Method and system for identifying key environmental elements influencing experience

Abstract

The embodiment of the present disclosure provides a kind of identification method and system of key environmental elements influencing experience feeling.The identification method includes: collecting the eye movement data of multiple testers, and simultaneously collecting the physiological characteristic data of tester;The eye movement data of each tester is analyzed and processed, and the eye movement index data of each tester is obtained;The ontology experience feeling score of each tester is determined based on physiological characteristic data;Based on the ontology experience feeling score of each tester and the eye movement index data, correlation analysis is carried out to determine the key environmental elements influencing the experience feeling of tester.Because physiological characteristic data objectively reflects the physiological characteristic change of tester's subconscious when observing environmental scene, the ontology experience feeling score obtained based on physiological characteristic data is also relatively objective to represent the feeling of tester to environmental scene, so the data in the method for determining the key environmental elements in the present scheme is more objective, and the identification result is also more accurate.

Description

Technical Field

[0001] This disclosure relates to the field of visual perception evaluation technology, specifically to a method and system for identifying key environmental elements that affect user experience. Background Technology

[0002] Numerous scientific studies and practical engineering practices have demonstrated that the rational layout of environmental elements such as buildings, green vegetation, and roads has a significant impact on the experience of people entering these environments, thereby affecting their level of relaxation and well-being, and ultimately influencing their behavior.

[0003] Based on the aforementioned research, methods for assessing test-taker experience have been proposed in areas such as regional planning layout, architectural design, and interior design. These methods determine whether test-takers have a good experience in specific planning and design scenarios. Furthermore, to enable feedback on planning and design modifications based on test-taker experience, related technologies have proposed methods for identifying key environmental elements based on test-taker experience and visual focus. To determine the environmental elements affecting experience, it is first necessary to determine the test-taker's real-time experience in the corresponding environmental scenario. Existing methods obtain test-takers' subjective evaluations of the environmental scenario through questionnaires and interviews, and then convert these subjective evaluations into corresponding experience scores.

[0004] The aforementioned methods for obtaining experience ratings are based on subjective evaluation. Influenced by the testers' subjectivity and rational, moderate evaluation approach, the corresponding rating results have insufficient objectivity, resulting in poor reliability in identifying key environmental elements. Summary of the Invention

[0005] To address the issue of unreliable identification of key environmental elements based on subjective evaluations obtained through questionnaires and interviews, embodiments of this disclosure provide a method and system for identifying key environmental elements that affect user experience.

[0006] In a first aspect, embodiments of this disclosure provide a method for identifying key environmental elements that affect user experience, including:

[0007] Eye movement data of multiple test subjects were collected while they observed the environmental scene within a preset time period, and physiological characteristic data of the test subjects were also collected within the preset time period. The physiological characteristic data is physiological data that is related to the test subject's experience when observing the environmental scene.

[0008] The eye movement data of each tester were analyzed and processed to obtain eye movement index data corresponding to various environmental elements observed by each tester in the environmental scene described.

[0009] Based on the physiological characteristic data, determine the proprioceptive experience score of each tester when observing the environmental scene;

[0010] Correlation analysis was conducted based on the proprioception scores of each tester and the eye-tracking data corresponding to various scene elements to identify the key environmental elements that affect the tester's experience.

[0011] Optionally, the method further includes: acquiring baseline physiological characteristic data of each test subject in a baseline scenario;

[0012] The step of determining the proprioceptive experience score of each tester when observing the environmental scene based on the physiological characteristic data includes: using the baseline physiological characteristic data of each tester as a reference, determining the proprioceptive experience score when observing the environmental scene based on the corresponding physiological characteristic data.

[0013] Optionally, the physiological characteristic data of the test subject while observing the environmental scene includes:

[0014] The heart rate variability index and / or skin conductance signal index are collected from the test subject while observing the environmental scene, and the heart rate variability index and / or skin conductance signal index are used as the physiological characteristic data.

[0015] Optionally, when the acquired physiological characteristic data includes more than one indicator, determining the ontological experience score of each tester when observing the environmental scene based on the physiological characteristic data includes:

[0016] The corresponding sub-indicators of ontological experience scores were determined based on various physiological characteristic parameters of each tester.

[0017] The ontological experience scores of each tester are weighted and summed to obtain the ontological experience score when observing the described environmental scene.

[0018] Optionally, the method further includes: while collecting eye-tracking data from multiple testers observing the environmental scene within a preset time period, conducting a subjective questionnaire survey on the environmental scene for each tester to obtain evaluation scores for each survey item, wherein each survey item reflects the tester's experience and feelings about the environmental scene.

[0019] Subjective experience scores are obtained based on the evaluation scores of each survey item;

[0020] The process involves performing correlation analysis between each tester's subjective experience score and the eye-tracking index data corresponding to various scene elements to determine the key environmental factors affecting the tester's experience. This includes: performing correlation analysis between each tester's subjective experience score and the subjective experience score and the eye-tracking index data corresponding to various scene elements to determine the key environmental factors affecting the tester's experience.

[0021] Optionally, the correlation analysis is performed between the proprioceptive experience score and the subjective experience score of each tester and the eye-tracking index data corresponding to various scene elements to obtain the corresponding correlation analysis results, including:

[0022] Based on the ontological experience scores and subjective experience scores of each tester, correlation analysis was performed with the eye movement index data corresponding to various scene elements to determine the eye movement index data with correlation, and to determine the importance ranking and importance coefficient of each environmental element under various eye movement index data.

[0023] Using the importance coefficient as a weighting coefficient, the ranking order of each environmental element under various eye-tracking index data is weighted and averaged to determine the overall importance score of each environmental element.

[0024] The key environmental factors that influence the test taker's experience are determined based on the overall importance score.

[0025] Optionally, the eye movement data of each test subject can be analyzed and processed to obtain eye movement index data corresponding to various environmental factors, including:

[0026] Based on the eye-tracking data of each tester, the environmental elements that were focused on at each moment were determined, and the moments when the same environmental element was focused were statistically analyzed to obtain the attention period corresponding to each environmental element.

[0027] Remove the periods of interest whose duration is less than the lower threshold and whose duration is greater than the upper threshold from the periods of interest to obtain the remaining periods of interest;

[0028] Based on the attention periods corresponding to various environmental elements, the following eye movement index data are obtained for each environmental element: total fixation duration, first fixation time, first fixation duration, and number of fixations.

[0029] Optionally, the method further includes: obtaining the relative projected area of ​​various environmental elements;

[0030] The process of analyzing and processing eye movement data from each test subject to obtain eye movement index data corresponding to various environmental factors also includes:

[0031] The total fixation time per unit area is calculated based on the total fixation time of various environmental elements and the corresponding relative projected area, and / or the number of fixations per unit area is calculated based on the number of fixations of various environmental elements and the corresponding relative projected area.

[0032] Optionally, the collection of eye-tracking data from multiple test subjects observing the environmental scene within a preset time period includes:

[0033] Virtual reality display equipment is used to show the test subjects an environmental scene for a preset duration, and eye movement data of the test subjects are collected while observing the environmental scene.

[0034] Secondly, embodiments of this disclosure provide a device for identifying key environmental elements that affect user experience, including:

[0035] The data acquisition unit is used to acquire eye movement data of multiple testers observing environmental scenes within a preset time period, and simultaneously acquire physiological characteristic data of testers observing environmental scenes. The physiological characteristic data is physiological data that is related to the experiential characteristics of testers when observing environmental scenes.

[0036] An eye-tracking data processing unit is used to analyze and process the eye-tracking data of each tester to obtain eye-tracking index data corresponding to various environmental elements observed by each tester in the environmental scene.

[0037] A physiological data processing unit is used to determine the ontological experience score of each test subject when observing the environmental scene based on the physiological characteristic data.

[0038] The analysis unit is used to perform correlation analysis based on the ontological experience scores of each tester and the eye-tracking index data corresponding to various scene elements, in order to identify the key environmental elements that affect the tester's experience.

[0039] Thirdly, embodiments of this disclosure provide a system for identifying key environmental elements that affect user experience, including:

[0040] Eye-tracking feature acquisition device, used to collect eye-tracking data of multiple test subjects as they observe an environmental scene within a preset time period;

[0041] A physiological feature acquisition device is used to acquire physiological feature data of a test subject when observing an environmental scene, wherein the physiological feature data is physiological data that is correlated with the test subject's experiential features when observing the environmental scene;

[0042] The data processing device is used to process and analyze the eye movement data of each tester to obtain eye movement index data corresponding to various environmental elements observed by each tester in the environmental scene; and to determine the proprioceptive experience score of each tester when observing the environmental scene based on the physiological characteristic data; and to perform correlation analysis based on the proprioceptive experience score of each tester and the eye movement index data corresponding to the observation of various scene elements to determine the key environmental elements that affect the tester's experience.

[0043] This embodiment of the scheme uses eye-tracking data from the test subject's observation of an environmental scene to determine a proprioceptive experience score. Then, it performs correlation analysis between the proprioceptive experience score and eye-tracking index data obtained from the eye-tracking data to identify key environmental elements affecting the test subject's experience. Because physiological characteristic data objectively reflects the subconscious physiological changes of the test subject while observing the environmental scene, the corresponding proprioceptive experience score obtained based on physiological characteristic data relatively objectively represents the test subject's perception of the environmental scene. Compared to methods using subjective questionnaires to determine key environmental elements, the data in this scheme is more objective, and the corresponding identification results are more accurate. Attached Figure Description

[0044] The accompanying drawings, which are incorporated in and form a part of this specification, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure.

[0045] To more clearly illustrate the technical solutions in the embodiments of this disclosure or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, those skilled in the art can obtain other drawings based on these drawings without any creative effort, wherein:

[0046] Figure 1 This is a flowchart of a method for identifying key environmental elements that affect user experience, provided in an embodiment of this disclosure.

[0047] Figure 2 This is a flowchart of a method for identifying key environmental elements that affect user experience, provided in another embodiment of this disclosure;

[0048] Figure 3 This is a schematic diagram of an experimental scenario for implementing the method in some embodiments of this disclosure;

[0049] Figure 4 These are images of test scenarios used in specific implementations;

[0050] Figure 5 This is a schematic diagram of the structure of the identification system provided in the embodiments of this disclosure. Detailed Implementation

[0051] Embodiments of this disclosure will now be described in more detail with reference to the accompanying drawings. While some embodiments of this disclosure are shown in the drawings, it should be understood that this disclosure can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of this disclosure. It should be understood that the accompanying drawings and embodiments of this disclosure are for illustrative purposes only and are not intended to limit the scope of protection of this disclosure.

[0052] The term "comprising" and its variations as used herein are open-ended inclusion, meaning "including but not limited to". The term "based on" means "at least partially based on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments". Definitions of other terms will be given in the description below. In this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations.

[0053] This disclosure provides a method for identifying key environmental elements that affect user experience, by analyzing objectively collected data to determine these key environmental elements.

[0054] Before analyzing the methods provided in the embodiments of this disclosure, we first analyze the concept of "experience." In the embodiments of this disclosure, "experience" refers to the overall psychological feeling a tester obtains based on visual observation of a specific environment. Experience characterizes whether the tester's psychological state is peaceful and stable when entering a particular environment, and whether they feel a sense of belonging to the environment; in other words, experience characterizes the strength of the tester's willingness to stay in the environment or their willingness to leave the environment.

[0055] Prior to implementing this embodiment, extensive psychological research revealed that the tester's experience is directly related to their emotional state in the environment. Therefore, the quality of the tester's experience can be reflected by characteristic data representing the tester's emotional state (specifically, physiological characteristic data reflecting the tester's emotional state), thereby identifying key environmental factors that affect the tester's experience.

[0056] Figure 1 This is a flowchart illustrating a method for identifying key environmental elements that affect user experience, as provided in this embodiment of the disclosure. Figure 1 As shown, the method for identifying key environmental elements that affect user experience provided in this embodiment includes steps S110-S140.

[0057] S110: Collect eye movement data of multiple test subjects while observing the environmental scene within a preset time period, and simultaneously collect physiological characteristic data of the test subjects within the preset time period.

[0058] As analyzed above, the experiential feeling discussed in this embodiment is the overall psychological feeling obtained based on visual observation of the environment. Accordingly, the perceptual information that affects the tester's experiential feeling is the visual information obtained by the tester through visual observation of the environment, specifically the visual information formed by the tester's visual observation of various environmental elements in the environment.

[0059] When observing various environmental elements in a scene, the test subject's eyes will change their gaze direction due to subconscious or unconscious control, thus focusing on a specific environmental element, different parts of a specific environmental element, or switching the focus from one environmental element to another. Achieving the aforementioned focusing or switching of attention requires the test subject to adaptively control the gaze direction of the eyes, which is to say, adaptively control the overall posture of the eyes.

[0060] Eye-tracking data is time-series data obtained by collecting the movement state and characteristics of a test subject's eyes. Therefore, based on eye-tracking data, information about the test subject's visual gaze direction can be obtained, thereby determining the environmental elements of the test subject's visual gaze. Based on this, the embodiment of this disclosure collects eye-tracking data of the test subject when observing the environmental scene.

[0061] Meanwhile, to enable cross-sectional comparisons and standardized analysis, and to avoid eye-tracking data losing its significance in representing the influence of environmental factors on the test subject's proprioceptive experience due to visual adaptation or attraction to specific areas of interest, eye-tracking data was collected from each test subject during a preset time period while observing the environmental scene. Furthermore, prior to the aforementioned preset scene, the test subjects did not visually observe the environmental scene.

[0062] In one specific implementation, the preset duration was set to 120 seconds to ensure that the tester could fully observe the various environmental elements in the environment while observing the scene, and at the same time, would not focus only on specific environmental elements because of interest in them.

[0063] In practice, an eye tracker can be used to record the eye movements of the test subject to obtain eye movement data that characterizes the eye movement trajectory.

[0064] In some approaches, to allow testers to experience environmental elements in a more immersive way, they can be placed in a real-world setting and observed from a specific vantage point. For example, in a city center square setting, testers could observe the square from the entrance.

[0065] In some embodiments, the environmental scene is a scene that has not yet been built. In this case, only 3D structural design drawings or 2D drawings of the environmental scene exist, without the actual scene itself. Rendering software can be used to process the 3D structural design drawings or 2D drawings to determine a projected image of the environmental scene viewed from a specific angle. This projected image is then presented to the test subject for observation. In some specific implementations, to avoid the reflection of the real environment on the test subject's eye movements when observing the projected image, a virtual reality display device can be used to display the environmental scene image to the test subject, and an eye tracker configured in the virtual reality display device can be used to collect the test subject's eye movement data.

[0066] Given the complexity of experimental setup and the difficulty of data collection, it's conceivable that using virtual reality (VR) display devices to show environmental scene images can ensure consistency in the environmental scene content observed by all test subjects, avoiding changes in environmental scene content caused by weather variations or changes in lighting characteristics. Simultaneously, using VR display devices also enables low-cost, repeatable sampling experiments, and by limiting the user's eye movement range and body posture range, subsequent steps for processing eye movement data become simpler.

[0067] Of course, in practice, real-world environmental scenes can also be photographed to create images, and the aforementioned virtual reality display equipment can be used to display these images, thereby reducing testing costs and minimizing the impact of uncontrollable factors.

[0068] In this embodiment of the disclosure, while collecting eye-tracking data of the test subject as they observe the environmental scene within a set time period, physiological characteristic data of the test subject in the aforementioned environmental scene is also collected. In other words, the physiological characteristic data is the user's physiological characteristic data within the preset time period.

[0069] It should be noted that the physiological characteristic data collected in this embodiment of the present disclosure are physiological data that are related to the test subject's experience when observing the environmental scene, and not all types of physiological data can be used as physiological characteristic data here.

[0070] Extensive preliminary physiological and psychological experiments in this embodiment revealed that heart rate variability and electrodermal signal indicators are physiological data correlated with a user's perceived experience when observing an environmental scene. Therefore, these two types of indicators can be used as the aforementioned physiological characteristic data, and corresponding physiological measurement instruments can be used to collect this data. In specific implementation, a smart bracelet with corresponding functions can be used to collect the aforementioned physiological characteristic data.

[0071] This section provides a simple analysis of why heart rate variability and electrodermal signal data are correlated with users' experiences when observing experimental scenarios.

[0072] Physiological analysis revealed that when users have a positive experience, their parasympathetic nerve activity increases while their sympathetic nerve activity decreases. When parasympathetic nerve activity is enhanced, the heart becomes more elastic, adaptable, and variable under the synergistic effect of the parasympathetic and sympathetic nervous systems; therefore, heart rate variability can be used as a physiological characteristic data point. Similarly, when users have a positive experience, either their parasympathetic nerve activity is enhanced or their sympathetic nerve activity is lowered. Under the synergistic effect of the parasympathetic and sympathetic nervous systems, the user's skin conductivity characteristics also change; therefore, skin electrical signal indicators can be used as the aforementioned physiological characteristic data point.

[0073] In practice, the aforementioned heart rate variability indicators may include the root mean square of successive differences (RMSSD) of heart rate intervals and the percentage of adjacent heartbeat intervals exceeding 50 ms (PN50).

[0074] The root mean square difference (RMSE) of heart rate intervals is obtained as follows: First, the distance between each normal heartbeat interval on the electrocardiogram is statistically analyzed. Then, the difference between adjacent heartbeat intervals is calculated, and the mean of the sum of squares of all differences is calculated. Finally, the square root of the mean of the sum of squares is taken to obtain the RMS of the heart rate interval. The unit of the RMS of the heart rate interval is usually milliseconds (ms). A higher RMS value indicates a larger short-term change in heart rate, stronger parasympathetic nerve activity, and reflects a relaxed and pleasant state, with a better perception of the surrounding environment.

[0075] The percentage of adjacent heartbeat intervals exceeding 50 ms is obtained as follows: First, the intervals between each normal heartbeat in the electrocardiogram are recorded. Then, the difference between adjacent heartbeat intervals is calculated, and the number of times the absolute value of the difference exceeds 50 ms is counted. Finally, the proportion of such differences in all heartbeat intervals is calculated, which is the percentage of adjacent heartbeat intervals exceeding 50 ms. This percentage reflects a short-term heart rate variability indicator, which is influenced by parasympathetic nervous system activity. It can reflect the immediate fluctuations in the test subject's emotional state and stress level, and thus the test subject's experience of their surrounding environment. A higher percentage of adjacent heartbeat intervals exceeding 50 ms indicates strong parasympathetic nervous system activity and greater cardiac variability, directly reflecting that the test subject is in a relaxed, pleasant, and calm emotional state.

[0076] In practice, the aforementioned skin conductivity signal indicators may include skin conductivity level (SCL) and skin conductivity response (SCR).

[0077] Skin conductivity levels can be obtained by measuring the skin's conductivity over a period of time and averaging the measured conductivity. Lower skin conductivity levels indicate lower activity of the pride-related neural pathways, typically corresponding to a relaxed and calm mood.

[0078] Skin conductivity is determined by recording the baseline conductivity value before a specific environmental stimulus and the peak conductivity value after the stimulus, and then subtracting the peak value from the baseline conductivity value. Skin conductivity reflects the instantaneous change in conductivity in response to a specific stimulus or emotional fluctuation. Because it is event-dependent, it is usually related to the intensity of the stimulus that causes the emotional fluctuation.

[0079] Lower skin conductivity indicates a weaker response to external stimuli and less emotional fluctuation. Lower skin conductivity is observed in individuals in a calm, relaxed state and in those who are insensitive to stimuli.

[0080] The applicant notes that the physiological characteristic data is not limited to the aforementioned root mean square difference of heart rate intervals, the percentage of adjacent heartbeat intervals exceeding 50 ms, skin conductance levels, and skin conductance responses. Rather, the use of these types of physiological characteristic data in this embodiment is due to the ease with which existing technologies can acquire the aforementioned heart rate data and skin conductance data. In other embodiments, other physiological data may be adaptively used as the aforementioned physiological characteristic data based on results obtained from research using medical technology, measurement technology, and physiological and psychological analysis techniques.

[0081] S120: Analyze and process the eye movement data of each tester to obtain eye movement index data corresponding to various environmental elements in the observation environment scene of each tester.

[0082] As analyzed earlier, eye-tracking data reflects the indicator data of various environmental elements observed by the tester in the scene. Statistical analysis based on eye-tracking data can yield eye-tracking indicator data corresponding to various environmental elements.

[0083] In practice, eye movement index data for various environmental factors may include at least one of the following: total fixation duration, first fixation time, first fixation duration, and number of fixations.

[0084] Total fixation time is the total time a test subject fixates on a specific environmental element within a preset timeframe.

[0085] First fixation time is the time from the start of the test to the first fixation of a particular environmental element; the shorter the first fixation time, the easier it is for the aforementioned environmental element to be noticed.

[0086] First fixation duration is the total time a test subject spends fixing their gaze on a particular environmental element for the first time; the longer the first fixation duration, the greater the attraction of the aforementioned environmental element to the test subject.

[0087] The number of fixations is the number of times a test subject fixates on a particular environmental element within a preset time period.

[0088] In practice, after acquiring eye-tracking data within a preset time period, the gaze direction of the test subject at each moment can be determined based on the eye-tracking data and benchmark calibration data (the benchmark calibration data is obtained by calibrating the test subject's eyes before collecting eye-tracking data). Subsequently, based on the content of environmental elements corresponding to each gaze direction, the environmental elements of attention at each moment are determined. After determining the environmental elements of attention at each moment, the computing device can perform statistical analysis based on the data corresponding to the environmental elements of attention at each moment to obtain the corresponding eye-tracking index data.

[0089] The core premise for determining the eye-tracking data corresponding to each environmental element in the aforementioned process is to determine the environmental element corresponding to each gaze direction. In some specific implementations, the relative relationship between the eye-tracking measurement device (eye tracker) used to collect user eye-tracking data and the various environmental elements in the environmental scene (or a rendered image or real-world image displaying the environmental scene) is determined. Accordingly, based on the aforementioned determined relative relationship, the corresponding attentional environmental element can be determined based on the eye-tracking data collected by the eye-tracking measurement device at various times.

[0090] In some embodiments, the inventors of this application used a virtual reality display device with an eye-tracking acquisition device to display a rendered image corresponding to an environmental scene to the user, and determined the positional area of ​​each scene element in the rendered image. Because the eye tracker and the display device in the virtual reality display device are relatively fixed, and the position of each scene element in the rendered image on the display device in the virtual reality display device is determined, the environmental element corresponding to the user's gaze direction can be determined through a simple coordinate transformation. For example, in one specific embodiment, the inventors used the built-in software provided by the virtual reality display device to annotate the rendered image and determine the pixel area where each environmental element is located in the rendered image. The virtual reality display device determines the gaze direction corresponding to each environmental element based on the relative positional relationship between the aforementioned pixel area, the display device, the tester's eye calibration data, and the eye tracker, and then uses the eye-tracking data to determine the eye-tracking index data corresponding to each environmental element.

[0091] In some embodiments, the corresponding eye movement index data can be obtained according to the following steps S121-S123.

[0092] S121: Based on the eye-tracking data of each tester, determine the environmental elements that are focused on at each moment, and statistically analyze the moments when the same environmental element is focused on to obtain the attention period corresponding to each environmental element.

[0093] Specifically, in S121, the method for determining the environmental elements of interest at each moment based on the eye-tracking data of each test subject is as described above and will not be repeated here. After obtaining the environmental elements of interest at each moment, the environmental elements corresponding to each moment are then arranged according to the time series, thus obtaining the sequence of environmental elements of interest. By sorting the aforementioned sequence of environmental elements and statistically analyzing the moments of interest in the same environmental element, the attention time period corresponding to each environmental element can be obtained.

[0094] S122: Remove the periods of attention that are shorter than the lower threshold and longer than the upper threshold, and obtain the remaining periods of attention.

[0095] Through the visual attention analysis mechanism, it was found that: (1) users cannot achieve eye accommodation to observe a certain visual element in a short period of time, that is, they cannot achieve effective fixation. Specifically, it takes users about 100ms to achieve effective fixation; (2) if users remain still for a long time and maintain a uniform gaze direction, it is likely that the user is not focusing on a certain content, but is more likely not paying attention to the content being gazed at, but thinking about other things (or is already in a state of distraction); through statistical analysis, it was found that if the test subject does not change the gaze direction for 5 seconds, it is likely that he or she is already in a state of distraction.

[0096] As analyzed earlier, the eye movement data corresponding to the inability to form effective fixation and the state of inattention cannot reflect the user's perception of environmental elements in the scene. Therefore, it is necessary to remove the aforementioned types of attention periods, that is, remove the periods with durations less than the lower threshold and those with durations greater than the upper threshold, to obtain the remaining periods.

[0097] S123: Based on the attention period corresponding to each environmental element, analyze and process the data to obtain at least one of the following eye-tracking indicators corresponding to each environmental element.

[0098] After obtaining the attention periods corresponding to each environmental element, statistical analysis can then be performed on the attention periods based on the definitions and calculation methods of the aforementioned eye movement index data to obtain the eye movement index data corresponding to each environmental element.

[0099] In some embodiments, eye-tracking metrics data, in addition to the aforementioned total fixation time, first fixation time, first fixation duration, and number of fixations, may also include total fixation time per unit area and first fixation duration per unit area. Visual observation experiments have revealed a correlation between the duration of eye focus on environmental elements and the visual area of ​​those elements in the user's vision. To reasonably reflect the degree of attention paid to various environmental elements by the test subjects, embodiments of this disclosure introduce the concept of "information density" to calculate total fixation time per unit area and first fixation duration per unit area.

[0100] In practice, the total fixation time per unit area and the duration of the first fixation per unit area can be calculated using the following steps S124-S125.

[0101] S124: Obtain the relative projected area of ​​various environmental elements.

[0102] The relative projected area of ​​each environmental element is the projected area of ​​each environmental element in the tester's field of vision. When the viewed scene is an actual scene, the imaging area of ​​each environmental element on the camera at the tester's observation position can be statistically analyzed to determine the imaging pixel area of ​​each environmental element, and this imaging pixel area is used as the relative projected area. In other embodiments, when the environmental scene is displayed as a rendered image, the imaging area of ​​each environmental element in the rendered image can be statistically analyzed, and the statistically obtained area is used as the relative projected area of ​​the corresponding environmental element. In a specific implementation, it is assumed that an environmental element is enclosed by a polygon with N vertices in the rendered image, and the coordinates of the N vertices are (x1, y1), (x2, y2), ..., (x...). n ,y n The relative projected area of ​​the aforementioned environmental elements

[0103]

[0104] S125: Calculate the total fixation time per unit area based on the total fixation time and relative projected area of ​​various environmental factors, and calculate the number of fixations per unit area based on the number of fixations per unit area of ​​various environmental factors and the corresponding relative projected area.

[0105] After obtaining the relative projected area of ​​each of the aforementioned environmental elements, dividing the total fixation time of a certain environmental element by its corresponding relative projected area yields the total fixation time per unit area; similarly, comparing the number of fixations of a certain environmental element with its relative projected area yields the number of fixations per unit area.

[0106] In S125, both the total fixation time per unit area and the number of fixations per unit area are calculated. In other embodiments, only one of the total fixation time per unit area and the number of fixations per unit area may be calculated.

[0107] S130: Determine the proprioceptive experience score of each tester when observing environmental scenes based on physiological characteristic data.

[0108] The proprioceptive experience score is a score that reflects the test subject's subconscious experience when observing an environmental scene, based on physiological characteristic data.

[0109] Determining each test subject's proprioceptive experience score when observing an environmental scene based on physiological characteristic data requires using physiological characteristic data of the test subjects in a control scene as a reference. Accordingly, to obtain the proprioceptive experience score in the environmental scene, it is also necessary to collect baseline physiological characteristic data of the test subjects observing the control scene. In practice, there can be one or multiple control environments.

[0110] After obtaining physiological characteristic data in both the environmental and control scenarios, a physiological characteristic data change model based on physiological experiments and the standard score of proprioceptive experience in the control environmental scenario can be used to calculate the test subject's proprioceptive experience score when observing the environmental scenario. In practice, the aforementioned physiological characteristic data change model may be a linear model or a non-linear model, which needs to be determined through physiological and psychological experiments based on the labeled scenario.

[0111] In some embodiments, only one type of physiological characteristic data is obtained from physiological measurements. Based on the aforementioned physiological characteristic data, the corresponding sub-index ontological experience score can be directly obtained, and the sub-index ontological experience score can be directly used as the ontological experience score for correlation analysis.

[0112] In some other embodiments, at least two types of physiological characteristic data are obtained from physiological measurements. In this case, the proprioceptive experience score can be determined using the following steps S131-S132.

[0113] S131: Determine the corresponding sub-indicator ontological experience score based on various physiological characteristic parameters of each tester.

[0114] After determining the various physiological characteristic parameters, the corresponding sub-indicators of ontological experience scores can be determined using the methods mentioned above.

[0115] S132: Weighted summation of the subject experience scores of each tester for each sub-index to obtain the subject experience score when observing the environmental scene.

[0116] After obtaining the sub-indicator ontological experience scores for each tester, the weighting of various indicators can be determined based on physiological-psychological experiments. The ontological experience scores of each sub-indicator are then summed in a weighted manner to obtain the ontological experience scores of each tester when observing the environmental scene.

[0117] S140: Based on the ontological experience scores of each tester and the eye-tracking index data corresponding to various scene elements, a correlation analysis is conducted to determine the key environmental elements that affect the tester's experience.

[0118] After obtaining the ontological experience scores of various testers using the method described above, and obtaining the eye-tracking index data corresponding to various scene elements, mathematical analysis tools can then be used to perform correlation analysis on the aforementioned data to obtain the key environmental elements affecting the tester's experience. In specific implementation, analysis tools provided by software such as Statistical Product and Service Solutions (SPSS) can be used to process the aforementioned data to obtain the key environmental elements affecting the tester's experience. Considering that the use of mathematical tools such as correlation analysis is not the focus of this solution, and that many existing technical solutions can already achieve the aforementioned correlation analysis, this disclosure will not elaborate on how to perform correlation analysis; for details, please refer to the content analysis of existing technologies.

[0119] In practice, correlation analysis can be used to determine the ranking and / or degree of influence of each environmental element on the test experience. Then, based on the aforementioned ranking and degree of influence, as well as the previously determined evaluation rules, the key environmental elements are identified.

[0120] In practice, correlation analysis can reveal that some eye movement metrics (such as the number of fixations) do not correlate with the test subject's proprioception score. In such cases, the influence ranking and / or influence score corresponding to these eye movement metrics can be excluded.

[0121] In practice, when there are multiple eye-tracking index data and the proprioceptive experience score for correlation analysis, the following S141-S143 can be used.

[0122] S141: Based on the ontological experience scores of each tester and the various eye-tracking index data corresponding to various scene elements, conduct correlation analysis to determine the eye-tracking index data with correlation, and determine the importance ranking and importance coefficient of each environmental element under various eye-tracking index data.

[0123] In S141, independent correlation analyses are performed on the test subject's proprioceptive experience score and various eye-tracking metrics to determine whether there is a correlation between the proprioceptive experience score and the eye-tracking metrics. If there is a correlation between the proprioceptive experience score and the eye-tracking metrics, the correlation coefficient obtained from the correlation analysis can be used as the aforementioned importance coefficient; the correlation ranking of each environmental element under the corresponding correlated eye-tracking metrics is used as the aforementioned importance ranking.

[0124] S142: Using the importance coefficient as the weighting coefficient, the ranking order of each environmental element under various eye-tracking index data is weighted and averaged to determine the overall importance score of each environmental element.

[0125] In S142, the ranking order obtained by sorting various environmental elements under the corresponding eye-tracking index is used as the score under its single index. The aforementioned correlation coefficient (specifically the correlation coefficient) is used as the weighting coefficient for weighted averaging. The weighted score branch of each environmental element is used as the corresponding overall importance score.

[0126] S143: Identify key environmental factors that affect the tester's experience based on the overall importance score.

[0127] After determining the overall importance score for each environmental element, the key environmental elements that influence the tester's experience are obtained by filtering the aforementioned overall importance scores and pre-determined screening rules. In practice, the first one or two environmental elements in the overall importance score can be selected as the key environmental elements.

[0128] The preceding steps S131-S132 involve analyzing the overall ontological experience score obtained by weighted summation of the tester's sub-indicator ontological experience scores against various eye-tracking metrics to identify key environmental elements. In other embodiments, correlation analysis can be performed on the ontological experience scores of various types with eye-tracking metrics data to obtain correlation indices and ranking orders. The corresponding importance ranking and importance coefficients can then be determined using methods such as those described in S141-S143, ultimately yielding the key environmental elements.

[0129] As previously analyzed, the present embodiment uses eye-tracking data from the test subject's observation of the environmental scene to determine the proprioceptive experience score. Then, it performs correlation analysis between the proprioceptive experience score and eye-tracking index data obtained from the eye-tracking data to identify the key environmental elements affecting the test subject's experience. Because physiological characteristic data objectively reflects the subconscious physiological changes of the test subject while observing the environmental scene, the corresponding proprioceptive experience score obtained from the physiological characteristic data relatively objectively represents the test subject's perception of the environmental scene. Compared to methods using subjective questionnaires to determine key environmental elements, the data in the method used in this embodiment is more objective, and the corresponding identification results are more accurate.

[0130] Undeniably, while subjective questionnaire surveys may be influenced by the test taker's rational thinking (moderate thinking), reasonable subjective questionnaire survey results can still reflect the test taker's experience to a certain extent, and thus identify key environmental factors affecting the user experience. Based on this, some embodiments of this disclosure also consider incorporating the survey results obtained from subjective questionnaires into the determination of key environmental factors.

[0131] Figure 2 This is a flowchart illustrating a method for identifying key environmental elements that affect user experience, provided in another embodiment of this disclosure. Figure 2 As shown, in another embodiment of this disclosure, the method for identifying key environmental elements that affect the user experience includes steps S210-S250.

[0132] S210: Collect eye movement data of multiple test subjects while observing the environmental scene within a preset time period, and simultaneously collect physiological characteristic data of the test subjects within a preset time period. At the same time, conduct a subjective questionnaire survey on the environmental scene for each test subject and obtain the evaluation score of each survey item.

[0133] Before implementing the scheme of the embodiments of this disclosure, a subjective perception questionnaire for environmental scenes is first created based on the identification target of the method. The subjective perception questionnaire is used to investigate the test subjects' subjective feelings when observing environmental scenes.

[0134] In one specific implementation, the subjective feeling questionnaire can adopt the form of a seven-point Likert scale. The questionnaire items can include an overall evaluation of the environmental scene and sub-item evaluations of different elements within the environmental scene. For example, in one embodiment, the subjective feelings involved in the evaluation items include spatial presence, spatial experience, and spatial intimacy. Items corresponding to spatial presence can include evaluations such as "spatial recognizability," "the ability of scene elements to distract attention," "sense of security," "spatial oppression," and "unity of regional style." Items corresponding to spatial experience can include evaluations such as "degree of psychological relaxation," "reduced psychological stress," "gaining rest," and "expectation of spatial exploration." Items corresponding to spatial intimacy can include evaluations such as "the scene reflects local customs and culture," "the degree of integration between the individual and the environment," "duration of stay," and "spatial attractiveness."

[0135] S220: Analyze and process the eye movement data of each tester to obtain eye movement index data corresponding to various environmental elements in the observation environment scene of each tester.

[0136] S230: Determine the proprioceptive experience score of each tester when observing environmental scenes based on physiological characteristic data.

[0137] The execution process of the corresponding steps in S210-S230 is the same as that in the previous embodiment, and will not be repeated here. For details, please refer to the previous description.

[0138] S240: Subjective experience score is obtained based on the evaluation scores of each survey item.

[0139] In practice, the evaluation scores of each survey item obtained from the aforementioned subjective questionnaire can be combined to obtain the subjective experience score. Furthermore, in some embodiments, subjective statistical tools in statistical analysis software can be used to analyze the scores of each survey item, remove scores from test takers that are not representative, and then sum the evaluation scores of each survey item according to a predetermined score summation method to obtain the subjective experience score.

[0140] S250: Based on the ontological and subjective experience scores of each tester, correlation analysis is performed with eye-tracking index data corresponding to various scene elements to determine the key environmental elements of the application tester's experience.

[0141] As analyzed above, the proprioceptive experience score and the subjective experience score reflect the tester's experience of the environmental scene from both objective and subjective perspectives, respectively. Therefore, in specific implementation, the proprioceptive experience score and the subjective experience score can be used to conduct correlation analysis with eye movement index data to obtain the correlation analysis results. The two analysis results can then be combined to determine the key environmental factors that affect the tester's experience.

[0142] In some embodiments, key environmental elements may be determined using the following steps S241-S243.

[0143] S241: Based on the ontological experience score and subjective experience score of each tester, conduct correlation analysis with the eye movement index data corresponding to various scene elements to determine the eye movement index data with correlation, and determine the importance ranking and importance coefficient of each environmental element under various eye movement index data.

[0144] S242: Using importance coefficients as weighting coefficients, the ranking order of each environmental element under various eye-tracking index data is weighted and averaged to determine the overall importance score of each environmental element.

[0145] S243: Identify key environmental factors that affect the tester's experience based on the overall importance score.

[0146] In practice, the implementation process of S241-S243 is the same as that of S141-S143 mentioned above, except that the data processing is different. Therefore, the relevant content will not be analyzed in detail here.

[0147] As analyzed above, in order to minimize the implementation cost of this embodiment, the aforementioned method is implemented using a combination of virtual reality devices, physiological monitoring devices (smart bracelets), and questionnaires. Figure 3 These are schematic diagrams illustrating experimental scenarios for implementing the method in some embodiments of this disclosure. For example... Figure 3 As shown, the hardware devices used to implement the present disclosure include a head-mounted display 1, a multimodal wristband 2 and a corresponding wristband terminal 3, a computing device 4 for processing source data and subsequent data, a questionnaire 5, and a locator for determining the test subject's eye movement data. Figure 4 These are images of test scenarios used in specific implementations. For example... Figure 4The test scene image shown is processed and labeled with various environmental elements, then projected onto a head-mounted display for observation by the test subject. Simultaneously, a multimodal wristband is controlled to collect data and presents questionnaire questions to the test subject via audio playback. The test subject answers quickly within a short time to obtain the aforementioned eye-tracking data, physiological characteristic data, and questionnaire item scores. After obtaining the aforementioned data, the computing device processes the data according to the aforementioned method to obtain the key environmental elements.

[0148] In addition to providing the aforementioned method for identifying key environmental elements that affect user experience, this disclosure also provides a system for identifying key environmental elements that affect user experience. Figure 5 This is a schematic diagram of the structure of a system for identifying key environmental elements that affect user experience, provided in an embodiment of this disclosure. For example... Figure 5 As shown, the identification system 500 for key environmental elements that affect the user experience includes an eye movement feature acquisition device 501, a physiological feature acquisition device 502, and a data processing device 503.

[0149] The eye movement feature acquisition device 501 is used to collect eye movement data of multiple test subjects when they observe environmental scenes within a preset time period.

[0150] The physiological feature acquisition device 502 is used to acquire the physiological feature data of the test subject when observing the environmental scene, wherein the physiological feature data is physiological data that is related to the test subject's experiential features when observing the environmental scene.

[0151] The data processing device 503 is used to process and analyze the eye movement data of each tester to obtain eye movement index data corresponding to various environmental elements in the observation environment scene of each tester; and to determine the proprioceptive experience score of each tester when observing the environmental scene based on physiological characteristic data; and to perform correlation analysis based on the proprioceptive experience score of each tester and the eye movement index data corresponding to various scene elements to determine the key environmental elements that affect the tester's experience.

[0152] In some embodiments, the data processing device 503 uses the baseline physiological characteristic data of each test subject as a reference to determine the ontological experience score when observing the environmental scene based on the corresponding physiological characteristic data.

[0153] In some embodiments, the data processing device 503 collects heart rate variability and / or skin conductance signal indicators of the test subject while observing the environmental scene, and uses the heart rate variability and / or skin conductance signal indicators as physiological characteristic data.

[0154] In some embodiments, when the acquired physiological characteristic data includes more than one indicator, the data processing device 503 determines the corresponding sub-indicator ontological experience score based on various physiological characteristic parameters of each tester; and performs a weighted summation of the sub-indicator ontological experience scores of each tester to obtain the ontological experience score when observing the environmental scene.

[0155] In some embodiments, while collecting eye-tracking data from multiple testers observing an environmental scene within a preset time period, a subjective questionnaire survey on the environmental scene is conducted for each tester, obtaining evaluation scores for each survey item. Each survey item reflects the tester's experience and perception of the environmental scene. The data processing device 503 obtains a subjective experience score based on the evaluation scores of each survey item. Subsequently, based on each tester's physical experience score and subjective experience score, a correlation analysis is performed with the eye-tracking index data corresponding to the observed scene elements to determine the key environmental elements affecting the tester's experience.

[0156] In some embodiments, the data processing device 503 performs correlation analysis with eye-tracking index data corresponding to various scene elements based on the ontological experience score and subjective experience score of each tester, respectively, to determine the eye-tracking index data with correlation, and to determine the importance ranking and importance coefficient of each environmental element under various eye-tracking index data; using the importance coefficient as a weighting coefficient, it performs a weighted average of the ranking order of each environmental element under various eye-tracking index data to determine the overall importance score of each environmental element; and determines the key environmental elements that affect the tester's experience based on the overall importance score.

[0157] In some embodiments, the data processing device 503 determines the environmental elements that are focused on at each moment based on the eye movement data of each tester, and performs statistics on the moments when the same environmental element is focused on to obtain the attention period corresponding to each environmental element; removes the attention periods with durations less than the lower threshold and those with durations greater than the upper threshold to obtain the remaining attention periods; and analyzes and processes the attention periods corresponding to each environmental element to obtain at least one of the following eye movement index data corresponding to each environmental element: total fixation duration, first fixation time, first fixation duration, and number of fixations.

[0158] In some embodiments, the data processing device 503 acquires the relative projected area of ​​various environmental elements, and calculates the corresponding total fixation time per unit area based on the total fixation time of various environmental elements and the corresponding relative projected area, and / or calculates the corresponding number of fixations per unit area based on the number of fixations of various environmental elements and the corresponding relative projected area.

[0159] According to embodiments of this disclosure, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of this disclosure include a computer program product comprising a computer program carried on a non-transitory computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network, installed from a storage device, or installed from a ROM. When the computer program is executed by the data processing device 503, it performs the functions defined in the methods of embodiments of this disclosure.

[0160] It should be noted that the computer-readable medium described above in this disclosure may be a computer-readable storage medium, a computer-readable signal medium, or any combination thereof.

[0161] Computer-readable storage media can be, for example—but not limited to—electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatuses, or devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this disclosure, a computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0162] The aforementioned computer-readable medium may be included in the aforementioned computing device; or it may exist independently and not assembled into the computing device.

[0163] Computer program code for performing the operations of this disclosure can be written in one or more programming languages ​​or a combination thereof, including but not limited to object-oriented programming languages ​​such as Java, Smarttalk, and C++, as well as conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the tester's computer, partially on the tester's computer, as a standalone software package, partially on the tester's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the tester's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0164] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram and / or flowchart, and combinations of blocks in block diagrams and / or flowcharts, may be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0165] The units described in the embodiments of this disclosure can be implemented in software or hardware. The names of the units are not necessarily limiting in certain circumstances. The functions described above can be performed at least in part by one or more hardware logic components. For example, exemplary types of hardware logic components that can be used, without limitation, include: Field Programmable Gate Arrays (FPGAs), Application-Specific Integrated Circuits (ASICs), Application Standard Products (ASSPs), System-on-Chip (SoCs), Complex Programmable Logic Devices (CPLDs), etc.

[0166] The above are merely specific embodiments of this disclosure, enabling those skilled in the art to understand or implement this disclosure. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this disclosure. Therefore, this disclosure is not to be limited to these embodiments, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for identifying key environmental elements that affect user experience, characterized in that, include: Eye movement data of multiple test subjects were collected while they observed the environmental scene within a preset time period, and physiological characteristic data of the test subjects were also collected within the preset time period. The physiological characteristic data is physiological data that is related to the test subject's experience when observing the environmental scene. The eye movement data of each tester are analyzed and processed to obtain eye movement index data corresponding to various environmental elements observed by each tester in the environmental scene; based on the physiological characteristic data, the proprioceptive experience score of each tester when observing the environmental scene is determined. Correlation analysis is performed based on the proprioceptive experience scores of each tester and the eye-tracking index data corresponding to various scene elements to determine the key environmental elements affecting the tester's experience. This includes: performing correlation analysis between the proprioceptive experience scores and subjective experience scores of each tester and the eye-tracking index data corresponding to various scene elements to determine the eye-tracking index data with correlation, and determining the importance ranking and importance coefficient of each environmental element under various eye-tracking index data. The various eye-tracking index data include: total fixation duration, first fixation time, first fixation duration, number of fixations, total fixation duration per unit area, and first fixation duration per unit area. Using the importance coefficient as a weighting coefficient, a weighted average is calculated for the ranking order of each environmental element under various eye-tracking index data to determine the overall importance score of each environmental element. The key environmental elements affecting the tester's experience are determined based on the overall importance score. The method further includes: While collecting eye-tracking data from multiple testers as they observed the environmental scene within a preset time period, a subjective questionnaire survey on the environmental scene was conducted on each tester to obtain evaluation scores for each survey item. Each survey item reflects the tester's experience and feelings about the environmental scene. Subjective experience scores are obtained based on the evaluation scores of each survey item; The process involves performing correlation analysis based on the subjective experience scores of each tester and the eye-tracking index data corresponding to various scene elements to determine the key environmental elements affecting the tester's experience. This includes: performing correlation analysis on the subjective experience scores of each tester and the eye-tracking index data corresponding to various scene elements to determine the key environmental elements affecting the tester's experience; and analyzing and processing the eye-tracking data of each tester to obtain the eye-tracking index data corresponding to various environmental elements. This includes: determining the environmental elements focused on at each moment based on the eye-tracking data of each tester, and statistically analyzing the moments focused on the same environmental element to obtain the attention period corresponding to each environmental element; and removing attention periods with durations shorter than the lower threshold and longer than the upper threshold to obtain the remaining attention periods. The eye movement index data is obtained by analyzing and processing the attention time periods corresponding to each environmental element. The method further includes: obtaining the relative projection area of ​​each environmental element; the analysis and processing of the eye movement data of each tester to obtain the eye movement index data corresponding to each environmental element also includes: calculating the total fixation time per unit area based on the total fixation time of each environmental element and the corresponding relative projection area, and / or, calculating the number of first fixations per unit area based on the number of fixations of each environmental element and the corresponding relative projection area.

2. The identification method according to claim 1, characterized in that, The method further includes: acquiring baseline physiological characteristic data of each test subject in a baseline scenario; The step of determining the proprioceptive experience score of each tester when observing the environmental scene based on the physiological characteristic data includes: using the baseline physiological characteristic data of each tester as a reference, determining the proprioceptive experience score when observing the environmental scene based on the corresponding physiological characteristic data.

3. The identification method according to claim 1, characterized in that, The physiological characteristic data collected from the test subjects while observing the environmental scene includes: The heart rate variability index and / or skin conductance signal index are collected from the test subject while observing the environmental scene, and the heart rate variability index and / or skin conductance signal index are used as the physiological characteristic data.

4. The identification method according to claim 3, characterized in that, When the acquired physiological characteristic data includes more than one indicator, the determination of each tester's ontological experience score when observing the environmental scene based on the physiological characteristic data includes: The corresponding sub-indicators of ontological experience scores were determined based on various physiological characteristic parameters of each tester. The ontological experience scores of each tester are weighted and summed to obtain the ontological experience score when observing the described environmental scene.

5. The identification method according to any one of claims 1-4, characterized in that, The collection of eye-tracking data from multiple test subjects observing the environmental scene within a preset time period includes: Virtual reality display equipment is used to show the test subjects an environmental scene for a preset duration, and eye movement data of the test subjects are collected while observing the environmental scene.

6. A system for identifying key environmental elements that affect user experience, characterized in that, include: Eye-tracking feature acquisition device, used to collect eye-tracking data of multiple test subjects as they observe an environmental scene within a preset time period; A physiological feature acquisition device is used to acquire physiological feature data of a test subject when observing an environmental scene, wherein the physiological feature data is physiological data that is correlated with the test subject's experiential features when observing the environmental scene; A data processing device is used to analyze and process the eye movement data of each tester to obtain eye movement index data corresponding to various environmental elements observed by each tester in the environmental scene; and to determine the proprioceptive experience score of each tester when observing the environmental scene based on the physiological characteristic data; and to perform correlation analysis based on the proprioceptive experience score of each tester and the eye movement index data corresponding to the observed various scene elements to determine the key environmental elements affecting the tester's experience, including: performing correlation analysis between each tester's proprioceptive experience score and subjective experience score and the eye movement index data corresponding to the observed various scene elements to determine the eye movement index data with correlation, and determining the importance ranking and importance coefficient of each environmental element under various eye movement index data, wherein the various eye movement index data include: total fixation duration, first fixation time, first fixation duration, number of fixations, total fixation time per unit area, and first fixation duration per unit area; using the importance coefficient as a weighting coefficient, a weighted average is performed on the ranking order of each environmental element under various eye movement index data to determine the overall importance score of each environmental element; and the key environmental elements affecting the tester's experience are determined based on the overall importance score. The data processing device is used to collect eye-tracking data from multiple test subjects observing environmental scenes within a preset time period, while simultaneously conducting a subjective questionnaire survey on the environmental scene for each test subject, obtaining evaluation scores for each survey item, where each survey item reflects the test subject's experience of the environmental scene; obtaining a subjective experience score based on the evaluation scores of each survey item; performing correlation analysis between each test subject's subjective experience score and the eye-tracking index data corresponding to various scene elements observed, based on the eye-tracking data of each test subject, identifying key environmental elements affecting the test subject's experience; determining the environmental elements focused on at each moment based on the eye-tracking data of each test subject, and statistically analyzing the moments focused on the same environmental element to obtain the attention period corresponding to each environmental element; removing attention periods with durations less than the lower threshold and durations higher than the upper threshold from the attention period to obtain the remaining attention period; analyzing and processing the attention period corresponding to each environmental element to obtain the eye-tracking index data; obtaining the relative projected area of ​​various environmental elements; calculating the corresponding total fixation time per unit area based on the total fixation time of various environmental elements and the corresponding relative projected area, and / or, calculating the corresponding number of fixations per unit area based on the number of fixations of various environmental elements and the corresponding relative projected area.

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