Display screen human-computer interaction interface optimization method and system based on virtual reality

Abstract

The invention is suitable for the technical field of virtual reality interaction management, and provides a display screen human-computer interaction interface optimization method and system based on virtual reality, and the method comprises the steps: screening out a specific option related to a relieving characteristic from a function panel when a user calls out the function panel in a virtual reality equipment interface, and storing the selected specific option in the virtual reality equipment interface; and acquiring an initial priority selection weight of the specific option, and acquiring a historical operation record of the wearable device of the user at the same time. By combining the physiological data, the emotional fluctuation and the pressure state of the user, the demand of the user in the current state is intelligently predicted, so that the display sequence of the menu of the virtual reality equipment is optimized. Particularly, by analyzing user features in historical operation records and combining data such as real-time heart rate and electrodermal response, the system can predict possible selection of the user after menu calling within a period of time after historical moments by observing physiological feature changes of the user.

Description

Technical Field

[0001] The present invention belongs to the technical field of virtual reality interaction management, and in particular relates to a method and system for optimizing a display screen human-computer interaction interface based on virtual reality. Background Art

[0002] With the rapid development of virtual reality technology, virtual reality devices are widely used in human-computer interaction, especially in the fields of health management, sports, and entertainment. Existing virtual reality interaction systems generally rely on manual user input or preset interaction modes to present interfaces and functional options. In these systems, users make selections through interface menus, and the system usually displays functional options based on static rules, rarely dynamically adjusting the displayed content based on the user's real-time physiological or emotional state. These existing technologies are often unable to flexibly adapt to user needs. In particular, when the user's physiological or emotional state changes, the interactive interface does not respond intelligently or promptly, resulting in an unsatisfactory user experience.

[0003] Existing virtual reality systems primarily rely on fixed patterns or manual input to adjust the interactive interface, lacking the ability to dynamically optimize menu presentation based on the user's real-time physiological data and emotional state. For example, the systems often fail to consider physiological data such as the user's current stress level, heart rate, or galvanic skin response, resulting in an inability to intelligently adjust the priority of functional options. Furthermore, existing technologies do not effectively utilize historical data to predict user needs, and are unable to proactively adjust menu item priorities by analyzing physiological trends. This often forces users to manually select items or wait for the system to respond. Summary of the Invention

[0004] The purpose of the present invention is to provide a method and system for optimizing the human-computer interaction interface of a display screen based on virtual reality, aiming to solve the problems raised in the background technology.

[0005] The present invention is implemented as follows: a method for optimizing a display screen human-computer interaction interface based on virtual reality, the method comprising:

[0006] When the user calls up a function panel in the virtual reality device interface, specific options related to soothing characteristics are filtered out from the function panel, and an initial priority weight of the specific option is obtained, and a historical operation record of the user's wearable device is obtained;

[0007] Parse the historical operation records to find several historical comparison moments that match the user's current motion state, and obtain local operation records within a predetermined time period after each historical comparison moment;

[0008] Analyze each local operation record to determine the user's average heart rate within a predetermined time period, and generate a first correction factor based on a plurality of temporal variation patterns of the average heart rates;

[0009] Obtaining the user's current galvanic skin response data, determining the standard galvanic skin response data of the user when the user is in a stable emotional state, quantifying the difference between the user's current galvanic skin response data and the standard galvanic skin response data, and generating a second correction factor;

[0010] The first correction factor and the second correction factor are combined to make a comprehensive adjustment to the initial priority weight of a specific option.

[0011] As a further limitation of the technical solution of the embodiment of the present invention, the steps of parsing the historical operation records, finding a number of historical comparison moments that match the user's current motion state, and obtaining local operation records within a predetermined time period after each historical comparison moment include:

[0012] Obtain the user's current heart rate, exercise duration, and average exercise intensity from the start of the exercise to the current moment, and based on these three factors, search historical operation records to locate several historical comparison moments that match these three factors;

[0013] A local operation record within a predetermined time period after each historical comparison moment is selected from the historical operation record.

[0014] As a further limitation of the technical solution of the embodiment of the present invention, the steps of parsing each local operation record, determining the average heart rate of the user within a predetermined time period, and generating a first correction factor based on a plurality of temporal variations of the average heart rates include:

[0015] Analyze each local operation record and calculate the user's average heart rate within a predetermined time period;

[0016] Correlating the average heart rate of several predetermined time periods with the corresponding historical control moments and plotting them into a time change curve;

[0017] The average slope of the time variation curve is calculated and used as a first correction factor generated based on multiple time variation patterns of average heart rates.

[0018] As a further limitation of the technical solution of the embodiment of the present invention, the steps of obtaining the user's current galvanic skin response data, determining the standard galvanic skin response data of the user when the user is in a stable emotional state, quantifying the difference between the user's current galvanic skin response data and the standard galvanic skin response data, and generating a second correction factor include:

[0019] Obtain the user's current skin electrical response data and evaluate the user's physical signs, gender and age;

[0020] Recalling a preset reference model, taking the user's physical signs, gender, age, current heart rate, exercise duration, and average exercise intensity from the start of exercise to the current moment as input, to determine the standard skin electrical response data when the user is in a stable emotional state in this state;

[0021] The difference between the user's current galvanic skin response data and the standard galvanic skin response data is quantified, and a second correction factor is generated.

[0022] As a further limitation of the technical solution of the embodiment of the present invention, the reference model refers to a pre-set standardized model based on the user's physical signs, gender, age, current heart rate, exercise duration and average exercise intensity. The reference model is used to generate standard skin electrical response data for different types of users when they are in a stable emotional state in different exercise situations.

[0023] As a further limitation of the technical solution of the embodiment of the present invention, the step of comprehensively adjusting the initial priority weight of the specific option in combination with the first correction factor and the second correction factor includes:

[0024] Retrieving the priority weight adjustment formula, and comprehensively adjusting the initial priority weight of the specific option in combination with the first correction factor and the second correction factor to obtain the optimized priority weight;

[0025] The optimized priority selection weights are applied to the display and selection priorities of the human-computer interaction interface based on virtual reality display screens.

[0026] As a further limitation of the technical solution of the embodiment of the present invention, the priority weight adjustment formula is: , where W opt Refers to the optimized priority weight, W init Refers to the initial preference weight, S avg Refers to the first correction factor, that is, the average slope of the time-varying curve, K1 refers to the adjustment coefficient corresponding to the first correction factor, D refers to the second correction factor, that is, the difference between the current galvanic skin response data and the standard galvanic skin response data, and K2 refers to the adjustment coefficient corresponding to the second correction factor;

[0027] In the priority weight adjustment formula, , where P current Refers to the current skin electrical response data, P benchmark Refers to standard galvanic skin response data.

[0028] A display screen human-computer interaction interface optimization system based on virtual reality, the system includes a data acquisition module, a comparison time positioning module, a first correction factor determination module, a second correction factor determination module and a selection weight adjustment module, wherein:

[0029] a data acquisition module, configured to, when a user calls up a function panel in a virtual reality device interface, filter out specific options related to soothing characteristics from the function panel, obtain an initial priority weight for the specific options, and simultaneously obtain historical operation records of the user's wearable device;

[0030] A comparison moment positioning module is used to analyze historical operation records, find several historical comparison moments that match the user's current motion state, and obtain local operation records within a predetermined time period after each historical comparison moment;

[0031] a first correction factor determination module, configured to analyze each local operation record, determine the user's average heart rate within a predetermined time period, and generate a first correction factor based on a plurality of temporal variation patterns of the average heart rates;

[0032] a second correction factor determination module, configured to obtain the user's current galvanic skin response data, determine the user's standard galvanic skin response data when the user is in a stable emotional state, quantify the difference between the user's current galvanic skin response data and the standard galvanic skin response data, and generate a second correction factor;

[0033] The selection weight adjustment module is used to comprehensively adjust the initial priority selection weight of a specific option by combining the first correction factor and the second correction factor.

[0034] As a further limitation of the technical solution of the embodiment of the present invention, the comparison time positioning module specifically includes:

[0035] A historical comparison moment positioning unit is used to obtain the user's current heart rate, exercise duration, and average exercise intensity from the start of exercise to the current moment, and based on these three factors, search the historical operation records to locate several historical comparison moments that match these three factors;

[0036] The local operation record selection unit is used to select local operation records within a predetermined time period after each historical comparison moment from the historical operation records.

[0037] As a further limitation of the technical solution of the embodiment of the present invention, the first correction factor determination module specifically includes:

[0038] an average heart rate calculation unit, configured to analyze each local operation record and calculate the user's average heart rate within a predetermined time period;

[0039] a time variation curve drawing unit, configured to associate the average heart rates of a plurality of predetermined time periods with corresponding historical control moments and draw the result into a time variation curve;

[0040] The first correction factor generating unit is used to calculate the average slope of the time variation curve and use it as the first correction factor generated based on multiple time variation rules of average heart rates.

[0041] Compared with the prior art, the present invention has the following beneficial effects:

[0042] This invention intelligently predicts the user's current needs by combining physiological data, emotional fluctuations, and stress levels, thereby optimizing the order in which menus are displayed in virtual reality devices. Specifically, by analyzing user characteristics from historical operation records and combining them with real-time heart rate, galvanic skin response data, and other data, the system can predict the user's likely choices after summoning the menu by observing changes in physiological characteristics over a period of time after the historical moment.

[0043] For example, if a user's heart rate gradually decreases and their galvanic skin response indicates reduced stress, the system predicts that the user may need relaxation-related options. Conversely, if their heart rate increases or their galvanic skin response increases, the system prioritizes options related to exercise or stress management. Compared to the static display methods used in existing technologies, this prediction-based dynamic adjustment mechanism can more accurately meet users' real-time needs, avoid information overload, and enhance the user experience. It has broad application potential in areas such as health management, exercise, and emotional regulation. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 A flowchart of a method provided by an embodiment of the present invention;

[0045] Figure 2 A flowchart of locating several historical comparison moments that match the user's current motion state in the method provided by an embodiment of the present invention;

[0046] Figure 3 A flow chart of generating a first correction factor in the method provided in an embodiment of the present invention;

[0047] Figure 4 A flow chart of generating a second correction factor in the method provided in an embodiment of the present invention;

[0048] Figure 5 A flow chart of comprehensively adjusting the initial priority selection weight of a specific option in the method provided in an embodiment of the present invention;

[0049] Figure 6 An application architecture diagram of the system provided by an embodiment of the present invention;

[0050] Figure 7 A structural block diagram of a comparison time positioning module in a system provided by an embodiment of the present invention;

[0051] Figure 8This is a structural block diagram of a first correction factor determination module in a system provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0052] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0053] Figure 1 A flow chart of a method provided by an embodiment of the present invention is shown.

[0054] Specifically, a method for optimizing a display screen human-computer interaction interface based on virtual reality comprises the following steps:

[0055] In step S100, when the user calls up the function panel in the virtual reality device interface, specific options related to soothing characteristics are filtered out from the function panel, and the initial priority weight of the specific options is obtained, and the historical operation records of the user's wearable device are obtained.

[0056] In embodiments of the present invention, the function panel in a virtual reality device interface refers to an interactive interface displayed by the virtual reality system, which contains multiple different types of options that the user can interact with as needed. These options are typically divided into multiple categories, including relaxation, exercise intensity adjustment, and task execution. Existing technologies allow for background adjustment of the options in the function panel, so that the options in the function panel are dynamically adjusted based on the user's current state or needs, prioritizing the functions that best match the user's needs.

[0057] "Specific options related to soothing features" specifically refer to features designed to help users relax or relieve stress, such as deep breathing exercises, meditation guidance, soothing music, stretching exercises, etc. However, in existing technologies, these options are usually displayed statically, and the display priority is not intelligently adjusted according to the user's real-time physiological state or emotional changes. Most current systems rely on preset rules or a fixed display order, and fail to dynamically prioritize these relaxation-related options based on user needs (such as when heart rate increases or stress increases). As a result, users may not get timely soothing features when they need them most.

[0058] "Initial priority weight" refers to the initial display priority of each option in the function panel, which is usually set by the system according to preset rules and the user's historical usage data. This weight not only affects the order of the options, but also involves the prominence of the options. For example, if an option is located above the panel or closer to the user's visual range and is easier to select, its initial priority weight is higher. In addition, ease of selection also includes placing options closer to the user's hand, making these options more convenient and easier to select during operation, further improving user interaction efficiency.

[0059] The present invention is applicable to users who have wearable devices. Wearable devices, such as smartwatches and fitness trackers, can collect physiological data (such as heart rate, galvanic skin response, and exercise intensity) from users and connect to virtual reality systems to provide real-time data support.

[0060] Obtaining the historical operation records of the user's wearable device is usually achieved by synchronizing or interfacing with the device. Historical operation records include the user's physiological data (such as heart rate, exercise duration, exercise intensity, etc.), and the user's operation behavior (such as the history of function selection, the frequency of option use, etc.). These records help the system understand the user's behavior patterns and needs in different situations. According to the claims, historical operation records should at least include the user's physiological state data (such as average heart rate, skin electrical response, exercise intensity, etc.) and corresponding operation history (such as selected function items, operation duration, frequency, etc.) when performing different types of exercise. This information will help the system analyze and adjust the display order of menu items to better adapt to the user's real-time needs.

[0061] Furthermore, the virtual reality-based display screen human-computer interaction interface optimization method further includes the following steps:

[0062] Step S200 , parsing the historical operation records, finding a number of historical comparison moments that match the user's current motion state, and obtaining local operation records within a predetermined time period after each historical comparison moment.

[0063] Specifically, Figure 2 A flow chart is shown for locating several historical comparison moments that are consistent with the user's current motion state.

[0064] The process of parsing the historical operation records, finding several historical comparison moments that match the user's current motion state, and obtaining local operation records within a predetermined time period after each historical comparison moment specifically includes the following steps:

[0065] Step S201: Obtain the user's current heart rate, exercise duration, and average exercise intensity from the start of exercise to the current moment, and search historical operation records based on these three factors to locate several historical comparison moments that match these three factors;

[0066] Step S202 : selecting local operation records within a predetermined time period after each historical comparison moment from the historical operation records.

[0067] In an embodiment of the present invention, to implement the step of "searching in historical operation records based on these three factors, and locating several historical control moments that match these three factors", it is first necessary to obtain the user's current heart rate, exercise duration, and average exercise intensity from the start of exercise to the current moment. These three factors are used to describe the user's current physiological state and exercise intensity. By comparing these three factors with the data in the historical operation records, the system can find historical control moments that match the current physiological state and exercise intensity. Specifically, based on the three indicators of heart rate, exercise duration and average exercise intensity, the system filters out moments in historical records that are close to or consistent with these indicator values, thereby obtaining historical control moments. These historical control moments are helpful in analyzing the user's behavior patterns and needs in similar exercise states, and provide a reference for subsequent operation record analysis.

[0068] The significance of locating historical comparison moments lies in that by comparing the user's current physiological state with similar situations in historical records, the system can more accurately identify the user's needs and behaviors under similar conditions. Through this process, the system can provide users with more personalized suggestions and feedback. For example, if the user's current heart rate is high, the system can predict the functional options the user may need by matching historical operation data under similar heart rates and prioritize displaying relevant options.

[0069] The predetermined time period is typically determined based on historically recorded typical periods and the user's exercise duration. It can be a fixed duration, such as 2, 5, or 10 minutes, or dynamically adjusted based on the user's historical behavior. The predetermined time period takes into account physiological changes during exercise, ensuring the system can capture changes in user needs and make appropriate optimization adjustments within an appropriate timeframe.

[0070] Furthermore, the virtual reality-based display screen human-computer interaction interface optimization method further includes the following steps:

[0071] Step S300 , analyzing each local operation record, determining the average heart rate of the user in a predetermined time period, and generating a first correction factor based on time variation patterns of several average heart rates.

[0072] Specifically, Figure 3 A flow chart for generating a first correction factor is shown.

[0073] The steps of analyzing each local operation record, determining the user's average heart rate within a predetermined time period, and generating a first correction factor based on a plurality of temporal variation patterns of the average heart rates specifically include the following steps:

[0074] Step S301: parse each local operation record and calculate the user's average heart rate within a predetermined time period;

[0075] Step S302, associating the average heart rates of several predetermined time periods with the corresponding historical reference moments and plotting them into a time variation curve;

[0076] Step S303: Calculate the average slope of the time variation curve and use it as a first correction factor generated based on multiple time variation patterns of average heart rates.

[0077] In this embodiment of the present invention, each local operation record is parsed to calculate the user's average heart rate over a predetermined time period. This operation is based on a preset period of time after the historical reference time. By extracting heart rate data from the historical operation records for that period and calculating the average heart rate for that period, the system can obtain the user's physiological status during that period. The preset time period is a period of time after the historical reference time, typically set based on the user's typical movement characteristics or behavioral patterns, and is intended to analyze the user's physiological responses in similar situations.

[0078] The X-axis of the time-varying curve represents time, starting from the preset period after the historical reference moment; the Y-axis represents average heart rate, the user's average heart rate during that period. By associating average heart rate data from multiple preset time periods with the corresponding historical reference moments and plotting them as a time-varying curve, the system can clearly observe how the user's physiological state changes over time, helping to assess their current exercise status.

[0079] The essence of using the average slope of the time-varying curve as the first correction factor generated based on the temporal variation patterns of multiple average heart rates lies in predicting the user's subsequent behavior. Based on the changing trends of heart rate, especially the rise or fall of heart rate, the system can predict the user's possible needs and choices. For example, if the heart rate continues to decrease, it indicates that the user may be tending to a resting state, and the system can predict that the user is more likely to choose to relax or restore related functions at this time; if the heart rate continues to rise, it indicates that the user may be willing to maintain a higher intensity of exercise, and the system can predict that the user may choose to continue exercising or increase the intensity.

[0080] The core of this predictive effect lies in the fact that by leveraging historical data on physiological changes in users, the system can dynamically infer the current user's needs and preferences under similar physiological conditions. This ability to predict current choices based on historical physiological performance not only enhances the system's intelligence but also greatly enhances the personalization and accuracy of interactions.

[0081] The benefit is that, based on the predicted correction factor, the system can show the user the most likely function options in advance, thereby improving the user experience, avoiding information overload, and ensuring that the user receives timely and accurate feedback during exercise or relaxation.

[0082] Furthermore, the virtual reality-based display screen human-computer interaction interface optimization method further includes the following steps:

[0083] Step S400: obtaining the user's current galvanic skin response data, determining the standard galvanic skin response data when the user is in a stable emotional state, quantifying the difference between the user's current galvanic skin response data and the standard galvanic skin response data, and generating a second correction factor.

[0084] Specifically, Figure 4 A flow chart for generating a second correction factor is shown.

[0085] The steps of obtaining the user's current galvanic skin response data, determining the standard galvanic skin response data when the user is in a stable emotional state, quantifying the difference between the user's current galvanic skin response data and the standard galvanic skin response data, and generating the second correction factor specifically include the following steps:

[0086] Step S401, obtaining the user's current galvanic skin response data and evaluating the user's physical signs, gender and age;

[0087] Step S402: Retrieving a preset reference model, taking the user's physical characteristics, gender, age, current heart rate, exercise duration, and average exercise intensity from the start of exercise to the current moment as input, to determine standard galvanic skin response data when the user is in a stable emotional state;

[0088] Step S403: quantify the difference between the user's current galvanic skin response data and the standard galvanic skin response data, and generate a second correction factor.

[0089] The reference model refers to a pre-set standardized model based on the user's physical signs, gender, age, current heart rate, exercise duration and average exercise intensity. The reference model is used to generate standard skin electrical response data for different types of users when they are in a stable emotional state in different exercise situations.

[0090] In this embodiment of the present invention, the reference model is derived from the analysis of extensive historical data and user characteristics. By collecting data on various users, including physical signs, gender, age, heart rate, exercise duration, and exercise intensity, and combining these data with their physiological responses (such as galvanic skin response), a standardized model based on these characteristics can be established using statistical and machine learning methods. This model is capable of predicting the galvanic skin responses of different users in various exercise scenarios, particularly when the user's emotions are stable.

[0091] Similar standardized models are already being used in some emotion monitoring and health management systems. In particular, psychological research has used big data to build standardized models of individual physiological responses, thereby predicting users' mood swings and physiological needs. Similar methods are already used in some wearable devices and smart health systems to analyze the relationship between physiological signals (such as galvanic skin response) and other physiological parameters. By comparing these with large amounts of user data, existing technologies are now able to provide personalized emotional state assessments and provide more appropriate function recommendations for users.

[0092] "Standardized galvanic skin response data under stable emotional conditions" refers to data collected when a user's emotions are stable or neutral, with no significant fluctuations. This data typically reflects the user's physiological responses when they are quiet, resting, or undisturbed by external factors. Standardized data is collected over a long period of time, reflecting the baseline galvanic skin response values ​​for different users without external emotional or physiological stress. This standard helps determine whether a user is experiencing emotional fluctuations in real-world applications, thereby adjusting system responses.

[0093] Quantifying the difference between a user's current galvanic skin response data and the standard galvanic skin response data can help the system determine the user's current emotional state or physiological load. For example, if the current galvanic skin response is significantly higher than the standard data, it may indicate that the user is in a state of high emotional tension or stress, and the system needs to make corresponding adjustments (such as prioritizing relaxation functions). If the current data is close to the standard data, it indicates that the user is relatively stable, and the system may choose to display other relevant functions.

[0094] The benefit of generating a second correction factor is that it can dynamically adjust the system's response, making the function panel display more intelligent and personalized. This correction factor quantifies the magnitude and speed of emotional fluctuations, helping the system make more accurate decisions. For example, if the system detects a user experiencing significant emotional fluctuations, the second correction factor can increase the priority of relaxation-related options; conversely, if the user is emotionally stable, the system can display other options that better match their current state. This ensures that the system's response is more aligned with the user's immediate needs, optimizing the interactive experience.

[0095] Furthermore, the virtual reality-based display screen human-computer interaction interface optimization method further includes the following steps:

[0096] Step S500 : Combining the first correction factor and the second correction factor, comprehensively adjust the initial priority selection weight of the specific option.

[0097] Specifically, Figure 5 A flow chart showing the comprehensive adjustment of initial preference weights for specific options.

[0098] The comprehensive adjustment of the initial preference weight of a specific option by combining the first correction factor and the second correction factor specifically includes the following steps:

[0099] Step S501: Retrieve the priority weight adjustment formula, and comprehensively adjust the initial priority weight of the specific option by combining the first correction factor and the second correction factor to obtain the optimized priority weight;

[0100] Step S502: Apply the optimized priority selection weights to the display and selection priorities of the human-computer interaction interface based on the virtual reality display screen.

[0101] The preferred weight adjustment formula is: , where W opt Refers to the optimized priority weight, W init Refers to the initial preference weight, S avg It refers to the first correction factor, that is, the average slope of the time-varying curve, K1 refers to the adjustment coefficient corresponding to the first correction factor, D refers to the second correction factor, that is, the difference between the current galvanic skin response data and the standard galvanic skin response data, and K2 refers to the adjustment coefficient corresponding to the second correction factor.

[0102] In the priority weight adjustment formula, , where P current Refers to the current skin electrical response data, P benchmark Refers to standard galvanic skin response data.

[0103] In an embodiment of the present invention, the initial priority weight of a specific option is comprehensively adjusted in combination with the first correction factor and the second correction factor in order to more comprehensively consider the physiological and emotional state of the user, thereby dynamically optimizing the display of functional options. The first correction factor is based on the variation pattern of the user's average heart rate, reflecting the user's physiological load and exercise intensity, while the second correction factor is based on the difference in the user's skin electrical response data, reflecting the user's emotional fluctuations. By combining these two factors, the system can more accurately judge the user's current needs. The linkage effect of the two is that the first correction factor reflects the user's exercise state, while the second correction factor focuses on emotional fluctuations. The two complement each other and jointly optimize the menu display priority, making the system more intelligent and flexible, and able to adjust functional options according to different physiological and emotional changes.

[0104] Through comprehensive adjustments, the system can optimize menu display in real time based on the user's heart rate and mood swings. For example, if a user's heart rate is high during exercise and their galvanic skin response indicates that they are nervous, the system will prioritize exercise-related options. If their heart rate gradually decreases and their galvanic skin response indicates that they are beginning to relax, the system can increase the priority of relaxation or recovery-related options. This linkage effect can enhance the user experience, make menu options more in line with the user's current needs, avoid information overload, and ensure that users receive the most appropriate recommendations at each stage.

[0105] The formula provided is merely a simple and intuitive application to help understand how to dynamically adjust preference weights based on a user's physiological and emotional state. Of course, in addition to the current calculation method, more complex methods can be employed, such as using machine learning models trained on historical user data to develop a more personalized preference weight adjustment algorithm. With more intelligent models, the system can better predict and optimize user needs, further enhancing its intelligence.

[0106] Suppose a user is exercising in a virtual reality device and summons the menu bar on the VR interface. The system first obtains the user's current physiological data from the wearable device, including heart rate, exercise duration, and average exercise intensity from the start of the exercise to the current moment. This data helps the system determine the user's exercise intensity and, based on these factors, searches the historical operation log for a historical reference moment that matches the current physiological state.

[0107] After acquiring historical data, the system analyzes the user's average heart rate over a predetermined period of time after that moment to generate a first correction factor. If the user's heart rate begins to decrease, indicating a gradual decrease in exercise intensity or a relaxation phase, the system predicts that the user may be more likely to require relaxation-related options. Therefore, the system prioritizes these options based on heart rate trends.

[0108] At the same time, the system will also obtain the user's skin electrical response data and compare it with the standard skin electrical response data preset based on the user's physical characteristics, gender, age, etc. If the user's skin electrical response differs significantly from the standard data, the system will generate a second correction factor, which reflects the user's current emotional fluctuations. If the user's skin electrical response is high, it means that the user may be in a state of tension. The system will predict that the user may need more relaxation or stress relief options, thereby further adjusting the priority of the options in the menu.

[0109] By combining the first and second correction factors, the system arrives at an optimized preference weight. At this point, the system dynamically adjusts the menu bar's display order, prioritizing the options most relevant to the user's current state. For example, if the user's physiological state indicates a need for relaxation, the system prioritizes relaxation-related options.

[0110] Ultimately, users will see an optimized menu bar where the order in which function items are displayed matches their current physiological and emotional needs, thereby improving the personalization and intelligence of the interaction and helping users obtain more accurate and timely feedback during exercise.

[0111] Further, Figure 6 The application architecture diagram of the system provided by the embodiment of the present invention is shown.

[0112] Among them, in another preferred embodiment provided by the present invention, a display screen human-computer interaction interface optimization system based on virtual reality includes:

[0113] The data acquisition module 100 is used to filter out specific options related to soothing characteristics from the function panel in the virtual reality device interface when the user calls up the function panel, obtain the initial priority weight of the specific option, and obtain the historical operation records of the user's wearable device.

[0114] In embodiments of the present invention, the function panel in a virtual reality device interface refers to an interactive interface displayed by the virtual reality system, which contains multiple different types of options that the user can interact with as needed. These options are typically divided into multiple categories, including relaxation, exercise intensity adjustment, and task execution. Existing technologies allow for background adjustment of the options in the function panel, so that the options in the function panel are dynamically adjusted based on the user's current state or needs, prioritizing the functions that best match the user's needs.

[0115] "Specific options related to soothing features" specifically refer to features designed to help users relax or relieve stress, such as deep breathing exercises, meditation guidance, soothing music, stretching exercises, etc. However, in existing technologies, these options are usually displayed statically, and the display priority is not intelligently adjusted according to the user's real-time physiological state or emotional changes. Most current systems rely on preset rules or a fixed display order, and fail to dynamically prioritize these relaxation-related options based on user needs (such as when heart rate increases or stress increases). As a result, users may not get timely soothing features when they need them most.

[0116] "Initial priority weight" refers to the initial display priority of each option in the function panel, which is usually set by the system according to preset rules and the user's historical usage data. This weight not only affects the order of the options, but also involves the prominence of the options. For example, if an option is located above the panel or closer to the user's visual range and is easier to select, its initial priority weight is higher. In addition, ease of selection also includes placing options closer to the user's hand, making these options more convenient and easier to select during operation, further improving user interaction efficiency.

[0117] The present invention is applicable to users who have wearable devices. Wearable devices, such as smartwatches and fitness trackers, can collect physiological data (such as heart rate, galvanic skin response, and exercise intensity) from users and connect to virtual reality systems to provide real-time data support.

[0118] Obtaining the historical operation records of the user's wearable device is usually achieved by synchronizing or interfacing with the device. Historical operation records include the user's physiological data (such as heart rate, exercise duration, exercise intensity, etc.), and the user's operation behavior (such as the history of function selection, the frequency of option use, etc.). These records help the system understand the user's behavior patterns and needs in different situations. According to the claims, historical operation records should at least include the user's physiological state data (such as average heart rate, skin electrical response, exercise intensity, etc.) and corresponding operation history (such as selected function items, operation duration, frequency, etc.) when performing different types of exercise. This information will help the system analyze and adjust the display order of menu items to better adapt to the user's real-time needs.

[0119] Furthermore, the virtual reality-based display screen human-computer interaction interface optimization system also includes:

[0120] The comparison moment positioning module 200 is used to analyze historical operation records, find out several historical comparison moments that match the user's current motion state, and obtain local operation records within a predetermined time period after each historical comparison moment.

[0121] Specifically, Figure 7 FIG. 2 shows a structural block diagram of a time-based positioning module 200 in a system provided by an embodiment of the present invention.

[0122] In a preferred embodiment of the present invention, the comparison time positioning module 200 specifically includes:

[0123] The historical comparison moment locating unit 201 is configured to obtain the user's current heart rate, exercise duration, and average exercise intensity from the start of exercise to the current moment, and search the historical operation records based on these three factors to locate several historical comparison moments that match these three factors;

[0124] The local operation record selection unit 202 is configured to select local operation records within a predetermined time period after each historical comparison moment from the historical operation records.

[0125] In an embodiment of the present invention, to implement the step of "searching in historical operation records based on these three factors, and locating several historical control moments that match these three factors", it is first necessary to obtain the user's current heart rate, exercise duration, and average exercise intensity from the start of exercise to the current moment. These three factors are used to describe the user's current physiological state and exercise intensity. By comparing these three factors with the data in the historical operation records, the system can find historical control moments that match the current physiological state and exercise intensity. Specifically, based on the three indicators of heart rate, exercise duration and average exercise intensity, the system filters out moments in historical records that are close to or consistent with these indicator values, thereby obtaining historical control moments. These historical control moments are helpful in analyzing the user's behavior patterns and needs in similar exercise states, and provide a reference for subsequent operation record analysis.

[0126] The significance of locating historical comparison moments lies in that by comparing the user's current physiological state with similar situations in historical records, the system can more accurately identify the user's needs and behaviors under similar conditions. Through this process, the system can provide users with more personalized suggestions and feedback. For example, if the user's current heart rate is high, the system can predict the functional options the user may need by matching historical operation data under similar heart rates and prioritize displaying relevant options.

[0127] The predetermined time period is typically determined based on historically recorded typical periods and the user's exercise duration. It can be a fixed duration, such as 2, 5, or 10 minutes, or dynamically adjusted based on the user's historical behavior. The predetermined time period takes into account physiological changes during exercise, ensuring the system can capture changes in user needs and make appropriate optimization adjustments within an appropriate timeframe.

[0128] Furthermore, the virtual reality-based display screen human-computer interaction interface optimization system also includes:

[0129] The first correction factor determination module 300 is used to analyze each local operation record, determine the average heart rate of the user in a predetermined time period, and generate a first correction factor based on a plurality of temporal variation patterns of the average heart rates.

[0130] Specifically, Figure 8 FIG. 4 shows a structural block diagram of the first correction factor determination module 300 in the system provided by an embodiment of the present invention.

[0131] In a preferred embodiment of the present invention, the first correction factor determination module 300 specifically includes:

[0132] The average heart rate calculation unit 301 is used to analyze each local operation record and calculate the user's average heart rate within a predetermined time period;

[0133] A time variation curve drawing unit 302 is used to associate the average heart rate of several predetermined time periods with the corresponding historical reference moments and draw them into a time variation curve;

[0134] The first correction factor generating unit 303 is configured to calculate the average slope of the time variation curve and use it as a first correction factor generated based on multiple time variation patterns of average heart rates.

[0135] In this embodiment of the present invention, each local operation record is parsed to calculate the user's average heart rate over a predetermined time period. This operation is based on a preset period of time after the historical reference time. By extracting heart rate data from the historical operation records for that period and calculating the average heart rate for that period, the system can obtain the user's physiological status during that period. The preset time period is a period of time after the historical reference time, typically set based on the user's typical movement characteristics or behavioral patterns, and is intended to analyze the user's physiological responses in similar situations.

[0136] The X-axis of the time-varying curve represents time, starting from the preset period after the historical reference moment; the Y-axis represents average heart rate, the user's average heart rate during that period. By associating average heart rate data from multiple preset time periods with the corresponding historical reference moments and plotting them as a time-varying curve, the system can clearly observe how the user's physiological state changes over time, helping to assess their current exercise status.

[0137] The essence of using the average slope of the time-varying curve as the first correction factor generated based on the temporal variation patterns of multiple average heart rates lies in predicting the user's subsequent behavior. Based on the changing trends of heart rate, especially the rise or fall of heart rate, the system can predict the user's possible needs and choices. For example, if the heart rate continues to decrease, it indicates that the user may be tending to a resting state, and the system can predict that the user is more likely to choose to relax or restore related functions at this time; if the heart rate continues to rise, it indicates that the user may be willing to maintain a higher intensity of exercise, and the system can predict that the user may choose to continue exercising or increase the intensity.

[0138] The core of this predictive effect lies in the fact that by leveraging historical data on physiological changes in users, the system can dynamically infer the current user's needs and preferences under similar physiological conditions. This ability to predict current choices based on historical physiological performance not only enhances the system's intelligence but also greatly enhances the personalization and accuracy of interactions.

[0139] The benefit is that, based on the predicted correction factor, the system can show the user the most likely function options in advance, thereby improving the user experience, avoiding information overload, and ensuring that the user receives timely and accurate feedback during exercise or relaxation.

[0140] Furthermore, the virtual reality-based display screen human-computer interaction interface optimization system also includes:

[0141] The second correction factor determination module 400 is used to obtain the user's current skin electrical response data, determine the standard skin electrical response data of the user when the user is in a stable emotional state, quantify the difference between the user's current skin electrical response data and the standard skin electrical response data, and generate a second correction factor.

[0142] In this embodiment of the present invention, the reference model is derived from the analysis of extensive historical data and user characteristics. By collecting data on various users, including physical signs, gender, age, heart rate, exercise duration, and exercise intensity, and combining these data with their physiological responses (such as galvanic skin response), a standardized model based on these characteristics can be established using statistical and machine learning methods. This model is capable of predicting the galvanic skin responses of different users in various exercise scenarios, particularly when the user's emotions are stable.

[0143] Similar standardized models are already being used in some emotion monitoring and health management systems. In particular, psychological research has used big data to build standardized models of individual physiological responses, thereby predicting users' mood swings and physiological needs. Similar methods are already used in some wearable devices and smart health systems to analyze the relationship between physiological signals (such as galvanic skin response) and other physiological parameters. By comparing these with large amounts of user data, existing technologies are now able to provide personalized emotional state assessments and provide more appropriate function recommendations for users.

[0144] "Standardized galvanic skin response data under stable emotional conditions" refers to data collected when a user's emotions are stable or neutral, with no significant fluctuations. This data typically reflects the user's physiological responses when they are quiet, resting, or undisturbed by external factors. Standardized data is collected over a long period of time, reflecting the baseline galvanic skin response values ​​for different users without external emotional or physiological stress. This standard helps determine whether a user is experiencing emotional fluctuations in real-world applications, thereby adjusting system responses.

[0145] Quantifying the difference between a user's current galvanic skin response data and the standard galvanic skin response data can help the system determine the user's current emotional state or physiological load. For example, if the current galvanic skin response is significantly higher than the standard data, it may indicate that the user is in a state of high emotional tension or stress, and the system needs to make corresponding adjustments (such as prioritizing relaxation functions). If the current data is close to the standard data, it indicates that the user is relatively stable, and the system may choose to display other relevant functions.

[0146] The benefit of generating a second correction factor is that it can dynamically adjust the system's response, making the function panel display more intelligent and personalized. This correction factor quantifies the magnitude and speed of emotional fluctuations, helping the system make more accurate decisions. For example, if the system detects a user experiencing significant emotional fluctuations, the second correction factor can increase the priority of relaxation-related options; conversely, if the user is emotionally stable, the system can display other options that better match their current state. This ensures that the system's response is more aligned with the user's immediate needs, optimizing the interactive experience.

[0147] Furthermore, the virtual reality-based display screen human-computer interaction interface optimization system also includes:

[0148] The selection weight adjustment module 500 is used to comprehensively adjust the initial priority selection weight of a specific option by combining the first correction factor and the second correction factor.

[0149] In an embodiment of the present invention, the initial priority weight of a specific option is comprehensively adjusted in combination with the first correction factor and the second correction factor in order to more comprehensively consider the physiological and emotional state of the user, thereby dynamically optimizing the display of functional options. The first correction factor is based on the variation pattern of the user's average heart rate, reflecting the user's physiological load and exercise intensity, while the second correction factor is based on the difference in the user's skin electrical response data, reflecting the user's emotional fluctuations. By combining these two factors, the system can more accurately judge the user's current needs. The linkage effect of the two is that the first correction factor reflects the user's exercise state, while the second correction factor focuses on emotional fluctuations. The two complement each other and jointly optimize the menu display priority, making the system more intelligent and flexible, and able to adjust functional options according to different physiological and emotional changes.

[0150] Through comprehensive adjustments, the system can optimize menu display in real time based on the user's heart rate and mood swings. For example, if a user's heart rate is high during exercise and their galvanic skin response indicates that they are nervous, the system will prioritize exercise-related options. If their heart rate gradually decreases and their galvanic skin response indicates that they are beginning to relax, the system can increase the priority of relaxation or recovery-related options. This linkage effect can enhance the user experience, make menu options more in line with the user's current needs, avoid information overload, and ensure that users receive the most appropriate recommendations at each stage.

[0151] The formula provided is merely a simple and intuitive application to help understand how to dynamically adjust preference weights based on a user's physiological and emotional state. Of course, in addition to the current calculation method, more complex methods can be employed, such as using machine learning models trained on historical user data to develop a more personalized preference weight adjustment algorithm. With more intelligent models, the system can better predict and optimize user needs, further enhancing its intelligence.

[0152] Suppose a user is exercising in a virtual reality device and summons the menu bar on the VR interface. The system first obtains the user's current physiological data from the wearable device, including heart rate, exercise duration, and average exercise intensity from the start of the exercise to the current moment. This data helps the system determine the user's exercise intensity and, based on these factors, searches the historical operation log for a historical reference moment that matches the current physiological state.

[0153] After acquiring historical data, the system analyzes the user's average heart rate over a predetermined period of time after that moment to generate a first correction factor. If the user's heart rate begins to decrease, indicating a gradual decrease in exercise intensity or a relaxation phase, the system predicts that the user may be more likely to require relaxation-related options. Therefore, the system prioritizes these options based on heart rate trends.

[0154] At the same time, the system will also obtain the user's skin electrical response data and compare it with the standard skin electrical response data preset based on the user's physical characteristics, gender, age, etc. If the user's skin electrical response differs significantly from the standard data, the system will generate a second correction factor, which reflects the user's current emotional fluctuations. If the user's skin electrical response is high, it means that the user may be in a state of tension. The system will predict that the user may need more relaxation or stress relief options, thereby further adjusting the priority of the options in the menu.

[0155] By combining the first and second correction factors, the system arrives at an optimized preference weight. At this point, the system dynamically adjusts the menu bar's display order, prioritizing the options most relevant to the user's current state. For example, if the user's physiological state indicates a need for relaxation, the system prioritizes relaxation-related options.

[0156] Ultimately, users will see an optimized menu bar where the order in which function items are displayed matches their current physiological and emotional needs, thereby improving the personalization and intelligence of the interaction and helping users obtain more accurate and timely feedback during exercise.

[0157] It should be understood that, although the various steps in the flow chart of each embodiment of the present invention are shown in sequence according to the indication of the arrows, these steps are not necessarily performed in sequence according to the order indicated by the arrows. Unless otherwise specified herein, the execution of these steps is not strictly limited in order, and these steps can be performed in other orders. Moreover, at least a portion of the steps in each embodiment may include a plurality of sub-steps or a plurality of stages, and these sub-steps or stages are not necessarily performed at the same time, but can be performed at different times, and the execution order of these sub-steps or stages is not necessarily performed in sequence, but can be performed in turn or alternately with at least a portion of other steps or sub-steps or stages of other steps.

[0158] Those skilled in the art will appreciate that all or part of the processes in the above-described method embodiments can be implemented by instructing the relevant hardware through a computer program. The program can be stored in a non-volatile computer-readable storage medium. When executed, the program can include the processes of the above-described method embodiments. Any reference to memory, storage, database, or other media used in the various embodiments provided herein may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct RAMbus dynamic RAM (DRDRAM), and RAMbus dynamic RAM (RDRAM).

[0159] The technical features of the above-mentioned embodiments can be combined arbitrarily. In order to make the description concise, not all possible combinations of the technical features in the above-mentioned embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0160] The above-described embodiments merely illustrate several implementations of the present invention, and while their descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that a person skilled in the art would be able to make numerous variations and improvements without departing from the spirit of the present invention, all of which fall within the scope of protection of the present invention. Therefore, the scope of protection of the present invention shall be determined by the appended claims.

[0161] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for optimizing a display screen human-computer interaction interface based on virtual reality, characterized in that: The method comprises: When the user calls up a function panel in the virtual reality device interface, specific options related to soothing characteristics are filtered out from the function panel, and an initial priority weight of the specific option is obtained, and a historical operation record of the user's wearable device is obtained; Parse the historical operation records to find several historical comparison moments that match the user's current motion state, and obtain local operation records within a predetermined time period after each historical comparison moment; Analyze each local operation record to determine the user's average heart rate within a predetermined time period, and generate a first correction factor based on a plurality of temporal variation patterns of the average heart rates; Obtaining the user's current galvanic skin response data, determining the standard galvanic skin response data of the user when the user is in a stable emotional state, quantifying the difference between the user's current galvanic skin response data and the standard galvanic skin response data, and generating a second correction factor; The first correction factor and the second correction factor are combined to make a comprehensive adjustment to the initial priority weight of a specific option.

2. The method for optimizing the display screen human-computer interaction interface based on virtual reality according to claim 1, characterized in that: The steps of parsing the historical operation records, finding a number of historical comparison moments that match the user's current motion state, and obtaining local operation records within a predetermined time period after each historical comparison moment include: Obtain the user's current heart rate, exercise duration, and average exercise intensity from the start of the exercise to the current moment. Based on these three factors, search historical operation records to locate several historical comparison moments that match these three factors. A local operation record within a predetermined time period after each historical comparison moment is selected from the historical operation record.

3. The method for optimizing the display screen human-computer interaction interface based on virtual reality according to claim 2, characterized in that: The steps of analyzing each local operation record, determining the average heart rate of the user within a predetermined time period, and generating a first correction factor based on a plurality of temporal variation patterns of the average heart rates include: Analyze each local operation record and calculate the user's average heart rate within a predetermined time period; Correlating the average heart rate of several predetermined time periods with the corresponding historical control moments and plotting them into a time change curve; The average slope of the time variation curve is calculated and used as a first correction factor generated based on multiple time variation patterns of average heart rates.

4. The method for optimizing the display screen human-computer interaction interface based on virtual reality according to claim 3, characterized in that: The steps of obtaining the user's current galvanic skin response data, determining the standard galvanic skin response data of the user when the user is in a stable emotional state, quantifying the difference between the user's current galvanic skin response data and the standard galvanic skin response data, and generating a second correction factor include: Obtain the user's current skin electrical response data and evaluate the user's physical signs, gender and age; Recalling a preset reference model, taking the user's physical signs, gender, age, current heart rate, exercise duration, and average exercise intensity from the start of exercise to the current moment as input, to determine the standard skin electrical response data when the user is in a stable emotional state in this state; The difference between the user's current galvanic skin response data and the standard galvanic skin response data is quantified, and a second correction factor is generated.

5. The method for optimizing the display screen human-computer interaction interface based on virtual reality according to claim 4, characterized in that: The reference model refers to a pre-set standardized model based on the user's physical signs, gender, age, current heart rate, exercise duration and average exercise intensity. The reference model is used to generate standard skin electrical response data for different types of users when they are in a stable emotional state in different exercise situations.

6. The method for optimizing the display screen human-computer interaction interface based on virtual reality according to claim 5, characterized in that: The steps of comprehensively adjusting the initial preference weight of a specific option by combining the first correction factor and the second correction factor include: Retrieving the priority weight adjustment formula, and comprehensively adjusting the initial priority weight of the specific option in combination with the first correction factor and the second correction factor to obtain the optimized priority weight; The optimized priority selection weights are applied to the display and selection priorities of the human-computer interaction interface based on virtual reality display screens.

7. The method for optimizing the display screen human-computer interaction interface based on virtual reality according to claim 6, characterized in that: The preferred weight adjustment formula is: , where W opt Refers to the optimized priority weight, W init Refers to the initial preference weight, S avg Refers to the first correction factor, that is, the average slope of the time-varying curve, K1 refers to the adjustment coefficient corresponding to the first correction factor, D refers to the second correction factor, that is, the difference between the current galvanic skin response data and the standard galvanic skin response data, and K2 refers to the adjustment coefficient corresponding to the second correction factor; In the priority weight adjustment formula, , where P current Refers to the current skin electrical response data, P benchmark Refers to standard galvanic skin response data.

8. A display screen human-computer interaction interface optimization system based on virtual reality, characterized in that: The system includes a data acquisition module, a comparison time positioning module, a first correction factor determination module, a second correction factor determination module, and a selection weight adjustment module, wherein: a data acquisition module, configured to, when a user calls up a function panel in a virtual reality device interface, filter out specific options related to soothing characteristics from the function panel, obtain an initial priority weight for the specific options, and simultaneously obtain historical operation records of the user's wearable device; A comparison moment positioning module is used to analyze historical operation records, find several historical comparison moments that match the user's current motion state, and obtain local operation records within a predetermined time period after each historical comparison moment; a first correction factor determination module, configured to analyze each local operation record, determine the user's average heart rate within a predetermined time period, and generate a first correction factor based on a plurality of temporal variation patterns of the average heart rates; a second correction factor determination module, configured to obtain the user's current galvanic skin response data, determine the user's standard galvanic skin response data when the user is in a stable emotional state, quantify the difference between the user's current galvanic skin response data and the standard galvanic skin response data, and generate a second correction factor; The selection weight adjustment module is used to comprehensively adjust the initial priority selection weight of a specific option by combining the first correction factor and the second correction factor.

9. The display screen human-computer interaction interface optimization system based on virtual reality according to claim 8, characterized in that: The control time positioning module specifically includes: A historical comparison moment positioning unit is used to obtain the user's current heart rate, exercise duration, and average exercise intensity from the start of exercise to the current moment, and based on these three factors, search the historical operation records to locate several historical comparison moments that match these three factors; The local operation record selection unit is used to select local operation records within a predetermined time period after each historical comparison moment from the historical operation records.

10. The display screen human-computer interaction interface optimization system based on virtual reality according to claim 9, characterized in that: The first correction factor determination module specifically includes: an average heart rate calculation unit, configured to analyze each local operation record and calculate the user's average heart rate within a predetermined time period; a time variation curve drawing unit, configured to associate the average heart rates of a plurality of predetermined time periods with corresponding historical control moments and draw the result into a time variation curve; The first correction factor generating unit is used to calculate the average slope of the time variation curve and use it as the first correction factor generated based on multiple time variation rules of average heart rates.

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