CN109325335A - System safety method, device, computer equipment and storage medium - Google Patents
System safety method, device, computer equipment and storage medium Download PDFInfo
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F21/00—Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
- G06F21/30—Authentication, i.e. establishing the identity or authorisation of security principals
- G06F21/31—User authentication
- G06F21/32—User authentication using biometric data, e.g. fingerprints, iris scans or voiceprints
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
Abstract
The embodiment of the invention discloses a kind of system safety method, device, computer equipment and storage medium, include the following steps: to obtain the screen shot that display area shows picture；The screen shot is input in preset first nerves network model, and obtains the first classification results of the screen shot of the first nerves network model output；First classification results are compared with preset conduct programming information, when first classification results are consistent with the conduct programming information, respond the user instruction of the user.Due to the user behavior information that classification results are screen shot characterization, i.e. which kind of operation user carries out with computer equipment.Whether the conduct programming information and classification results set when using computer by comparison user is consistent, it can determine whether user is operated using behavior of the computer in planning, rather than computer is used to carry out other operation contents, it realizes and user is helped to give up the improper task using computer.
Description
Technical field
The present embodiments relate to network safety filed, especially a kind of system safety method, device, computer equipment and
Storage medium.
Background technique
Along with computer equipment popularizing in people's lives, rapidly expanded using the crowd of computer, and
Population of adolescent is exactly that computer newly uses crowd mainstream populations, and with the quickening of computer spreading speed, teenager makes
It is being reduced year by year with the age of computer.Since population of adolescent lacks ability of self control, and transition online can be led using computer
It causes teenager to suffer from " network addiction ", and then it is made to lack interest to other business in studying and living, teen-age growth is caused
Adverse effect.
In the prior art, to prevent teens online generally by the way of are as follows: its identity information is checked before online,
Forbid its online if confirming as teenager.
But the inventor of the invention has found under study for action, computer is used as most important information tool social now
One of, total ban is unfavorable for teen-age growth using computer.And reasonably guide user using computer, it is few to blueness
The study and growth in year all have help.
Summary of the invention
The embodiment of the present invention provides system safety method, the dress that one kind can guide the proper use of computer equipment of user
It sets, computer equipment and storage medium.
In order to solve the above technical problems, the technical solution that the embodiment of the invention uses is: providing a germline
System safety method, includes the following steps:
Obtain the screen shot that display area shows picture；
The screen shot is input in preset first nerves network model, and obtains the first nerves network mould
First classification results of the screen shot of type output, wherein first classification results are the user of screen shot characterization
Behavioural information；
First classification results are compared with preset conduct programming information, when first classification results and institute
State conduct programming information it is consistent when, respond the user instruction of the user.
Optionally, further include following step before the step of screen shot for obtaining display area display picture:
Acquire the facial image of the user；
The certificate image of the facial image and the preset user is input to preset nervus opticus network model
In, and obtaining the first judging result of the nervus opticus network model output, wherein the nervus opticus network model is instruction
Practice to convergence state and is used to judge the neural network model of image similarity；
When the content of first judging result characterization is the facial image and inconsistent certificate image, forbid described
The log on request of user.
Optionally, further include following step after the step of facial image of the acquisition user:
The facial image is input in preset third nerve network model, and obtains the third nerve network mould
Second classification results of type output, wherein the third nerve network model is that training is used to judge face figure to convergence state
As deflection direction and the neural network model of deflection angle；
Second classification results are compared with preset command information, when second classification results and the finger
When enabling information inconsistent, forbid the log on request of the user.
Optionally, described that first classification results are compared with preset conduct programming information, when described first
It further include following after the step of responding the user instruction of the user when classification results are consistent with the conduct programming information
Step:
Obtain the duration of the user behavior of the user；
The duration is compared with preset time threshold；
When the duration is greater than the time threshold, forbid the user instruction for responding the user.
Optionally, when the user behavior payment behavior of the user behavior information representation；It is described that described first classifies
As a result it is compared with preset conduct programming information, when first classification results are consistent with the conduct programming information,
Further include following step after the step of responding the user instruction of the user:
Identify the payment amount in the screen shot；
The payment amount is compared with preset amount of money threshold value；
When the payment amount is greater than the amount of money threshold value, forbid the user instruction for responding the user.
Optionally, described when the payment amount is greater than the amount of money threshold value, forbid the user for responding the user to refer to
Further include following step after the step of enabling:
Information warning is sent to preset associated terminal, wherein the information warning includes whether to agree to the inquiry of payment
Information；
Receive the return information that the associated terminal replys the information warning；
When the reply content of return information characterization is to agree to payment, the payment instruction of the user is executed.
Optionally, further include following step before described the step of sending information warning to preset associated terminal:
Obtain the payment accounts that the payment behavior uses；
The payment terminal number with payment accounts binding is obtained according to the payment accounts；
The terminal for determining the payment terminal number characterization is the associated terminal.
In order to solve the above technical problems, the embodiment of the present invention also provides a kind of system safety devices, comprising:
Module is obtained, the screen shot of picture is shown for obtaining display area；
Processing module, for the screen shot to be input in preset first nerves network model, and described in acquisition
First classification results of the screen shot of first nerves network model output, wherein first classification results are the picture
The user behavior information of screenshot characterization；
Execution module, for first classification results to be compared with preset conduct programming information, when described
When one classification results are consistent with the conduct programming information, the user instruction of the user is responded.
Optionally, the system safety devices further include:
First acquisition submodule, for acquiring the facial image of the user；
First processing submodule, it is default for the certificate image of the facial image and the preset user to be input to
Nervus opticus network model in, and obtain the first judging result of nervus opticus network model output, wherein described the
Two neural network models are the neural network model that training is used to judge image similarity to convergence state；
First implementation sub-module, for being the facial image and certificate figure when the content of first judging result characterization
As it is inconsistent when, forbid the log on request of the user.
Optionally, the system safety devices further include:
Second processing submodule for the facial image to be input in preset third nerve network model, and obtains
Take the second classification results of the third nerve network model output, wherein the third nerve network model is that training is extremely received
State is held back for judging the neural network model in facial image deflection direction and deflection angle；
Second implementation sub-module, for second classification results to be compared with preset command information, when described
When second classification results and described instruction information are inconsistent, forbid the log on request of the user.
Optionally, the system safety devices further include:
Second acquisition submodule, the duration of the user behavior for obtaining the user；
Third handles submodule, for the duration to be compared with preset time threshold；
Third implementation sub-module, for forbidding responding the user when the duration is greater than the time threshold
User instruction.
Optionally, when the user behavior payment behavior of the user behavior information representation；The system safety devices are also
Include:
First identifies submodule, for identification the payment amount in the screen shot；
Fourth process submodule, for the payment amount to be compared with preset amount of money threshold value；
4th implementation sub-module, for forbidding responding the user when the payment amount is greater than the amount of money threshold value
User instruction.
Optionally, the system safety devices further include:
First sending submodule, for sending information warning to preset associated terminal, wherein the information warning includes
Whether agreement payment query message；
First receives submodule, and the return information of the information warning is replied for receiving the associated terminal；
5th implementation sub-module, the reply content for characterizing when the return information are when agreeing to payment, described in execution
The payment instruction of user.
Optionally, the system safety devices further include:
Third acquisition submodule, the payment accounts used for obtaining the payment behavior；
5th processing submodule, for obtaining the payment terminal number with payment accounts binding according to the payment accounts
Code；
6th implementation sub-module, for determining that the terminal of the payment terminal number characterization is the associated terminal.
In order to solve the above technical problems, the embodiment of the present invention also provides a kind of computer equipment, including memory and processing
Device is stored with computer-readable instruction in the memory, when the computer-readable instruction is executed by the processor, so that
The processor executes the step of system safety method described above.
In order to solve the above technical problems, the embodiment of the present invention also provides a kind of storage Jie for being stored with computer-readable instruction
Matter, when the computer-readable instruction is executed by one or more processors, so that one or more processors execute above-mentioned institute
The step of stating system safety method.
The beneficial effect of the embodiment of the present invention is: prompting user to fill in using planning when user is using computer, this makes
With the operation content for using computer to carry out including user in planning.When user's formal operation computer, computer screen is obtained
The screen shot of curtain display content, screen shot is input in preset first nerves network model, and obtain classification results.
Due to the user behavior information that classification results are screen shot characterization, i.e. which kind of operation user carries out with computer equipment.
It is whether consistent with classification results by comparing the conduct programming information that user is set when using computer, it can determine user
Whether operated using behavior of the computer in being planned, rather than computer is used to carry out other operation contents.Work as user
Computer when carrying out other operation contents, forbid respond user operational order, force user by program operation computer into
Row response work, achieve the purpose that guide the proper use of computer of user, realize help user give up it is improper use computer
Task.
Detailed description of the invention
To describe the technical solutions in the embodiments of the present invention more clearly, make required in being described below to embodiment
Attached drawing is briefly described, it should be apparent that, drawings in the following description are only some embodiments of the invention, for
For those skilled in the art, without creative efforts, it can also be obtained according to these attached drawings other attached
Figure.
Fig. 1 is the basic procedure schematic diagram of system safety method in the embodiment of the present invention；
Fig. 2 is the flow diagram that the embodiment of the present invention verifies subscriber identity information；
Fig. 3 is the flow diagram of user of embodiment of the present invention living body verifying；
Fig. 4 is the flow diagram that the embodiment of the present invention limits that user uses duration；
Fig. 5 is the flow diagram paid on restricted line of the embodiment of the present invention；
Fig. 6 is the flow diagram that the embodiment of the present invention verifies excess disbursement；
Fig. 7 is the flow diagram that the embodiment of the present invention obtains associated terminal；
Fig. 8 is the basic structure block diagram of system safety devices of the embodiment of the present invention；
Fig. 9 is computer equipment of embodiment of the present invention basic structure block diagram.
Specific embodiment
In order to enable those skilled in the art to better understand the solution of the present invention, below in conjunction in the embodiment of the present invention
Attached drawing, technical scheme in the embodiment of the invention is clearly and completely described.
In some processes of the description in description and claims of this specification and above-mentioned attached drawing, contain according to
Multiple operations that particular order occurs, but it should be clearly understood that these operations can not be what appears in this article suitable according to its
Sequence is executed or is executed parallel, and serial number of operation such as 101,102 etc. is only used for distinguishing each different operation, serial number
It itself does not represent and any executes sequence.In addition, these processes may include more or fewer operations, and these operations can
To execute or execute parallel in order.It should be noted that the description such as " first " herein, " second ", is for distinguishing not
Same message, equipment, module etc., does not represent sequencing, does not also limit " first " and " second " and be different type.
Following will be combined with the drawings in the embodiments of the present invention, and technical solution in the embodiment of the present invention carries out clear, complete
Site preparation description, it is clear that described embodiments are only a part of the embodiments of the present invention, instead of all the embodiments.It is based on
Embodiment in the present invention, those skilled in the art's every other implementation obtained without creative efforts
Example, shall fall within the protection scope of the present invention.
Those skilled in the art of the present technique are appreciated that " terminal " used herein above, " terminal device " both include wireless communication
The equipment of number receiver, only has the equipment of the wireless signal receiver of non-emissive ability, and including receiving and emitting hardware
Equipment, have on bidirectional communication link, can execute two-way communication reception and emit hardware equipment.This equipment
It may include: honeycomb or other communication equipments, shown with single line display or multi-line display or without multi-line
The honeycomb of device or other communication equipments；PCS (Personal Communications Service, PCS Personal Communications System), can
With combine voice, data processing, fax and/or communication ability；PDA (Personal Digital Assistant, it is personal
Digital assistants), it may include radio frequency receiver, pager, the Internet/intranet access, web browser, notepad, day
It goes through and/or GPS (Global Positioning System, global positioning system) receiver；Conventional laptop and/or palm
Type computer or other equipment, have and/or the conventional laptop including radio frequency receiver and/or palmtop computer or its
His equipment." terminal " used herein above, " terminal device " can be it is portable, can transport, be mounted on the vehicles (aviation,
Sea-freight and/or land) in, or be suitable for and/or be configured in local runtime, and/or with distribution form, operate in the earth
And/or any other position operation in space." terminal " used herein above, " terminal device " can also be communication terminal, on
Network termination, music/video playback terminal, such as can be PDA, MID (Mobile Internet Device, mobile Internet
Equipment) and/or mobile phone with music/video playing function, it is also possible to the equipment such as smart television, set-top box.
Referring to Fig. 1, Fig. 1 is the basic procedure schematic diagram of system safety method in the present embodiment.
As shown in Figure 1, a kind of system safety method, includes the following steps:
S1100, the screen shot that display area shows picture is obtained；
User, by sending the instruction of system screenshot, obtains computer equipment and currently shows picture when using computer equipment
The screen shot in face.But not limited to this, according to the difference of concrete application scene, the acquisition of screenshot picture is not limited to this, one
In a little selectivity embodiments, computer equipment has independent video card equipment, and computer equipment need to only read video card at work
Rendering figure in the device space can obtain screen shot.
In present embodiment, the acquisition of screen shot is timing acquisition, for example, obtaining every 3 minutes primary but unlimited
In this, the acquisition duration of screen shot can be any length of time of setting.In some selective embodiments, screen shot
Acquisition can also be real-time perfoming.
S1200, the screen shot is input in preset first nerves network model, and obtains the first nerves
First classification results of the screen shot of network model output, wherein first classification results are screen shot characterization
User behavior information；
The screen shot that interception obtains is input in preset first nerves network model.Wherein, the first nerves
Network model can be convolutional neural networks model (CNN), but first nerves network model can also be: deep neural network
The distorted pattern of model (DNN), Recognition with Recurrent Neural Network model (RNN) or above-mentioned three kinds of network models.
In present embodiment, first nerves network model is extremely restrained by preparatory training, for by carrying out to screen shot
Feature extraction and classification obtain, the user behavior information that screen shot is characterized.User behavior information include using computer into
Row (being not limited to): play games, listen to music, do one's assignment and browse webpage etc..In some selective embodiments, user behavior letter
Breath can also be that the bad behavior that user uses computer equipment to carry out (is not limited to) network gambling, watches unhealthy films and television programs
Or it carries out exceeding the behaviors such as the payment by the transfer of accounts of its limit of power.
In the present embodiment, the training process of first nerves network model are as follows:
The training sample set of the present embodiment is obtained by web crawlers or existing image data base.Training sample is concentrated
Including different types of computer screenshot picture.
Neural network model is in training in present embodiment, using several training sample sets (such as 1,000,000),
In, each training sample set includes the screen shot of a computer equipment.Before being trained to screen shot, need pair
The user behavior for the characterization image that each training sample is concentrated is prejudged, and anticipation can be carried out manually, such as picture is cut
When content in figure is game picture, then demarcating anticipation result is " playing games ", can obtain all training by above-mentioned mode
The anticipation result of sample set.And it defines the classification that anticipation result is the screen shot and judges information.
Training sample set is sequentially inputted in first nerves network model, screen shot successively passes through first nerves network
The convolutional layer of model, full articulamentum and classification layer.The result of classification layer output is the excitation output of first nerves network model
Screen shot classification results.In present embodiment, the classification results of first nerves network model are user's row of model judgement
For information.The result of definition excitation output is classification referring to information.
Category of model referring to the excited data that information is that first nerves network model is exported according to the screen shot of input,
It is not trained to before convergence in first nerves network model, classification is the biggish numerical value of discreteness referring to information, when the first mind
It is not trained to convergence through network model, classification is metastable data referring to information.
Judge classification judges whether information is consistent referring to information and classification by the loss function of first nerves network model.
Loss function is configured as in detection first nerves network model category of model referring to information, with it is intended that classification judge
The whether consistent detection function of information.When the output result of first nerves network model and classification judge the expectation of information
As a result it when inconsistent, needs to be corrected the weight in first nerves network model by inverse algorithms, so that first nerves
The output result of network model judges that the expected result of information is identical with classification.
When first nerves network model classification output output result and classification judge information expected result it is inconsistent when,
It needs to be corrected the weight in first nerves network model, so that the output result of first nerves network model is sentenced with classification
The expected result of disconnected information is identical.(such as 1,000,000 screen shots) are trained using multiple training samples when training, are passed through
Training and correction repeatedly, when the classification of first nerves network model output category data and each training sample is referring to information comparison
When accuracy reaches and (be not limited to) 99.9%, training terminates.At this point, the training of first nerves network model terminates, first nerves net
Network model is trained to convergence, and the user behavior information that can be characterized to screenshot picture is accurately judged.
S1300, first classification results are compared with preset conduct programming information, when the first classification knot
When fruit is consistent with the conduct programming information, the user instruction of the user is responded.
The first classification results (i.e. user behavior information) that the first nerves network model of training to convergence state is exported
It is compared with conduct programming information.Then user is allowed to continue to use computer when comparing consistent, then forbidden when comparing inconsistent
Execute user instruction.
When conduct programming information is that user enters operation interface using computer equipment, there is using computers to plan
Book, user need to need to fill in the actual conditions for using computer in planning book according to spontaneous, and format is XX moment-XX
Moment, which carries out " exercises on-line is write ", etc. using computer, will use the time of computer to be listed one by one with item.With
The using computers to plan book that family is filled in is defined as conduct programming information.
It in some selective embodiments, needs to audit the conduct programming information of user, such as makes in planning book
It must not exceed 1 hour with the time span that computer equipment is entertained, or cannot be filled in behavior planning information all
Such as, the unsound information such as pornofilm or gambling is seen.
When the first classification results and conduct programming information are inconsistent, then show the thing that user uses computer equipment to carry out
The planning usage behavior that Xiang Yuqi is filled in is different, at this point, computer equipment stops the operational order of response user.In some selections
Property embodiment in, forbid after executing user instruction, will show in the picture that is characterized to user behavior planning information of screen switching, or
Then the application program that person starts the characterization of conduct programming information representation continues to execute the new command of user.It is used with reaching guidance
The purpose of the proper use of computer equipment in family.
Above embodiment prompts user to fill in using planning when user is using computer, and it includes using in planning that this, which is used,
The operation content that family uses computer to carry out.When user's formal operation computer, the picture that computer screen shows content is obtained
Screen shot is input in preset first nerves network model, and obtains classification results by face screenshot.Since classification results are
Which kind of operation the user behavior information of screen shot characterization, i.e. user carry out with computer equipment.Existed by comparing user
Whether the conduct programming information set when using computer is consistent with classification results, can determine whether user uses computer
Behavior operation in being planned, rather than computer is used to carry out other operation contents.When subscriber computer is carrying out it
When his operation content, forbid the operational order for responding user, the work for forcing user to be responded by program operation computer reaches
To the purpose of the guidance proper use of computer of user, realizes and user is helped to give up the improper task using computer.
In some selective embodiments, to prevent from not meeting the teens online at online age, by verifying its identity
The mode of certificate (identity card, residence booklet or passport etc.) limits its online.But.Adolescents are by claiming the identity of others fraudulently
The event that information is surfed the Internet but remains incessant after repeated prohibition, and therefore, it is necessary to falsely use the behavior that identity is surfed the Internet to teenager to limit.
Referring to Fig. 2, Fig. 2 is the flow diagram that the present embodiment verifies subscriber identity information.
As shown in Fig. 2, further including following step before step S1100:
S1011, the facial image for acquiring the user；
User opens the power supply of computer equipment when using computer, when user enters operation system interface, needs
The identity of user is verified, the first step of verifying is the camera or peripheral apparatus integrated by computer equipment
The man face image acquiring of camera progress user.
S1012, the certificate image of the facial image and the preset user is input to preset nervus opticus net
In network model, and obtain the first judging result of the nervus opticus network model output, wherein the nervus opticus network mould
Type is the neural network model that training is used to judge image similarity to convergence state；
The facial image collected is input in nervus opticus network model, nervus opticus network model is to train extremely
Convergence state is used to judge the neural network model of image similarity.After facial image is input to nervus opticus network model,
Nervus opticus network model exports the first judging result.First judging result is nervus opticus network model to facial image and pre-
The comparison result of the certificate photograph similarity of storage, or the note that facial image and pre-stored registration user account when retains
The comparison result of volume head portrait similarity.
Wherein, the nervus opticus network model can be convolutional neural networks model (CNN), but nervus opticus network
Model can also be: deep neural network model (DNN), Recognition with Recurrent Neural Network model (RNN) or above-mentioned three kinds of network models
Distorted pattern.
In the present embodiment, the training process of nervus opticus network model are as follows:
The training sample set of the present embodiment is obtained by web crawlers or existing image data base.Training sample is concentrated
Including different types of facial image.
Neural network model is in training in present embodiment, using several training sample sets (such as 1,000,000),
In, each training sample set includes a face image data pair, including a facial image, and is carried out with the facial image
The comparison image of comparison, wherein comparing image is also facial image, and the same training sample is concentrated, facial image and comparison chart
As can it is identical also can be different.Before being trained to screen shot, the image table concentrated to each training sample is needed
The user behavior of sign is prejudged, and anticipation can be carried out manually, such as two pictures that training sample is concentrated are same
When facial image in personal different times or space, judges that two images are identical, can must own by above-mentioned mode
The anticipation result of training sample set.And it defines the classification that anticipation result is the screen shot and judges information.
Training sample set is sequentially inputted in nervus opticus network model, screen shot successively passes through nervus opticus network
The convolutional layer of model, full articulamentum and classification layer.The result of classification layer output is the excitation output of nervus opticus network model
Compare classification results.In present embodiment, the classification results of nervus opticus network model are the user behavior letter of model judgement
Breath.The result of definition excitation output is classification referring to information.Classifying is the face of nervus opticus network model extraction referring to information
The comparison result of the feature vector of image and comparison image.
Category of model referring to the excited data that information is that nervus opticus network model is exported according to the screen shot of input,
It is not trained to before convergence in nervus opticus network model, classification is the biggish numerical value of discreteness referring to information, when the second mind
It is not trained to convergence through network model, classification is metastable data referring to information.
Judge classification judges whether information is consistent referring to information and classification by the loss function of nervus opticus network model.
Loss function is configured as in detection nervus opticus network model category of model referring to information, with it is intended that classification judge
The whether consistent detection function of information.When the output result of nervus opticus network model and classification judge the expectation of information
As a result it when inconsistent, needs to be corrected the weight in nervus opticus network model by inverse algorithms, so that nervus opticus
The output result of network model judges that the expected result of information is identical with classification.
When nervus opticus network model classification output output result and classification judge information expected result it is inconsistent when,
It needs to be corrected the weight in nervus opticus network model, so that the output result of nervus opticus network model is sentenced with classification
The expected result of disconnected information is identical.Be trained when training using multiple training samples (such as 100 training sample sets are instructed repeatedly
Practice), by training and correction repeatedly, when the classification reference of nervus opticus network model output category data and each training sample
When information comparison accuracy reaches and (be not limited to) 99.9%, training terminates.At this point, the training of nervus opticus network model terminates, the
Two neural network models are trained to convergence, and the user behavior information that can be characterized to facial image is accurately judged.
S1013, when first judging result characterization content be the facial image and inconsistent certificate image when, prohibit
The only log on request of the user.
When the content of the first judging result characterization is the facial image and inconsistent certificate image, that is, determine face figure
As low with the similarity between the certificate photo of the certificate of user or the user images of retention, verification result is inconsistent, at this point, meter
Calculate machine equipment forbid execute user log on request, user can not login of operating system interface, control computer equipment.
It is verified by user images, can identify whether user has the legal right surfed the Internet using computer equipment.
Meanwhile can also reinforce computer equipment using safe.
In some embodiments, to prevent user from cheating using other people facial image photo to computer, at
Function unlocks login of operating system interface, needs when user logs in, and whether identification user is living body.Referring to Fig. 3, Fig. 3 is this
The flow diagram of embodiment user's living body verifying.
As shown in figure 3, after step S1011 further include:
S1021, the facial image is input in preset third nerve network model, and obtains the third nerve
Second classification results of network model output, wherein the third nerve network model is that training is used to judge to convergence state
Facial image deflects the neural network model of direction and deflection angle；
When acquiring user's facial image, computer equipment is sent out by built-in impression equipment or peripheral hardware sound equipment to user
Instruction is sent, command content is the angle for controlling user's face in camera visual field, for example, deflecting 30 degree to the left, being lifted up
45 degree or the order such as 60 degree of deflection to the right.After voice command publication, computer equipment acquires the facial image of user.And it will
The facial image is input in third nerve network model.
Third nerve network model is that training to convergence state is used to judge facial image deflection direction and deflection angle
Neural network model.Wherein, the third nerve network model can be convolutional neural networks model (CNN), but third is refreshing
Can also be through network model: deep neural network model (DNN), Recognition with Recurrent Neural Network model (RNN) or above-mentioned three kinds of nets
The distorted pattern of network model.
In this reality embodiment, third nerve network model is identical as the training method of first nerves network model, still
Trained sample set is changed to facial image by screen shot, and classification results are the inclined of human body head by user behavior information change
Turn direction and deflection angle.The people of user can be accurately judged by the third nerve network model of a large amount of training sample set
The deflection direction on body head and deflection angle.
S1022, second classification results are compared with preset command information, when second classification results with
When described instruction information is inconsistent, forbid the log on request of the user.
Second classification results of the output of third nerve network model are compared with the command information of setting, instruction letter
Breath and computer equipment control the information command that user face rotates by sound equipment.For example, command information is " to turn right
30 ° ", the face deflection angle that third nerve network model judges is " deflecting 30 degree to the right ", and the two fits like a glove, then sentences
Disconnected result is always.Show that user does not follow if the face deflection angle that third nerve network model judges is " 0 ° of deflection "
Execution is rotated, at this point, comparison result is inconsistent, computer equipment forbids responding the log on request of user.
By carrying out In vivo detection to facial image, capable of effectively avoiding illegal user or being not suitable for the user of online
The problem of computer is logged in is deceived by photo mode.
In some selective embodiments, the time of computer equipment is used for control user, is used by counting user
The duration of computer, and it is compared with the duration threshold value of setting, when being more than preset duration, computer equipment forbids ringing
Using any operational order at family, to guide the proper use of computer equipment of user.
Figure Fig. 4 is please referred to, Fig. 4 is the flow diagram that the present embodiment limits that user uses duration.
As shown in figure 4, further including following step after step S1300:
S1311, obtain the user user behavior duration；
Computer equipment passes through the starting duration of built-in timer logger computer equipment, then passes through the side of timing acquisition
Formula checks the use duration in timer, when use a length of user user behavior duration.
S1312, the duration is compared with preset time threshold；
The duration that will acquire is compared with the time threshold of setting, according to the difference of user behavior, different use
The time threshold of family behavior is also different.For example, it is 1 hour that user behavior, which is time threshold when playing games, user behavior is
The time threshold of study is 2 hours, and user must not exceed 3 hours at total surf time.
The total duration that computer equipment is used according to the duration of different user behaviors or user, with corresponding use
Family behavior is always compared using threshold value.
S1313, when the duration be greater than the time threshold when, forbid the user instruction for responding the user.
When comparison result duration is greater than time threshold, computer equipment forbids the operation for responding user's sending to refer to
It enables, to force user to stop using computer equipment, reaches the problem that control user uses computer equipment time-out.
In some embodiments, the minor user in part is when using computer equipment, commonly using the letter of parent
Consume or supplement with money on line with card, deposit card or electronic account.Therefore, it is necessary to additionally to underage users on line
Consumption is controlled.Referring to Fig. 5, Fig. 5 is the flow diagram paid on the present embodiment restricted line.
As shown in figure 5, after step S1300 further include:
Payment amount in S1321, the identification screen shot；
For user when being paid using computer equipment, computer equipment is by obtaining screen shot, and by the screenshot
It is input in first nerves network model, since the classification results being equipped in the classification results in first nerves network model include
Therefore payment behavior can judge whether user is paying by the classification results of first nerves network model.
Confirm that user after paying, is extracted the payment amount in screen shot by Word Input technology,
Pictograph is recognized for example, by using OCR, extracts the payment amount in screen shot.
S1322, the payment amount is compared with preset amount of money threshold value；
The payment amount read is compared with preset amount of money threshold value, amount of money threshold value is the teenage of parent's setting
Maximum spending amount can be configured by parent according to self-consumption ability or the amount for consuming control to child.
S1323, when the payment amount be greater than the amount of money threshold value when, forbid the user instruction for responding the user.
When payment amount is greater than amount of money threshold value, computer equipment forbids responding the user instruction of the user.Pass through control
The upper limit value of the customer transaction amount of money processed can control and control the consuming capacity of user in the range of restriction, will not be because of use
The impulsion at family brings unnecessary trouble to household.
In some embodiments, to avoid user from hiding the monitoring of above-mentioned technical proposal by way of repeatedly paying,
By the consumer record to set period of time (for example, one day) interior user, the sum of cumulative when consumer record is greater than amount of money threshold value
When, forbid the user instruction for responding the user.
In some embodiments, when user carries out excess disbursement, ability after the approval of parent or guardian is obtained
It is able to carry out execution.Referring to Fig. 6, Fig. 6 is the flow diagram that the present embodiment verifies excess disbursement.
As shown in fig. 6, further including following step after step S1323:
S1411, information warning is sent to preset associated terminal, wherein the information warning includes whether to agree to payment
Query message；
When detecting that user carrying out payment behavior, and when the amount of money paid is greater than the amount of money threshold value of setting, to associated
Terminal sends information warning, includes query message in information warning.For example, user XX paid on XX platform 5000 yuan it is whole, be
The no information warning for agreeing to payment.
Wherein associated terminal be when being registered by administrator right reserve telephone number, mailbox number or other immediately
Communicate account etc..In some embodiments, associated terminal refers to that payment accounts that user uses or draw bank are stuck in note
Reserved telephone number when volume.
S1412, the return information that the associated terminal replys the information warning is received；
The return information of the reply information warning of associated terminal transmission is got, the content of return information is whether to agree to use
Family is paid.
S1413, when the return information characterization reply content be agree to payment when, the payment for executing the user refers to
It enables.
When the reply content that the content of return information characterizes is to agree to payment, the payment that computer equipment executes user refers to
It enables, the payment information of user is sent to corresponding server end, realizes payment.If the content of return information characterization is to disagree
When payment, then forbid the delivery operation for executing user.
In some embodiments, for more accurately by the corresponding guardian of payment behavior transmission of user or account
Holder.When user pays, associated terminal is required to look up.Referring to Fig. 7, Fig. 7 is that the present embodiment obtains associated terminal
Flow diagram.
As shown in fig. 7, further including following step before step S1411:
S1401, the payment accounts that the payment behavior uses are obtained；
Obtain the electronic account that used payment card number or payment use when user carries out payment behavior.Meanwhile it reading
Take the payment accounts of the family payment card number used and electronic account.
S1402, the payment terminal number with payment accounts binding is obtained according to the payment accounts；
In present embodiment, when administrator (parent) registers, all Payment Cards being capable of providing in collector are understood
Number and electronic account information but do not include password, and the payment card number and corresponding payment terminal number of electronic account.
The payment terminal number with payment accounts binding can be directly obtained by the information of electronic account.
In some embodiments, after getting payment accounts, identify the payment accounts type and bank of deposit or
Platform.Above-mentioned identification can recognize rule by each bank's card number and be identified, then to the service where the mechanism of registration
Device system sends solicited message, the payment terminal number of request payment account binding.
S1403, the terminal for determining the payment terminal number characterization are the associated terminal.
After getting the payment terminal number of payment account binding, determine the terminal of payment terminal number characterization for association
Terminal.By the confirmation of associated terminal, information warning can be sent to maximally related user terminal, protected to greatest extent
The proprietary immediate interest of payment account.
In order to solve the above technical problems, the embodiment of the present invention also provides a kind of system safety devices.
Referring to Fig. 8, Fig. 8 is the basic structure block diagram of the present embodiment system safety devices.
As shown in figure 8, a kind of system safety devices, comprising: obtain module 2100, processing module 2200 and processing module
2300.Wherein, it obtains module 2100 and is used to obtain the screen shot that display area shows picture；Processing module 2200 will be for that will draw
Face screenshot is input in preset first nerves network model, and obtain the output of first nerves network model screen shot the
One classification results, wherein the first classification results are the user behavior information of screen shot characterization；Execution module 2300 is used for the
One classification results are compared with preset conduct programming information, when the first classification results and conduct programming information are inconsistent,
Forbid responding the user instruction of user.
System safety devices prompt user to fill in using planning when user is using computer, and it includes using in planning that this, which is used,
The operation content that family uses computer to carry out.When user's formal operation computer, the picture that computer screen shows content is obtained
Screen shot is input in preset first nerves network model, and obtains classification results by face screenshot.Since classification results are
Which kind of operation the user behavior information of screen shot characterization, i.e. user carry out with computer equipment.Existed by comparing user
Whether the conduct programming information set when using computer is consistent with classification results, can determine whether user uses computer
Behavior operation in being planned, rather than computer is used to carry out other operation contents.When subscriber computer is carrying out it
When his operation content, forbid the operational order for responding user, the work for forcing user to be responded by program operation computer reaches
To the purpose of the guidance proper use of computer of user, realizes and user is helped to give up the improper task using computer.
In some selective embodiments, system safety devices further include: the first acquisition submodule, the first processing submodule
With the first implementation sub-module.Wherein, the first acquisition submodule is used to acquire the facial image of user；First processing submodule is used for
The certificate image of facial image and preset user is input in preset nervus opticus network model, and obtains nervus opticus
First judging result of network model output, wherein nervus opticus network model is that training is used to judge image to convergence state
The neural network model of similarity；First implementation sub-module is used to when the content of the first judging result characterization be facial image and card
When part image is inconsistent, forbid the log on request of user.
In some selective embodiments, system safety devices further include: second processing submodule and second executes submodule
Block.Wherein, second processing submodule is for facial image to be input in preset third nerve network model, and obtains third
Second classification results of neural network model output, wherein third nerve network model is that training is used to judge to convergence state
Facial image deflects the neural network model of direction and deflection angle；Second implementation sub-module is for by the second classification results and in advance
If command information be compared, when the second classification results and command information it is inconsistent when, forbid the log on request of user.
In some selective embodiments, system safety devices further include: the second acquisition submodule, third handle submodule
With third implementation sub-module.Wherein, the second acquisition submodule is used to obtain the duration of the user behavior of user；Third processing
Submodule is for duration to be compared with preset time threshold；Third implementation sub-module is used to be greater than when duration
When time threshold, forbid the user instruction for responding user.
In some selective embodiments, when the user behavior payment behavior of user behavior information representation；System safety
Device further include: the first identification submodule, fourth process submodule and the 4th implementation sub-module.Wherein, the first identification submodule
Payment amount in screen shot for identification；Fourth process submodule is used to carry out payment amount and preset amount of money threshold value
It compares；4th implementation sub-module is used for the user instruction for forbidding responding user when payment amount is greater than amount of money threshold value.
In some selective embodiments, system safety devices further include: the first sending submodule, first receive submodule
5th implementation sub-module.Wherein, the first sending submodule is used to send information warning to preset associated terminal, wherein warning
Information includes whether to agree to the query message of payment；First, which receives submodule, replys returning for information warning for receiving associated terminal
Complex information；5th implementation sub-module is used to execute the payment of user when the reply content that return information characterizes is to agree to payment
Instruction.
In some selective embodiments, system safety devices further include: third acquisition submodule, third acquisition submodule
With the 5th processing submodule.Wherein, third acquisition submodule is for obtaining the payment accounts that payment behavior uses；5th processing
Module is used to obtain the payment terminal number with payment accounts binding according to payment accounts；6th implementation sub-module is for determining branch
The terminal for paying termination number characterization is associated terminal.
In order to solve the above technical problems, the embodiment of the present invention also provides computer equipment.It is this referring specifically to Fig. 9, Fig. 9
Embodiment computer equipment basic structure block diagram.
As shown in figure 9, the schematic diagram of internal structure of computer equipment.The computer equipment includes being connected by system bus
Processor, non-volatile memory medium, memory and network interface.Wherein, the non-volatile memories of the computer equipment are situated between
Matter is stored with operating system, database and computer-readable instruction, can be stored with control information sequence in database, the computer
When readable instruction is executed by processor, processor may make to realize a kind of system safety method.The processor of the computer equipment
For providing calculating and control ability, the operation of entire computer equipment is supported.It can be stored in the memory of the computer equipment
There is computer-readable instruction, when which is executed by processor, processor may make to execute a kind of system safety
Method.The network interface of the computer equipment is used for and terminal connection communication.It will be understood by those skilled in the art that showing in Fig. 9
Structure out, only the block diagram of part-structure relevant to application scheme, does not constitute and is applied to application scheme
The restriction of computer equipment thereon, specific computer equipment may include than more or fewer components as shown in the figure, or
Person combines certain components, or with different component layouts.
Processor obtains module 2100, processing module 2200 and execution module for executing in present embodiment in Fig. 8
2300 concrete function, program code and Various types of data needed for memory is stored with the above-mentioned module of execution.Network interface is used for
To the data transmission between user terminal or server.Memory in present embodiment is stored with facial image critical point detection
Program code needed for executing all submodules in device and data, server are capable of the program code and data of invoking server
Execute the function of all submodules.
Computer equipment prompts user to fill in using planning when user is using computer, and it includes user in planning that this, which is used,
The operation content carried out using computer.When user's formal operation computer, the picture that computer screen shows content is obtained
Screen shot is input in preset first nerves network model, and obtains classification results by screenshot.Since classification results are picture
Which kind of operation the user behavior information of face screenshot characterization, i.e. user carry out with computer equipment.Made by comparing user
Whether the conduct programming information set when with computer is consistent with classification results, can determine whether user uses computer to exist
Behavior operation in being planned, rather than computer is used to carry out other operation contents.When subscriber computer is carrying out other
When operation content, forbid the operational order for responding user, the work for forcing user to be responded by program operation computer reaches
The purpose of the proper use of computer of user is guided, realizes and user is helped to give up the improper task using computer.
The present invention also provides a kind of storage mediums for being stored with computer-readable instruction, and the computer-readable instruction is by one
When a or multiple processors execute, so that one or more processors execute system safety method described in any of the above-described embodiment
Step.
Those of ordinary skill in the art will appreciate that realizing all or part of the process in above-described embodiment method, being can be with
Relevant hardware is instructed to complete by computer program, which can be stored in a computer-readable storage and be situated between
In matter, the program is when being executed, it may include such as the process of the embodiment of above-mentioned each method.Wherein, storage medium above-mentioned can be
The non-volatile memory mediums such as magnetic disk, CD, read-only memory (Read-Only Memory, ROM) or random storage note
Recall body (Random Access Memory, RAM) etc..
It should be understood that although each step in the flow chart of attached drawing is successively shown according to the instruction of arrow,
These steps are not that the inevitable sequence according to arrow instruction successively executes.Unless expressly stating otherwise herein, these steps
Execution there is no stringent sequences to limit, can execute in the other order.Moreover, at least one in the flow chart of attached drawing
Part steps may include that perhaps these sub-steps of multiple stages or stage are not necessarily in synchronization to multiple sub-steps
Completion is executed, but can be executed at different times, execution sequence, which is also not necessarily, successively to be carried out, but can be with other
At least part of the sub-step or stage of step or other steps executes in turn or alternately.
Claims (10)
1. a kind of system safety method, which is characterized in that include the following steps:
Obtain the screen shot that display area shows picture；
The screen shot is input in preset first nerves network model, and it is defeated to obtain the first nerves network model
First classification results of screen shot out, wherein first classification results are the user behavior of screen shot characterization
Information；
First classification results are compared with preset conduct programming information, when first classification results and the row
When consistent for planning information, the user instruction of the user is responded.
2. system according to claim 1 safety method, which is characterized in that the picture for obtaining display area and showing picture
Further include following step before the step of face screenshot:
Acquire the facial image of the user；
The certificate image of the facial image and the preset user is input in preset nervus opticus network model, and
Obtain the first judging result of the nervus opticus network model output, wherein the nervus opticus network model is to train extremely
Convergence state is used to judge the neural network model of image similarity；
When the content of first judging result characterization is the facial image and inconsistent certificate image, forbid the user
Log on request.
3. system safety method according to claim 2, which is characterized in that the facial image of the acquisition user
Further include following step after step:
The facial image is input in preset third nerve network model, and it is defeated to obtain the third nerve network model
The second classification results out, wherein the third nerve network model is that training is used to judge that facial image is inclined to convergence state
Turn the neural network model of direction and deflection angle；
Second classification results are compared with preset command information, when second classification results and described instruction are believed
When ceasing inconsistent, forbid the log on request of the user.
4. system according to claim 1 safety method, which is characterized in that described by first classification results and default
Conduct programming information be compared, when first classification results are consistent with the conduct programming information, respond the use
Further include following step after the step of user instruction at family:
Obtain the duration of the user behavior of the user；
The duration is compared with preset time threshold；
When the duration is greater than the time threshold, forbid the user instruction for responding the user.
5. system according to claim 1 safety method, which is characterized in that as the user of the user behavior information representation
When behavior payment behavior；It is described that first classification results are compared with preset conduct programming information, when described first
It further include following after the step of responding the user instruction of the user when classification results are consistent with the conduct programming information
Step:
Identify the payment amount in the screen shot；
The payment amount is compared with preset amount of money threshold value；
When the payment amount is greater than the amount of money threshold value, forbid the user instruction for responding the user.
6. system safety method according to claim 5, which is characterized in that
It is described when the payment amount be greater than the amount of money threshold value when, forbid the step of responding the user instruction of the user it
Afterwards, further include following step:
Information warning is sent to preset associated terminal, wherein the information warning includes whether to agree to the query message of payment；
Receive the return information that the associated terminal replys the information warning；
When the reply content of return information characterization is to agree to payment, the payment instruction of the user is executed.
7. system safety method according to claim 6, which is characterized in that described send to preset associated terminal warns
Further include following step before the step of information:
Obtain the payment accounts that the payment behavior uses；
The payment terminal number with payment accounts binding is obtained according to the payment accounts；
The terminal for determining the payment terminal number characterization is the associated terminal.
8. a kind of system safety devices characterized by comprising
Module is obtained, the screen shot of picture is shown for obtaining display area；
Processing module for the screen shot to be input in preset first nerves network model, and obtains described first
First classification results of the screen shot of neural network model output, wherein first classification results are the screen shot
The user behavior information of characterization；
Execution module, for first classification results to be compared with preset conduct programming information, when described first point
When class result is consistent with the conduct programming information, the user instruction of the user is responded.
9. a kind of computer equipment, including memory and processor, it is stored with computer-readable instruction in the memory, it is described
When computer-readable instruction is executed by the processor, so that the processor executes such as any one of claims 1 to 7 right
It is required that the step of system safety method.
10. a kind of storage medium for being stored with computer-readable instruction, the computer-readable instruction is handled by one or more
When device executes, so that one or more processors execute the system secure side as described in any one of claims 1 to 7 claim
The step of method.
Priority Applications (2)
| Application Number | Priority Date | Filing Date | Title |
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| CN201811198452.XA CN109325335A (en) | 2018-10-15 | 2018-10-15 | System safety method, device, computer equipment and storage medium |
| PCT/CN2019/070374 WO2020077890A1 (en) | 2018-10-15 | 2019-01-04 | System security method and apparatus, computer device, and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
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| CN201811198452.XA CN109325335A (en) | 2018-10-15 | 2018-10-15 | System safety method, device, computer equipment and storage medium |
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| CN116305049A (en) * | 2023-05-11 | 2023-06-23 | 深圳市欧度利方科技有限公司 | Visual control system and method for tablet personal computer |
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