Dynamic adjustment method, system and equipment for user interface and storage medium

Abstract

The embodiment of the invention relates to the field of UI interfaces, and discloses a user interface dynamic adjustment method, system and device and a storage medium, and the method comprises the following steps: obtaining state data when a user operates a current UI interface; the state data at least comprises emotion expression data and operation behavior data; analyzing the emotion expression data to obtain a current emotion state of the user; analyzing the operation behavior data to obtain an operation behavior model of the user; and based on the current emotional state of the user and the operation behavior model of the user, utilizing an AI model to dynamically adjust and optimize the current UI interface, and displaying the adjusted and optimized UI interface. According to the user interface dynamic adjustment method disclosed by the invention, the problem of poor user experience caused by UI manual intervention adjustment is solved, so that the interface is automatically and dynamically generated according to the real-time state data of the user, manual intervention is reduced, the interface optimization response speed is increased, and the user experience of the user interface is improved.

Description

Technical Field

[0001] The embodiments of the present invention relate to the field of UI interface technology, and in particular to a method, system, device and storage medium for dynamically adjusting a user interface. Background Art

[0002] In modern Internet applications, user experience (UX) is one of the key factors that determine the success of a product. Among them, UI (user interface) construction refers to the creation and design of the interface through which users interact with computer systems, applications or websites.

[0003] Traditional front-end UI interface design often requires a lot of manual intervention and fixed templates. Designers select corresponding components and design styles to generate them. After the UI interface is initially designed, users cannot adjust it. In the prior art, in order to meet the user experience, the UI interface can be modified through the modification needs of user voice input or text input, but the user needs to actively provide feedback information, and manual intervention is still required. It is difficult to adjust the UI interface in combination with the user's real-time emotional state changes and demand changes, resulting in a mediocre user experience. Summary of the invention

[0004] The purpose of the present invention is to at least provide a method, system, device and storage medium for dynamic adjustment of user interface, which can at least solve the problem of poor user experience caused by manual intervention in UI adjustment, and at least achieve automatic dynamic generation of interface based on real-time status data of users, reduce manual intervention, and improve interface optimization response speed, thereby improving user interface usage experience.

[0005] To solve the above technical problems, at least one embodiment of the present application provides a method for dynamically adjusting a user interface, comprising:

[0006] Acquire the state data of the user when operating the current UI interface; the state data at least includes emotion expression data and operation behavior data;

[0007] Analyzing the emotion expression data to obtain the user's current emotion state;

[0008] Analyzing the operation behavior data to obtain an operation behavior model of the user;

[0009] Based on the user's current emotional state and the user's operational behavior model, the current UI interface is dynamically optimized using an AI model, and the optimized UI interface is displayed.

[0010] At least one embodiment of the present application further provides a user interface dynamic adjustment system, including:

[0011] A data acquisition module is used to acquire the state data of the user when operating the current UI interface; the state data at least includes emotional expression data and operation behavior data;

[0012] A data analysis module, used to analyze the emotion expression data to obtain the user's current emotion state; and used to analyze the operation behavior data to obtain the user's operation behavior model;

[0013] The UI interface adjustment module is used to dynamically adjust the current UI interface using an AI model based on the user's current emotional state and the user's operation behavior model, and display the adjusted UI interface.

[0014] At least one embodiment of the present application also provides an electronic device, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the above-mentioned user interface dynamic adjustment method.

[0015] At least one embodiment of the present application further provides a computer-readable storage medium storing a computer program, wherein the computer program implements the above-mentioned method for dynamically adjusting the user interface when executed by a processor.

[0016] The embodiments of the present application provide a method, system, device and storage medium for dynamic adjustment of user interface, which obtains the current emotional state of the user by analyzing the emotion expression data according to the real-time status data of the user using the current interface; analyzes the operation behavior data to obtain the operation behavior model of the user; based on the current emotional state of the user and the operation behavior model of the user, dynamically optimizes the current UI interface using an AI model, and displays the optimized UI interface, which can respond to the user's emotional changes and interaction changes in real time, provide a more intimate and humanized interaction experience, improve the user's interface experience, and has a fast response speed, automatically completes the design and adjustment of the interface, reduces the workload of manual design and maintenance, reduces labor costs, and continuously improves the adaptability of the interface and user experience without frequent manual intervention and adjustment.

[0017] In some optional embodiments, dynamically optimizing the current UI interface using an AI model based on the user's current emotional state and the user's operation behavior model includes:

[0018] Selecting a first UI interface model that matches the current emotional state of the user and the operation behavior model of the user from a preset UI interface model library, wherein the UI interface model library is pre-associated and stores a plurality of UI interface models corresponding to different emotional states and different operation behavior models;

[0019] Using the first UI interface model as prompt information, and using the AI ​​model to generate a UI interface to be optimized;

[0020] Using a pre-trained support vector machine to score user satisfaction with the UI interface to be optimized, to obtain a satisfaction score;

[0021] When the satisfaction score is greater than a preset score threshold, the corresponding UI interface to be optimized is used as the optimized UI interface.

[0022] In some optional embodiments, the UI interface model includes at least a color body, animation effects, a layout structure, and prompt information.

[0023] In some optional embodiments, the process of training the support vector machine includes:

[0024] Provide an initial support vector machine;

[0025] The initial support vector machine is trained using the satisfaction samples of the historical users so that the initial support vector machine learns the scoring behavior for the UI interface to obtain the trained support vector machine; the satisfaction samples include: historical UI interfaces and historical users' satisfaction scores for the historical UI interfaces.

[0026] In some optional embodiments, the user satisfaction score of the UI interface to be optimized is scored by the pre-trained support vector machine to obtain the satisfaction score, which includes:

[0027] The support vector machine is used to score each UI interface feature of the UI interface to be optimized to obtain a feature score corresponding to each UI interface feature; the UI interface feature includes at least one of the color and layout of the UI interface.

[0028] In some optional embodiments, the process of constructing the UI interface model library includes:

[0029] Acquire historical emotion expression data and historical operation behavior data when historical users operate historical UI interfaces; the historical operation behavior data includes at least one of click rate, sliding operation and stay time;

[0030] Extract features from the historical emotion expression data and the historical operation behavior data to determine the emotion state and operation behavior model of the historical user when operating the historical UI interface;

[0031] Building a UI response strategy library for responding to a plurality of said historical emotional states;

[0032] The UI response strategy library and the operation behavior model formed when the historical user operates the historical UI interface are associated and stored to obtain the UI interface model library.

[0033] In some optional embodiments, the emotion expression data includes facial expression data and voice data;

[0034] The analyzing the emotion expression data to obtain the current emotion state of the user includes:

[0035] Using a convolutional neural network to perform feature extraction processing on the user's facial expression data to obtain a facial emotion feature set;

[0036] Using a recurrent neural network to perform feature extraction processing on the user's voice data to obtain a voice emotion feature set;

[0037] The facial emotion feature set and the voice feature set are input into a multimodal emotion analysis model for analysis to obtain the current emotional state of the user. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] One or more embodiments are exemplarily described by the pictures in the corresponding drawings, and these exemplary descriptions do not constitute limitations on the embodiments.

[0039] Figure 1 This is a process of a user interface dynamic adjustment method provided by an embodiment of the present application. Figure 1 ;

[0040] Figure 2 This is a process of a user interface dynamic adjustment method provided by an embodiment of the present application. Figure 2 ;

[0041] Figure 3 This is a process of a user interface dynamic adjustment method provided by an embodiment of the present application. Figure 3 ;

[0042] Figure 4 This is a process of a user interface dynamic adjustment method provided by an embodiment of the present application. Figure 4 ;

[0043] Figure 5 This is a process of a user interface dynamic adjustment method provided by an embodiment of the present application. Figure 5 ;

[0044] Figure 6 is a schematic diagram of a user interface dynamic adjustment device provided by another embodiment of the present application;

[0045] Figure 7 is a structural schematic diagram of an electronic device provided by another embodiment of the present application;

[0046] Figure 8 It is a schematic diagram of the structure of a computer-readable storage medium provided by another embodiment of the present application. DETAILED DESCRIPTION

[0047] To make the purpose, technical scheme and advantages of the embodiments of the present application clearer, the embodiments of the present application will be described in detail below in conjunction with the accompanying drawings. However, it will be appreciated by those skilled in the art that in the present application, many technical details are proposed in order to enable the reader to better understand the present application. However, even without these technical details and various changes and modifications based on the following embodiments, the technical scheme claimed in the present application can also be implemented. The division of the following embodiments is for the convenience of description, and the specific implementation of the present application should not be construed as any limitation, and the various embodiments can be combined and referenced with each other under the premise of no contradiction.

[0048] In order to facilitate understanding of the embodiments of the present application, relevant content about the UI interface is first introduced here.

[0049] In modern Internet applications, user experience (UX) is one of the key factors that determine the success of a product. Among them, UI (user interface) construction refers to the creation and design of the interface through which users interact with computer systems, applications or websites.

[0050] Traditional front-end UI interface design often requires a lot of manual intervention and fixed templates. Designers select corresponding components and design styles to generate them. After the UI interface is initially designed, users cannot adjust it. In the prior art, in order to meet the user experience, the UI interface can be modified through the modification needs of user voice input or text input, but the user needs to actively provide feedback information, and manual intervention is still required. It is difficult to adjust the UI interface in combination with the user's real-time emotional state changes and demand changes, resulting in a mediocre user experience.

[0051] In order to solve the technical problem of poor user experience caused by manual intervention in UI, the present invention proposes a method for dynamic adjustment of user interface. The implementation details of the method for dynamic adjustment of user interface of this embodiment are described in detail below. The following content is only the implementation details provided for easy understanding and is not necessary for implementing this solution.

[0052] Embodiment 1:

[0053] The user interface dynamic adjustment method of this embodiment can be applied to electronic devices with communication, computing and data storage capabilities. The specific process can be as follows: Figure 1 As shown, steps 101-104 are included, specifically including:

[0054] Step 101, obtaining status data of the user in the process of operating the current UI interface; the status data at least includes emotion expression data and operation behavior data;

[0055] Specifically, the UI interface is an interface that can be loaded on a terminal with a display screen, and the status data is relevant data generated when the user uses the current UI interface on the terminal.

[0056] In some examples, the state data includes emotional expression data and operational behavior data. Among them, emotional expression data includes user facial expression data and voice data, which are collected through the camera, microphone and other devices of the terminal that loads the UI interface in this embodiment. Operational behavior data includes user clicks, slides, dwell time and other operation data.

[0057] In some examples, the status data also includes the surrounding usage environment data of the UI interface terminal, so as to combine the environmental data to analyze the environmental characteristics, and use the AI ​​model combined with the environmental characteristics to generate a UI interface that conforms to the current usage environment to meet the user usage needs in different environments.

[0058] In some embodiments, further, the acquired status data needs to be preprocessed before entering step 102, and the preprocessing process includes:

[0059] Data cleaning: Remove duplicate, erroneous, and abnormal data to ensure data accuracy and reliability.

[0060] Data standardization: Normalize the data to eliminate the impact of different dimensions on data analysis.

[0061] Step 102, analyzing the emotion expression data to obtain the user's current emotion state;

[0062] Specifically, emotional states include happiness, sadness, anger, etc.

[0063] In some examples, the emotion expression data includes facial expression data and voice data. Step 102, such as Figure 2 As shown, specifically including:

[0064] Step 1021: Perform feature extraction processing on the facial expression data of the user using a convolutional neural network to obtain a facial emotion feature set;

[0065] Step 1022: Perform feature extraction processing on the user's voice data using a recurrent neural network to obtain a voice emotion feature set;

[0066] Step 1023: input the facial emotion feature set and the voice feature set into a multimodal emotion analysis model for analysis to obtain the current emotional state of the user.

[0067] Specifically, by using convolutional neural networks (CNN) to process facial expression data, using recurrent neural networks (RNN) to process voice data, and combining multimodal sentiment analysis technology, various emotional features are integrated to identify the user's emotional state.

[0068] Step 103, analyzing the operation behavior data to obtain the user's operation behavior model;

[0069] Specifically, step 103 includes: analyzing the user operation behavior data using a machine learning algorithm, extracting the user's behavior patterns and preferences, and building a user operation behavior model.

[0070] Step 104, based on the user's current emotional state and the user's operation behavior model, dynamically optimize the current UI interface using an AI model, and display the optimized UI interface.

[0071] Specifically, by using an AI-driven model to combine the user's current emotional state and the user's operational behavior model, a UI interface is generated to dynamically optimize the current UI interface, so that by using AI algorithms and sentiment analysis technology, the system can generate a front-end interface that adapts to the current emotional state in a short time, reducing user waiting time and improving the system's interactive response speed. The interface can be quickly adjusted according to the user's immediate feedback, allowing the user to obtain the best interface experience in a short time. In addition, the interface can be dynamically generated according to the user's real-time emotional state, so that the interface is more in line with the user's current emotional needs, thereby improving the user's satisfaction and pleasure. The interface can respond to the user's emotional changes in real time, provide a more intimate and humane interactive experience, and enhance the user's emotional resonance.

[0072] In some embodiments, step 104, such as Figure 3 As shown, including:

[0073] Step 201: Select a first UI interface model that matches the current emotional state of the user and the operation behavior model of the user from a preset UI interface model library, wherein the UI interface model library has pre-associated storage of multiple UI interface models corresponding to different emotional states and different operation behavior models;

[0074] Step 202: using the first UI interface model as prompt information, and using the AI ​​model to generate a UI interface to be optimized;

[0075] Step 203: Using a pre-trained support vector machine to score user satisfaction with the UI interface to be optimized, to obtain a satisfaction score;

[0076] Specifically, step 203 includes: using the support vector machine to score each UI interface feature of the UI interface to be optimized to obtain a feature score corresponding to each UI interface feature; the UI interface feature includes at least one of the color and layout of the UI interface.

[0077] In this embodiment, the UI interface features include color harmony, layout clarity, interactive convenience, aesthetics and ease of use. Each UI interface feature of the UI interface to be optimized is scored, and the user's satisfaction with the UI interface and each feature of the UI interface is reflected through numerical values.

[0078] Step 204: When the satisfaction score is greater than a preset score threshold, the corresponding UI interface to be optimized is used as the optimized UI interface.

[0079] Specifically, after using the AI ​​model to generate the UI interface to be optimized, the support vector machine is used to perform satisfaction scoring on the UI interface to be optimized. If the satisfaction score is greater than the preset score threshold, the corresponding UI interface to be optimized will be used as the tuned UI interface. If the satisfaction score is lower than the preset score threshold, then return to step 201-step 203 and use the AI ​​model to regenerate the UI interface to be optimized until the preset score threshold is met. The corresponding UI interface to be optimized will be used as the tuned UI interface to ensure that the optimized UI interface meets the user satisfaction requirements, ensure one-time optimization, avoid repeated optimization, and improve user experience.

[0080] In some embodiments, when the satisfaction score is lower than a preset score threshold, when returning to step 201-step 202, the scoring results of each UI interface feature are combined with the AI ​​model to generate a pseudo-optimized UI interface, thereby improving the accuracy of the regenerated pseudo-optimized UI interface and providing a UI interface that meets user needs.

[0081] Furthermore, the UI interface model at least includes a color main body, an animation effect, a layout structure and prompt information. The pre-built UI interface model library is built based on historical emotion data and historical operation behavior data.

[0082] In some embodiments, Figure 4 As shown, the process of building the UI interface model library includes:

[0083] Step 301: Obtain historical emotion expression data and historical operation behavior data when the historical user operates the historical UI interface; the historical operation behavior data includes at least one of click rate, sliding operation and stay time;

[0084] Step 302: extract features from the historical emotion expression data and the historical operation behavior data to determine the emotion state and operation behavior model of the historical user when operating the historical UI interface;

[0085] Step 303: construct a UI response strategy library for responding to a plurality of historical emotional states;

[0086] Step 304: associate and store the UI response strategy library and the operation behavior model formed when the historical user operates the historical UI interface to obtain the UI interface model library.

[0087] In this embodiment, when the UI response strategy library and the operation behavior model formed when the historical user operates the historical UI interface are associated and stored, the UI interface model library is generated through the AI ​​model. The generated UI interface model library verifies the model through methods such as cross-validation and leave-out validation to evaluate the prediction performance and generalization ability of the generated interface. The model is tuned according to the verification results to improve the prediction accuracy and reliability.

[0088] In some embodiments, Figure 5 As shown, the training process of the pre-trained support vector machine includes:

[0089] Step 401: providing an initial support vector machine;

[0090] Step 402: Use the satisfaction samples of the historical users to train the initial support vector machine so that the initial support vector machine learns the scoring behavior for the UI interface and obtains the trained support vector machine; the satisfaction samples include: historical UI interfaces and historical users' satisfaction scores for the historical UI interfaces.

[0091] Furthermore, for the training of support vector machines, it is also necessary to select a kernel function: In SVM (support vector machine), the choice of kernel function has a great impact on the performance of the model. Commonly used kernel functions include linear kernel, radial basis function (RBF), etc. According to the specific characteristics of the problem and the data distribution, select a suitable kernel function, and then train the SVM after selecting the kernel function: Use the training set data to train the SVM model and find the best hyperplane that can classify user satisfaction. During the training process, the performance of the model can be optimized by adjusting the model's hyperparameters (such as regularization coefficient C, penalty coefficient γ, etc.).

[0092] In this embodiment, the RBF radial basis function kernel is selected, and then the SVM model is trained using the training set and the kernel function.

[0093] The user interface dynamic adjustment method provided in this embodiment obtains the user's current emotional state by analyzing the emotion expression data according to the real-time status data of the user using the current interface; analyzes the operation behavior data to obtain the user's operation behavior model; based on the user's current emotional state and the user's operation behavior model, dynamically optimizes the current UI interface using an AI model, and displays the optimized UI interface, which can respond to the user's emotional changes and interaction changes in real time, provide a more intimate and humanized interaction experience, improve the user's interface experience, and has a fast response speed, automatically completes the design and adjustment of the interface, reduces the workload of manual design and maintenance, reduces labor costs, and continuously improves the adaptability of the interface and user experience without frequent manual intervention and adjustment.

[0094] Embodiment 2:

[0095] Another embodiment of the present application relates to a user interface dynamic adjustment system. The implementation details of the user interface dynamic adjustment system of this embodiment are specifically described below. The following content is only for the convenience of understanding the implementation details provided, and is not necessary for the implementation of this solution. The schematic diagram of the user interface dynamic adjustment system of this embodiment can be as follows Figure 6 As shown, it includes a data acquisition module 801, a data analysis module 802 and a UI interface adjustment module 803.

[0096] The data acquisition module 801 is used to acquire the state data of the user when operating the current UI interface; the state data at least includes emotion expression data and operation behavior data;

[0097] The data analysis module 802 is used to analyze the emotion expression data to obtain the current emotion state of the user; and to analyze the operation behavior data to obtain the operation behavior model of the user;

[0098] The UI interface adjustment module 803 is used to dynamically adjust the current UI interface using an AI model based on the user's current emotional state and the user's operation behavior model, and display the adjusted UI interface.

[0099] It is worth mentioning that all modules involved in this embodiment are logic modules. In practical applications, a logic unit can be a physical unit, a part of a physical unit, or a combination of multiple physical units. In addition, in order to highlight the innovative part of this application, this embodiment does not introduce units that are not closely related to solving the technical problems proposed by this application, but this does not mean that there are no other units in this embodiment.

[0100] Embodiment three:

[0101] Another embodiment of the present application relates to an electronic device, such as Figure 7 As shown, it includes: at least one processor 901; and a memory 902 that is communicatively connected to the at least one processor 901; wherein the memory 902 stores instructions that can be executed by the at least one processor 901, and the instructions are executed by the at least one processor 901 so that the at least one processor 901 can execute the user interface dynamic adjustment method in the above-mentioned embodiments.

[0102] Among them, the memory and the processor are connected in a bus manner, and the bus may include any number of interconnected buses and bridges, and the bus connects various circuits of one or more processors and memories together. The bus can also connect various other circuits such as peripherals, voltage regulators, and power management circuits, which are well known in the art and are therefore not further described herein. The bus interface provides an interface between the bus and the transceiver. The transceiver can be one element or multiple elements, such as multiple receivers and transmitters, providing a unit for communicating with various other devices on a transmission medium. The data processed by the processor is transmitted on a wireless medium via an antenna, and further, the antenna also receives data and transmits the data to the processor.

[0103] The processor is responsible for managing the bus and general processing, and can also provide various functions, including timing, peripheral interfaces, voltage regulation, power management, and other control functions. Memory can be used to store data used by the processor when performing operations.

[0104] Embodiment 4:

[0105] Another embodiment of the present application relates to a computer-readable storage medium, such as Figure 8 As shown, a computer program 31 is stored. When the computer program 31 is executed by a processor, the above method embodiment is implemented.

[0106] That is, those skilled in the art can understand that all or part of the steps in the above-mentioned embodiment method can be completed by instructing the relevant hardware through a program, and the program is stored in a storage medium, including a number of instructions to enable a device (which can be a single-chip microcomputer, chip, etc.) or a processor to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read-Only Memory, referred to as: ROM), random access memory (Random Access Memory, referred to as: RAM), disk or optical disk and other media that can store program codes.

[0107] Those skilled in the art will appreciate that the above embodiments are specific embodiments for implementing the present application, and in actual applications, various changes may be made thereto in form and detail without departing from the spirit and scope of the present application.

Claims

1. A method for dynamically adjusting a user interface, characterized in that: include: Get the status data of the user operating the current UI interface; The state data at least includes emotion expression data and operation behavior data; Analyzing the emotion expression data to obtain the user's current emotion state; Analyzing the operation behavior data to obtain an operation behavior model of the user; Based on the user's current emotional state and the user's operational behavior model, the current UI interface is dynamically optimized using an AI model, and the optimized UI interface is displayed.

2. The user interface dynamic adjustment method according to claim 1, characterized in that: The dynamically optimizing the current UI interface using an AI model based on the current emotional state of the user and the operation behavior model of the user includes: Selecting a first UI interface model that matches the current emotional state of the user and the operation behavior model of the user from a preset UI interface model library, wherein the UI interface model library is pre-associated and stores a plurality of UI interface models corresponding to different emotional states and different operation behavior models; Using the first UI interface model as prompt information, and using the AI ​​model to generate a UI interface to be optimized; Using a pre-trained support vector machine to score user satisfaction with the UI interface to be optimized, to obtain a satisfaction score; When the satisfaction score is greater than a preset score threshold, the corresponding UI interface to be optimized is used as the optimized UI interface.

3. According to the user interface dynamic adjustment method described in claim 2, the UI interface model at least includes a color body, animation effects, a layout structure and prompt information.

4. The method for dynamically adjusting a user interface according to claim 2, characterized in that: The process of training the support vector machine includes: Provide an initial support vector machine; The initial support vector machine is trained using the satisfaction samples of the historical users so that the initial support vector machine learns the scoring behavior for the UI interface to obtain the trained support vector machine; the satisfaction samples include: historical UI interfaces and historical users' satisfaction scores for the historical UI interfaces.

5. A user interface dynamic adjustment method according to claim 4, characterized in that: The user satisfaction score of the UI interface to be optimized is scored by the pre-trained support vector machine, and the satisfaction score obtained includes: The support vector machine is used to score each UI interface feature of the UI interface to be optimized to obtain a feature score corresponding to each UI interface feature; the UI interface feature includes at least one of the color and layout of the UI interface.

6. The method for dynamically adjusting a user interface according to claim 2, characterized in that: The process of building the UI interface model library includes: Acquire historical emotion expression data and historical operation behavior data when historical users operate historical UI interfaces; the historical operation behavior data includes at least one of click rate, sliding operation and stay time; Extract features from the historical emotion expression data and the historical operation behavior data to determine the emotion state and operation behavior model of the historical user when operating the historical UI interface; Building a UI response strategy library for responding to a plurality of said historical emotional states; The UI response strategy library and the operation behavior model formed when the historical user operates the historical UI interface are associated and stored to obtain the UI interface model library.

7. The user interface dynamic adjustment method according to claim 1, characterized in that: The emotion expression data includes facial expression data and voice data; The analyzing the emotion expression data to obtain the current emotion state of the user includes: Using a convolutional neural network to perform feature extraction processing on the user's facial expression data to obtain a facial emotion feature set; Using a recurrent neural network to perform feature extraction processing on the user's voice data to obtain a voice emotion feature set; The facial emotion feature set and the voice feature set are input into a multimodal emotion analysis model for analysis to obtain the current emotional state of the user.

8. A user interface dynamic adjustment system, characterized in that: include: The data acquisition module is used to obtain the status data of the user operating the current UI interface; The state data at least includes emotion expression data and operation behavior data; A data analysis module, used to analyze the emotion expression data to obtain the user's current emotion state; and for analyzing the operation behavior data to obtain an operation behavior model of the user; The UI interface adjustment module is used to dynamically adjust the current UI interface using an AI model based on the user's current emotional state and the user's operation behavior model, and display the adjusted UI interface.

9. An electronic device, characterized in that: include: at least one processor; as well as, a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the user interface dynamic adjustment method as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method for dynamically adjusting a user interface according to any one of claims 1 to 7 is implemented.

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