CN116048363A - Display method, system, equipment and medium of software interface based on artificial intelligence - Google Patents
Display method, system, equipment and medium of software interface based on artificial intelligence Download PDFInfo
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F3/00—Input arrangements for transferring data to be processed into a form capable of being handled by the computer; Output arrangements for transferring data from processing unit to output unit, e.g. interface arrangements
- G06F3/01—Input arrangements or combined input and output arrangements for interaction between user and computer
- G06F3/048—Interaction techniques based on graphical user interfaces [GUI]
- G06F3/0487—Interaction techniques based on graphical user interfaces [GUI] using specific features provided by the input device, e.g. functions controlled by the rotation of a mouse with dual sensing arrangements, or of the nature of the input device, e.g. tap gestures based on pressure sensed by a digitiser
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F3/00—Input arrangements for transferring data to be processed into a form capable of being handled by the computer; Output arrangements for transferring data from processing unit to output unit, e.g. interface arrangements
- G06F3/01—Input arrangements or combined input and output arrangements for interaction between user and computer
- G06F3/048—Interaction techniques based on graphical user interfaces [GUI]
- G06F3/0481—Interaction techniques based on graphical user interfaces [GUI] based on specific properties of the displayed interaction object or a metaphor-based environment, e.g. interaction with desktop elements like windows or icons, or assisted by a cursor's changing behaviour or appearance
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F9/00—Arrangements for program control, e.g. control units
- G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
- G06F9/44—Arrangements for executing specific programs
- G06F9/451—Execution arrangements for user interfaces
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
- G06N3/084—Backpropagation, e.g. using gradient descent
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
- G06V10/82—Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/40—Scenes; Scene-specific elements in video content
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D10/00—Energy efficient computing, e.g. low power processors, power management or thermal management
Abstract
The invention provides a display method, a system, equipment and a medium of a software interface based on artificial intelligence, and relates to the technical field of software interface display; determining a finger touch sensitivity of the user based on the device operation video usage sensitivity determination model of the user; acquiring user information and software information, wherein the user information comprises user vision information, user age, sex and health status; determining the font size of the text of the software interface corresponding to the software information by using a font size determining model based on the finger touch sensitivity of the user, the user information and the software information; the display content of the software interface corresponding to the software information is determined based on the text font size of the software interface corresponding to the software information.
Description
Technical Field
The invention relates to the technical field of software interface display, in particular to a method, a system, equipment and a medium for displaying a software interface based on artificial intelligence.
Background
With the development of internet technology, more and more software has been developed. Conventional software interfaces typically consist of fixed templates and layouts, the content of which is of a fixed size when opened by the user. Since the software display interface is usually laid out for young people to observe and use, the software display interface will have a smaller text size when displayed so that more information can be displayed. For the elderly or people with vision disorder, the displayed characters in the software interface are smaller when the software interface is used, so that the situation of poor eyesight or false touch can be caused. The traditional software interface display method mostly sets an 'senile mode' and an 'teenager mode' in software, and when a user manually opens the 'senile mode', fonts in the software can be increased, so that the user can see clearly. However, for the old people with better eyesight or better physical condition, the display amount of information in the "senile mode" is smaller, and more display contents can be browsed by frequent operation, which is not very beneficial to the use of users, and the situations that the fonts in the "senile mode" are too large and the fonts in the "teenager mode" are too small are inconvenient for the users to use are caused.
Therefore, how to intelligently adjust the font size of characters in a software interface and improve the software use experience of a user is a current problem to be solved urgently.
Disclosure of Invention
The invention mainly solves the technical problem of intelligently adjusting the font size of characters in a software interface and improving the software use experience of a user.
According to a first aspect, the present invention provides a method for displaying a software interface based on artificial intelligence, including: acquiring a device operation video of a user; determining a finger touch sensitivity of the user based on the device operation video usage sensitivity determination model of the user; acquiring user information and software information, wherein the user information comprises user vision information, user age, sex and health status; determining the font size of the text of the software interface corresponding to the software information by using a font size determining model based on the finger touch sensitivity of the user, the user information and the software information; and determining the display content of the software interface corresponding to the software information based on the text font size of the software interface corresponding to the software information.
Still further, the acquiring the device operation video of the user includes: and obtaining a screen recording video after performing screen recording operation on a device screen of the user, and taking the screen recording video as a device operation video of the user.
Still further, the sensitivity determination model is a long-short period neural network model, the input of the sensitivity determination model is a device operation video of the user, and the output of the sensitivity determination model is finger touch sensitivity of the user.
Still further, the method further comprises: and determining the brightness of the device screen based on the user vision information and the ambient illumination intensity.
Still further, the method further comprises: and if the brightness of the equipment screen is higher than the brightness threshold value, reminding the user through a popup window.
According to a second aspect, the present invention provides a display system for an artificial intelligence based software interface, comprising: the first acquisition module is used for acquiring equipment operation videos of users; a sensitivity determination module for determining a finger touch sensitivity of the user using a sensitivity determination model based on the device operation video of the user; the second acquisition module is used for acquiring user information and software information, wherein the user information comprises user vision information, user age, sex and health status; a text font size determining module, configured to determine a text font size of a software interface corresponding to the software information based on the finger touch sensitivity of the user, the user information, and the software information, where the text font size determining module includes a font size determining model; and the display content determining module is used for determining the display content of the software interface corresponding to the software information based on the text font size of the software interface corresponding to the software information.
Still further, the first acquisition module is further configured to: and obtaining a screen recording video after performing screen recording operation on a device screen of the user, and taking the screen recording video as a device operation video of the user.
Still further, the system further comprises a brightness determining module, wherein the brightness determining module is further used for: and determining the brightness of the device screen based on the user vision information and the ambient illumination intensity.
According to a third aspect, the present invention provides an electronic device comprising: a memory; a processor; a computer program; wherein the computer program is stored in the memory and configured to be executed by the processor to implement the method described above.
According to a fourth aspect, the present invention provides a computer readable storage medium having stored thereon a program executable by a processor to implement a method as in any of the above aspects.
The invention provides a display method, a system, equipment and a medium of a software interface based on artificial intelligence, wherein the method comprises the steps of obtaining equipment operation video of a user; determining a finger touch sensitivity of the user based on the device operation video usage sensitivity determination model of the user; acquiring user information and software information, wherein the user information comprises user vision information, user age, sex and health status; determining the font size of the text of the software interface corresponding to the software information by using a font size determining model based on the finger touch sensitivity of the user, the user information and the software information; and determining the display content of the software interface corresponding to the software information based on the text font size of the software interface corresponding to the software information. The method can intelligently adjust the font size of the characters in the software interface and improve the software use experience of the user.
Drawings
FIG. 1 is a schematic flow chart of a method for displaying an artificial intelligence-based software interface according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of display interval inconsistency between displayed characters of each row according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of a text provided by an embodiment of the present invention not displayed according to a determined text font size;
FIG. 4 is a schematic diagram of a display system of an artificial intelligence based software interface according to an embodiment of the present invention;
fig. 5 is a schematic diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
The invention will be described in further detail below with reference to the drawings by means of specific embodiments. Wherein like elements in different embodiments are numbered alike in association. In the following embodiments, numerous specific details are set forth in order to provide a better understanding of the present invention.
In an embodiment of the present invention, a method for displaying a software interface based on artificial intelligence is provided as shown in fig. 1, where the method for displaying a software interface based on artificial intelligence includes steps S1 to S5:
step S1, acquiring a device operation video of a user.
The user's device operation video represents a video recorded by the user's device operation. The length of time the user's device operates the video may be 1 minute, 3 minutes, 5 minutes, 10 minutes, etc. In some embodiments, the screen recording video can be obtained after the screen recording operation is performed on the device screen of the user, and the screen recording video is used as the device operation video of the user. The screen recording video represents a screen recording video obtained by recording a screen of a device display screen of a user through screen recording software of the device.
The device operation video of the user refers to a dynamic image recorded in an electric signal mode and consists of a plurality of continuous static images in time. Wherein each image is a frame of video data.
And step S2, determining the finger touch sensitivity of the user based on the device operation video of the user by using a sensitivity determination model.
The finger touch sensitivity of the user indicates how easily the user can properly operate the device by touching. The finger touch sensitivity of the user is high, so that the user is skilled in touch operation, the error touch condition in the operation is less, and the touch button in the screen can be accurately clicked. The low touch sensitivity of the user's finger indicates that the touch operation of the user is relatively slow, and the false touch condition in the operation is relatively large, and the font is required to be enlarged to increase the touch range of the user's finger so as to reduce the false touch condition. The finger touch sensitivity of the user may be a value between 0 and 1, the greater the value, the higher the finger touch sensitivity of the user.
The device operation video of the user records the operation condition of the device operated by the user through touching, and the device operation video of the user can reflect the finger touching sensitivity of the user. For example, when the user's device operates the video display user to click the touch button in the screen and then rapidly click back, it is indicated that the user has a false touch condition, and the touch sensitivity of the user's finger is low. For another example, when the user's device operates the video display user to click the touch button on the screen, the user often needs to continuously click the touch button twice or more, which means that the user's finger is inflexible, the operation is relatively slow, and the touch sensitivity of the user's finger is low. For another example, when the user device operates the video display user to continuously click in a non-click area far away from the touch button, it is indicated that the user cannot correctly click the touch button, and if the clicked position is far away from the correct touch button, it is indicated that the finger touch sensitivity of the user is lower. The touch buttons may be font links, web page links, application icons, and the like. The sensitivity determination model may determine a finger touch sensitivity of the user by processing the device operation video of the user.
The sensitivity determination model is a long-short-term neural network model. The long-term neural network model is one implementation of artificial intelligence. The Long and Short Term neural network model includes a Long and Short Term neural network (LSTM), which is one of RNNs (Recurrent Neural Network, recurrent neural networks). The long-term and short-term neural network model can process sequence data with any length, capture sequence information and output results based on the association relationship of front data and rear data in the sequence. The device operation videos of the users in the continuous time period are processed through the long-short-term neural network model, so that the characteristics of the association relationship among the device operation videos of the users in each time point can be output, and the output characteristics are more accurate and comprehensive.
The input of the sensitivity determination model comprises a device operation video of the user, and the output of the sensitivity determination model is finger touch sensitivity of the user.
The sensitivity determination model may be trained by training samples in the historical data. The training sample comprises sample input data and a label corresponding to the sample input data, wherein the sample input data is equipment operation video of a sample user, and the label is finger touch sensitivity of the sample user. The output label of the training sample can be obtained through artificial labeling. For example, the user may view a device operation video of the sample user and determine the finger touch sensitivity of the sample user by querying the touch operation condition of the user. In some embodiments, the initial sensitivity determination model may be trained by a gradient descent method to obtain a trained sensitivity determination model. Specifically, according to the training sample, constructing a loss function of the sensitivity determination model, adjusting parameters of the sensitivity determination model through the loss function of the sensitivity determination model until the loss function value converges or is smaller than a preset threshold, and completing training. The loss function may include, but is not limited to, a log (log) loss function, a square loss function, an exponential loss function, a range loss function, an absolute value loss function, and the like.
After training is completed, inputting the device operation video of the user to a sensitivity determination model after training is completed, and outputting to obtain the finger touch sensitivity of the user.
And step S3, acquiring user information and software information, wherein the user information comprises user vision information, user age, sex and health state.
The user information includes user vision information, user age, gender, and health status.
The user vision information includes the naked eyes of the user, the eyesight of wearing glasses, the degree of astigmatism and the axial direction of astigmatism.
The health status may include a health status, sub-health status, disease status.
The software information includes software code, software modules, typesetting modes of the software interfaces, text fonts in the software interfaces, and the like.
In some embodiments, the user may be provided with user information by means of a pop-up window.
In some embodiments, the brightness of the device screen may be determined based on the user vision information, ambient light intensity. The worse the user's vision information, the higher the screen brightness may be so that the user can clearly see the screen. The greater the ambient light intensity, the higher the screen brightness may be so that the screen can be brighter and clearer. In some embodiments, the brightness of the device screen may be determined by a screen brightness determination model. The screen brightness determining model can comprehensively consider the vision information of the user and the ambient illumination intensity, so as to determine the proper screen brightness. The screen brightness determination model is a deep neural network model. The deep neural network model includes a deep neural network (Deep Neural Networks, DNN). The deep neural network model is one implementation of artificial intelligence. The deep neural network may include a plurality of processing layers, each processing layer being composed of a plurality of neurons, each neuron matrixing data. The parameters used by the matrix may be obtained by training. The deep neural network may include a recurrent neural network (Recurrent Neural Network, RNN), a convolutional neural network (Convolutional Neural Networks, CNN), a generating countermeasure network (Generative Adversarial Networks, GAN), and so on. The input of the screen brightness determining model is the vision information of the user and the ambient illumination intensity, and the output of the screen brightness determining model is the brightness of the equipment screen.
In some embodiments, if the brightness of the device screen is higher than a brightness threshold, the user is alerted through a pop-up window. For example, the popup content is "the brightness of the current device screen is high, please note eye-safe". The brightness threshold may be set by human experience or automatically by a machine.
And S4, determining the font size of the text of the software interface corresponding to the software information by using a font size determination model based on the finger touch sensitivity of the user, the user information and the software information.
The text font size of the software interface represents the size of the font displayed on the software interface. The font size may be a first font, a second font, a third font, etc. The font size may also be 11 pounds, 12 pounds, 13 pounds, 14 pounds, etc.
The font size determining model is a deep neural network model. The deep neural network model includes a deep neural network (Deep Neural Networks, DNN). The deep neural network model is one implementation of artificial intelligence. The deep neural network may include a plurality of processing layers, each processing layer being composed of a plurality of neurons, each neuron matrixing data. The parameters used by the matrix may be obtained by training. The deep neural network may include a recurrent neural network (Recurrent Neural Network, RNN), a convolutional neural network (Convolutional Neural Networks, CNN), a generating countermeasure network (Generative Adversarial Networks, GAN), and so on. The input of the font size determining model is finger touch sensitivity of the user, the user information and the software information, and the output of the font size determining model is the text font size of the software interface corresponding to the software information.
When determining the font size of the text of the software interface corresponding to the software information, the finger touch sensitivity and the user information need to be comprehensively considered. For example, the lower the finger touch sensitivity of the user is, the larger the text font of the software interface can be, so that the user can click conveniently, the false touch of the user is avoided, the higher the finger touch sensitivity of the user is, the text font of the software interface can be reduced, the user can conveniently browse more information at the same time, and the frequent operation of the device caused by the user for browsing more information is reduced. For another example, the worse the vision in the user vision information, the larger the character font of the software interface can be, so that the user can conveniently view the vision, and the better the vision in the user vision information, the smaller the character font of the software interface can be, so that the user can conveniently browse more information at the same time. For another example, the older the user is, the larger the text font of the software interface may be to facilitate visibility by the user. The font size determining model comprehensively considers finger touch sensitivity and user information when outputting the font size of the text of the software interface corresponding to the software information, so that the final result is more accurate.
The font-sizing model may be trained by training samples. The input of the training sample is finger touch sensitivity of a sample user, sample user information and sample software information, the output label of the training sample is the character font size of a software interface corresponding to the sample software information, and the output label of the training sample can be obtained through artificial labeling. In some embodiments, the font-size determining model may be trained by a gradient descent method to obtain a trained font-size determining model. Specifically, according to the training sample, constructing a loss function of the font size determining model, adjusting the parameters of the font size determining model through the loss function of the font size determining model until the loss function value is converged or smaller than a preset threshold value, and finishing training. The loss function may include, but is not limited to, a log (log) loss function, a square loss function, an exponential loss function, a range loss function, an absolute value loss function, and the like.
And S5, determining the display content of the software interface corresponding to the software information based on the text font size of the software interface corresponding to the software information.
And after determining the text font size of the software interface corresponding to the software information, adjusting the text in the display content in the software interface to the text font size of the software interface corresponding to the software information, and displaying the display content in the software interface.
Because the device models of different users are different, each text in the display content of the software interface corresponding to the software information may have a display error. For example, there may be a display interval inconsistency between the display text of each line and the display text of the next line in the display content, and fig. 2 is a schematic diagram of the display interval inconsistency between the display text of each line according to the embodiment of the present invention. As shown in fig. 2, the display pitch is not uniform between each of the first, second, and third rows. For another example, it may also happen that a certain text in the display content is not displayed according to the determined text font size, and fig. 3 is a schematic diagram that the text provided in the embodiment of the present invention is not displayed according to the determined text font size, and as shown in fig. 3, the middle text in the first line is not displayed according to the determined text font size. For another example, there may be cases where the text pitch between the displayed text is too large.
In some embodiments, the display content of the software interface corresponding to the software information may also be checked through the neural network model to determine whether each text in the display content has a display error. The input of the graphic neural network model is a plurality of nodes and a plurality of edges of the display content of the software interface, the plurality of nodes are a plurality of characters, each character is a node, the plurality of edges are relations among the characters, the characteristics of the nodes can be the size and the format of each character, the characteristics of the edges in the plurality of edges can be the distance and the direction among the characters, and the output of the graphic neural network model is that the display state of the display content is correct or the display state is incorrect.
The graph neural network model includes a graph neural network (Graph Neural Network, GNN) and a full connectivity layer. A graph neural network is a neural network that acts directly on a graph, which is a data structure made up of two parts, nodes and edges. The graph neural network is one implementation of artificial intelligence. The graph neural network model is based on an information propagation mechanism, and each node updates its own node state by exchanging information with each other until a certain stable value is reached.
In some embodiments, the Graph neural network may include a Graph convolution network (Graph Convolution Networks, GCN), a Graph attention network (Graph Attention Networks), a Graph self encoder (Graph Autoencoders), a Graph generation network (Graph Generative Networks), and a Graph Spatial-temporal network (Graph Spatial-temporal Networks).
In some embodiments, the graph neural network model may include a multi-layer graph neural network. In the training or practical application process of the multi-layer graph neural network, each node of each layer receives information from nodes connected with the nodes (such as adjacent nodes) and performs information fusion between the nodes, and after the multi-layer graph neural network is passed, the nodes in each layer can perform information fusion with nodes which are farther away from the nodes (such as nodes which are not connected with the nodes or adjacent to the nodes), so that accuracy is improved.
The graph neural network model can be obtained through training of training samples. The input of the training sample comprises a plurality of nodes and a plurality of edges, the nodes are a plurality of characters, the edges are relations among the characters, and the output of the training sample is correct in display state or incorrect in display state. In some embodiments, the graph neural network model may be trained by a gradient descent method to obtain a trained graph neural network model. Specifically, according to the training sample, constructing a loss function of the graph neural network model, and adjusting parameters of the graph neural network model through the loss function of the graph neural network model until the loss function value converges or is smaller than a preset threshold value, and finishing training. The loss function may include, but is not limited to, a log (log) loss function, a square loss function, an exponential loss function, a range loss function, an absolute value loss function, and the like.
After training is completed, a plurality of nodes and a plurality of edges of the display content of the software interface can be input into the graph neural network model, and the display state of the display content is correct or the display state is incorrect.
Based on the same inventive concept, fig. 4 is a schematic diagram of a display system of an artificial intelligence based software interface according to an embodiment of the present invention, where the display system of the artificial intelligence based software interface includes:
a first obtaining module 41, configured to obtain a device operation video of a user;
a sensitivity determination module 42 for determining a finger touch sensitivity of the user using a sensitivity determination model based on the device operation video of the user;
a second obtaining module 43, configured to obtain user information and software information, where the user information includes user vision information, user age, gender, and health status;
a text font size determining module 44, configured to determine a text font size of a software interface corresponding to the software information based on the finger touch sensitivity of the user, the user information, and the software information, where the text font size determining module includes a font size determining model;
and the display content determining module 45 is configured to determine display content of the software interface corresponding to the software information based on the text font size of the software interface corresponding to the software information.
Based on the same inventive concept, an embodiment of the present invention provides an electronic device, as shown in fig. 5, including:
a processor 51; a memory 52 for storing executable program instructions in the processor 51; wherein the processor 51 is configured to execute to implement a method of displaying an artificial intelligence based software interface as provided above, the method comprising: acquiring a device operation video of a user; determining a finger touch sensitivity of the user based on the device operation video usage sensitivity determination model of the user; acquiring user information and software information, wherein the user information comprises user vision information, user age, sex and health status; determining the font size of the text of the software interface corresponding to the software information by using a font size determining model based on the finger touch sensitivity of the user, the user information and the software information; and determining the display content of the software interface corresponding to the software information based on the text font size of the software interface corresponding to the software information.
Based on the same inventive concept, the present embodiment provides a non-transitory computer readable storage medium, which when executed by the processor 51 of the electronic device, enables the electronic device to perform a display method implementing the artificial intelligence-based software interface provided as described above, the method comprising obtaining a device operation video of a user; determining a finger touch sensitivity of the user based on the device operation video usage sensitivity determination model of the user; acquiring user information and software information, wherein the user information comprises user vision information, user age, sex and health status; determining the font size of the text of the software interface corresponding to the software information by using a font size determining model based on the finger touch sensitivity of the user, the user information and the software information; and determining the display content of the software interface corresponding to the software information based on the text font size of the software interface corresponding to the software information.
Finally, it should be understood that the embodiments described in this specification are merely illustrative of the principles of the embodiments of this specification. Other variations are possible within the scope of this description. Thus, by way of example, and not limitation, alternative configurations of embodiments of the present specification may be considered as consistent with the teachings of the present specification. Accordingly, the embodiments of the present specification are not limited to only the embodiments explicitly described and depicted in the present specification.
Claims (10)
1. A method for displaying a software interface based on artificial intelligence, comprising:
acquiring a device operation video of a user;
determining a finger touch sensitivity of the user based on the device operation video usage sensitivity determination model of the user;
acquiring user information and software information, wherein the user information comprises user vision information, user age, sex and health status;
determining the font size of the text of the software interface corresponding to the software information by using a font size determining model based on the finger touch sensitivity of the user, the user information and the software information;
and determining the display content of the software interface corresponding to the software information based on the text font size of the software interface corresponding to the software information.
2. The method for displaying an artificial intelligence based software interface according to claim 1, wherein the obtaining a device operation video of a user comprises: and obtaining a screen recording video after performing screen recording operation on a device screen of the user, and taking the screen recording video as a device operation video of the user.
3. The method for displaying an artificial intelligence based software interface according to claim 1, wherein the sensitivity determination model is a long-short term neural network model, an input of the sensitivity determination model is a device operation video of the user, and an output of the sensitivity determination model is a finger touch sensitivity of the user.
4. The method for displaying an artificial intelligence based software interface according to claim 2, wherein the method further comprises: and determining the brightness of the device screen based on the user vision information and the ambient illumination intensity.
5. The method for displaying an artificial intelligence based software interface according to claim 4, wherein the method further comprises: and if the brightness of the equipment screen is higher than the brightness threshold value, reminding the user through a popup window.
6. A display system for an artificial intelligence based software interface, comprising:
the first acquisition module is used for acquiring equipment operation videos of users;
a sensitivity determination module for determining a finger touch sensitivity of the user using a sensitivity determination model based on the device operation video of the user;
the second acquisition module is used for acquiring user information and software information, wherein the user information comprises user vision information, user age, sex and health status;
a text font size determining module, configured to determine a text font size of a software interface corresponding to the software information based on the finger touch sensitivity of the user, the user information, and the software information, where the text font size determining module includes a font size determining model;
and the display content determining module is used for determining the display content of the software interface corresponding to the software information based on the text font size of the software interface corresponding to the software information.
7. The display system of an artificial intelligence based software interface according to claim 6, wherein the first acquisition module is further configured to: and obtaining a screen recording video after performing screen recording operation on a device screen of the user, and taking the screen recording video as a device operation video of the user.
8. The display system of an artificial intelligence based software interface according to claim 6, further comprising a brightness determination module further configured to: and determining the brightness of the device screen based on the user vision information and the ambient illumination intensity.
9. An electronic device, comprising: a memory; a processor; a computer program; wherein the computer program is stored in the memory and configured to be executed by the processor to implement the method of displaying an artificial intelligence based software interface according to any one of claims 1 to 5.
10. A computer readable storage medium having stored thereon a computer program, which when executed by a processor implements a method of displaying an artificial intelligence based software interface according to any of claims 1 to 5.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
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| CN202310346873.7A CN116048363B (en) | 2023-04-03 | 2023-04-03 | Display method, system, equipment and medium of software interface based on artificial intelligence |
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| CN202310346873.7A CN116048363B (en) | 2023-04-03 | 2023-04-03 | Display method, system, equipment and medium of software interface based on artificial intelligence |
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