CN109359901A - Method and device, medium and electronic equipment are determined based on the business risk of block chain - Google Patents
Method and device, medium and electronic equipment are determined based on the business risk of block chain Download PDFInfo
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Abstract
The invention discloses a kind of business risks based on block chain to determine method and device, medium and electronic equipment, is related to block chain technical field.The business risk based on block chain determines that method includes: to store multiple business risk results and venture influence information corresponding with each business risk result by block chain network；One machine learning model is trained using the multiple business risk results and venture influence information corresponding with each business risk result of block chain network storage；If detecting enterprise's related information of the first enterprise of typing in the block chain network, in the machine learning model after enterprise's related information input is trained, with the business risk result corresponding with enterprise's related information of determination first enterprise.The disclosure can determine risk existing for enterprise.
Description
Technical field
This disclosure relates to block chain technical field, in particular to a kind of business risk determination side based on block chain
Method, business risk determining device, storage medium and electronic equipment based on block chain.
Background technique
With the development of economy, more and more enterprises have been emerged.Enterprise is during operation, due to internal factor
It can change with the management style of the effect of external factor, enterprise.Wherein, variation of the internal factor for example including personnel, money
Gold abnormality etc., variation, the operation exception of upstream and downstream firms etc. of the external factor for example including the market demand.
In order to which enterprise smoothly can operate and get a promotion, need to assess enterprise's potential risks.Currently, enterprise
During risk assessment, need artificially to analyze the factor that may threaten enterprise development, and obtain risk control
Conclusion.
However, artificially when assessing risk, in fact it could happen that factor considers the problems such as incomplete, subjective judgement, causes
Risk judgment inaccuracy.
It should be noted that information is only used for reinforcing the reason to the background of the disclosure disclosed in above-mentioned background technology part
Solution, therefore may include the information not constituted to the prior art known to persons of ordinary skill in the art.
Summary of the invention
A kind of business risk based on block chain of being designed to provide of the disclosure determines method, the enterprise based on block chain
Risk determining device, storage medium and electronic equipment, and then risk judgment inaccuracy is overcome the problems, such as at least to a certain extent.
According to one aspect of the disclosure, a kind of business risk based on block chain is provided and determines method, comprising: passes through area
Block chain network stores multiple business risk results and venture influence information corresponding with each business risk result；Using institute
State the multiple business risk results and venture influence information corresponding with each business risk result of the storage of block chain network
One machine learning model is trained；If detecting enterprise's association letter of the first enterprise of typing in the block chain network
Breath, then by the machine learning model after enterprise's related information input training, with determination first enterprise with it is described
The corresponding business risk result of enterprise's related information.
In a kind of exemplary embodiment of the disclosure, the business risk determines method further include: determining with described the
Relevant second enterprise of one enterprise；The business risk of second enterprise is determined according to the business risk result of first enterprise
As a result.
In a kind of exemplary embodiment of the disclosure, described is determined according to the business risk result of first enterprise
The business risk result of two enterprises comprises determining that the degree of correlation between first enterprise and second enterprise；Based on described
The degree of correlation and the business risk result of first enterprise determine the business risk result of second enterprise.
In a kind of exemplary embodiment of the disclosure, the business risk knot based on the degree of correlation and first enterprise
Fruit determines that the business risk result of second enterprise includes: to judge whether the degree of correlation meets default degree of correlation requirement；Such as
The degree of correlation described in fruit meets default degree of correlation requirement, then the business risk result based on the degree of correlation and first enterprise is true
The business risk result of fixed second enterprise.
In a kind of exemplary embodiment of the disclosure, enterprise's related information of first enterprise is rendered as image；
It wherein, include: that image recognition is carried out to described image by the machine learning model after enterprise's related information input training；It will
The machine learning model after the input training of enterprise's related information after image recognition.
In a kind of exemplary embodiment of the disclosure, the business risk result is characterized as being business risk grade；Its
In, storing multiple business risk results by block chain network includes: the enterprise's wind obtained based on business risk event determination
Dangerous grade；The business risk grade is stored to block chain network.
In a kind of exemplary embodiment of the disclosure, the business risk grade is divided into positive risk class and bears
To risk class, if it is determined that the business risk result for going out first enterprise is negative to risk class, then the business risk
Determine method further include: send a warning message to first enterprise and/or enterprise relevant to first enterprise.
According to one aspect of the disclosure, a kind of business risk determining device based on block chain is provided, including information is deposited
Store up module, model training module and the first risk determining module.
Specifically, information storage module be used to store by block chain network multiple business risk results and with it is each described
The corresponding venture influence information of business risk result；Model training module is used for multiple enterprises using block chain network storage
Industry Risk Results and venture influence information corresponding with each business risk result are trained a machine learning model；
It, will if the first risk determining module is used to detect enterprise's related information of the first enterprise of typing in the block chain network
In machine learning model after enterprise's related information input training, with being associated with the enterprise for determination first enterprise
The corresponding business risk result of information.
In a kind of exemplary embodiment of the disclosure, business risk determining device further includes enterprise's determining module and second
Risk determining module.
Specifically, enterprise's determining module is for determining the second enterprise relevant to first enterprise；Second risk determines
Module is used to determine the business risk result of second enterprise according to the business risk result of first enterprise.
In a kind of exemplary embodiment of the disclosure, the second risk determining module is configured as: determining described
The degree of correlation between one enterprise and second enterprise；Business risk result based on the degree of correlation and first enterprise is true
The business risk result of fixed second enterprise.
In a kind of exemplary embodiment of the disclosure, the second risk determining module is configured as: judging the phase
Whether Guan Du meets default degree of correlation requirement；If the degree of correlation meets default degree of correlation requirement, it is based on the degree of correlation
The business risk result of second enterprise is determined with the business risk result of first enterprise.
In a kind of exemplary embodiment of the disclosure, enterprise's related information of first enterprise is rendered as image；
Wherein, the first risk determining module is configured as: carrying out image recognition to described image；Enterprise after image recognition is associated with letter
Machine learning model after breath input training.
In a kind of exemplary embodiment of the disclosure, the business risk result is characterized as being business risk grade；Its
In, information storage module is configured as: obtaining based on business risk event the business risk grade of determination；By enterprise's wind
Dangerous grade is stored to block chain network.
In a kind of exemplary embodiment of the disclosure, the business risk grade is divided into positive risk class and bears
To risk class；Wherein, business risk determining device further includes alarm sending module.
Specifically, alarm sending module is used to send out to first enterprise and/or enterprise relevant to first enterprise
Send warning information.
According to one aspect of the disclosure, a kind of storage medium is provided, computer program, the computer are stored thereon with
The business risk determination side described in above-mentioned any one exemplary embodiment based on block chain is realized when program is executed by processor
Method.
According to one aspect of the disclosure, a kind of electronic equipment is provided, comprising: processor；And memory, for storing
The executable instruction of the processor；Wherein, the processor is configured to above-mentioned to execute via the executable instruction is executed
Business risk described in any one exemplary embodiment based on block chain determines method.
In the technical solution provided by some embodiments of the present disclosure, multiple business risks are stored by block chain network
As a result and venture influence information corresponding with each business risk result, and machine learning model is instructed using these information
Practice, if detecting enterprise's related information of the first enterprise of typing in block chain network, enterprise's related information is inputted and is trained
In machine learning model afterwards, to determine the business risk result corresponding with enterprise's related information of the first enterprise.One side
Face, the scheme based on the disclosure can effectively determine out the risk of enterprise, avoid artificially assess to risk leading to risk
Judge the problem of inaccuracy；On the other hand, the disclosure in block chain network by storing business risk result and corresponding
Venture influence information makes it possible to guarantee by block chain network that these information can not distort, and can be based on block chain
The traceable processing of network stored to realize these information, and then effectively ensure that the safety of information is shared；In another aspect, this public affairs
Business risk can be carried out based on the business risk result and corresponding venture influence information stored in block chain network by opening
Judgement, peomotes effective popularization of the block chain technical application in terms of business risk prediction.
It should be understood that above general description and following detailed description be only it is exemplary and explanatory, not
The disclosure can be limited.
Detailed description of the invention
The drawings herein are incorporated into the specification and forms part of this specification, and shows the implementation for meeting the disclosure
Example, and together with specification for explaining the principles of this disclosure.It should be evident that the accompanying drawings in the following description is only the disclosure
Some embodiments for those of ordinary skill in the art without creative efforts, can also basis
These attached drawings obtain other attached drawings.In the accompanying drawings:
Fig. 1 diagrammatically illustrates the business risk determination side based on block chain according to an exemplary embodiment of the present disclosure
The flow chart of method；
Fig. 2 diagrammatically illustrates the business risk based on block chain according to an exemplary embodiment of the present disclosure and determines system
The block diagram of system；
Fig. 3 diagrammatically illustrates the business risk based on block chain according to an exemplary embodiment of the present disclosure and determines dress
The block diagram set；
Fig. 4 diagrammatically illustrates true according to the business risk based on block chain of the another exemplary embodiment of the disclosure
Determine the block diagram of device；
Fig. 5 diagrammatically illustrates true according to the business risk based on block chain of another illustrative embodiments of the disclosure
Determine the block diagram of device；
Fig. 6 shows the schematic diagram of storage medium according to an exemplary embodiment of the present disclosure；And
Fig. 7 diagrammatically illustrates the block diagram of electronic equipment according to an exemplary embodiment of the present disclosure.
Specific embodiment
Example embodiment is described more fully with reference to the drawings.However, example embodiment can be with a variety of shapes
Formula is implemented, and is not understood as limited to example set forth herein；On the contrary, thesing embodiments are provided so that the disclosure will more
Fully and completely, and by the design of example embodiment comprehensively it is communicated to those skilled in the art.Described feature, knot
Structure or characteristic can be incorporated in any suitable manner in one or more embodiments.In the following description, it provides perhaps
More details fully understand embodiment of the present disclosure to provide.It will be appreciated, however, by one skilled in the art that can
It is omitted with technical solution of the disclosure one or more in the specific detail, or others side can be used
Method, constituent element, device, step etc..In other cases, be not shown in detail or describe known solution to avoid a presumptuous guest usurps the role of the host and
So that all aspects of this disclosure thicken.
In addition, attached drawing is only the schematic illustrations of the disclosure, it is not necessarily drawn to scale.Identical attached drawing mark in figure
Note indicates same or similar part, thus will omit repetition thereof.Some block diagrams shown in the drawings are function
Energy entity, not necessarily must be corresponding with physically or logically independent entity.These function can be realized using software form
Energy entity, or these functional entitys are realized in one or more hardware modules or integrated circuit, or at heterogeneous networks and/or place
These functional entitys are realized in reason device device and/or microcontroller device.
Flow chart shown in the drawings is merely illustrative, it is not necessary to including all steps.For example, the step of having
It can also decompose, and the step of having can merge or part merges, therefore the sequence actually executed is possible to according to the actual situation
Change.
Business risk described below based on block chain determines that method can be realized based on server, in such case
Under, the business risk determining device of the disclosure can be only fitted in the server.However, the business risk of the disclosure determines method
It can also be realized by terminal device, not do particular determination in this illustrative embodiment to this.It should be noted that being made below
Term " first ", " second " are merely to the purpose distinguished, and should not be used as the limitation of the disclosure.
The business risk based on block chain that Fig. 1 diagrammatically illustrates the illustrative embodiments of the disclosure determines method
Flow chart.With reference to Fig. 1, the business risk based on block chain determines that method may comprise steps of:
S12. multiple business risk results and corresponding with each business risk result are stored by block chain network
Venture influence information.
In the illustrative embodiments of the disclosure, venture influence information can be the information for influencing enterprise operation.Specifically
, venture influence information may include marketing policy information, personnel's transition information, product price fluctuation information, it is of the same trade other
Enterprise operation information, upstream and downstream firms information etc..In addition, business risk result may include enterprise experience risk and caused by fortune
Battalion is as a result, for example, falling stock prices, stockpiling of unsold product, abnormal, the product price raising of capital turnover etc..
It is understood that each business risk result is caused by corresponding venture influence information, server can be incited somebody to action
Multiple business risk results and venture influence information corresponding with each business risk result are uploaded to block chain network.Wherein,
Multiple business risk results described here can be all as a result, also may refer to caused by the risk of enterprise's experience in history
Some results caused by the risk of enterprise's experience in history.In addition, venture influence information can be made of much information, for example,
One venture influence information may include the development of industry where upstream firm supply raw material deficiency and policy limitation enterprise.
According to some embodiments of the present disclosure, business risk result can be characterized as being business risk grade, enterprise's wind
Dangerous grade can be taking human as being divided, for example, being divided into ten grades.In addition, business risk grade can be divided into positive risk
Grade and negative sense risk class, wherein positive risk can form advantage to enterprise, and negative sense risk can produce the operation of enterprise
Raw negative influence.In the case where being ten grades by business risk grade classification, positive risk class may include grade 1 to 5, benefit
Good degree is for example gradually increased；Negative sense risk class may include grade -1 to -5, and the journey of negative influence is generated to enterprise operation
Degree is for example gradually increased.
During storing multiple business risk results by block chain network, it is possible, firstly, to artificially to historical
Business risk result is judged, with the corresponding business risk grade of determination；Next, the enterprise that server can will be determined
Risk class is stored to block chain network.
For example, storing to the information of block chain network can include but is not limited to: financial public sentiment, financial policy adjustment
The big variation of information, public figure's event, staple commodities price (e.g., 3% or more), product information, financial report information, Gu Dongxin
Breath, the big variation (e.g., 3% or more) of stock price, enterprise event and being associated between the lag time of movement of stock prices, enterprise
Relationship (e.g., supply chain relationship, mutually other company informations of the same industry etc.).Furthermore it is also possible to will be helpful to further determine that enterprise
The picture and video of industry risk are uploaded to block chain network.
The disclosure is made it possible to by storing business risk result and corresponding venture influence information in block chain network
It is enough to guarantee that these information be distorted by block chain network, and these can be realized based on the storage of block chain network
The traceable processing of information, and then effectively ensure that the safety of information is shared.
S14. using the block chain network storage multiple business risk results and with each business risk result
Corresponding venture influence information is trained a machine learning model.
According to some embodiments of the present disclosure, the machine learning model of the disclosure can be convolutional neural networks model.Example
Such as, the machine learning module can be configured to the convolutional neural networks comprising 256 convolution kernels, the size of each convolution kernel is equal
It is 2 × 2, sliding step 1.It is easily understood that the purpose of dimensionality reduction is realized using pond layer in convolutional neural networks,
Finally, each calculated characteristic pattern is inputted a full articulamentum, the corresponding enterprise of venture influence information is calculated by softmax function
Industry Risk Results.For example, the output of convolutional neural networks can be business risk grade recited above.
Specifically, can using venture influence information as the input of model, using business risk result as the output of model,
To be trained to above-mentioned convolutional neural networks model.Trained process namely determines each ginseng in convolutional neural networks model
Several processes does not do particular determination to trained detailed process in this illustrative embodiment.
Furthermore it is also possible to realize machine learning model described in the disclosure using other models, these models for example may be used
To include Naive Bayes Classification Model, nearest neighbor algorithm (KNN) model etc..
S16. if enterprise's related information of the first enterprise of typing in the block chain network is detected, by the enterprise
In machine learning model after related information input training, with the corresponding with enterprise's related information of determination first enterprise
Business risk result.
Enterprise's related information of first enterprise can be any information related with the first enterprise.Server can be by first
Enterprise's related information of enterprise is input to the machine learning model after above-mentioned training, and using the output of machine learning model as
The business risk result corresponding with enterprise's related information of one enterprise.
In addition, it should be noted that enterprise's related information is not limited to a type of information, it can also include the first enterprise
Own situation, industry public sentiment, the emergency event of upstream and downstream firms, a variety of or whole information in market reaction.
According to some embodiments of the present disclosure, the input of machine learning model is text information.A kind of situation, if enterprise
Related information is rendered as image (for example, screenshot on website), then server can carry out image recognition to the image, and will
The machine learning model after the input training of enterprise's related information after image recognition.Specifically, can be using refreshing with above-mentioned convolution
Image is identified through network different another convolutional neural networks to determine the corresponding text information of image, and will identification
The machine learning model after text information input training out.
It, will be wherein in addition, can be cleaned to text information after identifying the corresponding text information of image
The unrelated information deletion of one enterprise.
Another situation, if enterprise's related information is rendered as the form (for example, broadcast, news-video) of voice,
Server can identify voice using the speech recognition schemes of the relevant technologies, to determine the corresponding text envelope of voice
Breath, and the text information identified by speech recognition technology is inputted into the machine learning model after training.
According to some embodiments of the present disclosure, server can determine the second enterprise relevant to the first enterprise, and according to
The business risk result for the first enterprise that the above method is determined determines the business risk result of the second enterprise.For example, the first enterprise
Industry belongs to program of real estate enterprise, and the enterprise for providing building materials to the first enterprise can be used as the second enterprise.
Firstly, server can determine the degree of correlation between the first enterprise and the second enterprise.Specifically, in one embodiment
In, the degree of correlation between enterprise can be configured taking human as based on the relationship between enterprise's upstream and downstream；In another embodiment
In, the degree of correlation between industry can be artificially determined in advance, it will be between the affiliated industry of the affiliated industry of the first enterprise and the second enterprise
The degree of correlation be determined as the degree of correlation between the first enterprise and the second enterprise.
It is easily understood that the degree of correlation determined can be a numerical value, for example, indicating the degree of correlation with 1 to 100, wherein
100 indicate perfectly correlated, and 1 indicates almost uncorrelated.Furthermore it is also possible to determining that the degree of correlation is normalized, use 0 to
1 numerical value indicates the degree of correlation, and particular determination is not done to this in this illustrative embodiment.
Next, in the case where determining the degree of correlation between the first enterprise and the second enterprise, can based on the degree of correlation and
The business risk result of first enterprise determines the business risk result of the second enterprise.Specifically, if by business risk result table
Sign is business risk grade, then the business risk grade of the second enterprise can be determined using following formula:
X2=w*x1
Wherein, x1 indicates the business risk grade of the first enterprise, and x2 indicates the business risk grade of the second enterprise, and w is indicated
Weight, the weight can be corresponding with the degree of correlation between the first enterprise and the second enterprise, for example, can be by the correlation after normalization
Degree is used as weight.
According to other embodiments of the disclosure, before determining the business risk result of the second enterprise, it can be determined that the
Whether the degree of correlation between one enterprise and the second enterprise meets default degree of correlation requirement, wherein the default degree of correlation requires may is that
Whether the degree of correlation between the first enterprise and the second enterprise is greater than a default relevance threshold.In addition, the default degree of correlation requires also
It may is that whether the degree of correlation between the first enterprise and the second enterprise is greater than a default relevance threshold and the first enterprise and second
There are the relationships of business upstream and downstream for enterprise.The disclosure requires not doing specifically limited to the default degree of correlation.
If it is judged that the degree of correlation between the first enterprise and the second enterprise meets default degree of correlation requirement, then can be based on
Above description determines the business risk result of the second enterprise.
According to some embodiments of the present disclosure, it is based on the description above, if it is determined that go out the business risk knot of the first enterprise
Fruit is negative to risk class, then server can send a warning message to the first enterprise and/or enterprise relevant to the first enterprise,
With prompt, there may be risks, and then facilitate the measure of corporate planning anticipating risk.
It should be noted that although describing each step of method in the disclosure in the accompanying drawings with particular order, this is simultaneously
Undesired or hint must execute these steps in this particular order, or have to carry out the ability of step shown in whole
Realize desired result.Additional or alternative, it is convenient to omit multiple steps are merged into a step and executed by certain steps,
And/or a step is decomposed into execution of multiple steps etc..
Below with reference to business risk based on block chain of the Fig. 2 to the illustrative embodiments of the disclosure determine system into
Row explanation.
With reference to Fig. 2, the business risk based on block chain of the illustrative embodiments of the disclosure determines that system may include
Block chain network constructs subsystem 210, data format definition subsystem 220, information storage subsystem 230, business risk and determines
Subsystem 240, System Performance Analysis subsystem 250.
Specifically, block chain network building subsystem 210 for block chain node building, update and maintenance mechanism and
Building, update and the maintenance of block chain network.Such as can be using base of insurance company operating agency as minimum node, and it is based on one
The participations of a or multiple insurance pool/companies constructs block chain network.
Data format definition subsystem 220 can store letter involved in the disclosure according to data structure predetermined
Breath, to guarantee the high efficiency of information storage and information processing.Wherein, input can be business risk result and corresponding enterprise
The corresponding venture influence information of Risk Results, in addition, the information of input can also include helping to further determine that business risk
Picture and information, the public-key cryptography of related personnel and the signature such as video.Output can be the correlation of business risk predictive information
The storage links of voucher material, the relevant enterprise table listings influenced by an event and the risks such as impacted degree and direction to
Relevant enterprise or personal sending prompting, public-key cryptography (account address) of relevant information visitor etc..
Specifically, data structure predetermined can be as shown in table 1:
Table 1
In the data structure shown in table 1, since business risk result and corresponding wind direction influence information and other materials
It would generally believe comprising bigger information of data volumes such as some images, documents, therefore in order to improve storage efficiency and solve block
Excessive problem is ceased, in an embodiment of the present invention, the bigger material such as image can be stored in area in the form of a link
In block, value of this link is exactly the cryptographic Hash encrypted by hash function to material, such as SHA1 etc., this logical
Cross hash function obtain pointer link mode can guarantee that content can not distort.And actual material can both be stored in block
In the local storage device of chain node, and it can be stored in a manner of cloud storage.Meanwhile in order to guarantee the highly reliable of material storage
Property, material can be stored by the way of redundancy encoding, for example using RS coding (i.e. Reed-Solomon codes,
It is a kind of channel coding of forward error correction, effective to the multinomial as caused by correction over-sampling data) or LDPC (Low
Density Parity Check Code, low density parity check code) mode etc. of coding carries out at redundancy encoding material
Reason.
Information storage subsystem 230 is for storing above- mentioned information.Specifically, each information can pass through the format of above-mentioned table 1
It is uploaded in block chain network, to be stored by information storage subsystem 230.
Business risk determines that subsystem 240 can use the above-mentioned business risk based on block chain and determine method to determine
The risk of enterprise, details are not described herein.
System Performance Analysis subsystem 250 can be used for assessing above-mentioned business risk and determine method, and then assess enterprise's wind
Timeliness, validity and the accuracy nearly predicted help to effectively realize business risk prediction management by block chain network
In effective popularization of the promotion block chain technical application in terms of business risk prediction.
Further, a kind of business risk determining device based on block chain is additionally provided in this example embodiment.
Fig. 3 diagrammatically illustrates the business risk determining device based on block chain of the illustrative embodiments of the disclosure
Block diagram.With reference to Fig. 3, the business risk determining device 3 based on block chain according to an exemplary embodiment of the present disclosure can be with
Including information storage module 31, model training module 33 and the first risk determining module 35.
Specifically, information storage module 31 can be used for storing by block chain network multiple business risk results and with
The corresponding venture influence information of each business risk result；Model training module 33 can be used for utilizing the block chain network
Multiple business risk results of storage and venture influence information corresponding with each business risk result are to a machine learning
Model is trained；If the first risk determining module 35 can be used for detecting the first enterprise of typing in the block chain network
Enterprise's related information, then by enterprise's related information input training after machine learning model in, with determination described first
The business risk result corresponding with enterprise's related information of enterprise.
The business risk determining device based on block chain based on disclosure illustrative embodiments, on the one hand, based on this
Disclosed scheme can effectively determine out the risk of enterprise, avoid artificially assess to risk causing risk judgment inaccurate
True problem；On the other hand, the disclosure in block chain network by storing business risk result and corresponding venture influence
Information makes it possible to guarantee by block chain network that these information can not distort, and being capable of depositing based on block chain network
The traceable processing to realize these information is stored up, and then effectively ensures that the safety of information is shared；In another aspect, the disclosure can be with base
The business risk result and corresponding venture influence information that store in block chain network judges business risk, helps
In effective popularization of the promotion block chain technical application in terms of business risk prediction.
According to an exemplary embodiment of the present disclosure, with reference to Fig. 4, business risk determining device 4 is determined compared to business risk
Device 3 can also include enterprise's determining module 41 and the second risk determining module 43.
Specifically, enterprise's determining module 41 is determined for the second enterprise relevant to first enterprise；Second wind
Dangerous determining module 43 can be used for determining the business risk of second enterprise according to the business risk result of first enterprise
As a result.
According to an exemplary embodiment of the present disclosure, the second risk determining module is configured as: determining first enterprise
The degree of correlation between industry and second enterprise；Institute is determined based on the business risk result of the degree of correlation and first enterprise
State the business risk result of the second enterprise.
According to an exemplary embodiment of the present disclosure, the second risk determining module is configured as: judging the degree of correlation
Whether satisfaction presets degree of correlation requirement；If the degree of correlation meets default degree of correlation requirement, it is based on the degree of correlation and institute
The business risk result for stating the first enterprise determines the business risk result of second enterprise.
According to an exemplary embodiment of the present disclosure, enterprise's related information of first enterprise is rendered as image；Wherein,
First risk determining module is configured as: carrying out image recognition to described image；Enterprise's related information after image recognition is defeated
Machine learning model after entering training.
According to an exemplary embodiment of the present disclosure, the business risk result is characterized as being business risk grade；Wherein, believe
Breath memory module is configured as: obtaining based on business risk event the business risk grade of determination；By described business risk etc.
Grade is stored to block chain network.
According to an exemplary embodiment of the present disclosure, the business risk grade is divided into positive risk class and negative sense wind
Dangerous grade；Wherein, with reference to Fig. 5, business risk determining device 5 can also include alarm hair compared to business risk determining device 3
Send module 51.
Specifically, alarm sending module 51 can be used for first enterprise and/or relevant to first enterprise
Enterprise sends a warning message.
In addition, it is to be understood that alarm sending module 51 may be included in business risk determining device 4.
Since each functional module and the above method of the program analysis of running performance device of embodiment of the present invention are invented
It is identical in embodiment, therefore details are not described herein.
In an exemplary embodiment of the disclosure, a kind of computer readable storage medium is additionally provided, energy is stored thereon with
Enough realize the program product of this specification above method.In some possible embodiments, various aspects of the invention may be used also
In the form of being embodied as a kind of program product comprising program code, when described program product is run on the terminal device, institute
Program code is stated for executing the terminal device described in above-mentioned " illustrative methods " part of this specification according to this hair
The step of bright various illustrative embodiments.
Refering to what is shown in Fig. 6, describing the program product for realizing the above method of embodiment according to the present invention
600, can using portable compact disc read only memory (CD-ROM) and including program code, and can in terminal device,
Such as it is run on PC.However, program product of the invention is without being limited thereto, in this document, readable storage medium storing program for executing can be with
To be any include or the tangible medium of storage program, the program can be commanded execution system, device or device use or
It is in connection.
Described program product can be using any combination of one or more readable mediums.Readable medium can be readable letter
Number medium or readable storage medium storing program for executing.Readable storage medium storing program for executing for example can be but be not limited to electricity, magnetic, optical, electromagnetic, infrared ray or
System, device or the device of semiconductor, or any above combination.The more specific example of readable storage medium storing program for executing is (non exhaustive
List) include: electrical connection with one or more conducting wires, portable disc, hard disk, random access memory (RAM), read-only
Memory (ROM), erasable programmable read only memory (EPROM or flash memory), optical fiber, portable compact disc read only memory
(CD-ROM), light storage device, magnetic memory device or above-mentioned any appropriate combination.
Computer-readable signal media may include in a base band or as carrier wave a part propagate data-signal,
In carry readable program code.The data-signal of this propagation can take various forms, including but not limited to electromagnetic signal,
Optical signal or above-mentioned any appropriate combination.Readable signal medium can also be any readable Jie other than readable storage medium storing program for executing
Matter, the readable medium can send, propagate or transmit for by instruction execution system, device or device use or and its
The program of combined use.
The program code for including on readable medium can transmit with any suitable medium, including but not limited to wirelessly, have
Line, optical cable, RF etc. or above-mentioned any appropriate combination.
The program for executing operation of the present invention can be write with any combination of one or more programming languages
Code, described program design language include object oriented program language-Java, C++ etc., further include conventional
Procedural programming language-such as " C " language or similar programming language.Program code can be fully in user
It calculates and executes in equipment, partly executes on a user device, being executed as an independent software package, partially in user's calculating
Upper side point is executed on a remote computing or is executed in remote computing device or server completely.It is being related to far
Journey calculates in the situation of equipment, and remote computing device can pass through the network of any kind, including local area network (LAN) or wide area network
(WAN), it is connected to user calculating equipment, or, it may be connected to external computing device (such as utilize ISP
To be connected by internet).
In an exemplary embodiment of the disclosure, a kind of electronic equipment that can be realized the above method is additionally provided.
Person of ordinary skill in the field it is understood that various aspects of the invention can be implemented as system, method or
Program product.Therefore, various aspects of the invention can be embodied in the following forms, it may be assumed that complete hardware embodiment, complete
The embodiment combined in terms of full Software Implementation (including firmware, microcode etc.) or hardware and software, can unite here
Referred to as circuit, " module " or " system ".
The electronic equipment 700 of this embodiment according to the present invention is described referring to Fig. 7.The electronics that Fig. 7 is shown
Equipment 700 is only an example, should not function to the embodiment of the present invention and use scope bring any restrictions.
As shown in fig. 7, electronic equipment 700 is showed in the form of universal computing device.The component of electronic equipment 700 can wrap
It includes but is not limited to: at least one above-mentioned processing unit 710, at least one above-mentioned storage unit 720, the different system components of connection
The bus 730 of (including storage unit 720 and processing unit 710), display unit 740.
Wherein, the storage unit is stored with program code, and said program code can be held by the processing unit 710
Row, so that various according to the present invention described in the execution of the processing unit 710 above-mentioned " illustrative methods " part of this specification
The step of illustrative embodiments.For example, the processing unit 710 can execute step S12 as shown in fig. 1: passing through area
Block chain network stores multiple business risk results and venture influence information corresponding with each business risk result；Step
S14: multiple business risk results and wind corresponding with each business risk result using block chain network storage
Danger influences information and is trained to a machine learning model；Step S16: if detecting typing first in the block chain network
Enterprise's related information of enterprise then inputs enterprise's related information in the machine learning model after training, described in determination
The business risk result corresponding with enterprise's related information of first enterprise.
Storage unit 720 may include the readable medium of volatile memory cell form, such as Random Access Storage Unit
(RAM) 7201 and/or cache memory unit 7202, it can further include read-only memory unit (ROM) 7203.
Storage unit 720 can also include program/utility with one group of (at least one) program module 7205
7204, such program module 7205 includes but is not limited to: operating system, one or more application program, other program moulds
It may include the realization of network environment in block and program data, each of these examples or certain combination.
Bus 730 can be to indicate one of a few class bus structures or a variety of, including storage unit bus or storage
Cell controller, peripheral bus, graphics acceleration port, processing unit use any bus structures in a variety of bus structures
Local bus.
Electronic equipment 700 can also be with one or more external equipments 800 (such as keyboard, sensing equipment, bluetooth equipment
Deng) communication, can also be enabled a user to one or more equipment interact with the electronic equipment 700 communicate, and/or with make
Any equipment (such as the router, modulation /demodulation that the electronic equipment 700 can be communicated with one or more of the other calculating equipment
Device etc.) communication.This communication can be carried out by input/output (I/O) interface 750.Also, electronic equipment 700 can be with
By network adapter 760 and one or more network (such as local area network (LAN), wide area network (WAN) and/or public network,
Such as internet) communication.As shown, network adapter 760 is communicated by bus 730 with other modules of electronic equipment 700.
It should be understood that although not shown in the drawings, other hardware and/or software module can not used in conjunction with electronic equipment 700, including but not
Be limited to: microcode, device driver, redundant processing unit, external disk drive array, RAID system, tape drive and
Data backup storage system etc..
Through the above description of the embodiments, those skilled in the art is it can be readily appreciated that example described herein is implemented
Mode can also be realized by software realization in such a way that software is in conjunction with necessary hardware.Therefore, according to the disclosure
The technical solution of embodiment can be embodied in the form of software products, which can store non-volatile at one
Property storage medium (can be CD-ROM, USB flash disk, mobile hard disk etc.) in or network on, including some instructions are so that a calculating
Equipment (can be personal computer, server, terminal installation or network equipment etc.) is executed according to disclosure embodiment
Method.
In addition, above-mentioned attached drawing is only the schematic theory of processing included by method according to an exemplary embodiment of the present invention
It is bright, rather than limit purpose.It can be readily appreciated that the time that above-mentioned processing shown in the drawings did not indicated or limited these processing is suitable
Sequence.In addition, be also easy to understand, these processing, which can be, for example either synchronously or asynchronously to be executed in multiple modules.
It should be noted that although being referred to several modules or list for acting the equipment executed in the above detailed description
Member, but this division is not enforceable.In fact, according to embodiment of the present disclosure, it is above-described two or more
Module or the feature and function of unit can embody in a module or unit.Conversely, an above-described mould
The feature and function of block or unit can be to be embodied by multiple modules or unit with further division.
Those skilled in the art after considering the specification and implementing the invention disclosed here, will readily occur to its of the disclosure
His embodiment.This application is intended to cover any variations, uses, or adaptations of the disclosure, these modifications, purposes or
Adaptive change follow the general principles of this disclosure and including the undocumented common knowledge in the art of the disclosure or
Conventional techniques.The description and examples are only to be considered as illustrative, and the true scope and spirit of the disclosure are by claim
It points out.
It should be understood that the present disclosure is not limited to the precise structures that have been described above and shown in the drawings, and
And various modifications and changes may be made without departing from the scope thereof.The scope of the present disclosure is only limited by the attached claims.
Claims (10)
1. a kind of business risk based on block chain determines method characterized by comprising
Multiple business risk results and venture influence corresponding with each business risk result are stored by block chain network
Information；
Multiple business risk results and wind corresponding with each business risk result using block chain network storage
Danger influences information and is trained to a machine learning model；
If detecting enterprise's related information of the first enterprise of typing in the block chain network, by enterprise's related information
In machine learning model after input training, with enterprise's wind corresponding with enterprise's related information of determination first enterprise
Dangerous result.
2. the business risk according to claim 1 based on block chain determines method, which is characterized in that the business risk
Determine method further include:
Determine the second enterprise relevant to first enterprise；
The business risk result of second enterprise is determined according to the business risk result of first enterprise.
3. the business risk according to claim 2 based on block chain determines method, which is characterized in that according to described first
The business risk result of enterprise determines that the business risk result of second enterprise includes:
Determine the degree of correlation between first enterprise and second enterprise；
The business risk result of second enterprise is determined based on the business risk result of the degree of correlation and first enterprise.
4. the business risk according to claim 3 based on block chain determines method, which is characterized in that be based on the correlation
The business risk result of degree and first enterprise determines that the business risk result of second enterprise includes:
Judge whether the degree of correlation meets default degree of correlation requirement；
If the degree of correlation meets default degree of correlation requirement, the business risk based on the degree of correlation and first enterprise
As a result the business risk result of second enterprise is determined.
5. the business risk according to claim 1 based on block chain determines method, which is characterized in that first enterprise
Enterprise's related information be rendered as image；Wherein, by the machine learning model packet after enterprise's related information input training
It includes:
Image recognition is carried out to described image；
By the machine learning model after enterprise's related information input training after image recognition.
6. the business risk according to any one of claim 1 to 5 based on block chain determines method, which is characterized in that
The business risk result is characterized as being business risk grade；Wherein, multiple business risk results are stored by block chain network
Include:
The business risk grade of determination is obtained based on business risk event；
The business risk grade is stored to block chain network.
7. the business risk according to claim 6 based on block chain determines method, which is characterized in that the business risk
Grade is divided into positive risk class and negative sense risk class, if it is determined that the business risk result of first enterprise is out
Negative sense risk class, then the business risk determines method further include:
It sends a warning message to first enterprise and/or enterprise relevant to first enterprise.
8. a kind of business risk determining device based on block chain characterized by comprising
Information storage module, for by block chain network store multiple business risk results and with each business risk knot
The corresponding venture influence information of fruit；
Model training module, for using the block chain network storage multiple business risk results and with each enterprise
The corresponding venture influence information of Risk Results is trained a machine learning model；
First risk determining module, if for detecting that the enterprise of the first enterprise of typing in the block chain network is associated with letter
Breath, then by the machine learning model after enterprise's related information input training, with determination first enterprise with it is described
The corresponding business risk result of enterprise's related information.
9. a kind of storage medium, is stored thereon with computer program, which is characterized in that the computer program is executed by processor
Business risk described in Shi Shixian any one of claims 1 to 7 based on block chain determines method.
10. a kind of electronic equipment characterized by comprising
Processor；And
Memory, for storing the executable instruction of the processor；
Wherein, the processor is configured to come described in any one of perform claim requirement 1 to 7 via the execution executable instruction
The business risk based on block chain determine method.
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| CN201811528428.8A CN109359901A (en) | 2018-12-13 | 2018-12-13 | Method and device, medium and electronic equipment are determined based on the business risk of block chain |
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|---|---|---|---|
| CN201811528428.8A CN109359901A (en) | 2018-12-13 | 2018-12-13 | Method and device, medium and electronic equipment are determined based on the business risk of block chain |
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| CN109359901A true CN109359901A (en) | 2019-02-19 |
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| CN201811528428.8A Pending CN109359901A (en) | 2018-12-13 | 2018-12-13 | Method and device, medium and electronic equipment are determined based on the business risk of block chain |
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| CN110070432A (en) * | 2019-03-15 | 2019-07-30 | 深圳壹账通智能科技有限公司 | Supply chain finance evaluation method, device, storage medium and terminal |
| CN111709639A (en) * | 2020-06-14 | 2020-09-25 | 湖南三湘银行股份有限公司 | Grading method suitable for heavy machinery manufacturing enterprises of bank system |
| CN111858835A (en) * | 2020-07-31 | 2020-10-30 | 平安国际智慧城市科技股份有限公司 | Enterprise relation display method and related equipment |
| CN112529696A (en) * | 2020-12-24 | 2021-03-19 | 潍坊信至科技发展有限公司 | Financial wind control system based on block chain and public sentiment |
| CN115115290A (en) * | 2022-08-29 | 2022-09-27 | 深圳装速配科技有限公司 | Building material supplier management system and method based on risk assessment |
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| CN112529696A (en) * | 2020-12-24 | 2021-03-19 | 潍坊信至科技发展有限公司 | Financial wind control system based on block chain and public sentiment |
| CN112529696B (en) * | 2020-12-24 | 2021-06-25 | 优观融资租赁(中国)有限公司 | Financial wind control system based on block chain and public sentiment |
| CN115115290A (en) * | 2022-08-29 | 2022-09-27 | 深圳装速配科技有限公司 | Building material supplier management system and method based on risk assessment |
| CN117035695A (en) * | 2023-10-08 | 2023-11-10 | 之江实验室 | Information early warning method and device, readable storage medium and electronic equipment |
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