CN111639318A - Wind control method based on gesture monitoring on mobile terminal and related device - Google Patents
Wind control method based on gesture monitoring on mobile terminal and related device Download PDFInfo
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Abstract
The application relates to a wind control method and a related device based on gesture monitoring on a mobile terminal, belonging to the technical field of risk monitoring and comprising the following steps: monitoring and continuously collecting interface operation characteristic data generated in a human-computer interaction process; acquiring interface operation characteristic data in a preset time period; dividing the interface operation characteristic data into a plurality of sections of sub-interface operation characteristic data; after adding a terminal service label corresponding to the human-computer interaction process to each sub-interface operation characteristic data, inputting a wind control model to obtain a sub-gesture label and an abnormal score of each sub-interface operation characteristic data; and calculating the corresponding wind control score of the user according to the sub-gesture label of each sub-interface operation characteristic data, the abnormal score of the sub-gesture label and the corresponding terminal service label of each sub-interface operation characteristic data so as to determine whether the risk exists. The reliability of the wind control on the mobile terminal is effectively improved. The invention also relates to a block chain technology, and the sub-interface operation characteristic data is stored in the block chain.
Description
Technical Field
The application relates to the technical field of risk monitoring, in particular to a wind control method and a related device based on gesture monitoring on a mobile terminal.
Background
Generally, when risk monitoring is performed, a method of judging a gesture is used for user monitoring, an image acquired by a camera device is used, or a method of providing a light emitting unit is used, an image generated by reflecting a light beam by a hand is obtained, a hand image is obtained through a processing circuit, all hand skeletons on the image are identified, whether identified human skeleton information is matched with human skeleton information stored in a database is judged, and therefore the identity of a user is identified for wind control. However, these methods are inevitably used for high-definition devices, and consume a large amount of cost, easily cause unnecessary troubles, and have poor user experience; in addition, in a mobile terminal scene, the devices do not exist, the user identity detection cannot be performed by using the user behavior characteristics, and the reliability of the wind control is low.
It is to be noted that the information disclosed in the above background section is only for enhancement of understanding of the background of the present application and therefore may include information that does not constitute prior art known to a person of ordinary skill in the art.
Disclosure of Invention
An object of this application is to provide a wind accuse scheme based on gesture monitoring on mobile terminal, and then effectively promote the reliability of wind accuse on the mobile terminal to a certain extent at least, effectively promote user experience when reducing the wind accuse cost.
According to one aspect of the application, a wind control method based on gesture monitoring on a mobile terminal is provided, and the method is characterized by comprising the following steps:
monitoring a human-computer interaction process of a user on a mobile terminal interface, and continuously collecting interface operation characteristic data generated in the human-computer interaction process and storing the interface operation characteristic data to a preset position;
when the wind control point is triggered, acquiring interface operation characteristic data in a preset time period from the preset position;
dividing the interface operation characteristic data in the preset time period into a plurality of sections of sub-interface operation characteristic data with the length being a unit operation time period, wherein the unit operation time period represents a time period for finishing a single operation action;
after a terminal service tag corresponding to the human-computer interaction process is added to each sub-interface operation characteristic data, inputting a wind control model of the mobile terminal to obtain a sub-gesture tag of each sub-interface operation characteristic data and an abnormal score of the sub-gesture tag;
and calculating the corresponding wind control score of the user according to the sub-gesture label of each sub-interface operation characteristic data, the abnormal score of the sub-gesture label and the corresponding terminal service label of each sub-interface operation characteristic data so as to determine whether the user has risk in the man-machine interaction process of the mobile terminal.
In an exemplary embodiment of the present application, the interface operation characteristic data includes:
at least three types of interfaces operate interface operation characteristic data generated on the interface of the mobile terminal.
In an exemplary embodiment of the application, the acquiring, from the predetermined position, the interface operation characteristic data within a predetermined time period when the wind control point is triggered includes:
determining the type of a wind control point when the wind control point is triggered;
determining a preset time period for acquiring interface operation characteristic data from the preset position according to the type of the wind control point;
and acquiring interface operation characteristic data in the preset time period from the preset position.
In an exemplary embodiment of the present application, the storing the sub-interface operation characteristic data in a block chain, the dividing the interface operation characteristic data in the predetermined time period into sub-interface operation characteristic data with a plurality of lengths as a unit operation time period, where the unit operation time period represents a time period for completing a single operation action, includes:
determining a first operation action included in the interface operation characteristic data in the preset time period;
when the number of the types of the first operation actions is lower than a preset threshold, searching for second operation actions included in interface operation characteristic data in a second preset time period adjacent to the preset time period until the number of the types of the operation actions in the second operation actions and the first operation actions exceeds the preset threshold;
and dividing the interface operation characteristic data in the preset time period and the second preset time period into sub-interface operation characteristic data with a plurality of sections of lengths as unit operation time periods according to the starting time point and the ending time point of each operation action in the second operation action and the first operation action.
In an exemplary embodiment of the present application, the training method of the wind control model includes:
acquiring an interface operation characteristic data sample set, wherein each sample comprises a plurality of sections of interface operation characteristic data, and each section of interface operation characteristic data is added with a corresponding terminal service label, a corresponding sub-gesture label and an abnormal score of each sub-gesture label in a human-computer interaction process;
respectively inputting the data of each sample into a wind control model to obtain the sub-gesture labels of each section of interface operation characteristic data predicted by the wind control model and the abnormal scores of the sub-gesture labels;
and if the difference value between the abnormal score of the sub-gesture label of the predicted interface operation characteristic data of each section and the abnormal score of the sub-gesture label of the interface operation characteristic data calibrated in advance for the sample is larger than a preset threshold value or the difference value between the sub-gesture label of the predicted interface operation characteristic data of each section and the sub-gesture label of the interface operation characteristic data calibrated in advance for the sample is inconsistent, adjusting the coefficient of the wind control model until the difference value is consistent.
In an exemplary embodiment of the present application, the wind control model is a logistic regression model.
In an exemplary embodiment of the present application, calculating a wind control score corresponding to the user according to a sub-gesture tag of each sub-interface operation characteristic data, an abnormal score of the sub-gesture tag, and a terminal service tag corresponding to each sub-interface operation characteristic data includes:
and calculating the wind control score corresponding to the user according to the formula Y-a 1-Y _ d1+ a 2-Y _ d2+. + an-Y _ di, wherein the an represents the wind control weight coefficient corresponding to the terminal service label n, the Y _ di represents the abnormal score of the ith sub-gesture label, and the an and the Y _ di correspond to the same sub-interface operation characteristic data.
According to one aspect of the application, a wind control device based on gesture monitoring on a mobile terminal is provided, and the device is characterized by comprising:
the system comprises an acquisition module, a display module and a display module, wherein the acquisition module is used for monitoring the human-computer interaction process of a user on a mobile terminal interface, and continuously acquiring interface operation characteristic data generated in the human-computer interaction process and storing the interface operation characteristic data to a preset position;
the acquisition module is used for acquiring interface operation characteristic data in a preset time period from the preset position when the wind control point is triggered;
the dividing module is used for dividing the interface operation characteristic data in the preset time period into a plurality of sections of sub-interface operation characteristic data with the length being a unit operation time period, wherein the unit operation time period represents a time period for finishing a single operation action;
the prediction module is used for inputting a wind control model of the mobile terminal after adding a corresponding terminal service tag in a human-computer interaction process to each sub-interface operation characteristic data to obtain a sub-gesture tag of each sub-interface operation characteristic data and an abnormal score of the sub-gesture tag;
and the determining module is used for calculating the corresponding wind control score of the user according to the sub-gesture label of each sub-interface operation characteristic data, the abnormal score of the sub-gesture label and the corresponding terminal service label of each sub-interface operation characteristic data so as to determine whether the user has risk in the man-machine interaction process of the mobile terminal.
According to an aspect of the application, there is provided a computer readable storage medium having computer readable instructions stored thereon which, when executed by a processor, implement the method of any of the above.
According to an aspect of the present application, there is provided an electronic device including:
a processor; and
a memory for storing computer readable instructions of the processor; wherein the processor is configured to perform any of the methods described above via execution of the computer-readable instructions.
The method comprises the steps of firstly, monitoring a human-computer interaction process of a user on a mobile terminal interface, continuously collecting interface operation characteristic data generated in the human-computer interaction process, and storing the interface operation characteristic data to a preset position; the interface operation characteristic data of the user in the man-machine interaction process of the mobile terminal interface are continuously collected, real-time monitoring can be carried out, the interface operation characteristic data are stored in the preset position, and the required interface operation characteristic data can be obtained in real time according to requirements in the subsequent steps. Then, when the wind control point is triggered, interface operation characteristic data in a preset time period are obtained from a preset position; and interface operation characteristic data can be acquired at a set wind control point according to requirements for risk analysis. Then, dividing the interface operation characteristic data in a preset time period into a plurality of sections of sub-interface operation characteristic data with the length being a unit operation time period, wherein the unit operation time period represents a time period for finishing a single operation action; the interface operation characteristic data are divided into sub-interface operation characteristic data corresponding to a single operation action according to the operation action, analysis can be respectively carried out based on behavior habits of each operation action, and the analysis accuracy is guaranteed. Further, after a terminal service label corresponding to the human-computer interaction process is added to each sub-interface operation characteristic data, inputting a wind control model of the mobile terminal to obtain a sub-gesture label of each sub-interface operation characteristic data and an abnormal score of the sub-gesture label; by adding the corresponding terminal service tags to the sub-interface operation characteristic data, whether the user behavior embodied by the sub-interface operation characteristic data is consistent with the user gesture habits of the specific terminal service process can be analyzed through the user terminal service operation process limitation, the sub-gesture tags and the abnormal scores of the sub-gesture tags are obtained through the wind control model analysis, and further, the accuracy of the analysis of the user behavior habits is ensured. And finally, calculating the wind control score corresponding to the user according to the sub-gesture label of each sub-interface operation characteristic data, the abnormal score of the sub-gesture label and the corresponding terminal service label of each sub-interface operation characteristic data so as to determine whether the user has risk in the man-machine interaction process of the mobile terminal, and calculating the wind control score of the current user according to the characteristics of the abnormal score and the terminal service, so that the wind control reliability of the mobile terminal is realized, the wind control cost is reduced, and the user experience is effectively improved.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the application.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and together with the description, serve to explain the principles of the application. It is obvious that the drawings in the following description are only some embodiments of the application, and that for a person skilled in the art, other drawings can be derived from them without inventive effort.
Fig. 1 schematically shows a flow chart of a wind control method based on gesture monitoring on a mobile terminal.
Fig. 2 schematically shows an application scenario example of a wind control method based on gesture monitoring on a mobile terminal.
Fig. 3 schematically shows a flow chart of a wind control method based on gesture monitoring on another mobile terminal.
Fig. 4 schematically shows a block diagram of a wind control device based on gesture monitoring on a mobile terminal.
Fig. 5 schematically illustrates an example block diagram of an electronic device for implementing the wind control method based on gesture monitoring on the mobile terminal.
Fig. 6 schematically illustrates a computer-readable storage medium for implementing the above-described wind control method based on gesture monitoring on a mobile terminal.
Detailed Description
Example embodiments will now be described more fully with reference to the accompanying drawings. Example embodiments may, however, be embodied in many different forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of example embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to give a thorough understanding of embodiments of the application. One skilled in the relevant art will recognize, however, that the subject matter of the present application can be practiced without one or more of the specific details, or with other methods, components, devices, steps, and so forth. In other instances, well-known technical solutions have not been shown or described in detail to avoid obscuring aspects of the present application.
Furthermore, the drawings are merely schematic illustrations of the present application and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and thus their repetitive description will be omitted. Some of the block diagrams shown in the figures are functional entities and do not necessarily correspond to physically or logically separate entities. These functional entities may be implemented in the form of software, or in one or more hardware modules or integrated circuits, or in different networks and/or processor devices and/or microcontroller devices.
In the present exemplary embodiment, a wind control method based on gesture monitoring on a mobile terminal is first provided, and the wind control method based on gesture monitoring on the mobile terminal may be run on a server, or may also be run on a server cluster or a cloud server, and the like. Referring to fig. 1, the wind control method based on gesture monitoring on the mobile terminal may include the following steps:
step S110, monitoring a human-computer interaction process of a user on a mobile terminal interface, and continuously collecting interface operation characteristic data generated in the human-computer interaction process and storing the interface operation characteristic data to a preset position;
step S120, when the wind control point is triggered, interface operation characteristic data in a preset time period are obtained from the preset position;
step S130, dividing the interface operation characteristic data in the preset time period into a plurality of sections of sub-interface operation characteristic data with the length as a unit operation time period, wherein the unit operation time period represents a time period for finishing a single operation action;
step S140, after adding a terminal service tag corresponding to the human-computer interaction process to each sub-interface operation characteristic data, inputting a wind control model of the mobile terminal to obtain a sub-gesture tag of each sub-interface operation characteristic data and an abnormal score of the sub-gesture tag;
step S150, calculating the corresponding wind control score of the user according to the sub-gesture label of each sub-interface operation characteristic data, the abnormal score of the sub-gesture label and the corresponding terminal service label of each sub-interface operation characteristic data, so as to determine whether the user has risk in the man-machine interaction process of the mobile terminal.
Firstly, monitoring a human-computer interaction process of a user on a mobile terminal interface, continuously acquiring interface operation characteristic data generated in the human-computer interaction process, and storing the interface operation characteristic data to a preset position; the interface operation characteristic data of the user in the man-machine interaction process of the mobile terminal interface are continuously collected, real-time monitoring can be carried out, the interface operation characteristic data are stored in the preset position, and the required interface operation characteristic data can be obtained in real time according to requirements in the subsequent steps. Then, when the wind control point is triggered, interface operation characteristic data in a preset time period are obtained from a preset position; and interface operation characteristic data can be acquired at a set wind control point according to requirements for risk analysis. Then, dividing the interface operation characteristic data in a preset time period into a plurality of sections of sub-interface operation characteristic data with the length being a unit operation time period, wherein the unit operation time period represents a time period for finishing a single operation action; the interface operation characteristic data are divided into sub-interface operation characteristic data corresponding to a single operation action according to the operation action, analysis can be respectively carried out based on behavior habits of each operation action, and the analysis accuracy is guaranteed. Further, after a terminal service label corresponding to the human-computer interaction process is added to each sub-interface operation characteristic data, inputting a wind control model of the mobile terminal to obtain a sub-gesture label of each sub-interface operation characteristic data and an abnormal score of the sub-gesture label; by adding the corresponding terminal service tags to the sub-interface operation characteristic data, whether the user behavior embodied by the sub-interface operation characteristic data is consistent with the user gesture habits of the specific terminal service process can be analyzed through the user terminal service operation process limitation, the sub-gesture tags and the abnormal scores of the sub-gesture tags are obtained through the wind control model analysis, and further, the accuracy of the analysis of the user behavior habits is ensured. And finally, calculating the wind control score corresponding to the user according to the sub-gesture label of each sub-interface operation characteristic data, the abnormal score of the sub-gesture label and the corresponding terminal service label of each sub-interface operation characteristic data so as to determine whether the user has risk in the man-machine interaction process of the mobile terminal, and calculating the wind control score of the current user according to the characteristics of the abnormal score and the terminal service, so that the wind control reliability of the mobile terminal is realized, the wind control cost is reduced, and the user experience is effectively improved.
Hereinafter, each step in the wind control method based on gesture monitoring on the mobile terminal in the exemplary embodiment will be explained and explained in detail with reference to the drawings.
In step S110, a human-computer interaction process of a user on a mobile terminal interface is monitored, and interface operation characteristic data generated in the human-computer interaction process is continuously collected and stored to a predetermined position.
In the embodiment of the present example, referring to fig. 2, there is provided a system architecture diagram to which an embodiment of a wind control method based on gesture monitoring on a mobile terminal of the present application may be applied, where the system architecture includes: the mobile terminal 202 can monitor the human-computer interaction process of a user on a mobile terminal interface, continuously collect interface operation characteristic data generated in the human-computer interaction process and store the interface operation characteristic data to the server 201. It is understood that, in the subsequent step, the interface operation characteristic data generated in the human-computer interaction process may also be continuously collected and stored in the mobile terminal 202, if the conditions allow this. The server 201 may be any device with processing capability, such as a computer, a microprocessor, and the like, and the mobile terminal 202 may be any device with a terminal interface, such as a mobile phone, a tablet computer, and the like, which are not limited herein.
The human-computer interaction process is a process in which a user performs terminal control by touching an end interface, for example, operation processes of operation types such as password login, article turning, screen sliding and page turning are performed.
The interface operation characteristic data is user behavior characteristic data which is reflected on an interface by the touch operation of a user on the interface of the mobile terminal. The key stroke area, the key stroke pressure, the key stroke position track, the key stroke time and other relevant data corresponding to various types of operation can be obtained.
The preset position can be a local database of the mobile terminal, and can also be a cloud or a preset server.
The interface operation characteristic data of the user in the man-machine interaction process of the mobile terminal interface are continuously collected, real-time monitoring can be carried out, the interface operation characteristic data are stored in the preset position, and the required interface operation characteristic data can be obtained in real time according to requirements in the subsequent steps.
In one embodiment, the interface operation characteristic data includes:
at least three types of interfaces operate interface operation characteristic data generated on the interface of the mobile terminal.
The different types of interface operations can include password login, article turning, screen sliding and page turning. The applicant has found that the analysis needs in each case can be guaranteed when the predetermined number of interface operations is at least 3.
It can be understood that by setting the number of interface operation types in the interface operation characteristic data according to requirements, for example, 4 types or 5 types, etc., the analysis accuracy can be ensured under the condition of adjusting the appropriate analysis efficiency according to the analysis requirements.
In step S120, when the wind control point is triggered, interface operation characteristic data within a predetermined time period is acquired from the predetermined position.
In the embodiment of the present example, the wind control point may be any agreed service point, for example, a payment node corresponding to a certain service handling process. But may be any given point in time, such as 9 am of each day, etc.
The predetermined period of time may be a predetermined period of time after the point in time at which the wind control point is triggered. When the time is a predetermined time period after the time point at which the wind control point is triggered, the wind control point is, for example, a service start node; the predetermined time period may be a predetermined time period before the point in time that the wind control point is triggered, for example, a transfer node, when the predetermined time period before the point in time that the wind control point is triggered.
Therefore, interface operation characteristic data can be acquired according to requirements at set wind control points for risk analysis.
In one embodiment, referring to fig. 3, when the wind control point is triggered, the interface operation characteristic data in a predetermined time period is acquired from the predetermined position, and the method includes:
step S310, when a wind control point is triggered, determining the type of the wind control point;
step S320, determining a preset time period for acquiring interface operation characteristic data from the preset position according to the type of the wind control point;
and step S330, acquiring interface operation characteristic data in the preset time period from the preset position.
The type of the wind control point may be a set attribute of the wind control point, for example, a wind control point of a time point attribute, a wind control point of service node data, and the attribute may be further subdivided into a morning time point or a noon time point, and a payment node or a start node, and the like.
The predetermined time period for acquiring the interface operation characteristic data from the predetermined position is determined according to the type of the wind control point, and may be determined according to a preset time period determination table (including the type of the wind control point and the corresponding predetermined time period characteristic), a predetermined time period characteristic (for example, 5 hours before the current time point, etc.) is queried from the table based on the type of the wind control point, and then the predetermined time period is determined.
In step S130, the interface operation characteristic data in the predetermined time period is divided into a plurality of sub-interface operation characteristic data with a length as a unit operation time period, where the unit operation time period represents a time period for completing a single operation action.
In the embodiment of the present example, the unit operation time period represents a time period for completing a single operation action, for example, completing one password login, one article scroll, and one page flip operation action. When the interface operation characteristic data is collected, the starting time point and the ending time point of one operation can be recorded and used for dividing the sub-interface operation characteristic data of unit operation time period; and the corresponding sub-interface operation characteristic data can be recorded after one operation is finished. It should be emphasized that, in order to further ensure the privacy and security of the sub-interface operation characteristic data, the sub-interface operation characteristic data may also be stored in a node of a block chain.
The human-computer interaction data in the preset time period are divided into a plurality of sections of interaction data, so that the integrity of one action can be ensured, and the accurate analysis of the operation gesture can be performed in the subsequent steps.
In one embodiment, dividing the interface operation characteristic data in the predetermined time period into a plurality of sub-interface operation characteristic data with a length as a unit operation time period, where the unit operation time period represents a time period for completing a single operation action, includes:
determining a first operation action included in the interface operation characteristic data in the preset time period;
when the number of the types of the first operation actions is lower than a preset threshold, searching for second operation actions included in interface operation characteristic data in a second preset time period adjacent to the preset time period until the number of the types of the operation actions in the second operation actions and the first operation actions exceeds the preset threshold;
and dividing the interface operation characteristic data in the preset time period and the second preset time period into sub-interface operation characteristic data with a plurality of sections of lengths as unit operation time periods according to the starting time point and the ending time point of each operation action in the second operation action and the first operation action.
The different types of interface operations can include password login, article turning, screen sliding and page turning. The predetermined threshold may be at least 3, and at least 3 may ensure the accuracy of the analysis. When the number of types of the first operation actions is lower than a preset threshold, searching for a second operation action included in the interface operation characteristic data in a second preset time period (which may include a second preset time period before or after the preset time period) adjacent to the preset time period until the number of types of operation actions in the second operation action and the first operation action exceeds the preset threshold, and indicating that data of enough types of operation actions can be acquired. Furthermore, according to the starting time point and the ending time point of each operation action in the second operation action and the first operation action, the interface operation characteristic data in the preset time period and the second preset time period are divided into sub-interface operation characteristic data with a plurality of sections of lengths as unit operation time periods, and the gesture analysis accuracy can be effectively guaranteed.
In step S140, after adding a terminal service tag corresponding to the human-computer interaction process to each sub-interface operation characteristic data, inputting a wind control model of the mobile terminal to obtain a sub-gesture tag of each sub-interface operation characteristic data and an abnormal score of the sub-gesture tag.
In the exemplary embodiment, the mobile terminal's wind control model may be trained from a collection of interaction data sets for a particular user of the terminal.
The sub-gesture labels are, for example, right-handed operation, left-handed operation, double-handed operation, left-handed operation, right-handed operation, left-handed operation, right-handed operation, and right-handed operation.
The terminal service tag may be a service type of the terminal service controlled by the operation action corresponding to the sub-interface operation characteristic data, for example, a tag for starting or unlocking a certain app.
And the abnormal score representation of the sub-gesture labels is the score of the sub-gesture obtained by calculation and analysis according to the operation characteristic data of each sub-interface and the corresponding terminal service label, wherein the higher the score is, the higher the abnormal possibility is.
By adding the corresponding terminal service label to each sub-interface operation characteristic data, whether the user behavior embodied by each sub-interface operation characteristic data is consistent with the user gesture habit in the specific terminal service process can be analyzed through the limitation of the user terminal service operation process, the sub-gesture label and the abnormal score of the sub-gesture label are obtained through the analysis of the wind control model, and further, the accuracy of the analysis of the user behavior habit is ensured.
In one embodiment, the training method of the wind control model comprises the following steps:
acquiring an interface operation characteristic data sample set, wherein each sample comprises a plurality of sections of interface operation characteristic data, and each section of interface operation characteristic data is added with a corresponding terminal service label, a corresponding sub-gesture label and an abnormal score of each sub-gesture label in a human-computer interaction process;
respectively inputting the data of each sample into a wind control model to obtain the sub-gesture labels of each section of interface operation characteristic data predicted by the wind control model and the abnormal scores of the sub-gesture labels;
and if the difference value between the abnormal score of the sub-gesture label of the predicted interface operation characteristic data of each section and the abnormal score of the sub-gesture label of the interface operation characteristic data calibrated in advance for the sample is larger than a preset threshold value or the difference value between the sub-gesture label of the predicted interface operation characteristic data of each section and the sub-gesture label of the interface operation characteristic data calibrated in advance for the sample is inconsistent, adjusting the coefficient of the wind control model until the difference value is consistent.
When the data of the samples of all mobile terminal users are input into the wind control model, the difference value between the obtained abnormal score of the sub-gesture label of each predicted interactive data segment and the abnormal score of the sub-gesture label of each interactive data segment calibrated in advance for the samples is smaller than a preset threshold value, the sub-gesture label of each predicted interactive data segment is consistent with the sub-gesture label of each interactive data segment calibrated in advance for the samples, the training is finished, and the reliable wind control model can be obtained through the training.
In one embodiment, the wind control model is a logistic regression model.
The logistic regression model may use the softmax function to construct a model to solve the multi-classification problem.
The softmax regression classifier needs to learn the function as:
where k is the number of gesture categories (e.g., right-handed, left-handed, double-handed),
and biA weight vector and an offset scalar for the ith class.
Wherein
Probability of being labeled as jth class, which can be regarded as sample X, and
the softmax regression classification model has a plurality of outputs, the number of the outputs is equal to the number of the classes, the output is the probability that the sample X is of each class, and finally the type of predicting the sample is the class with the highest probability (during training, the difference between the predicted probability and the calibrated probability is smaller than a preset threshold value).
Obtained by learning
And biThus, the target loss function is established as:
the cost function of the above equation is also referred to as: a log-likelihood cost function.
In the case of binary classification, the log-likelihood cost function can be converted to a cross-entropy cost function.
Wherein m is the number of training set samples, k is the number of classes, 1 {. cndot.) is a sexual function when y is(j)When l is true, the function value is 1, otherwise 0, i.e. when the sample type is correct, the function value is 1.
Using logarithmic properties
The loss function is expanded with:
and (4) continuing to unfold:
minimizing loss function and chain partial derivative by gradient descent method, using J (w, b) pair
The derivation may be:
the iteration is therefore performed by the gradient descent method:
similarly, minimizing the loss function by the gradient descent method can also obtain the optimal value of bi.
In step S150, a wind control score corresponding to the user is calculated according to the sub-gesture label of each sub-interface operation characteristic data, the abnormal score of the sub-gesture label, and the terminal service label corresponding to each sub-interface operation characteristic data, so as to determine whether the user has a risk in the human-computer interaction process of the mobile terminal.
In the embodiment of the present example, the wind control score of the current user may be obtained by calculating a weighted sum of the abnormal scores of the sub-gesture tags and combining the characteristics of the corresponding terminal service (the importance of the terminal service to the user authentication may be embodied as an importance score or a weight coefficient). Whether the current user has risk in the man-machine interaction process of the mobile terminal is judged according to the height of the wind control score, the higher the wind control score is, the higher the risk is, for example, when the wind control score exceeds a preset wind control score threshold, the preset wind control score threshold can be obtained from a preset wind control score threshold lookup table (including the combination of the terminal service tags and the corresponding wind control score threshold) according to the combination of the terminal service tags, and the user is determined to have risk in the man-machine interaction process of the mobile terminal. Furthermore, the reliability of the wind control of the mobile terminal is realized, the wind control cost is reduced, and the user experience is effectively improved.
In one embodiment, calculating a wind control score corresponding to the user according to a sub-gesture tag of each sub-interface operation characteristic data, an abnormal score of the sub-gesture tag, and a terminal service tag corresponding to each sub-interface operation characteristic data includes:
and calculating the wind control score corresponding to the user according to the formula Y-a 1-Y _ d1+ a 2-Y _ d2+. + an-Y _ di, wherein the an represents the wind control weight coefficient corresponding to the terminal service label n, the Y _ di represents the abnormal score of the ith sub-gesture label, and the an and the Y _ di correspond to the same sub-interface operation characteristic data.
In one embodiment, the method may further include:
and if the user is determined to have risk in the man-machine interaction process of the mobile terminal, carrying out risk prompt through a preset prompt action.
The predetermined prompting action may be to lock the mobile terminal or to generate an alert message to a particular terminal, etc.
In one embodiment, the method may further include:
and if the user is determined to have risk in the man-machine interaction process of the mobile terminal, starting a wind control system to perform user authentication.
The user authentication performed by the wind control system can be face recognition authentication, account password authentication and the like.
In one embodiment, the method may further include:
if the wind control system carries out user authentication, the authentication has no risk, and a system interface and a keyboard are adjusted to accord with the gesture habit of the user.
After learning of habits of the user, the position of the screen, the interface design, the positions of the keyboards, the position angles of articles and the like can be adjusted in real time, for example, if the habits of the user are both hands, but one-hand operation is performed suddenly at a certain time, a risk prompt can be given to a certain extent, and meanwhile, if the risk judgment is not performed, the user can be considered to be inconvenient to operate under certain conditions, so that corresponding interface adjustment is performed, and the user can have better interactive experience.
The application also provides a wind control device based on gesture monitoring on the mobile terminal. Referring to fig. 4, the wind control device based on gesture monitoring on the mobile terminal includes an acquisition module 410, an acquisition module 420, a division module 430, a prediction module 440, and a determination module 450. Wherein:
the acquisition module 410 may be configured to monitor a human-computer interaction process of a user on a mobile terminal interface, and continuously acquire interface operation characteristic data generated in the human-computer interaction process and store the interface operation characteristic data to a predetermined position;
the obtaining module 420 may be configured to obtain interface operation characteristic data within a predetermined time period from the predetermined position when the wind control point is triggered;
the dividing module 430 may be configured to divide the interface operation characteristic data in the predetermined time period into sub-interface operation characteristic data with a plurality of lengths as a unit operation time period, where the unit operation time period represents a time period in which a single operation action is completed;
the prediction module 440 may be configured to, after adding a terminal service tag corresponding to the human-computer interaction process to each sub-interface operation characteristic data, input a wind control model of the mobile terminal to obtain a sub-gesture tag of each sub-interface operation characteristic data and an abnormal score of the sub-gesture tag;
the determining module 450 may be configured to calculate a wind control score corresponding to the user according to the sub-gesture tag of each sub-interface operation characteristic data, the abnormal score of the sub-gesture tag, and the terminal service tag corresponding to each sub-interface operation characteristic data, so as to determine whether the user has a risk in a human-computer interaction process of the mobile terminal. It should be emphasized that, in order to further ensure the privacy and security of the sub-interface operation characteristic data, the sub-interface operation characteristic data may also be stored in a node of a block chain.
The specific details of each module in the wind control device based on gesture monitoring on the mobile terminal have been described in detail in the wind control method based on gesture monitoring on the corresponding mobile terminal, and therefore are not described herein again.
It should be noted that although in the above detailed description several modules or units of the device for action execution are mentioned, such a division is not mandatory. Indeed, the features and functionality of two or more modules or units described above may be embodied in one module or unit, according to embodiments of the application. Conversely, the features and functions of one module or unit described above may be further divided into embodiments by a plurality of modules or units.
Moreover, although the steps of the methods herein are depicted in the drawings in a particular order, this does not require or imply that the steps must be performed in this particular order, or that all of the depicted steps must be performed, to achieve desirable results. Additionally or alternatively, certain steps may be omitted, multiple steps combined into one step execution, and/or one step broken down into multiple step executions, etc.
Through the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein may be implemented by software, or by software in combination with necessary hardware. Therefore, the technical solution according to the embodiments of the present application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a usb disk, a removable hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which can be a personal computer, a server, a mobile terminal, or a network device, etc.) to execute the method according to the embodiments of the present application.
In an exemplary embodiment of the present application, there is also provided an electronic device capable of implementing the above method.
As will be appreciated by one skilled in the art, aspects of the present invention may be embodied as a system, method or program product. Thus, various aspects of the invention may be embodied in the form of: an entirely hardware embodiment, an entirely software embodiment (including firmware, microcode, etc.) or an embodiment combining hardware and software aspects that may all generally be referred to herein as a "circuit," module "or" system.
An electronic device 500 according to this embodiment of the invention is described below with reference to fig. 5. The electronic device 500 shown in fig. 5 is only an example and should not bring any limitation to the functions and the scope of use of the embodiments of the present invention.
As shown in fig. 5, the electronic device 500 is embodied in the form of a general purpose computing device. The components of the electronic device 500 may include, but are not limited to: the at least one processing unit 510, the at least one memory unit 520, and a bus 530 that couples various system components including the memory unit 520 and the processing unit 510.
Wherein the storage unit stores program code that is executable by the processing unit 510 to cause the processing unit 510 to perform steps according to various exemplary embodiments of the present invention as described in the above section "exemplary methods" of the present specification. For example, the processing unit 510 may execute the steps shown in fig. 1, step S110, monitoring a human-computer interaction process of a user on a mobile terminal interface, and continuously collecting interface operation characteristic data generated in the human-computer interaction process and storing the interface operation characteristic data to a predetermined position; step S120, when the wind control point is triggered, interface operation characteristic data in a preset time period are obtained from the preset position; step S130, dividing the interface operation characteristic data in the preset time period into a plurality of sections of sub-interface operation characteristic data with the length as a unit operation time period, wherein the unit operation time period represents a time period for finishing a single operation action; step S140, after adding a terminal service tag corresponding to the human-computer interaction process to each sub-interface operation characteristic data, inputting a wind control model of the mobile terminal to obtain a sub-gesture tag of each sub-interface operation characteristic data and an abnormal score of the sub-gesture tag; step S150, calculating the corresponding wind control score of the user according to the sub-gesture label of each sub-interface operation characteristic data, the abnormal score of the sub-gesture label and the corresponding terminal service label of each sub-interface operation characteristic data, so as to determine whether the user has risk in the man-machine interaction process of the mobile terminal.
The memory unit 520 may include a readable medium in the form of a volatile memory unit, such as a random access memory unit (RAM)5201 and/or a cache memory unit 5202, and may further include a read only memory unit (ROM) 5203.
The electronic device 500 may also communicate with one or more external devices 700 (e.g., keyboard, pointing device, bluetooth device, etc.), with one or more devices that enable a client to interact with the electronic device 500, and/or with any devices (e.g., router, modem, etc.) that enable the electronic device 500 to communicate with one or more other computing devices. Such communication may occur through input/output (I/O) interface 550, and may also include a display unit 540 coupled to input/output (I/O) interface 550. Also, the electronic device 500 may communicate with one or more networks (e.g., a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network, such as the internet) via the network adapter 560. As shown, the network adapter 560 communicates with the other modules of the electronic device 500 over the bus 530. It should be appreciated that although not shown in the figures, other hardware and/or software modules may be used in conjunction with the electronic device 500, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, among others.
Through the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein may be implemented by software, or by software in combination with necessary hardware. Therefore, the technical solution according to the embodiments of the present application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a usb disk, a removable hard disk, etc.) or on a network, and includes several instructions to make a computing device (which can be a personal computer, a server, a terminal device, or a network device, etc.) execute the method according to the embodiments of the present application.
In an exemplary embodiment of the present application, there is also provided a computer-readable storage medium having stored thereon a program product capable of implementing the above-described method of the present specification. In some possible embodiments, aspects of the invention may also be implemented in the form of a program product comprising program code means for causing a terminal device to carry out the steps according to various exemplary embodiments of the invention described in the above section "exemplary methods" of the present description, when said program product is run on the terminal device.
Referring to fig. 6, a program product 600 for implementing the above method according to an embodiment of the present invention is described, which may employ a portable compact disc read only memory (CD-ROM) and include program code, and may be run on a terminal device, such as a personal computer. However, the program product of the present invention is not limited in this regard and, in the present document, a readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
The program product may employ any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium include: an electrical connection having one or more wires, a portable disk, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
A computer readable signal medium may include a propagated data signal with readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A readable signal medium may also be any readable medium that is not a readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.
Program code embodied on a readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
The block chain is a novel application mode of computer technologies such as distributed data storage, point-to-point transmission, a consensus mechanism, an encryption algorithm and the like. A block chain (Blockchain), which is essentially a decentralized database, is a series of data blocks associated by using a cryptographic method, and each data block contains information of a batch of network transactions, so as to verify the validity (anti-counterfeiting) of the information and generate a next block. The blockchain may include a blockchain underlying platform, a platform product service layer, an application service layer, and the like.
Program code for carrying out operations for aspects of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the client computing device, partly on the client device, as a stand-alone software package, partly on the client computing device and partly on a remote computing device, or entirely on the remote computing device or server. In the case of a remote computing device, the remote computing device may be connected to the client computing device over any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computing device (e.g., over the internet using an internet service provider).
Furthermore, the above-described figures are merely schematic illustrations of processes involved in methods according to exemplary embodiments of the invention, and are not intended to be limiting. It will be readily understood that the processes shown in the above figures are not intended to indicate or limit the chronological order of the processes. In addition, it is also readily understood that these processes may be performed synchronously or asynchronously, e.g., in multiple modules.
Other embodiments of the present application will be apparent to those skilled in the art from consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of the invention following, in general, the principles of the application and including such departures from the present disclosure as come within known or customary practice within the art to which the invention pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the application being indicated by the following claims.
Claims (10)
1. A wind control method based on gesture monitoring on a mobile terminal is characterized by comprising the following steps:
monitoring a human-computer interaction process of a user on a mobile terminal interface, and continuously collecting interface operation characteristic data generated in the human-computer interaction process and storing the interface operation characteristic data to a preset position;
when the wind control point is triggered, acquiring interface operation characteristic data in a preset time period from the preset position;
dividing the interface operation characteristic data in the preset time period into a plurality of sections of sub-interface operation characteristic data with the length being a unit operation time period, wherein the unit operation time period represents a time period for finishing a single operation action;
after a terminal service tag corresponding to the human-computer interaction process is added to each sub-interface operation characteristic data, inputting a wind control model of the mobile terminal to obtain a sub-gesture tag of each sub-interface operation characteristic data and an abnormal score of the sub-gesture tag;
and calculating the corresponding wind control score of the user according to the sub-gesture label of each sub-interface operation characteristic data, the abnormal score of the sub-gesture label and the corresponding terminal service label of each sub-interface operation characteristic data so as to determine whether the user has risk in the man-machine interaction process of the mobile terminal.
2. The method of claim 1, wherein the interface operates on feature data comprising:
at least three types of interfaces operate interface operation characteristic data generated on the interface of the mobile terminal.
3. The method according to claim 1 or 2, wherein the obtaining interface operation characteristic data from the predetermined position within a predetermined time period when the wind control point is triggered comprises:
determining the type of a wind control point when the wind control point is triggered;
determining a preset time period for acquiring interface operation characteristic data from the preset position according to the type of the wind control point;
and acquiring interface operation characteristic data in the preset time period from the preset position.
4. The method according to claim 1, wherein the sub-interface operation characteristic data is stored in a block chain, and the dividing the interface operation characteristic data in the predetermined time period into sub-interface operation characteristic data with a plurality of lengths as a unit operation time period, wherein the unit operation time period represents a time period for completing a single operation action comprises:
determining a first operation action included in the interface operation characteristic data in the preset time period;
when the number of the types of the first operation actions is lower than a preset threshold, searching for second operation actions included in interface operation characteristic data in a second preset time period adjacent to the preset time period until the number of the types of the operation actions in the second operation actions and the first operation actions exceeds the preset threshold;
and dividing the interface operation characteristic data in the preset time period and the second preset time period into sub-interface operation characteristic data with a plurality of sections of lengths as unit operation time periods according to the starting time point and the ending time point of each operation action in the second operation action and the first operation action.
5. The method of claim 1, wherein the training method of the wind control model comprises:
acquiring an interface operation characteristic data sample set, wherein each sample comprises a plurality of sections of interface operation characteristic data, and each section of interface operation characteristic data is added with a corresponding terminal service label, a corresponding sub-gesture label and an abnormal score of each sub-gesture label in a human-computer interaction process;
respectively inputting the data of each sample into a wind control model to obtain the sub-gesture labels of each section of interface operation characteristic data predicted by the wind control model and the abnormal scores of the sub-gesture labels;
and if the difference value between the abnormal score of the sub-gesture label of the predicted interface operation characteristic data of each section and the abnormal score of the sub-gesture label of the interface operation characteristic data calibrated in advance for the sample is larger than a preset threshold value or the difference value between the sub-gesture label of the predicted interface operation characteristic data of each section and the sub-gesture label of the interface operation characteristic data calibrated in advance for the sample is inconsistent, adjusting the coefficient of the wind control model until the difference value is consistent.
6. The method of claim 1 or 5, wherein the wind control model is a logistic regression model.
7. The method according to claim 1, wherein calculating the corresponding wind control score of the user according to the sub-gesture label of each sub-interface operation characteristic data, the abnormal score of the sub-gesture label, and the corresponding terminal service label of each sub-interface operation characteristic data comprises:
and calculating the wind control score corresponding to the user according to the formula Y-a 1-Y _ d1+ a 2-Y _ d2+. + an-Y _ di, wherein Y represents the wind control score corresponding to the user, an represents the wind control weight coefficient corresponding to the terminal service label n, Y _ di represents the abnormal score of the ith sub-gesture label, and an and Y _ di correspond to the same sub-interface operation characteristic data.
8. A wind control device based on gesture monitoring on a mobile terminal is characterized by comprising:
the system comprises an acquisition module, a display module and a display module, wherein the acquisition module is used for monitoring the human-computer interaction process of a user on a mobile terminal interface, and continuously acquiring interface operation characteristic data generated in the human-computer interaction process and storing the interface operation characteristic data to a preset position;
the acquisition module is used for acquiring interface operation characteristic data in a preset time period from the preset position when the wind control point is triggered;
the dividing module is used for dividing the interface operation characteristic data in the preset time period into a plurality of sections of sub-interface operation characteristic data with the length being a unit operation time period, wherein the unit operation time period represents a time period for finishing a single operation action;
the prediction module is used for inputting a wind control model of the mobile terminal after adding a corresponding terminal service tag in a human-computer interaction process to each sub-interface operation characteristic data to obtain a sub-gesture tag of each sub-interface operation characteristic data and an abnormal score of the sub-gesture tag;
and the determining module is used for calculating the corresponding wind control score of the user according to the sub-gesture label of each sub-interface operation characteristic data, the abnormal score of the sub-gesture label and the corresponding terminal service label of each sub-interface operation characteristic data so as to determine whether the user has risk in the man-machine interaction process of the mobile terminal.
9. A computer readable storage medium having computer readable instructions stored thereon which, when executed by a processor, implement the method of any of claims 1-7.
10. An electronic device, comprising:
a processor; and
a memory for storing computer readable instructions of the processor; wherein the processor is configured to perform the method of any of claims 1-7 via execution of the computer-readable instructions.
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