Mobile equipment page adjusting method and device, equipment and medium

Abstract

The invention discloses a mobile equipment page adjusting method and device, equipment and a medium. The method comprises the steps of obtaining a use data set of current mobile equipment; the use data set comprises data of a historical use mode provided by a user; calculating the influence degree corresponding to each parameter in the use data set; performing weighted calculation on the influence degree corresponding to each parameter to obtain a user prediction age of the current mobile device; and adjusting the page according to the predicted age of the user. According to the embodiment of the invention, the automation degree of page adjustment can be improved when the elderly user accesses the page by using the mobile equipment.

Description

Technical Field

[0001] The present invention relates to the field of mobile terminal technology, and in particular to a method, device, equipment and medium for adjusting a page of a mobile device. Background Art

[0002] With the rapid development and popularization of mobile devices, elderly users also use a large number of mobile devices in their daily lives, such as mobile phones or tablets. When elderly users use mobile devices to visit web pages, the font, resolution, layout and other contents of the page need to be adaptively adjusted according to the needs of the elderly users to accommodate the elderly users' browsing of the web.

[0003] Currently, existing mobile device users manually turn on the senior mode to adjust the font, resolution, layout and other content of the page.

[0004] However, the above method has the defect of lack of automation and cannot adjust the page according to the actual needs of elderly users. Summary of the invention

[0005] The present invention provides a mobile device page adjustment method, device, equipment and medium. The embodiments of the present invention can improve the automation level of page adjustment when elderly users use mobile devices to access pages.

[0006] In a first aspect, an embodiment of the present invention provides a method for adjusting a page on a mobile device, the method comprising:

[0007] Obtaining a usage data set of the current mobile device; the usage data set includes: historical usage data provided by the user;

[0008] Calculate the impact degree corresponding to each parameter in the use data set;

[0009] Perform weighted calculation on the influence degree corresponding to each parameter to obtain the predicted age of the user of the current mobile device;

[0010] Adjust the page based on the user's predicted age.

[0011] In a second aspect, an embodiment of the present invention further provides a mobile device page adjustment device, the device comprising:

[0012] The data set acquisition module is used to acquire the usage data set of the current mobile device; the usage data set includes: the data of the historical usage mode provided by the user;

[0013] An impact degree calculation module is used to calculate the impact degree corresponding to each parameter in the usage data set;

[0014] The age prediction module is used to perform weighted calculation on the influence degree corresponding to each parameter to obtain the predicted age of the user of the current mobile device;

[0015] The page adjustment module is used to adjust the page according to the user's predicted age.

[0016] In a third aspect, an embodiment of the present invention further provides a mobile device page adjustment device, the mobile device page adjustment device comprising:

[0017] at least one processor; and

[0018] a memory communicatively connected to at least one processor; wherein,

[0019] The memory stores a computer program that can be executed by at least one processor. The computer program is executed by at least one processor so that the at least one processor can execute the mobile device page adjustment method of any embodiment of the present invention.

[0020] In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the mobile device page adjustment method of any embodiment of the present invention when executed.

[0021] The technical solution of the embodiment of the present invention can obtain the specific usage of the device by obtaining the usage data set of the current mobile device, providing a data basis for subsequent analysis; by using the historical usage data provided by the user as the usage data set, it can reflect the user's historical usage habits and preferences for the current mobile device, providing a basis for personalized adjustment; by calculating the influence degree corresponding to each parameter in the usage data set, it can identify the influence degree of each parameter on the user's predicted age, thereby optimizing the accuracy of adjusting the page based on the predicted age; by performing weighted calculation on the influence degree corresponding to each parameter, the user's age can be predicted more accurately, and the page can be adjusted according to the user's predicted age, so as to achieve a page display that is more in line with the user's age characteristics and improve the user experience; it solves the problem that when elderly users use mobile devices to access pages, the page layout can be automatically adjusted, thereby improving the elderly users' page access experience.

[0022] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0024] Figure 1 A flow chart of a mobile device page adjustment method provided by an embodiment of the present invention;

[0025] Figure 2 A flow chart of a mobile device page adjustment method provided by an embodiment of the present invention;

[0026] Figure 3 A schematic diagram of the structure of a page adjustment device for a mobile device provided by an embodiment of the present invention;

[0027] Figure 4 A schematic structural diagram of a page adjustment device for a mobile device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0028] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0029] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0030] In the technical solution of the embodiment of the present invention, the acquisition, storage and application of the usage data set of the current mobile device, the historical usage data provided by the user and the system setting information of the current mobile device, etc., all comply with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0031] Figure 1 A flow chart of a mobile device page adjustment method provided by an embodiment of the present invention. The embodiment of the present invention is applicable to the situation of mobile device page adjustment, and the method can be executed by a mobile device page adjustment device, and the mobile device page adjustment device can be implemented in the form of hardware and / or software.

[0032] See also Figure 1 The mobile device page adjustment method shown includes:

[0033] S101, obtaining a usage data set of a current mobile device; the usage data set includes: data on historical usage patterns provided by a user.

[0034] Mobile devices may refer to electronic devices that are easy for users to carry and have computing, display and communication functions. Mobile devices may include: smart phones, tablet computers and / or smart watches.

[0035] The usage data set may refer to a data set generated by a user in the process of using a mobile device. When a user uses a mobile device, the user operation behavior data and system settings are determined as the usage data set; wherein, the user operation behavior data may refer to the data generated when the user interacts with the mobile device, and the user operation behavior data may include screen click behavior data, touch gesture data, voice data, and false touch information data; the screen click behavior data may refer to the user's click frequency, click position, and continuous click situation on the mobile device; the touch gesture may refer to the user's touch gesture record data on the touch screen of the mobile device; the voice data may refer to the voice data input by the user to the mobile device, such as voice bar, call and voice command, etc.; the false touch information may refer to the false operation record data caused by the user's operation error; the system setting may refer to the display setting, volume setting, and input method category setting of the mobile device. The usage data set can be obtained through the sensor of the mobile device, as well as the interaction data between the user and the operating system or application of the mobile device. The user's usage data set can be used to obtain the user's usage mode when using the mobile device, and the user's behavior pattern can be determined based on the usage mode, providing a data basis for the subsequent prediction of the user's age range.

[0036] Among them, the historical usage pattern may refer to the user's usage pattern of the mobile device in the historical time period. The historical usage pattern is used to accurately determine the user's usage pattern of the mobile device in the historical time period. By analyzing the specific way in which the user uses the mobile device in the historical time period, the user's age range can be predicted based on the specific usage pattern, thereby identifying whether the user belongs to the elderly user group. Exemplarily, the historical usage pattern may refer to the font setting of the mobile device by the user in the historical time period; the historical usage pattern may also refer to the application installed by the user in the historical time period; the historical usage pattern may also refer to the touch operation performed by the user when using the mobile device in the historical time period.

[0037] S102: Calculate the influence degree corresponding to each parameter in the usage data set.

[0038] Among them, a parameter may refer to a specific attribute that describes a feature or behavior of a piece of data in a usage data set. A parameter may represent an adjustment item of a mobile device, a user's interaction method, or a perception dimension of a mobile device. For example, a parameter may involve a user's behavior, preference, or device configuration. A parameter does not represent a specific numerical value. For example, a parameter may refer to a volume setting or brightness setting of a mobile device; a parameter may also refer to the number and / or frequency of accidental touches by a user.

[0039] The degree of influence may refer to the degree of influence of a certain parameter on the predicted age of the user. Since different parameters have different correlations with the age of the user, different parameters have different degrees of influence on the user's age. For example, the volume setting parameter and the font size setting parameter have different degrees of influence on the judgment of the user's age. When the correlation between the volume setting parameter and the user's age is less than the correlation between the font size setting parameter and the user's age, the volume setting parameter has a smaller influence on the predicted age of the user than the font size setting parameter. The degree of influence can accurately quantify the effect of different parameters on the predicted age of the user, and help analyze which parameters play a more critical role in the result of the predicted age of the final user.

[0040] In a specific example, the font size setting has a great impact on the readability of elderly users. A larger font can help elderly users with impaired eyesight better identify information. Therefore, when the font size of a mobile device is greater than a certain set font size threshold, it can be judged that the user of the mobile device is more likely to be an elderly user, and thus it can be determined that the font size setting parameter has a greater impact on the user's age prediction; while the keyboard type setting has a lower judgment accuracy for elderly users. As elderly users age, their finger joints become thicker and their fingers become less sensitive. During typing, they cannot accurately press the small keys on the keyboard. Therefore, elderly users are more likely to set the keyboard type to a handwriting keyboard type. When the keyboard type of the mobile device is set to a handwriting keyboard type, it can be judged that the user of the mobile device is more likely to be an elderly user, and thus it can be determined that the keyboard type setting parameter has a greater impact on the user's age prediction; and the font size setting parameter and the keyboard type setting parameter have different impacts on the predicted user age.

[0041] S103 , performing weighted calculation on the influence degree corresponding to each parameter to obtain the predicted age of the user of the current mobile device.

[0042] Among them, weighted calculation can refer to weighted calculation by assigning different weight values ​​according to the degree of influence corresponding to each parameter. Since the degree of influence of different parameters is different, different weight values ​​can be assigned to different parameters according to the degree of influence. Parameters that play a greater role in the result of the final user's predicted age are assigned a larger weight value, and parameters that play a lesser role in the result of the final user's predicted age are assigned a smaller weight value. The role of weighted calculation is to make the result more accurate and in line with actual needs by weighting the different degrees of influence corresponding to each parameter. In this way, the degree of influence of each parameter on the user's predicted age can be better quantified.

[0043] Among them, the user predicted age may refer to the approximate age of the user who uses the mobile device. The user predicted age is not the actual accurate birth age of the user, but refers to the predicted age that is in accordance with the user's behavioral characteristics and physiological changes. The user predicted age estimates the user's age by obtaining a set of usage data. Since the behavior and physiological characteristics of the elderly user group are different from those of young people, by learning these behavioral patterns and using data analysis techniques such as weighted calculation or machine learning, a user predicted age that is in accordance with their physiological characteristics is calculated. The role of the user predicted age is to predict the user's age based on his or her behavioral characteristics, so as to provide the user with a page that is more suitable for his or her physiological and cognitive abilities. By making a reasonable prediction of the user's age, a more reasonable page adjustment can be made to ensure that the user's experience when using the page is more friendly and efficient.

[0044] S104: Adjust the page according to the user's predicted age.

[0045] The page is adjusted according to different predicted ages of users, so that the page of the mobile device can provide appropriate page display according to the needs of users of different age groups, ensuring that elderly users are more comfortable and convenient when browsing the page.

[0046] It can be seen that in the embodiments of the present application, by obtaining the usage data set of the current mobile device, the specific usage of the device can be obtained, providing a data basis for subsequent analysis; by using the historical usage data provided by the user as the usage data set, the user's historical usage habits and preferences for the current mobile device can be reflected, providing a basis for personalized adjustment; by calculating the degree of influence corresponding to each parameter in the usage data set, the degree of influence of each parameter on the user's predicted age can be identified, thereby optimizing the accuracy of adjusting the page based on the predicted age; by performing weighted calculation on the degree of influence corresponding to each parameter, the user's age can be predicted more accurately, and the page can be adjusted according to the user's predicted age, so as to achieve a page display that is more in line with the user's age characteristics and improve the user experience; it solves the problem that when elderly users use mobile devices to access pages, the page layout can be automatically adjusted, thereby improving the elderly users' experience of accessing pages.

[0047] In an optional embodiment, the "historical usage data provided by the user" is refined into "historical usage data provided by the user includes at least one of the following: false touch information, voice information, brightness setting, typing speed, keyboard type and application usage" to improve the page adjustment operation of the mobile device.

[0048] It should be noted that for the parts not described in detail in the embodiments of the present invention, reference may be made to the descriptions of other embodiments. Figure 2 A flow chart of a mobile device page adjustment method provided by an embodiment of the present invention.

[0049] See also Figure 2 The mobile device page adjustment method shown includes:

[0050] S201. Obtain a usage data set of the current mobile device; the usage data set includes data on historical usage provided by the user, including at least one of the following: false touch information, voice information, brightness setting, typing speed, keyboard type, viewing distance information, and used applications.

[0051] Among them, the false touch information may refer to the user inputting a trigger operation different from the required operation when using a mobile device, usually referring to an erroneous touch operation. False touches are usually related to the physiological characteristics of elderly users, such as hand tremors or unstable finger control. When the user touches the screen, multiple false operations may occur due to fingertip vibrations or body function degradation. False touch operations may include misclicks or misslides. False touch information can be obtained through sensor devices of mobile devices, such as accelerometers and gyroscopes. These sensors can monitor the vibration frequency and amplitude during touch, so as to determine whether a false touch has occurred. Therefore, it is necessary to obtain the false touch information of the current mobile device, and then estimate whether the current user is an elderly user.

[0052] Among them, voice information may refer to the voice data generated when the user interacts with the mobile device through voice. Voice information can be used to analyze the user's accent, speech speed and other characteristics. For example, since most elderly users prefer to use dialects, the proportion of elderly users who use Mandarin is relatively small, and the elderly users speak slowly, so the user's age can be judged by the user's accent and speech speed and other characteristics. Voice information can be collected by devices such as microphones to analyze the spectral characteristics, phoneme characteristics, and frequency band distribution of the audio in the voice information. Therefore, it is necessary to obtain the voice information of the current mobile device to estimate whether the current user is an elderly user.

[0053] The typing speed may refer to the speed at which a user inputs text through an input method on a mobile device. Due to factors such as decreased vision, inflexible finger control, or slow understanding of pages, the typing speed of elderly users is slower. Therefore, it is necessary to obtain the typing speed of the current mobile device to estimate whether the current user is an elderly user.

[0054] The keyboard type may refer to the keyboard input type used by the user when inputting text on the mobile device. As elderly users have reduced finger flexibility or vision impairment, they are more inclined to use handwriting keyboards or voice input instead of traditional physical or virtual keyboards. By recording the keyboard type used by the user when inputting, the user's operating habits can be inferred, and then whether the current user is an elderly user can be estimated.

[0055] The viewing distance information may refer to the viewing distance between the user and the display screen of the mobile device when the user is using the mobile device. Since most elderly users are farsighted, when it is detected that the viewing distance reaches the specified farsighted viewing distance threshold, it can be determined that the current user is farsighted. By obtaining the viewing distance information, it can be inferred whether the user is farsighted, and then it can be estimated whether the current user is an elderly user.

[0056] Among them, the use of applications can refer to the application information installed by the user on the mobile device. Users of different age groups have different preferences and habits when choosing and using applications. Elderly users usually prefer simple and easy-to-use applications, such as health management, social chat and news applications. In addition, due to the elderly users' level of cognition of mobile devices, the elderly users have poor recognition ability for fraudulent software or fraudulent websites, which makes it easy for elderly users to download and / or browse fraudulent software and fraudulent websites. By obtaining relevant information about the user's use of applications, it is estimated whether the current user is an elderly user.

[0057] S202: Calculate the influence degree corresponding to each parameter in the usage data set.

[0058] Optionally, when the parameter in the data set is the false touch information, the calculation of the influence degree corresponding to the false touch information can be realized in the following manner: the false touch information can include information such as the number of false touches, the false touch area and the false touch duration; wherein the number of false touches can be the number of false touch operations that the user has made in the historical time period; the false touch area can be the area where the false touch operation of the user has been made; the false touch duration can be the time for the false touch operation to continue after the user has made the false touch operation; the information such as the number of false touches, the false touch area and the false touch duration is quantified and converted into a specific numerical value or score for calculating the influence degree. For example, assuming that the false touch operation count is increased by one each time the user makes a false touch operation, when condition 1 is met and the number of false touches is greater than the false touch number threshold, it is determined that condition 1 is met; when condition 2 is met, when the false touch operation occurs in the specified false touch area and the false touch duration of the false touch operation is greater than the false touch duration threshold, it is confirmed that condition 2 is met. When both conditions 1 and 2 are met, the influence degree corresponding to the false touch information is calculated. When at least one of conditions 1 and 2 is not met, the influence degree corresponding to the false touch information is not calculated.

[0059] Since the influence of each parameter is different, different parameters may be assigned different scores. For the parameter being false touch information, assuming that the score of the false touch information is X, the influence of the false touch information is X.

[0060] Optionally, when the parameter in the usage data set is voice information, the calculation of the influence degree corresponding to the voice information can be achieved in the following manner: obtaining voice information from the historical usage data provided by the user, and extracting information such as the accent category, spectrum characteristics, phoneme characteristics, and frequency band distribution from the voice information. The user's age range can be determined by analyzing the accent characteristics of the voice. For example, a Mandarin accent may correspond to a young user, while an accent with a local dialect may correspond to an elderly user. Different values ​​or scores are assigned according to the accent type, such as a Mandarin accent is assigned an influence degree of a, and a dialect accent is assigned an influence degree of b, and a is less than b; according to the voice spectrum characteristics, the influence degree corresponding to different frequency intervals is set, for example, a high frequency influence degree is a, a medium frequency influence degree is b, and a low frequency influence degree is c. Since a lower voice frequency may correspond to an elderly user and a higher voice frequency may correspond to a young user, a is less than b, and b is less than c. Different information in the voice information has different influence degrees. When the accent category is a dialect, the spectrum is a specific age spectrum segment, and the frequency band distribution is high frequency, the influence degree corresponding to the voice information is calculated.

[0061] Optionally, when the parameter in the data set is a brightness setting, calculating the impact level corresponding to the brightness setting can be achieved in the following manner: the brightness setting as a parameter can reflect the user's visual preference. As age increases, vision may gradually deteriorate. Therefore, elderly users may tend to adjust the screen brightness to a brighter level to compensate for blurred vision. Brighter brightness settings are usually associated with elderly users, especially in low-light environments. Lower brightness settings may indicate that the user's vision is no longer as sensitive, and may be a non-elderly user. A higher brightness setting may indicate that the user is older and has a higher impact level. A lower set brightness value may indicate that the user is younger and has a lower impact level. For example, brightness setting A may correspond to an impact level a, brightness setting B may correspond to an impact level b, and brightness setting C may correspond to an impact level c.

[0062] Optionally, when the parameter in the data set used is typing speed, calculating the impact degree corresponding to the typing speed can be achieved in the following way: the typing speed reflects how fast the user enters text. As they age, the typing speed of elderly users may slow down. Users with slower typing speeds may be elderly people, while faster typing speeds usually represent non-elderly users. By analyzing the user's typing frequency and input time, the number of words entered per minute is obtained. For example, when the typing speed is A, it can correspond to an impact degree a, and the typing speed B can correspond to an impact degree b.

[0063] Optionally, when the parameter in the usage data set is the keyboard type, calculating the impact degree corresponding to the keyboard type can be achieved in the following manner: obtaining the keyboard type used by the user, which may include a handwriting keyboard and a traditional keyboard. Elderly users usually use handwriting keyboards, and non-elderly users usually use traditional keyboards. For example, when the keyboard type is a handwriting keyboard, the impact degree a corresponding to the keyboard type being a handwriting keyboard can be determined, and when the keyboard type is a traditional keyboard, the impact degree b corresponding to the keyboard type being a traditional keyboard can be determined.

[0064] Optionally, when the parameter in the data set used is line of sight information, the calculation of the influence degree corresponding to the line of sight information can be achieved in the following manner: obtain the line of sight distance of the user through a sensor, and confirm the line of sight distance as the line of sight information. There are differences in the line of sight distance when different age groups use mobile devices. Elderly users usually have farsightedness, which leads to a larger line of sight distance for elderly users and a smaller line of sight distance for non-elderly users. For example, when the line of sight distance is greater than x, it can be determined that the influence degree corresponding to the line of sight information at this time is a, and when the line of sight distance is less than or equal to x, it can be determined that the influence degree corresponding to the line of sight information at this time is b.

[0065] Optionally, when the parameter in the usage data set is the usage application, the calculation of the influence degree corresponding to the usage application can be achieved in the following way: the types of applications used by different age groups are different, and elderly users usually focus on using applications such as news, weather, and communication, and elderly users usually do not use game-type applications. Therefore, there are differences in the types of applications used by users of different age groups, that is, the influence degree corresponding to the usage applications of users of different age groups is also different. For example, when the application types are concentrated in applications such as news, weather, and communication, it can be determined that the influence degree corresponding to the usage application at this time is a, and when the application types are concentrated in applications such as entertainment and social media, it can be determined that the influence degree corresponding to the usage application at this time is b.

[0066] S203: Perform weighted calculation on the influence degree corresponding to each parameter to obtain the predicted age of the user of the current mobile device.

[0067] S204: Adjust the page according to the user's predicted age.

[0068] It can be seen that in this embodiment, by using the historical usage data provided by the user including false touch information, voice information, brightness setting, typing speed, keyboard type, viewing distance information and used applications, the user's behavior habits and needs can be understood more comprehensively and multi-dimensionally; this rich usage data set helps to more accurately analyze the user's preferences and usage patterns, and thus provide support for personalized adjustment and optimization of mobile device pages; through the historical usage data provided by each user, the mobile device can provide more intelligent and personalized services according to the user's specific usage, and make more practical adaptive adjustments to the page according to the user's predicted age.

[0069] In some embodiments, the parameter set includes false touch information;

[0070] Calculate the influence of each parameter in the parameter set, including:

[0071] For the false touch information in the parameter set, obtain the hand vibration recognition information, interactive feedback information and gesture action information of the user when touching the current mobile device;

[0072] According to the hand vibration recognition information, the interactive feedback information and the gesture action information, the accidental touch operation abnormality detection is performed to obtain the accidental touch operation abnormality result;

[0073] According to the abnormal result of the accidental touch operation, the impact degree corresponding to the accidental touch information is determined.

[0074] Among them, the hand vibration identification information may refer to the vibration data caused by shaking or instability of the user's hand when using the touch screen of the mobile device monitored by the sensor of the mobile device. The built-in sensor of the mobile device can sense the tiny vibration of the hand in real time. The built-in sensor of the mobile device may include sensors such as accelerometers and gyroscopes. Through the hand vibration identification information of the historical period, it can be determined whether the accidental touch behavior occurs due to physiological reasons such as hand shaking of the elderly.

[0075] The interactive feedback information may refer to the response of the mobile device after the user interacts with the mobile device. The interactive feedback delay may refer to the time required for the mobile device to respond to the operation when the user clicks or touches the page of the mobile device. If the operating system of the mobile device fails to respond in time after the user performs the operation, or the response time is too long, or multiple operations fail to successfully feedback, it can be inferred that the interactive feedback information is an accidental touch behavior.

[0076] Among them, gesture action information can refer to user gesture data captured by the mobile device sensor. For multi-gesture operations, the mobile device can monitor the completion of the user's gestures, and the mobile device detects whether there is an incorrect multi-gesture operation through sensors or touch screen data. Or if multiple invalid click operations are detected in a short period of time, it can be considered that the elderly user's hand shaking caused the accidental touch.

[0077] Among them, the detection of abnormal mis-touch operation can refer to analyzing the user's operation data to determine whether the user's operation behavior contains abnormal mis-touch operation behavior, and record the number of mis-touch operations. It can be determined whether there are abnormalities in the user's hand vibration recognition information, interactive feedback information, and gesture action information. When the above information is abnormal, it can be determined that the current user's operation contains mis-touch operation, and the number of mis-touch operations of the user can be recorded.

[0078] Among them, the abnormal result of the accidental touch operation may refer to the result of determining whether the number of the user's accidental touch operations is abnormal. When the number of the user's accidental touch operations is greater than the threshold number of accidental touch operations, it can be determined that the current user has a large number of accidental touch operations, and the abnormal result of the accidental touch operation is determined to be abnormal; when the number of the user's accidental touch operations is less than the threshold number of accidental touch operations, it can be determined that the current user does not have a large number of accidental touch operations, and the abnormal result of the accidental touch operation is determined to be normal. When the abnormal result of the accidental touch operation is normal, it can be inferred that the user is not an elderly user, and when the abnormal result of the accidental touch operation is abnormal, it can be inferred that the user is an elderly user.

[0079] It can be seen that in this embodiment, by obtaining hand vibration recognition information, interactive feedback information and gesture action information, it is possible to fully monitor whether false touches occur during user operations, determine whether the user has false touches due to hand vibration, feedback delays or unstable gestures, and quantify the severity of the false touches; by determining the degree of influence of the false touch information based on the false touch operation abnormal results, it is possible to quantitatively evaluate the influence of the false touch behavior on the user operation, and further determine whether the occurrence of false touches is within the expected range; if the false touch operation abnormal results indicate that the user frequently has false touches, it can be inferred that the influence of the false touch information is large; if the false touch operation abnormal results indicate that the user does not frequently have false touches, it can be inferred that the influence of the false touch information is small.

[0080] In some embodiments, the parameters in the parameter set include: voice information;

[0081] Calculate the user's predicted age corresponding to each parameter in the parameter set, including:

[0082] Get voice information;

[0083] According to the voice information, performing voice analysis on the voice information to obtain a voice analysis result; the voice analysis result includes at least one of the following: accent category, phoneme frequency and voice frequency band;

[0084] According to the speech analysis result, the predicted age of the user corresponding to the speech information is determined.

[0085] Among them, speech analysis can refer to the process of processing and interpreting speech information. Since the features in speech information of users of different age groups are different, speech analysis of speech information is helpful to predict the user's predicted age.

[0086] The speech analysis result may refer to the output data after the speech information is analyzed, and the speech analysis result may include accent category, phoneme frequency and voice frequency segment.

[0087] The accent category may refer to whether the user's accent belongs to a local dialect category or a Mandarin category. By inputting the elderly user's voice into the speech recognition system and comparing it with the pronunciation models of multiple local dialects, it can be further determined whether there is an accent in the voice. Voices from different regions may differ in intonation, voice rhythm, and tone.

[0088] In a specific example, a speech recognition system can be implemented through a machine learning algorithm, and the specific implementation process is as follows: first, a speech data set is constructed, and the speech data set includes speech samples of different local dialects and speech samples of Mandarin, wherein the speech samples of different local dialects and speech samples of Mandarin are all from speech samples of elderly users; secondly, the features of each speech sample in the speech data set are extracted, and the Mel-frequency cepstral coefficient, pitch, duration, rhythm, volume, and speaking speed of each speech sample are extracted respectively; then, a classification model is trained through a machine learning algorithm such as a support vector machine or a decision tree; finally, the speech information in the usage data set of the current mobile device is used as the input of the classification model to identify the accent category of the speech information.

[0089] Among them, the phoneme frequency may refer to the pronunciation frequency of different phonemes in the voice information. Phonemes are the smallest speech units in a language, and different phonemes correspond to different sounds, such as vowels and consonants. The phoneme frequency reflects the pronunciation characteristics of the speaking user, and the pronunciation characteristics include the speed of speech and the pitch of the voice. The phoneme frequency can be obtained through spectrum analysis, which analyzes the energy distribution of different frequency bands in the voice. Phonemes with higher frequencies usually represent the clear pronunciation of young people, while phonemes with low frequencies are usually associated with the voices of older people. The phoneme frequency can be used to analyze the pronunciation characteristics of the user. As the age increases, the human body's vocal organs degenerate, resulting in a decrease in the voice frequency. Therefore, by analyzing the phoneme frequency, it can help to infer the predicted age of the user corresponding to the voice information.

[0090] Among them, the speech band can refer to the division of different frequency ranges in the voice information. The speech band analysis determines the pronunciation characteristics of the speaker by detecting the energy distribution in different frequency ranges in the voice information. The speech band characteristics of people of different age groups are different. The elderly usually have more significant attenuation in the high-frequency part. The speech band is mainly divided into low frequency (80-200 Hz), medium frequency (200-500 Hz) and high frequency (above 500 Hz). Therefore, the speech band can also indirectly reflect the predicted age of the user corresponding to the voice information.

[0091] It can be seen that in this embodiment, by obtaining the user's voice information, it is possible to provide data support for subsequent voice analysis; by performing voice analysis on the voice information, it is possible to extract key information from the user's voice, including but not limited to accent category, phoneme frequency and voice frequency segment; by obtaining the analysis results of the accent category, phoneme frequency and voice frequency segment, and by determining the user's predicted age based on the voice analysis results, it is possible to comprehensively analyze the user's voice features and accurately infer the user's predicted age.

[0092] In some embodiments, adjusting the page based on the predicted age of the user includes:

[0093] According to the predicted age of the user, the parameters of the page are adjusted; the parameters of the page include at least one of the following: size, color, cognitive visual item and interactive depth.

[0094] Among them, the size may refer to the size of each text, picture, button, etc. in the page. For elderly users, the size of the font, picture and button is adjusted to be larger than the original page size, which is easier for elderly users to identify and click. The adjustment of different page sizes varies for different ages. When the user's predicted age is under 50 years old, the user is a non-elderly user, and the size of the page is not adjusted; when the user's predicted age is over 50 years old, the user is an elderly user, and the size of the page is increased. The size can be adjusted by adjusting the style of each element through the CSS (Cascading Style Sheets) or UI (User Interface) framework at the front end of the page. For example, the font size can be adjusted through the font-size attribute in CSS, the size of icons and pictures can be adjusted through attributes such as width and height in CSS, the button size can be adjusted through attributes such as width, height and padding in CSS, and the spacing between elements can be adjusted through attributes such as margin and padding in CSS.

[0095] Here, color may refer to the color contrast of an image on a page. Since elderly users have poor color recognition ability, the color contrast on a page can be improved for elderly users to help them improve their color recognition. Adjusting the color contrast on a page can be achieved in the following ways: obtaining the original color combination of the page; calculating the color contrast using the WCAG (Web Content Accessibility Guidelines) contrast calculator; adjusting the color contrast based on the predicted age of the current user. For example, if the current user is an elderly user, the text and background colors can be adjusted to increase the contrast. The background color can be adjusted from white to light gray, and the text black can be adjusted to a darker black.

[0096] Among them, cognitive visual items may refer to visible items related to the user's cognitive ability and visual ability. As age increases, the cognitive ability and visual ability of elderly users weaken. For example, as cognitive ability declines, elderly users find it more difficult to understand abstract graphics or symbols and tend to recognize intuitive and visual elements more easily. In addition, elderly users may experience color vision decline, especially a decline in the ability to distinguish blue.

[0097] Among them, interaction depth can refer to the complexity and level of interaction between users and pages. In user groups of different age groups, the design of interaction depth needs to be adjusted accordingly according to the user's operating habits, cognitive ability, and technical proficiency. Interaction depth not only involves the functions of page operations, but also includes the complexity of the operation process and the design of the hierarchical structure. As users age, elderly users have slower operation speeds and poorer memory and operating habits. Therefore, when adjusting the page, the interaction depth needs to be reduced to facilitate the operation of elderly users.

[0098] It can be seen that in this embodiment, by adjusting the page according to the user's predicted age, the page can be adaptively optimized according to the needs of users of different age groups; by adjusting the page parameters according to the user's predicted age, the design of the page can be made more in line with the visual and interaction needs of users of this age group; by adjusting the page size, color, cognitive visual items and interaction depth, the usability of the page can be improved, ensuring that the adjusted page is more suitable for the usage needs and cognitive abilities of users of different age groups, thereby improving the overall user experience.

[0099] In some embodiments, the cognitive visual items include color proportions and skeuomorphic icons;

[0100] According to the user's predicted age, adjust the parameters of the page, including:

[0101] Determine the level of awareness based on the user's predicted age;

[0102] According to the degree of cognition, the color proportion of the page and the number of realistic icons on the page are adjusted.

[0103] Among them, the cognitive level can refer to the user's cognitive ability level when processing information, understanding pages and performing page operations, as well as the user's ability to recognize special colors. The level of cognitive level affects the user's ability to understand, process and interact with the content of the page, as well as the user's ability to recognize special colors. Due to physiological and psychological changes, the cognitive ability of elderly users will gradually decline, so cognitive visual items need to be adjusted. For users with lower cognitive levels, that is, elderly users, page design needs to be more simplified, using more intuitive and clear elements to reduce the user's cognitive burden and help elderly users use the page more easily.

[0104] The color proportion may refer to the proportion of colors that are difficult to identify. For elderly users with low cognitive ability, since the elderly users' ability to identify blue is reduced, the page can reduce the proportion of blue to make the page more intuitive and clear, helping elderly users to browse the page more clearly.

[0105] The number of skeuomorphic icons may refer to the number of skeuomorphic icons on a page. Skeuomorphic icons are designed with objects or scenes in real life as models, so that the icons can more intuitively reflect functions or operations. Elderly users have a weaker understanding of abstract icons, so skeuomorphic icons are more suitable for elderly users. By increasing the number of skeuomorphic icons, the page can provide elderly users with clearer and more intuitive operation prompts.

[0106] It can be seen that in this embodiment, by adjusting the page parameters according to the user's predicted age, personalized page design can be achieved, so that the display of the page is more in line with the needs and usage habits of elderly users; by determining the cognitive level according to the user's predicted age, the user's cognitive ability and needs can be effectively evaluated, which is helpful to formulate a page design strategy suitable for the user, ensuring that the page content and functions match the user's cognitive ability, thereby reducing the difficulty of user operation; by adjusting the color proportion and the number of skeuomorphic icons of the page according to the cognitive level, the needs of users with different cognitive levels can be better met, and the convenience and readability of the page for elderly users are improved.

[0107] In some embodiments, the parameters of the page include an interaction level;

[0108] According to the user's predicted age, adjust the parameters of the page, including:

[0109] Determine the complexity of the operation based on the user's predicted age;

[0110] Adjust the interaction level of the page according to the complexity of the operation.

[0111] The operation complexity refers to the difficulty of the steps and tasks that need to be performed during the page operation process. For elderly users, the more interaction levels a page has, the higher the operation complexity, which affects the elderly users' use and browsing of the page. Therefore, the page operation complexity of elderly users can be reduced by reducing the interaction level of the page. For example, the content of the page with a deeper interaction level can be moved to the page with a shallow interaction level.

[0112] In a specific example, content that originally required jumping to multiple pages to access can be directly displayed on the main page or secondary page. This can be achieved by integrating the information of multiple deep pages into different modules or panels of one page. For example, using single-page application technology, different content can be embedded in the page in the form of modules, and different module views can be switched after clicking to avoid page jumps, thereby reducing the interaction level of the page; or dynamic loading technology can be used to load deep content on demand. The homepage displays some basic content, and when the user scrolls down the page, more deep information is automatically loaded to avoid users clicking on new pages.

[0113] It can be seen that in this embodiment, by determining the operation complexity based on the user's predicted age, the user's abilities and needs when operating the page can be reasonably evaluated, reducing the tediousness caused by excessive operation complexity, and improving the operation comfort of elderly users; by adjusting the interaction level of the page according to the operation complexity, the page design can be optimized to ensure that the interaction process of the page is in line with the abilities of elderly users, thereby avoiding excessive operation burden on elderly users.

[0114] Figure 3 A schematic diagram of the structure of a mobile device page adjustment device provided by an embodiment of the present invention. The embodiment of the present invention is applicable to the situation of mobile device page adjustment, the device can execute a mobile device page adjustment method, and the device can be implemented in the form of hardware and / or software.

[0115] See also Figure 3 The mobile device page adjustment device shown includes: a data set acquisition module 301, an influence degree calculation module 302, an age prediction module 303 and a page adjustment module 304, wherein:

[0116] The data set acquisition module 301 is used to acquire the usage data set of the current mobile device; the usage data set includes: the data of the historical usage mode provided by the user;

[0117] The influence degree calculation module 302 is used to calculate the influence degree corresponding to each parameter in the usage data set;

[0118] Age prediction module 303, used to perform weighted calculation on the influence degree corresponding to each parameter to obtain the predicted age of the user of the current mobile device;

[0119] The page adjustment module 304 is used to adjust the page according to the predicted age of the user.

[0120] The technical solution of the embodiment of the present invention can obtain the specific usage of the device by obtaining the usage data set of the current mobile device, providing a data basis for subsequent analysis; by using the historical usage data provided by the user as the usage data set, it can reflect the user's historical usage habits and preferences for the current mobile device, providing a basis for personalized adjustment; by calculating the influence degree corresponding to each parameter in the usage data set, it can identify the influence degree of each parameter on the user's predicted age, thereby optimizing the accuracy of adjusting the page based on the predicted age; by performing weighted calculation on the influence degree corresponding to each parameter, the user's age can be predicted more accurately, and the page can be adjusted according to the user's predicted age, so as to achieve a page display that is more in line with the user's age characteristics and improve the user experience; it solves the problem that when elderly users use mobile devices to access pages, the page layout can be automatically adjusted, thereby improving the elderly users' page access experience.

[0121] In some embodiments, the historical usage data provided by the user includes at least one of the following: false touch information, voice information, brightness setting, typing speed, keyboard type, viewing distance information, and used applications.

[0122] In some embodiments, the parameter set includes false touch information. In calculating the influence degree corresponding to each parameter in the parameter set, the influence degree calculation module 302 is specifically used to:

[0123] For the false touch information in the parameter set, obtain the hand vibration recognition information, interactive feedback information and gesture action information of the user when touching the current mobile device;

[0124] According to the hand vibration recognition information, the interactive feedback information and the gesture action information, the accidental touch operation abnormality detection is performed to obtain the accidental touch operation abnormality result;

[0125] According to the abnormal result of the accidental touch operation, the impact degree corresponding to the accidental touch information is determined.

[0126] In some embodiments, the parameters in the parameter set include voice information. In calculating the predicted age of the user corresponding to each parameter in the parameter set, the influence degree calculation module 302 is specifically used to:

[0127] Get voice information;

[0128] According to the voice information, performing voice analysis on the voice information to obtain a voice analysis result; the voice analysis result includes at least one of the following: accent category, phoneme frequency and voice frequency band;

[0129] According to the speech analysis result, the predicted age of the user corresponding to the speech information is determined.

[0130] In some embodiments, in adjusting the page according to the predicted age of the user, the page adjustment module 304 is specifically used to:

[0131] According to the predicted age of the user, the parameters of the page are adjusted; the parameters of the page include at least one of the following: size, color, cognitive visual item and interactive depth.

[0132] In some embodiments, the cognitive visual items include color proportions and skeuomorphic icons. In adjusting the parameters of the page according to the predicted age of the user, the page adjustment module 304 is specifically used to:

[0133] Determine the level of awareness based on the user's predicted age;

[0134] According to the degree of cognition, the color proportion of the page and the number of realistic icons on the page are adjusted.

[0135] In some embodiments, the parameters of the page include an interaction level. In adjusting the parameters of the page according to the predicted age of the user, the page adjustment module 304 is specifically used to:

[0136] Determine the complexity of the operation based on the user's predicted age;

[0137] Adjust the interaction level of the page according to the complexity of the operation.

[0138] The mobile device page adjustment device provided in the embodiment of the present invention can execute the mobile device page adjustment method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects for executing the mobile device page adjustment method.

[0139] Figure 4 A schematic structural diagram of a page adjustment device for a mobile device provided by an embodiment of the present invention.

[0140] like Figure 4As shown, the mobile device page adjustment device 400 includes at least one processor 401, and a memory connected to the at least one processor 401, such as a read-only memory (ROM) 402, a random access memory (RAM) 403, etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor 401 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 402 or the computer program loaded from the storage unit 408 to the random access memory (RAM) 403. In RAM403, various programs and data required for the operation of the mobile device page adjustment device 400 can also be stored. The processor 401, ROM402 and RAM403 are connected to each other through a bus 404. An input / output (I / O) interface 405 is also connected to the bus 404.

[0141] Multiple components in the mobile device page adjustment device 400 are connected to the I / O interface 405, including: an input unit 406, such as a keyboard, a mouse, etc.; an output unit 407, such as various types of displays, speakers, etc.; a storage unit 408, such as a disk, an optical disk, etc.; and a communication unit 409, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 409 allows the mobile device page adjustment device 400 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0142] The processor 401 may be a variety of general and / or dedicated processing components with processing and computing capabilities. Some examples of the processor 401 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 401 executes the various methods and processes described above, such as a mobile device page adjustment method.

[0143] In some embodiments, the mobile device page adjustment method may be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 408. In some embodiments, part or all of the computer program may be loaded and / or installed on the mobile device page adjustment device 400 via the ROM 402 and / or the communication unit 409. When the computer program is loaded into the RAM 403 and executed by the processor 401, one or more steps of the mobile device page adjustment method described above may be performed. Alternatively, in other embodiments, the processor 401 may be configured to execute the mobile device page adjustment method in any other appropriate manner (e.g., by means of firmware).

[0144] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0145] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that when the computer program is executed by the processor, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer program may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0146] In the context of the present invention, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, device, or equipment. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or equipment, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0147] In order to provide interaction with the user, the systems and techniques described herein can be implemented on an operation detection device, which has: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or a trackball), through which the user can provide input to the mobile device page adjustment device. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and the input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0148] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0149] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The client and server relationship is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS (Virtual Private Server) services.

[0150] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and this document does not limit this.

[0151] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A mobile device page adjustment method, characterized in that: The method comprises: Acquire a usage data set of the current mobile device; the usage data set includes: data on historical usage patterns provided by a user; Calculating the influence degree corresponding to each parameter in the usage data set; Performing weighted calculation on the influence degree corresponding to each parameter to obtain the predicted age of the user of the current mobile device; The page is adjusted according to the predicted age of the user.

2. The method according to claim 1, characterized in that The historical usage data provided by the user includes at least one of the following: false touch information, voice information, brightness setting, typing speed, keyboard type, viewing distance information and used applications.

3. The method according to claim 2, characterized in that The parameter set includes false touch information; The calculating the influence degree corresponding to each parameter in the parameter set includes: For the false touch information in the parameter set, obtaining the hand vibration recognition information, the interactive feedback information and the gesture action information of the user when touching the current mobile device; Performing mis-touch operation abnormality detection according to the hand vibration recognition information, the interactive feedback information and the gesture action information to obtain a mis-touch operation abnormality result; According to the abnormal result of the mis-touch operation, the impact degree corresponding to the mis-touch information is determined.

4. The method according to claim 2, characterized in that: The parameters in the parameter set include voice information; The calculating the predicted age of the user corresponding to each parameter in the parameter set includes: Acquiring the voice information; According to the voice information, performing voice analysis on the voice information to obtain a voice analysis result; the voice analysis result includes at least one of the following: accent category, phoneme frequency and voice frequency segment; The predicted age of the user corresponding to the voice information is determined according to the voice analysis result.

5. The method according to claim 1, characterized in that The adjusting the page according to the predicted age of the user includes: According to the predicted age of the user, the parameters of the page are adjusted; the parameters of the page include at least one of the following: size, color, cognitive visual item and interactive depth.

6. The method according to claim 5, characterized in that The cognitive visual items include color proportion and the number of skeuomorphic icons; The adjusting the parameters of the page according to the predicted age of the user includes: determining a level of cognition based on the predicted age of the user; According to the degree of cognition, the color ratio of the page and the number of realistic icons on the page are adjusted.

7. The method according to claim 5, characterized in that The parameters of the page include interaction level; The adjusting the parameters of the page according to the predicted age of the user includes: Determining the operation complexity according to the predicted age of the user; The interaction level of the page is adjusted according to the complexity of the operation.

8. A mobile device page adjustment device, characterized in that: include: A data set acquisition module is used to acquire the usage data set of the current mobile device; The usage data set includes: data on historical usage patterns provided by the user; An influence degree calculation module, used to calculate the influence degree corresponding to each parameter in the usage data set; An age prediction module, used to perform weighted calculation on the influence degree corresponding to each parameter to obtain the predicted age of the user of the current mobile device; The page adjustment module is used to adjust the page according to the predicted age of the user.

9. A mobile device page adjustment device, characterized in that: The mobile device page adjustment device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the mobile device page adjustment method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the mobile device page adjustment method according to any one of claims 1 to 7 when executed.

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