CN109726905A - A kind of method and system of enterprise value portrait evaluation - Google Patents
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Abstract
The present embodiments relate to a kind of method and systems of enterprise value portrait evaluation, which comprises establishes the value portrait evaluation model of enterprise；Obtain the data of the value portrait for evaluating the enterprise；The data are analyzed based on the evaluation model, determine the value image of the enterprise.From there through enterprise value portrait evaluation method, it can be by establishing medium-sized and small enterprises value portrait evaluation model, using reasonable evaluation method, it is capable of the state of development of objective evaluation medium-sized and small enterprises and its constructional aspect of place industry, scientifically medium-sized and small enterprises value portrait is evaluated, predicts future developing trend.
Description
Technical field
The present embodiments relate to artificial intelligence field more particularly to a kind of enterprise value portrait evaluation method and be
System.
Background technique
In recent years in investment activity, Target Enterprise, the i.e. value of invested enterprise usually like looking at flowers in a fog, enable investment
It is indefinite that person ponders, then, the investment decision link mostly important at investor to the value assessment of Target Enterprise.Value assessment
Refer to that both parties buy or sell the value judgement made to target (equity or assets).Investor passes through certain method meter
The value of Target Enterprise is calculated, to provide basis of price for whether transaction is feasible.In investment process, the appraisal to Target Enterprise is
Whether it is successfully crucial.In terms of investor's angle, the either investor of shareholder or Target Enterprise, it is desirable to transaction value
Be conducive to one's own side.Since two-sided information is grasped in insufficient or subjective understanding there are deviation, cannot be fixed a price by a side and
Force at other side.At this moment it just needs that intermediary is engaged to make value assessment from the angle of economic technology.
Come in terms of first against enterprise development scale and personnel amount two to enterprise carry out it is large-scale, medium-sized, small-sized with it is miniature
Enterprise classifying enterprise, it is different further according to the time is set up, distinguish establishment type enterprise, growing enterprise and adult form enterprise.And it is directed to
These three types of enterprises form three sets of appraisement systems, for enterprise investment value of testing and assessing.
Summary of the invention
In consideration of it, to solve above-mentioned technical problem or partial technical problems, the embodiment of the present invention provide a kind of enterprise value
The method and system of portrait evaluation.
In a first aspect, the application provides a kind of method of enterprise value portrait evaluation, comprising:
Establish the value portrait evaluation model of enterprise；Obtain the data of the value portrait for evaluating the enterprise；It is based on
The evaluation model analyzes the data, determines the value image of the enterprise.
In a possible embodiment, the value portrait evaluation model for establishing enterprise, comprising: be based on the enterprise
Target data and the corresponding target score of the target data, using default dimension carry out enterprise value portrait carry out study instruction
Practice, obtains value portrait evaluation model；Wherein, the target score is that expert beats the enterprise according to the target data
Point.
In a possible embodiment, the default dimension include at least following one: innovation and development, base power,
Business growth, management benefit, Project Benefit and credit risk.
It is described that the data are analyzed based on the evaluation model in a possible embodiment, described in determination
The value image of enterprise, comprising: the analysis that based on the value portrait evaluation model data are carried out with default dimension, according to
The evaluation information of each dimension determines the various dimensions value image of the enterprise.
In a possible embodiment, the method also includes: described in being crawled according to the information crawler of the enterprise
The public data of enterprise；It is described that the data are analyzed based on the evaluation model, comprising: to be based on the evaluation model pair
The data and the public data are analyzed.
Second aspect, this application provides a kind of enterprise value portrait evaluation systems, comprising: modeling module, for establishing
The value portrait evaluation model of enterprise；Module is obtained, for obtaining the data of the value portrait for evaluating the enterprise；Analysis
Module determines the value image of the enterprise for analyzing based on the evaluation model the data.
In a possible embodiment, the modeling module, specifically for based on the enterprise target data and institute
The corresponding target score of target data is stated, enterprise value portrait is carried out using default dimension and carries out learning training, obtains value picture
As evaluation model.
In a possible embodiment, the default dimension include at least following one: innovation and development, base power,
Business growth, management benefit, Project Benefit and credit risk.
In a possible embodiment, the analysis module is specifically used for evaluation model pair of drawing a portrait based on the value
The data carry out the analysis of default dimension, determine that the various dimensions of the enterprise are worth image according to the evaluation information of each dimension.
In a possible embodiment, the acquisition module is also used to be crawled according to the information crawler of the enterprise
The public data of the enterprise；The analysis module is specifically used for based on the evaluation model to the data and the disclosure
Data are analyzed.
The method and system of enterprise value portrait evaluation provided in an embodiment of the present invention is worth picture by establishing medium-sized and small enterprises
As evaluation model is capable of the state of development of objective evaluation medium-sized and small enterprises and its knot of place industry using reasonable evaluation method
Structure situation scientifically evaluates medium-sized and small enterprises value portrait, predicts future developing trend.
Enterprise value portrait can help government administration section to obtain more comprehensive enterprise operation Information of Development, to administration
Enterprise's development has accurate assurance in area, so as to reasonably formulate sme development support policy, carries out
Reasonable industrial pattern.
Enterprise value is drawn a portrait to the holographic portraits of medium-sized and small enterprises so that financial institution can be more accurately to medium-sized and small enterprises
Growing potential make assessment, more good investment target is quickly filtered out from a large amount of medium-sized and small enterprises, by further examining
Lock onto target after core preferably guarantees investment return, prevention and control risk.Enterprise value portrait enables financial institution from a large amount of low
It frees in benefit repetitive operation and is engaged in the work of high benefit value, promote its business fast-developing.
Enterprise value portrait evaluates enterprise value portrait by the analysis of six big dimensions, assists enterprise to find out hiding
In the main problem of deep layer, so that it carries out specific aim improvement.Be conducive to enterprise to mark industry overall development status, specify itself
Locating competitive position has the developing state of current and future and clearly recognizes, and is conducive to enterprise and makes reasonable Developing Decision.
Meanwhile approval of the evaluation result of enterprise value portrait by financial institution and government garden, enterprise can be helped to finance
The policy for expanding and seeking garden and government administration section is helped.
Detailed description of the invention
Fig. 1 is a kind of flow chart of the evaluation method of enterprise value portrait provided in an embodiment of the present invention；
Fig. 2 is basic model schematic diagram provided in an embodiment of the present invention；
Fig. 3 is the GBRT fit procedure schematic diagram in basic model provided in an embodiment of the present invention；
Fig. 4 is integrated study module diagram provided in an embodiment of the present invention；
Fig. 5 is semi-supervised learning module diagram provided in an embodiment of the present invention；
Fig. 6 is a kind of structural schematic diagram of the evaluation system of enterprise value portrait provided in an embodiment of the present invention.
Specific embodiment
In order to make the object, technical scheme and advantages of the embodiment of the invention clearer, below in conjunction with the embodiment of the present invention
In attached drawing, technical scheme in the embodiment of the invention is clearly and completely described, it is clear that described embodiment is
A part of the embodiment of the present invention, instead of all the embodiments.Based on the embodiments of the present invention, those of ordinary skill in the art
Every other embodiment obtained without making creative work, shall fall within the protection scope of the present invention.
In order to facilitate understanding of embodiments of the present invention, it is further explained below in conjunction with attached drawing with specific embodiment
Illustrate, does not constitute the restriction to the embodiment of the present invention.
Fig. 1 is the embodiment of the present invention, a kind of enterprise value portrait evaluation method flow diagram provided, as shown in Figure 1,
This method specifically includes:
S101, the value portrait evaluation model for establishing enterprise.
Establish the value portrait evaluation model of enterprise, comprising:
The corresponding target score of target data and target data based on enterprise carries out enterprise value picture using default dimension
As carrying out learning training, value portrait evaluation model is obtained；Wherein, target score is that expert beats enterprise according to target data
Point.
Default dimension includes at least following one: innovation and development, base power, business growth, management benefit, Project Benefit
And credit risk.
Wherein, the specific code of points of default dimension provided in this embodiment is as follows:
The code of points of innovation and development:
Pass through the patent of invention situation to enterprise, new variety of plant, national variety of crops, national new drug, country one
Grade Chinese medicine protects kind, and integrated circuit G- Design patent rights, new patent, design patent, software copyright and works write
Make power to be counted.One type patent gets a mark of 100；It only obtains 2 class patents and obtains 50 points；One kind is obtained with 2 classes, is got a mark of 100.
The code of points of base power:
By to enterprises registration capital, the ratio between paid-up capital and registered capital, affiliated industry, industry administrative examination and approval and qualification
Certificate is counted.Wherein large enterprise's registered capital is higher than the 20% of income from sales, and score 10 is divided, lower than 20%, score 6
Point.The registered capital of medium-sized and small enterprises is lower than 20%, 50%, 80%, 100% in the specific gravity of income from sales, is higher than 100%, obtains
Dividing is 2,4,8,6,6 points respectively.Minuscule-type-enterprise is higher than its income from sales and obtains 10 points, obtains 5 points lower than income from sales.Paid-up capital
The ratio between with registered capital: being equal to 1, obtain 30 points, less than 1, obtain 15 points.
The code of points of business growth:
To the TN phase total assets of enterprise, TN phase owner's equity total value, TN-1 phase total assets, the TN-1 phase owner
Total equity, the TN phase, the TN phase was relative to TN-1 phase total liabilities change rate, TN phase phase relative to TN-1 phase total assets change rate
TN-1 phase owner's equity change rate is counted and calculated.Standards of grading: it could be located in confirmation in systems.
The code of points of management benefit:
Whether the management level of enterprise are participated in assigning, the de facto control of target enterprise is caused to shift, trade mark letter and
Website, which is put on record, to be counted.Standards of grading: whether management level, which participate in, is assigned, and is, then obtains 20 points, no is 0 point；Target is caused to be looked forward to
The de facto control of industry shifts, and is then to obtain 10 points, otherwise obtains 0 point；Trademark information is then to obtain 40 points, otherwise obtains 0 point；Net
Station is put on record, is then to obtain 30 points, is otherwise obtained 0 point.
The code of points of Project Benefit:
By the TN phase total assets to enterprise, the TN phase is responsible for total value, TN phase owner's equity total value, and the TN phase does business receipts
Enter, TN phase operating profit, TN phase net profit, TN net profit on sales rate, TN Return on Assets, TN stockholder's equity net profit margin, the TN-1 phase
Total assets, TN-1 phase are responsible for total value, TN-1 phase owner's equity total value, TN-1 phase operating income, TN-1 phase operating profit,
TN-1 phase net profit, TN-1 net profit on sales rate, TN-1 Return on Assets, TN-1 stockholder's equity net profit margin, TN-2 phase assets are total
Volume, TN-2 phase are responsible for total value, TN-2 phase owner's equity total value, TN-2 phase operating income, TN-2 phase operating profit, and the TN-2 phase is net
Profit, TN-2 net profit on sales rate, TN-2 Return on Assets, TN-2 stockholder's equity net profit margin are counted and are calculated.Scoring mark
It is quasi-: confirmation could be located in systems.
Credit scoring rule:
To the executed person of enterprise, information of breaking one's promise, administrative penalty is broken the law on a serious scale, tax arrear bulletin, manages exception, and the administration of justice is clapped
It sells, tax grading and asset-liability ratio are counted and calculated.Code of points: asset-liability ratio.Nothing obtains 20 points, less than 30%,
18 points are obtained, less than 50%, 12 points is obtained, less than 65%, obtains 8 points, is greater than 65%, obtains 0 point.Tax grading.A grades obtain 5 points, and B grades obtain
3 points, C grades obtain 1 point, and off grade obtains 2 points.
All kinds of specific targets of project investment value assessment mainly in numerous information announcing elements, choose investment in enterprise valence
Value evaluation has the index of correlation, by the marking reported situations to index, completes the evaluation to project.Entire evaluation is altogether
700 points, six of them dimension, each 100 points, in addition basis point, 100 points.
Above-mentioned six default dimensions are first class index, include one group of two-level index under each first class index, obtain enterprise
All two-level index quantized datas, for can not actually obtain the index of quantized data, using the average value of all future trouble enterprises
Quantized data as the index.
The two-level index that innovation and development includes: registered capital, scientific research personnel's number, scientific research personnel's average operation time limit, undergraduate course
Above scientific research personnel's accounting, the above employee's ratio of junior college, patent hold several and scientific research cooperative.
The two-level index that base power includes: current year liquidity ratio, current year total assets, current year asset load
Rate, position in industry, occupation rate of market and paid-up capital.
The two-level index that business growth includes: whether new high-tech enterprise, there is listing in core technology source in rival
Company, if listed company is holding, and the current year puts into R & D Cost, the credit extending channel of industry where enterprise.
The two-level index that management benefit includes: management level number, management level average age, management level highest educational background, 30~
40 years old employee's ratios, 30 years old or less employee's ratio and employee's sum.
The two-level index that Project Benefit includes: current year main business income, succeeding year main business income are also surveyed, market
Occupation rate, current year income from main operation rate, the item number of succeeding year net assets and research and development.
The two-level index that credit risk includes: whether enterprise occurred serious accident for nearly 1 year, and enterprise nearly 1 year is
No that severe environments illegal activities occurred, whether enterprise occurred great quality accident, current year asset-liability ratio, sheet for nearly 1 year
Annual liquidity ratio and prior year year income from main operation rate.
It can be basic model as shown in Figure 2 in the value portrait evaluation model that the present embodiment is related to:
Basic model uses GBRT algorithm, and wherein CRBT algorithm expression formula is as follows:
Fm(x)=Fm-1(x)+ρmh(x；am)。
The basic principle of GBRT is similar with common Boosting algorithm, based on the simple regression tree model of structure,
According to the fitting result of last round of model, constructs new regression tree and model is added, correct the error of last round of model, repeatedly changing
Dai Hou obtains a high-precision final mask by combining multiple regression tree models.But GBRT and common Boosting is calculated
The difference of method maximum is that the calculating of GBRT each time is to reduce last residual error, gradient direction of the meeting in residual error reduction
On establish a new model.In Gradient Boost, each new model is in order to enable the residual error of model is past before
Gradient direction is reduced, and the way for increasing weighting with sample of the Boosting algorithm to mistake has very big difference.Do so phase
The advantage of traditional Boosting algorithm is that the efficiency of processing data can be obviously improved and effectively prevent over-fitting, to different
Often the resistance of point or extremum is stronger.
Further, GBRT fit procedure by combining multiple simple regression trees of structure as shown in figure 3, obtained more preferable
Fitting effect.
S102, the data that the value for evaluating enterprise is drawn a portrait are obtained.
The public data of S103, the enterprise crawled according to the information crawler of enterprise.
The data for obtaining the value portrait for evaluating enterprise can be the data that enterprise reports, such as financial statement.
Enterprise's public data that crawler crawls is the relevant data of enterprise's public sentiment, including news, microblogging, forum, blog are more
Channel sociological intelligence.
S104, the data and the public data are analyzed based on the evaluation model, determines the value of enterprise
Portrait.
Enterprise value draws a portrait model from innovation and development, base power, business growth, management benefit, Project Benefit, credit wind
Danger sets out enterprise is analyzed and evaluated, and determines the value portrait of enterprise, wherein be worth portrait avatar can there are many,
For example, the forms such as score value, curve, table, histogram.
The evaluation method it should be noted that enterprise value provided in this embodiment is drawn a portrait, can also pass through integrated study mould
Block and semi-supervised learning module auxiliary foundation model, and then realize enterprise value portrait evaluation.
Integrated analysis module may be simultaneously present multiple models as shown in figure 4, in the case where current training sample is less
Performance on training set is close, as the basic model GBRT selected by us, super ginseng that can be different by setting
Number, obtains GBRT model of multiple performances without significant difference, only chooses the model put up the best performance on training set in actual use
And it is clearly risky to eliminate other models, it is possible to show relatively poor model for actually using faced number
According to better prediction effect can be obtained instead.Specifically, in terms of statistics, since the hypothesis space of learning tasks is past
Toward that very greatly, may there are multiple hypothesis to reach equal performance on training set, if may be led at this time because falsely dropping using single learner
It causes Generalization Capability bad, can then reduce this risk in conjunction with multiple learners；In terms of calculating, learning algorithm is often
Local minimum is fallen into, Generalization Capability corresponding to some local minimum points may be very bad, and can reduce by associative learning device
Fall into the risk of local minimum point.
In terms of expression, vacation that the true hypothesis of certain learning tasks may not be considered in current learning algorithm
If then certainly invalid using single learner at this time in space, and by combining multiple learners, due to assuming that space has accordingly
Expanded, it is possible to which learn better approximation.Therefore, suitable combination strategy can be chosen, multiple models is combined, reduces wind
Danger, the stability of lift scheme entirety.And Stacking is then a kind of powerful Ensemble Learning Algorithms, can be used for combining multiple
GBRT model.
Stacking algorithm first goes out primary learner from initial training concentration training, then " generates " a new data set
For the secondary learner of training, in this new data set, the output of primary learner is taken as sample input feature vector, and initial
The label of sample is still taken as sample to mark.For enterprise value portrait model, we choose GBRT as primary learner, choosing
Take MLR (Multiple Linear Regression) as secondary model, the output of comprehensive multiple GBRT obtain it is final at
Long value divides prediction result.
Semi-supervised learning module as shown in figure 5, with the enterprise for uploading data increase, the quantity of unmarked sample is inevitable
It will be much larger than the quantity of the marked sample by expert estimation, by the information that is included using unmarked sample rather than only
Marked sample training pattern is relied on, is capable of the Generalization Capability of further lift scheme, for example, although unmarked sample is not direct
Comprising mark information, but if they and marked sample are from same Data Source Independent with profile samples, then they
The information about data distribution for being included obviously contributes to establish model.Enterprise value portrait model uses semi-supervised
Learning algorithm is Tri-training, belongs to the method based on disagreement, using multiple learners, by between multiple learners
Disagreement utilizes Unlabeled data, since enterprise value portrait model will train multiple learners in integrated study module, because
This Tri-training after sample size is enough will be embedded in well in enterprise value portrait model.
Co-training is initially for " multiple view " (multi-view) design data.In many practical applications, one
A data object often possesses multiple property sets simultaneously, and each property set just constitutes a view.Co-training is initial
Foundation is the compatible complementarity using multiple view, it is assumed that data have multiple abundant and conditional sampling views, can use a letter
Single method utilizes Unlabeled data: a classifier is respectively trained out based on marked sample first on each view, so
After allow each classifier to select oneself " most safe " unmarked sample respectively to assign pseudo- label, and pseudo- marker samples are provided
To other classifiers as newly-increased marked sample for training renewal learning device, then iteration carries out this process.Although side
Method is simple, but existing theoretical proof, if view sufficiently and conditional sampling, passes through Co-training using unmarked sample
The Generalization Capability of Weak Classifier is promoted to any height.Also, in the ungratified situation of conditional independence, pass through Co-
Training, performance still can have a degree of promotion, in this embodiment it is not even necessary to which data possess multiple view, only need between base learner
There are significant disagreements (or difference) can pass through Co-training improving performance.
It, can be small in objective evaluation using reasonable evaluation method by establishing medium-sized and small enterprises value portrait evaluation model
The state of development of enterprise and its constructional aspect of place industry scientifically evaluate medium-sized and small enterprises value portrait, and prediction is not
Carry out development trend.
Enterprise value portrait can help government administration section to obtain more comprehensive enterprise operation Information of Development, to administration
Enterprise's development has accurate assurance in area, so as to reasonably formulate sme development support policy, carries out
Reasonable industrial pattern.
Enterprise value is drawn a portrait to the holographic portraits of medium-sized and small enterprises so that financial institution can be more accurately to medium-sized and small enterprises
Growing potential make assessment, more good investment target is quickly filtered out from a large amount of medium-sized and small enterprises, by further examining
Lock onto target after core preferably guarantees investment return, prevention and control risk.Enterprise value portrait enables financial institution from a large amount of low
It frees in benefit repetitive operation and is engaged in the work of high benefit value, promote its business fast-developing.
Enterprise value portrait evaluates enterprise value portrait by the analysis of six big dimensions, assists enterprise to find out hiding
In the main problem of deep layer, so that it carries out specific aim improvement.Be conducive to enterprise to mark industry overall development status, specify itself
Locating competitive position has the developing state of current and future and clearly recognizes, and is conducive to enterprise and makes reasonable Developing Decision.
Meanwhile approval of the evaluation result of enterprise value portrait by financial institution and government garden, enterprise can be helped to finance
The policy for expanding and seeking garden and government administration section is helped.
Fig. 6 is a kind of structural schematic diagram of the evaluation system of enterprise value portrait provided in an embodiment of the present invention, such as Fig. 6 institute
Show, which includes:
Modeling module 601, for establishing the value portrait evaluation model of enterprise；
Module 602 is obtained, for obtaining the data of the value portrait for evaluating the enterprise；
Analysis module 603 determines the value of the enterprise for analyzing based on the evaluation model the data
Image.
Optionally, the modeling module 601, specifically for target data and the target data pair based on the enterprise
The target score answered carries out enterprise value portrait using default dimension and carries out learning training, obtains value portrait evaluation model.
Optionally, the default dimension includes at least following one: innovation and development, base power, business growth, management effect
Benefit, Project Benefit and credit risk.
Optionally, the analysis module 603 is specifically used for carrying out the data based on value portrait evaluation model
The analysis of default dimension determines that the various dimensions of the enterprise are worth image according to the evaluation information of each dimension.
Optionally, the acquisition module 601, the public affairs for the enterprise for being also used to be crawled according to the information crawler of the enterprise
Open data；
The analysis module 603 is specifically used for carrying out the data and the public data based on the evaluation model
Analysis.
The evaluation system of enterprise value portrait provided in this embodiment can be enterprise value portrait as shown in Figure 6
Evaluation system can be performed all steps in the evaluation method of enterprise value portrait as shown in figure 1, and then realize enterprise shown in Fig. 1
It is worth the technical effect of the evaluation method of portrait, specifically please refers to Fig. 1 associated description, for succinct description, therefore not to repeat here.
Professional should further appreciate that, described in conjunction with the examples disclosed in the embodiments of the present disclosure
Unit and algorithm steps, can be realized with electronic hardware, computer software, or a combination of the two, hard in order to clearly demonstrate
The interchangeability of part and software generally describes each exemplary composition and step according to function in the above description.
These functions are implemented in hardware or software actually, the specific application and design constraint depending on technical solution.
Professional technician can use different methods to achieve the described function each specific application, but this realization
It should not be considered as beyond the scope of the present invention.
The step of method described in conjunction with the examples disclosed in this document or algorithm, can be executed with hardware, processor
The combination of software module or the two is implemented.Software module can be placed in random access memory (RAM), memory, read-only memory
(ROM), electrically programmable ROM, electrically erasable ROM, register, hard disk, moveable magnetic disc, CD-ROM or technical field
In any other form of storage medium well known to interior.
Above-described specific embodiment has carried out further the purpose of the present invention, technical scheme and beneficial effects
It is described in detail, it should be understood that being not intended to limit the present invention the foregoing is merely a specific embodiment of the invention
Protection scope, all within the spirits and principles of the present invention, any modification, equivalent substitution, improvement and etc. done should all include
Within protection scope of the present invention.
Claims (10)
- The evaluation method 1. a kind of enterprise value is drawn a portrait characterized by comprisingEstablish the value portrait evaluation model of enterprise；Obtain the data of the value portrait for evaluating the enterprise；The data are analyzed based on the evaluation model, determine the value image of the enterprise.
- 2. the method according to claim 1, wherein the value portrait evaluation model for establishing enterprise, comprising:Target data and the corresponding target score of the target data based on the enterprise carry out enterprise's valence using default dimension Value portrait carries out learning training, obtains value portrait evaluation model；Wherein, the target score is marking of the expert according to the target data to the enterprise.
- 3. according to the method described in claim 2, it is characterized in that, the default dimension includes at least following one:Innovation and development, base power, business growth, management benefit, Project Benefit and credit risk.
- 4. according to the method described in claim 3, it is characterized in that, described divide the data based on the evaluation model Analysis, determines the value image of the enterprise, comprising:The analysis that based on the value portrait evaluation model data are carried out with default dimension, according to the evaluation information of each dimension Determine the various dimensions value image of the enterprise.
- 5. the method according to claim 1, wherein the method also includes:According to the public data for the enterprise that the information crawler of the enterprise crawls；It is described that the data are analyzed based on the evaluation model, comprising:The data and the public data are analyzed based on the evaluation model.
- The evaluation system 6. a kind of enterprise value is drawn a portrait characterized by comprisingModeling module, for establishing the value portrait evaluation model of enterprise；Module is obtained, for obtaining the data of the value portrait for evaluating the enterprise；Analysis module determines the value image of the enterprise for analyzing based on the evaluation model the data.
- 7. system according to claim 6, which is characterized in that the modeling module, specifically for based on the enterprise Target data and the corresponding target score of the target data carry out enterprise value portrait using default dimension and carry out study instruction Practice, obtains value portrait evaluation model；Wherein, the target score is marking of the expert according to the target data to the enterprise.
- 8. system according to claim 7, which is characterized in that the default dimension includes at least following one:Innovation and development, base power, business growth, management benefit, Project Benefit and credit risk.
- 9. system according to claim 8, which is characterized in that the analysis module is specifically used for being based on the value picture As evaluation model carries out the analysis of default dimension to the data, the multidimensional of the enterprise is determined according to the evaluation information of each dimension Degree value image.
- 10. system according to claim 9, which is characterized in that the acquisition module is also used to the letter according to the enterprise The public data for the enterprise that breath crawler crawls；The analysis module, specifically for being analyzed based on the evaluation model the data and the public data.
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Cited By (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110599041A (en) * | 2019-09-16 | 2019-12-20 | 未来地图(深圳)智能科技有限公司 | Enterprise target management method, cloud and system based on artificial intelligence technology |
| CN110675011A (en) * | 2019-06-21 | 2020-01-10 | 广东电网物资有限公司 | Imaging method and use method of supplier portrait of material equipment in power industry |
| CN111144778A (en) * | 2019-12-30 | 2020-05-12 | 智慧神州(北京)科技有限公司 | Management and evaluation method and system based on data account |
| CN111861262A (en) * | 2020-07-30 | 2020-10-30 | 国网山东省电力公司寿光市供电公司 | Enterprise perspective portrait method and terminal based on energy big data |
| CN112101732A (en) * | 2020-08-18 | 2020-12-18 | 北京大学 | Enterprise ecological efficiency evaluation method based on GEP index system |
| CN112148760A (en) * | 2020-10-10 | 2020-12-29 | 北京火眼神算数据科技有限公司 | Big data screening method and device |
| CN112434158A (en) * | 2020-11-13 | 2021-03-02 | 北京创业光荣信息科技有限责任公司 | Enterprise label acquisition method and device, storage medium and computer equipment |
| CN113723737A (en) * | 2021-05-11 | 2021-11-30 | 天元大数据信用管理有限公司 | Enterprise portrait-based policy matching method, device, equipment and medium |
| CN114140036A (en) * | 2022-02-08 | 2022-03-04 | 北京融信数联科技有限公司 | Enterprise portrait method, system and readable storage medium based on data analysis |
| CN115827934A (en) * | 2023-02-21 | 2023-03-21 | 四川省计算机研究院 | Enterprise portrait intelligent analysis system and method based on unified social credit code |
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| CN110675011A (en) * | 2019-06-21 | 2020-01-10 | 广东电网物资有限公司 | Imaging method and use method of supplier portrait of material equipment in power industry |
| CN110599041A (en) * | 2019-09-16 | 2019-12-20 | 未来地图(深圳)智能科技有限公司 | Enterprise target management method, cloud and system based on artificial intelligence technology |
| CN111144778A (en) * | 2019-12-30 | 2020-05-12 | 智慧神州(北京)科技有限公司 | Management and evaluation method and system based on data account |
| CN111861262A (en) * | 2020-07-30 | 2020-10-30 | 国网山东省电力公司寿光市供电公司 | Enterprise perspective portrait method and terminal based on energy big data |
| CN112101732B (en) * | 2020-08-18 | 2022-10-11 | 北京大学 | Enterprise ecological efficiency evaluation method based on GEP index system |
| CN112101732A (en) * | 2020-08-18 | 2020-12-18 | 北京大学 | Enterprise ecological efficiency evaluation method based on GEP index system |
| CN112148760A (en) * | 2020-10-10 | 2020-12-29 | 北京火眼神算数据科技有限公司 | Big data screening method and device |
| CN112434158A (en) * | 2020-11-13 | 2021-03-02 | 北京创业光荣信息科技有限责任公司 | Enterprise label acquisition method and device, storage medium and computer equipment |
| CN112434158B (en) * | 2020-11-13 | 2024-05-28 | 海创汇科技创业发展股份有限公司 | Enterprise tag acquisition method, enterprise tag acquisition device, storage medium and computer equipment |
| CN113723737A (en) * | 2021-05-11 | 2021-11-30 | 天元大数据信用管理有限公司 | Enterprise portrait-based policy matching method, device, equipment and medium |
| WO2023087398A1 (en) * | 2021-11-19 | 2023-05-25 | 南方科技大学 | Quantitative evaluation method for science and technology enterprise, intelligent terminal, and storage medium |
| CN114140036A (en) * | 2022-02-08 | 2022-03-04 | 北京融信数联科技有限公司 | Enterprise portrait method, system and readable storage medium based on data analysis |
| CN115827934A (en) * | 2023-02-21 | 2023-03-21 | 四川省计算机研究院 | Enterprise portrait intelligent analysis system and method based on unified social credit code |
| CN115827934B (en) * | 2023-02-21 | 2023-05-09 | 四川省计算机研究院 | Enterprise portrait intelligent analysis system and method based on unified social credit code |
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