A thermos cup outer surface tactile feedback interaction method

Abstract

The present application relates to the technical field of vacuum cup outer surface interaction, and discloses a vacuum cup outer surface tactile feedback interaction method. The method first divides the target vacuum cup outer surface into several interaction regions according to holding structure and functional partition boundary, each region containing at least one tactile sensor, and obtains contact pressure, temperature sensing and user interaction data to form a multi-source data set. Then, a tactile feedback parameter and an interaction efficiency evaluation index are constructed, an initial feedback intensity value is dynamically generated through a double-threshold trigger mechanism and an adjustment model, and the initial value is dynamically corrected according to the functional correlation of adjacent interaction regions, and then a priority response table is generated in descending order, and mode switching display is performed in combination with an interaction control module. Through the multi-source data fusion and dynamic correction mechanism, the method improves the accuracy and user experience of vacuum cup outer surface interaction, and is suitable for vacuum cup intelligent interaction scenes.

Description

A tactile feedback interaction method for the outer surface of a thermos cup Technical Field

[0001] This invention relates to the field of interactive technology for the outer surface of thermos cups, specifically to a tactile feedback interactive method for the outer surface of thermos cups. Background Technology

[0002] In daily life, insulated water bottles, as common household items, have evolved beyond simply keeping things warm; people's demands for their interactive experience are gradually increasing. Traditional insulated water bottles have relatively simple external surface interaction methods, mostly offering only basic touch functionality, failing to provide personalized feedback based on user scenarios and needs. For example, existing insulated water bottles cannot accurately sense the user's contact pressure and temperature changes when held, failing to provide appropriate tactile feedback, resulting in a poor interactive experience.

[0003] With the development of smart wearable devices and smart homes, users' demand for intelligent interaction is growing. In the thermos cup industry, how to achieve more intelligent and user-friendly interaction has become an urgent problem to be solved. Currently, although some smart thermos cups on the market have added some interactive functions, these functions often lack comprehensive processing and analysis of multi-source data. For example, only considering contact pressure data while ignoring temperature sensing data and user interaction data, they cannot fully understand the user's usage status and needs, resulting in inaccurate and untimely feedback.

[0004] Existing interactive methods for the outer surface of thermos cups lack dynamism and flexibility in adjusting feedback intensity. They cannot adjust feedback intensity in real time based on the functional relationships between adjacent interactive areas, resulting in poor coordination between feedback effects from different functional areas and impacting overall interaction efficiency. For example, when a user is using a specific functional area of ​​the thermos cup, adjacent functional areas may not provide corresponding feedback support based on their functional relationships, requiring the user to perform more actions to complete the task.

[0005] Existing methods for evaluating interaction performance are often insufficiently comprehensive and scientific. They fail to promptly assess and adjust interaction effectiveness based on metrics such as user satisfaction and feedback frequency, hindering continuous optimization of the interaction system and its ability to meet evolving user needs. For example, if a user's satisfaction with a particular interaction function is low, the system may not detect and adjust it in a timely manner, negatively impacting the user experience.

[0006] Existing methods for interacting with the outer surface of thermos cups lack precision and rationality in calculating spatial interaction and functional complementarity indices. They fail to accurately assess the spatial relationships and functional complementarity between interaction areas, leading to inaccurate corrections to feedback intensity and impacting interaction effectiveness. For example, when calculating spatial interaction indices, factors such as contact distance and interaction frequency are not adequately considered, resulting in inaccurate assessments of the ease and closeness of interaction. Summary of the Invention

[0007] The purpose of this invention is to provide a tactile feedback interaction method for the outer surface of a thermos cup to solve the problems mentioned in the background art.

[0008] To achieve the above objectives, the present invention provides a tactile feedback interaction method for the outer surface of a thermos cup, the method comprising:

[0009] The outer surface of the target thermos cup is divided into several interactive areas. Contact pressure data, temperature sensing data and user interaction data in each interactive area are obtained to form a multi-source dataset.

[0010] Based on multi-source datasets, haptic feedback parameters and interaction effectiveness evaluation indicators are constructed, and initial feedback intensity values ​​are dynamically generated through a dual-threshold triggering mechanism and adjustment model.

[0011] The initial feedback intensity value of each interaction area is dynamically corrected based on the functional correlation between adjacent interaction areas, and the corrected feedback intensity value is generated.

[0012] The corrected feedback intensity values ​​are sorted in descending order to generate a priority response form, which is then displayed in a mode switching manner in conjunction with the interactive control module.

[0013] Preferably, the specific division rules for the outer surface of the target thermos cup are as follows:

[0014] The outer surface of the target is divided into touch units with an area difference within a preset threshold range based on the grip structure and functional partition boundaries of the thermos cup. Each touch unit contains at least one tactile sensor and is used as an interaction area.

[0015] Preferably, the specific construction process of the haptic feedback parameters is as follows:

[0016] Identify the degree of external surface contact pressure based on contact pressure data;

[0017] Contact duration and preference records are extracted from user interaction data, and feedback sensitivity parameters are calculated using a pre-defined weighting model.

[0018] A contact comfort index is generated based on the relationship between feedback sensitivity parameters and corresponding preset standard thresholds.

[0019] A feedback response score is generated based on the frequency and speed of feedback within a preset time window in the interaction log.

[0020] Functional adaptability indicators are extracted from user interaction data, and tactile feedback parameter values ​​are calculated by combining contact comfort index, feedback response score, and functional adaptability indicators.

[0021] Preferably, the specific construction process of the interaction performance evaluation index is as follows:

[0022] Extract feedback frequency, response latency, and user satisfaction from user interaction data, and then standardize the data.

[0023] The interaction effectiveness evaluation score is obtained by weighted and fused calculation of the standardized indicators.

[0024] Specifically, when user satisfaction falls below a set threshold, the interaction performance evaluation score is updated. The rules for updating the overall interaction performance score are as follows:

[0025] If the ratio of the number of consecutive monitoring periods with user satisfaction below the set threshold to the total number of monitoring periods exceeds the limit, the setting of the deduction ratio threshold will be triggered, and the interaction performance evaluation score will be deducted accordingly based on the deduction ratio threshold.

[0026] Otherwise, the system divides users into tiered intervals based on the difference between their satisfaction level and the set threshold, and deducts users according to an increasing percentage, with the maximum deduction being the set deduction percentage threshold.

[0027] Preferably, the specific analysis process for the initial feedback intensity value is as follows:

[0028] Set warning thresholds for haptic feedback parameters and interaction performance evaluation indicators;

[0029] If any evaluation index of a certain interaction area is lower than the set warning threshold, its initial feedback intensity value is assigned to 1.

[0030] If there are indicators that exceed the set warning threshold, the initial feedback intensity value is calculated using an adjustment function based on the tactile feedback parameters and interaction performance evaluation indicators.

[0031] Preferably, the specific execution steps of the dynamic correction include:

[0032] Randomly select an interaction area as the target interaction area and obtain the initial feedback intensity values ​​of all its adjacent interaction areas;

[0033] Based on the contact distance and interaction frequency between the target interaction area and adjacent interaction areas, the final spatial interaction index value is generated.

[0034] By matching the functional types of adjacent interactive areas with the target interactive area, functional complementarity index values ​​are generated.

[0035] Correction coefficients are assigned based on the final spatial interaction index value and the functional complementarity index value;

[0036] The initial feedback intensity value of the target interaction area is weighted and corrected based on the correction coefficient to generate the corrected feedback intensity value.

[0037] Iterate through all interactive areas and correct them sequentially, then output the corrected feedback intensity value for each interactive area.

[0038] Preferably, the specific process for generating the spatial interaction index value includes:

[0039] The ease of contact is calculated based on the distance between the target interactive area and adjacent interactive areas.

[0040] The interaction density is calculated based on the interaction frequency between the target interaction area and adjacent interaction areas.

[0041] The spatial interaction index value is generated by weighted fusion of the convenience of contact and the density of interaction, and a compensation coefficient is set according to the contact type of adjacent interaction areas.

[0042] The final spatial interaction index value is obtained by compensating the spatial interaction index value with a corresponding compensation coefficient.

[0043] The compensation coefficient is set according to the contact type, satisfying the following rules:

[0044] If the contact type is direct contact, a first compensation coefficient value is assigned; if the contact type is intermittent contact, a second compensation coefficient value is assigned, and the first compensation coefficient value is greater than the second compensation coefficient value.

[0045] Preferably, the specific process for generating the functional complementarity index value includes:

[0046] Match the dominant function type of adjacent interaction areas with the function type of the target interaction area;

[0047] If it belongs to the preset complementary type combination, the functional complementarity index value is calculated according to the matching functional types and the preset weight of the corresponding functional types;

[0048] If they are not complementary combinations, the correlation index is calculated based on the interaction compatibility specification, and the functional complementarity index value is obtained by mapping the correlation index through a preset mapping function.

[0049] Preferably, the specific calculation process of the correlation index is as follows:

[0050] Obtain historical interaction datasets and then count the co-occurrence frequency of corresponding function types between the target interaction area and adjacent interaction areas;

[0051] Based on the function type dependency weight table, query the initial value of the function dependency weight of the target interaction area and the adjacent interaction areas for the corresponding function types;

[0052] Calculate the compatibility coefficient between the target interaction area and adjacent interaction areas based on the interaction compatibility rules of the thermos cup;

[0053] The modified functional dependency weight values ​​are obtained by constraining the initial values ​​of the functional dependency weights based on the compatibility coefficient.

[0054] The comprehensive correction function relies on weight values ​​and co-occurrence frequencies to calculate the correlation index.

[0055] Preferably, the specific allocation process of the correction coefficient is as follows:

[0056] Obtain the interaction mode category of the thermos cup, and set the spatial interaction weight and functional complementarity weight according to the interaction mode category;

[0057] The correction coefficient is obtained based on the set weights, the final spatial interaction index value, and the functional complementarity index value.

[0058] Compared with the prior art, the beneficial effects of the present invention are:

[0059] This patent proposes a tactile feedback interaction method for the outer surface of a thermos cup. The outer surface of the target thermos cup is divided into several interactive areas. By acquiring contact pressure data, temperature sensing data, and user interaction data from each interactive area, a multi-source dataset is formed, providing a rich data foundation for subsequent tactile feedback parameter construction and interaction performance evaluation. Based on the multi-source dataset, tactile feedback parameters and interaction performance evaluation indicators are constructed. An initial feedback intensity value is dynamically generated through a dual-threshold triggering mechanism and adjustment model. This allows the initial feedback intensity value to be dynamically adjusted according to actual interaction data and evaluation indicators, improving the accuracy and adaptability of the feedback.

[0060] The initial feedback intensity values ​​of each interactive area are dynamically adjusted based on the functional relationships between adjacent interactive areas, generating corrected feedback intensity values. This dynamic adjustment method fully considers the functional relationships between adjacent interactive areas, enabling the feedback intensity of different functional areas to coordinate with each other, improving the overall interaction efficiency and user experience. For example, when a user is using a certain functional area, adjacent functional areas can automatically adjust their feedback intensity based on their functional relationships, providing the user with more convenient operation support.

[0061] The corrected feedback intensity values ​​are sorted in descending order to generate a priority response form, which is then displayed in a mode-switching manner in conjunction with the interaction control module. The generation of the priority response form allows the system to prioritize important interactive events based on the magnitude of the feedback intensity, improving the response speed and efficiency of the interaction. The mode-switching display automatically switches to the appropriate interaction mode according to different usage scenarios and user needs, providing users with a personalized interactive experience.

[0062] In terms of the division of the interactive area, the outer surface of the target is divided into touch units with an area difference within a preset threshold range based on the grip structure and functional partition boundaries of the thermos cup. Each touch unit contains at least one tactile sensor, ensuring that the division of the interactive area conforms to ergonomics and functional requirements, and improving the user's comfort and convenience of interaction.

[0063] The process of constructing haptic feedback parameters comprehensively considers multiple factors such as contact pressure data, contact duration in user interaction data, preference records, feedback frequency, response speed, and functional adaptability indicators. This ensures that the haptic feedback parameters can fully reflect the user's interaction state and needs, providing a strong guarantee for generating accurate feedback intensity values.

[0064] The process of constructing interaction performance evaluation indicators involves extracting feedback frequency, response latency, and user satisfaction from user interaction data, performing standardized processing and weighted fusion calculations, and dynamically updating the interaction performance evaluation score based on user satisfaction. This makes the interaction performance evaluation more comprehensive, scientific, and real-time, enabling timely identification and adjustment of problems in the interaction process.

[0065] The analysis process of the initial feedback intensity value, by setting warning thresholds for tactile feedback parameters and interaction effectiveness evaluation indicators, can promptly adjust the initial feedback intensity value when the evaluation indicator is lower than the set warning threshold, ensuring that the feedback intensity value is always within a reasonable range, thus improving the reliability and stability of the feedback.

[0066] The specific steps of dynamic correction are to generate final spatial interaction index values ​​and functional complementarity index values, and to assign correction coefficients based on these index values ​​to perform weighted correction on the initial feedback intensity values. This ensures that the corrected feedback intensity values ​​can fully consider the spatial relationships and functional complementarity between interactive areas, thereby improving the accuracy and rationality of the feedback intensity values.

[0067] The generation process of spatial interaction index values ​​takes into account factors such as contact location distance, interaction frequency, and contact type. Through weighted fusion and the setting of compensation coefficients, the spatial interaction index values ​​can accurately reflect the convenience and density of spatial interaction between interaction areas, providing an accurate spatial basis for the correction of feedback intensity values.

[0068] The generation process of the functional complementarity index value is achieved by matching the functional types of adjacent interaction areas with the target interaction area and calculating the value based on the combination of complementary types or interaction compatibility specifications. This ensures that the functional complementarity index value can accurately reflect the functional complementarity relationship between interaction areas, providing a reasonable functional basis for the correction of the feedback intensity value.

[0069] The process of allocating the correction coefficient is based on setting spatial interaction weight and functional complementarity weight according to the interaction mode category. It is then combined with the final spatial interaction index value and functional complementarity index value, which enables the correction coefficient to be dynamically adjusted according to different interaction modes and index values, thereby improving the flexibility and adaptability of feedback intensity value correction. Attached Figure Description

[0070] Figure 1 is a schematic diagram illustrating the working principle of the tactile feedback interaction method on the outer surface of the thermos cup according to the present invention.

[0071] Figure 2 is a flowchart of the outer surface division rules;

[0072] Figure 3 is a flowchart of the haptic feedback parameter construction process;

[0073] Figure 4 is a flowchart of the dynamic correction execution;

[0074] Figure 5 is a flowchart of the spatial interaction index value generation process. Detailed Implementation

[0075] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0076] Please refer to Figures 1-5. This invention provides a tactile feedback interaction method for the outer surface of a thermos cup. The specific implementation steps are as follows:

[0077] The outer surface of the target thermos cup is divided into several interactive areas. Contact pressure data, temperature sensing data, and user interaction data within each interactive area are acquired to form a multi-source dataset. Specifically, based on the thermos cup's grip structure and functional partition boundaries, the target's outer surface is divided into touch units with area differences within a preset threshold range. Each touch unit contains at least one tactile sensor, which serves as the interactive area.

[0078] Tactile feedback parameters and interaction performance evaluation indicators are constructed based on multi-source datasets, and initial feedback intensity values ​​are dynamically generated through a dual-threshold triggering mechanism and adjustment model. Specifically, the construction of tactile feedback parameters requires identifying the degree of pressure applied to the outer surface based on contact pressure data; extracting contact duration and preference records from user interaction data, and calculating feedback sensitivity parameters using a pre-defined weighting model; generating a contact comfort index based on the relationship between the feedback sensitivity parameters and corresponding pre-defined standard thresholds; generating a feedback response score based on the feedback frequency and response speed within a pre-defined time window in the interaction records; and extracting functional adaptability indicators from user interaction data, comprehensively calculating tactile feedback parameter values ​​by combining the contact comfort index, feedback response score, and functional adaptability indicators. The construction of interaction performance evaluation indicators involves extracting feedback frequency, response latency, and user satisfaction from user interaction data and standardizing them. The standardized indicators are then weighted and fused to obtain the interaction performance evaluation score. When user satisfaction falls below a set threshold, the interaction performance evaluation score is updated. If the ratio of consecutive monitoring periods with user satisfaction below the set threshold to the total monitoring periods exceeds a limit, a deduction ratio threshold is set, and the interaction performance evaluation score is deducted accordingly. Otherwise, the score is divided into tiered intervals based on the difference between user satisfaction and the set threshold, with deductions increasing proportionally, up to the set deduction ratio threshold. The generation of the initial feedback intensity value requires setting warning thresholds for haptic feedback parameters and interaction performance evaluation indicators. If any evaluation indicator in a certain interaction area falls below the set warning threshold, its initial feedback intensity value is assigned to 1. If there are indicators above the set warning threshold, the initial feedback intensity value is calculated using an adjustment function based on the haptic feedback parameters and interaction performance evaluation indicators.

[0079] The initial feedback intensity value of each interaction area is dynamically corrected based on the functional correlation between adjacent interaction areas, and the corrected feedback intensity value is generated. The specific execution steps are as follows: A random interactive area is selected as the target interactive area, and the initial feedback intensity values ​​of all its adjacent interactive areas are obtained. Based on the contact distance and interaction frequency between the target interactive area and its adjacent interactive areas, a final spatial interaction index value is generated. The ease of contact is calculated based on the contact distance between the target interactive area and its adjacent interactive areas, and the interaction density is calculated based on the interaction frequency between the target interactive area and its adjacent interactive areas. The ease of contact and the interaction density are combined and weighted to obtain the generated spatial interaction index value. Compensation coefficients are set according to the contact type of adjacent interactive areas: a first compensation coefficient value is assigned if the contact type is direct contact, and a second compensation coefficient value is assigned if the contact type is intermittent contact, with the first compensation coefficient value being greater than the second compensation coefficient value. The spatial interaction index value is then compensated accordingly using these compensation coefficients to obtain the final spatial interaction index value. Furthermore, a functional complementarity index value is generated by matching the functional types of adjacent interactive areas with the target interactive area. The dominant functional type of the adjacent interactive areas is matched with the functional type of the target interactive area. If they belong to a preset complementary type combination, the functional complementarity index is calculated based on the preset weights of each matched functional type. If the target value is not a complementary combination, the correlation index is calculated based on the interaction compatibility specification. Historical interaction datasets are obtained to statistically analyze the co-occurrence frequency of the corresponding functional types between the target interaction area and adjacent interaction areas. The initial values ​​of the functional dependency weights for the corresponding functional types of the target interaction area and adjacent interaction areas are retrieved from the functional type dependency weight table. The compatibility coefficient between the target interaction area and adjacent interaction areas is calculated according to the interaction compatibility rules of the thermos cup. The initial values ​​of the functional dependency weights are constrained based on the compatibility coefficients to obtain the corrected functional dependency weight values. The correlation index is calculated by combining the corrected functional dependency weight values ​​and the co-occurrence frequency. The functional complementarity index value is then mapped using a preset mapping function based on the correlation index. Correction coefficients are assigned based on the final spatial interaction index value and the functional complementarity index value. The interaction mode category of the thermos cup is obtained. Spatial interaction weights and functional complementarity weights are set according to the interaction mode category. Correction coefficients are obtained based on the set weights, the final spatial interaction index value, and the functional complementarity index value. The initial feedback intensity value of the target interaction area is weighted and corrected based on the correction coefficients to generate the corrected feedback intensity value. All interaction areas are traversed sequentially to complete the correction, and the corrected feedback intensity values ​​for all interaction areas are output.

[0080] The corrected feedback intensity values ​​are sorted in descending order to generate a priority response form, which is then displayed in a mode switching manner in conjunction with the interactive control module.

[0081] Example 1: When dividing the interactive area of ​​the target thermos cup's outer surface, specific rules and procedures must be followed. First, the cup's grip structure must be fully considered, as the part and manner in which the user holds the cup directly affects the interaction effect and experience. For example, the handle of the thermos cup is where the user frequently holds it, and its structural design needs to meet ergonomic requirements for easy gripping. The main body of the cup may be touched when the user drinks or places it down. Different grip structures have different functions and usage frequencies, which is one of the important bases for dividing the interactive area.

[0082] Meanwhile, the boundaries of functional zones are also a key factor in defining interactive areas. Insulated cups typically have different functional areas, such as a temperature display area to show the temperature of the liquid inside, and function button areas for different settings like keeping warm or cooling. These functional zones have clear boundaries, and each area performs a specific function. When defining interactive areas, these boundaries should be used as a reference to ensure that the division of interactive areas corresponds to the functional areas, allowing for more accurate acquisition of interactive data from each functional area.

[0083] Based on the aforementioned grip structure and functional partition boundaries, the target's outer surface is divided into several touch units. During this division, the area difference between each touch unit needs to be controlled within a preset threshold range. This is to ensure relative consistency in area among the various interaction areas, avoiding uneven sensor data collection due to excessive area differences. For example, if a touch unit's area is too large, the tactile sensors within that area may not accurately reflect contact at different locations; conversely, an area that is too small may increase the difficulty and cost of sensor placement. Therefore, controlling the area difference through a preset threshold ensures that each touch unit maintains relative uniformity in area, thus providing a stable foundation for subsequent data acquisition.

[0084] Each touch unit contains at least one tactile sensor, which is a key component for acquiring contact pressure data, temperature sensing data, and user interaction data. The tactile sensor detects the contact pressure between the user and the outer surface of the thermos. When the user holds or touches the thermos, the sensor converts the pressure signal into an electrical signal, which is then collected and processed by the system. Temperature sensing data detects temperature changes on the outer surface of the thermos, which is crucial for reflecting the temperature of the liquid inside and the user's temperature perception. User interaction data includes information such as the duration of contact between the user and the interactive area, interaction frequency, and operation methods. Collecting this data helps the system understand the user's habits and needs.

[0085] Taking the controller as an example, because the contact area between the user's palm and the controller is relatively large when holding it, and the pressure distribution may vary in different areas, the controller can be divided into multiple touch units of similar size. Each touch unit contains a tactile sensor, allowing for more detailed collection of contact pressure data from different locations on the controller. For instance, by dividing the controller into upper, middle, and lower sections, the sensor within each unit can detect the pressure of the user's fingers at different locations, thus providing the system with more comprehensive contact pressure data.

[0086] The main body of the cup may be divided into different touch units based on functional zones. For example, the temperature display area could be a separate touch unit. The sensor within this unit can not only sense contact pressure but also, in conjunction with temperature sensing, acquire temperature data for that area. When a user touches the temperature display area, the sensor can simultaneously collect both contact pressure and temperature information. The system can then use this data to determine whether the user is checking the temperature and provide appropriate feedback.

[0087] When defining the interaction area, the placement and arrangement of sensors also need to be considered. The sensors must be positioned to accurately detect user contact and operation without affecting the thermos's appearance or performance. For example, sensors can be embedded in the outer surface of the thermos, flush with it. This ensures both a comfortable grip and high sensitivity when in contact with the user.

[0088] In addition, the defined interaction areas need to be numbered and labeled for subsequent data collection and processing. Each interaction area has a unique identifier, allowing the system to accurately record and store the data for that area. For example, during data collection, the system records contact pressure data, temperature sensing data, and user interaction data for each interaction area, and associates this data with the corresponding interaction area identifier for easier subsequent analysis and processing.

[0089] Through the above steps, the outer surface of the target thermos cup is reasonably divided into several interactive areas. Each interactive area's touch unit is equipped with at least one tactile sensor, which can comprehensively and accurately acquire multi-source data from each area, providing reliable data support for subsequent steps such as tactile feedback parameter construction, interactive performance evaluation index construction, initial feedback intensity value generation, dynamic correction, and priority response form generation.

[0090] Example 2: When constructing tactile feedback parameters, key information needs to be extracted from multi-source datasets and implemented through a series of processing steps. The degree of pressure applied to the outer surface is identified based on contact pressure data. The pressure signals collected by the tactile sensor are converted into numerical data. The system analyzes this data; for example, when a user holds a thermos, the pressure distribution varies in different interaction areas. The handle area may exhibit different pressure values ​​depending on the grip strength, while the pressure is relatively lower when contacting the side of the cup. By analyzing the magnitude and distribution of these values, the system determines the degree of contact pressure in that area, for example, classifying the pressure values ​​into different levels such as light, medium, and heavy, providing basic data for subsequent processing.

[0091] Contact duration and preference records are extracted from user interaction data. Contact duration refers to the length of time a user maintains contact with a specific interactive area. For example, when a user adjusts the temperature function, the duration of contact between their finger and the corresponding button area is recorded by the system. Preference records include the user's choice of feedback mode during use. For instance, some users may prefer vibration feedback, while others prefer a slight tactile feedback. This information is collected and stored through user actions or system settings.

[0092] The feedback sensitivity parameter is calculated using a pre-defined weighted allocation model. In this model, contact duration and preference records are assigned different weight values, based on statistical analysis of user habits. For example, extensive user data analysis reveals that interactions with longer contact durations typically require more sensitive feedback, thus assigning a higher weight to contact duration in the model. Preference records, reflecting users' personalized needs, are also assigned weights according to their importance. The system inputs the extracted contact duration and preference record data into the model and calculates the feedback sensitivity parameter through weighted summation. This parameter reflects the user's sensitivity to feedback in a specific interaction area.

[0093] A contact comfort index is generated based on the relationship between feedback sensitivity parameters and corresponding preset standard thresholds. These preset standard thresholds are reasonable ranges determined based on ergonomics and user experience research. For example, when the feedback sensitivity parameters are within a certain range, most users will feel that the feedback intensity is moderate and the comfort level is high. The system compares the calculated feedback sensitivity parameters with these thresholds. If the parameters fall within the comfort range, the contact comfort index is correspondingly higher; if the parameters deviate from the comfort range, the index decreases, thereby quantifying the user's perception of comfort from the feedback.

[0094] A feedback response score is generated based on the frequency and speed of feedback within a preset time window in the interaction log. The preset time window can be one minute, five minutes, etc. Within this time period, the system counts the number of times feedback occurs in a specific interaction area (feedback frequency) and records the time interval between each user action and the system response (response speed). Interaction areas with high feedback frequency and fast response speed indicate a more efficient system feedback mechanism in that area, resulting in a higher feedback response score; conversely, a lower score indicates a lower score.

[0095] Functionality adaptability metrics are extracted from user interaction data. These metrics measure whether the feedback of an interactive area matches the function of that area. For example, the feedback in a temperature display area should be related to temperature information, and the feedback in a function button area should correspond to the button operation function. The system analyzes the feedback effects and operational behaviors of users when using different functions to determine the degree of adaptability between feedback and function, thereby extracting the functionality adaptability metrics.

[0096] The haptic feedback parameter value is calculated by combining the contact comfort index, feedback response score, and functional adaptability index. These three indicators reflect the feedback characteristics of the interactive area from different dimensions: the contact comfort index reflects the user's subjective feeling, the feedback response score reflects the system's feedback efficiency, and the functional adaptability index measures the matching degree between feedback and function. The system integrates these three indicators according to certain weights, which are set based on the importance of each indicator to haptic feedback. For example, the contact comfort index may account for 40% of the weight, the feedback response score for 30%, and the functional adaptability index for 30%. The final haptic feedback parameter value is obtained by weighted summation. This parameter value comprehensively reflects the haptic feedback characteristics of the interactive area and provides a key basis for the subsequent generation of initial feedback intensity values.

[0097] Throughout the entire construction process, data collection and processing must maintain accuracy and consistency. For example, the collection of contact pressure data must ensure the accuracy of the sensors to avoid inaccurate identification of the degree of contact pressure due to sensor errors; the recording of user interaction data must fully record every interaction behavior, including contact duration, preference selection, feedback frequency, etc., to prevent data loss from affecting subsequent calculations. At the same time, preset weighting models, standard thresholds, and other parameters need to be reasonably set according to actual application scenarios and user needs. For example, for users of different age groups, it may be necessary to adjust the threshold range of the contact comfort index to meet the needs of different user groups.

[0098] Furthermore, the system needs to have a data update and optimization mechanism. As users spend more time using the system, their habits and preferences may change. Therefore, it is necessary to regularly analyze user interaction data and update parameters such as the weight allocation model and standard thresholds to make the construction of haptic feedback parameters more in line with users' actual needs. For example, when it is found that the number of users who prefer a certain feedback pattern has increased significantly, the system can adjust the weight of the preference record in the weight allocation model to adapt to changes in user needs.

[0099] Example 3: When constructing interaction performance evaluation indicators, it is necessary to extract multiple key indicators from user interaction data and process them systematically. First, three core data types are extracted from multi-source datasets: feedback frequency, response latency, and user satisfaction. Feedback frequency refers to the number of times a user effectively interacts with the interactive area per unit of time, such as the number of times a user touches the temperature adjustment area within 10 minutes; response latency is the time interval between when a user completes an interactive operation and when the system generates tactile feedback, such as the time from pressing the keep-warm button to when the handle generates vibration feedback; user satisfaction is indirectly obtained through user-input ratings (such as 1-5 star ratings) or behavioral data during use (such as the number of repeated operations and operation pause times).

[0100] Next, these three types of indicators are standardized, transforming data with different dimensions into values ​​within a unified range. Taking feedback frequency as an example, suppose the maximum historical feedback frequency for a certain interaction area is... If the current feedback frequency is F, then the standardized feedback frequency is... It can be represented as:

[0101]

[0102] Where F represents the number of feedback times collected per unit time. This represents the maximum number of responses in the history of this interaction area. The value range is [0,1]. Similarly, the standardization of response delay requires converting the time value into a relative value. Assuming the minimum response delay is... The maximum response latency is If the current response delay is T, then the standardized response delay is... for:

[0103]

[0104] T is the actual measured response time. and These are the minimum and maximum response times from historical data, respectively. The larger the value, the faster the response time. Standardization of user satisfaction directly maps the original rating (e.g., 1-5 stars) linearly to the [0,1] interval, for example, 5 stars corresponds to 1, and 1 star corresponds to 0.

[0105] After standardization, the interaction performance evaluation score is calculated by weighting and integrating the various indicators. Let w1 be the weight of feedback frequency, w2 be the weight of response latency, and w3 be the weight of user satisfaction, with w1 + w2 + w3 = 1. Then, the formula for calculating the interaction performance evaluation score S is:

[0106]

[0107] Where U' is the standardized user satisfaction value. The weights are set based on user surveys and usage scenario analysis. For example, in high-frequency operation scenarios, the weight w1 for feedback frequency can be set to 0.4, the weight w2 for response latency can be set to 0.3, and the weight w3 for user satisfaction can be set to 0.3.

[0108] When user satisfaction U' falls below a set threshold U0, the interaction performance evaluation score update mechanism is triggered. At this point, the ratio R of the number of consecutive monitoring periods C where user satisfaction is below U0 to the total number of monitoring periods N needs to be calculated, i.e.:

[0109]

[0110] If R exceeds the preset ratio threshold R0, the deduction mechanism is triggered, and the score S is deducted according to the deduction ratio threshold K. The score S1 after deduction is:

[0111]

[0112] Where K is the pre-set maximum deduction ratio, such as 0.2. If R does not exceed R0, the deduction is applied in tiered intervals based on the difference between user satisfaction and U0. For example, when U0=0.6, a 5% deduction is applied when U' is in [0.5,0.6), and a 10% deduction is applied when it is in [0.4,0.5). The deduction ratio increases as the difference increases, and the maximum deduction does not exceed K.

[0113] In practical applications, data acquisition must ensure both timeliness and accuracy. Feedback frequency statistics require real-time recording of user action events, and response latency measurements must be accurate to the millisecond level, which can be calculated using the difference between the system clock and the timestamp of the sensor trigger signal. User satisfaction data can be collected by guiding users to rate their performance through an embedded interactive interface, or indirectly inferred by analyzing the smoothness of user actions (such as success rate and number of erroneous operations).

[0114] The standardization process requires dynamic updates. , and Equal baseline values ​​are used. For example, historical data is traversed every 24 hours to update the maximum feedback frequency and response latency extreme values ​​for each interaction area, ensuring that standardized parameters match the current user usage patterns. Weight parameters w1, w2, and w3 can also be adjusted based on cluster analysis of long-term usage data; for example, if it is found that users' sensitivity to response speed increases, the weight of w2 can be increased.

[0115] When a score update is triggered, the system needs to record the reason and magnitude of each deduction, forming a historical log. For example, if a certain interactive area has a user satisfaction score below U0=0.6 for 7 out of 10 consecutive monitoring periods (each period is 1 hour), with a total monitoring period N=10 and calculated R=0.7, and if R0=0.5, then a deduction of K=0.2 is triggered. The original score S=0.8, and after the deduction, S1=0.8×(1-0.2)=0.64. This dynamic adjustment mechanism allows the evaluation indicators to more closely reflect the actual user experience, avoiding a decline in user interaction experience due to a failure of the system feedback mechanism.

[0116] In addition, the system must have anomaly data filtering capabilities. If the feedback frequency changes abruptly within a monitoring period (e.g., exceeding 3 times the historical average), it may be due to sensor malfunction or data transmission errors; the data for that period should be flagged and excluded from R calculations. Outliers in response delay (e.g., exceeding...) (Even those twice the size of the standard) need to be removed using a sliding window filtering algorithm to ensure the accuracy of the standardization process.

[0117] Example 4: When generating the initial feedback intensity value, it is necessary to first set the tactile feedback parameter warning threshold and the interaction performance evaluation index warning threshold. These two thresholds are reference standards determined based on the design requirements of the thermos cup tactile feedback interaction system and user habits. For example, the tactile feedback parameter warning threshold may be set to a value that can meet the basic interaction needs of most users, while the interaction performance evaluation index warning threshold is used to determine whether the system's interaction effect is within the normal range.

[0118] Taking a specific interactive area as an example, let's assume this area is the temperature control touch area on a thermos cup. After the system collects contact pressure data, temperature sensing data, and user interaction data for this area, it first calculates the tactile feedback parameters and interaction performance evaluation indicators for that area. If, during a user operation, the calculated tactile feedback parameter value for this area is 35, while the set tactile feedback parameter warning threshold is 40, and the user satisfaction score in the interaction performance evaluation indicator is 3 out of 5, which is lower than the set user satisfaction warning threshold of 4, then if any evaluation indicator for this interactive area falls below the set warning threshold, the system will directly assign its initial feedback intensity value to 1. This means that the feedback effect of this area may not have met expectations and needs adjustment.

[0119] Another example is the anti-slip touch area on the handle of a thermos cup. When processing data from this area, the system calculates a tactile feedback parameter of 50, which is higher than the set warning threshold of 40. Simultaneously, the feedback frequency (8 times per minute), response latency (150 milliseconds), and user satisfaction score (4.5 points) in the interaction performance evaluation metrics all exceed their respective warning thresholds. In this case, the system will calculate the initial feedback intensity value using an adjustment function based on the tactile feedback parameter and the interaction performance evaluation metrics.

[0120] The adjustment function takes into account the values ​​of these two indicators. For example, a higher haptic feedback parameter indicates better feedback characteristics in that area and a greater contribution to the initial feedback intensity value; a higher interaction performance evaluation index also indicates better system interaction in that area, which will similarly increase the initial feedback intensity value. Assuming the adjustment function calculates a weighted sum of the haptic feedback parameter and the interaction performance evaluation index, with the haptic feedback parameter having a weight of 0.6 and the interaction performance evaluation index having a weight of 0.4, and given that the haptic feedback parameter for the anti-slip touch area is 50 and the calculated interaction performance evaluation index is 0.8 (assuming a standardized value), then the initial feedback intensity value calculated by the adjustment function is 50 × 0.6 + 0.8 × 100 × 0.4 (assuming the interaction performance evaluation index is converted to a value of the same order of magnitude as the haptic feedback parameter), i.e., 30 + 32 = 62.

[0121] In practical applications, different interaction areas may have different threshold settings and adjustment function logic. For example, in the opening and closing control interaction area of ​​a thermos lid, because users require a high degree of sensitivity to feedback during operation, the tactile feedback parameter warning threshold may be set to 45, higher than the 40 threshold for the body area. At the same time, the weight of the tactile feedback parameter in the adjustment function may be set to 0.7 to further emphasize the influence of this parameter on the initial feedback intensity value.

[0122] When a user uses the thermos, the system monitors the evaluation metrics of each interactive area in real time. If the evaluation metric of a certain interactive area remains below the warning threshold for a period of time, its initial feedback intensity value will remain at 1 until the evaluation metric rises above the warning threshold. For areas where the evaluation metric is above the warning threshold, the system continuously updates the haptic feedback parameters and interaction performance evaluation metrics based on real-time data, and recalculates the initial feedback intensity value through an adjustment function to adapt to different user states and needs.

[0123] For example, when a user first picks up the thermos in the morning, they might grip the handle with greater force. This changes the contact pressure data of the anti-slip touch area, causing the tactile feedback parameters to increase. The system will then recalculate the initial feedback intensity value for that area and increase it accordingly to provide feedback more consistent with the user's current grip. Conversely, when the user puts down the thermos and picks it up again after a period of time, the grip may be lighter, the contact pressure data will decrease, the tactile feedback parameters will drop, and the initial feedback intensity value will be adjusted accordingly.

[0124] Furthermore, different user habits can also affect the generation of the initial feedback intensity value. For example, some users prefer stronger haptic feedback, and the system may adjust the parameters of the adjustment function in the user settings to calculate a higher initial feedback intensity value under the same evaluation metric. Conversely, for users who prefer weaker feedback, the system will make the opposite adjustment.

[0125] Throughout the generation of the initial feedback intensity value, the system needs to ensure the accuracy and real-time nature of data acquisition. The tactile sensor needs to accurately detect changes in the user's contact pressure and temperature, and the data processing module needs to analyze and calculate the collected data in a timely manner to ensure that the initial feedback intensity value reflects the current interaction state promptly. Simultaneously, the threshold setting and the parameters of the adjustment function also need continuous optimization and adjustment based on actual usage to ensure that the generated initial feedback intensity value better meets the user's interaction needs.

[0126] Example 5: When dynamically correcting the initial feedback intensity value, each interaction area needs to be processed according to specific logic and steps. An interaction area is randomly selected as the target interaction area, for example, a touch unit on the handle of a thermos cup. Then, the initial feedback intensity values ​​of all its adjacent interaction areas are obtained. Assume that the target interaction area is adjacent to a touch unit at the top of the handle and another touch unit in the middle of the handle, with initial feedback intensity values ​​of 60 and 55 respectively.

[0127] Based on the contact distance and interaction frequency between the target interaction area and adjacent interaction areas, a final spatial interaction index value is generated. When calculating the contact distance, the actual physical distance between the target interaction area and adjacent interaction areas is measured. For example, the distance between the target interaction area and the top touch unit is 2 cm, and the distance to the middle touch unit is 1.5 cm; the closer the distance, the higher the ease of contact. Then, the interaction frequency between the target interaction area and adjacent interaction areas is statistically analyzed over a period of time. For example, within 10 minutes, the target interaction area interacts with the top touch unit 5 times and with the middle touch unit 8 times; the higher the interaction frequency, the higher the interaction density.

[0128] The spatial interaction index value is obtained by weighted fusion of the ease of contact and the density of interaction. Assuming the weight of ease of contact is 0.4 and the weight of density of interaction is 0.6, the ease of contact between the target interaction area and the top touch unit can be converted into a value of 80 (a certain conversion rule corresponds to a distance of 2 cm), and the density of interaction is 70 (a corresponding value corresponds to 5 interactions). Therefore, the spatial interaction index value is 80×0.4+70×0.6=32+42=74. The ease of contact with the middle touch unit is 90 (a higher value corresponds to a distance of 1.5 cm), and the density of interaction is 85 (a higher value corresponds to 8 interactions). Therefore, the spatial interaction index value is 90×0.4+85×0.6=36+51=87.

[0129] Compensation coefficients are set based on the contact type of adjacent interactive areas. If the target interactive area is in direct contact with the top touch unit without any other structural separation, a first compensation coefficient value, such as 1.2, is assigned. If there is a slight gap between the target interactive area and the middle touch unit due to the handle's structural design, it is considered intermittent contact and a second compensation coefficient value, such as 1.0, is assigned. The spatial interaction index value is compensated using these compensation coefficients to obtain the final spatial interaction index value. The final spatial interaction index value for the top touch unit is 74 × 1.2 = 88.8, and for the middle touch unit it is 87 × 1.0 = 87.

[0130] Next, by matching the function types of adjacent interaction areas with the target interaction area, a functional complementarity index value is generated. Assuming the target interaction area's function type is anti-slip feedback, the top touch unit's dominant function type is grip pressure sensing, and the middle touch unit's function type is temperature indication, the function types of adjacent interaction areas are matched with the target interaction area's function type to determine if they belong to a preset complementary type combination.

[0131] Anti-slip feedback and grip strength sensing may be preset complementary types, as changes in grip strength affect the need for anti-slip feedback. In this case, the functional complementarity index value is calculated based on the matched function types and their corresponding preset weights. Assuming the weight of anti-slip feedback is 0.5 and the weight of grip strength sensing is 0.5, their functional complementarity score can be calculated as 90 using preset rules. Therefore, the functional complementarity index value is 90 × (0.5 + 0.5) = 90.

[0132] However, anti-slip feedback and temperature alerts may not be a pre-defined complementary combination. In this case, it is necessary to calculate the correlation index based on the interaction compatibility specification. First, obtain the historical interaction dataset and count the co-occurrence frequency of the target interaction area and the corresponding function type of the central touch unit. For example, in the past 100 interactions, the anti-slip feedback and temperature alert were triggered simultaneously 15 times, with a co-occurrence frequency of 15%. Then, according to the function type dependency weight table, look up the initial value of the function dependency weight of the corresponding function type of the target interaction area and the central touch unit, assuming it is 60.

[0133] The compatibility coefficient is calculated based on the interaction compatibility rules of the thermos cup. For example, temperature prompts and anti-slip feedback do not have obvious functional conflicts, so the compatibility coefficient is 0.8. The initial value of the functional dependency weight is constrained based on the compatibility coefficient, resulting in a corrected functional dependency weight value of 60 × 0.8 = 48. The correlation index is calculated by combining the corrected functional dependency weight value and the co-occurrence frequency, resulting in 48 + 15 = 63. Then, the functional complementarity index value is obtained by mapping the correlation index through a preset mapping function, assuming the mapping function maps 63 to 70.

[0134] Obtain the interaction mode category of the thermos cup, such as the current daily use mode. Set spatial interaction weight and functional complementarity weight based on the interaction mode category. Assume the spatial interaction weight is 0.6 and the functional complementarity weight is 0.4. Calculate the correction coefficient based on the set weights, the final spatial interaction index value, and the functional complementarity index value. For the top touch unit, the correction coefficient is 88.8 × 0.6 + 90 × 0.4 = 53.28 + 36 = 89.28; for the middle touch unit, the correction coefficient is 87 × 0.6 + 70 × 0.4 = 52.2 + 28 = 80.2.

[0135] The initial feedback intensity value of the target interaction area is weighted and corrected based on the correction coefficient to generate the corrected feedback intensity value. Assuming the initial feedback intensity value of the target interaction area is 58, the influence weight of the correction coefficient for the top touch unit is 0.3, the influence weight of the middle touch unit is 0.3 (and there may be other adjacent areas with a total influence weight of 0.4; assuming the comprehensive correction influence of other adjacent areas is 60 × 0.4 = 24), then the corrected feedback intensity value is 58 + (89.28 × 0.3 + 80.2 × 0.3 + 24) = 58 + (26.784 + 24.06 + 24) = 58 + 74.844 = 132.844, rounded to 133.

[0136] Following the steps described above, all interactive areas are traversed and corrections are performed sequentially. For example, after correcting the target interactive area of ​​the handle area, a specific interactive area of ​​the cup body is selected as the new target interactive area. The process of obtaining the initial feedback intensity values ​​of adjacent areas, calculating the spatial interaction index values ​​and functional complementarity index values, determining the correction coefficients, and weighting the initial feedback intensity values ​​is repeated until all interactive areas have been corrected. Finally, the corrected feedback intensity values ​​for all interactive areas are output.

[0137] Throughout the dynamic correction process, it is crucial to ensure the accuracy of data collection, including measuring the distance to the contact point, statistically analyzing the interaction frequency, and matching functional types. Simultaneously, the determination of the interaction mode category must be adjusted in real-time based on the user's current usage scenario. For example, when a user puts the thermos in their bag, the interaction mode may switch to an anti-accidental touch mode. In this case, the spatial interaction weight and functional complementarity weight will be adjusted accordingly to adapt to different usage scenario requirements. Furthermore, the preset complementary type combinations and interaction compatibility rules need to be reasonably set based on the thermos's design and user habits to ensure the accuracy of functional complementarity index values. This will ensure that the corrected feedback intensity value better matches actual interaction needs, enhancing the user's tactile feedback interaction experience.

[0138] It should be noted that, in this document, relational terms such as "first" and "second" are used only 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. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0139] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A tactile feedback interaction method for the outer surface of a thermos cup, characterized in that, The method includes: dividing the outer surface of the target thermos cup into several interactive areas, acquiring contact pressure data, temperature sensing data, and user interaction data in each interactive area to form a multi-source dataset; constructing tactile feedback parameters and interaction performance evaluation indicators based on the multi-source dataset, and dynamically generating initial feedback intensity values ​​through a dual-threshold triggering mechanism and adjustment model; dynamically correcting the initial feedback intensity values ​​of each interactive area according to the functional correlation of adjacent interactive areas to generate corrected feedback intensity values; sorting the corrected feedback intensity values ​​in descending order to generate a priority response form, and displaying the mode switching in conjunction with the interaction control module; the specific construction process of the tactile feedback parameters is as follows: identifying the degree of contact pressure on the outer surface based on contact pressure data; extracting contact duration and preference records from user interaction data, and calculating feedback sensitivity parameters through a preset weight allocation model; generating a contact comfort index based on the relationship between the feedback sensitivity parameters and the corresponding preset standard threshold; generating a feedback response score based on the feedback frequency and response speed within a preset time window in the interaction records; extracting functional adaptability indicators from user interaction data, and comprehensively considering the contact comfort index, feedback response score, and functional adaptability. The haptic feedback parameter values ​​are calculated using the following methods: The interaction performance evaluation index is constructed as follows: Feedback frequency, response latency, and user satisfaction are extracted from user interaction data and standardized. The standardized indicators are weighted and fused to obtain the interaction performance evaluation score. When user satisfaction is below a set threshold, the interaction performance evaluation score is updated. The update rule for the comprehensive interaction performance score is as follows: If the ratio of consecutive monitoring periods with user satisfaction below the set threshold to the total monitoring periods exceeds the limit, a deduction ratio threshold is set, and the interaction performance evaluation score is deducted accordingly. Otherwise, a tiered interval is divided based on the difference between user satisfaction and the set threshold, and deductions are made at an increasing rate, with the upper limit of the deduction being the set deduction ratio threshold. The specific analysis process for the initial feedback intensity value is as follows: A haptic feedback parameter warning threshold and an interaction performance evaluation index warning threshold are set. If any evaluation index in a certain interaction area is below the set warning threshold, its initial feedback intensity value is assigned to 1. If there is an index above the set warning threshold, the initial feedback intensity value is calculated using an adjustment function based on the haptic feedback parameter and the interaction performance evaluation index.

2. The tactile feedback interaction method for the outer surface of a thermos cup according to claim 1, characterized in that: The specific division rules for the outer surface of the target thermos cup are as follows: the outer surface of the target is divided into touch units with an area difference within a preset threshold range based on the grip structure and functional partition boundaries of the thermos cup. Each touch unit contains at least one tactile sensor and is used as the interaction area.

3. The tactile feedback interaction method for the outer surface of a thermos cup according to claim 1, characterized in that: The specific execution steps of the dynamic correction include: randomly selecting an interaction area as the target interaction area and obtaining the initial feedback intensity values ​​of all its adjacent interaction areas; generating a final spatial interaction index value based on the contact distance and interaction frequency between the target interaction area and its adjacent interaction areas; generating a functional complementarity index value by matching the functional types of adjacent interaction areas and the target interaction area; allocating correction coefficients according to the final spatial interaction index value and the functional complementarity index value; performing weighted correction on the initial feedback intensity value of the target interaction area based on the correction coefficients to generate a corrected feedback intensity value; traversing all interaction areas to complete the correction in sequence, and outputting the corrected feedback intensity value corresponding to all interaction areas.

4. The tactile feedback interaction method for the outer surface of a thermos cup according to claim 3, characterized in that: The specific process for generating the spatial interaction index value includes: calculating the ease of contact based on the contact distance between the target interaction area and adjacent interaction areas; calculating the interaction density based on the interaction frequency between the target interaction area and adjacent interaction areas; generating the spatial interaction index value by weighted fusion of the ease of contact and the interaction density, and setting a compensation coefficient based on the contact type of adjacent interaction areas; and obtaining the final spatial interaction index value by corresponding compensation of the spatial interaction index value using the compensation coefficient. The compensation coefficient based on the contact type satisfies the following rule: if the contact type is direct contact, a first compensation coefficient value is assigned; if the contact type is intermittent contact, a second compensation coefficient value is assigned; and the first compensation coefficient value is greater than the second compensation coefficient value.

5. The tactile feedback interaction method for the outer surface of a thermos cup according to claim 3, characterized in that: The specific process for generating the functional complementarity index value includes: matching the dominant functional type of adjacent interaction areas with the functional type of the target interaction area; if they belong to a preset complementary type combination, calculating the functional complementarity index value based on the matched functional types and their corresponding preset weights; if they do not belong to a complementary combination, calculating the correlation index based on the interaction compatibility specification, and obtaining the functional complementarity index value by mapping the correlation index through a preset mapping function.

6. The tactile feedback interaction method for the outer surface of a thermos cup according to claim 5, characterized in that: The specific calculation process of the correlation index is as follows: obtain historical interaction datasets and then count the co-occurrence frequency of corresponding function types between the target interaction area and adjacent interaction areas; query the initial value of function dependency weight of corresponding function types between the target interaction area and adjacent interaction areas according to the function type dependency weight table; calculate the compatibility coefficient between the target interaction area and adjacent interaction areas according to the interaction compatibility rules of the thermos cup. The modified functional dependency weight values ​​are obtained by constraining the initial values ​​of the functional dependency weights based on the compatibility coefficient. The comprehensive correction function relies on weight values ​​and co-occurrence frequencies to calculate the correlation index.

7. The tactile feedback interaction method for the outer surface of a thermos cup according to claim 3, characterized in that: The specific allocation process of the correction coefficient is as follows: obtain the interaction mode category of the thermos cup, set the spatial interaction weight and functional complementarity weight according to the interaction mode category; obtain the correction coefficient according to the set weight, the final spatial interaction index value and the functional complementarity index value.

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