WO2020114352A1 - Anti-misoperation processing of computing device - Google Patents

Anti-misoperation processing of computing device Download PDF

Info

Publication number
WO2020114352A1
WO2020114352A1 PCT/CN2019/122406 CN2019122406W WO2020114352A1 WO 2020114352 A1 WO2020114352 A1 WO 2020114352A1 CN 2019122406 W CN2019122406 W CN 2019122406W WO 2020114352 A1 WO2020114352 A1 WO 2020114352A1
Authority
WO
WIPO (PCT)
Prior art keywords
user
misoperation
operating environment
recognition
terminal interface
Prior art date
Application number
PCT/CN2019/122406
Other languages
French (fr)
Chinese (zh)
Inventor
李陆启
李一山
纪伟
周熠
张鑫淼
杨飞宇
Original Assignee
北京三快在线科技有限公司
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
Priority to CN201811482942.2 priority Critical
Priority to CN201811482942.2A priority patent/CN109558032B/en
Application filed by 北京三快在线科技有限公司 filed Critical 北京三快在线科技有限公司
Publication of WO2020114352A1 publication Critical patent/WO2020114352A1/en

Links

Images

Classifications

    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRIC DIGITAL DATA PROCESSING
    • G06F3/00Input 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
    • G06F3/01Input arrangements or combined input and output arrangements for interaction between user and computer
    • G06F3/03Arrangements for converting the position or the displacement of a member into a coded form
    • G06F3/041Digitisers, e.g. for touch screens or touch pads, characterised by the transducing means
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRIC DIGITAL DATA PROCESSING
    • G06F3/00Input 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
    • G06F3/01Input arrangements or combined input and output arrangements for interaction between user and computer
    • G06F3/03Arrangements for converting the position or the displacement of a member into a coded form
    • G06F3/033Pointing devices displaced or positioned by the user, e.g. mice, trackballs, pens or joysticks; Accessories therefor
    • G06F3/0354Pointing 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

Abstract

Disclosed are an operation processing method and apparatus, and an electronic device and a computer-readable medium. The method comprises: based on a user operation detected on a terminal interface, reading the values of a plurality of operation environment factors (S110); inputting the values of the plurality of operation environment factors into a pre-trained operation recognition model to obtain a probability value of the user operation being a misoperation (S120), wherein the operation recognition model comprises weight coefficients corresponding to the multiple operation environment factors; and according to a value range where the probability value falls, making the terminal interface trigger a corresponding subsequent response (S130).

Description

Anti-misoperation handling of computing equipment Technical field

The present disclosure relates to the field of Internet technology, and in particular, to an operation processing method and apparatus, and a computing device.

Background technique

At present, mobile terminals are widely used in people's life and work. When people use mobile terminals, they usually operate them by touching the screen or clicking with a mouse. According to statistics, up to 50% of mobile ad clicks are accidentally clicked, and in addition to mobile ads, other types of functions and pages in mobile touch screen applications also have a large number of unintentional wrong clicks, for example, the user's attention when operating It is not on the guide element, just the wrong touch will lead to a mistake; for example, when the new content replaces the old content instantaneously, the user does not have enough time to judge, and intends to click on the old element, but clicks on the newly appeared element, causing the mistake.

The above information disclosed in the background section is only for enhancing the understanding of the background of the present disclosure, so it may include information that does not constitute prior art known to those of ordinary skill in the art.

Summary of the invention

In view of this, the purpose of the present disclosure is to provide an operation processing method and apparatus and a computing device, so as to overcome, at least to some extent, one or more problems due to limitations and defects of the related art.

Other features and advantages of the present disclosure will become apparent through the following detailed description, or partly learned through the practice of the present disclosure.

According to a first aspect of an embodiment of the present disclosure, an operation processing method is proposed. The method includes: reading values of a plurality of operating environment factors based on user operations detected by a terminal interface; The value is input into a pre-trained operation recognition model to obtain a probability value that the user operation belongs to a misoperation. The operation recognition model includes weight coefficients corresponding to the plurality of operating environment factors; and The range of values enables the terminal interface to trigger a corresponding subsequent response.

According to a second aspect of the embodiments of the present disclosure, there is provided an operation processing device, which includes: an environment detection module configured to read values of a plurality of operation environment factors based on user operations detected by a terminal interface; an operation recognition module , Set to input values of the plurality of operating environment factors into a pre-trained operation recognition model to obtain a probability value that the user operation belongs to a misoperation, and the operation recognition model includes corresponding to the plurality of operation environment factors A weighting factor; and an operation response module, which is set to cause the terminal interface to trigger a corresponding subsequent response according to the numerical range that the probability value falls within.

According to a third 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 execute the method as described in the first aspect above.

According to a fourth aspect of the embodiments 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 execute as described in the first aspect above Methods.

According to the technical solution provided by the embodiments of the present disclosure, an operation recognition model is established based on the user's historical operation to output the user's misoperation probability in real time, and the subsequent response is controlled accordingly, which can improve the recognition accuracy rate and accurately classify to provide more accurate data and save Traffic waste and improve user experience.

It should be understood that the above general description and the following detailed description are only exemplary and explanatory, and do not limit the present disclosure.

BRIEF DESCRIPTION

The above and other objects, features, and advantages of the present disclosure will become more apparent by describing in detail example embodiments thereof with reference to the accompanying drawings. The drawings described below are just some embodiments of the present disclosure. For those of ordinary skill in the art, without paying any creative work, other drawings may be obtained based on these drawings.

Fig. 1 is a flow chart of an operation processing method according to an exemplary embodiment.

Fig. 2 is a flow chart of an operation processing method according to an exemplary embodiment.

Fig. 3 is a schematic diagram of an interface for receiving user feedback according to an exemplary embodiment.

FIG. 4 is an exemplary flowchart of step S220 in the embodiment shown in FIG. 2.

Fig. 5 is a flow chart of an operation processing method according to an exemplary embodiment.

Fig. 6 is a flow chart showing an operation processing method according to another exemplary embodiment.

Fig. 7 is a schematic diagram of a calculation process of an operation process according to another exemplary embodiment.

8 is a structural block diagram of an operation processing device according to an embodiment of the present disclosure.

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 drawings. However, the exemplary embodiments can be implemented in various forms, and should not be construed as being limited to the embodiments set forth herein; on the contrary, providing these embodiments makes the present disclosure comprehensive and complete, and fully conveys the concept of the exemplary embodiments For those skilled in the art. The same reference numerals in the drawings denote the same or similar parts, and thus their repeated description will be omitted.

Furthermore, the described features, structures, or characteristics may be combined in one or more embodiments in any suitable manner. In the following description, many specific details are provided to give a full understanding of the embodiments of the present disclosure. However, those skilled in the art will realize that the technical solutions of the present disclosure may be practiced without one or more of the specific details, or other methods, components, devices, steps, etc. may be adopted. In other instances, well-known methods, devices, implementations, or operations have not been shown or described in detail to avoid obscuring aspects of the present disclosure.

The block diagrams shown in the drawings are merely functional entities and do not necessarily have to correspond to physically independent entities. That is, these functional entities can be implemented in the form of software, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and/or processor devices and/or microcontroller devices entity.

The flow charts shown in the drawings are only exemplary illustrations, and it is not necessary to include all contents and operations/steps, nor to be executed in the order described. For example, some operations/steps can also be decomposed, and some operations/steps can be merged or partially merged, so the order of actual execution may change according to the actual situation.

It should 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 component from another. Therefore, the first component discussed below may be referred to as the second component without departing from the teachings of the concepts of the present disclosure. As used herein, the term "and/or" includes any and all combinations of one or more of the associated listed items.

Those skilled in the art can understand that the drawings are only schematic diagrams of example embodiments, and the modules or processes in the drawings are not necessarily required to implement the present disclosure, and therefore cannot be used to limit the protection scope of the present disclosure.

In a method and system for preventing false clicks of advertisements based on pressure sensing technology, it receives the pressure of the user's finger on the advertisement bar and judges whether it reaches the set value; wherein, when the pressure is greater than the first set value, the preset is triggered The first event is to enter the advertisement interface linked to the advertisement bar or display a full-screen advertisement interface to the user. With this method, the true meaning of the user clicking on the advertisement can be relatively accurately simulated, and various mistouches can be relatively accurately prevented, thereby giving the user a better user experience.

However, in this method, the judgment basis of the false click of the advertisement is the finger pressure of a single user, and in comparing the finger pressure with the set value, the diversity of different user groups and objective environments is not considered, so it may be There will be some inaccurate judgments of operation errors. In addition, the problem of misplacement caused by the replacement of old and new content cannot be solved.

Therefore, there is a need for a new operation processing method and apparatus and computing equipment.

Fig. 1 is a flow chart of an operation processing method according to an exemplary embodiment. As shown in FIG. 1, the method of this embodiment includes the following steps S110-S130. In one embodiment, the method of this embodiment may be executed on a user terminal (eg, mobile phone, tablet computer).

In step S110, based on the user operation detected on the terminal interface, the values of a plurality of operating environment factors are read.

User operations may include, for example, but not limited to, clicking on advertisements, game links, product links, and so on. The operating environment factor is the environmental data collected by the user terminal when the user operates the terminal interface. The operating environment factors may include, for example, but not limited to, display layout categories, hardware environment categories, user information categories, and guide element content categories. The display layout class refers to the parameters involved in the display of the operation object in the terminal interface. The display layout class may include, for example, but not limited to, the visible percentage of the user operation object (the percentage of the visible area of the operation object in the area of the operation page), and the user operation position (for example The distance between the coordinates of the user's operation position and the center point of the operation object), the display duration of the operation object (the time interval between the loading and display of the operation object until the operation occurs), the duration of the user interface rest (the interval between the scrollable list scrolling and the time when the user operation occurs) 2. The display time of "in-situ content replacement" (the time interval from when the new page content is loaded and the original content is replaced until the user operation occurs), etc. The hardware environment category refers to the hardware-related parameters of the current terminal, which may include, for example, but not limited to CPU (Central Processing Unit) occupancy rate, available memory, stuck duration, etc. The user information category refers to parameters related to the current user that can affect their operating preferences, which may include, for example, but not limited to, the user's age, gender, job category, education, etc. The guide element content class refers to the relevant parameters of the current operation object, which may include, for example, but not limited to the theme, nature, etc. of the operation object.

In step S120, the values of a plurality of operating environment factors are input into a pre-trained operation recognition model to obtain a probability value that a user operation belongs to an erroneous operation. The operation recognition model includes weight coefficients corresponding to the plurality of operating environment factors.

The operation recognition model can be used to calculate the probability value that the user operation is a misoperation. In one embodiment, the operation recognition model may further include a linear function between the probability that each operating environment factor causes a misoperation and the operating environment factor. In one embodiment, when inputting the operating environment factors into the operation recognition model, the probability of misoperation caused by each operating environment factor can be obtained first according to the linear function, and the probability of misoperation caused by each operating environment factor and its corresponding The weight coefficients are weighted and summed, and the sum value is the probability value that the user operation is a misoperation.

In step S130, the terminal interface triggers a corresponding subsequent response according to the numerical range that the probability value falls within.

In one embodiment, the misoperation attribute of the current user operation may be determined according to the numerical range in which the probability value falls. Corresponding subsequent responses can be distinguished based on misoperation attributes. In one embodiment, when the probability value falls within the first numerical range, it is confirmed that the user operation is a misoperation, and the terminal interface is stopped from responding to the user operation. In one embodiment, when the probability value falls within the second numerical range, the terminal interface is caused to generate a prompt for receiving user feedback, and confirm whether to continue the response to the user operation based on the received user feedback. In one embodiment, when the probability value falls within the third numerical range, it is confirmed that the user operation is not a misoperation, and the terminal interface continues to respond to the user operation.

In one embodiment, multiple operating environment factors and misoperation attributes of the current user operation may also be used as training data of the operation recognition model to update the operation recognition model in real time to improve its recognition accuracy.

Fig. 2 is a flowchart of an operation processing method according to an exemplary embodiment. As shown in Fig. 2, the method of this embodiment includes the following steps S210-S250.

In step S210, sample data for training an operation recognition model is collected, the sample data includes multiple historical operations of the detected user, historical values of operating environment factors corresponding to multiple historical operations, and multiple historical operations Misuse attribute.

As described above, the result of step S130 can be obtained in real time to update the sample data for training the operation recognition model.

The misoperation attribute can indicate whether the corresponding user operation is a misoperation. In one embodiment, the method for acquiring the misoperation attribute may be, for example: calculating the return operation time value based on the historical operation, and the return operation time value represents the time elapsed from the detection of the operation triggered by the user to trigger the terminal interface to the detection of the return operation; When the returned operation time value is less than the preset time value, a prompt interface for receiving user feedback is generated; and based on the user feedback received on the prompt interface, the misoperation attribute of the historical operation is determined. The prompt interface for receiving user feedback may be as shown in FIG. 3, for example. The operation that the user triggers the terminal interface switching is, for example, the operation of clicking a certain link. When the operation triggers a jump, a return operation is detected to return to the clicked page when the user triggered the terminal interface switching operation. The above two operations The interval between them is the return operation time value. The value of the preset time may be calculated and obtained based on actual statistical data or mechanism data, and the embodiments of the present disclosure have no limitation on this.

In one embodiment, the method for acquiring the misoperation attribute may, for example, determine whether the historical operation meets the following conditions: the historical operation includes the first operation, the second operation, and the third operation in sequence; the first page, the first Two pages and a first page; the first operation is detected when the in-situ replacement of the display element occurs on the first page, the terminal interface switches to the second page in response to the first operation, and the second operation is detected on the second page, The terminal interface returns to the first page in response to the second operation, detects a third operation for the display element before in-situ replacement occurs on the first page; and when the historical operation meets the above conditions, it is determined that the first operation is a misoperation. If the first operation is a misoperation, it can be determined that the historical operation is a misoperation. Here, in-place replacement refers to that the original display content of a certain display area is replaced with new display content on the page. In-situ replacement may occur, for example, but not limited to, during the rolling display of advertisements or during the rotation of different advertisements. The first page is the terminal interface, which contains the display elements that the user intends to click, such as the advertisement page; the first operation is the operation that the user clicks on the advertisement page of the in-place replacement area when the in-situ replacement occurs, that is, the advertisement opened by the user is not The originally displayed advertisement on the first page; the second page is a jump page in response to the first operation, that is, a new advertisement page after in-situ replacement; the second operation may be a user's return operation, which is used to return 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-situ replacement occurs on the first page, that is, a click operation performed on the advertisement page that the user intends to click.

In step S220, a weight coefficient corresponding to a plurality of operating environment factors is calculated based on the sample data.

The weight coefficients are included in the operation recognition model. The calculation method can be, for example, a neural network to learn the sample data. Multiple operation environment factors in the sample data are used as the input of the neural network, and the probability of misoperation is used as the expected output. After iterative training, to Obtain weight coefficients corresponding to multiple operating environment factors.

In step S230, based on the user operation detected on the terminal interface, the values of a plurality of operating environment factors are read.

In step S240, the values of a plurality of operating environment factors are input into a pre-trained operation recognition model to obtain a probability value that a user operation belongs to a misoperation. The operation recognition model includes weight coefficients corresponding to the plurality of operating environment factors.

In step S250, the terminal interface triggers a corresponding subsequent response according to the numerical range that the probability value falls within.

Steps S230-S250 have been explained in detail in FIG. 1 and will not be repeated here.

In one embodiment, step S220 may be implemented as the exemplary process shown in FIG. 4, including the following steps S410-S430.

In step S410, cluster analysis is performed on a plurality of operating environment factors according to the degree of influence caused by misoperation.

Clustering is a process of dividing a collection of physical or abstract objects into multiple classes composed of similar objects. In this embodiment, a clustering algorithm can be used to classify the samples according to the values of multiple operating environment factors.

In step S420, a linear fitting is performed on each operating environment factor, and the probability of misoperation caused by the operating environment factor is calculated.

Linear fitting can characterize the linear function between each operating environment factor and the probability that the operating environment factor causes a misoperation. The linear function obtained by linear fitting can be used to determine the probability of malfunction due to the value of the current operating environment factor.

In step S430, based on the back-propagation BP neural network, the probability of erroneous operation caused by multiple operating environment factors is used as input to perform forward calculation, and reverse correction is performed according to the erroneous operation attribute, and iteratively obtains corresponding to multiple operating environment factors Weight coefficient.

BP (backpropagation) neural network is a multi-layer feedforward neural network trained according to the error back propagation algorithm. The BP neural network algorithm includes two processes of forward propagation of signals and back propagation of errors. The weight coefficient may be represented by, for example, the weight value between the input neuron and the hidden layer neuron in the BP neural network.

Fig. 5 is a flow chart of an operation processing method according to an exemplary embodiment. As shown in the figure, the method of this embodiment includes the following steps S510-S520.

In step S510, the values of a plurality of operating environment factors are respectively input into a linear function to obtain the probability of misoperation caused by each operating environment factor.

In one embodiment, the linear function may be acquired by step S420, for example.

In step S520, based on the weighted sum of the probability of erroneous operation caused by each operating environment factor and the weighting coefficient, the probability value that the user operation belongs to the erroneous operation is obtained.

In one embodiment, assuming that there are n operating environment factors, the probability of misoperation is expressed as P 1 , P 2 , ..., P n , and their corresponding weight coefficients are expressed as W 1 , W 2 , respectively. .., W n , the formula for calculating the probability that the user operation is a misoperation can be expressed as:

Figure PCTCN2019122406-appb-000001

Fig. 6 is a flow chart showing an operation processing method according to another exemplary embodiment. As shown in the figure, the method of this embodiment includes the following steps S610-S630.

In step S610, sample data is collected.

The sample data can be obtained, for example, by collecting and extracting multiple historical operations of the user. The sample data may include, for example, user information type data, display layout type data, hardware environment type data, and guide element content type data.

In one embodiment, the value of the user's erroneous response time can also be collected, that is, after clicking the guidance factor, click the back button to return to the original page N seconds after the page jump occurs. Take N seconds as the value of the delayed reaction time. The value of the reaction time to the misoperation can be used, for example, to determine the misoperation attribute of the current user operation. The method for determining the misoperation attribute has been described in detail in the embodiment of step S210, and will not be repeated here.

In step S620, the weight coefficients of multiple environmental factors are calculated to obtain an operation recognition model. Among them, the weight coefficients of environmental factors can be calculated by the BP neural network. Taking the probability of misoperation caused by multiple operating environment factors as input, the weight coefficient of the BP neural network is reversely corrected according to the misoperation attribute, and by learning the sample data, the weight value of a set of input layers to the hidden layer can be obtained , That is, the weight coefficient desired in this step.

In step S630, the operation recognition model is delivered 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 operating environment factor. By weighting and summing it with the weight coefficient, the probability value that the user operation belongs to misoperation can be obtained. The misoperation recognition process can be shown in FIG. 7 for example.

Further, different subsequent responses can be selected through probability values. For example, a threshold of 0.5 is set. When the probability value is greater than 0.5, the user operation is considered to be a misoperation, and the user can choose to terminate the response to the user operation; when the probability value is less than 0.5, the user operation is not considered to be a misoperation, and the user can continue to respond to the current User actions. Further, an intermediate value range can be set. When the probability value falls within the value range, it is considered that the misoperation attribute of the user operation is not very certain. For example, a prompt interface for receiving user feedback may be generated. The interface may be, for example, FIG. 3 As shown.

According to the operation processing method of this embodiment, by collecting a plurality of operation environment factors during the operation of the user, and using a pre-trained operation to identify the probability value of the model misoperation. Triggering the subsequent response of the terminal interface according to the probability value can improve the accuracy of identifying whether the user operation is a misoperation, prevent entering the other interface due to the user's misoperation, thereby reducing the terminal's misoperation rate, improving the terminal's processing efficiency, and avoiding errors The waste of traffic generated by operations improves the conversion rate of advertisements and operations, makes advertising billing and sharing more accurate, and improves the user experience. ; Further, by continuously updating the sample data of historical operations, the operation recognition model can be updated in real time to adapt to the real-time update of webpage content and user data.

The following is an embodiment of the apparatus of the present disclosure, which can be used to execute the embodiment of the method of the present disclosure. For details not disclosed in the device embodiments of the present disclosure, please refer to the method embodiments of the present disclosure.

8 is a structural block diagram of an operation processing device according to an embodiment of the present disclosure. As shown in the figure, the device of this embodiment includes a bomb environment detection module 801, an operation recognition module 802, and an operation response module 803.

The environment detection module 801 is configured to read the values of multiple operation environment factors based on the user operation detected by the terminal interface.

The operation recognition module 802 is configured to input values of a plurality of operation environment factors into a pre-trained operation recognition model to obtain a probability value that a user operation is a misoperation. The operation recognition model includes weight coefficients corresponding to the plurality of operation environment factors.

The operation response module 803 is configured to cause the terminal interface to trigger a corresponding subsequent response according to the numerical range that the probability value falls within.

Optionally, the environment detection module 801 can also collect sample data for training the operation recognition model, the sample data includes multiple historical operations of the detected user, historical values of operating environment factors corresponding to the multiple historical operations, and Misoperation attributes corresponding to multiple historical operations. Further, a weight coefficient corresponding to multiple operating environment factors may be calculated based on the sample data to apply the weight coefficient to the operation recognition module 802.

Optionally, the operation response module 803 may calculate the return operation time value according to the historical operation. The return operation time value represents the time elapsed from the detection of the operation triggered by the user terminal interface switch to the detection of the return operation; When the time value is set, a prompt interface for receiving user feedback is generated; and based on the user feedback received on the prompt interface, the misoperation attribute of the historical operation is determined.

Optionally, the operation recognition module 802 may be further configured to determine whether the historical operation meets the following conditions: the historical operation includes the first operation, the second operation, and the third operation in sequence; the first page and the second page are displayed in sequence on the terminal interface And the first page; the first operation is detected when the in-situ replacement of the display element occurs on the first page, the terminal interface switches to the second page in response to the first operation, the second operation is detected on the second page, the terminal interface In response to the second operation returning to the first page, a third operation for the display element before in-situ replacement on the first page is detected; and when the historical operation meets the above conditions, it is determined that the first operation is a misoperation to obtain the Misoperation attributes of historical operations.

Alternatively, the weighting coefficient of the operation recognition module 802 may be obtained by first performing cluster analysis on multiple operating environment factors according to the degree of influence caused by misoperation; linearly fitting each operating environment factor to calculate the operating environment The probability of misoperation caused by factors; and based on the back propagation BP neural network, the probability of misoperation caused by multiple operating environment factors is used as the input for forward calculation, and the reverse correction is made according to the attribute of misoperation, and multiple operating environments are obtained through iteration The weighting factor corresponding to the factor.

Optionally, the operation recognition module 802 is also provided with a probability of misoperation caused by each operating environment factor. When calculating the probability of misoperation caused by each operating environment factor, the operation recognition module 802 may first input the values of multiple operating environment factors into a linear function to obtain the probability of misoperation caused by each operating environment factor; and based on each The probability of misoperation caused by the operating environment factor and the weighted sum of the weight coefficients are used to obtain the probability value that the user operation belongs to misoperation.

Optionally, the operation response module 803 may be set to confirm that the user operation is a misoperation when the probability value falls within the first numerical range, and cause the terminal interface to stop responding to the user operation; when the probability value falls within the second numerical range , Make the terminal interface generate a prompt for receiving user feedback, and confirm whether to continue to respond to the user operation based on the received user feedback; and when the probability value falls within the third numerical range, confirm that the user operation is not a misuse, and make The terminal interface continues to respond to user operations.

According to the operation processing device of this embodiment, by collecting a plurality of operation environment factors during the operation of the user, and using a pre-trained operation to identify the probability value of the model misoperation. Triggering the subsequent response of the terminal interface according to the probability value can improve the accuracy of identifying whether the user operation is a misoperation, prevent entering the other interface due to the user's misoperation, thereby reducing the terminal's misoperation rate, improving the terminal's processing efficiency, and avoiding errors The waste of traffic generated by operations improves the conversion rate of advertisements and operations, makes advertising billing and sharing more accurate, and improves the user experience. ; Further, by continuously updating the sample data of historical operations, the operation recognition model can be updated in real time to adapt to the real-time update of webpage content and user data.

This embodiment provides an operation processing apparatus based on the same inventive concept as the above method embodiment. This embodiment can be used to implement the operation processing method provided in the above embodiment.

It should be noted that although several modules or units of the device for action execution are mentioned in the above detailed description, this division is not mandatory. In fact, according to the embodiments of the present disclosure, the features and functions of the two or more modules or units described above may be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided into multiple modules or units to be embodied. The components displayed as modules or units may or may not be physical units, that is, they may be located in one place, or may be distributed on multiple network units. Part or all of the modules can be selected according to actual needs to achieve the purpose of the wooden disclosure scheme. Those of ordinary skill in the art can understand and implement without paying creative labor.

Through the description of the above embodiments, those skilled in the art can easily understand that the example embodiments described above can be implemented by software, or by software in combination with necessary hardware.

For example, in an example embodiment, a computer-readable storage medium is also provided, on which a computer program is stored, and when the program is executed by a processor, the steps of the method described in any one of the foregoing embodiments may be implemented. For the specific steps of the method, reference may be made to the detailed description in the foregoing embodiments, and details are not repeated here. The computer-readable storage medium may be ROM, random access memory (RAM), CD-ROM, magnetic tape, floppy disk, optical data storage device, or the like.

In another example embodiment, a computing device is also provided. The device may be a mobile terminal such as a mobile phone or a tablet computer, or may be a terminal device such as a desktop computer or a server, which is not limited in this example embodiment. 9 shows a schematic diagram of a computing device 90 according to an example embodiment of the present disclosure. For example, the device 90 may be provided as a mobile terminal. 9, the device 90 includes a processing component 91, which further includes one or more processors, and memory resources represented by the memory 92 for storing instructions executable by the processing component 91, such as application programs. The application programs stored in the memory 92 may include one or more modules each corresponding to a set of instructions. In addition, the processing component 91 is configured to execute instructions to perform the above-mentioned operation processing method.

Among them, the memory can be used to store software programs and modules, and the processor executes various functional applications and data processing by running the software programs and modules stored in the memory. The memory may include a high-speed random access memory, and may also include a 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 memories remotely provided with respect to the processor, and these remote memories may be connected to a computer terminal or a mobile terminal through a network. Examples of the above network include but are not limited to the Internet, intranet, local area network, mobile communication network, 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 the network, and an input/output (I/O) interface 95. The device 90 may operate based on an operating system stored in the memory 92, such as Android, IOS, or the like.

Those skilled in the art will easily think of other embodiments of the present disclosure after considering the description and practicing the invention disclosed herein. This application is intended to cover any variations, uses, or adaptive changes of the present disclosure that follow the general principles of the present disclosure and include common general knowledge or customary technical means in the technical field not disclosed in the present disclosure . The description and examples are to be considered exemplary only, and the true scope and spirit of this disclosure are pointed out by the appended claims.

Although the present disclosure has been described with reference to several exemplary embodiments, it should be understood that the terms used are illustrative and exemplary rather than limiting. Since the present disclosure can be embodied in various forms without departing from the spirit or essence of the application, it should be understood that the above-mentioned embodiments are not limited to any of the foregoing details, but should be widely interpreted within the spirit and scope defined by the appended claims Therefore, all changes and modifications falling within the scope of the claims or their equivalents shall be covered by the appended claims.

Claims (10)

  1. An operation processing method, including:
    Based on user operations detected on the terminal interface, read values of multiple operating environment factors;
    Input the values of the plurality of operating environment factors into a pre-trained operation recognition model to obtain a probability value that the user operation belongs to a misoperation, and the operation recognition model includes weight coefficients corresponding to the plurality of operation environment factors; as well as
    According to the numerical range that the probability value falls within, the terminal interface is caused to trigger a corresponding subsequent response.
  2. The method of claim 1, further comprising:
    Collecting sample data for training the operation recognition model, the sample data includes a plurality of detected historical operations of the user, historical values of operating environment factors corresponding to the plurality of historical operations, and corresponding to all Describe the misoperation attributes of multiple historical operations; and
    The weighting coefficients corresponding to the plurality of operating environment factors are calculated based on the sample data.
  3. The method according to claim 2, wherein collecting sample data for training the operation recognition model includes: calculating a return operation time value according to the historical operation, the return operation time value representing the time from detection to user The time elapsed from the operation that triggered the terminal interface switch until the return operation was detected;
    When the return operation time value is less than the preset time value, generating a prompt interface for receiving user feedback; and
    Based on user feedback received on the prompt interface, the misoperation attribute of the historical operation is determined.
  4. The method of claim 2, wherein collecting sample data for training the operation recognition model includes:
    Determine whether the historical operation meets the following conditions: the historical operation includes the first operation, the second operation, and the third operation in sequence; the first page, the second page, and the first page are displayed in sequence on the terminal interface Detecting the first operation when the in-situ replacement of the display element occurs on the first page, and the terminal interface switches to the second page in response to the first operation, on the second page Detecting the second operation, the terminal interface returns to the first page in response to the second operation, and detects a third operation for the display element on the first page before in-situ replacement occurs; and
    When the historical operation meets the above conditions, it is determined that the first operation belongs to a misoperation to obtain the misoperation attribute of the historical operation.
  5. The method of claim 2, wherein calculating the weighting coefficients corresponding to the plurality of operating environment factors based on the sample data includes:
    Perform cluster analysis on the multiple operating environment factors according to the degree of influence caused by misoperation;
    Linearly fitting each operating environment factor to calculate the probability of misoperation caused by the operating environment factor; and
    Based on the back-propagation BP neural network, the probability of misoperation caused by the multiple operating environment factors is used as an input for forward calculation, and the reverse correction is performed according to the attribute of the misoperation, and the multiple operating environment factor correspondences are obtained through iteration The weighting coefficient of.
  6. The method of claim 1, wherein the operation recognition model further includes a linear function between the probability of each operating environment factor causing a misoperation and the operating environment factor,
    Entering the values of the multiple operating environment factors into a pre-trained operation recognition model to obtain the probability value that the user operation is a misoperation includes:
    Input 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
    Based on the weighted sum of the probability of misoperation caused by each operating environment factor and the weight coefficient, the probability value that the user operation belongs to misoperation is obtained.
  7. The method according to claim 1, wherein causing the terminal interface to trigger a corresponding subsequent response according to the numerical range that the probability value falls within includes any one of the following steps:
    When the probability value falls within the first numerical range, confirm that the user operation is a misoperation, and cause the terminal interface to stop responding to the user operation;
    When the probability value falls within the second numerical range, causing the terminal interface to generate a prompt for receiving user feedback, and confirming whether to continue to respond to the user operation based on the received user feedback; and
    When the probability value falls within the third numerical range, it is confirmed that the user operation is not a misoperation, and the terminal interface continues to respond to the user operation.
  8. An operation processing device, characterized in that it includes:
    The environment detection module is set to read the values of multiple operating environment factors based on user operations detected by the terminal interface;
    An operation recognition module, configured to input values of the plurality of operation environment factors into a pre-trained operation recognition model to obtain a probability value that the user operation is a misoperation, and the operation recognition model includes the plurality of operation environments The weighting factor corresponding to the factor; and
    The operation response module is configured to cause the terminal interface to trigger a corresponding subsequent response according to the numerical range that the probability value falls within.
  9. A storage medium storing a computer program, which when executed by a processor of a computer, causes the processor to execute the method according to any one of claims 1-7.
  10. A computing device, including:
    processor;
    A memory storing instructions executable by the processor;
    Wherein the processor is configured to perform the method according to any one of claims 1-7.
PCT/CN2019/122406 2018-12-05 2019-12-02 Anti-misoperation processing of computing device WO2020114352A1 (en)

Priority Applications (2)

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

Publications (1)

Publication Number Publication Date
WO2020114352A1 true WO2020114352A1 (en) 2020-06-11

Family

ID=65868988

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/CN2019/122406 WO2020114352A1 (en) 2018-12-05 2019-12-02 Anti-misoperation processing of computing device

Country Status (2)

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

Families Citing this family (1)

* 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

Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101645711A (en) * 2009-08-31 2010-02-10 深圳华为通信技术有限公司 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
CN105183538A (en) * 2014-06-03 2015-12-23 联想(北京)有限公司 Information processing method and electronic device
CN106095295A (en) * 2016-06-15 2016-11-09 维沃移动通信有限公司 A kind of processing method based on fingerprint recognition and mobile terminal
CN106203380A (en) * 2016-07-20 2016-12-07 中国科学院计算技术研究所 Ultrasound wave gesture identification method and system
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
CN109558032A (en) * 2018-12-05 2019-04-02 北京三快在线科技有限公司 Operation processing method, device and computer equipment

Family Cites Families (10)

* 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
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
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
WO2017200883A1 (en) * 2016-05-17 2017-11-23 Silicon Storage Technology, Inc. Deep learning neural network classifier using non-volatile memory array
US20180121794A1 (en) * 2016-11-03 2018-05-03 Avanseus Holdings Pte. Ltd. Method and system for machine failure prediction
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
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
CN108021292A (en) * 2017-10-27 2018-05-11 努比亚技术有限公司 Light sensation control trigger control method, equipment and computer-readable recording medium
CN108021235A (en) * 2017-12-27 2018-05-11 广东欧珀移动通信有限公司 Method, apparatus, terminal and the storage medium of trigger action
CN108710847A (en) * 2018-05-15 2018-10-26 北京旷视科技有限公司 Scene recognition method, device and electronic equipment

Patent Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101645711A (en) * 2009-08-31 2010-02-10 深圳华为通信技术有限公司 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
CN105183538A (en) * 2014-06-03 2015-12-23 联想(北京)有限公司 Information processing method and electronic device
CN106095295A (en) * 2016-06-15 2016-11-09 维沃移动通信有限公司 A kind of processing method based on fingerprint recognition and mobile terminal
CN106203380A (en) * 2016-07-20 2016-12-07 中国科学院计算技术研究所 Ultrasound wave gesture identification method and system
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
CN109558032A (en) * 2018-12-05 2019-04-02 北京三快在线科技有限公司 Operation processing method, device and computer equipment

Also Published As

Publication number Publication date
CN109558032B (en) 2020-09-04
CN109558032A (en) 2019-04-02

Similar Documents

Publication Publication Date Title
Pudlo et al. Reliable ABC model choice via random forests
Karimi et al. News recommender systems–Survey and roads ahead
AU2016346497B2 (en) Method and system for performing a probabilistic topic analysis of search queries for a customer support system
US9342798B2 (en) Customized predictive analytical model training
Lee et al. Improving the accuracy of top-n recommendation using a preference model
Ditzler et al. Learning in nonstationary environments: A survey
Lughofer Single-pass active learning with conflict and ignorance
US10671679B2 (en) Method and system for enhanced content recommendation
CN107423442B (en) Application recommendation method and system based on user portrait behavior analysis, storage medium and computer equipment
Chen et al. Usher: Improving data quality with dynamic forms
US10402749B2 (en) Customizable machine learning models
US10817931B2 (en) Systems and methods for selecting third party content based on feedback
US9053436B2 (en) Methods and system for providing simultaneous multi-task ensemble learning
US10127522B2 (en) Automatic profiling of social media users
US9378202B2 (en) Semantic clustering
US8972397B2 (en) Auto-detection of historical search context
US20140207441A1 (en) Semantic Clustering And User Interfaces
US8229786B2 (en) Click probability with missing features in sponsored search
US20190073232A1 (en) Application documentation effectiveness monitoring and feedback
US20190102652A1 (en) Information pushing method, storage medium and server
JP5031206B2 (en) Fit exponential model
US10671933B2 (en) Method and apparatus for evaluating predictive model
CN106202453B (en) Multimedia resource recommendation method and device
JP5450051B2 (en) Behavioral targeting system
CN101556553B (en) Defect prediction method and system based on requirement change