CN109063952A - Strategy generating and risk control method and device - Google Patents
Strategy generating and risk control method and device Download PDFInfo
- Publication number
- CN109063952A CN109063952A CN201810623948.0A CN201810623948A CN109063952A CN 109063952 A CN109063952 A CN 109063952A CN 201810623948 A CN201810623948 A CN 201810623948A CN 109063952 A CN109063952 A CN 109063952A
- Authority
- CN
- China
- Prior art keywords
- air control
- control strategy
- control rule
- target
- strategy
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0635—Risk analysis of enterprise or organisation activities
Landscapes
- Business, Economics & Management (AREA)
- Human Resources & Organizations (AREA)
- Engineering & Computer Science (AREA)
- Strategic Management (AREA)
- Entrepreneurship & Innovation (AREA)
- Economics (AREA)
- Operations Research (AREA)
- Game Theory and Decision Science (AREA)
- Development Economics (AREA)
- Marketing (AREA)
- Educational Administration (AREA)
- Quality & Reliability (AREA)
- Tourism & Hospitality (AREA)
- Physics & Mathematics (AREA)
- General Business, Economics & Management (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Wind Motors (AREA)
- Air Conditioning Control Device (AREA)
Abstract
The present invention provides a kind of strategy generating and risk control method and device, the target air control rule in air control strategy is adjusted by setting constraint condition, the automation of implementation rule is selected and is combined, and so as to optimize automatically to air control strategy, improves the reliability of air control strategy；Meanwhile above-mentioned air control strategy-generating method is not necessarily to artificial interference, improves the timeliness for generating air control strategy.
Description
Technical field
This specification is related to technical field of risk control more particularly to strategy generating and risk control method and device.
Background technique
In today of internet fast development, safety is as guarantee business health, the defender of stable development, and effect is just
It is gradually highlighting, wherein deploying to ensure effective monitoring and control of illegal activities for risk policy is its important ring.With the diversity of business and the continuous upgrading of confrontation, have
Necessity improves traditional risk policy generating mode.
Summary of the invention
Based on this, the present invention provides strategy generating and risk control methods and device.
According to a first aspect of the embodiments of the present invention, a kind of strategy-generating method is provided, which comprises from pre- Mr.
At a plurality of air control rule in select at least one target air control rule, and air control plan is generated according to the target air control rule
Slightly；If target variable is unsatisfactory for preset constraint condition, the target air control rule in the air control strategy is adjusted,
In, the target variable is that training sample is inputted to the output variable obtained after the air control strategy.
Optionally, a plurality of air control rule is generated by multiple decision trees, and each decision tree generates wherein at least one
Air control rule.
Optionally, before generating air control strategy according to the target air control rule, the method also includes: it determines to each
Identical target air control rule merges in plan tree.
Optionally, after identical target air control rule merges in each decision tree, the method also includes:
If the target air control rule has different decision conditions in each decision tree, wherein one is chosen according to the constraint condition
Decision condition of a decision condition as the target air control rule after merging.
Optionally, the step of being adjusted to the target air control rule in the air control strategy includes: to reject the air control
At least one target air control rule in strategy；And/or at least one target air control rule in the adjustment air control strategy
Decision condition；And/or at least one target air control rule in the air control strategy is replaced using the air control rule.
Optionally, the method also includes: according to test sample verify air control strategy adjusted whether meet it is described about
Beam condition.
Optionally, the method also includes: training sample is extracted from database；According to training sample generation
Air control rule.
Optionally, the method also includes: the training sample is updated.
Optionally, before generating the air control rule according to the training sample, the method also includes: to the instruction
Practice sample progress data cleansing and feature is derivative.
Optionally, the method also includes: obtain the multiple air control strategies for meeting default constraint condition；From the multiple wind
Optimal air control strategy is selected in control strategy.
Optionally, the constraint condition is accuracy constraint condition.
According to a second aspect of the embodiments of the present invention, a kind of risk control method is provided, which comprises according to air control
Strategy judges business datum with the presence or absence of risk；Wherein, the air control strategy by any embodiment air control strategy-generating method
It generates.
According to a third aspect of the embodiments of the present invention, a kind of air control strategy generating device is provided, described device includes: strategy
Generation module, for selecting at least one target air control rule from pre-generated a plurality of air control rule, and according to the mesh
It marks air control rule and generates air control strategy；Developing Tactics module, if preset constraint condition is unsatisfactory for for target variable, to described
Target air control rule in air control strategy is adjusted, wherein the target variable is that training sample is inputted to the air control plan
The output variable slightly obtained afterwards.
Optionally, a plurality of air control rule is generated by multiple decision trees, and each decision tree generates wherein at least one
Air control rule.
Optionally, described device further include: merging module, for target air control rule identical in each decision tree into
Row merges.
Optionally, described device further include: threshold value selecting module, if for the target air control rule in each decision tree
In have different decision conditions, according to the constraint condition select one of decision condition as merging after target wind
The decision condition of regulatory control then.
Optionally, Developing Tactics module includes: culling unit, for rejecting at least one target in the air control strategy
Air control rule；And/or adjustment unit, for adjusting the decision item of at least one target air control rule in the air control strategy
Part；And/or replacement unit, for using the air control rule at least one target air control rule in the air control strategy into
Row replacement.
Optionally, described device further include: authentication module, for being according to test sample verifying air control strategy adjusted
It is no to meet the constraint condition.
Optionally, described device further include: extraction module, for extracting training sample from database；Air control rule is raw
At module, for generating the air control rule according to the training sample.
Optionally, described device further include: update module, for being updated to the training sample.
Optionally, described device further include: preprocessing module, for carrying out data cleansing and feature to the training sample
It is derivative.
Optionally, described device further include: module is obtained, for obtaining the multiple air control plans for meeting default constraint condition
Slightly；Strategy selection module, for selecting optimal air control strategy from the multiple air control strategy.
Optionally, the constraint condition is accuracy constraint condition.
According to a fourth aspect of the embodiments of the present invention, a kind of risk control device is provided, described device includes: judgement mould
Block, for judging business datum with the presence or absence of risk according to air control strategy；Wherein, the air control strategy by any embodiment wind
Strategy-generating method is controlled to generate.
According to a fifth aspect of the embodiments of the present invention, a kind of computer readable storage medium is provided, calculating is stored thereon with
Machine program realizes the method for any embodiment when the program is executed by processor.
According to a sixth aspect of the embodiments of the present invention, a kind of computer equipment, including memory, processor and storage are provided
On a memory and the computer program that can run on a processor, which is characterized in that when the processor executes described program
The method for realizing any embodiment.
Using the embodiment of the present invention, the target air control rule in air control strategy is carried out by setting constraint condition
Adjustment, the automation of implementation rule are selected and are combined, and so as to optimize automatically to air control strategy, improve air control strategy
Reliability；Meanwhile above-mentioned air control strategy-generating method is not necessarily to artificial interference, improves the timeliness for generating air control strategy.
It should be understood that above general description and following detailed description be only it is exemplary and explanatory, not
It can the limitation present invention.
Detailed description of the invention
The drawings herein are incorporated into the specification and forms part of this specification, and shows and meets implementation of the invention
Example, and be used to explain the principle of the present invention together with specification.
Fig. 1 is the schematic diagram of this specification one embodiment risk antagonistic process.
Fig. 2 is the flow chart of air control strategy-generating method in this specification one embodiment.
Fig. 3 is the decision tree schematic diagram in this specification one embodiment.
Fig. 4 is this specification one embodiment risk control method schematic diagram.
Fig. 5 is air control strategy generating and the overview flow chart of risk control in this specification one embodiment.
Fig. 6 is the block diagram of air control strategy generating device in this specification one embodiment.
Fig. 7 is the block diagram of this specification one embodiment risk control device.
Fig. 8 is the block diagram of computer equipment where device in this specification one embodiment.
Specific embodiment
Example embodiments are described in detail here, and the example is illustrated in the accompanying drawings.Following description is related to
When attached drawing, unless otherwise indicated, the same numbers in different drawings indicate the same or similar elements.Following exemplary embodiment
Described in embodiment do not represent all embodiments consistented with the present invention.On the contrary, they be only with it is such as appended
The example of device and method being described in detail in claims, some aspects of the invention are consistent.
It is only to be not intended to limit the invention merely for for the purpose of describing particular embodiments in terminology used in the present invention.
It is also intended in the present invention and the "an" of singular used in the attached claims, " described " and "the" including majority
Form, unless the context clearly indicates other meaning.It is also understood that term "and/or" used herein refers to and wraps
It may be combined containing one or more associated any or all of project listed.
It will be appreciated that though various information, but this may be described using term first, second, third, etc. in the present invention
A little information should not necessarily be limited by these terms.These terms are only used to for same type of information being distinguished from each other out.For example, not departing from
In the case where the scope of the invention, the first information can also be referred to as the second information, and similarly, the second information can also be referred to as
One information.Depending on context, word as used in this " if " can be construed to " ... when " or " when ...
When " or " in response to determination ".
As shown in Figure 1, being the schematic diagram of this specification one embodiment risk antagonistic process.Can exist in daily life
Various risks, these risks may bring certain hidden danger.For example, when user is in electricity such as computer, mobile phone, tablet computers
When executing delivery operation in sub- equipment 102, it is understood that there may be the risks such as system vulnerability bring hidden danger to the assets security of user.For
Carry out risk control, ensures the assets security of user, can be extracted by bottom data of the network to user, and will mention
The bottom data got is stored into database 104.Server 106 can acquire training sample from database 104, and according to
Certain algorithm generates air control strategy, and the air control strategy of generation can be used for carrying out risk resisting.Certainly, due to air control strategy
In the presence of the risk of generation also can make corresponding upgrading with the progress of technology over time, therefore, in risk resisting
In the process, it is often also required to the upgrading with risk and updates air control strategy.To form the risk resisting side of a set of closed loop
Case.
As shown in Fig. 2, being the flow chart of air control strategy-generating method in this specification one embodiment.The air control strategy
Generation method can comprise the following steps that
Step 202: at least one target air control rule is selected from pre-generated a plurality of air control rule, and according to described
Target air control rule generates air control strategy；
Step 204: if target variable is unsatisfactory for preset constraint condition, to the target air control rule in the air control strategy
It is adjusted, wherein the target variable is that training sample is inputted to the output variable obtained after the air control strategy.
It in step 202, can be regular to generate a plurality of air control according to certain or certain algorithms in advance, wherein different
Air control rule can be generated by identical algorithm, can also be generated by different algorithms.For example, can by decision Tree algorithms come
Generate a plurality of air control rule.The a plurality of air control rule can be the air control rule generated by same decision tree.It is described a plurality of
Air control rule can also be generated by multiple decision trees, and each decision tree generates wherein at least one air control rule.For example, it is assumed that
3 decision trees are shared, then air control rule A, B, C can be generated in decision tree 1, and air control rule D and E, decision can be generated in decision tree 2
Air control rule F and G can be generated in tree 3, then the air control rule got in step 202 includes air control rule A~G.
As shown in figure 3, showing the embodiment for generating 2 decision trees.Wherein, each rule is by father node, one
A child node and branch's (i.e. decision condition) are constituted, and each leaf node ultimately generated respectively corresponds a label.
As can be seen that in the embodiment shown in fig. 3, there are the identical rules in part in decision tree 2 for decision tree 1.Certainly, this is
One kind there may be the case where, in actual conditions, the rule in every decision tree of generation may be all different.
After obtaining air control rule, at least one target wind first can be selected from pre-generated a plurality of air control rule
Regulatory control then, then generates air control strategy by way of iteration.
In step 204, target variable is that business datum is imported the variable obtained after air control strategy, can be used for characterizing
Classification belonging to business datum.Several groups training sample can be obtained from database, and training sample is imported into air control strategy, is obtained
To target variable, then judge whether the corresponding target variable of each training sample and the actual classification of training sample are consistent, with
Verify the constraint condition.
This specification embodiment is adjusted the target air control rule in air control strategy by setting constraint condition, real
The automation of existing rule is selected and is combined, and so as to optimize automatically to air control strategy, improves the reliable of air control strategy
Property；Meanwhile above-mentioned air control strategy-generating method is not necessarily to artificial interference, improves the timeliness for generating air control strategy.
In one embodiment, the training sample in database can be extracted, and according to training sample generation
Air control rule.Training sample can be obtained from client.For example, for Alipay business, when available user's registration, passes through
The business datum etc. sent when the user information and user that client uploads are paid using Alipay by client
Deng.First each rule of generation can be added in a regular collection, then select at least one from regular collection again
Target air control rule, for generating air control strategy.
Before generating air control rule, data cleansing can also be carried out to training sample and feature is derivative.Wherein, data are clear
Washing is the process that data are examined and verified again, it is therefore intended that deletes mistake existing for duplicate message, correction, and provides
Data consistency.Feature derivative is to carry out certain combination to the existing feature of finger, generates the significant feature of new tool.
For there are the situations of identical air control rule, in order to reduce the redundancy of rule, advised according to the target air control
Before then generating air control strategy, identical target air control rule can be merged.Further, if identical target wind
Regulatory control then has different decision conditions in each decision tree, can choose one of decision item according to the constraint condition
Decision condition of the part as the target air control rule after merging.
In one embodiment, the step of target air control rule in the air control strategy being adjusted include with down toward
It is few any: to reject at least one target air control rule in the air control strategy；And/or in the adjustment air control strategy at least
The decision condition of one target air control rule；And/or using the air control rule at least one mesh in the air control strategy
Mark air control rule is replaced.
For example, pre-generated air control rule includes { A, B, C, D, E, F, G }, the target air control rule of selection is A, B, C.
Whether the air control strategy that then can determine whether that A, B, C are constituted meets constraint condition, if not satisfied, can delete from air control strategy a certain
Item or a few target air control rules, for example, deleting A, then only including B and C in air control strategy adjusted.Also it is adjustable certain
The decision condition of one or a few target air control rule, for example, the decision condition of B is adjusted to " big from " being more than or equal to 90 "
In equal to 95 ".It can also use, any one air control rule in D, E, F, G substitutes the wherein regular (example of a target air control
Such as, C).It is of course also possible to be performed simultaneously deletion, adjustment decision condition and replacement.
In one embodiment, when obtaining air control strategy, the available multiple air control strategies for meeting default constraint condition,
Optimal air control strategy is selected from the multiple air control strategy again.For example, it is assumed that being obtained after being adjusted to target air control rule
The air control strategy for meeting constraint condition got includes strategy X1, strategy X2 and strategy X3, then can select from X1, X2, X3
Optimal air control strategy, as final air control strategy.Wherein, optimal can be is best suitable for constraint condition.For example, when constraint item
When part is accuracy rate constraint condition, the highest air control strategy of accuracy rate can be selected as final air control plan from X1, X2, X3
Slightly.In this way, can further obtain preferably air control strategy by postsearch screening.
After obtaining air control strategy, can be verified according to test sample air control strategy adjusted whether meet it is described about
Beam condition.Test sample can be extracted from newest business datum.For example, in available business datum 20% business number
According to as test sample, verify whether finally obtained air control strategy meets constraint condition.If it is satisfied, then can be directly used
The air control strategy carries out risk control to the data of entire operation system；If conditions are not met, can then regenerate air control strategy.
In this way, the influence to entire operation system is reduced.
In one embodiment, the training sample can also be updated.Risk pair is being carried out using air control strategy
In anti-process, risk can upgrade according to the improvement of air control strategy, can generate new business under the risk effect after upgrading
Therefore data can be updated training sample according to new business datum.The mode of update, which can be, to be regularly updated, example
Such as, every other week or a month equal times carried out a bottom data and extract, according to the bottom data extracted to training sample
Originally it is updated.Data update can also be carried out in such a way that condition triggers.
Constraint condition in any of the above-described embodiment can be accuracy rate constraint condition, that is, be obtained by air control strategy
Target variable and the quantity of the consistent training sample of concrete class account for the ratio of training sample sum.Assuming that a total of N item training
Sample in the target variable obtained by air control strategy, there is that M target variable is consistent with the actual classification of corresponding training sample,
Then accuracy rate can be denoted as M/N.If accuracy rate is greater than preset accuracy rate threshold value, it is believed that meeting accuracy rate constraint condition；It is no
Then, it is believed that be unsatisfactory for accuracy rate constraint condition.
Constraint condition in any of the above-described embodiment is also possible to risk coverage rate constraint condition, that is, passes through air control strategy
The risk identified accounts for the ratio of the risk sum of physical presence.Assuming that there are risks for shared N training sample, pass through air control plan
Slightly identifying is M there are the quantity of the training sample of risk, then risk coverage rate can be denoted as M/N.If risk coverage rate is greater than
Preset risk coverage rate threshold value, then it is believed that meeting risk coverage rate constraint condition；Otherwise it is assumed that being unsatisfactory for risk coverage rate
Constraint condition.
This specification embodiment has the advantage that
(1) without artificial selection rule, the timeliness of strategy generating is improved.
(2) rule is reasonably assessed by setting constraint condition, improves the reliability of rule and strategy.
(3) based on the regular generation strategy in more decision trees, the robustness of generation strategy is improved.
(4) different rules is merged, screened, arranged, reduce the redundancy of rule.
As shown in figure 4, this specification embodiment additionally provides a kind of risk control method, the method can include:
Judge business datum with the presence or absence of risk according to air control strategy；Wherein, the air control strategy is by any of the above-described implementation
The air control strategy-generating method of example generates.
In the present embodiment, by business datum import air control strategy, the classification of available business datum, such as: do not deposit
Classification in risk, the classification there are risk, there are classifications of high risk etc..In this way, risk control can be carried out
System, ensures the safety of business datum.
As shown in figure 5, be air control strategy generating and the overview flow chart of risk control in this specification one embodiment, it can
The following steps are included:
Step 502: bottom data extracts.Wherein, bottom data may be from mobile phone, computer, the tablet computer that user uses
Equal electronic equipments.
Step 504: data cleansing and feature are derivative.
Step 506: data obtained in step 504 are stored in database backup.
Step 508: target variable is defined, such as: there are risks.
Step 510: obtaining training sample from database, execute stub algorithm, obtain air control rule.
Step 512: air control strategy being generated according to air control rule, the air control in any of the above-described embodiment can be used in generating mode
Strategy-generating method.
Step 514: the air control rule that step 512 generates being tested using test sample, according to wind after the completion of test
Regulatory control then fights risk.
Corresponding with the embodiment of preceding method, the present invention also provides device, computer storage media and computers to set
Standby embodiment.
As shown in fig. 6, being the block diagram of air control strategy generating device in this specification one embodiment.Described device includes:
Policy generation module 602, for selecting at least one target air control to advise from pre-generated a plurality of air control rule
Then, and according to the target air control rule air control strategy is generated；
Developing Tactics module 604, if being unsatisfactory for preset constraint condition for target variable, in the air control strategy
Target air control rule is adjusted, wherein the target variable be obtained after training sample to be inputted to the air control strategy it is defeated
Variable out.
The specific details of the realization process of the function of modules and effect are shown in above-mentioned air control strategy generating side in above-mentioned apparatus
The realization process of step is corresponded in method, details are not described herein.
As shown in fig. 7, being the block diagram of this specification one embodiment risk control device.Described device includes:
Judgment module 702, for judging business datum with the presence or absence of risk according to air control strategy；Wherein, the air control plan
Slightly generated by the air control strategy-generating method of any embodiment.
The specific details of the realization process of the function of modules and effect are shown in above-mentioned risk control method in above-mentioned apparatus
The realization process of corresponding step, details are not described herein.
The embodiment of data processing equipment during this specification on-line payment can be applied on a computing device, example
Such as server or terminal device.Installation practice can also pass through hardware or software and hardware combining by software realization
Mode is realized.It taking software implementation as an example, is the processor by file process where it as the device on a logical meaning
Computer program instructions corresponding in nonvolatile memory are read into memory what operation was formed.For hardware view,
As shown in figure 8, for a kind of hardware configuration of computer equipment where the data processing equipment during this specification on-line payment
Figure is implemented other than processor 802 shown in Fig. 8, memory 804, network interface 806 and nonvolatile memory 808
Server or electronic equipment in example where device can also include other generally according to the actual functional capability of the computer equipment
Hardware repeats no more this.
For device embodiment, since it corresponds essentially to embodiment of the method, so related place is referring to method reality
Apply the part explanation of example.The apparatus embodiments described above are merely exemplary, wherein described be used as separation unit
The module of explanation may or may not be physically separated, and the component shown as module can be or can also be with
It is not physical module, it can it is in one place, or may be distributed on multiple network modules.It can be according to actual
The purpose for needing to select some or all of the modules therein to realize this specification scheme.Those of ordinary skill in the art are not
In the case where making the creative labor, it can understand and implement.
Correspondingly, this specification embodiment also provides a kind of computer storage medium, is stored with journey in the storage medium
Sequence realizes the method in any of the above-described embodiment when described program is executed by processor.
Correspondingly, this specification embodiment also provides a kind of computer equipment, including memory, processor and is stored in
On reservoir and the computer program that can run on a processor, the processor realize any of the above-described implementation when executing described program
Method in example.
Those skilled in the art after considering the specification and implementing the invention disclosed here, will readily occur to its of the disclosure
Its embodiment.The disclosure is intended to cover any variations, uses, or adaptations of the disclosure, these modifications, purposes or
Person's adaptive change follows 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 following
Claim is pointed 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 accompanying claims.
The foregoing is merely the preferred embodiments of the disclosure, not to limit the disclosure, all essences in the disclosure
Within mind and principle, any modification, equivalent substitution, improvement and etc. done be should be included within the scope of disclosure protection.
Claims (16)
1. a kind of air control strategy-generating method, which comprises
At least one target air control rule is selected from pre-generated a plurality of air control rule, and according to the target air control rule
Generate air control strategy；
If target variable is unsatisfactory for preset constraint condition, the target air control rule in the air control strategy is adjusted,
In, the target variable is that training sample is inputted to the output variable obtained after the air control strategy.
2. according to the method described in claim 1, a plurality of air control rule is generated by multiple decision trees, and each decision tree is raw
At wherein at least one air control rule.
3. according to the method described in claim 2, according to the target air control rule generate air control strategy before, the method
Further include:
Target air control rule identical in each decision tree is merged.
4. according to the method described in claim 3, after identical target air control rule merges in each decision tree,
The method also includes:
If the target air control rule has different decision conditions in each decision tree, it is chosen according to the constraint condition
In a decision condition as merge after target air control rule decision condition.
5. according to the method described in claim 1, the step of being adjusted to the target air control rule in air control strategy packet
It includes:
Reject at least one target air control rule in the air control strategy；And/or
Adjust the decision condition of at least one target air control rule in the air control strategy；And/or
At least one target air control rule in the air control strategy is replaced using the air control rule.
6. according to the method described in claim 1, the method also includes:
Verify whether air control strategy adjusted meets the constraint condition according to test sample.
7. according to the method described in claim 1, the method also includes:
Training sample is extracted from database；
The air control rule is generated according to the training sample.
8. according to the method described in claim 7, the method also includes:
The training sample is updated.
9. according to the method described in claim 7, before generating air control rule according to the training sample, the method
Further include:
Data cleansing is carried out to the training sample and feature is derivative.
10. according to the method described in claim 1, the method also includes:
Obtain the multiple air control strategies for meeting default constraint condition；
Optimal air control strategy is selected from the multiple air control strategy.
11. the constraint condition is accuracy constraint condition according to claim 1 to method described in 10 any one.
12. a kind of risk control method, which comprises
Judge business datum with the presence or absence of risk according to air control strategy；
Wherein, air control strategy air control strategy-generating method as described in claim 1 to 11 any one generates.
13. a kind of air control strategy generating device, described device include:
Policy generation module, for selecting at least one target air control rule, and root from pre-generated a plurality of air control rule
Air control strategy is generated according to the target air control rule；
Developing Tactics module, if being unsatisfactory for preset constraint condition for target variable, to the target wind in the air control strategy
Regulatory control is then adjusted, wherein the target variable is that training sample is inputted to the output variable obtained after the air control strategy.
14. a kind of risk control device, described device include:
Judgment module, for judging business datum with the presence or absence of risk according to air control strategy；
Wherein, air control strategy air control strategy-generating method as described in claim 1 to 11 any one generates.
15. a kind of computer readable storage medium, is stored thereon with computer program, power is realized when which is executed by processor
Benefit requires method described in 1 to 11 any one.
16. a kind of computer equipment including memory, processor and stores the meter that can be run on a memory and on a processor
Calculation machine program, the processor realize method described in claim 1 to 11 any one when executing described program.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810623948.0A CN109063952B (en) | 2018-06-15 | 2018-06-15 | Policy generation and risk control method and device |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810623948.0A CN109063952B (en) | 2018-06-15 | 2018-06-15 | Policy generation and risk control method and device |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN109063952A true CN109063952A (en) | 2018-12-21 |
| CN109063952B CN109063952B (en) | 2022-03-29 |
Family
ID=64820430
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201810623948.0A Active CN109063952B (en) | 2018-06-15 | 2018-06-15 | Policy generation and risk control method and device |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109063952B (en) |
Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109559232A (en) * | 2019-01-03 | 2019-04-02 | 深圳壹账通智能科技有限公司 | Transaction data processing method, device, computer equipment and storage medium |
| CN111047220A (en) * | 2019-12-27 | 2020-04-21 | 支付宝(杭州)信息技术有限公司 | Method, device, equipment and readable medium for determining condition of wind control threshold |
| CN111784119A (en) * | 2020-06-12 | 2020-10-16 | 支付宝(杭州)信息技术有限公司 | Wind control strategy migration method, strategy package generation method and device |
| CN111861703A (en) * | 2020-07-10 | 2020-10-30 | 深圳无域科技技术有限公司 | Data-driven wind control strategy rule generation method and system and risk control method and system |
| CN111915422A (en) * | 2020-07-15 | 2020-11-10 | 北京云从科技有限公司 | Wind control model scheduling method and device, machine readable medium and equipment |
| CN112861955A (en) * | 2021-02-01 | 2021-05-28 | 深圳无域科技技术有限公司 | Risk model strategy generation system and method |
| CN113657779A (en) * | 2021-08-20 | 2021-11-16 | 杭州时趣信息技术有限公司 | Dynamically-configured wind control decision method, system, equipment and storage medium |
| CN114897595A (en) * | 2022-04-11 | 2022-08-12 | 深圳无域科技技术有限公司 | Data processing method and device, electronic equipment and storage medium |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106878236A (en) * | 2015-12-11 | 2017-06-20 | 阿里巴巴集团控股有限公司 | A kind of user's request processing method and equipment |
| CN107545360A (en) * | 2017-07-28 | 2018-01-05 | 浙江邦盛科技有限公司 | A kind of air control intelligent rules deriving method and system based on decision tree |
| CN108074179A (en) * | 2017-12-07 | 2018-05-25 | 深圳乐信软件技术有限公司 | Financial air control tactics configuring method, system, server and storage medium |
| CN108090829A (en) * | 2017-12-21 | 2018-05-29 | 深圳市买买提信息科技有限公司 | A kind of data managing method, data administrator and electronic equipment |
-
2018
- 2018-06-15 CN CN201810623948.0A patent/CN109063952B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106878236A (en) * | 2015-12-11 | 2017-06-20 | 阿里巴巴集团控股有限公司 | A kind of user's request processing method and equipment |
| CN107545360A (en) * | 2017-07-28 | 2018-01-05 | 浙江邦盛科技有限公司 | A kind of air control intelligent rules deriving method and system based on decision tree |
| CN108074179A (en) * | 2017-12-07 | 2018-05-25 | 深圳乐信软件技术有限公司 | Financial air control tactics configuring method, system, server and storage medium |
| CN108090829A (en) * | 2017-12-21 | 2018-05-29 | 深圳市买买提信息科技有限公司 | A kind of data managing method, data administrator and electronic equipment |
Cited By (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109559232A (en) * | 2019-01-03 | 2019-04-02 | 深圳壹账通智能科技有限公司 | Transaction data processing method, device, computer equipment and storage medium |
| CN111047220A (en) * | 2019-12-27 | 2020-04-21 | 支付宝(杭州)信息技术有限公司 | Method, device, equipment and readable medium for determining condition of wind control threshold |
| CN111784119A (en) * | 2020-06-12 | 2020-10-16 | 支付宝(杭州)信息技术有限公司 | Wind control strategy migration method, strategy package generation method and device |
| CN111784119B (en) * | 2020-06-12 | 2022-06-03 | 支付宝(杭州)信息技术有限公司 | Wind control strategy migration method, strategy package generation method and device |
| CN111861703A (en) * | 2020-07-10 | 2020-10-30 | 深圳无域科技技术有限公司 | Data-driven wind control strategy rule generation method and system and risk control method and system |
| CN111861703B (en) * | 2020-07-10 | 2024-05-10 | 深圳无域科技技术有限公司 | Data-driven wind control strategy rule generation method and system and risk control method and system |
| CN111915422A (en) * | 2020-07-15 | 2020-11-10 | 北京云从科技有限公司 | Wind control model scheduling method and device, machine readable medium and equipment |
| CN112861955A (en) * | 2021-02-01 | 2021-05-28 | 深圳无域科技技术有限公司 | Risk model strategy generation system and method |
| CN113657779A (en) * | 2021-08-20 | 2021-11-16 | 杭州时趣信息技术有限公司 | Dynamically-configured wind control decision method, system, equipment and storage medium |
| CN113657779B (en) * | 2021-08-20 | 2024-01-09 | 杭州时趣信息技术有限公司 | Dynamic configuration wind control decision method, system, equipment and storage medium |
| CN114897595A (en) * | 2022-04-11 | 2022-08-12 | 深圳无域科技技术有限公司 | Data processing method and device, electronic equipment and storage medium |
Also Published As
| Publication number | Publication date |
|---|---|
| CN109063952B (en) | 2022-03-29 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109063952A (en) | Strategy generating and risk control method and device | |
| US11651082B2 (en) | Blockchain applicability framework | |
| AU2019305079A1 (en) | Blockchain transaction safety using smart contracts | |
| CN108876133A (en) | Risk assessment processing method, device, server and medium based on business information | |
| US20200356678A1 (en) | Cybersecurity vulnerability mitigation framework | |
| CN110399550A (en) | A kind of information recommendation method and device | |
| CN108512827A (en) | The identification of abnormal login and method for building up, the device of supervised learning model | |
| CN107835982A (en) | Method and apparatus for management security in a computer network | |
| CN108629413A (en) | Neural network model training, trading activity Risk Identification Method and device | |
| CN105590055A (en) | Method and apparatus for identifying trustworthy user behavior in network interaction system | |
| CN111382956A (en) | Enterprise group relationship mining method and device | |
| CN114422224B (en) | Threat information intelligent analysis method and system for attack tracing | |
| CN113628049B (en) | Conflict arbitration method of blockchain intelligent contracts based on group intelligence | |
| CN109523124A (en) | Asset data processing method, device, computer equipment and storage medium | |
| Brown et al. | An extended study on multi-objective security games | |
| CN108346098A (en) | A kind of method and device of air control rule digging | |
| CN109690571A (en) | Group echo system and method based on study | |
| CN114072808A (en) | Classification model for controlling manufacturing process | |
| CN106874306A (en) | People information portrait Compare System Key Performance Indicator evaluating method | |
| CN114679341A (en) | Network intrusion attack analysis method, equipment and medium combined with ERP system | |
| US11954174B2 (en) | Sharing financial crime knowledge | |
| CN109859001A (en) | A kind of method and device carrying out source of houses update based on block chain | |
| WO2021138591A1 (en) | Blockchain cybersecurity solutions | |
| CN107835174A (en) | A kind of anti-fake system of account book based on Internet of Things and method | |
| CN110009473A (en) | Data processing method, device, equipment and storage medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| TA01 | Transfer of patent application right |
Effective date of registration: 20200922 Address after: Cayman Enterprise Centre, 27 Hospital Road, George Town, Grand Cayman Islands Applicant after: Innovative advanced technology Co.,Ltd. Address before: Cayman Enterprise Centre, 27 Hospital Road, George Town, Grand Cayman Islands Applicant before: Advanced innovation technology Co.,Ltd. Effective date of registration: 20200922 Address after: Cayman Enterprise Centre, 27 Hospital Road, George Town, Grand Cayman Islands Applicant after: Advanced innovation technology Co.,Ltd. Address before: A four-storey 847 mailbox in Grand Cayman Capital Building, British Cayman Islands Applicant before: Alibaba Group Holding Ltd. |
|
| TA01 | Transfer of patent application right | ||
| TA01 | Transfer of patent application right |
Effective date of registration: 20220307 Address after: 201208 room 1607, 16 / F, No. 447, Nanquan North Road, Pudong New Area (Shanghai) pilot Free Trade Zone, Shanghai Applicant after: Ant Zhian safety technology (Shanghai) Co.,Ltd. Address before: Ky1-9008 Cayman Enterprise Centre, 27 Hospital Road, George Town, Grand Cayman Islands, ky1-9008 Applicant before: Innovative advanced technology Co.,Ltd. |
|
| TA01 | Transfer of patent application right | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |