CN109558032B - Operation processing method and device and computer equipment - Google Patents

Operation processing method and device and computer equipment Download PDF

Info

Publication number
CN109558032B
CN109558032B CN201811482942.2A CN201811482942A CN109558032B CN 109558032 B CN109558032 B CN 109558032B CN 201811482942 A CN201811482942 A CN 201811482942A CN 109558032 B CN109558032 B CN 109558032B
Authority
CN
China
Prior art keywords
misoperation
user
probability
operating environment
terminal interface
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Active
Application number
CN201811482942.2A
Other languages
Chinese (zh)
Other versions
CN109558032A (en
Inventor
李陆启
李一山
纪伟
周熠
张鑫淼
杨飞宇
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Beijing Sankuai Online Technology Co Ltd
Original Assignee
Beijing Sankuai Online Technology Co Ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Beijing Sankuai Online Technology Co Ltd filed Critical Beijing Sankuai Online Technology Co Ltd
Priority to CN201811482942.2A priority Critical patent/CN109558032B/en
Publication of CN109558032A publication Critical patent/CN109558032A/en
Priority to PCT/CN2019/122406 priority patent/WO2020114352A1/en
Application granted granted Critical
Publication of CN109558032B publication Critical patent/CN109558032B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F3/00—Input arrangements for transferring data to be processed into a form capable of being handled by the computer; Output arrangements for transferring data from processing unit to output unit, e.g. interface arrangements
    • G06F3/01—Input arrangements or combined input and output arrangements for interaction between user and computer
    • G06F3/03—Arrangements for converting the position or the displacement of a member into a coded form
    • G06F3/041—Digitisers, e.g. for touch screens or touch pads, characterised by the transducing means
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F3/00—Input arrangements for transferring data to be processed into a form capable of being handled by the computer; Output arrangements for transferring data from processing unit to output unit, e.g. interface arrangements
    • G06F3/01—Input arrangements or combined input and output arrangements for interaction between user and computer
    • G06F3/03—Arrangements for converting the position or the displacement of a member into a coded form
    • G06F3/033—Pointing devices displaced or positioned by the user, e.g. mice, trackballs, pens or joysticks; Accessories therefor
    • G06F3/0354—Pointing devices displaced or positioned by the user, e.g. mice, trackballs, pens or joysticks; Accessories therefor with detection of 2D relative movements between the device, or an operating part thereof, and a plane or surface, e.g. 2D mice, trackballs, pens or pucks

Landscapes

  • Engineering & Computer Science (AREA)
  • General Engineering & Computer Science (AREA)
  • Theoretical Computer Science (AREA)
  • Human Computer Interaction (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • User Interface Of Digital Computer (AREA)

Abstract

The disclosure relates to an operation processing method, an operation processing device, an electronic device and a computer readable medium. The method comprises the following steps: reading values of a plurality of operating environment factors based on user operation detected by a terminal interface; inputting the values of the plurality of operating environment factors into a pre-trained operation recognition model to obtain a probability value of the user operation belonging to misoperation, wherein the operation recognition model comprises weight coefficients corresponding to the plurality of operating environment factors; and triggering corresponding subsequent response by the terminal interface according to the value range in which the probability value falls. The operation processing method, the operation processing device, the electronic equipment and the computer readable medium can calculate the probability that the user operation belongs to the misoperation based on various factors, so that the identification accuracy is improved.

Description

Operation processing method and device and computer equipment
Technical Field
The present disclosure relates to the field of internet technologies, and in particular, to an operation processing method and apparatus, and a computing device.
Background
At present, mobile terminals are widely applied to life and work of people, and people usually operate the mobile terminals by touching a screen or clicking a mouse when using the mobile terminals. Statistically, up to 50% of mobile advertisement clicks are inadvertently clicked, and besides mobile advertisements, there are a lot of error clicks of non-subjective will on other types of functions and pages in mobile touch screen applications, for example, the user does not pay attention to the guide element during operation, but the error touches will cause error clicks; for another example, when the new content replaces the old content instantaneously, the user does not have enough time to judge and intends to click the old element, but the new element is clicked, so that a point error is caused.
In the prior art, several patents have been made to solve such problems. The prior art scheme is a method and a system for preventing the mistaken clicking of the advertisement based on the pressure sensing technology, wherein the method receives the finger pressure of a user on an advertisement strip and judges whether the finger pressure reaches a set value; when the pressure is larger than a first set value, triggering a preset first event, wherein the first event is entering an advertisement interface linked with an advertisement bar or displaying a full-screen advertisement interface to a user. By the method, the real meaning expression of clicking the advertisement by the user can be accurately simulated, various risks of mistaken touch can be accurately prevented, and better user experience can be provided for the user.
However, in the prior art, the judgment basis is that only a single user has finger pressure, and in the step of comparing the finger pressure with the set value, the diversity of different user groups and objective environments is not considered, so that part of error point operation judgment is inaccurate. In addition, the problem of error point caused by the replacement of new and old contents in the above example cannot be solved.
Therefore, a new operation processing method and apparatus, and a computing device are needed.
The above information disclosed in this background section is only for enhancement of understanding of the background of the disclosure and therefore it may contain information that does not constitute prior art that is already known to a person of ordinary skill in the art.
Disclosure of Invention
In view of the foregoing, it is an object of the present disclosure to provide an operation processing method and apparatus and a computing device, thereby overcoming, at least to some extent, one or more of the problems due to the limitations and disadvantages of the related art.
Additional features and advantages of the disclosure will be set forth in the detailed description which follows, or in part will be obvious from the description, or may be learned by practice of the disclosure.
According to an aspect of the embodiments of the present disclosure, an operation processing method is provided, which includes: reading values of a plurality of operating environment factors based on user operation detected by a terminal interface; inputting the values of the plurality of operating environment factors into a pre-trained operation recognition model to obtain a probability value of the user operation belonging to misoperation, wherein the operation recognition model comprises weight coefficients corresponding to the plurality of operating environment factors; and triggering corresponding subsequent response by the terminal interface according to the value range in which the probability value falls.
According to another aspect of the embodiments of the present disclosure, there is provided an operation processing apparatus including: the environment detection module is set to read values of a plurality of operation environment factors based on user operation detected by the terminal interface; the operation recognition module is used for inputting the values of the multiple operation environment factors into a pre-trained operation recognition model to obtain a probability value of the user operation belonging to the misoperation, and the operation recognition model comprises weight coefficients corresponding to the multiple operation environment factors; and the operation response module is set to enable the terminal interface to trigger corresponding subsequent response according to the value range in which the probability value falls.
According to an aspect of the embodiments of the present disclosure, there is provided a storage medium storing a computer program which, when executed by a processor of a computer, causes the computer to perform the method according to any one of the above embodiments.
According to an aspect of an embodiment of the present disclosure, there is provided a computing device including: a processor; a memory storing instructions executable by the processor; wherein the processor is configured to perform a method as described in any of the above embodiments.
According to the technical scheme provided by the embodiment of the disclosure, the operation identification model is established based on the historical operation of the user so as to output the misoperation probability of the user in real time, and follow-up response is controlled accordingly, so that the identification accuracy can be improved, the data is accurately classified so as to provide more accurate data, the flow waste is saved, and the user experience is 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 disclosure.
Drawings
The above and other objects, features and advantages of the present disclosure will become more apparent by describing in detail exemplary embodiments thereof with reference to the attached drawings. The drawings described below are merely some embodiments of the present disclosure, and other drawings may be derived from those drawings by those of ordinary skill in the art without inventive effort.
FIG. 1 is a flow diagram illustrating a method of operational processing in accordance with an exemplary embodiment.
FIG. 2 is a flow diagram illustrating a method of operational processing in accordance with an exemplary embodiment.
FIG. 3 is a diagram illustrating an interface for receiving user feedback, according to an example embodiment.
Fig. 4 is an exemplary flowchart of step S220 in the embodiment shown in fig. 2.
FIG. 5 is a flowchart illustrating a method of operational processing in accordance with an exemplary embodiment.
FIG. 6 is a flowchart illustrating a method of operational processing in accordance with another exemplary embodiment.
FIG. 7 is a diagram illustrating a computational process of an operational process according to another exemplary embodiment.
Fig. 8 is a block diagram of an operation processing device according to an embodiment of the present disclosure.
FIG. 9 is a schematic diagram of a computing device according to an embodiment of the present disclosure.
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 embodiments 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 same reference numerals denote the same or similar parts in the drawings, and thus, a repetitive description thereof will be omitted.
Furthermore, 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 disclosure. One skilled in the relevant art will recognize, however, that the subject matter of the present disclosure 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 methods, devices, implementations, or operations have not been shown or described in detail to avoid obscuring aspects of the disclosure.
The block diagrams shown in the figures are functional entities only and do not necessarily correspond to physically separate entities. I.e. 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 means and/or microcontroller means.
The flow charts shown in the drawings are merely illustrative and do not necessarily include all of the contents and operations/steps, nor do they necessarily have to be performed in the order described. For example, some operations/steps may be decomposed, and some operations/steps may be combined or partially combined, so that the actual execution sequence may be changed according to the actual situation.
It will be understood that, although the terms first, second, third, etc. may be used herein to describe various components, these components should not be limited by these terms. These terms are used to distinguish one element from another. Thus, a first component discussed below may be termed a second component without departing from the teachings of the disclosed concept. As used herein, the term "and/or" includes any and all combinations of one or more of the associated listed items.
It is to be understood by those skilled in the art that the drawings are merely schematic representations of exemplary embodiments, and that the blocks or processes shown in the drawings are not necessarily required to practice the present disclosure and are, therefore, not intended to limit the scope of the present disclosure.
FIG. 1 is a flow diagram illustrating a method of operational processing in accordance with an exemplary embodiment. As shown, the method of the present embodiment includes the following steps S110 to S130. In one embodiment, the method of the present embodiment may be executed in a user terminal (e.g., a mobile phone, a tablet computer).
In step S110, values of a plurality of operating environment factors are read based on a user operation detected by the terminal interface.
User actions may include, for example, but are not limited to, clicking on an advertisement, game link, merchandise link, and the like. The operation environment factor is environment data collected by the user terminal when the user operates the terminal interface. The operating environment factors may include, for example, but are not limited to, a presentation layout class, a hardware environment class, a user information class, and a guide element content class. The display layout class refers to parameters involved when the operation object is displayed in the terminal interface, and the display layout class may include, but is not limited to, a visible percentage of the user operation object (percentage of an operation object visible region in an operation page area), a user operation position (e.g., a distance between a user operation position coordinate and an operation object center point), an operation object display duration (a time interval from the operation object loading display to the operation occurrence), a user interface static duration (a time interval from the slidable list scrolling pause to the user operation occurrence), a display time of "content in-situ substitution" (a time interval from the page content loading completion to the user operation occurrence when the original content is replaced), and the like. The hardware environment class refers to parameters related to hardware in the current terminal, which may include, for example, but not limited to, CPU occupancy, available memory, stuck duration, etc. The user information class refers to parameters related to the current user that may affect their operating preferences, which may include, for example, but not limited to, the user's age, gender, job category, academic calendar, and the like. The content of the guide element refers to the relevant parameters of the current operation object, which may include, for example, but not limited to, the topic, nature, etc. of the operation object.
In step S120, values of the multiple operating environment factors are input into a pre-trained operation recognition model to obtain a probability value of the user operation belonging to the misoperation, where the operation recognition model includes weight coefficients corresponding to the multiple operating environment factors.
The operation recognition model may be used to calculate a probability value that the user operation belongs to a false operation. In one embodiment, the operation recognition model may further include a linear function between the probability that each operating environment factor causes a malfunction and the operating environment factor. In one embodiment, when the operation environment factors are input into the operation recognition model, the probability of each operation environment factor causing misoperation is obtained according to a linear function, and the probability of each operation environment factor causing misoperation and the corresponding weight coefficient are subjected to weighted summation, wherein the summation is the probability value of the user belonging to the misoperation.
In step S130, the terminal interface triggers a corresponding subsequent response according to the value range in which the probability value falls.
In one embodiment, the misoperation attribute of the current user operation can be determined according to the value range in which the probability value falls. Corresponding subsequent responses may be distinguished based on the misoperation attribute. In one embodiment, when the probability value falls into the first value range, the fact that the user operation belongs to the misoperation is confirmed, and the terminal interface stops responding to the user operation. In one embodiment, when the probability value falls within the second value range, the terminal interface is caused to generate a prompt for receiving user feedback, and whether to continue a response to the user operation is confirmed based on the received user feedback. In one embodiment, when the probability value falls into the third value range, the user operation is confirmed not to belong to the misoperation, and the terminal interface is enabled to continue responding to the user operation.
In one embodiment, a plurality of operating environment factors operated by a current user and misoperation attributes of the operating environment factors can be used as training data of the operation recognition model, so that the operation recognition model is updated in real time to improve the recognition accuracy of the operation recognition model.
Fig. 2 is a flowchart illustrating an operation processing method according to an exemplary embodiment, and as shown, the method of the present embodiment includes the following steps S210-S220.
In step S210, sample data for training the operation recognition model is collected, where the sample data includes a plurality of detected historical operations of the user, historical values of the operating environment factors corresponding to the historical operations, and misoperation attributes corresponding to the historical operations.
As previously described, the results of step S130 may be obtained in real-time to update the sample data used to train the operation recognition model.
The misoperation attribute can indicate whether the corresponding user operation belongs to misoperation. In one embodiment, the method for obtaining the misoperation attribute may be, for example: calculating a return operation time value according to historical operation, wherein the return operation time value represents the time from the detection of the operation of triggering the terminal interface switching by the user to the detection of the return operation; when the return operation time value is smaller than the preset time value, generating a prompt interface for receiving user feedback; and determining misoperation attributes of the historical operations based on the user feedback received at the prompt interface. A prompt interface for receiving user feedback may be, for example, as shown in fig. 3. The operation of triggering the terminal interface switching by the user is, for example, an operation of clicking a certain link, when the operation triggers the jump, a return operation is detected again to return to the clicked page when the user triggers the operation of triggering the terminal interface switching, and the interval time between the two operations is the return operation time value. The value of the preset time can be obtained by calculation according to actual statistical data or mechanism data, and the embodiment of the disclosure has no limitation.
In one embodiment, the method for obtaining the misoperation attribute may, for example, determine whether the historical operation meets the following condition: the historical operation comprises a first operation, a second operation and a third operation which are sequentially performed; sequentially displaying a first page, a second page and the first page on a terminal interface; detecting a first operation when in-situ replacement of a display element occurs on a first page, switching a terminal interface to a second page in response to the first operation, detecting a second operation on the second page, returning the terminal interface to the first page in response to the second operation, and detecting a third operation aiming at the display element before in-situ replacement occurs on the first page; and determining that the first operation belongs to the misoperation when the historical operation meets the conditions. The in-place replacement refers to that original display content in a certain display area is replaced by new display content in a page. The in-place replacement may occur, for example and without limitation, during a rolling presentation of the advertisement or during a rotation presentation of different advertisements. The first page is a terminal interface and comprises display elements which are intended to be clicked by a user, such as an advertisement page; the first operation is that a user clicks an advertisement page in an in-situ replacement occurrence region when in-situ replacement occurs, namely, the advertisement opened by the user is not the original displayed advertisement in the first page; the second page is a jump page responding to the first operation, namely a new advertisement page after in-situ replacement, and the second operation can be a return operation of a user and is used for returning to the first page. The third operation may be, for example, a click operation performed by the user on the display element before the in-place replacement occurs in the first page, that is, a click operation performed on the advertisement page that the user intends to click.
In step S220, weight coefficients corresponding to the plurality of operating environment factors are calculated based on the sample data.
The weight coefficients are included in the operation recognition model, and the calculation method can, for example, learn sample data by using a neural network, wherein a plurality of operation environment factors in the sample data are used as input of the neural network, and misoperation attributes are used as expected output, and the weight coefficients corresponding to the plurality of operation environment factors are obtained through iterative training.
In step S230, values of a plurality of operating environment factors are read based on the user operation detected by the terminal interface.
In step S240, values of the multiple operating environment factors are input into a pre-trained operation recognition model to obtain a probability value of the user operation belonging to the misoperation, where the operation recognition model includes weight coefficients corresponding to the multiple operating environment factors.
In step S250, the terminal interface triggers a corresponding subsequent response according to the value range in which the probability value falls.
Steps S230-S250 have already been explained in detail in FIG. 1 and are not described here.
In one embodiment, step S220 may be implemented as the exemplary flow illustrated in FIG. 4, including the following steps S410-S430.
In step S410, a plurality of operation environment factors are subjected to cluster analysis according to the degree of influence of the erroneous operation.
Clustering is a process of dividing a collection of physical or abstract objects into classes composed of similar objects. In this embodiment, the samples may be classified according to values of a plurality of operating environment factors using a clustering algorithm.
In step S420, a linear fit is performed for each operation environment factor, and the probability that the operation environment factor causes a malfunction is calculated.
The linear fit may characterize a linear function between each operating environment factor and the probability that the operating environment factor causes a malfunction. And the probability of misoperation caused by the linear function obtained by linear fitting can be determined by the value of the current operating environment factor.
In step S430, based on the back propagation BP neural network, forward calculation is performed with the probability of the multiple operation environment factors causing the misoperation as an input, reverse correction is performed according to the misoperation attribute, and the weighting coefficients corresponding to the multiple operation environment factors are obtained through iteration.
The bp (back propagation) neural network is a multi-layer feedforward neural network trained according to an error back propagation algorithm. The BP neural network algorithm comprises two processes of forward propagation of signals and backward propagation of errors. The weight coefficients may be represented, for example, by weight values between input neurons and hidden layer neurons in a BP neural network.
FIG. 5 is a flowchart illustrating a method of operational processing in accordance with an exemplary embodiment. As shown, the method of the present embodiment includes the following steps S510 to S520.
In step S510, values of the plurality of operating environment factors are respectively input to the linear function, so as to obtain a probability that each operating environment factor causes an erroneous operation.
In one embodiment, the linear function may be obtained, for example, by step S420.
In step S520, a weighted sum of the probability of the misoperation caused by each operation environment factor and the weight coefficient is obtained to obtain a probability value that the user operation belongs to the misoperation.
In one embodiment, assume that there are n operating environment factors that cause a probability of false operation, denoted P respectively1，P2，...，PnThe corresponding weight coefficients are respectively expressed as W1，W2，...，WnThen, the formula for calculating the probability value of the user operation belonging to the misoperation can be expressed as:
Figure BDA0001893816430000081
FIG. 6 is a flowchart illustrating a method of operational processing in accordance with another exemplary embodiment. As shown, the method of the present embodiment includes the following steps S610-S620.
In step S610, sample data is acquired.
Sample data may be obtained, for example, by collecting and extracting a plurality of historical operations of the user. The sample data may include, for example, user information class data, presentation layout class data, hardware environment class data, and guide element content class data.
In one embodiment, the user's error point reaction time value may also be collected, that is, after clicking the guide factor, the return button is clicked N seconds after the page jumps to return to the original page. N seconds was used as the reaction time value of the error point. The error point reaction time value can be used for judging the error operation attribute of the current user operation, for example. The method for determining the misoperation attribute has been described in detail in the embodiment of step S210, and is not described herein again.
In step S620, weighting coefficients of a plurality of environmental factors are calculated to obtain an operation recognition model. Wherein, the weight coefficient of the environmental factor can be calculated by a BP neural network. And taking the probability of misoperation caused by a plurality of operation environment factors as input, reversely correcting the weight coefficient of the BP neural network according to the misoperation attribute, and learning sample data to obtain the weight value of a group of input layers to the hidden layer, namely the weight coefficient expected to be obtained in the step.
In step S630, the operation recognition model is issued to the user terminal to perform real-time judgment on the user operation.
The input of the operation recognition model is the probability of misoperation caused by the operation environment factor, and the probability value of the misoperation of the user operation can be obtained by performing weighted summation on the probability and the weight coefficient. The malfunction identification process may be as shown in fig. 7, for example.
Further, different subsequent responses may be selected by the probability value. For example, a threshold value of 0.5 is set, when the probability value is greater than 0.5, the user operation is considered to be misoperation, and the response to the user operation can be selected to be terminated; and when the probability value is less than 0.5, the user operation is not considered as the misoperation, and the current user operation can be selected to be continuously responded. Further, an intermediate value range may be set, and when the probability value falls into the value range, the misoperation attribute of the user operation is considered to be less certain, and a prompt interface for receiving user feedback may be generated, for example, as shown in fig. 3.
According to the operation processing method of the embodiment, the probability value of the misoperation of the model is identified by collecting a plurality of operation environment factors in the user operation process and using the operation trained in advance. Whether the user operation is misoperation can be identified accurately by triggering the subsequent response of the terminal interface according to the probability value, and other interfaces are prevented from entering due to the misoperation of the user, so that the misoperation rate of the terminal is reduced, the processing efficiency of the terminal is improved, the waste of flow generated by misoperation is avoided, the advertisement and operation conversion rate is improved, the advertisement charging and the division are more accurate, and the user experience is improved. (ii) a Furthermore, the operation identification model can be updated in real time by continuously updating the sample data of the historical operation so as to adapt to the real-time update of the webpage content and the user data.
The following are embodiments of the disclosed apparatus that may be used to perform embodiments of the disclosed methods. For details not disclosed in the embodiments of the apparatus of the present disclosure, refer to the embodiments of the method of the present disclosure.
Fig. 8 is a block diagram of an operation processing apparatus according to an embodiment of the present disclosure, and as shown in the drawing, the apparatus of this embodiment includes a pop environment detection module 801, an operation identification module 802, and an operation response module 803.
The environment detection module 801 is configured to read values of a plurality of operating environment factors based on user operations detected by the terminal interface.
The operation recognition module 802 is configured to input values of the plurality of operation environment factors into a pre-trained operation recognition model to obtain a probability value of the user operation belonging to the misoperation, and the operation recognition model includes weight coefficients corresponding to the plurality of operation environment factors.
The operation response module 803 is set to trigger a corresponding subsequent response by the terminal interface according to the value range in which the probability value falls.
Optionally, sample data for training the operation recognition model may be further collected by the environment detection module 801, where the sample data includes a plurality of detected historical operations of the user, historical values of the operation environment factors corresponding to the plurality of detected historical operations, and misoperation attributes corresponding to the plurality of detected historical operations. Further, weight coefficients corresponding to a plurality of operating environment factors may be calculated based on the sample data to apply the weight coefficients to the operation identifying module 802.
Alternatively, the operation response module 803 may calculate a return operation time value according to the historical operation, where the return operation time value represents the time elapsed from the detection of the operation of the user triggering the terminal interface switch to the detection of the return operation; when the return operation time value is smaller than the preset time value, generating a prompt interface for receiving user feedback; and determining misoperation attributes of the historical operations based on the user feedback received at the prompt interface.
Optionally, the operation identification module 802 may be further configured to determine whether the historical operation meets the following condition: the historical operation comprises a first operation, a second operation and a third operation which are sequentially performed; sequentially displaying a first page, a second page and the first page on a terminal interface; detecting a first operation when in-situ replacement of a display element occurs on a first page, switching a terminal interface to a second page in response to the first operation, detecting a second operation on the second page, returning the terminal interface to the first page in response to the second operation, and detecting a third operation aiming at the display element before in-situ replacement occurs on the first page; and determining that the first operation belongs to the misoperation when the historical operation meets the conditions.
Optionally, the obtaining of the weighting factor of the operation identification module 802 may, for example, first perform cluster analysis on a plurality of operation environment factors according to the influence degree causing the misoperation; performing linear fitting on each operating environment factor, and calculating the probability of misoperation caused by the operating environment factor; and based on the back propagation BP neural network, performing forward calculation by taking the probability of misoperation caused by the multiple operation environment factors as input, performing reverse correction according to the misoperation attribute, and obtaining the weight coefficients corresponding to the multiple operation environment factors through iteration.
Optionally, the operation identification module 802 is further provided with a probability that each of the operation environment factors causes a malfunction. When calculating the probability of each operating environment factor causing the misoperation, the operation identification module 802 may first input the values of the plurality of operating environment factors into the linear function respectively to obtain the probability of each operating environment factor causing the misoperation; and based on the weighted summation of the probability of misoperation caused by each operation environment factor and the weight coefficient, obtaining the probability value of the misoperation of the user operation.
Optionally, the operation response module 803 may be configured to, when the probability value falls within the first numerical value range, confirm that the user operation belongs to the misoperation, and stop the response of the terminal interface to the user operation; when the probability value falls into a second numerical value range, enabling the terminal interface to generate a prompt for receiving user feedback, and confirming whether to continue responding to the user operation or not based on the received user feedback; and when the probability value falls into a third numerical value range, confirming that the user operation does not belong to the misoperation, and enabling the terminal interface to continue responding to the user operation.
According to the operation processing device of the embodiment, the probability value of the model misoperation is identified by collecting a plurality of operation environment factors in the user operation process and using the operation trained in advance. Whether the user operation is misoperation can be identified accurately by triggering the subsequent response of the terminal interface according to the probability value, and other interfaces are prevented from entering due to the misoperation of the user, so that the misoperation rate of the terminal is reduced, the processing efficiency of the terminal is improved, the waste of flow generated by misoperation is avoided, the advertisement and operation conversion rate is improved, the advertisement charging and the division are more accurate, and the user experience is improved. (ii) a Furthermore, the operation identification model can be updated in real time by continuously updating the sample data of the historical operation so as to adapt to the real-time update of the webpage content and the user data.
The present embodiment provides an operation processing apparatus based on the same inventive concept as the above-described method embodiment, and can be used to implement the operation processing method provided in the above-described embodiment.
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 present disclosure. 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. The components shown as modules or units may or may not be physical units, i.e. may be located in one place or may also be distributed over a plurality of network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the wood-disclosed scheme. One of ordinary skill in the art can understand and implement it without inventive effort.
Through the description of the above embodiments, those skilled in the art will readily understand that the above described exemplary embodiments may be implemented by software, or by software in combination with necessary hardware.
For example, in an example embodiment, there is also provided a computer readable storage medium, on which a computer program is stored, which when executed by a processor, may implement the steps of the method described in any of the above embodiments. The detailed description of the steps of the method can refer to the detailed description in the foregoing embodiments, and the detailed description is omitted here. The computer readable storage medium may be a ROM, a Random Access Memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, and the like.
In another example embodiment, a computing device is further provided, where the computing device may be a mobile terminal such as a mobile phone and a tablet computer, and may also be a terminal device such as a desktop computer and a server, which is not limited in this example embodiment. FIG. 9 shows a schematic diagram of a computing device 90 in an example embodiment according to the present disclosure. For example, the device 90 may be provided as a mobile terminal. Referring to fig. 9, the device 90 includes a processing component 91 that further includes one or more processors, and memory resources, represented by memory 92, for storing instructions, such as applications, that are executable by the processing component 91. The application programs stored in memory 92 may include one or more modules that each correspond to a set of instructions. Further, the processing component 91 is configured to execute instructions to perform the above-described operation processing method.
The memory may be used for storing software programs and modules, and the processor may execute various functional applications and data processing by operating the software programs and modules stored in the memory. The memory may include high speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory may further include memory located remotely from the processor, which may be connected to a computer terminal or a mobile terminal through a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The device 90 may also include a power component 93 configured to perform power management of the device 90, a wired or wireless network interface 94 configured to connect the device 90 to a network, and an input output (I/O) interface 95. The device 90 may operate based on an operating system, such as Android, IOS, or the like, stored in the memory 92.
Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure disclosed herein. This application is intended to cover any variations, uses, or adaptations of the disclosure following, in general, the principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.
While the present disclosure has been described with reference to several exemplary embodiments, it is understood that the terminology used is intended to be in the nature of words of description and illustration, rather than of limitation. As the present disclosure may be embodied in several forms without departing from the spirit or essential characteristics thereof, it should also be understood that the above-described embodiments are not limited by any of the details of the foregoing description, but rather should be construed broadly within its spirit and scope as defined in the appended claims, and therefore all changes and modifications that fall within the meets and bounds of the claims, or equivalences of such meets and bounds are therefore intended to be embraced by the appended claims.

Claims (9)

1. An operation processing method, comprising:
reading values of a plurality of operating environment factors based on user operation detected by a terminal interface;
inputting the values of the plurality of operating environment factors into a pre-trained operation recognition model to obtain a probability value of the user operation belonging to misoperation, wherein the operation recognition model comprises weight coefficients corresponding to the plurality of operating environment factors; and
triggering corresponding subsequent response by the terminal interface according to the value range of the probability value,
the operation recognition model further includes a linear function between the probability of the misoperation caused by each operation environment factor and the operation environment factor, and the values of the operation environment factors are input into a pre-trained operation recognition model to obtain the probability value of the misoperation of the user, including:
respectively inputting the values of the plurality of operating environment factors into the linear function to obtain the probability of misoperation caused by each operating environment factor; and
and obtaining a probability value of the misoperation of the user operation based on the weighted summation of the probability of the misoperation caused by each operation environment factor and the weight coefficient.
2. The method of claim 1, further comprising:
collecting sample data for training the operation recognition model, wherein the sample data comprises a plurality of detected historical operations of the user, historical values of operating environment factors corresponding to the historical operations, and misoperation attributes corresponding to the historical operations; and
and calculating the weight coefficients corresponding to the multiple operating environment factors based on the sample data.
3. The method of claim 2, wherein said collecting sample data for training said operational recognition model comprises:
calculating a return operation time value according to the historical operation, wherein the return operation time value represents the time from the detection of the operation of triggering the terminal interface switching by the user to the detection of the return operation;
when the return operation time value is smaller than a preset time value, generating a prompt interface for receiving user feedback; and
and determining the misoperation attribute of the historical operation based on the user feedback received at the prompt interface.
4. The method of claim 2, wherein said collecting sample data for training said operational recognition model comprises:
judging whether the historical operation meets the following conditions: the historical operation comprises a first operation, a second operation and a third operation which are sequentially performed; sequentially displaying a first page, a second page and the first page on the terminal interface; detecting the first operation when in-situ replacement of a display element occurs on the first page, switching the terminal interface to the second page in response to the first operation, detecting the second operation on the second page, returning the terminal interface to the first page in response to the second operation, and detecting a third operation for the display element before in-situ replacement occurs on the first page; and
and when the historical operation meets the conditions, determining that the first operation belongs to misoperation.
5. The method of claim 2, wherein said calculating said weight coefficients corresponding to said plurality of operating environment factors based on said sample data comprises:
performing cluster analysis on the plurality of operating environment factors according to the influence degree of misoperation;
performing linear fitting on each operating environment factor, and calculating the probability of misoperation caused by the operating environment factor; and
and based on a back propagation BP neural network, performing forward calculation by taking the probability of misoperation caused by the multiple operation environment factors as input, performing reverse correction according to the misoperation attribute, and obtaining the weight coefficients corresponding to the multiple operation environment factors through iteration.
6. The method of claim 1, wherein the step of enabling the terminal interface to trigger the corresponding subsequent response according to the value range in which the probability value falls comprises any of the following steps:
when the probability value falls into a first numerical value range, confirming that the user operation belongs to misoperation, and enabling the terminal interface to stop responding to the user operation;
when the probability value falls into a second numerical value range, enabling the terminal interface to generate a prompt for receiving user feedback, and confirming whether to continue responding to the user operation or not based on the received user feedback; and
and when the probability value falls into a third numerical value range, confirming that the user operation does not belong to misoperation, and enabling the terminal interface to continue responding to the user operation.
7. An operation processing apparatus characterized by comprising:
the environment detection module is set to read values of a plurality of operation environment factors based on user operation detected by the terminal interface;
the operation recognition module is used for inputting the values of the multiple operation environment factors into a pre-trained operation recognition model to obtain a probability value of the user operation belonging to the misoperation, and the operation recognition model comprises weight coefficients corresponding to the multiple operation environment factors; and
an operation response module which is set to enable the terminal interface to trigger corresponding subsequent response according to the value range in which the probability value falls,
wherein the operation identification model further comprises a linear function between the probability of each operating environment factor causing a false operation and the operating environment factor, and the operation identification module is further configured to:
respectively inputting the values of the plurality of operating environment factors into the linear function to obtain the probability of misoperation caused by each operating environment factor; and
and obtaining a probability value of the misoperation of the user operation based on the weighted summation of the probability of the misoperation caused by each operation environment factor and the weight coefficient.
8. A storage medium storing a computer program which, when executed by a processor of a computer, causes the computer to perform the method of any one of claims 1-6.
9. A computing device, comprising:
a processor;
a memory storing instructions executable by the processor;
wherein the processor is configured to perform the method of any one of claims 1-6.
CN201811482942.2A 2018-12-05 2018-12-05 Operation processing method and device and computer equipment Active CN109558032B (en)

Priority Applications (2)

Application Number Priority Date Filing Date Title
CN201811482942.2A CN109558032B (en) 2018-12-05 2018-12-05 Operation processing method and device and computer equipment
PCT/CN2019/122406 WO2020114352A1 (en) 2018-12-05 2019-12-02 Anti-misoperation processing of computing device

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201811482942.2A CN109558032B (en) 2018-12-05 2018-12-05 Operation processing method and device and computer equipment

Publications (2)

Publication Number Publication Date
CN109558032A CN109558032A (en) 2019-04-02
CN109558032B true CN109558032B (en) 2020-09-04

Family

ID=65868988

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201811482942.2A Active CN109558032B (en) 2018-12-05 2018-12-05 Operation processing method and device and computer equipment

Country Status (2)

Country Link
CN (1) CN109558032B (en)
WO (1) WO2020114352A1 (en)

Families Citing this family (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109558032B (en) * 2018-12-05 2020-09-04 北京三快在线科技有限公司 Operation processing method and device and computer equipment
CN111913618B (en) * 2020-07-08 2022-04-19 核动力运行研究所 Detection method and device
CN112395200B (en) * 2020-11-25 2024-04-02 电信科学技术第十研究所有限公司 Method and device for determining probability of software reliability operation profile
CN113176847A (en) * 2021-04-07 2021-07-27 上海墨案智能科技有限公司 Method and equipment for preventing mistaken touch of ink screen
CN114386688B (en) * 2022-01-07 2022-12-06 逑美科技(上海)有限公司 User intention prediction method and system based on multi-data fusion
CN117686757A (en) * 2023-12-07 2024-03-12 浙江万昌电力设备有限公司 Intelligent early warning method and system for outdoor power metering box

Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN107519641A (en) * 2017-08-04 2017-12-29 网易（杭州）网络有限公司 Control method, apparatus, storage medium and the mobile terminal of game skill release

Family Cites Families (16)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US6954744B2 (en) * 2001-08-29 2005-10-11 Honeywell International, Inc. Combinatorial approach for supervised neural network learning
CN101645711B (en) * 2009-08-31 2014-06-04 华为终端有限公司 Keyboard error correction method and device
CN102314436A (en) * 2010-06-30 2012-01-11 国际商业机器公司 Webpage automatic adjusting method and system
TWI557722B (en) * 2012-11-15 2016-11-11 緯創資通股份有限公司 Method to filter out speech interference, system using the same, and computer readable recording medium
CN105183538B (en) * 2014-06-03 2019-10-29 联想(北京)有限公司 A kind of information processing method and electronic equipment
US9959517B2 (en) * 2014-12-22 2018-05-01 International Business Machines Corporation Self-organizing neural network approach to the automatic layout of business process diagrams
KR102182583B1 (en) * 2016-05-17 2020-11-24 실리콘 스토리지 테크놀로지 인크 Deep learning neural network classifier using nonvolatile memory array
CN106095295B (en) * 2016-06-15 2020-09-25 维沃移动通信有限公司 Processing method based on fingerprint identification and mobile terminal
CN106203380B (en) * 2016-07-20 2019-11-29 中国科学院计算技术研究所 Ultrasonic wave gesture identification method and system
US11494654B2 (en) * 2016-11-03 2022-11-08 Avanseus Holdings Pte. Ltd. Method for machine failure prediction using memory depth values
CN107358169A (en) * 2017-06-21 2017-11-17 厦门中控智慧信息技术有限公司 A kind of facial expression recognizing method and expression recognition device
CN108021292B (en) * 2017-10-27 2021-01-26 努比亚技术有限公司 Light sensing control trigger control method and device and computer readable storage medium
CN108089897A (en) * 2017-12-21 2018-05-29 广东欧珀移动通信有限公司 Startup management method, device, storage medium and the mobile terminal of application program
CN108021235B (en) * 2017-12-27 2021-03-09 Oppo广东移动通信有限公司 Method, device, terminal and storage medium for triggering operation
CN108710847B (en) * 2018-05-15 2020-11-27 北京旷视科技有限公司 Scene recognition method and device and electronic equipment
CN109558032B (en) * 2018-12-05 2020-09-04 北京三快在线科技有限公司 Operation processing method and device and computer equipment

Patent Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN107519641A (en) * 2017-08-04 2017-12-29 网易（杭州）网络有限公司 Control method, apparatus, storage medium and the mobile terminal of game skill release

Also Published As

Publication number Publication date
WO2020114352A1 (en) 2020-06-11
CN109558032A (en) 2019-04-02

Similar Documents

Publication Publication Date Title
CN109558032B (en) Operation processing method and device and computer equipment
US11048530B1 (en) Predictive action modeling to streamline user interface
US11551239B2 (en) Characterizing and modifying user experience of computing environments based on behavior logs
WO2019153604A1 (en) Device and method for creating human/machine identification model, and computer readable storage medium
CN110163647B (en) Data processing method and device
WO2017190610A1 (en) Target user orientation method and device, and computer storage medium
US10331221B2 (en) Methods for analysing user interactions with a user interface
CN111354237A (en) Context-based deep knowledge tracking method and computer readable medium thereof
US8346710B2 (en) Evaluating statistical significance of test statistics using placebo actions
US11610580B2 (en) System and method for determining reasons for anomalies using cross entropy ranking of textual items
US20210366006A1 (en) Ranking of business object
US10878451B2 (en) Change point detection in a multi-armed bandit recommendation system
CN111401041A (en) Problem prediction method and device, electronic equipment and storage medium
CN110288403A (en) It keeps and plays frame method of ejecting, system, equipment and storage medium
CN114445143A (en) Service data prediction method, device, equipment and medium
CN111667024B (en) Content pushing method, device, computer equipment and storage medium
CN110169021B (en) Method and apparatus for filtering multiple messages
CN115564517A (en) Commodity recommendation method, prediction model training method and related equipment
US20170148035A1 (en) Buying Stage Determination in a Digital Medium Environment
CN111881007B (en) Operation behavior judgment method, device, equipment and computer readable storage medium
CN107786376B (en) Content pushing method and device and computer equipment
CN112084408B (en) List data screening method, device, computer equipment and storage medium
US20210201177A1 (en) Prediction system, prediction method, and information storage medium
EP4099250A1 (en) Method and device for determining quality of user experience of products
CN111382346B (en) Method and system for recommending content

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant