Design method and system of touch interaction interface

Abstract

The invention provides a design method and system of a touch interaction interface. Wherein user behavior habits are collected, key elements are extracted, a user participation degree evaluation system is constructed, and a behavior habit analysis report is generated; carrying out situation modeling processing by using a situation awareness layout optimization algorithm, carrying out adaptability analysis by adopting an environment adaptation technology, and generating environment adaptation interface configuration; a long-term preference tracking algorithm is used to record and learn personalized preference settings to add personalized components, a progressive interface evolution method is used to carry out progressive evolution processing, an interactive feedback mechanism is introduced step by step, an interactive module is added, and an optimized layout scheme is generated; and constructing a learning library according to the user behavior habits, importing interaction behavior data to perform behavior pre-judgment, adjusting interface elements and a response mode, and generating a touch interaction interface optimization strategy. According to the technical scheme provided by the invention, the man-machine interaction fluency and the user experience of the touch interaction interface are improved.

Description

Technical Field

[0001] The embodiments of the present application relate to the field of human-computer interaction technology, and in particular, to a design method and system for a touch interaction interface. Background Art

[0002] In the application scenarios of smart medical boxes, the touch interactive interface plays a vital role. With the development of technology and the improvement of people's awareness of health management, smart medical boxes need to not only have basic drug storage functions, but also integrate intelligent services such as health data monitoring, emergency call response, and medication reminders. Therefore, higher requirements are put forward for the design of the operation interface, including but not limited to: ensuring the convenience of operation to adapt to users of different ages and technical proficiency; anti-mistouch design to avoid unnecessary misoperation, especially for emergency response; and providing intuitive and easy-to-understand identification symbols so that users can quickly understand and use the device correctly. These features are of great significance to improving user experience and ensuring medical safety.

[0003] Smart medical kits currently on the market are usually equipped with touch screens as the main human-computer interaction method. In order to prevent misoperation, some products have introduced pop-up windows for secondary click confirmation, sliding to unlock, or screen lock mechanisms.

[0004] However, existing solutions often adopt a one-size-fits-all design approach and fail to fully consider individual differences, which may cause some users to feel that the operation is not intuitive or convenient enough. Secondly, although measures such as pop-up windows for secondary confirmation are adopted, in actual use, especially in emergency situations, such mechanisms may increase operation time and affect efficiency. Moreover, a simple one-time confirmation mechanism cannot completely eliminate the possibility of misoperation. Summary of the invention

[0005] The embodiments of the present application provide a method and system for designing a touch interactive interface to solve the problem XX in the prior art.

[0006] In a first aspect, an embodiment of the present application provides a method for designing a touch interaction interface, comprising:

[0007] Collect user behavior habits, extract key factors that affect the user behavior habits, and build a user engagement evaluation system based on the key factors to analyze the touch frequency and depth in the user behavior habits and generate a behavior habit analysis report; the key factors refer to a set of factors that have a significant impact on the user behavior habits in a specific usage scenario, including visual feedback response time, gesture operation convenience, and ambient lighting;

[0008] Using the context-aware layout optimization algorithm, extract the user's visual attention distribution and the reaction of the immediate physical environment in the behavior habit analysis report, perform context modeling processing, and use environmental adaptation technology to perform adaptability analysis on the visual clarity and auditory reception parameters in the context modeling processing results to obtain the user's adaptability to the immediate physical environment and generate an environmental adaptation interface configuration;

[0009] Using a long-term preference tracking algorithm, the personalized preference settings in the behavior habit analysis report are continuously recorded and learned, the continuous recording and learning results are input into the environment adaptation interface configuration, personalized components are added to the environment adaptation interface configuration, and a progressive interface evolution method is used to progressively evolve the environment adaptation interface configuration after the personalized components are added, so as to gradually introduce an interactive feedback mechanism, add interactive modules to the environment adaptation interface configuration, and generate an optimized layout plan;

[0010] A learning library is constructed according to the user behavior habits, the interactive behavior data in the learning library is imported into the interactive prediction module in the interactive module, a behavior prediction operation is performed on the interactive behavior data, and the interface elements and response methods in the optimized layout scheme are adjusted according to the behavior prediction operation results to improve the fluency of human-computer interaction and generate a touch interactive interface optimization strategy.

[0011] Optionally, the use of a context-aware layout optimization algorithm to extract the user's visual attention distribution and the reaction of the immediate physical environment in the behavior habit analysis report, perform context modeling processing, and use environmental adaptation technology to perform adaptability analysis on the visual clarity and auditory reception parameters in the context modeling processing result to obtain the user's adaptability to the immediate physical environment and generate an environmental adaptation interface configuration, including:

[0012] Extracting the visual focus area and operation frequency of the user in different usage scenarios based on the behavior habit analysis report, performing statistical analysis on the visual focus area and operation frequency to obtain parameter distribution characteristics, and generating a usage scenario parameter distribution map;

[0013] Use the context-aware layout optimization algorithm to extract the user's visual attention distribution and the immediate physical environment response in the behavior habit analysis report, and combine the parameter distribution in the usage scenario parameter distribution map as basic input to perform context modeling processing and generate a multi-dimensional context model;

[0014] Adopting environmental adaptation technology, performing adaptability analysis on visual clarity and auditory reception parameters in the multidimensional situation model to obtain the user's adaptability to the instant physical environment, optimizing the visual clarity and auditory reception parameters according to the adaptability, and generating an environmental adaptation processing result;

[0015] A virtual user experience environment is created, and adaptability parameters in the environment adaptation processing result are imported to verify the adaptability level of the environment adaptation processing result, and an environment adaptation interface configuration is generated.

[0016] Optionally, based on the usage scenario parameter distribution map, a context-aware layout optimization algorithm is used to extract the user's visual attention distribution and the reaction of the immediate physical environment in the behavior habit analysis report, and the parameter distribution in the usage scenario parameter distribution map is combined as a basic input to perform context modeling processing to generate a multidimensional context model, including:

[0017] Extracting usage scenario parameter distribution features based on the usage scenario parameter distribution map, performing cluster analysis on the usage scenario parameter distribution features to identify typical behavior patterns of users, and generating a situational behavior pattern library;

[0018] Using the context-aware layout optimization algorithm, extract the user's visual attention distribution and the immediate physical environment reaction in the behavior habit analysis report, and match them one by one with the behavior patterns in different situations in the context behavior pattern library to generate a context matching result;

[0019] Conduct multiple rounds of iterative testing on different context matching results, record the results of multiple rounds of iterative testing to identify potential improvement points in the one-by-one matching process, and generate optimized context matching results;

[0020] The context matching degree of the user behavior characteristics in the optimized context matching result is extracted, and the context matching degree is retrospectively analyzed. The retrospective analysis result is combined with the parameter distribution in the usage scenario parameter distribution map as a basic input to perform context modeling processing to generate a multidimensional context model.

[0021] Optionally, based on the multidimensional situational model, using environmental adaptation technology, performing adaptability analysis on visual clarity and auditory reception parameters in the multidimensional situational model to obtain the user's adaptability to the immediate physical environment, optimizing the visual clarity and auditory reception parameters according to the adaptability, and generating an environmental adaptation processing result, including:

[0022] Performing feature extraction processing based on the multidimensional situation model to obtain a multidimensional environment adaptation feature vector, performing standardization processing on the multidimensional environment adaptation feature vector, and generating an environment adaptation feature matrix;

[0023] Adopting environmental adaptation technology, performing adaptability analysis on visual clarity and auditory reception parameters in the multidimensional situation model, vectorizing the adaptability analysis results and inputting them into the environmental adaptation feature matrix to generate an optimized environmental adaptation feature matrix;

[0024] By using a correlation analysis method, the correlation of all environmental adaptation feature vectors in the optimized environmental adaptation feature matrix is ​​analyzed to generate a feature vector correlation table;

[0025] Based on the correlation analysis results in the feature vector association table, feature fusion processing is performed on the highly correlated environment adaptation feature vectors to reduce the dimensional redundancy of the optimized environment adaptation feature matrix and generate an environment adaptation processing result.

[0026] Optionally, the long-term preference tracking algorithm is used to continuously record and learn the personalized preference settings in the behavior habit analysis report, and the continuous recording and learning results are input into the environment adaptation interface configuration, and personalized components are added to the environment adaptation interface configuration. A progressive interface evolution method is used to progressively evolve the environment adaptation interface configuration after the personalized components are added, so as to gradually introduce an interactive feedback mechanism, add an interactive module to the environment adaptation interface configuration, and generate an optimized layout plan, including:

[0027] By using a time series analysis method, the operation frequencies of users in different time periods under the environment adaptation interface configuration are counted, high-frequency operation areas and low-frequency operation areas are identified, and a user behavior trajectory diagram is generated;

[0028] Based on the user behavior trajectory diagram, using a long-term preference tracking algorithm, continuously recording and learning the personalized preference settings in the behavior habit analysis report, inputting the continuous recording and learning results into the environment adaptation interface configuration, adding personalized components to the environment adaptation interface configuration, and generating a user preference configuration;

[0029] Based on the user preference configuration, a progressive interface evolution method is used to progressively evolve the environment adaptation interface configuration after adding personalized components, so as to gradually introduce an interactive feedback mechanism and generate a progressive evolution interface configuration;

[0030] Based on the progressively evolving interface configuration, an interactive module is added, and the user participation and module convenience of the interactive module are tested through a scenario simulation test method to generate an optimized layout solution.

[0031] Optionally, based on the user behavior trajectory diagram, using a long-term preference tracking algorithm, continuously recording and learning the personalized preference settings in the behavior habit analysis report, inputting the continuous recording and learning results into the environment adaptation interface configuration, adding personalized components to the environment adaptation interface configuration, and generating a user preference configuration, including:

[0032] Based on the user behavior trajectory map, identifying the operation habits and path preferences in the user behavior trajectory map through a behavior sequence analysis method, and generating a behavior pattern heat map;

[0033] Using a long-term preference tracking algorithm, the personalized preference settings in the behavior habit analysis report and the path preference in the behavior pattern heat map are continuously recorded and learned, and the continuous recording and learning results are input into the environment adaptation interface configuration to generate personalized and path preference configurations;

[0034] Extracting personalized components from the personalized and path preference configurations, and adding the personalized components to the environment adaptation interface configuration to generate a personalized interface configuration;

[0035] An intelligent fusion mechanism is introduced to integrate the personalized interface configuration, map the personalized interface configuration to the corresponding interface element in the environment adaptation interface configuration, and generate a user preference configuration.

[0036] Optionally, the step of building a learning library according to the user behavior habits, importing the interactive behavior data in the learning library into the interactive prediction module in the interactive module, performing a behavior prediction operation on the interactive behavior data, and adjusting the interface elements and response methods of the optimized layout scheme according to the behavior prediction operation results to improve the fluency of human-computer interaction and generate a touch interaction interface optimization strategy, including:

[0037] Constructing a scene context model, classifying and annotating the behavior patterns of different usage scenarios in the optimized layout scheme, and generating a scene feature map;

[0038] By introducing a user behavior monitoring module into the optimized layout scheme, the user's operation data during the implementation of the optimized layout scheme is monitored in real time, and the real-time monitoring results are integrated with the scene feature parameters in the scene feature map to generate a multi-dimensional interactive data stream;

[0039] Building a learning library according to the user behavior habits, synchronizing the interactive behavior data in the learning library with the multi-dimensional interactive data stream, and importing the synchronous processing result into the interactive prediction module in the interactive module to perform a behavior prediction operation and generate an interactive behavior prediction result;

[0040] The predicted interactive behavior result is used as an adjustment standard to adjust the interface elements and response methods of the optimized layout solution to improve the smoothness of human-computer interaction and generate a touch interactive interface optimization strategy.

[0041] In a second aspect, an embodiment of the present application provides a system for designing a touch interactive interface, including:

[0042] A construction module is used to collect user behavior habits, extract key factors that affect the user behavior habits, and construct a user participation evaluation system based on the key factors to analyze the touch frequency and depth in the user behavior habits and generate a behavior habit analysis report; the key factors refer to a set of factors that have a significant impact on the user behavior habits in a specific usage scenario, including visual feedback response time, gesture operation convenience, and ambient lighting;

[0043] An analysis module, which is used to use a context-aware layout optimization algorithm to extract the user's visual attention distribution and the reaction of the immediate physical environment in the behavior habit analysis report, perform context modeling processing, and use environmental adaptation technology to perform adaptability analysis on the visual clarity and auditory reception parameters in the context modeling processing results to obtain the user's adaptability to the immediate physical environment and generate an environmental adaptation interface configuration;

[0044] A learning module, for continuously recording and learning the personalized preference settings in the behavior habit analysis report using a long-term preference tracking algorithm, inputting the continuous recording and learning results into the environment adaptation interface configuration, adding personalized components to the environment adaptation interface configuration, using a progressive interface evolution method, and performing progressive evolution processing on the environment adaptation interface configuration after adding the personalized components, so as to gradually introduce an interactive feedback mechanism, add an interactive module to the environment adaptation interface configuration, and generate an optimized layout plan;

[0045] The adjustment module is used to build a learning library according to the user's behavior habits, import the interactive behavior data in the learning library into the interactive prediction module in the interactive module, perform behavior prediction operations on the interactive behavior data, and adjust the interface elements and response methods of the optimized layout scheme according to the behavior prediction operation results to improve the fluency of human-computer interaction and generate a touch interaction interface optimization strategy.

[0046] In a third aspect, an embodiment of the present application provides a computing device, comprising a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are used to be called and executed by the processing component to implement a touch interaction interface design method as described in the first aspect.

[0047] In a fourth aspect, an embodiment of the present application provides a computer storage medium storing a computer program, which, when executed by a computer, implements a method for designing a touch interactive interface as described in the first aspect.

[0048] In the embodiment of the present application, user behavior habits are collected, key factors affecting the user behavior habits are extracted, and a user participation evaluation system is constructed based on the key factors to analyze the touch frequency and depth in the user behavior habits and generate a behavior habit analysis report; the key factors refer to a set of factors that have a significant impact on the user behavior habits in a specific usage scenario, including visual feedback response time, gesture operation convenience and ambient lighting; a context-aware layout optimization algorithm is used to extract the user's visual attention distribution and the reaction of the immediate physical environment in the behavior habit analysis report, and context modeling is performed. The environmental adaptation technology is used to perform adaptive analysis on the visual clarity and auditory reception parameters in the context modeling processing results to obtain the user's adaptability to the immediate physical environment and generate an environmental adaptation interface configuration; long-term preferences are used. Tracking algorithm, continuously record and learn the personalized preference settings in the behavior habit analysis report, input the continuous record and learning results into the environment adaptation interface configuration, add personalized components to the environment adaptation interface configuration, adopt progressive interface evolution method, progressively evolve the environment adaptation interface configuration after adding personalized components, so as to gradually introduce interactive feedback mechanism, add interactive module to the environment adaptation interface configuration, and generate optimized layout scheme; build learning library according to the user behavior habits, import interactive behavior data in the learning library into interactive prediction module in the interactive module, perform behavior prediction operation on the interactive behavior data, adjust the interface elements and response methods in the optimized layout scheme according to the behavior prediction operation results, so as to improve the fluency of human-computer interaction, and generate touch interaction interface optimization strategy. By systematically collecting and analyzing the key elements in user behavior habits, a user participation evaluation system is constructed, which can accurately identify the key factors affecting user behavior. Through the detailed analysis of touch frequency and depth, the generated behavior habit analysis report provides a solid data foundation for subsequent optimization. The application of context-aware layout optimization algorithm ensures that the interface configuration can adapt to different real-time physical environments and improves the user experience. The combination of long-term preference tracking algorithm and progressive interface evolution method makes the interface configuration not only personalized, but also gradually optimized over time, enhancing user participation and satisfaction. Finally, by introducing the interaction prediction module, accurate prediction of user behavior is achieved, significantly improving the fluency and efficiency of human-computer interaction.

[0049] Furthermore, by extracting the visual focus area and operation frequency of users in different usage scenarios in detail, a distribution map of usage scenario parameters was generated, providing rich data support for situational modeling. The situational modeling was performed by combining these parameters with the situational awareness layout optimization algorithm, and a multi-dimensional situational model was generated to ensure the comprehensiveness and accuracy of the model. The application of environmental adaptation technology further optimized the visual clarity and auditory reception parameters, making the interface configuration more in line with the user's immediate physical environment. The creation of a virtual user experience environment verified the effectiveness of the environmental adaptation processing results, ensuring that the final generated environmental adaptation interface configuration is highly adaptable and reliable, thereby significantly improving the user's immersion and operational convenience.

[0050] Furthermore, the user's operation frequency in different time periods is counted through time series analysis methods, high-frequency and low-frequency operation areas are identified, and user behavior trajectory diagrams are generated, providing an intuitive basis for personalized configuration. The application of long-term preference tracking algorithms ensures the continuous recording and learning of user personalized preferences, so that the environment adaptation interface configuration can continue to evolve and be closer to the actual needs of users. The adoption of the progressive interface evolution method makes the process of adding personalized components smooth and natural, avoiding the discomfort caused to users by abrupt changes. The application of scenario simulation testing methods ensures the user participation and convenience of interactive modules. The optimized layout solution finally generated not only improves user satisfaction, but also enhances the flexibility and adaptability of the system.

[0051] These and other aspects of the present application will become more clearly understood in the description of the following embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, a brief introduction will be given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0053] Figure 1 A flow chart of a method for designing a touch interactive interface provided in an embodiment of the present application;

[0054] Figure 2 A schematic diagram of the structure of a touch interactive interface design system provided in an embodiment of the present application;

[0055] Figure 3 A schematic diagram of the structure of a computing device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0056] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application.

[0057] In some of the processes described in the specification and claims of this application and the above-mentioned figures, multiple operations that appear in a specific order are included, but it should be clearly understood that these operations may not be executed in the order in which they appear in this article or executed in parallel. The serial numbers of the operations, such as 101, 102, etc., are only used to distinguish between different operations, and the serial numbers themselves do not represent any execution order. In addition, these processes may include more or fewer operations, and these operations may be executed in sequence or in parallel. It should be noted that the descriptions of "first", "second", etc. in this article are used to distinguish different messages, devices, modules, etc., do not represent the order of precedence, and do not limit the "first" and "second" to be different types.

[0058] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of this application.

[0059] Figure 1 A flow chart of a method for designing a touch interactive interface is provided for an embodiment of the present application, such as Figure 1 As shown, the method includes:

[0060] 101. Collect user behavior habits, extract key factors that affect the user behavior habits, and build a user engagement evaluation system based on the key factors to analyze the touch frequency and depth in the user behavior habits and generate a behavior habit analysis report; the key factors refer to a set of factors that have a significant impact on the user behavior habits in a specific usage scenario, including visual feedback response time, gesture operation convenience, and ambient lighting;

[0061] In this step, user behavior habits refer to a series of operation modes and preferences of users when using the touch interaction interface, including but not limited to gesture operations such as clicking, sliding, and long pressing, as well as visual attention distribution.

[0062] Key factors refer to a set of factors that have a significant impact on user behavior habits in specific usage scenarios. These factors include visual feedback response time, gesture operation convenience, and ambient lighting.

[0063] The user engagement evaluation system is a comprehensive evaluation framework used to analyze user engagement in different scenarios, including indicators such as touch frequency and depth.

[0064] The behavior habit analysis report is generated by analyzing user behavior habit data, which contains detailed user behavior characteristics and impact assessment of key factors.

[0065] In the embodiment of the present application, first, the system collects user behavior data through various sensors and log records; second, extracts key factors that affect user behavior and builds a user engagement evaluation system; third, analyzes the touch frequency and depth in user behavior and identifies key behavior patterns; finally, generates a detailed behavior habit analysis report to provide a basis for subsequent optimization.

[0066] Assuming that users frequently use the touch interaction interface of the medical device screen, especially in environments with large changes in lighting conditions such as operating rooms or emergency departments, the system will record the user's operating behavior under different lighting conditions, including click frequency, sliding path and dwell time; first, the system collects the user's operating behavior data under different lighting conditions; second, the system analysis finds that in a strong light environment, users are more inclined to use large icons and high-contrast interfaces to improve visibility, and in a low-light environment, users prefer soft background light and larger fonts to reduce eyestrain; third, based on these data, the system generates a behavioral habit analysis report, revealing the user's operating preferences and needs under different lighting conditions; finally, the system uses these analysis results for subsequent interface optimization design to ensure that the touch interaction interface of the medical device screen can automatically adjust according to the ambient light, thereby improving the operating efficiency and accuracy of doctors and nurses.

[0067] 102. Use a context-aware layout optimization algorithm to extract the user's visual attention distribution and the reaction of the immediate physical environment in the behavior habit analysis report, perform context modeling processing, and use environmental adaptation technology to perform adaptability analysis on the visual clarity and auditory reception parameters in the context modeling processing results to obtain the user's adaptability to the immediate physical environment and generate an environmental adaptation interface configuration;

[0068] In this step, the context-aware layout optimization algorithm is an intelligent algorithm that aims to dynamically adjust the interface layout based on the user's behavior habit analysis report and immediate physical environment response to improve the user experience.

[0069] Visual attention distribution refers to the distribution of the user's visual focus when using the interface, reflecting the user's focus and operation intention.

[0070] The immediate physical environment includes the current environmental conditions, such as light intensity, noise level, etc., which will affect the user's operating experience.

[0071] Situational modeling is the process of combining user behavior habits and environmental conditions to generate a multidimensional situational model, which is used to simulate user operation behaviors in different situations.

[0072] Environmental adaptation technology is a series of technical means used to adjust the visual clarity and auditory reception parameters of the interface so that the interface can better adapt to the user's immediate physical environment.

[0073] Environmentally adaptive interface configuration refers to an optimized interface configuration that can automatically adjust according to user behavior habits and environmental conditions to improve user experience.

[0074] In the embodiment of the present application, first, the system extracts the visual attention distribution data in the behavior habit analysis report; second, it performs situational modeling processing in combination with the user's immediate physical environment reaction; third, it uses environmental adaptation technology to optimize visual clarity and auditory reception parameters; finally, it generates an interface configuration that adapts to the user's immediate physical environment.

[0075] For example, continuing with the above example, assume that the system has generated a behavioral habit analysis report; first, the system extracts the user's visual attention distribution and finds that the user's main focus areas on the medical device screen are the patient information display area and the operation control panel; second, combined with the light intensity of the immediate physical environment, the system generates a multidimensional situational model and optimizes the visual clarity and auditory reception parameters of the interface to ensure optimal visibility and audibility under different lighting conditions (such as strong light in the operating room or weak light in the emergency department); third, the system adjusts the layout and design of the patient information display area and the operation control panel to make them more clearly visible and easy to operate; finally, the system generates an adaptable environmental adaptation interface configuration to improve the operating efficiency and accuracy of medical staff.

[0076] 103. Using a long-term preference tracking algorithm, continuously record and learn the personalized preference settings in the behavior habit analysis report, input the continuous record and learning results into the environment adaptation interface configuration, add personalized components to the environment adaptation interface configuration, use a progressive interface evolution method, and perform progressive evolution processing on the environment adaptation interface configuration after adding the personalized components, so as to gradually introduce an interactive feedback mechanism, add an interactive module to the environment adaptation interface configuration, and generate an optimized layout plan;

[0077] In this step, the long-term preference tracking algorithm is a machine learning algorithm used to continuously record and learn users' personalized preference settings in order to better understand users' needs.

[0078] Personalized preference settings refer to the specific preferences formed by users during long-term use, such as interface layout, function location, etc.

[0079] Path preference refers to the path and operation method commonly used by users when using the interface, reflecting the user's habits and needs.

[0080] The progressive interface evolution method is a method of gradually optimizing the interface configuration, which gradually adapts the interface to user needs by continuously introducing new elements and feedback mechanisms.

[0081] The interactive feedback mechanism refers to collecting feedback information through interaction between users and the system, which is used to improve the interface configuration.

[0082] An optimized layout solution refers to a highly personalized interface configuration generated after long-term preference tracking and progressive evolution, which can meet the specific needs of users.

[0083] In the embodiment of the present application, first, the system uses a long-term preference tracking algorithm to continuously record and learn user behavior habits; second, the learning results are input into the environment adaptation interface configuration to add personalized elements; third, a progressive interface evolution method is adopted to gradually introduce an interactive feedback mechanism; finally, an optimized layout plan is generated to ensure that the interface configuration is highly personalized and meets user needs.

[0084] For example, continuing with the above example, assume that the system has generated an environmental adaptation interface configuration; first, the system uses a long-term preference tracking algorithm to record the user's operation frequency in different time periods, and identifies high-frequency operation areas (such as emergency buttons, common function menus) and low-frequency operation areas (such as setting options, help guides); secondly, based on these data, the system generates a user behavior trajectory map and adds personalized elements to the environmental adaptation interface configuration, such as adjusting the location of common functions to a more accessible place and increasing the icon size to improve click convenience; thirdly, the system gradually introduces interactive feedback mechanisms, such as adding shortcut menus and gesture operations, to further simplify complex operation processes; finally, the system generates an optimized layout plan to make the interface more in line with the user's long-term preferences and immediate needs, thereby improving the safety and efficiency of medical operations.

[0085] 104. Build a learning library based on the user behavior habits, import the interactive behavior data in the learning library into the interactive prediction module in the interactive module, perform a behavior prediction operation on the interactive behavior data, and adjust the interface elements and response methods in the optimized layout scheme according to the behavior prediction operation results to improve the fluency of human-computer interaction and generate a touch interactive interface optimization strategy.

[0086] In this step, the learning library refers to a database that stores user interaction behavior data for training and optimizing interface configuration.

[0087] Interaction behavior data refers to all operation records of users when using the interface, including gesture operations such as clicking, sliding, and long pressing.

[0088] The interaction prediction module is an intelligent module that is used to predict the user's next operation and adjust the interface configuration in advance.

[0089] Behavior prediction operation refers to predicting the user's possible next action by analyzing the user's historical behavior data in order to optimize the interface response.

[0090] Interface elements and response methods refer to the various components in the interface and their response logic, such as the click response speed of a button and the way a menu expands.

[0091] Touch interaction interface optimization strategy refers to a comprehensive solution that improves the smoothness of human-computer interaction and user experience through a series of optimization measures.

[0092] In the embodiment of the present application, first, the system constructs a learning library based on user behavior habits and stores interactive behavior data; secondly, the data in the learning library is imported into the interactive prediction module to perform behavior prediction operations; thirdly, the interface elements and response methods are adjusted according to the prediction results; finally, a touch interactive interface optimization strategy is generated to improve the fluency of human-computer interaction.

[0093] For example, continuing with the above example, assume that the system has generated an optimized layout plan; first, the system builds a learning library based on user behavior habits and imports all interaction behavior data into the interaction prediction module; second, the system analyzes the user's historical operation records to predict the user's possible next operation, such as clicking a button or sliding a menu, especially in emergency situations. The need to quickly access key functions; third, based on these prediction results, the system adjusts interface elements and response methods in advance to ensure that operations respond quickly and accurately, and reduce operational errors caused by interface delays; finally, the system generates a touch interaction interface optimization strategy, which significantly improves the fluency of human-computer interaction and user experience, especially in high-pressure environments (such as emergency scenarios), ensuring that medical staff can complete operations efficiently and accurately.

[0094] In order to improve the convenience of operation for medical staff when using the medical equipment screen, the system should design a simple and intuitive interface layout. Commonly used function buttons should be placed in easy-to-reach locations and arranged reasonably according to the frequency of use. For frequently used functions (such as freezing and alarm pause), the operating efficiency can be improved by increasing the button size or setting up quick access. In addition, the system can introduce an intelligent prediction mechanism to prepare the function options that may be needed in advance based on the user's historical behavior and current operation scenario, reduce the user's operation steps, and further simplify the interaction process.

[0095] To prevent accidental touches and misoperations, the system can add a secondary confirmation pop-up window before key operations (such as parameter adjustment, starting emergency procedures) to ensure that users have enough time to confirm their operating intentions. For example, when adjusting ventilator parameters or switching adult / child mode, the system can pop up a confirmation dialog box, requiring the user to click the confirmation button again or enter a specific command to perform the operation. The slide-to-unlock mechanism can also be used for high-risk operations, such as turning off the alarm sound or stopping the device, by sliding a specific path to verify the user's true intentions and avoid accidental triggering.

[0096] In some cases, such as when moving equipment or during surgery, the system can provide a screen lock function to prevent the device from changing settings due to accidental touch. When the device is locked, only limited operations (such as viewing waveforms) are allowed, and all other setting changes are disabled. Unlocking can be done in a secure way such as password, fingerprint recognition or facial recognition to ensure that only authorized personnel can perform key operations. This design not only improves the security of the system, but also reduces the possibility of misoperation.

[0097] The interface design should fully consider the working habits and operational needs of medical staff, so that the interface layout and interaction logic are as close to the actual workflow as possible. For example, commonly used function buttons can be grouped according to the frequency of operation and importance, and placed in a conspicuous and easy-to-click position; while for advanced settings that are rarely used, they can be hidden in the secondary menu to keep the main interface simple. At the same time, the system can also provide custom interface configuration options, allowing users to adjust the position and size of interface elements according to personal preferences, so as to better adapt to different operating habits.

[0098] The identification symbols of medical equipment should be clear and follow international standards and industry practices to ensure that all users can quickly understand their meaning. For important operation prompts or warning information, eye-catching colors (such as red for warning and green for normal) and easily recognizable icons should be used. In addition, the system can combine text instructions or voice prompts to help users understand and execute operation instructions more accurately. In particular, the operation instructions for emergency situations should be designed to be more intuitive and easy to understand so that medical staff can respond correctly in the shortest time.

[0099] In order to solve more complex situational adaptation problems, in some embodiments, the use of the situational awareness layout optimization algorithm in step 102 to generate an environment adaptation interface configuration includes: extracting the user's visual focus area and operation frequency in different usage scenarios based on the behavior habit analysis report, performing statistical analysis on the visual focus area and operation frequency to obtain parameter distribution characteristics, and generating a usage scenario parameter distribution map; using the situational awareness layout optimization algorithm to extract the user's visual attention distribution and the reaction of the immediate physical environment in the behavior habit analysis report, combining the parameter distribution in the usage scenario parameter distribution map as basic input, performing situational modeling processing, and generating a multidimensional situational model; using the environment adaptation technology to perform adaptability analysis on the visual clarity and auditory reception parameters in the multidimensional situational model to obtain the user's adaptability to the immediate physical environment, optimizing the visual clarity and auditory reception parameters according to the adaptability, and generating an environment adaptation processing result; creating a virtual user experience environment, importing the adaptability parameters in the environment adaptation processing result to verify the adaptability level of the environment adaptation processing result, and generating an environment adaptation interface configuration.

[0100] In this embodiment, the usage scenario parameter distribution map refers to a parameter distribution feature map generated by statistically analyzing the visual focus area and operation frequency in different usage scenarios of users. It is used to capture the user's behavior patterns and preferences in different scenarios.

[0101] The visual focus area refers to the screen area that the user focuses on when using a touch interactive interface, reflecting the user's attention distribution.

[0102] Operation frequency refers to the frequency with which users perform various gesture operations (such as clicking and sliding) in a specific area, reflecting the user's operating habits.

[0103] The multidimensional situational model is a multidimensional situational simulation model constructed by combining the user's behavior habit analysis report and the usage scenario parameter distribution map. It is used to predict the user's operating behavior and needs in different situations.

[0104] The virtual user experience environment is a test platform that simulates the real usage environment and is used to verify the effectiveness and adaptability level of the environmental adaptation processing results.

[0105] In the embodiment of the present application, firstly, the system extracts the visual focus areas and operation frequencies of users in different usage scenarios based on the behavior habit analysis report; secondly, statistical analysis is performed on these visual focus areas and operation frequencies to obtain parameter distribution characteristics and generate a usage scenario parameter distribution map; thirdly, a context-aware layout optimization algorithm is used, combined with the parameter distribution in the usage scenario parameter distribution map as the basic input, to perform context modeling processing and generate a multi-dimensional context model; finally, environmental adaptation technology is used to perform adaptive analysis on the visual clarity and auditory reception parameters in the multi-dimensional context model, and these parameters are optimized according to the analysis results to generate an environmental adaptation processing result, thereby ensuring that the interface configuration can better adapt to the user's immediate physical environment.

[0106] Here is a specific example:

[0107] Assuming that users frequently use medical equipment screens to view patient information and perform treatment operations in hospital wards, the system needs to optimize the interface configuration according to different scenarios. First, the system extracts the main visual focus areas (such as vital signs data and medical order records) and operation frequencies (such as quick access to medical records and click frequencies of buttons to adjust drug dosages) when users view patient information, and generates corresponding usage scenario parameter distribution maps. Second, the system combines these data and uses a context-aware layout optimization algorithm to generate a multidimensional context model containing multiple usage scenarios to adapt to different medical operation needs. Third, the system uses environmental adaptation technology to optimize the visual clarity and auditory reception parameters of the patient information display interface to ensure the operating experience of medical staff under different lighting conditions, especially in low-light environments to improve visibility and reduce eye fatigue. Finally, a virtual user experience environment is created, the adaptability parameters in the environmental adaptation processing results are imported, their adaptability levels are verified, and the final environmental adaptation interface configuration is generated to ensure that medical staff have the best operating experience when using medical equipment screens in wards.

[0108] In order to solve more complex situational adaptation problems, in some embodiments, the generation of a multidimensional situational model based on the usage scenario parameter distribution map described in step 102 includes: extracting usage scenario parameter distribution features based on the usage scenario parameter distribution map, performing cluster analysis on the usage scenario parameter distribution features to identify the user's typical behavior patterns, and generating a situational behavior pattern library; using a situation-aware layout optimization algorithm to extract the user's visual attention distribution and the reaction of the immediate physical environment in the behavior habit analysis report, and matching them one by one with the behavior patterns in different situations in the situational behavior pattern library to generate a situational matching result; performing multiple rounds of iterative testing on different situational matching results, recording the results of multiple rounds of iterative testing to identify potential improvement points in the one-to-one matching process, and generating an optimized situational matching result; extracting the situational matching degree of the user behavior features in the optimized situational matching result, performing a retrospective analysis on the situational matching degree, and using the retrospective analysis results combined with the parameter distribution in the usage scenario parameter distribution map as basic inputs to perform situational modeling processing to generate a multidimensional situational model.

[0109] In this embodiment, the usage scenario parameter distribution map refers to a parameter distribution feature map generated by statistically analyzing the visual focus area and operation frequency of users in different usage scenarios. It is used to capture the user's behavior patterns and preferences in different scenarios.

[0110] The usage scenario parameter distribution features are key data features extracted from the usage scenario parameter distribution map through statistical analysis, reflecting the user's behavior patterns and needs in specific scenarios.

[0111] Cluster analysis is a data analysis method that aims to group similar data points to identify typical user behavior patterns. This method can help the system understand the user's operating habits in different scenarios.

[0112] The situational behavior pattern library is a database that stores a variety of typical behavior patterns. These patterns are extracted from a large amount of user behavior data through cluster analysis and are used to match the user's current behavior pattern.

[0113] The situation matching result refers to the result generated by matching the user's current behavior pattern with the typical pattern in the situation behavior pattern library through an algorithm, which reflects the user's operation intention in a specific situation.

[0114] Multiple rounds of iterative testing refer to multiple tests on different scenario matching results, recording the results of each test to identify potential improvement points and ensure the optimization of the final configuration.

[0115] Situational matching refers to the degree of matching between user behavior characteristics and typical patterns in the situational behavior pattern library, reflecting the consistency of user behavior in a specific situation.

[0116] Retrospective analysis is the review and analysis of historical data in the context matching process to discover potential improvement opportunities and optimize subsequent matching processing.

[0117] In the embodiment of the present application, first, the system extracts the characteristics of the usage scenario parameter distribution based on the usage scenario parameter distribution map; secondly, cluster analysis is performed on these characteristics to identify the user's typical behavior patterns and generate a situational behavior pattern library; thirdly, a situational awareness layout optimization algorithm is used to extract the user's visual attention distribution and the reaction of the immediate physical environment in the behavior habit analysis report, and match them one by one with the behavior patterns in different situations in the situational behavior pattern library to generate a situational matching result; finally, multiple rounds of iterative testing are performed on different situational matching results, and the results of multiple rounds of iterative testing are recorded to identify potential improvement points in the one-to-one matching process, generate optimized situational matching results, and extract the situational matching degree of the user behavior characteristics therein, and combine them with the parameter distribution in the usage scenario parameter distribution map as the basic input to perform situational modeling and generate a multi-dimensional situational model.

[0118] Here is a specific example:

[0119] For example, assuming that frontline medical staff and experts from rear hospitals conduct remote consultation and guidance, the system needs to optimize the touch interaction interface configuration according to different scenarios; first, the system extracts the user's main visual focus area (such as patient imaging data, real-time video window) and operation frequency (such as switching patient medical records, adjusting video angles, and controlling volume) during remote consultation based on the usage scenario parameter distribution map, and performs cluster analysis on these parameters to identify the user's typical behavior patterns and generate a situational behavior pattern library; secondly, the system uses the situational awareness layout optimization algorithm to extract the user's visual attention distribution and the reaction of the immediate physical environment during remote consultation, and matches them one by one with the behavior patterns in different situations in the situational behavior pattern library to generate situational matching results; thirdly, the system performs multiple rounds of iterative testing on different situational matching results, records the results of each test, identifies potential improvement points in the one-by-one matching process, and generates optimized situational matching results; finally, the system extracts the situational matching degree of the user behavior characteristics in the optimized situational matching results, combines the parameter distribution in the usage scenario parameter distribution map, performs situational modeling, and generates a multidimensional situational model to ensure that frontline medical staff and rear experts obtain the best interactive experience in different consultation scenarios.

[0120] In order to solve more complex environmental adaptation problems, in some embodiments, the generation of environmental adaptation processing results based on the multidimensional situational model in step 102 includes: performing feature extraction processing based on the multidimensional situational model to obtain a multidimensional environmental adaptation feature vector, and standardizing the multidimensional environmental adaptation feature vector to generate an environmental adaptation feature matrix; using environmental adaptation technology to perform adaptability analysis on the visual clarity and auditory reception parameters in the multidimensional situational model, and quantizing the adaptability analysis results and inputting them into the environmental adaptation feature matrix to generate an optimized environmental adaptation feature matrix; using a correlation analysis method to analyze the correlation of all environmental adaptation feature vectors in the optimized environmental adaptation feature matrix to generate a feature vector association table; based on the correlation analysis results in the feature vector association table, performing feature fusion processing on environmental adaptation feature vectors with high correlation to reduce the dimensional redundancy of the optimized environmental adaptation feature matrix and generate an environmental adaptation processing result.

[0121] In this embodiment, the multidimensional situational model is a multidimensional situational simulation model constructed by combining the user's behavior habit analysis report and the usage scenario parameter distribution map, which is used to predict the user's operating behavior and needs in different situations.

[0122] Feature extraction refers to the process of obtaining key environmental adaptation feature vectors from the multidimensional situation model. These feature vectors reflect the user's needs for visual clarity and auditory reception parameters in a specific situation.

[0123] The multi-dimensional environmental adaptation feature vector is a series of feature vectors extracted from the multi-dimensional situational model. Each vector contains data in multiple dimensions, such as light intensity, noise level, etc., which are used to describe the user's immediate physical environment.

[0124] Standardization is the process of uniformly scaling the multidimensional environmental adaptation feature vectors to ensure the comparability between different features and generate an environmental adaptation feature matrix.

[0125] The optimized environmental adaptation characteristic matrix is ​​a characteristic matrix that is further optimized based on the original environmental adaptation characteristic matrix and combined with the adaptability analysis results, reflecting the adjusted optimal environmental adaptation parameters.

[0126] The correlation analysis method is a data analysis method that aims to identify the correlation and dependency between feature vectors and generate a feature vector correlation table.

[0127] The feature vector association table records the associations between all environmental adaptation feature vectors, helping the system understand which features have strong correlations.

[0128] Feature fusion processing refers to merging highly correlated environmental adaptation feature vectors into new comprehensive feature vectors to reduce the dimensional redundancy of the optimized environmental adaptation feature matrix, simplify the data structure, and improve computational efficiency.

[0129] In the embodiment of the present application, first, the system performs feature extraction processing based on the multidimensional situational model, obtains a multidimensional environmental adaptation feature vector, and standardizes the feature vector to generate an environmental adaptation feature matrix; secondly, the environmental adaptation technology is used to perform adaptability analysis on the visual clarity and auditory reception parameters in the multidimensional situational model, and the adaptability analysis results are vectorized and input into the environmental adaptation feature matrix to generate an optimized environmental adaptation feature matrix; thirdly, through the correlation analysis method, the correlation of all environmental adaptation feature vectors in the optimized environmental adaptation feature matrix is ​​analyzed to generate a feature vector association table; finally, based on the correlation analysis results in the feature vector association table, feature fusion processing is performed on the environmental adaptation feature vectors with high correlation to reduce the dimensional redundancy of the optimized environmental adaptation feature matrix and generate the final environmental adaptation processing result.

[0130] Here is a specific example:

[0131] For example, assuming that the emergency transport equipment is used in an outdoor working environment, the system needs to optimize the interface configuration and alarm performance according to the immediate physical environment; first, the system performs feature extraction processing based on the multidimensional situational model to obtain multidimensional environmental adaptation feature vectors (such as light intensity, background noise), and standardizes these feature vectors to generate an environmental adaptation feature matrix; second, the system uses environmental adaptation technology to perform adaptive analysis on the visual clarity (such as icon size, contrast) and auditory reception parameters (such as alarm volume, frequency) of the emergency equipment interface, and quantizes the analysis results and inputs them into the environmental adaptation feature matrix to generate an optimized environmental adaptation feature matrix; third, the system uses correlation The analysis method analyzes the correlation of all environmental adaptation feature vectors in the optimized environmental adaptation feature matrix, generates a feature vector association table, and finds that there is a strong correlation between light intensity and icon size and color, and a strong correlation between background noise and alarm volume and frequency; finally, based on the correlation analysis results in the feature vector association table, the system performs feature fusion processing on highly correlated environmental adaptation feature vectors (such as light intensity and icon size and color, background noise and alarm volume and frequency), reduces the dimensional redundancy of the optimized environmental adaptation feature matrix, and generates the final environmental adaptation processing results to ensure that emergency personnel obtain the best operating experience and the highest warning performance in complex outdoor environments. Specifically, during the emergency transportation process, when the light intensity is high, the system automatically enlarges the alarm information pop-up box and adjusts its color to high-contrast red or yellow to improve visibility and warning effect. At the same time, if the background noise is detected to be large, the system will automatically increase the volume of the alarm sound and adjust it to a more recognizable frequency to ensure that emergency personnel will not miss critical alarms. Through these adaptive adjustments, the system significantly improves the availability and reliability of emergency equipment in outdoor working environments, and enhances the speed and accuracy of emergency response. Through the above steps, it is ensured that the emergency transport equipment provides the best user experience and the highest warning performance in complex outdoor environments, improves the safety and efficiency of emergency operations, and reduces operational errors caused by interface discomfort or unclear alarms.

[0132] In order to meet the needs of personalized interface configuration, in some embodiments, the use of a long-term preference tracking algorithm in step 103 to generate an optimized layout plan includes: using a time series analysis method to count the user's operation frequency in different time periods based on the environmental adaptation interface configuration, identifying high-frequency operation areas and low-frequency operation areas, and generating a user behavior trajectory map; based on the user behavior trajectory map, using a long-term preference tracking algorithm to continuously record and learn the personalized preference settings in the behavior habit analysis report, input the continuous recording and learning results into the environmental adaptation interface configuration, add personalized components to the environmental adaptation interface configuration, and generate a user preference configuration; based on the user preference configuration, using a progressive interface evolution method to progressively evolve the environmental adaptation interface configuration after adding personalized components, so as to gradually introduce an interactive feedback mechanism and generate a progressive evolution interface configuration; based on the progressive evolution interface configuration, adding an interactive module, testing the user participation and module convenience of the interactive module through a scenario simulation test method, and generating an optimized layout plan.

[0133] In this embodiment, the time series analysis method is a statistical analysis method used to analyze the trends and patterns of data changes over time. This method can identify the user's operation frequency in different time periods and help the system understand the user's long-term behavior patterns.

[0134] High-frequency operation areas and low-frequency operation areas refer to areas that users frequently use (such as commonly used function buttons) and areas that are less frequently used (such as auxiliary settings menus) when using a touch interactive interface. These areas reflect users' operating habits and preferences.

[0135] The user behavior trajectory chart is a chart generated by statistically analyzing the user's operation frequency in different time periods. It shows the user's behavior path and preferences and is used to guide interface optimization.

[0136] User preference configuration refers to a personalized configuration generated through a long-term preference tracking algorithm, which includes the user's specific preferences in terms of interface layout, function location, etc.

[0137] The interactive feedback mechanism refers to collecting feedback information through interaction between users and the system, which is used to improve the interface configuration and ensure that the interface can dynamically respond to user needs.

[0138] The scenario simulation test method is an evaluation method that tests the newly added interactive modules by simulating real usage scenarios to verify their user experience effects.

[0139] In the embodiment of the present application, first, the system uses a time series analysis method to count the user's operation frequency in different time periods based on the environment adaptation interface configuration, identify high-frequency operation areas and low-frequency operation areas, and generate a user behavior trajectory map; secondly, based on the user behavior trajectory map, the system uses a long-term preference tracking algorithm to continuously record and learn the personalized preference settings in the behavior habit analysis report, and inputs the learning results into the environment adaptation interface configuration, adds personalized components, and generates a user preference configuration; thirdly, based on the user preference configuration, the system adopts a progressive interface evolution method to progressively evolve the environment adaptation interface configuration after adding personalized components, gradually introduces an interactive feedback mechanism, and generates a progressive evolution interface configuration; finally, the system adds an interactive module based on the progressive evolution interface configuration, and tests the user participation and module convenience of the interactive module through a scenario simulation test method, and finally generates an optimized layout plan.

[0140] Here is a specific example:

[0141] For example, assuming that medical staff frequently use ventilators, the system needs to optimize the interface configuration based on the user's long-term preferences and operating habits; first, the system uses time series analysis methods to count the user's operating frequency of the ventilator's commonly used adjustment parameter boxes (such as tidal volume, respiratory rate, respiratory ratio, oxygen concentration, and positive end-expiratory pressure) in different operating scenarios, identify high-frequency operating areas (such as respiratory rate and oxygen concentration adjustment areas) and low-frequency operating areas (such as positive end-expiratory pressure settings), and generate a user behavior trajectory map; secondly, based on the user behavior trajectory map, the system uses a long-term preference tracking algorithm to continuously record the user's habits in different operating scenarios, and inputs the learning results into the environmental adaptation interface configuration to add personalized components, For example, the position of the high-frequency operation parameter box is adjusted to an area of ​​the screen that is easier to reach, and its display size is increased to improve the convenience of operation, generating a user preference configuration; secondly, based on the user preference configuration, the system adopts a progressive interface evolution method, gradually introduces an interactive feedback mechanism, such as adding shortcut menus and gesture operations, to ensure that the adjustment of common parameters is more intuitive and efficient, and generates a progressive evolution interface configuration; finally, the system adds interactive modules based on the progressive evolution interface configuration, such as intelligent alarm prompts and parameter suggestion functions, and tests the user participation and module convenience of the interactive module through a scenario simulation test method, and finally generates an optimized layout plan to ensure that medical staff get the best operating experience when using the ventilator. Specifically, in the daily use of the ventilator, when the system detects that a parameter (such as respiratory rate) is frequently adjusted, the parameter box will be automatically moved to a prominent position in the center or edge of the screen and appropriately enlarged for quick access. For less used parameters (such as positive end-expiratory pressure), they can be placed in a less important position, but still maintain visibility and operability. In addition, the system can also predict the parameters that users may need to adjust based on historical data, and prepare corresponding shortcut operation paths in advance to further simplify the operation process. Through these optimization measures, the system significantly improves the human-computer interaction efficiency of the ventilator interface and enhances the convenience and accuracy of the work of medical staff. Through the above steps, it is ensured that the commonly used adjustment parameter box and parameter display box of the ventilator can be reasonably adjusted to the most appropriate position according to the operating habits and frequency of use, which improves the operating efficiency and accuracy of medical staff, reduces the operating errors caused by unreasonable interface design, and thus ensures the treatment effect and safety of patients.

[0142] In order to meet the needs of personalized interface configuration, in some embodiments, the generation of user preference configuration based on the user behavior trajectory map described in step 103 includes: based on the user behavior trajectory map, identifying the operating habits and path preferences in the user behavior trajectory map through a behavior sequence analysis method to generate a behavior pattern heat map; using a long-term preference tracking algorithm to continuously record and learn the personalized preference settings in the behavior habit analysis report and the path preferences in the behavior pattern heat map, and input the continuous recording and learning results into the environment adaptation interface configuration to generate a personalized and path preference configuration; extracting personalized components from the personalized and path preference configuration, and adding the personalized components to the environment adaptation interface configuration to generate a personalized interface configuration; introducing an intelligent fusion mechanism to integrate the personalized interface configuration, mapping the personalized interface configuration to the corresponding interface elements in the environment adaptation interface configuration, and generating a user preference configuration.

[0143] In this embodiment, the user behavior trajectory diagram is a diagram generated by statistically analyzing the user's operation frequency in different time periods, showing the user's behavior path and preferences, and is used to guide interface optimization.

[0144] Behavior sequence analysis is a data analysis technique that aims to reveal users' habits and preferences by identifying the temporal sequence and path patterns of their operations. This method can capture complex patterns in user behavior, such as common operation sequences and path choices.

[0145] A behavior pattern heat map is a visualization heat map generated by identifying the operating habits and path preferences in the user behavior trajectory map. It shows the areas and operation paths that users frequently use on the interface, helping the system understand the user's operating patterns.

[0146] Personalization and path preference configuration refers to a highly personalized interface configuration generated by combining the user's behavior pattern heat map and behavior habit analysis report, which reflects the user's long-term preferences and frequently used paths.

[0147] Personalization components are specific personalized elements extracted from the personalization and path preference configurations, such as the location of commonly used functions, icon size, etc. These elements will be added to the environment adaptation interface configuration.

[0148] The intelligent fusion mechanism is an integrated processing method that aims to reasonably map various elements in the personalized interface configuration to the corresponding interface elements in the environment adaptation interface configuration to ensure seamless integration of the personalized configuration.

[0149] User preference configuration refers to the interface configuration that contains user personalized preferences and is ultimately generated after being processed by an intelligent fusion mechanism, which can provide a highly customized user experience.

[0150] In the embodiment of the present application, firstly, the system identifies the operating habits and path preferences in the user behavior trajectory map based on the user behavior trajectory map through a behavior sequence analysis method, and generates a behavior pattern heat map; secondly, the system uses a long-term preference tracking algorithm to continuously record and learn the personalized preference settings in the behavior habit analysis report and the path preferences in the behavior pattern heat map, and inputs the learning results into the environment adaptation interface configuration to generate a personalized and path preference configuration; thirdly, the system extracts personalized components from the personalized and path preference configuration, and adds these personalized components to the environment adaptation interface configuration to generate a personalized interface configuration; finally, the system introduces an intelligent fusion mechanism to integrate the personalized interface configuration, maps the personalized interface configuration to the corresponding interface elements in the environment adaptation interface configuration, and generates the final user preference configuration.

[0151] Here is a specific example:

[0152] For example, assuming that medical staff frequently use ECG monitors, the system needs to optimize the interface configuration according to the user's long-term preferences; first, based on the user behavior trajectory diagram, the system uses the behavior sequence analysis method to identify the user's preference settings in different operation scenarios (such as frequently clicked monitoring function buttons, waveform freezing, alarm pause, printing waveforms, blood pressure measurement, etc.) and generate a behavior pattern heat map; secondly, the system uses a long-term preference tracking algorithm to continuously record the user's operating habits and path preferences in different time periods, and input the learning results into the environment adaptation interface configuration to generate personalized and path preference configurations, such as adjusting the position of the monitoring function buttons and adding commonly used functions (such as waveform Secondly, the system extracts personalized components (such as the adjusted monitoring function button position and quick entry) from the personalized and path preference configuration, and adds these personalized components to the environmental adaptation interface configuration to generate a personalized interface configuration; finally, the system introduces an intelligent fusion mechanism to integrate the personalized interface configuration, and maps the personalized interface configuration to the corresponding interface elements in the environmental adaptation interface configuration, such as mapping the monitoring function button to a prominent position on the main interface, and mapping the quick entry of common functions to the bottom navigation bar or sidebar, to generate the final user preference configuration to ensure that medical staff get the best operating experience when using the ECG monitor. Specifically, during the operation of the ECG monitor, when the system detects that a certain function (such as waveform freezing) is frequently used, the function button will be automatically moved to a prominent position in the center or edge of the screen and appropriately enlarged for quick access. For other commonly used functions (such as alarm pause, waveform printing, blood pressure measurement), the system will place them in an easily accessible position according to the frequency of use, and provide a quick operation path. In addition, the system can also predict the functions that users may need based on historical data, and prepare corresponding quick operation paths in advance to further simplify the operation process. For example, if the system finds that a medical staff member often prints immediately after viewing the waveform, it can directly provide a print option on the waveform display interface to reduce the number of operation steps. Through the above steps, it is ensured that the ECG monitor's monitoring function buttons, waveform freeze, alarm pause, print waveform, blood pressure measurement and other preference settings can be reasonably adjusted to the most appropriate position according to the user's long-term preferences, improving the operating efficiency and accuracy of medical staff, reducing operational errors caused by unreasonable interface design, and thus ensuring the monitoring effect and safety of patients.

[0153] In order to solve the problem of human-computer interaction fluency, in some embodiments, the step 104 of generating a touch interaction interface optimization strategy based on user behavior habits includes: constructing a scene context model, classifying and annotating the behavior patterns of different usage scenarios in the optimization layout scheme, and generating a scene feature map; introducing a user behavior monitoring module in the optimization layout scheme, monitoring the user's operation data in real time during the implementation of the optimization layout scheme, and fusing the real-time monitoring results with the scene feature parameters in the scene feature map to generate a multi-dimensional interaction data stream; constructing a learning library based on the user behavior habits, synchronously processing the interaction behavior data in the learning library with the multi-dimensional interaction data stream, and importing the synchronization processing results into the interaction prediction module in the interactive module to perform behavior prediction operations and generate interaction behavior prediction results; using the interaction behavior prediction results as adjustment criteria, adjusting the interface elements and response methods of the optimization layout scheme to improve the human-computer interaction fluency and generate a touch interaction interface optimization strategy.

[0154] In this embodiment, the scenario context model is a model used to classify and label user behavior patterns in different usage scenarios. It analyzes the user's operation data, identifies the behavior characteristics in a specific situation, and generates a corresponding scenario feature map.

[0155] The scenario feature map refers to a chart generated by classifying and annotating the behavioral patterns of different usage scenarios in the optimized layout solution. It shows the key behavioral characteristics in each scenario and helps the system understand user needs in different situations.

[0156] The user behavior monitoring module is a component that monitors user operation data in real time. It can capture all user operation behaviors during the use of the optimized layout solution, such as clicks, slides, etc.

[0157] The multi-dimensional interactive data stream refers to the data stream generated by fusing the real-time monitoring results of the user behavior monitoring module with the scene feature parameters in the scene feature map. It contains the time series information and scene features of the user's operation behavior, and provides rich interactive data.

[0158] The learning library is a database that stores user interaction behavior data and is used to train and optimize interface configurations. The data in the learning library can be processed and used to predict future user behavior.

[0159] The interaction prediction module is an intelligent module used to predict the user's next operation and adjust the interface configuration in advance. It generates interaction behavior prediction results by analyzing historical behavior data and real-time interaction data streams.

[0160] The interactive behavior prediction results refer to the prediction results of the user's future operation behavior through the interactive prediction module. These results are used to guide the adjustment of interface elements and response methods to improve the user experience.

[0161] In the embodiment of the present application, first, the system constructs a scene context model, classifies and annotates the behavior patterns of different usage scenarios in the optimized layout scheme, and generates a scene feature map; secondly, the system introduces a user behavior monitoring module into the optimized layout scheme, monitors the user's operation data in real time during the implementation of the optimized layout scheme, and integrates the real-time monitoring results with the scene feature parameters in the scene feature map to generate a multi-dimensional interactive data stream; thirdly, the system constructs a learning library according to the user's behavioral habits, synchronizes the interactive behavior data in the learning library with the multi-dimensional interactive data stream, and imports the synchronized processing results into the interactive prediction module in the interactive module to perform behavior prediction operations and generate interactive behavior prediction results; finally, the system uses the interactive behavior prediction results as the adjustment standard to adjust the interface elements and response methods of the optimized layout scheme to improve the fluency of human-computer interaction and generate a touch interactive interface optimization strategy.

[0162] Here is a specific example:

[0163] For example, assuming that medical staff use the screen of medical equipment in the operating room for intraoperative monitoring and operation guidance, the system needs to optimize the touch interactive interface configuration according to the stages and urgency of the surgical process; first, the system builds a scene context model, classifies and annotates the behavior patterns of different stages of the operation (such as preoperative preparation, operation, and postoperative recovery), and generates a surgical process feature map; secondly, the system introduces a real-time environmental perception module to monitor the user's operation data during the operation (such as clicking on specific monitoring parameters and adjusting equipment settings) in real time, and combines the real-time monitoring results with the characteristic parameters in the surgical process feature map (such as the location of the key parameter monitoring area and the layout of the emergency alarm button) in combination with the environmental factors in the operating room (such as lighting conditions and noise levels). The system combines the interactive behavior data in the learning library (such as frequently used functions during surgery) to generate a multi-dimensional interactive data stream; thirdly, the system builds a dynamic learning library according to the different stages of the surgical process, synchronously processes the interactive behavior data in the learning library (such as frequently used functions during surgery) and the multi-dimensional interactive data stream, and imports the synchronous processing results into the prediction engine in the interactive module to perform behavior prediction operations and generate interactive behavior prediction results, such as predicting that the user is about to check a certain vital sign or adjust the ventilator parameters; finally, the system uses the interactive behavior prediction results as the adjustment standard to dynamically adjust the interface elements and response methods of the optimized layout plan, such as displaying the shortcut entrance to key parameters in advance or directly opening the relevant setting page to improve the fluency of human-computer interaction, and generate the final touch interactive interface optimization strategy to ensure that medical staff get the best operating experience during surgery.

[0164] The present application considers that in order to solve the problems in the prior art due to insufficient situational adaptability, untimely response and limited ability to process complex user behavior patterns, the present invention embodiment proposes this optional solution to solve these problems, and thus proposes a new optional solution, which includes:

[0165] Based on the situational behavior pattern library, a situational awareness layout optimization algorithm is used to extract the user's visual attention distribution and the reaction of the immediate physical environment in the behavior habit analysis report, and match them one by one with the behavior patterns in different situations in the situational behavior pattern library to generate situational matching results, including:

[0166] Based on the situational behavior pattern library, the user's visual attention distribution and the reaction of the immediate physical environment are collected and annotated with high resolution, and the high-resolution collection and annotation results are subjected to feature extraction and dimensionality reduction processing by a principal component analysis method to generate a situational matching score;

[0167] The context match score is calculated using the following formula:

[0168]

[0169] Where S is the situation matching score; w i is the weight coefficient of the i-th situation mode; C i is the behavioral pattern characteristic of the i-th situation pattern; C ref is the reference behavior pattern characteristic; σ C is the standard deviation of the behavior pattern characteristics; α is the influence coefficient of visual attention distribution; V i is the visual attention distribution in the i-th situation mode; V avg is the average visual attention distribution; V max and V min are the maximum and minimum visual attention distributions, respectively; i is the index of the situation mode, from 1 to N; N is the number of situation modes;

[0170] Based on the situation matching score, a time response scoring mechanism is introduced, and a nonlinear correction coefficient is introduced to adjust the time response score influence weight, and the possible reaction time in similar situations in the future is predicted through time series analysis, and the situation matching score sensitivity coefficient is dynamically adjusted to generate a situation fitness evaluation value;

[0171] The situational fitness evaluation value is calculated using the following formula:

[0172]

[0173] Among them, A is the situational fitness evaluation value; S is the situational matching score; θ is the situational matching score threshold; k is the sensitivity coefficient of the situational matching score; γ is the influence coefficient of the time response score; T i T is the time response score of the i-th situation mode; avg is the average time response score; T max and T min are the maximum and minimum time response scores, respectively; δ is the nonlinear correction coefficient of the time response score; i is the index of the situation mode, from 1 to N; N is the number of situation modes;

[0174] Based on the situational fitness evaluation value, a sensitivity analysis method is applied to evaluate the response of the situational fitness evaluation value to changes in key parameters, identify the situational patterns with significant responses, introduce a weight adjustment mechanism, adjust the weight coefficient of each situational fitness evaluation value according to the long-term behavior trend of users in the situational behavior pattern library, and generate a situational matching result.

[0175] This method aims to improve the accuracy and response efficiency of context matching. It collects and annotates the user's visual attention distribution and immediate physical environment response with high resolution, and combines the context behavior pattern library for one-by-one matching processing to generate context matching scores and context fitness evaluation values. By introducing a time response scoring mechanism and a nonlinear correction coefficient to adjust the time response score influence weight, and dynamically adjusting the context matching score sensitivity coefficient, it ensures that the touch interaction interface can intelligently adapt to user needs. This not only solves the problems in the existing technology, but also provides a more scientific and effective solution for personalized touch interaction design.

[0176] Assuming that medical staff use portable medical device screens for rapid diagnosis and treatment in the emergency department, the system needs to optimize the touch interaction interface configuration according to the type and urgency of the emergency; assuming that the user behavior pattern weight coefficient w i =[0.3,0.4,0.3], user behavior pattern feature C i =[0.7,0.8,0.6], reference behavior pattern feature C ref =0.75, standard deviation of behavioral pattern characteristics σ C =0.1, visual attention distribution influence coefficient α = 0.5, user visual attention distribution V i =[0.9,0.85,0.8], average visual attention distribution V avg =0.85, maximum visual attention distribution V max =0.95, minimum visual attention distribution V min =0.75;

[0177]

[0178] Assume that the context matching score threshold θ = 0.8, the context matching score sensitivity coefficient k = 1.5, the time response score influence coefficient γ = 0.3, and the user operation time T i =[0.8,0.9,0.7], average time response score T avg =0.8, maximum time response score T max =0.95, minimum time response score T min =0.65, time response score nonlinear correction coefficient δ = 0.2;

[0179]

[0180] Assuming that the threshold is set to 0.85, since the calculated result 0.92 is greater than the set threshold, it shows that the situational fitness evaluation value has high validity and accuracy, which can ensure that the optimized configuration of the touch interactive interface in different emergency situations can effectively improve the operating efficiency of medical staff. This is because the higher situational fitness evaluation value reflects that the system can intelligently adjust the interface layout in the current emergency situation to meet the personalized needs of medical staff without affecting the operational fluency and response speed. Through the above steps, the accurate capture and response to user behavior patterns are ensured, the intelligence level of the touch interactive interface and the quality of user experience are improved, and the adaptability and responsiveness of the entire system are enhanced. The present application takes into account that in order to solve the problems in the prior art due to the inaccurate analysis of user behavior patterns, insufficient personalized configuration, and limited adaptability to complex environments, the invention embodiment proposes this optional solution to solve these problems, and therefore proposes a new optional solution, which includes:

[0181] Based on the behavior pattern heat map, the personalized preference settings in the behavior habit analysis report and the path preferences in the behavior pattern heat map are continuously recorded and learned using a long-term preference tracking algorithm, and the continuous recording and learning results are input into the environment adaptation interface configuration to generate personalized and path preference configurations, including:

[0182] Based on the behavior pattern heat map, multi-time point behavior feature extraction is performed to obtain discrete time series features of the behavior pattern in the behavior pattern heat map, and a time decay model is introduced to provide a time-dependent basis for the discrete time series features to generate a dynamic adjustment coefficient of the path preference;

[0183] The path preference dynamic adjustment coefficient is calculated using the following formula:

[0184]

[0185] Among them, D j (t) is the dynamic adjustment coefficient of the path preference of the jth user at time t; α iis the importance factor of the pth time point in the behavior pattern heat map; H pj (t) is the path preference heat value of the jth user at the pth time point; λ is the time decay rate; ρ is the path preference heat difference influence coefficient; is the average value of the path preference heat of the jth user; p is the index of the observed time point, from 1 to Q; Q is the number of observed time points; t is the time variable;

[0186] Based on the dynamic adjustment coefficient of the path preference, an environmental characteristic impact assessment is performed, by introducing an ideal central value as a reference standard, measuring the deviation between the environmental characteristic impact of the dynamic adjustment coefficient of the path preference and the ideal state, and introducing a time-varying trend of the dynamic adjustment coefficient of the path preference to generate personalized environmental adaptability;

[0187] The personalized environmental adaptability is calculated by the following formula:

[0188]

[0189] Among them, A j (t) is the personalized environmental fitness of the jth user at time t; η l is the impact intensity of environmental characteristics l on fitness; E jl (t) is the score of the jth user under the lth environmental feature; C l is the ideal center value of the environmental characteristic l; σ E is the standard deviation of the environmental feature score; l is the index of the environmental feature, from 1 to M; M is the number of environmental features; D jl (t) is the dynamic adjustment coefficient of the path preference of the jth user based on the lth environmental feature and time t; D ref is the reference value of the dynamic adjustment coefficient of path preference; γ' is the influence coefficient of the dynamic adjustment coefficient of path preference; δ' is the influence coefficient of time fluctuation; ω is the time fluctuation frequency; φ is the time fluctuation phase offset; t is the time variable;

[0190] Based on the personalized environmental fitness, a dynamic path optimization mechanism is introduced. According to the long-term preference tracking data in the behavior pattern heat map, the weight of the path preference dynamic adjustment coefficient is dynamically updated. The dynamic update result and the personalized environmental fitness are subjected to multi-dimensional preference fusion processing to generate personalized and path preference configuration.

[0191] This method aims to improve the personalized adaptability of the touch interaction interface in different situations. It continuously records and learns the user's personalized preference settings through a long-term preference tracking algorithm, and combines the path preference in the behavior pattern heat map to generate personalized and path preference configurations. By introducing the time decay model and the ideal center value as reference standards, the path preference and environmental characteristics are dynamically adjusted to ensure that the system can respond to user needs in real time and intelligently. This not only solves the problems in the existing technology, but also provides a more scientific and effective solution for personalized touch interaction design.

[0192] In the dynamic adjustment coefficient of path preference, the behavior pattern heat accumulation term This part is used to measure the cumulative value of the path preference heat of the jth user at different time points; the time decay model is used to give newer behavior patterns a higher weight; the path preference heat difference correction term This part is used to correct the impact of path preference heat difference; by introducing the path preference heat difference impact coefficient, the accuracy of the score is enhanced;

[0193] Among them, α p Obtained by evaluating the importance of different time points in the user behavior pattern heat map; H pj (t) is obtained by real-time monitoring of the path preference heat in the user behavior pattern heat map; λ is the time decay rate set according to the experimental data; ρ is the path preference heat difference influence coefficient adjusted according to the experimental data; It is obtained by statistically analyzing the path preference heat in the user behavior pattern heat map; p and Q are obtained by the observed time point index and quantity respectively; t is obtained by the time variable;

[0194] In personalized environmental fitness, environmental characteristics affect the intensity term This part is used to measure the impact of the jth user's rating under different environmental characteristics on the personalized environmental adaptability. By introducing the ideal center value as a reference standard, the deviation is measured; the path preference dynamically adjusts the influence term 1+γ'·sin(π / 2·(D jl (t)) / D ref ): This part is used to measure the impact of the dynamic adjustment coefficient of path preference on the personalized environment adaptability. By introducing the sine function to simulate periodic changes, the dynamic adjustment ability of the score is enhanced; Time fluctuation influence term 1+δ'·cos(ωt+φ): This part is used to measure the impact of time fluctuation on the personalized environment adaptability. By introducing the cosine function to simulate time fluctuation, the score is closer to the actual user behavior;

[0195] Among them, η l is the intensity of the impact of environmental characteristics on fitness adjusted according to experimental data; E jl(t) obtained by real-time monitoring of users’ ratings under different environmental characteristics; C l and σ E The ideal central value and standard deviation of the environmental feature score obtained by statistical analysis of a large amount of user data; l and M are obtained by the index and number of environmental features respectively; D jl (t) Calculated by the dynamic adjustment coefficient formula of path preference; D ref is the reference value of the dynamic adjustment coefficient of the path preference set according to the experimental data; γ' is the influence coefficient of the dynamic adjustment coefficient of the path preference adjusted according to the experimental data; δ' is the time fluctuation influence coefficient adjusted according to the experimental data; ω and φ are the time fluctuation frequency and phase offset set according to the experimental data; t is obtained through the time variable;

[0196] Assuming that medical staff frequently use ECG monitors in the intensive care unit, the system needs to optimize the interface configuration according to the monitoring needs of different patients to improve operational convenience and safety; assuming that the user behavior pattern weight coefficient α p =[0.9,0.8,0.7], user path preference heat value H pj (t) = [0.85, 0.8, 0.75], time decay rate λ = 0.1, path preference heat difference influence coefficient ρ = 0.3, user path preference heat average value The number of observation time points Q = 3, the time variable t = 1;

[0197]

[0198] Assume that environmental characteristics affect the intensity η l =[0.4,0.5], user rating E under different environmental characteristics jl (t) = [0.9, 0.85], the ideal center value C of the environmental characteristics l =[0.8,0.8], standard deviation of environmental feature scores σ E =0.1, path preference dynamic adjustment coefficient D jl (t) = [0.82, 0.82], reference value D of the dynamic adjustment coefficient of path preference ref =0.8, the influence coefficient of the dynamic adjustment coefficient of the path preference γ'=0.2, the influence coefficient of the time fluctuation δ'=0.1, the time fluctuation frequency ω=0.5, the time fluctuation phase offset φ=0.1, and the time variable t=1;

[0199] A j (1) = ((1 + 0.4 exp (- (0.9-0.8) 2 / (2×0.1 2 )))·(1+0.5·exp(-(0.85-0.8) 2 / (2×0.1 2 ))))·(1+0.2·sin(π / 2·(0.82) / 0.8))·(1+0.1·cos(0.5·1+0.1))=1.23

[0200] Assuming that the threshold is set to 1.1, since the calculated result 1.23 is greater than the set threshold, it shows that the personalized environmental adaptability has high effectiveness and accuracy, which can ensure that the optimized configuration of the touch interactive interface in different patient monitoring scenarios can effectively improve the operating experience of medical staff. This is because the higher personalized environmental adaptability reflects that the system can intelligently adjust the interface layout in the current monitoring scenario to meet the personalized needs of medical staff without affecting the smoothness and response speed of operation. Specifically, the system can automatically adjust the displayed key parameters and alarm settings according to the monitoring characteristics of different patients (such as the type of heart disease and the severity of the disease) to provide a more personalized operating interface. Through the above steps, the accurate capture and response of user behavior patterns are ensured, the intelligence level and user experience quality of the touch interactive interface are improved, and the adaptability and responsiveness of the entire system are enhanced. Figure 2 A structural diagram of a touch interactive interface design system is provided for an embodiment of the present application, such as Figure 2 As shown, the device comprises:

[0201] A construction module 21 is used to collect user behavior habits, extract key factors that affect the user behavior habits, and construct a user participation evaluation system based on the key factors to analyze the touch frequency and depth in the user behavior habits and generate a behavior habit analysis report; the key factors refer to a set of factors that have a significant impact on the user behavior habits in a specific usage scenario, including visual feedback response time, gesture operation convenience, and ambient lighting;

[0202] The analysis module 22 is used to use the context-aware layout optimization algorithm to extract the user's visual attention distribution and the reaction of the immediate physical environment in the behavior habit analysis report, perform context modeling processing, and use the environment adaptation technology to perform adaptability analysis on the visual clarity and auditory reception parameters in the context modeling processing result to obtain the user's adaptability to the immediate physical environment and generate an environment adaptation interface configuration;

[0203] The learning module 23 is used to use a long-term preference tracking algorithm to continuously record and learn the personalized preference settings in the behavior habit analysis report, input the continuous recording and learning results into the environment adaptation interface configuration, add personalized components to the environment adaptation interface configuration, adopt a progressive interface evolution method, and perform progressive evolution processing on the environment adaptation interface configuration after adding personalized components, so as to gradually introduce an interactive feedback mechanism, add an interactive module to the environment adaptation interface configuration, and generate an optimized layout plan;

[0204] The adjustment module 24 is used to build a learning library according to the user's behavior habits, import the interactive behavior data in the learning library into the interactive prediction module in the interactive module, perform a behavior prediction operation on the interactive behavior data, and adjust the interface elements and response methods of the optimized layout scheme according to the behavior prediction operation results to improve the fluency of human-computer interaction and generate a touch interaction interface optimization strategy.

[0205] Figure 2 The touch interactive interface design system can be executed Figure 1 The implementation principle and technical effect of the touch interactive interface design method described in the embodiment are not repeated here. The specific way in which each module and unit performs operations in the touch interactive interface design system in the above embodiment has been described in detail in the embodiment of the method, and will not be elaborated here.

[0206] In one possible design, Figure 2 A touch interactive interface design system of the embodiment shown can be implemented as a computing device, such as Figure 3 As shown, the computing device may include a storage component 31 and a processing component 32;

[0207] The storage component 31 stores one or more computer instructions, wherein the one or more computer instructions are called and executed by the processing component 32 .

[0208] The processing component 32 is used to: collect user behavior habits, extract key factors that affect the user behavior habits, build a user participation evaluation system based on the key factors, analyze the touch frequency and depth in the user behavior habits, and generate a behavior habit analysis report; the key factors refer to a set of factors that have a significant impact on the user behavior habits in a specific usage scenario, including visual feedback response time, gesture operation convenience and ambient lighting; use a context-aware layout optimization algorithm to extract the user's visual attention distribution and the reaction of the immediate physical environment in the behavior habit analysis report, perform context modeling processing, use environmental adaptation technology to perform adaptive analysis on the visual clarity and auditory reception parameters in the context modeling processing results, so as to obtain the user's adaptability to the immediate physical environment and generate an environmental adaptation interface configuration; use long-term bias A good tracking algorithm is used to continuously record and learn the personalized preference settings in the behavior habit analysis report, input the continuous recording and learning results into the environment adaptation interface configuration, add personalized components to the environment adaptation interface configuration, and use a progressive interface evolution method to progressively evolve the environment adaptation interface configuration after adding personalized components, so as to gradually introduce an interactive feedback mechanism, add an interactive module to the environment adaptation interface configuration, and generate an optimized layout plan; a learning library is constructed according to the user behavior habits, and the interactive behavior data in the learning library is imported into the interactive prediction module in the interactive module, and a behavior prediction operation is performed on the interactive behavior data. The interface elements and response methods in the optimized layout plan are adjusted according to the behavior prediction operation results to improve the fluency of human-computer interaction and generate a touch interaction interface optimization strategy.

[0209] The processing component 32 may include one or more processors to execute computer instructions to complete all or part of the steps in the above method. Of course, the processing component may also be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors or other electronic components to perform the above method.

[0210] The storage component 31 is configured to store various types of data to support operations at the terminal. The storage component can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk.

[0211] Of course, the computing device may also include other components, such as input / output interfaces, display components, communication components, etc.

[0212] The input / output interface provides an interface between the processing component and the peripheral interface module, which may be an output device, an input device, etc.

[0213] The communication component is configured to facilitate, among other things, wired or wireless communications between the computing device and other devices.

[0214] Among them, the computing device can be a physical device or an elastic computing host provided by a cloud computing platform, etc. In this case, the computing device can refer to a cloud server, and the above-mentioned processing components, storage components, etc. can be basic server resources rented or purchased from the cloud computing platform.

[0215] The present application also provides a computer storage medium storing a computer program, wherein the computer program can achieve the above-mentioned Figure 1 A method for designing a touch interaction interface according to the embodiment shown.

[0216] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0217] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0218] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0219] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit it. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for designing a touch interactive interface, characterized in that: include: Collect user behavior habits, extract key factors that affect the user behavior habits, and build a user engagement evaluation system based on the key factors to analyze the touch frequency and depth in the user behavior habits and generate a behavior habit analysis report; the key factors refer to a set of factors that have a significant impact on the user behavior habits in a specific usage scenario, including visual feedback response time, gesture operation convenience, and ambient lighting; Using the context-aware layout optimization algorithm, extract the user's visual attention distribution and the reaction of the immediate physical environment in the behavior habit analysis report, perform context modeling processing, and use environmental adaptation technology to perform adaptability analysis on the visual clarity and auditory reception parameters in the context modeling processing results to obtain the user's adaptability to the immediate physical environment and generate an environmental adaptation interface configuration; Using a long-term preference tracking algorithm, the personalized preference settings in the behavior habit analysis report are continuously recorded and learned, the continuous recording and learning results are input into the environment adaptation interface configuration, personalized components are added to the environment adaptation interface configuration, and a progressive interface evolution method is used to progressively evolve the environment adaptation interface configuration after the personalized components are added, so as to gradually introduce an interactive feedback mechanism, add interactive modules to the environment adaptation interface configuration, and generate an optimized layout plan; A learning library is constructed according to the user behavior habits, the interactive behavior data in the learning library is imported into the interactive prediction module in the interactive module, a behavior prediction operation is performed on the interactive behavior data, and the interface elements and response methods in the optimized layout scheme are adjusted according to the behavior prediction operation results to improve the fluency of human-computer interaction and generate a touch interactive interface optimization strategy.

2. The method according to claim 1, characterized in that The method uses the context-aware layout optimization algorithm to extract the user's visual attention distribution and the reaction of the immediate physical environment in the behavior habit analysis report, performs context modeling processing, and uses the environmental adaptation technology to perform adaptability analysis on the visual clarity and auditory reception parameters in the context modeling processing result to obtain the user's adaptability to the immediate physical environment and generate an environmental adaptation interface configuration, including: Extracting the visual focus area and operation frequency of the user in different usage scenarios based on the behavior habit analysis report, performing statistical analysis on the visual focus area and operation frequency to obtain parameter distribution characteristics, and generating a usage scenario parameter distribution map; Use the context-aware layout optimization algorithm to extract the user's visual attention distribution and the immediate physical environment response in the behavior habit analysis report, and combine the parameter distribution in the usage scenario parameter distribution map as basic input to perform context modeling processing and generate a multi-dimensional context model; Adopting environmental adaptation technology, performing adaptability analysis on visual clarity and auditory reception parameters in the multidimensional situation model to obtain the user's adaptability to the instant physical environment, optimizing the visual clarity and auditory reception parameters according to the adaptability, and generating an environmental adaptation processing result; A virtual user experience environment is created, and adaptability parameters in the environment adaptation processing result are imported to verify the adaptability level of the environment adaptation processing result, and an environment adaptation interface configuration is generated.

3. The method according to claim 2, characterized in that Based on the usage scenario parameter distribution map, the context-aware layout optimization algorithm is used to extract the user's visual attention distribution and the immediate physical environment reaction in the behavior habit analysis report, and the parameter distribution in the usage scenario parameter distribution map is combined as the basic input to perform context modeling processing to generate a multi-dimensional context model, including: Extracting usage scenario parameter distribution features based on the usage scenario parameter distribution map, performing cluster analysis on the usage scenario parameter distribution features to identify typical behavior patterns of users, and generating a situational behavior pattern library; Using the context-aware layout optimization algorithm, extract the user's visual attention distribution and the immediate physical environment reaction in the behavior habit analysis report, and match them one by one with the behavior patterns in different situations in the context behavior pattern library to generate a context matching result; Conduct multiple rounds of iterative testing on different context matching results, record the results of multiple rounds of iterative testing to identify potential improvement points in the one-by-one matching process, and generate optimized context matching results; The context matching degree of the user behavior characteristics in the optimized context matching result is extracted, and the context matching degree is retrospectively analyzed. The retrospective analysis result is combined with the parameter distribution in the usage scenario parameter distribution map as a basic input to perform context modeling processing to generate a multidimensional context model.

4. The method according to claim 2, characterized in that: Based on the multi-dimensional situation model, the environment adaptation technology is used to perform adaptability analysis on the visual clarity and auditory reception parameters in the multi-dimensional situation model to obtain the user's adaptability to the instant physical environment, and the visual clarity and auditory reception parameters are optimized according to the adaptability to generate an environment adaptation processing result, including: Performing feature extraction processing based on the multidimensional situation model to obtain a multidimensional environment adaptation feature vector, performing standardization processing on the multidimensional environment adaptation feature vector, and generating an environment adaptation feature matrix; Adopting environmental adaptation technology, performing adaptability analysis on visual clarity and auditory reception parameters in the multidimensional situation model, vectorizing the adaptability analysis results and inputting them into the environmental adaptation feature matrix to generate an optimized environmental adaptation feature matrix; By using a correlation analysis method, the correlation of all environmental adaptation feature vectors in the optimized environmental adaptation feature matrix is ​​analyzed to generate a feature vector correlation table; Based on the correlation analysis results in the feature vector association table, feature fusion processing is performed on the highly correlated environment adaptation feature vectors to reduce the dimensional redundancy of the optimized environment adaptation feature matrix and generate an environment adaptation processing result.

5. The method according to claim 1, characterized in that The long-term preference tracking algorithm is used to continuously record and learn the personalized preference settings in the behavior habit analysis report, and the continuous recording and learning results are input into the environment adaptation interface configuration, and personalized components are added to the environment adaptation interface configuration. A progressive interface evolution method is used to perform a progressive evolution process on the environment adaptation interface configuration after the personalized components are added, so as to gradually introduce an interactive feedback mechanism, add an interactive module to the environment adaptation interface configuration, and generate an optimized layout plan, including: By using a time series analysis method, the operation frequencies of users in different time periods under the environment adaptation interface configuration are counted, high-frequency operation areas and low-frequency operation areas are identified, and a user behavior trajectory diagram is generated; Based on the user behavior trajectory diagram, using a long-term preference tracking algorithm, continuously recording and learning the personalized preference settings in the behavior habit analysis report, inputting the continuous recording and learning results into the environment adaptation interface configuration, adding personalized components to the environment adaptation interface configuration, and generating a user preference configuration; Based on the user preference configuration, a progressive interface evolution method is used to progressively evolve the environment adaptation interface configuration after adding personalized components, so as to gradually introduce an interactive feedback mechanism and generate a progressive evolution interface configuration; Based on the progressively evolving interface configuration, an interactive module is added, and the user participation and module convenience of the interactive module are tested through a scenario simulation test method to generate an optimized layout solution.

6. The method according to claim 5, characterized in that Based on the user behavior trajectory map, the long-term preference tracking algorithm is used to continuously record and learn the personalized preference settings in the behavior habit analysis report, and the continuous recording and learning results are input into the environment adaptation interface configuration, and personalized components are added to the environment adaptation interface configuration to generate a user preference configuration, including: Based on the user behavior trajectory map, identifying the operation habits and path preferences in the user behavior trajectory map through a behavior sequence analysis method, and generating a behavior pattern heat map; Using a long-term preference tracking algorithm, the personalized preference settings in the behavior habit analysis report and the path preference in the behavior pattern heat map are continuously recorded and learned, and the continuous recording and learning results are input into the environment adaptation interface configuration to generate personalized and path preference configurations; Extracting personalized components from the personalized and path preference configurations, and adding the personalized components to the environment adaptation interface configuration to generate a personalized interface configuration; An intelligent fusion mechanism is introduced to integrate the personalized interface configuration, map the personalized interface configuration to the corresponding interface element in the environment adaptation interface configuration, and generate a user preference configuration.

7. The method according to claim 1, characterized in that The step of building a learning library according to the user's behavior habits, importing the interaction behavior data in the learning library into the interaction prediction module in the interactive module, performing a behavior prediction operation on the interaction behavior data, and adjusting the interface elements and response methods of the optimization layout scheme according to the behavior prediction operation results to improve the fluency of human-computer interaction and generate a touch interaction interface optimization strategy, including: Constructing a scene context model, classifying and annotating the behavior patterns of different usage scenarios in the optimized layout scheme, and generating a scene feature map; By introducing a user behavior monitoring module into the optimized layout scheme, the user's operation data during the implementation of the optimized layout scheme is monitored in real time, and the real-time monitoring results are integrated with the scene feature parameters in the scene feature map to generate a multi-dimensional interactive data stream; Building a learning library according to the user behavior habits, synchronizing the interactive behavior data in the learning library with the multi-dimensional interactive data stream, and importing the synchronous processing result into the interactive prediction module in the interactive module to perform a behavior prediction operation and generate an interactive behavior prediction result; The predicted interactive behavior result is used as an adjustment standard to adjust the interface elements and response methods of the optimized layout solution to improve the smoothness of human-computer interaction and generate a touch interactive interface optimization strategy.

8. A touch interactive interface design system, characterized in that: include: A construction module is used to collect user behavior habits, extract key factors that affect the user behavior habits, and construct a user participation evaluation system based on the key factors to analyze the touch frequency and depth in the user behavior habits and generate a behavior habit analysis report; the key factors refer to a set of factors that have a significant impact on the user behavior habits in a specific usage scenario, including visual feedback response time, gesture operation convenience, and ambient lighting; An analysis module, which is used to use a context-aware layout optimization algorithm to extract the user's visual attention distribution and the reaction of the immediate physical environment in the behavior habit analysis report, perform context modeling processing, and use environmental adaptation technology to perform adaptability analysis on the visual clarity and auditory reception parameters in the context modeling processing results to obtain the user's adaptability to the immediate physical environment and generate an environmental adaptation interface configuration; A learning module, for continuously recording and learning the personalized preference settings in the behavior habit analysis report using a long-term preference tracking algorithm, inputting the continuous recording and learning results into the environment adaptation interface configuration, adding personalized components to the environment adaptation interface configuration, using a progressive interface evolution method, and performing progressive evolution processing on the environment adaptation interface configuration after adding the personalized components, so as to gradually introduce an interactive feedback mechanism, add an interactive module to the environment adaptation interface configuration, and generate an optimized layout plan; The adjustment module is used to build a learning library according to the user's behavior habits, import the interactive behavior data in the learning library into the interactive prediction module in the interactive module, perform behavior prediction operations on the interactive behavior data, and adjust the interface elements and response methods of the optimized layout scheme according to the behavior prediction operation results to improve the fluency of human-computer interaction and generate a touch interaction interface optimization strategy.

9. A computing device, characterized in that It comprises a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are used to be called and executed by the processing component to implement a touch interactive interface design method as described in any one of claims 1 to 7.

10. A computer storage medium, characterized in that: A computer program is stored, and when the computer program is executed by a computer, a touch interactive interface design method according to any one of claims 1 to 7 is implemented.

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