fig. 5 is some electronic equipments that the application executes the credit information processing method that the embodiment of the present invention one or two provides
Hardware architecture diagram.
Specific implementation mode
In order to make those skilled in the art more fully understand the technical solution in the embodiment of the present invention, below in conjunction with the present invention
Attached drawing in embodiment, technical scheme in the embodiment of the invention is clearly and completely described, it is clear that described reality
It is a part of the embodiment of the embodiment of the present invention to apply example only, instead of all the embodiments.Based on the implementation in the embodiment of the present invention
Example, the every other embodiment that those of ordinary skill in the art are obtained should all belong to the range of protection of the embodiment of the present invention.
The specific implementation of embodiment is further illustrated the present invention below in conjunction with the accompanying drawings.
Fig. 1 is the credit information process flow figure that the embodiment of the present invention one provides.As shown, the embodiment of the present invention
The one credit information processing method provided specifically includes following steps：
S101 determines that N number of evaluative dimension of target object and the dimension weight of each evaluative dimension, N are more than or equal to 2
Integer.
In this step, the N number of above evaluation dimension of target object can be determined according to experimental data or practical experience data
Degree, so that can be evaluated from multiple evaluative dimensions target object；Meanwhile also determining the dimension power of each evaluative dimension
Weight, so as to which balance can be weighted to the dimension evaluation of estimate of evaluative dimension by dimension weight, in subsequent step to be conducive to carry
The confidence level of the practical comprehensive evaluation value of high target object.
Optionally, N is more than or equal to 5, is advisable with 5.
S102 is generated according to the data to be evaluated of N number of evaluative dimension of the target object obtained from background server
The dimension evaluation of estimate of N number of evaluative dimension of target object.
With " assessment of keeping a promise " " management ability " " enterprise's speciality " " financial analysis " " the concerning taxes credit " of target object " enterprise A "
It, can be by accessing the public of the units such as the tax, the administration for industry and commerce, judicial authority and telecom operators for 5 evaluative dimensions
Database service interface, that is, background server obtains the data to be evaluated of this 5 dimensions of enterprise A, to ensure these data to be evaluated
Real-time, integrality and legitimacy.Then, scoring example can be carried out to the data to be evaluated of above-mentioned 5 evaluative dimensions of enterprise A
Scoring is such as compareed automatically by the dimension evaluation of estimate standards of grading of pre-set target object, and then generates above-mentioned the 5 of enterprise A
The dimension evaluation of estimate of a evaluative dimension, such as：By scoring, it is generated to the dimension evaluation of estimate of above-mentioned 5 evaluative dimensions of enterprise A
It it is respectively 90 points, 85 points, 95 points, 88 points, 92 points by taking hundred-mark system as an example.
Step S103, according to the dimension evaluation of estimate of N number of evaluative dimension of target object and each evaluative dimension
Dimension weight generates the practical comprehensive evaluation value of target object.
Still in step S102 enterprise A and above-mentioned data instance, practical synthesis of above-mentioned 5 evaluative dimensions to enterprise A
There are different degrees of differences for the contribution of evaluation of estimate, therefore can be by the dimension weight of each dimension to corresponding evaluative dimension
Dimension evaluation of estimate is weighted processing (sample data is shown in Table 1), for example, can be generated by following formula 1 enterprise A reality it is comprehensive
Close evaluation of estimate.In practical application, which can be used as practical synthesis credit appraisal value to use.
Table 1：
Calculation formula 1：
Wherein, DSiFor the dimension evaluation of estimate of i-th of evaluative dimension of target object, θiFor i-th of evaluative dimension of target object
Corresponding dimension weight, TS are the practical comprehensive evaluation value of target object.
According to above-mentioned calculation formula, the comprehensive evaluation value that can obtain enterprise A is 89.41 points, which, which belongs in hundred-mark system, comments
The higher score value of valence.
Optionally, further include after step S103：
S104 obtains the practical overall merit classification results of target object according to the practical comprehensive evaluation value of target object.
The step can be specifically：
According to the comprehensive evaluation value of pre-set target object and overall merit classification mapping table, will with it is described
The corresponding overall merit classification of practical comprehensive evaluation value is determined as the practical overall merit classification results of target object.
Specifically, the correspondence of the comprehensive evaluation value and overall merit classification of a target object can be pre-set
Table.In general, the quantity that can be classified according to overall merit, is correspondingly divided into identical quantity by the value range of comprehensive evaluation value
Each subrange, is corresponded to an overall merit classification by subrange respectively, and the synthesis that a target object thus can be arranged is commented
The mapping table of value and overall merit classification.Then by the comprehensive evaluation value value model where the practical comprehensive evaluation value
Overall merit classification corresponding to the subrange enclosed is determined as the practical overall merit classification results of target object.It may make in this way
Seem more intuitive, humanized to the practical overall merit of target object.
Still divided in the case of the practical comprehensive evaluation value 89.41 of target object " enterprise A ", an overall merit can be pre-set
The mapping table (as shown in table 2) of value and overall merit classification, i.e.,：The value range [0,100] of comprehensive evaluation value is divided
For " [0,50), [50,65), [65,80), [80,90), [90,100] " five subranges, this five subranges corresponding " pole respectively
It is low, low, medium, high, high " five overall merit classification.
The practical comprehensive evaluation value of enterprise A be 89.41 fall [80,90) this subrange, then it is subrange is corresponding comprehensive
The practical overall merit classification results that classification of assessment "high" is determined as enterprise A are closed, that is, show that " business standing is good, is suitble to mesh
Preceding regular traffic contact ".
Table 2：
| Comprehensive evaluation value | Overall merit is classified | Explanation |
| 90 (containing) -100 (containing) | It is high | The business standing is very excellent, is suitble to any business contact |
| 80 (containing) -90 | It is high | The business standing is good, is suitble to current regular traffic contact |
| 65 (containing) -80 | It is medium | The business standing is fine, be suitble to it is guaranteed under the conditions of carry out business contact |
| 50 (containing) -65 | It is low | The business standing is general, it is necessary to business contact is carried out under the conditions of guaranteed |
| 0 (containing) -50 | It is extremely low | The business standing is bad, is not suitable for business contact, please pays attention to caution |
The embodiment of the present invention one is commented each dimension by obtaining the dimension evaluation of estimate of multiple evaluative dimensions of target object
Value and corresponding dimension weight combination processing are equivalent to and combine the evaluation processing that target object has carried out many aspects, from
And the confidence level higher of the practical comprehensive evaluation value of target object caused by can making, and then overcome in the prior art because of evaluation
Dimension is single, unilateral and make the problem relatively low to the confidence level of the evaluation result of target object.
Fig. 2 is credit information process flow figure provided by Embodiment 2 of the present invention.As shown in Fig. 2, in embodiment one
On the basis of, credit information processing method provided by Embodiment 2 of the present invention specifically includes：
S201 determines that N number of evaluative dimension of target object and the dimension weight of each evaluative dimension, N are more than or equal to 2
Integer.
Step S201 is consistent with step S101, and details are not described herein for specific method.
One or more evaluation index is respectively set to N number of evaluative dimension of target object in S202.
That is, refineing to one or more again to each evaluative dimension in N number of evaluative dimension of target object
Evaluation index.
S203, according to whole evaluation indexes under N number of evaluative dimension of the target object obtained from background server
Data to be evaluated obtain the metrics evaluation value for each evaluation index being respectively set under N number of evaluative dimension of target object.
Specifically, the common data of the units such as the access tax, the administration for industry and commerce, judicial authority and telecom operators can be passed through
Service interface, that is, background server obtains the data to be evaluated of whole evaluation indexes under N number of evaluative dimension of target object, to protect
Demonstrate,prove the real-time, integrality and legitimacy of these data to be evaluated.Then according to the data to be evaluated and pre-set mesh
The standards of grading for marking each evaluation index of object are obtained and each of are respectively set under N number of evaluative dimension of target object
The metrics evaluation value of evaluation index.
That is, the standards of grading of each evaluation index of target object can be pre-set for example with the shape of grade form
Formula embodies the standards of grading, then by the data to be evaluated of whole evaluation indexes under N number of evaluative dimension of target object and is somebody's turn to do
Standards of grading carry out control scoring, can obtain the metrics evaluation value of each evaluation index, that is, get target object
The metrics evaluation value for each evaluation index being respectively set under N number of evaluative dimension.Do so may make to it is each evaluation refer to
Target evaluation comparison specification, so that the accuracy higher of generated data.
S204, according to the metrics evaluation for each evaluation index being respectively set under N number of evaluative dimension of target object
Value, generates the dimension evaluation of estimate of N number of evaluative dimension of target object.
For example, to each evaluative dimension in N number of evaluative dimension of target object, to each of under the evaluative dimension
The metrics evaluation value of evaluation index is summed after being weighted, and obtains the dimension evaluation of estimate of the evaluative dimension, and then generate target pair
The corresponding N number of dimension evaluation of estimate of the N number of evaluative dimension of elephant.
S205, according to the dimension evaluation of estimate of N number of evaluative dimension of target object and the dimension of each evaluative dimension
Weight generates the practical comprehensive evaluation value of target object.
This step is consistent with step S103, and details are not described herein for specific method.
Optionally, step S204 can be specifically：
The each evaluation index being respectively set for N number of evaluative dimension of target object accordingly weigh by one index of setting
Weight.For example, to each evaluative dimension in N number of evaluative dimension of target object, it can be according to each under the evaluative dimension
The index weights of each evaluation index are arranged to the contribution of the dimension evaluation of estimate of the evaluative dimension in a evaluation index.
According to the metrics evaluation value for each evaluation index being respectively set under N number of evaluative dimension of target object, and
In conjunction with the index weights of corresponding evaluation index, the dimension evaluation of estimate of N number of evaluative dimension of target object is obtained.For example, right
Each evaluative dimension of N number of evaluative dimension of target object each of can calculate separately under the evaluative dimension evaluation and refer to
The product of target metrics evaluation value and corresponding index weights point, then sum to each product, will be obtained and be determined as phase
Answer the dimension evaluation of estimate of evaluative dimension.
Index weights weighting is carried out by the metrics evaluation value to each evaluation index under evaluative dimension as a result, to
So that the dimension evaluation of estimate of obtained evaluative dimension can more reflect the truth of corresponding evaluative dimension.
The embodiment of the present invention two comments the dimension of N number of evaluative dimension of target object on the basis of embodiment one
The acquisition of value is refined, further specification and the acquisition modes for optimizing dimension evaluation of estimate.
Fig. 3 is the credit information processing unit structure chart that the embodiment of the present invention three provides.As shown in figure 3, the present invention is implemented
The credit information processing unit that example three provides specifically includes：
Evaluative dimension determining module 1, the dimension of N number of evaluative dimension and each evaluative dimension for determining target object
Weight, N are the integer more than or equal to 2；
Optionally, N is more than the in 5, is advisable with 5.
Dimension evaluation of estimate generation module 2, for N number of evaluation dimension according to the target object obtained from background server
The data to be evaluated of degree, generate the dimension evaluation of estimate of N number of evaluative dimension of target object；
Further, dimension evaluation of estimate generation module 2 be specifically used for according to the data to be evaluated of N number of evaluative dimension and
The dimension evaluation of estimate standards of grading of pre-set target object, the dimension for generating N number of evaluative dimension of target object are commented
Value.
Comprehensive evaluation value generation module 3 is used for the dimension evaluation of estimate of N number of evaluative dimension according to target object, with
And the dimension weight of each evaluative dimension, generate the practical comprehensive evaluation value of target object.
Optionally, the credit information processing unit further includes：
Classification of assessment result determining module 4 obtains target object for the practical comprehensive evaluation value according to target object
Practical overall merit classification results.
Further, classification of assessment result determining module 4 is specifically used for being commented according to the synthesis of pre-set target object
Overall merit corresponding with the practical comprehensive evaluation value is classified and is determined as by the mapping table of value and overall merit classification
The practical overall merit classification results of target object.
The credit information processing unit that the embodiment of the present invention three provides is specifically used for executing the credit letter that embodiment one provides
Processing method is ceased, realization principle, method and function and usage etc. are similar with embodiment one, repeat no more here.
Fig. 4 is the credit information processing unit structure chart that the embodiment of the present invention four provides.As shown in figure 4, in embodiment three
On the basis of, dimension evaluation of estimate generation module 2 specifically includes：
Evaluation index setting unit 21 is respectively set one or more for N number of evaluative dimension to target object
A evaluation index；
Metrics evaluation value acquiring unit 22, for N number of evaluation according to the target object obtained from background server
The data to be evaluated of whole evaluation indexes under dimension are obtained and each of are respectively set under N number of evaluative dimension of target object
The metrics evaluation value of evaluation index；
Further, metrics evaluation value acquiring unit 22 is specifically used for according to the data to be evaluated and pre-set mesh
The standards of grading for marking each evaluation index of object are obtained and each of are respectively set under N number of evaluative dimension of target object
The metrics evaluation value of evaluation index.
Dimension evaluation of estimate generation unit 23, it is every for being respectively set under N number of evaluative dimension according to target object
The metrics evaluation value of a evaluation index generates the dimension evaluation of estimate of N number of evaluative dimension of target object.
Further, dimension evaluation of estimate generation unit 23 includes：
Subelement, each evaluation for being respectively set for N number of evaluative dimension of target object is arranged in index weights
An index weights are accordingly arranged in index；
Dimension evaluation of estimate generates subelement, every for being respectively set under N number of evaluative dimension according to target object
The metrics evaluation value of a evaluation index, and the index weights of corresponding evaluation index are combined, generate N number of evaluation of target object
The dimension evaluation of estimate of dimension.
Further, dimension evaluation of estimate generates subelement and is specifically used for the every of N number of evaluative dimension of target object
A evaluative dimension calculates separately the metrics evaluation value and corresponding index weights point of each evaluation index under the evaluative dimension
Product, then sum to each product, will be obtained and be determined as the dimension evaluation of estimate of corresponding evaluative dimension.
The credit information processing unit that the embodiment of the present invention four provides is specifically used for executing the credit letter that embodiment two provides
Processing method is ceased, realization principle, function and usage etc. are similar with embodiment two, repeat no more here.
Fig. 5 is some electronic equipments that the application executes the credit information processing method that the embodiment of the present invention one or two provides
Hardware architecture diagram.According to Fig.5, which includes：One or more processors 510 and memory 520,
In Fig. 5 by taking a processor 510 as an example.
The equipment for executing the credit information processing method can also include：Input unit 530 and output device 540.
Processor 510, memory 520, input unit 530 and output device 540 can pass through bus or other modes
It connects, in Fig. 5 for being connected by bus.
Memory 520 is used as a kind of non-volatile computer readable storage medium storing program for executing, can be used for storing non-volatile software journey
Sequence, non-volatile computer executable program and module, such as the credit information processing method pair in the embodiment of the present application
Program instruction/the module answered.Processor 510 by operation be stored in non-volatile software program in memory 520, instruction with
And module, the various function application to execute server and data processing, that is, realize the credit information processing method.
Memory 520 may include storing program area and storage data field, wherein storing program area can store operation system
System, the required application program of at least one function；Storage data field can store according to embodiments of the present invention three or four offers
Credit information processing unit uses created data etc..In addition, memory 520 may include high-speed random access memory
520, can also include nonvolatile memory 520, a for example, at least magnetic disk storage 520, flush memory device or other are non-
Volatile solid-state 520.In some embodiments, it includes remotely located relative to processor 510 that memory 520 is optional
Memory 520, these remote memories 520 can pass through network connection to the credit information processing unit.Above-mentioned network
Example include but not limited to internet, intranet, LAN, mobile radio communication and combinations thereof.
Input unit 530 can receive the number or character information of input, and generate and the credit information processing unit
User setting and function control related key signals input.Input unit 530 may include pressing the equipment such as module.
One or more of modules are stored in the memory 520, when by one or more of processors
When 510 execution, the credit information processing method is executed.
The said goods can perform the method that the corresponding embodiment of the application is provided, and have the corresponding function module of execution method
And advantageous effect.The not technical detail of detailed description in the present embodiment, reference can be made to the side that the corresponding embodiment of the application is provided
Method.
The electronic equipment of the embodiment of the present application exists in a variety of forms, including but not limited to:
(1) mobile communication equipment:The characteristics of this kind of equipment is that have mobile communication function, and to provide speech, data
Communication is main target.This Terminal Type includes:Smart mobile phone (such as i Phone), multimedia handset, functional mobile phone, and
Low-end mobile phone etc..
(2) super mobile personal computer equipment:This kind of equipment belongs to the scope of personal computer, there is calculating and processing work(
Can, generally also have mobile Internet access characteristic.This Terminal Type includes:PDA, MID and UMPC equipment etc., such as iPad.
(3) portable entertainment device:This kind of equipment can show and play multimedia content.Such equipment includes:Audio,
Video player (such as iPod), handheld device, e-book and intelligent toy and portable car-mounted navigation equipment.
(4) server:The equipment for providing the service of calculating, the composition of server include that processor, hard disk, memory, system are total
Line etc., server is similar with general computer architecture, but due to needing to provide highly reliable service, in processing energy
Power, stability, reliability, safety, scalability, manageability etc. are more demanding.
(5) other electronic devices with data interaction function.
The apparatus embodiments described above are merely exemplary, wherein the module illustrated as separating component can
It is physically separated with being or may not be, the component shown as module may or may not be physics mould
Block, you can be located at a place, or may be distributed on multiple network modules.It can be selected according to the actual needs
In some or all of module achieve the purpose of the solution of this embodiment.Those of ordinary skill in the art are not paying creativeness
Labour in the case of, you can to understand and implement.
Through the above description of the embodiments, those skilled in the art can be understood that each embodiment can
It is realized by the mode of software plus required general hardware platform, naturally it is also possible to pass through hardware.Based on this understanding, on
Stating technical solution, substantially the part that contributes to existing technology can be expressed in the form of software products in other words, should
Computer software product can store in a computer-readable storage medium, the computer readable recording medium storing program for performing include for
Any mechanism of the readable form storage of computer (such as computer) or transmission information.For example, machine readable media includes only
Read memory (ROM), random access memory (RAM), magnetic disk storage medium, optical storage media, flash medium, electricity, light,
The transmitting signal (for example, carrier wave, infrared signal, digital signal etc.) etc. of sound or other forms, which includes
Some instructions are used so that a computer equipment (can be personal computer, server or the network equipment etc.) executes respectively
Method described in certain parts of a embodiment or embodiment.
Finally it should be noted that：The above embodiments are only used to illustrate the technical solution of the present invention., rather than its limitations；To the greatest extent
Pipe is described the invention in detail with reference to foregoing embodiments, it will be understood by those of ordinary skill in the art that：It is still
Either which part or all technical features can be carried out so that technical scheme described in the above embodiments is modified
Equivalent replacement；And these modifications or replacements, it does not separate the essence of the corresponding technical solution various embodiments of the present invention technical side
The range of case.
Claims (17)
1. a kind of credit information processing method, which is characterized in that including：
Determine that N number of evaluative dimension of target object and the dimension weight of each evaluative dimension, N are the integer more than or equal to 2；
According to the data to be evaluated of N number of evaluative dimension of the target object obtained from background server, target object is generated
N number of evaluative dimension dimension evaluation of estimate；
According to the dimension evaluation of estimate of N number of evaluative dimension of target object and the dimension weight of each evaluative dimension, generate
The practical comprehensive evaluation value of target object.
2. credit information processing method according to claim 1, which is characterized in that described according to target object N number of is commented
The dimension evaluation of estimate of valence dimension and the dimension weight of each evaluative dimension, generate target object practical comprehensive evaluation value it
After further include：According to the practical comprehensive evaluation value of target object, the practical overall merit classification results of target object are obtained.
3. credit information processing method according to claim 2, which is characterized in that commented according to the practical synthesis of target object
Value, the practical overall merit classification results for obtaining target object include：
It, will be with the reality according to the mapping table of the comprehensive evaluation value of pre-set target object and overall merit classification
The corresponding overall merit classification of comprehensive evaluation value is determined as the practical overall merit classification results of target object.
4. credit information processing method according to claim 1, which is characterized in that according to the mesh obtained from background server
The data to be evaluated for marking N number of evaluative dimension of object, generate the dimension evaluation of estimate of N number of evaluative dimension of target object
Including：According to the dimension evaluation of estimate standards of grading of the data to be evaluated of N number of evaluative dimension and pre-set target object,
Generate the dimension evaluation of estimate of N number of evaluative dimension of target object.
5. credit information processing method according to claim 1, which is characterized in that according to the mesh obtained from background server
The data to be evaluated for marking N number of evaluative dimension of object, generate the dimension evaluation of estimate of N number of evaluative dimension of target object
Including：
One or more evaluation index is respectively set to N number of evaluative dimension of target object；
According to the valence mumber to be evaluated of whole evaluation indexes under N number of evaluative dimension of the target object obtained from background server
According to obtaining the metrics evaluation value for each evaluation index being respectively set under N number of evaluative dimension of target object；
According to the metrics evaluation value for each evaluation index being respectively set under N number of evaluative dimension of target object, mesh is generated
Mark the dimension evaluation of estimate of N number of evaluative dimension of object.
6. credit information processing method according to claim 5, which is characterized in that according to the mesh obtained from background server
The data to be evaluated for marking whole evaluation indexes under N number of evaluative dimension of object obtain N number of evaluation dimension of target object
The metrics evaluation value for each evaluation index being respectively set under degree includes：
According to the standards of grading of the data to be evaluated and each evaluation index of pre-set target object, target pair is obtained
The metrics evaluation value for each evaluation index being respectively set under N number of evaluative dimension of elephant.
7. credit information processing method according to claim 5, which is characterized in that described according to target object N number of is commented
The metrics evaluation value for each evaluation index being respectively set under valence dimension generates the dimension of N number of evaluative dimension of target object
Evaluation of estimate is spent, including：
For each evaluation index that N number of evaluative dimension of target object is respectively set, one index weights is accordingly set；
According to the metrics evaluation value for each evaluation index being respectively set under N number of evaluative dimension of target object, and combine
The index weights of corresponding evaluation index, obtain the dimension evaluation of estimate of N number of evaluative dimension of target object.
8. credit information processing method according to claim 7, which is characterized in that described according to target object N number of is commented
The metrics evaluation value for each evaluation index being respectively set under valence dimension, and the index weights of corresponding evaluation index are combined, it obtains
The dimension evaluation of estimate of N number of evaluative dimension of target object, including：
To each evaluative dimension of N number of evaluative dimension of target object, each evaluation under the evaluative dimension is calculated separately
The product of the metrics evaluation value of index and corresponding index weights point, then sum to each product, will be obtained and be determined as
The dimension evaluation of estimate of corresponding evaluative dimension.
9. credit information processing method according to claim 1, which is characterized in that the N is the integer more than or equal to 5.
10. a kind of credit information processing unit, which is characterized in that including：
Evaluative dimension determining module, the dimension weight of N number of evaluative dimension and each evaluative dimension for determining target object,
N is the integer more than or equal to 2；
Dimension evaluation of estimate generation module, for according to the N number of evaluative dimension of target object obtained from background server
Data to be evaluated generate the dimension evaluation of estimate of N number of evaluative dimension of target object；
Comprehensive evaluation value generation module, for according to the dimension evaluation of estimate of N number of evaluative dimension of target object and each
The dimension weight of evaluative dimension generates the practical comprehensive evaluation value of target object.
11. credit information processing unit according to claim 10, which is characterized in that further include：Classification of assessment result is true
Cover half block obtains the practical overall merit classification results of target object for the practical comprehensive evaluation value according to target object.
12. credit information processing unit according to claim 10, which is characterized in that classification of assessment result determining module has
Body is used for the mapping table of the comprehensive evaluation value and overall merit classification according to pre-set target object, will be with the reality
Comprehensive evaluation value corresponding overall merit classification in border is determined as the practical overall merit classification results of target object.
13. credit information processing unit according to claim 10, which is characterized in that dimension evaluation of estimate generation module is specific
For according to the data to be evaluated of N number of evaluative dimension and the dimension evaluation of estimate standards of grading of pre-set target object,
Generate the dimension evaluation of estimate of N number of evaluative dimension of target object.
14. credit information processing unit according to claim 10, which is characterized in that dimension evaluation of estimate generation module packet
It includes：
One or more evaluation is respectively set for N number of evaluative dimension to target object in evaluation index setting unit
Index；
Metrics evaluation value acquiring unit, under N number of evaluative dimension according to the target object obtained from background server
The data to be evaluated of whole evaluation indexes obtain and evaluation each of is respectively set under N number of evaluative dimension of target object refers to
Target metrics evaluation value；
Dimension evaluation of estimate generation unit, each evaluation for being respectively set under N number of evaluative dimension according to target object
The metrics evaluation value of index generates the dimension evaluation of estimate of N number of evaluative dimension of target object.
15. credit information processing unit according to claim 14, which is characterized in that metrics evaluation value acquiring unit is specific
For the standards of grading according to the data to be evaluated and each evaluation index of pre-set target object, target pair is obtained
The metrics evaluation value for each evaluation index being respectively set under N number of evaluative dimension of elephant.
16. credit information processing unit according to claim 14, which is characterized in that dimension evaluation of estimate generation unit packet
It includes：
Subelement, each evaluation index for being respectively set for N number of evaluative dimension of target object is arranged in index weights
Accordingly one index weights of setting；
Dimension evaluation of estimate generates subelement, is commented for each of being respectively set under N number of evaluative dimension according to target object
The metrics evaluation value of valence index, and the index weights of corresponding evaluation index are combined, generate N number of evaluative dimension of target object
Dimension evaluation of estimate.
17. credit information processing unit according to claim 16, which is characterized in that dimension evaluation of estimate generates subelement tool
Body is used for each evaluative dimension to N number of evaluative dimension of target object, each of calculates separately under the evaluative dimension and to comment
The product of the metrics evaluation value of valence index and corresponding index weights point, then sum to each product, it will be obtained and determining
For the dimension evaluation of estimate of corresponding evaluative dimension.
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| CN110751403A (en) * | 2019-10-21 | 2020-02-04 | 中国民航信息网络股份有限公司 | Credit scoring method and device |
| CN112396430A (en) * | 2020-11-09 | 2021-02-23 | 中国南方电网有限责任公司 | Processing method and system for enterprise evaluation |
| CN114691152A (en) * | 2020-12-31 | 2022-07-01 | 中国联合网络通信集团有限公司 | Method and device for recommending data resource service application program interface |
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| CN109840847A (en) * | 2018-12-29 | 2019-06-04 | 航天信息股份有限公司 | One kind going core Supply chain financing method |
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