A system and method for assisting users with visual and tactile impairments to use an e-commerce platform

Abstract

The present invention discloses a system and method for assisting users with visual and tactile impairments to use an e-commerce platform, which relates to the fields of human-computer interaction technology and e-commerce technology, and is used to solve the problem that people with visual and tactile impairments cannot conveniently use e-commerce platforms; it includes a data acquisition module, a dynamic auxiliary adjustment module, a user customization module and a data storage module, and each module is signal-connected. The data acquisition module extracts the feature data of the page in real time, and performs analysis and storage. The dynamic auxiliary adjustment module calculates the comprehensive adjustment coefficient and dynamically optimizes the font, color, size, etc. of the page elements. The user customization module supports personalized adjustments such as fonts, colors, and layouts through a visual interface, and dynamically predicts and adjusts the page layout in combination with real-time interactive behavior data to enhance the user experience. The present invention provides an efficient and convenient user experience through multi-dimensional data analysis and dynamic adjustment.

Description

Technical Field

[0001] The present invention relates to the fields of human-computer interaction technology and e-commerce technology, and more specifically, to a system and method for assisting users with visual and tactile impairments in using an e-commerce platform. Background Art

[0002] With the popularization of the Internet and the rapid development of e-commerce, online shopping has become an important way for people to obtain goods and services. However, for the elderly and users with visual and tactile impairments, the existing e-commerce platforms have not fully considered their special needs in terms of interface design and operation methods. These users often face problems such as difficulty in interface recognition, inconvenient operation, and accidental touch when browsing products, viewing details, adding shopping carts, paying, and communicating with customer service, which seriously affects their shopping experience and quality of life. In the prior art, although there are some auxiliary tools for visually impaired users, such as screen readers and magnifiers, these tools usually have single functions and cannot provide comprehensive assistance for the complex interactive processes of e-commerce platforms. At the same time, the page layouts and operation methods of different e-commerce platforms are different, and there is a lack of a universal solution.

[0003] In view of the above problems, the present invention proposes a solution. Summary of the invention

[0004] In order to overcome the above-mentioned defects of the prior art, embodiments of the present invention provide a system and method for assisting users with visual and tactile impairments to use an e-commerce platform, so as to solve the problems raised in the above-mentioned background technology.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] In a preferred embodiment, it includes: a data acquisition module, a dynamic auxiliary adjustment module, a user customization module and a data storage module, and the modules are signal-connected;

[0007] The data storage module is used to store all data in the platform processing process;

[0008] The data acquisition module is used to obtain the real-time page feature data of the product list page, product details page, payment settlement page and customer service communication page, and upload the obtained real-time page feature data to the data storage module;

[0009] The dynamic auxiliary adjustment module is mainly used for dynamic adjustment and auxiliary use of each page;

[0010] The user customized module displays the configuration files in the user customized module through a visual interface through the JSON visual editor.

[0011] In a preferred embodiment, when the page is loaded, the data acquisition module extracts feature information by accessing the attributes or content of each commodity element in the page.

[0012] In a preferred embodiment, the dynamic auxiliary adjustment module determines the user's physical state coefficient by comprehensively analyzing the degree of visual impairment and tactile impairment of different users;

[0013] The use environment coefficient is determined through comprehensive analysis of different lighting use environments and different noise use environments, and the user's specific adjustment coefficient is determined in combination with the user's physical condition coefficient and the use environment coefficient. According to the user's specific adjustment coefficient, corresponding dynamic adjustments are made for different users;

[0014] The use environment coefficient is obtained as follows:

[0015] Step H1: The dynamic auxiliary adjustment module uses the DeviceLightEvent interface in JavaScript to access the light sensor data of the device, obtain the brightness value L and the color temperature value S in the current environment, set the brightness threshold Yd, and compare the obtained brightness value data L with the brightness threshold Yd. When the brightness value L is less than or equal to Yd, the dynamic auxiliary adjustment module determines that the current environment is low light; when the brightness value L is greater than or equal to Yd, the dynamic auxiliary adjustment module determines that the current environment is strong light; set the color temperature threshold Ys, and compare the obtained color temperature value data S with the color temperature threshold Ys. When the color temperature value S is less than or equal to Ys, the dynamic auxiliary adjustment module determines that the current environment is warm light; when the color temperature value S is greater than or equal to Ys, the dynamic auxiliary adjustment module determines that the current environment is cold light;

[0016] The dynamic auxiliary adjustment module calculates the brightness condition coefficient based on the relationship between the brightness value L and the brightness threshold value Yd, specifically according to the formula: Lc=L / Yd, where Lc is the brightness condition coefficient; the color temperature condition coefficient is calculated based on the relationship between the color temperature value S and the color temperature threshold value Ys, specifically according to the formula: Sc=S / Ys, where Sc is the color temperature condition coefficient. After obtaining the brightness condition coefficient Lc and the color temperature condition coefficient Sc, the dynamic auxiliary adjustment module combines the brightness condition coefficient Lc and the color temperature condition coefficient Sc by weighted averaging to determine the illumination condition coefficient Gc; specifically according to the formula: Gc=α×Lc+(1+α)×Sc; where α is a weighting coefficient, which is adjusted according to different usage scenarios;

[0017] Step H2: The dynamic auxiliary adjustment module accesses the microphone data of the device through the Web Audio API in the browser, captures the surrounding environmental sound, obtains the noise value Z and the noise duration T in the current environment, sets the noise threshold Yz, and compares the obtained noise value data Z with the noise threshold Yz. When the noise value Z is less than or equal to Yz, the dynamic auxiliary adjustment module determines that the current environment is a low-noise environment; when the noise value Z is greater than or equal to Yz, the dynamic auxiliary adjustment module determines that the current environment is a high-noise environment; sets the time threshold Yt, compares the obtained noise duration T with the time threshold Yt, when the noise duration T is less than or equal to Yt, the dynamic auxiliary adjustment module determines that the current environment is short-term noise; when the noise duration T is greater than or equal to Yt, the dynamic auxiliary adjustment module determines that the current environment is continuous noise;

[0018] The dynamic auxiliary adjustment module calculates the influence of the noise value on the condition coefficient based on the noise value Z and the noise threshold value Yz, specifically according to the formula: Zc=Z / Yz, where Zc is the influence coefficient of the noise value on the noise condition coefficient; calculates the influence of the sound duration on the noise condition coefficient based on the noise duration T and the time threshold value Yt: specifically according to the formula: Tc=T / Yt, where Tc is the influence coefficient of the noise duration on the noise condition coefficient; the dynamic auxiliary adjustment module determines the noise condition coefficient ZYc by combining the noise value influence coefficient Zc and the sound duration influence coefficient Tc in a weighted average manner; specifically according to the formula: ZYc=Qz×Zc+Qt×Tc; where Qz represents the weight of the noise value influence coefficient, and Qt represents the weight of the noise duration influence coefficient;

[0019] Step H3: The dynamic auxiliary adjustment module determines the use environment coefficient Hc by weighted average calculation based on the illumination condition coefficient and the noise condition coefficient, specifically according to the formula: Hc=Qg×Gc+Qzy×ZYc, where Qg represents the weight of the illumination condition coefficient, and Qzy represents the weight of the noise condition coefficient;

[0020] The user's physical condition coefficient is obtained as follows:

[0021] The dynamic auxiliary adjustment module sends a request to the data storage module through the RESTful API to obtain the user's visual impairment and tactile impairment, and converts the user's visual impairment and tactile impairment into standardized values ​​through a preset mapping rule, and calculates the user's physical condition coefficient by weighted average calculation based on the user's visual impairment and tactile impairment values, specifically according to the formula: user physical condition coefficient = Qa × visual impairment + Qb × tactile impairment, where Qa represents the weight of the visual impairment, and Qb represents the weight of the tactile impairment;

[0022] The dynamic auxiliary adjustment module will combine the user's physical condition coefficient and the use environment coefficient to calculate the user's specific adjustment coefficient by weighted average; the specific formula is: user specific adjustment coefficient = Qq × user physical condition coefficient + Qp × use environment coefficient, where Qq represents the weight of the user's physical condition coefficient, and Qp represents the weight of the use environment coefficient;

[0023] The dynamic auxiliary adjustment module updates the style of page elements in real time by dynamically modifying the variables of the CSS style sheet based on the user's specific adjustment coefficient, specifically according to the formula: new parameter = old parameter × (1 + user specific adjustment coefficient).

[0024] In a preferred embodiment, the user customization module obtains the stored JSON configuration file from the data storage module through an asynchronous request via a RESTful API when the page is loaded, parses it into an object, and dynamically generates a corresponding visual control;

[0025] The user customization module listens to control events through onChange, obtains user input data, and writes the corresponding JSON configuration items in real time, storing them in the form of key-value pairs; the user configuration is applied in real time through CSS variables or by directly modifying the inline style of the page DOM elements.

[0026] In a preferred embodiment, the user customization module captures real-time interactive behavior data by using JavaScript event monitoring, and calculates a new user adjustment coefficient by weighted average in combination with the user's real-time interactive behavior data and the user's specific adjustment coefficient, specifically according to the formula: new user adjustment coefficient = (click frequency × Qu) + (page dwell time × Qi) + (input box operation times × Qo) + (voice input times × Qr), where Qu represents the weight of the click frequency, Qi represents the weight of the page dwell time, Qo represents the weight of the input box operation times, and Qr represents the weight of the voice input times;

[0027] The user customization module combines the current adjustment coefficient with the initial adjustment coefficient, and uses the weighted average method to predict the future adjustment coefficient of the user's page element, specifically based on the formula: future adjustment coefficient of page element = new adjustment coefficient × Qf + user-specific adjustment coefficient × (1-Qj), where Qf represents the weight of the new adjustment coefficient, and Qj represents the weight of the user-specific adjustment coefficient. The threshold value Yw of the future adjustment coefficient of the page element is set. When the future adjustment coefficient of the page element is ≤Yw, the relevant page element should be reduced; when the future adjustment coefficient of the page element is >Yw, the relevant page element should be enlarged; and the real-time update of the page is achieved through dynamic setting of CSS variables and DOM operations;

[0028] The user-customized module regularly collects new interactive behavior data through the event listener focus, uses the new user behavior data, calculates the new future adjustment coefficient according to the established weights and algorithms, and uses JavaScript's set Interval method to execute the adjustment logic at set time intervals.

[0029] In a preferred embodiment;

[0030] Step 1: Get the real-time page feature data of the product list;

[0031] Step 2: Based on the real-time feature data of the product details page, determine the user's specific adjustment coefficient by combining the user's physical condition coefficient and the usage environment coefficient, and dynamically adjust the product details page according to the user's specific adjustment coefficient;

[0032] Step 3: Provide a personalized adjustment interface, allowing users to adjust interface parameters by themselves;

[0033] Step 4: Use JavaScript event monitoring to capture real-time interactive behavior data. By combining the user's real-time interactive behavior data with the user's specific adjustment coefficient, comprehensive analysis is performed to predict the future adjustment coefficients of page elements, and each page is automatically adjusted based on the future coefficients that are continuously updated by the user.

[0034] The present invention discloses a system and method for assisting users with visual and tactile impairments to use an e-commerce platform, which relates to the fields of human-computer interaction technology and e-commerce technology, and is used to solve the problem that people with visual and tactile impairments cannot conveniently use e-commerce platforms; it includes a data acquisition module, a dynamic auxiliary adjustment module, a user customization module and a data storage module, and each module is signal-connected. The data acquisition module extracts the feature data of the page in real time, and performs analysis and storage. The dynamic auxiliary adjustment module calculates the comprehensive adjustment coefficient, and dynamically optimizes the font, color, size, etc. of the page elements. The user customization module supports personalized adjustments such as fonts, colors, and layouts through a visual interface, and dynamically predicts and adjusts the page layout in combination with real-time interactive behavior data to enhance the user experience. The present invention provides an efficient and convenient user experience through multi-dimensional data analysis and dynamic adjustment: BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 This is a schematic diagram of the system structure of the present invention for assisting users with visual and tactile impairments to use an e-commerce platform.

[0036] Figure 2 This is an operation flow chart of a method for assisting visually and tactilely impaired users to use an e-commerce platform according to the present invention. DETAILED DESCRIPTION

[0037] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0038] Example 1

[0039] The invention discloses a system for assisting users with poor vision and tactile sense to conveniently use an e-commerce platform, comprising: a data acquisition module, a dynamic auxiliary adjustment module, a user customization module and a data storage module, and signal connections are made between the modules.

[0040] The data storage module is used to store all data during the platform processing process.

[0041] The data acquisition module is mainly used to obtain the real-time page feature data of the product list page, product details page, payment settlement page and customer service communication page; and upload the obtained real-time page feature data to the data storage module.

[0042] Specifically, the product list page data includes: product pictures, names, prices, number of reviews, and the position and size of each element on the page; the product details page data includes: such as "Add to Cart", "Buy Now", and product specification selection box; the payment settlement page data includes: password input box, verification code input box, and confirmation button; the customer service communication page data includes: text input box, message bubble, and send button;

[0043] The data acquisition module extracts the feature data of each real-time page using the element feature extractor, parses the real-time page feature data through the DOM parser, and uploads the parsed data to the data storage module.

[0044] Specifically, after the user enters the product list page and the page is loaded, the data acquisition module extracts feature information by accessing the attributes or content of each product element in the page; the data acquisition module obtains the coordinate information of the product element, including its horizontal and vertical position, width and height, and dynamically calculates the display position and size of each product in the page through the browser's layout engine. For example: the product image is obtained through its link address, the product name is extracted through its corresponding text content, and the price and number of reviews are read from the tags that specifically represent this information. The following data is obtained:

[0045] Product name: Smart watch;

[0046] Product price: 299 yuan;

[0047] Number of reviews: 123 reviews;

[0048] The data acquisition module obtains the position and size of each product card on the page through the browser layout engine and obtains the following data:

[0049] The upper left corner of product card A is at (100,200), the width is 300 pixels, and the height is 400 pixels.

[0050] The upper left corner of product card B is at (420,200), the width is 300 pixels, and the height is 400 pixels.

[0051] At the same time, the data acquisition module uses HTML form tags to build a user health data input interface, allowing users to fill in their own physical condition data, and uses CSS for style design to ensure that the interface is simple and easy to use; among them, the user health data input interface contains multiple input boxes for users to fill in their physical condition data. Common input items include: degree of visual impairment, degree of tactile impairment. When the user fills in information on the health data input interface, the data acquisition module captures the user's input data through JavaScript and performs real-time verification to check whether the degree of impairment entered by the user is reasonable. If the user enters invalid data, the interface will display a prompt message to guide the user to re-enter. For example: After entering the user health data input interface, the user fills in his or her physical condition in each input box and obtains the following data:

[0052] Degree of visual impairment: Mild impairment;

[0053] Degree of tactile impairment: severe impairment;

[0054] The data acquisition module structures the extracted feature data through the DOM parser, organizes it into a standardized data format, and uploads the data to the data storage module through the HTTP protocol for further use by other modules.

[0055] The dynamic auxiliary adjustment module is mainly used for dynamic adjustment and auxiliary use of each page.

[0056] It should be noted that users with different physical conditions and usage environments need to make different adjustments, and the dynamic auxiliary adjustment module makes different adjustments according to users with different physical conditions and usage environments. In this embodiment, the dynamic auxiliary adjustment module determines the user's physical condition coefficient through a comprehensive analysis of the degree of visual impairment and the degree of tactile impairment of different users; determines the usage environment coefficient through a comprehensive analysis of different lighting usage environments and different noise usage environments, and determines the user's specific adjustment coefficient in combination with the user's physical condition coefficient and the usage environment coefficient, and makes corresponding dynamic adjustments to different users according to the user's specific adjustment coefficient.

[0057] Specifically, the use environment coefficient is obtained as follows:

[0058] Step H1: The dynamic auxiliary adjustment module uses the DeviceLightEvent interface in JavaScript to access the light sensor data of the device, including the brightness value L and the color temperature value S in the current environment. After obtaining the brightness value and color temperature value data, the dynamic auxiliary adjustment module determines whether the current environment is low light or strong light based on these data. In this embodiment, a brightness threshold value Yd is set, and the obtained brightness value data L is compared with the brightness threshold value Yd. When the brightness value L is less than or equal to Yd, the dynamic auxiliary adjustment module determines that the current environment is low light; when the brightness value L is greater than or equal to Yd, the dynamic auxiliary adjustment module determines that the current environment is strong light. For example: the brightness value in the current environment is 150, and the brightness threshold is set to 200. The dynamic auxiliary adjustment module compares the brightness value in the current environment with the brightness threshold to obtain that the current environment is low light. Set the color temperature threshold Ys, and compare the obtained color temperature value data S with the color temperature threshold Ys. When the color temperature value S is less than or equal to Ys, the dynamic auxiliary adjustment module determines that the current environment is warm light; when the color temperature value S is greater than or equal to Ys, the dynamic auxiliary adjustment module determines that the current environment is cold light. For example: the color temperature value in the current environment is 3000k, and the color temperature threshold is set to 4500k. The dynamic auxiliary adjustment module compares the color temperature value in the current environment with the color temperature threshold and obtains that the current use environment is low light.

[0059] The dynamic auxiliary adjustment module determines the illumination condition coefficient by combining the brightness value and the color temperature value for comprehensive analysis. Specifically: the brightness condition coefficient is calculated based on the relationship between the brightness value L and the brightness threshold value Yd, specifically according to the formula: Lc=L / Yd, where Lc is the brightness condition coefficient; the color temperature condition coefficient is calculated based on the relationship between the color temperature value S and the color temperature threshold value Ys, specifically according to the formula: Sc=S / Ys, where Sc is the color temperature condition coefficient. After obtaining the brightness condition coefficient Lc and the color temperature condition coefficient Sc, the dynamic auxiliary adjustment module determines the illumination condition coefficient Gc by combining the brightness condition coefficient Lc and the color temperature condition coefficient Sc in a weighted average manner. Specifically according to the formula: Gc=α×Lc+(1+α)×Sc; where α is a weighting coefficient, which is adjusted according to different usage scenarios and is 1 by default. In this embodiment, if the influence of the brightness condition needs to be emphasized more, the α value can be increased; if the influence of the color temperature needs to be emphasized, the α value can be reduced. For example, by substituting the brightness value and the color temperature value data into the formula, the brightness condition coefficient is 0.25, the color temperature condition coefficient is 0.67, and the illumination condition coefficient Gc=0.25.

[0060] Step H2: The dynamic auxiliary adjustment module accesses the microphone data of the device through the Web Audio API in the browser to capture the surrounding environmental sound, including the noise value Z and the noise duration T in the current environment. After obtaining the noise value and the noise duration, the dynamic auxiliary adjustment module determines the current noise environment based on these data. In this embodiment, the noise threshold Yz is set, and the obtained noise value data Z is compared with the noise threshold Yz. When the noise value Z is less than or equal to Yz, the dynamic auxiliary adjustment module determines that the current environment is a low noise environment; when the noise value Z is greater than or equal to Yz, the dynamic auxiliary adjustment module determines that the current environment is a high noise environment. For example: the noise value in the current environment is 25, the noise threshold is set to 75, and the dynamic auxiliary adjustment module compares the noise value in the current environment with the noise threshold to obtain low noise in the current environment. Set the time threshold Yt, compare the obtained noise duration T with the time threshold Yt, when the noise duration T is less than or equal to Yt, the dynamic auxiliary adjustment module determines that the current environment is short-term noise; when the noise duration T is greater than or equal to Yt, the dynamic auxiliary adjustment module determines that the current environment is continuous noise. For example, if the noise duration in the current environment is 2 seconds and the time threshold is set to 5 seconds, the dynamic auxiliary adjustment module compares the noise duration in the current environment with the time threshold and obtains that the noise in the current environment is short-term.

[0061] The dynamic auxiliary adjustment module determines the noise condition coefficient by combining the noise value and the noise duration for comprehensive analysis. Specifically, the influence of the noise value on the condition coefficient is calculated based on the noise value Z and the noise threshold Yz, according to the formula: Zc=Z / Yz, where Zc is the influence coefficient of the noise value on the noise condition coefficient; the influence of the sound duration on the noise condition coefficient is calculated based on the noise duration T and the time threshold Yt: according to the formula: Tc=T / Yt, where Tc is the influence coefficient of the noise duration on the noise condition coefficient. After obtaining the noise value influence coefficient Zc and the sound duration influence coefficient Tc, the dynamic auxiliary adjustment module determines the noise condition coefficient ZYc by combining the noise value influence coefficient Zc and the sound duration influence coefficient Tc by weighted averaging. Specifically according to the formula: ZYc=Qz×Zc+Qt×Tc; where Qz represents the weight of the noise value influence coefficient, and Qt represents the weight of the noise duration influence coefficient. For example: through calculation, the weight Qz of the noise value influence coefficient is 0.7, and the weight Qt of the noise duration influence coefficient is 0.3. Substituting the example data into the formula, it is calculated that the noise value influence coefficient is 0.33, the noise duration influence coefficient is 0.4, and the noise condition coefficient ZYc=0.7×0.33+0.3×0.4=0.351.

[0062] Step H3: The dynamic auxiliary adjustment module determines the use environment coefficient Hc by combining the illumination condition coefficient and the noise condition coefficient through comprehensive analysis. Specifically, the dynamic auxiliary adjustment module determines the use environment coefficient Hc by weighted average calculation based on the illumination condition coefficient and the noise condition coefficient, specifically according to the formula: Hc = Qg × Gc + Qzy × ZYc, where Qg represents the weight of the illumination condition coefficient, and Qzy represents the weight of the noise condition coefficient. For example: by calculating, the weight Qg of the illumination condition coefficient is 0.6, and the weight Qzy of the noise condition coefficient is 0.4. Substituting the sample data into the formula, the use environment coefficient Hc is calculated to be 0.2904.

[0063] The user's physical condition coefficient is obtained as follows:

[0064] The dynamic auxiliary adjustment module sends a request to the data storage module through the RESTful API, obtains the physical condition data of the current user, and then comprehensively analyzes the degree of visual impairment and the degree of tactile impairment of the user to determine the user's physical condition coefficient. Specifically, in this embodiment, the dynamic auxiliary adjustment module quantifies the degree of visual impairment and the degree of tactile impairment of the user into standardized values ​​through a preset mapping rule, which is convenient for unified calculation and comprehensive analysis. For example: the user inputs the degree of visual impairment as mild blur and the degree of tactile impairment as severe paralysis, and the mild blur is quantified as 0.5 and the severe paralysis is quantified as 0.8. The user's physical condition coefficient is determined by combining the user's visual impairment degree value and the tactile impairment degree value. The specific formula is: User physical condition coefficient = Qa × visual impairment degree + Qb × tactile impairment degree, where Qa represents the weight of the degree of visual impairment, and Qb represents the weight of the degree of tactile impairment. For example: by calculating, the visual impairment degree weight Qa is 0.7, and the tactile impairment degree weight Qb is 0.3. The example data is brought into the formula to calculate the user's physical condition coefficient as 0.59.

[0065] The dynamic auxiliary adjustment module will combine the user's physical condition coefficient and the usage environment coefficient, and calculate the user's specific adjustment coefficient by weighted average. This parameter determines how the system dynamically adjusts the elements on the page, including: font size, button size, color contrast, etc. Specific formula: User specific adjustment coefficient = Qq × user physical condition coefficient + Qp × usage environment coefficient, where Qq represents the weight of the user's physical condition coefficient, and Qp represents the weight of the usage environment coefficient. For example: by calculation, the user's physical condition coefficient weight Qq is 0.7, and the usage environment coefficient weight Qp is 0.3. Substituting the example data into the formula, the user's specific adjustment coefficient is 0.5183.

[0066] The dynamic auxiliary adjustment module dynamically modifies the variables of the CSS style sheet based on the user's specific adjustment coefficient, updates the style of the page elements in real time, and dynamically adjusts the elements on the interface to improve the user experience. The specific formula is: new parameter = old parameter × (1 + user specific adjustment coefficient). For example: through the user's specific adjustment coefficient, the default font size of 16px is calculated through the formula: new font size = default font size × (1 + comprehensive adjustment coefficient), and the new font size is calculated to be 24.3px.

[0067] It should be noted that the thresholds are derived through analysis and testing of various environments, and the specific values ​​may vary depending on the usage scenario.

[0068] The user customization module is mainly used to provide users with a highly personalized experience. Specifically, in the user customization module, the configuration files in the user customization module are displayed through a visual interface through a JSON visual editor. After the user enters the visual interface settings, the user can adjust the font size, color theme, button size, and gesture sensitivity parameters to improve the user experience through personalized design. After the settings are completed, the user customization module will update the configuration files in real time and apply the adjustments immediately. The user can see the effect without refreshing the page.

[0069] Specifically, when the page is loaded, the user customization module obtains the stored JSON configuration file from the data storage module through an asynchronous request through the RESTful API, parses it into an object, and dynamically generates the corresponding visual control. The user customization module generates the corresponding slider, color palette or input box control according to the structure of the configuration file. For example:

[0070] Font Size: A slider control that allows the user to adjust between a minimum and maximum value.

[0071] Color Theme: A swatch control that lets users select a predefined color scheme.

[0072] Button size: Slider control to adjust the aspect ratio.

[0073] After the user adjusts the slider, selects a color, or enters a new value, the user customization module listens to the control event through onChange to obtain the user's input data. The user customization module writes the user input value to the corresponding JSON configuration item in real time and stores it in the form of a key-value pair. For example: if the user adjusts the font size to 16px, the user customization module updates the value of the configuration item fontSize to 16; if the user selects a new color theme #FF5733, the user customization module updates the value of colorTheme to the color code. The user configuration is applied in real time through CSS variables or by directly modifying the inline style of the page DOM element. The user customization module also temporarily stores the modified content through the browser's IndexedDB local storage to prevent the user from losing data before saving the configuration. If the user logs in to the account, the system can also bind the configuration to the account to achieve multi-device synchronization.

[0074] In this embodiment, the user customization module uses JavaScript event monitoring to capture real-time interactive behavior data, such as click frequency, page dwell time, input box operation, and voice input. By combining the user's real-time interactive behavior data with the user's specific adjustment coefficient, comprehensive analysis is performed to predict the future adjustment coefficient of the page element, and each page is automatically adjusted regularly according to the future coefficient continuously updated by the user.

[0075] The user-customized module captures real-time interactive behavior data by using JavaScript event monitoring. Specifically, it monitors users clicking on page buttons and page links, and counts the number of clicks per unit time to analyze the user's click frequency and behavior patterns. By monitoring page loading and unloading events, the timestamp of the user entering the page is recorded, and the end time is recorded when the page is about to be unloaded, and the time difference between the two is calculated to obtain the residence time. By monitoring the focus change and loss of focus events of the input box, the time the user stays in the input box is measured. And using the speech recognition technology in the Web Speech API, the user's voice input is captured and converted into text. The acquired real-time user interactive behavior data is as follows:

[0076] Product A (smart watch):

[0077] Click frequency: 5 times

[0078] Dwell time: 45 seconds

[0079] Number of input box operations: 3 times

[0080] Voice input times: 2 times

[0081] Product B (Bluetooth headset):

[0082] Click frequency: 3 times

[0083] Dwell time: 30 seconds

[0084] Number of input box operations: 2 times

[0085] Voice input times: 1 time

[0086] The user customization module calculates the new user adjustment coefficient by combining the user's real-time interactive behavior data with the user's specific adjustment coefficient in a weighted average manner, according to the formula: New user adjustment coefficient = (click frequency × Qu) + (page dwell time × Qi) + (input box operation times × Qo) + (voice input times × Qr), where Qu represents the weight of the click frequency, Qi represents the weight of the page dwell time, Qo represents the weight of the input box operation times, and Qr represents the weight of the voice input times. For example: by calculation, the click frequency weight Qu = 0.4; the dwell time weight Qi = 0.3; the input box operation times weight Qo = 0.2; the voice input times weight Qr = 0.1, and the sample data of product A (in the smart watch) is substituted to obtain the product A adjustment coefficient = 16.3; the sample data of product B (Bluetooth headset) is substituted to obtain the product B adjustment coefficient = 10.7.

[0087] Furthermore, the user customization module combines the current adjustment coefficient with the initial adjustment coefficient, and uses the weighted average method to predict the future adjustment coefficient of the user page element, according to the formula: future adjustment coefficient of the page element = new adjustment coefficient × Qf + user specific adjustment coefficient × (1-Qj), where Qf represents the weight of the new adjustment coefficient, and Qj represents the weight of the user specific adjustment coefficient. For example, through calculation, the weight of the new adjustment coefficient is 0.7, and the user specific adjustment coefficient is 0.3. Substituting the example data into the calculation, the future adjustment coefficient of product A is 11.56549; the future adjustment coefficient of product B is 7.64549.

[0088] In this embodiment, after obtaining the future adjustment coefficient data of the page element, the user customization module sets the future adjustment coefficient threshold value Yw of the page element. When the future adjustment coefficient of the page element is ≤Yw, the page layout is compact and the relevant page elements should be reduced; when the future adjustment coefficient of the page element is >Yw, the page layout is loose and the relevant page elements should be enlarged; through the dynamic setting of CSS variables and DOM operations, the real-time update of the page is realized. For example: in this embodiment, the future adjustment threshold value Yw of the page element is set to 15, and the example data is compared with the future adjustment threshold value of the page element to obtain that product A should be enlarged in the future and product B should be reduced in the future.

[0089] At the same time, the user customization module regularly collects new interactive behavior data through the event listener focus, uses the new user behavior data, calculates the new future adjustment coefficient according to the established weights and algorithms, and uses JavaScript's setInterval method to execute the adjustment logic at set time intervals.

[0090] Specifically, the user-defined module runs the adjustment logic every five minutes, uses setInterval, sets the trigger interval to 300,000 milliseconds, and captures the user's new behavior data within 5 minutes. For example: Product A: 6 new clicks, 60 seconds of new dwell time; Product B: 2 new clicks, 20 seconds of new dwell time.

[0091] It should be noted that the above algorithms are all conventional technical means and will not be described in detail here.

[0092] Example 2

[0093] The present invention discloses a method for assisting users with impaired vision and tactile sense to conveniently use an e-commerce platform, comprising the following steps:

[0094] Step 1: Get the real-time page feature data of the product list.

[0095] Step 2: Based on the real-time page feature data of the product details, determine the user's specific adjustment coefficient by combining the user's physical condition coefficient and the usage environment coefficient, and dynamically adjust the product details page based on the user's specific adjustment coefficient.

[0096] Step 3: Provide a personalized adjustment interface to allow users to adjust interface parameters by themselves.

[0097] Step 4: Use JavaScript event monitoring to capture real-time interactive behavior data. By combining the user's real-time interactive behavior data with the user's specific adjustment coefficient, comprehensive analysis is performed to predict the future adjustment coefficients of page elements, and each page is automatically adjusted based on the future coefficients that are continuously updated by the user.

[0098] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0099] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments may be implemented in whole or in part in the form of a computer program product.

[0100] Those of ordinary skill in the art will appreciate that the modules and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application of the technical solution and the invention constraints. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0101] In addition, each functional module in each embodiment of the present application may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.

[0102] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0103] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A system for assisting users with visual and tactile impairments to use an e-commerce platform, characterized in that: include: Data acquisition module, dynamic auxiliary adjustment module, user customization module and data storage module, and signal connection between each module; The data storage module is used to store all data in the platform processing process; The data acquisition module is used to obtain the real-time page feature data of the product list page, product details page, payment settlement page and customer service communication page, and upload the obtained real-time page feature data to the data storage module; The dynamic auxiliary adjustment module is mainly used for dynamic adjustment and auxiliary use of each page; The user customized module displays the configuration files in the user customized module through a visual interface through the JSON visual editor.

2. The system for assisting users with visual and tactile impairments to use an e-commerce platform according to claim 1, characterized in that: When the page is loaded, the data acquisition module extracts feature information by accessing the attributes or content of each product element in the page.

3. The system for assisting users with visual and tactile impairments to use an e-commerce platform according to claim 2, characterized in that: The dynamic auxiliary adjustment module determines the user's physical condition coefficient through comprehensive analysis of the degree of visual impairment and tactile impairment of different users; The use environment coefficient is determined through comprehensive analysis of different lighting use environments and different noise use environments, and the user specific adjustment coefficient is determined in combination with the user's physical condition coefficient and the use environment coefficient. According to the user's specific adjustment coefficient, corresponding dynamic adjustments are made for different users; The use environment coefficient is obtained as follows: Step H1: The dynamic auxiliary adjustment module uses the DeviceLightEvent interface in JavaScript to access the light sensor data of the device, obtain the brightness value L and the color temperature value S in the current environment, set the brightness threshold Yd, and compare the obtained brightness value data L with the brightness threshold Yd. When the brightness value L is less than or equal to Yd, the dynamic auxiliary adjustment module determines that the current environment is low light; when the brightness value L is greater than or equal to Yd, the dynamic auxiliary adjustment module determines that the current environment is strong light; set the color temperature threshold Ys, and compare the obtained color temperature value data S with the color temperature threshold Ys. When the color temperature value S is less than or equal to Ys, the dynamic auxiliary adjustment module determines that the current environment is warm light; when the color temperature value S is greater than or equal to Ys, the dynamic auxiliary adjustment module determines that the current environment is cold light; The dynamic auxiliary adjustment module calculates the brightness condition coefficient based on the relationship between the brightness value L and the brightness threshold value Yd, specifically according to the formula: Lc=L / Yd, where Lc is the brightness condition coefficient; the color temperature condition coefficient is calculated based on the relationship between the color temperature value S and the color temperature threshold value Ys, specifically according to the formula: Sc=S / Ys, where Sc is the color temperature condition coefficient. After obtaining the brightness condition coefficient Lc and the color temperature condition coefficient Sc, the dynamic auxiliary adjustment module combines the brightness condition coefficient Lc and the color temperature condition coefficient Sc by weighted averaging to determine the illumination condition coefficient Gc; specifically according to the formula: Gc=α×Lc+(1+α)×Sc; where α is a weighting coefficient, which is adjusted according to different usage scenarios; Step H2: The dynamic auxiliary adjustment module accesses the microphone data of the device through the Web Audio API in the browser, captures the surrounding environmental sound, obtains the noise value Z and noise duration T in the current environment, sets the noise threshold Yz, and compares the obtained noise value data Z with the noise threshold Yz. When the noise value Z is less than or equal to Yz, the dynamic auxiliary adjustment module determines that the current environment is a low-noise environment; when the noise value Z is greater than or equal to Yz, the dynamic auxiliary adjustment module determines that the current environment is a high-noise environment; sets the time threshold Yt, compares the obtained noise duration T with the time threshold Yt. When the noise duration T is less than or equal to Yt, the dynamic auxiliary adjustment module determines that the current environment is short-term noise; when the noise duration T is greater than or equal to Yt, the dynamic auxiliary adjustment module determines that the current environment is continuous noise; The dynamic auxiliary adjustment module calculates the influence of the noise value on the condition coefficient based on the noise value Z and the noise threshold value Yz, specifically according to the formula: Zc=Z / Yz, where Zc is the influence coefficient of the noise value on the noise condition coefficient; calculates the influence of the sound duration on the noise condition coefficient based on the noise duration T and the time threshold value Yt: specifically according to the formula: Tc=T / Yt, where Tc is the influence coefficient of the noise duration on the noise condition coefficient; the dynamic auxiliary adjustment module determines the noise condition coefficient ZYc by combining the noise value influence coefficient Zc and the sound duration influence coefficient Tc in a weighted average manner; specifically according to the formula: ZYc=Qz×Zc+Qt×Tc; where Qz represents the weight of the noise value influence coefficient, and Qt represents the weight of the noise duration influence coefficient; Step H3: The dynamic auxiliary adjustment module determines the use environment coefficient Hc by weighted average calculation based on the illumination condition coefficient and the noise condition coefficient, specifically according to the formula: Hc=Qg×Gc+Qzy×ZYc, where Qg represents the weight of the illumination condition coefficient, and Qzy represents the weight of the noise condition coefficient; The user's physical condition coefficient is obtained as follows: The dynamic auxiliary adjustment module sends a request to the data storage module through the RESTful API to obtain the user's visual impairment and tactile impairment levels, and converts the user's visual impairment and tactile impairment levels into standardized values ​​through preset mapping rules, and calculates the user's physical condition coefficient by weighted average calculation based on the user's visual impairment and tactile impairment values, specifically according to the formula: User physical condition coefficient = Qa × visual impairment level + Qb × tactile impairment level, where Qa represents the weight of the visual impairment level, and Qb represents the weight of the tactile impairment level; The dynamic auxiliary adjustment module will combine the user's physical condition coefficient and the use environment coefficient to calculate the user's specific adjustment coefficient by weighted average; the specific formula is: user specific adjustment coefficient = Qq × user physical condition coefficient + Qp × use environment coefficient, where Qq represents the weight of the user's physical condition coefficient, and Qp represents the weight of the use environment coefficient; The dynamic auxiliary adjustment module updates the style of page elements in real time by dynamically modifying the variables of the CSS style sheet based on the user's specific adjustment coefficient, specifically according to the formula: new parameter = old parameter × (1 + user specific adjustment coefficient).

4. The system for assisting users with visual and tactile impairments to use an e-commerce platform according to claim 3, characterized in that: When the page is loaded, the user-customized module obtains the stored JSON configuration file from the data storage module through the RESTful API through asynchronous requests, parses it into objects, and dynamically generates the corresponding visual controls; The user customization module listens to control events through onChange, obtains user input data, and writes corresponding JSON configuration items in real time, storing them in the form of key-value pairs; user configuration is applied in real time through CSS variables or by directly modifying the inline style of the page DOM elements.

5. The system for assisting users with visual and tactile impairments to use an e-commerce platform according to claim 4, characterized in that: The user customization module uses JavaScript event monitoring to capture real-time interactive behavior data, and combines the user's real-time interactive behavior data with the user's specific adjustment coefficient to calculate a new user adjustment coefficient in a weighted average manner. The specific formula is: New user adjustment coefficient = (click frequency × Qu) + (page dwell time × Qi) + (input box operation times × Qo) + (voice input times × Qr), where Qu represents the weight of click frequency, Qi represents the weight of page dwell time, Qo represents the weight of input box operation times, and Qr represents the weight of voice input times; The user customization module combines the current adjustment coefficient with the initial adjustment coefficient, and uses the weighted average method to predict the future adjustment coefficient of the user's page element, specifically based on the formula: future adjustment coefficient of page element = new adjustment coefficient × Qf + user-specific adjustment coefficient × (1-Qj), where Qf represents the weight of the new adjustment coefficient, and Qj represents the weight of the user-specific adjustment coefficient. The threshold value Yw of the future adjustment coefficient of the page element is set. When the future adjustment coefficient of the page element is ≤Yw, the relevant page element should be reduced; when the future adjustment coefficient of the page element is >Yw, the relevant page element should be enlarged; and the real-time update of the page is achieved through dynamic setting of CSS variables and DOM operations; The user-customized module regularly collects new interactive behavior data through the event listener focus, uses the new user behavior data to calculate the new future adjustment coefficient according to the established weights and algorithms, and uses JavaScript's setInterval method to execute the adjustment logic at set time intervals.

6. A method for assisting users with impaired vision and tactile sense to conveniently use an e-commerce platform, based on the method for assisting users with impaired vision and tactile sense to conveniently use an e-commerce platform as claimed in any one of claims 1 to 5, characterized in that: Step 1: Get the real-time page feature data of the product list; Step 2: Based on the real-time feature data of the product details page, determine the user's specific adjustment coefficient by combining the user's physical condition coefficient and the usage environment coefficient, and dynamically adjust the product details page according to the user's specific adjustment coefficient; Step 3: Provide a personalized adjustment interface, allowing users to adjust interface parameters by themselves; Step 4: Use JavaScript event monitoring to capture real-time interactive behavior data. By combining the user's real-time interactive behavior data with the user's specific adjustment coefficient, comprehensive analysis is performed to predict the future adjustment coefficients of page elements. Each page is automatically adjusted based on the user's continuously updated future coefficients.

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