CN109992435A - Equipment uses abnormality determination method, device and computer storage medium - Google Patents
Equipment uses abnormality determination method, device and computer storage medium Download PDFInfo
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- CN109992435A CN109992435A CN201910145792.4A CN201910145792A CN109992435A CN 109992435 A CN109992435 A CN 109992435A CN 201910145792 A CN201910145792 A CN 201910145792A CN 109992435 A CN109992435 A CN 109992435A
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- 238000000034 method Methods 0.000 title claims abstract description 23
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- 230000002159 abnormal effect Effects 0.000 claims abstract description 28
- 238000013178 mathematical model Methods 0.000 claims abstract description 25
- 238000010276 construction Methods 0.000 claims description 6
- 238000007689 inspection Methods 0.000 claims description 2
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Abstract
A kind of equipment uses abnormality determination method, device and computer storage medium, which comprises obtains the incidence relation between the corresponding user of equipment, constructs the corresponding relational network of user with incidence relation；Obtain the attribute data of the relational network；The attribute data of relational network is inputted into abnormality detection mathematical model, obtains abnormality detection scoring；Abnormality detection is scored and is compared with scale, determines whether relational network is abnormal, and the corresponding equipment of user in abnormal relational network is determined as using warping apparatus.Using the above scheme, it may be implemented in the case where no worker monitor, determine that the equipment for abnormal service condition occur and user reduce time cost while reducing the economic cost of artificial monitoring device by data calculated result.
Description
Technical field
The present invention relates to no worker monitor fields more particularly to a kind of equipment to use abnormality determination method, device and calculating
Machine storage medium.
Background technique
The Self-Service class machine that scene many of works as unmanned monitoring is consumed with becoming increasingly popular for self-oriented service, under line
Device.Such equipment is generally in the state of no worker monitor during the work time, therefore whether user abides by phase when using equipment
It closes usage criteria and relevant contract is the problem of manager pays close attention to emphatically.
In the prior art, usually by manually being checked one by one to equipment and user.
However, scheme higher cost in the prior art, and take a long time.
Summary of the invention
Present invention solves the technical problem that being monitoring of tools higher cost, take a long time.
In order to solve the above technical problems, the embodiment of the present invention, which provides a kind of equipment, uses abnormality determination method, comprising: obtain
Incidence relation between the corresponding user of equipment constructs the corresponding relational network of user with incidence relation；Obtain the pass
It is the attribute data of network；The attribute data of the relational network is inputted into abnormality detection mathematical model, abnormality detection is obtained and comments
Point；The abnormality detection is scored and is compared with preset scale, determines whether the relational network is abnormal, and will be different
The corresponding equipment of user in normal relational network is determined as using warping apparatus.
Optionally, when the number of the equipment is multiple, the association between the corresponding user of the multiple equipment is obtained
Relationship constructs the user with incidence relation connection and the corresponding relational network of non-user.
Optionally, the attribute data comprises at least one of the following: number, incidence relation quantity；The incidence relation packet
It includes: in-degree incidence relation and out-degree incidence relation.
Optionally, it when detecting in the relational network in the presence of user is newly increased, will be newly increased in the relational network
The corresponding attribute data of user inputs abnormality detection mathematical model, obtains additional abnormality detection scoring.
Optionally, when detecting the relational network newly increased, the attribute data of the relational network newly increased is defeated
Enter abnormality detection mathematical model, obtains additional abnormality detection scoring.
Optionally, by abnormality detection scoring and the additional abnormality detection scoring and value and the scale into
Row compares.
The present invention also provides a kind of equipment to use abnormity determining device, comprising: construction unit, it is corresponding for obtaining equipment
Incidence relation between user constructs the corresponding relational network of user with incidence relation；Acquiring unit, it is described for obtaining
The attribute data of relational network；Computing unit, for the attribute data of the relational network to be inputted abnormality detection mathematical model,
Obtain abnormality detection scoring；Judging unit is compared for scoring the abnormality detection with preset scale, determines
Whether the relational network is abnormal, and the corresponding equipment of user in abnormal relational network is determined as using warping apparatus.
Optionally, the construction unit is also used to obtain the multiple equipment pair when the number of the equipment is multiple
The incidence relation between user answered constructs the user with incidence relation connection and the corresponding relational network of non-user.
Optionally, the attribute data comprises at least one of the following: number, incidence relation quantity；The incidence relation packet
It includes: in-degree incidence relation and out-degree incidence relation.
Optionally, the computing unit is also used to when detecting in the relational network in the presence of user is newly increased, by institute
It states and newly increases the corresponding attribute data input abnormality detection mathematical model of user in relational network, obtain additional abnormality detection and comment
Point.
Optionally, the computing unit is also used to when detecting the relational network newly increased, by the pass newly increased
It is the attribute data input abnormality detection mathematical model of network, obtains additional abnormality detection scoring.
Optionally, the judging unit is also used to score the abnormality detection and the additional abnormality detection scoring
It is compared with value with the scale.
The present invention also provides a kind of computer readable storage mediums, are stored thereon with computer instruction, the computer can
Reading storage medium is non-volatile memory medium or non-transitory storage media, and the computer instruction executes any of the above-described when running
The equipment of item uses the step of abnormality determination method.
Abnormity determining device is used the present invention also provides a kind of equipment, including memory and processor, on the memory
It is stored with computer instruction, the computer instruction equipment that the processor executes any of the above-described when running is sentenced using abnormal
The step of determining method.
Compared with prior art, the technical solution of the embodiment of the present invention has the advantages that
By obtaining the incidence relation between the corresponding user of equipment, the user with incidence relation is configured to network of personal connections
Network；Obtain the attribute data of the relational network；The attribute data of relational network is inputted into abnormality detection mathematical model, is obtained different
Often detection scoring；Abnormality detection is scored and is compared with scale, determines whether relational network is abnormal, by abnormal relationship
The corresponding equipment of user in network is determined as using warping apparatus.Using the above scheme, the feelings in no worker monitor may be implemented
Under condition, the equipment for abnormal service condition occur and user are determined by data calculated result, is reducing artificial monitoring device
Economic cost while, reduce time cost.
Detailed description of the invention
Fig. 1 is the flow diagram that equipment provided in an embodiment of the present invention uses abnormality determination method；
Fig. 2 is the structural schematic diagram that equipment provided in an embodiment of the present invention uses abnormity determining device.
Specific embodiment
In the prior art, usually by manually to the Self-Service class equipment of no worker monitor and corresponding user carry out by
A investigation.
However, scheme higher cost in the prior art, and take a long time.
It, will be with incidence relation by obtaining the incidence relation between the corresponding user of equipment in the embodiment of the present invention
User is configured to relational network；Obtain the attribute data of the relational network；By the abnormal inspection of attribute data input of relational network
Mathematical model is surveyed, abnormality detection scoring is obtained；Abnormality detection is scored and is compared with scale, whether determines relational network
It is abnormal, the corresponding equipment of user in abnormal relational network is determined as using warping apparatus.Using the above scheme, Ke Yishi
In the case where present no worker monitor, the equipment for abnormal service condition occur and user are determined by data calculated result, is being subtracted
While having lacked the economic cost of artificial monitoring device, time cost is reduced.
It is understandable to enable above-mentioned purpose of the invention, feature and beneficial effect to become apparent, with reference to the accompanying drawing to this
The specific embodiment of invention is described in detail.
Refering to fig. 1, the flow diagram that abnormality determination method is used for equipment provided in an embodiment of the present invention, leads to below
Specific steps are crossed to be described in detail.
Step S101 obtains the incidence relation between the corresponding user of equipment, and constructing has the user of incidence relation corresponding
Relational network.
In specific implementation, equipment can be set in retail shop.User can operate in equipment, select expectation purchase
The type of merchandize bought.Equipment can provide a user the amount of money section of debt-credit according to the type of merchandize of the selected expectation purchase of user.
The amount of money section for the debt-credit that user can provide according to equipment, the amount of money needed for selecting.Needed for equipment can be selected according to user
Number provides a user loaning bill.User buys corresponding commodity according to the loaning bill that equipment provides.
For example, user is in selecting " mobile phone " this type of merchandize in equipment, equipment is provided according to type of merchandize for user
1000 yuans to 10000 yuans lendable amount of money sections.The amount of money needed for user selects in equipment is 5000
Member, equipment provide a user 5000 yuan of loaning bill, 5000 yuan of purchase mobile phones that user uses equipment to provide.
In specific implementation, communication relations, connection relationship and identity of the incidence relation between user between user
Existing connection relationship between the users such as relationship.For example, user A and user B are the user of equipment, between user A and user B
There are message registrations, then determine there is incidence relation between user A and user B.
In specific implementation, for user when using equipment, equipment can obtain relevant information based on the authorization of user,
Among multiple information using the user of equipment, determining has the user of incidence relation.
In specific implementation, relational network may include single device relational network and more device relationships networks.
In specific implementation, single device relational network can be the relational network of the corresponding user of a certain equipment individual.Example
Such as, user A, user B and user C are the user of selected equipment, have incidence relation between user A and user B, user A with
There is incidence relation between user C, then determine with user A, user B and the relational network of user C building for the selected equipment
Corresponding single device relational network.
In specific implementation, more device relationships networks can be the relational network of the corresponding user of multiple equipment individual.Example
Such as, user A, user B are the user of equipment A, and the user that user C and user D are equipment B has between user A and user B and closes
Connection relationship has incidence relation between user A and user C, have incidence relation between user C and user D, then determines with user
A, user B, user C and the relational network of user D building are equipment A more device relationships networks corresponding with equipment B.
In the embodiment of the present invention, when the number of the equipment be it is multiple when, obtain the corresponding user of the multiple equipment it
Between incidence relation, construct have incidence relation connection user and the corresponding relational network of non-user.
In specific implementation, non-user is not used the people or crowd of equipment.
For example, user A is the user of equipment A, user B is the user of equipment B, exists between user A and non-user C and is associated with
Relationship, there are incidence relations between user B and non-user C, then are with the relational network that user A, user B and non-user C are constructed
The corresponding more device relationships networks of equipment A, equipment B.
In specific implementation, a certain equipment individual can correspond to multiple single device relational networks, and multiple equipment individual can be with
Corresponding multiple more device relationships networks.
Step S102 obtains the attribute data of the relational network.
In specific implementation, attribute data can be the relevant information of user authorization facility acquisition and user in equipment
The relevant information generated in use process.
In the embodiment of the present invention, the attribute data may include following at least one: number, incidence relation quantity.
In specific implementation, the incidence relation may include: in-degree incidence relation and out-degree incidence relation.For example, needle
It is out-degree incidence relation by the incidence relation that user A is directed toward other users for user A, is directed toward user A's by other users
Incidence relation is in-degree incidence relation.
In specific implementation, incidence relation quantity is the quantity of incidence relation present in relational network.For example, single device
In relational network, there is incidence relation between user A and user B, there is incidence relation, then this sets up between user A and user C
Incidence relation quantity in standby relational network is 2.
The attribute data of the relational network is inputted abnormality detection mathematical model by step S103, is obtained abnormality detection and is commented
Point.
In specific implementation, the case where user can be violated to criterion used in connection with and relevant contract when using equipment
It is considered as equipment and uses exception.
In specific implementation, abnormality detection mathematical model is constructed by abnormality detection mathematical algorithm.
In specific implementation, the abnormality detection mathematical model compares the attribute data of relational network and normal data
It is right, abnormality detection scoring is calculated.For example, when the attribute data of input is number, it is different when corresponding normal data is 5
Often detection mathematical model tends to number being evaluated as exception beyond 5 relational network.
In specific implementation, the abnormality detection scoring that abnormality detection mathematical model is calculated can be used for characterization of relation net
There is abnormal degree in network.
In specific implementation, when inputting the attribute data of multiple types, the attribute data of various species can be carried out
Weighted calculation, weight can be set by manager according to practical application scene accordingly.
The abnormality detection is scored and is compared with preset scale, determines the relational network by step S104
It is whether abnormal, and the corresponding equipment of user in abnormal relational network is determined as using warping apparatus.
In specific implementation, the scale shows which kind of intensity of anomaly relational network is judged as exception.Standard
The specific number of scoring can be set by manager according to practical application scene accordingly.
In specific implementation, when abnormality detection scoring is higher than scale, it is possible to determine that relational network is abnormal.
In specific implementation, when relational network exception, the corresponding equipment of user in relational network is then judged as making
Use warping apparatus.
In the embodiment of the present invention, when detecting in the relational network in the presence of user is newly increased, by the relational network
The corresponding attribute data input abnormality detection mathematical model of user is inside newly increased, additional abnormality detection scoring is obtained.
In the embodiment of the present invention, when detecting the relational network newly increased, by the category of the relational network newly increased
Property data input abnormality detection mathematical model, obtain additional abnormality detection scoring.
In the embodiment of the present invention, by abnormality detection scoring with additional abnormality detection scoring and value and the scale into
Row compares.
In specific implementation, as the time that equipment investment uses increases, corresponding user is consequently increased, and is increased in user
After adding, new user may be increased in known relational network, it is also possible to will increase new relational network, in above situation
When appearance, the attribute data of the part newly increased is only calculated, obtains adding abnormality detection scoring accordingly.Abnormality detection is scored
Score with additional abnormality detection and value is compared with the scale, and then whether predicting relation network is abnormal, to subtract
Few calculation amount, promotes the efficiency of determination flow.
In the embodiment of the present invention, equipment can be deployed in device end using the related algorithm of abnormality determination method, by setting
It is standby to be executed, it can also be deployed in the background management system of equipment, executed by background management system.
Therefore by obtaining the incidence relation between the corresponding user of equipment, the user with incidence relation is constructed
Corresponding relational network；Obtain the attribute data of the relational network；The attribute data of relational network is inputted into abnormality detection number
Model is learned, abnormality detection scoring is obtained；Abnormality detection is scored and is compared with scale, determines whether relational network is different
Often, the corresponding equipment of user in abnormal relational network is determined as using warping apparatus.Using the above scheme, it may be implemented
In the case where no worker monitor, the equipment for abnormal service condition occur and user are determined by data calculated result, is being reduced
While the economic cost of artificial monitoring device, time cost is reduced.
Referring to Fig.2, it uses the structural schematic diagram of abnormity determining device 20 for equipment provided in an embodiment of the present invention, wherein
Specifically include: construction unit 201, for obtaining the incidence relation between the corresponding user of equipment, constructing has incidence relation
The corresponding relational network of user；Acquiring unit 202, for obtaining the attribute data of the relational network；Computing unit 203 is used
In the attribute data of the relational network is inputted abnormality detection mathematical model, abnormality detection scoring is obtained；Judging unit 204,
It being compared for scoring the abnormality detection with preset scale, determining whether the relational network is abnormal, and will
The corresponding equipment of user in abnormal relational network is determined as using warping apparatus.
In the embodiment of the present invention, the construction unit 201 can be also used for obtaining when the number of the equipment is multiple
The incidence relation between the corresponding user of the multiple equipment is taken, constructing has the user of incidence relation connection and non-user corresponding
Relational network.
In the embodiment of the present invention, the attribute data may include following at least one: number, incidence relation quantity；Institute
Stating incidence relation includes: in-degree incidence relation and out-degree incidence relation.
In the embodiment of the present invention, the computing unit 203 can be also used for detecting in the relational network exist newly
When increasing user, the corresponding attribute data input abnormality detection mathematical model of user will be newly increased in the relational network, is obtained
Additional abnormality detection scoring.
In the embodiment of the present invention, the computing unit 203 be can be also used for when detecting the relational network newly increased,
The attribute data of the relational network newly increased is inputted into abnormality detection mathematical model, obtains additional abnormality detection scoring.
In the embodiment of the present invention, the judging unit 204 can be also used for scoring the abnormality detection and add with described
Abnormality detection scores and value is compared with the scale.
The present invention also provides a kind of computer readable storage mediums, are stored thereon with computer instruction, the computer can
Reading storage medium is non-volatile memory medium or non-transitory storage media, and it is real that the present invention is executed when the computer instruction is run
Apply the step of equipment of example offer is using abnormality determination method.
Abnormity determining device is used the present invention also provides a kind of equipment, including memory and processor, on the memory
It is stored with computer instruction, computer instruction processor execution equipment use provided in an embodiment of the present invention when running
The step of abnormality determination method.
Those of ordinary skill in the art will appreciate that all or part of the steps in the various methods of above-described embodiment is can
It is completed with indicating relevant hardware by program, which can store in computer readable storage medium, and storage is situated between
Matter may include: ROM, RAM, disk or CD etc..
Although present disclosure is as above, present invention is not limited to this.Anyone skilled in the art are not departing from this
It in the spirit and scope of invention, can make various changes or modifications, therefore protection scope of the present invention should be with claim institute
Subject to the range of restriction.
Claims (14)
1. a kind of equipment uses abnormality determination method characterized by comprising
The incidence relation between the corresponding user of equipment is obtained, the corresponding relational network of user with incidence relation is constructed；
Obtain the attribute data of the relational network；
The attribute data of the relational network is inputted into abnormality detection mathematical model, obtains abnormality detection scoring；
The abnormality detection is scored and is compared with preset scale, determines whether the relational network is abnormal, and will
The corresponding equipment of user in abnormal relational network is determined as using warping apparatus.
2. equipment according to claim 1 uses abnormality determination method, which is characterized in that described to obtain the use for using equipment
Incidence relation between family constructs the corresponding relational network of user with incidence relation, comprising:
When the number of the equipment is multiple, the incidence relation between the corresponding user of the multiple equipment, building tool are obtained
The user of relevant connection and the corresponding relational network of non-user.
3. equipment according to claim 1 uses abnormality determination method, which is characterized in that the attribute data includes following
It is at least one: number, incidence relation quantity；The incidence relation includes: in-degree incidence relation and out-degree incidence relation.
4. equipment according to claim 1 uses abnormality determination method, which is characterized in that described by the relational network
Attribute data input abnormality detection mathematical model, obtain abnormality detection scoring after, further includes:
When detecting in the relational network in the presence of user is newly increased, the corresponding category of user will be newly increased in the relational network
Property data input abnormality detection mathematical model, obtain additional abnormality detection scoring.
5. equipment according to claim 1 uses abnormality determination method, which is characterized in that described by the relational network
Attribute data input abnormality detection mathematical model, obtain abnormality detection scoring after, further includes:
When detecting the relational network newly increased, the attribute data of the relational network newly increased is inputted into abnormality detection number
Model is learned, additional abnormality detection scoring is obtained.
6. equipment according to claim 4 or 5 uses abnormality determination method, which is characterized in that described by the abnormal inspection
Assessment point is compared with preset scale, comprising:
Abnormality detection scoring is scored with the additional abnormality detection and value is compared with the scale.
7. a kind of equipment uses abnormity determining device characterized by comprising
Construction unit, for obtaining the incidence relation between the corresponding user of equipment, constructing has the user of incidence relation corresponding
Relational network；
Acquiring unit, for obtaining the attribute data of the relational network；
Computing unit obtains abnormality detection and comments for the attribute data of the relational network to be inputted abnormality detection mathematical model
Point；
Judging unit being compared with preset scale for scoring the abnormality detection, determining the relational network
It is whether abnormal, and the corresponding equipment of user in abnormal relational network is determined as using warping apparatus.
8. equipment according to claim 7 uses abnormity determining device, which is characterized in that the construction unit is also used to
When the number of the equipment is multiple, the incidence relation between the corresponding user of the multiple equipment is obtained, building, which has, closes
The user of connection relationship connection and the corresponding relational network of non-user.
9. equipment according to claim 7 uses abnormity determining device, which is characterized in that the attribute data includes following
It is at least one: number, incidence relation quantity；The incidence relation includes: in-degree incidence relation and out-degree incidence relation.
10. equipment according to claim 7 uses abnormity determining device, which is characterized in that the computing unit is also used to
When detecting in the relational network in the presence of user is newly increased, the corresponding attribute number of user will be newly increased in the relational network
According to input abnormality detection mathematical model, additional abnormality detection scoring is obtained.
11. equipment according to claim 7 uses abnormity determining device, which is characterized in that the computing unit is also used to
When detecting the relational network newly increased, the attribute data of the relational network newly increased is inputted into abnormality detection mathematical modulo
Type obtains additional abnormality detection scoring.
12. equipment described in 0 or 11 uses abnormity determining device according to claim 1, which is characterized in that the judging unit,
With the additional abnormality detection scoring and value that is also used to score the abnormality detection is compared with the scale.
13. a kind of computer readable storage medium, is stored thereon with computer instruction, the computer readable storage medium is non-
Volatile storage medium or non-transitory storage media, which is characterized in that the computer instruction run when perform claim require 1~
6 described in any item equipment use the step of abnormality determination method.
14. a kind of equipment uses abnormity determining device, including memory and processor, computer is stored on the memory and is referred to
It enables, which is characterized in that the computer instruction 1~6 described in any item equipment of processor perform claim requirement when running
The step of using abnormality determination method.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
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| CN201910145792.4A CN109992435A (en) | 2019-02-26 | 2019-02-26 | Equipment uses abnormality determination method, device and computer storage medium |
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| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910145792.4A CN109992435A (en) | 2019-02-26 | 2019-02-26 | Equipment uses abnormality determination method, device and computer storage medium |
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| CN109992435A true CN109992435A (en) | 2019-07-09 |
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| CN201910145792.4A Pending CN109992435A (en) | 2019-02-26 | 2019-02-26 | Equipment uses abnormality determination method, device and computer storage medium |
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116701914A (en) * | 2023-06-21 | 2023-09-05 | 广东星云开物科技股份有限公司 | Hardware equipment abnormal use identification method, device, storage device and system |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104933621A (en) * | 2015-06-19 | 2015-09-23 | 天睿信科技术（北京）有限公司 | Big data analysis system and method for guarantee ring |
| CN107229951A (en) * | 2017-05-31 | 2017-10-03 | 北京知道创宇信息技术有限公司 | Predict method and computing device of the user with the presence or absence of malicious act |
| CN107945024A (en) * | 2017-12-12 | 2018-04-20 | 厦门市美亚柏科信息股份有限公司 | Identify that internet finance borrowing enterprise manages abnormal method, terminal device and storage medium |
| CN109064313A (en) * | 2018-07-20 | 2018-12-21 | 重庆富民银行股份有限公司 | Warning monitoring system after the loan of knowledge based graphical spectrum technology |
-
2019
- 2019-02-26 CN CN201910145792.4A patent/CN109992435A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104933621A (en) * | 2015-06-19 | 2015-09-23 | 天睿信科技术（北京）有限公司 | Big data analysis system and method for guarantee ring |
| CN107229951A (en) * | 2017-05-31 | 2017-10-03 | 北京知道创宇信息技术有限公司 | Predict method and computing device of the user with the presence or absence of malicious act |
| CN107945024A (en) * | 2017-12-12 | 2018-04-20 | 厦门市美亚柏科信息股份有限公司 | Identify that internet finance borrowing enterprise manages abnormal method, terminal device and storage medium |
| CN109064313A (en) * | 2018-07-20 | 2018-12-21 | 重庆富民银行股份有限公司 | Warning monitoring system after the loan of knowledge based graphical spectrum technology |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116701914A (en) * | 2023-06-21 | 2023-09-05 | 广东星云开物科技股份有限公司 | Hardware equipment abnormal use identification method, device, storage device and system |
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