Intelligent interaction system and method based on space-time coupling matrix and multi-mode perception

Abstract

The invention provides an intelligent interaction system and method based on a space-time coupling matrix and multi-mode perception. The method comprises the following steps that a user selects and triggers interaction equipment to capture user information; acquiring latitude and longitude data, importing the latitude and longitude data into a space-time coupling matrix, and calculating rough basic information through a dynamic grid decision system; carrying out embedded fusion and analysis on the data and true sun time data, and generating personalized signature content corresponding to specific information of an AI model database of a selected field; user feedback interaction enters the next step of analysis, and an AI large model database is imported to generate detailed signature analysis content; the user portrait model is constructed to provide data support for the scene, and the user experience and the operation efficiency are improved. According to the invention, the personalized signature is generated by combining the space-time coupling matrix with the AI large model algorithm, and intelligent upgrading of the scene blessing device is realized by using the multi-modal sensor technology. The traditional culture and the modern digital technology are deeply fused, the culture kernel is reserved, and the user experience and the commercial value are improved.

Description

Technical Field

[0001] This invention relates to the field of interactive entertainment and cultural and creative technology, and in particular to an intelligent interactive system and method based on spatiotemporal coupling matrix and multimodal perception. Background Technology

[0002] Existing interactive blessing devices mostly adopt random or fixed fortune-telling models, lacking dynamic connection with the user's real time and space, and failing to provide personalized interpretations of personality and other personal information. For example, traditional blessing relies on fixed times or random number readings, making it difficult to meet users' needs for "real-time" and "personalized" experiences. In addition, existing technologies have not fully integrated emerging technologies such as blockchain and AI, resulting in problems such as unclear content copyright ownership and monotonous interactive experiences.

[0003] Existing cultural and tourism scenarios suffer from the following shortcomings: Insufficient scenario adaptability: Interactive installations often focus on a single function (such as QR code scanning for lotteries), lacking modular design to adapt to diverse scenarios such as "Gen Z trendy play / family travel / senior citizen travel." According to a 2024 survey by the Ministry of Culture and Tourism, approximately 76% of interactive installations have a user repeat experience rate of less than 30% due to fixed themes; the average hardware update cycle is 2.5 years, and maintenance costs account for 28% of total investment. Low accuracy in behavior prediction: User behavior analysis relies on linear statistics or simple machine learning models, resulting in large prediction errors. Existing technologies generally have a prediction accuracy rate of less than 65% and cannot explain the spatiotemporal correlation of prediction results, making it difficult to guide dynamic resource scheduling in scenarios. Inefficient risk control decisions: Scenario resource scheduling (shuttle buses, restrooms) relies on human experience, lacking a data-driven risk control engine based on "spatiotemporal-behavior-emotion."

[0004] In addition, common customer group analysis methods currently fall into two categories. One is based on traditional manual sampling surveys and observation, which involves manually analyzing user numbers, characteristics, and activities to analyze customer composition and behavioral patterns. However, this method is resource-intensive, has a long data collection cycle, high costs, small sample sizes, and struggles to obtain timely and accurate customer information, resulting in poor real-time performance and hindering optimization of scenario management and operations. The other method is based on mobile internet and intelligent data collection and analysis, using mobile apps, Wi-Fi signals, and other facilities to collect user location information, movement trajectories, and dwell time to monitor customer groups. This method offers convenient data acquisition, strong real-time performance, and can provide early warnings of congestion points, but it struggles to effectively identify customer attributes and requires users to carry smartphones for data collection. Summary of the Invention

[0005] This invention proposes an intelligent interaction system and method based on spatiotemporal coupling matrix and multimodal perception. It can generate AI personalized signatures for the user's selected domain through the event probability prediction method of spatiotemporal coupling matrix, thereby enhancing interactivity.

[0006] The technical solution of this invention is implemented as follows: an intelligent interaction method based on spatiotemporal coupling matrix and multimodal perception, comprising the following steps:

[0007] Step 1. Set up an interactive device with multiple domain options in a fixed scene. When the user selects a domain option, the interactive device detects the triggering behavior through sensors and captures user information through a camera.

[0008] Step 2. Obtain the precise time of user triggering and current GPS coordinates, acquire latitude and longitude data, import the spatiotemporal coupling matrix, and deduce the approximate basic information through the dynamic grid decision system;

[0009] Step 3. Combine the user's feature information captured by the camera with the true solar time data for embedding, fusion and analysis, and correspond to the specific information in the AI ​​model database of the selected field to generate a simplified version of the personalized signature content;

[0010] Step 4. If the user interacts by clicking / scanning the code, the system will proceed to the next step of analysis, importing the AI ​​large model database to generate detailed interpretation of the divination text;

[0011] Step 5. Build user profile models using user interaction and consumption behavior data to provide data support for scenario marketing and route planning;

[0012] Step 6. The digital twin passenger flow heat map generated in real time at the edge can dynamically adjust the configuration of scene resources, improving user experience and operational efficiency.

[0013] Preferably, the step two, which involves calculating the spatiotemporal coupling matrix event based on the GPS coordinates, includes the following steps:

[0014] Step 1: Calculate the sexagenary cycle matrix G based on the current system time T and geographical coordinates (L, B);

[0015] Step 2: Calculate the energy value Ei of each palace based on G within the nine-square grid coordinate system Ω;

[0016] Step 3: Output the event probability distribution P based on Ei and the preset gate weight W.

[0017] Preferably, the sensor in step one includes an infrared / TOF / capacitive sensor that captures user trigger signals in real time, including proximity, touch, and pressing actions.

[0018] Preferably, in step three, the user's characteristic information captured by the camera includes gender, age, expression, and clothing color information, and this information is captured and imported into the database.

[0019] Preferably, in steps five and six, a user profile model is constructed using user interaction and consumption behavior data. The total number of people participating in the interaction and the gender and age ratio are statistically analyzed. Furthermore, the gender and age ratio of people participating in the interaction and consumption are statistically analyzed and imported into the database, which can be used to recommend the next tour route.

[0020] An intelligent interactive system based on spatiotemporal coupling matrix and multimodal perception includes a device housing, a trigger module, a data acquisition module, a control processing module, a network module, and a printing module. Each module is housed inside the device housing, which is a multi-faceted prism.

[0021] Preferably, the device housing is a hexagonal prism, with each face independently corresponding to a field option, and the corresponding option is marked with text and graphic symbols and a corresponding trigger module.

[0022] Preferably, each face of the hexagonal prism corresponds to career / studies / wealth / marriage / health / peace.

[0023] Preferably, the data acquisition module has a camera installed on each side, the camera is connected to the control processing module and the network module, and transmits data to the control processing module.

[0024] Preferably, the triggering module includes, but is not limited to, an infrared sensor, a TOF sensor, and a capacitive sensor, which are connected to the control processing module.

[0025] Compared with existing technologies, the advantages of this invention are as follows: This invention generates personalized fortune slips by combining a spatiotemporal coupling matrix with AI large-scale model algorithms, and achieves an intelligent upgrade of the scene-based blessing device using multimodal sensor technology. Its innovation lies in the deep integration of traditional culture and modern digital technology, preserving the cultural core while enhancing user interaction and commercial value through technological means.

[0026] The core value of this format lies in "amplifying the sense of ritual through technology"—the physical hexagonal facet provides a tangible ritual carrier, simplifies the interaction threshold, and the large model transforms "praying for blessings" from "one-way wishing" into "two-way dialogue." As long as attention is paid to details such as hardware stability, content accuracy, and lightweight subsequent conversion, it has the potential to become a differentiated and explosive scenario, especially with strong applicability in the fields of cultural tourism, commercial traffic generation, and emotional consumption. Attached Figure Description

[0027] Figure 1 This is a block diagram of the overall system architecture of the present invention;

[0028] Figure 2 This is a block diagram of the multimodal data acquisition module of the present invention;

[0029] Figure 3This is a block diagram of the trigger module architecture of the present invention;

[0030] Figure 4 This is a block diagram of the positioning and communication module architecture of the present invention;

[0031] Figure 5 This is a flowchart of the true solar time calculation process of the present invention;

[0032] Figure 6 This is a block diagram of the feature vector encoding module of the present invention;

[0033] Figure 7 This is a block diagram of the edge-side heatmap generation module of the present invention;

[0034] Figure 8 This is a schematic diagram of the structure of a cultural and tourism interactive device according to an embodiment of the present invention.

[0035] In the diagram: 1. Equipment casing; 2. Trigger module; 3. Data acquisition module; 4. Control processing module; 5. Network module; 6. Printing module. Detailed Implementation

[0036] 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.

[0037] Example: See Figures 1-8 ,

[0038] This invention relates to an intelligent interactive system based on spatiotemporal coupling matrix and multimodal perception, comprising: a hardware perception layer: a hexagonal prism device, each facet having a detachable magnetic theme panel (with a built-in NFC chip storing theme identifiers), integrating an infrared proximity sensor (detection distance 0.1-1m, accuracy 2mm), a TOF distance sensor (measurement range 2cm-100cm, accuracy 2mm), a high-precision GPS (error ≤5m), and a 1080P camera;

[0039] Spatiotemporal calculation layer: True solar time calculation module (calculates true solar time TST based on formulas 1-4), nine-square grid spatiotemporal matrix generation module (converts geographic coordinates into a 3×3 matrix G, with each element Gij corresponding to the cultural and tourism context as the dimension weight, calculated using formulas 5-6);

[0040] Intelligent Generation Layer: Cloud-based AI generation module (using a fine-tuned GPT-3.5 model, and generating personalized prompts by integrating scene tags, user profiles, and spatiotemporal parameters through Prompt templates);

[0041] Data closed-loop layer: Spatiotemporal behavioral risk control engine (real-time analysis of interactive data, dynamic optimization of resource allocation, including heat map scheduling module and inventory early warning module).

[0042] The theme panels of the hexagonal prism device can be quickly changed via magnetic attraction. Each panel corresponds to a cultural tourism theme (a six-dimensional behavioral preference label) and has a built-in NFC chip to store the corresponding theme identifier. The identifier uses a 6-bit binary code, with each 2 bits corresponding to a weight level of one dimension.

[0043] The true solar time calculation module uses formula 1: TST = LST + Δt, where Δt is calculated using formulas 2-4, and the parameters of the formulas include:

[0044] d_n: Day number; year: Current year; λ: Longitude; φ: Latitude; h_s: Solar altitude angle; A_s: Solar azimuth angle.

[0045] The nine-square grid spatiotemporal matrix generation module converts geographic coordinates (λ,φ) into Cartesian coordinates (x,y) and constructs a 3×3 matrix G. The central element G22 corresponds to the current coordinates, and the surrounding elements Gij are calculated using formulas 5-6. The formula parameters include: α, β, γ: preset weight coefficients that satisfy α+β+γ=1.

[0046] h_s: Solar altitude angle; Crowd density_ij: Historical crowd density data; Sentiment value_ij: User sentiment analysis results.

[0047] An intelligent interaction method based on spatiotemporal coupling matrix and multimodal perception, implemented using the aforementioned intelligent interaction system, includes the following steps:

[0048] Step 1. Set up an interactive device with multiple domain options in a fixed scene. When the user selects a domain option, the interactive device detects the triggering behavior through sensors and captures user information through a camera.

[0049] Step 2. Obtain the precise time of user triggering and current GPS coordinates, acquire latitude and longitude data, import the spatiotemporal coupling matrix, and deduce the approximate basic information through the dynamic grid decision system;

[0050] Step 3. Combine the user's feature information captured by the camera with the true solar time data for embedding, fusion and analysis, and correspond to the specific information in the AI ​​model database of the selected field to generate a simplified version of the personalized signature content;

[0051] Step 4. If the user interacts by clicking / scanning the code, the system will proceed to the next step of analysis, importing the AI ​​large model database to generate detailed interpretation of the divination text;

[0052] Step 5. Build user profile models using user interaction and consumption behavior data to provide data support for scenario marketing and route planning;

[0053] Step Six. The digital twin passenger flow heat map generated in real time at the edge can dynamically adjust scene resource configuration, improving user experience and operational efficiency. It receives user location and triggered behavior data in real time to generate a dynamic passenger flow heat map, which is used for intelligent scheduling of scene resources, such as peak-hour shuttle bus diversion and optimization of toilet cleaning frequency.

[0054] The specific steps for calculating the spatiotemporal coupling matrix event based on the GPS coordinates in step two are as follows:

[0055] Step 1: Calculate the sexagenary cycle matrix G based on the current system time T and geographical coordinates (L, B);

[0056] Step 2: Calculate the energy value Ei of each palace based on G within the nine-square grid coordinate system Ω;

[0057] Step 3: Output the event probability distribution P based on Ei and the preset gate weight W.

[0058] The nine-square grid spatiotemporal matrix generation module converts geographic coordinates into a 3×3 matrix G, where each element Gij corresponds to the weight of the "six-dimensional label of behavioral preferences".

[0059] The nine-square grid spatiotemporal matrix generation module converts geographic coordinates (λ,φ) into Cartesian coordinates (x,y) and constructs a 3×3 matrix G. The central element G22 corresponds to the current coordinates, and the surrounding elements Gij (i,j=1,2,3,i≠j) are the weights of the neighboring regions. The weight calculation integrates true solar time and historical pedestrian flow data.

[0060] Formula 5: Gij = α × h_s + β × crowd density_ij + γ × mood value_ij

[0061] Formula 6: α + β + γ = 1

[0062] Where h_s is the solar altitude angle (calculated using formula 7-9), crowd density_ij is historical crowd density data, emotion value_ij is the user emotion analysis result, and α, β, and γ are preset weight coefficients.

[0063] Formula 7: sin(h_s)=cos(ω)cos(φ)+sin(φ)sin(δ)

[0064] Formula 8: cos(A_s)=[sin(φ)cos(δ)-cos(φ)sin(δ)cos(ω)] / sin(h_s)

[0065] Formula 9: δ = 23.45° × sin(360° × (d_n + 284) / 365)

[0066] Where ω is the hour angle (calculated using formula 10), φ is the geographic latitude (degrees), δ is the solar declination (degrees), and A_s is the solar azimuth (degrees).

[0067] Formula 10: ω = 15° × (TST - 12)

[0068] Calculation of solar azimuth

[0069] The solar azimuth calculation module calculates the solar azimuth angle A_s according to formula 11:

[0070] Formula 11: A_s = 180° - arccos(cos(A_s))

[0071] The RGB light strip control module dynamically adjusts the colors based on the element values ​​of the nine-grid spacetime matrix.

[0072] Formula 12: RGB=(Gij×2.55,(100-Gij)×2.55,0)

[0073] Where Gij represents the element value of the nine-grid spatiotemporal matrix (range 0-100), and RGB represents the color channel value (range 0-255).

[0074] The spatiotemporal coupling matrix can be replaced by a true solar time algorithm for big data import. The true solar time algorithm specifically calculates the solar angle θ = 2π × (day - N). o ) / 365.2422

[0075] Annual correction factor N o =79.6764+0.2422×(year-1985)-int((year-1985) / 4)

[0076] True flat solar time difference Δt (with second-level accuracy)

[0077] Δt=0.0028-1.9857·sinθ+9.9059·sin2θ-7.0924·cosθ-0.6882·cos2θ

[0078] Longitude-corrected mean solar time (LST) = Beijing time - (120 - local longitude) × 4 min

[0079] Final true solar time TST = LST + Δt

[0080] Where LST is the local standard time (hours), d_n is the date sequence number (calculated from January 1st), year is the current year, and θ is the day angle (radians).

[0081] The sensors in step one include infrared / TOF / capacitive sensors. These sensors capture user trigger signals in real time, including proximity, touch, and pressing actions. Infrared sensor: detects whether the user is approaching the target area. TOF sensor: accurately measures the distance between the user and the target. Capacitive sensor: detects whether the user touches or presses the target marking surface.

[0082] In step three, the camera captures user feature information including gender, age, facial expression, and clothing color. This information is captured and imported into the database. Gender recognition: Facial recognition technology is used to determine the user's gender. Age range: The user's age range is estimated through facial feature analysis. Facial expression recognition: A deep learning model is used to recognize the user's facial expressions. Clothing color: Computer vision technology is used to extract the main colors of the user's clothing.

[0083] In steps five and six, a user profile model is constructed using user interaction and consumption behavior data. The total number of people participating in the interaction and the gender and age ratio are statistically analyzed. Furthermore, the gender and age ratio of people participating in the interaction and consumption are statistically analyzed and imported into the database, which can be used to recommend the next tour route.

[0084] Figure 8 As shown, an interactive cultural tourism device for users in a given scenario includes a device housing 1, a trigger module 2, a data acquisition module 3, a control processing module 4, a network module 5, and a printing module 6. All modules are housed within the device housing 1, which is a multi-faceted prism. It features a built-in high-precision GPS and supports 4G / 5G / NB-IoT communication to ensure accurate user location acquisition and data transmission. GPS positioning: This acquires the user's precise location coordinates. The network module 5 supports multiple communication methods, ensuring stable and real-time data transmission. The network module 5 is also a cloud-based AI generation module: it uses a fine-tuned GPT-3.5 model and integrates scene tags, user profiles, and spatiotemporal parameters through a Prompt template to generate personalized prompts. The printing module 6 prints the personalized prompts as paper documents, which are then distributed to users with a QR code that allows them to scan and proceed to the next interactive stage.

[0085] The device housing 1 is a hexagonal prism, with each face independently corresponding to a field option, and each option is marked with a text and graphic identifier and a corresponding trigger module 2. The hexagonal prism device features a detachable magnetic theme panel on each face (with a built-in NFC chip storing the theme identifier), the trigger module 2 integrates an infrared proximity sensor and a TOF distance sensor, and the data acquisition module 3 integrates a high-precision GPS and a 1080P camera.

[0086] The hexagonal prism (900mm high, 400mm in diameter inscribed circle, made of aluminum alloy) features magnetic theme panels on each facet (220mm x 220mm, supporting quick theme switching with six-dimensional behavioral preference tags). This design allows for rapid theme panel switching during different festivals (such as Spring Festival and Lantern Festival) or special events (such as IP collaboration exhibitions), increasing hardware reusability by 300%.

[0087] NFC module: 13.56MHz, ISO 14443A protocol, sensing distance ≤3cm (anti-accidental touch), storage capacity 128B, used to store theme identifiers (6-dimensional weight parameters, each 2 bits corresponds to the weight level of one dimension).

[0088] Environmental perception: Infrared proximity sensor: detection distance 0.1-1m, accuracy 2mm, used to detect when a user approaches;

[0089] TOF distance sensor: Guangwei N01 model, measuring range 2cm-100cm, accuracy 2mm, strong resistance to sunlight interference;

[0090] High-precision GPS: error ≤ 5m, used to obtain user's geographical location and time information;

[0091] 1080P camera: Supports face recognition / expression analysis / clothing color recognition (HSV color space) for collecting user visual information.

[0092] Interactive feedback: Each facet edge is embedded with an RGB light strip (supports single-color constant on / blinking), used to dynamically adjust the color according to the spatiotemporal matrix element values;

[0093] It has a built-in 0.5W buzzer (plays a prompt sound when triggered) to provide interactive feedback.

[0094] Explanation of technical terms

[0095] 1. True Solar Time (TST): The time determined by the actual position of the sun in the sky. It differs from Beijing time and is affected by longitude.

[0096] 2. Nine-grid spatiotemporal matrix: The geographic coordinates (λ,φ) are discretized into a 3×3 grid, and a dynamic weight matrix is ​​generated by combining true solar time parameters to predict user behavior.

[0097] 3. Spatiotemporal coupling matrix: A matrix obtained by coupling time, space and behavioral features, used to guide the generation of personalized prompts and resource scheduling.

[0098] 4. NFC chip: A near-field communication chip compliant with the ISO 14443A protocol, used to store subject identifiers and weight parameters.

[0099] 5. Emotional Value_ij: The user's emotional value calculated by the facial expression analysis algorithm, ranging from 0 to 100, used to adjust the weights of the nine-square spatiotemporal matrix.

[0100] Each face of the hexagonal prism corresponds to an interaction type, such as career / studies / wealth / marriage / health / peace; it can also be set to correspond to six blessing directions: fortune, prosperity, longevity, happiness, wealth, and peace. The system uses infrared / TOF / capacitive sensors to detect user proximity, touch, and press actions, triggering the blessing process for the corresponding face (fortune / prosperity / longevity / happiness / wealth / peace); and a camera captures real-time user gender, age, facial expression, and clothing color information.

[0101] Encode spatiotemporal, behavioral, and individual features into vectors; Large model prompt: Input vector + ancient book fortune-telling template to generate personalized fortune-telling text (Example: "A good match is near, but you must first let go of old friends" → combined with the user's gender (female), expression (smiling), and clothing (red dress) to optimize to "Girl, the red dress makes you look very good. A good match is near, but you must first let go of old friends' entanglements").

[0102] RLHF Feedback: After users click "Detailed Analysis", feedback (likes / dislikes) is collected to train the model and continuously optimize the signature.

[0103] The data acquisition module 3 has a camera installed on each side. The camera is connected to the control processing module 4 and the network module 5, and transmits the data to the control processing module 4.

[0104] The triggering module 2 includes, but is not limited to, an infrared sensor, a TOF sensor, and a capacitive sensor, which are connected to the control processing module 4.

[0105] Edge computing and cloud collaborative architecture

[0106] To improve the response speed and interactive experience of generated signature content, this invention adopts a distributed computing architecture of "edge devices + cloud-based large model":

[0107] 1. Edge Computing Nodes: The system's main control module utilizes edge computing platforms such as the Raspberry Pi 4B, running a lightweight Linux system. It features NFC signal parsing, user behavior preprocessing, and local lighting and sound effect control. The system incorporates caching and rollback mechanisms, allowing for the retrieval of locally preset content in offline environments to ensure uninterrupted interaction.

[0108] 2. Cloud-based large model service: Based on a finely tuned large language model (such as GPT-3.5 or a domestic model), it is deployed on an elastic cloud service platform. It receives interaction requests through API interfaces, generates personalized prompts of about 100-150 characters based on "topic number + user scenario characteristics", and sends them back to the device.

[0109] 3. Multimodal sensing module integration: including NFC recognition, light and sound effect feedback, receipt printing, 4G communication, infrared and TOF distance sensors. All modules are connected to edge devices via standard I2C, USB or GPIO interfaces.

[0110] This architecture effectively achieves a low-latency, highly available interactive experience in cultural and tourism scenarios, significantly enhancing the independence of device deployment and the smoothness of user experience.

[0111] Feature encoding layer: Transforms user features into machine-readable vectors; through multimodal perception (sensors + camera), it encodes user behavior (which side is touched / stay time) and individual characteristics (gender / age / expression / clothing) into high-dimensional vectors, enabling large models to understand "who the user is and what they are doing".

[0112] The Prompt generation module designs few-shot prompt templates to guide the cloud-based large model to learn the logic of "traditional calculation rules + personalized expression". The few-shot prompt template includes task description, rule explanation and example input and output. Example input is true solar time, latitude and longitude parameters and individual characteristics. Example output is a personalized blessing text, including recent luck, traditional calculation basis and personalized description.

[0113] The personalized prompt generation module uses a fine-tuned GPT-3.5 model, integrating scene tags, user profiles, and spatiotemporal parameters through a Prompt template to generate personalized prompts. The Prompt template includes:

[0114] Task description (e.g., "Generate personalized prompts based on spatiotemporal parameters");

[0115] Rule constraints (such as "integration of six-dimensional behavioral preference labels");

[0116] Example input / output (e.g., input "Wealthy face + Male / 40 years old / Frowning / Black coat + Shopping mall", output "It is better to defend than to attack at this time...Starbucks today offers a 10 RMB discount on orders over 50 RMB").

[0117] Example 1.

[0118] Take a 5A-level scenic spot, "Blessing Hexagonal Prism," as an example:

[0119] Hardware deployment: The hexagonal prism is 2 meters high, with a capacitive touch area and camera on each side, and a built-in GPS module on the top;

[0120] Software Flow: User touches the "Wealth" face → Sensor triggers → GPS (31.2304°N, 121.4737°E) is acquired → True solar time is calculated (October 1, 2024, 14:30, True Solar Time 14:28) → Large model generates the fortune reading "Wealth is shining brightly, but you must first give up small gains" → Combined with the user's expression (smiling) and clothing (yellow shirt), it is optimized to "Sir, the yellow shirt makes you look very noble, wealth is shining brightly! But today it is advisable to give up small gains and give the slow-moving samples to old customers, tomorrow's orders will double~" → User scans the code and pays 9.9 yuan to unlock detailed analysis (including "Tomorrow the partner will wear a blue suit, give a 3% discount when negotiating the price") → Data is uploaded to the blockchain for evidence, and the user is marked as "high willingness to spend + sensitive to wealth" → Subsequent "Open the Treasury" activity is pushed.

[0121] Example 2. For instance, "female / 25 years old / smiling / red dress" can be broken down into: gender vector [0.9, 0.1] (0.9 represents female tendency), age segmentation vector (1 for the 20-30 age range), micro-expression classification (smiling corresponds to a positive emotion vector [0.8, 0.2]), and clothing color embedding (red may be associated with the semantic vector of "enthusiasm / impulsiveness"). These vectors are concatenated with spatiotemporal vectors (true solar time + latitude and longitude embedding) as input to the larger model.

[0122] Input: Time (Hangzhou, True Solar Time 14:00) + Characteristics (Male / 40 years old / Frowning / Black coat) → Output: "Brother, the black coat makes you look composed. Don't sign any new contracts recently; your old business will keep you safe."

[0123] Input: Time (Xi'an, True Solar Time 9:00) + Features (Female / 18 years old / Smiling / Pink Dress) → Output: "Girl, your pink dress makes you look charming, auspicious for marriage. At the teahouse in the south of the city, a destined person will come to greet you, and good fortune will come."

[0124] Business closed loop layer:

[0125] Blockchain NFT ownership verification: The signature is uploaded to the blockchain when it is generated to ensure copyright.

[0126] Paid analysis: Scan the QR code to unlock a detailed fortune reading (such as "Fortune: Wear blue clothes next week to discuss cooperation");

[0127] Data feedback: Building user profiles for targeted marketing in specific scenarios (such as promoting parent-child blessing activities to mothers);

[0128] Heat map scheduling: Digital twin passenger flow heat maps are generated at the edge to dynamically adjust resources such as shuttle buses and toilets.

[0129] In addition, user tour guidance can be provided through user feature identification dimensions.

[0130] Gender matching

[0131] For female users: We recommend flower-themed routes / spots with soft lighting for photos (e.g., "This rose garden would perfectly complement your pale purple dress").

[0132] For male users: Highlight technology-themed experiences (e.g., "The mecha exhibition hall is open for a limited time").

[0133] Age stratification

[0134] Children (3-12 years old): Trigger cartoon voice guidance (e.g., "Pikachu leads you to discover dinosaur fossils!")

[0135] Teenagers (13-25 years old): Push AR effects to popular photo spots (e.g., "Generate your cyberpunk-style profile picture")

[0136] Seniors (50+): Automatically switch to large-font navigation (e.g., "Rest area and free tea available 50 meters ahead")

[0137] Micro-expression recognition

[0138] Downturned mouth: Push heartwarming content (e.g., "Would you like to watch a video of a newborn panda?").

[0139] Pupil dilation: Instantly recommend surprise items (e.g., "You are currently only 200 meters away from the limited-time performance")

[0140] Clothing color analysis

[0141] Cool-toned clothing: Recommended for modern art exhibitions (e.g., "This installation art creates an interesting dialogue with your blue shirt").

[0142] Warm-toned clothing: Guiding the way to natural landscape areas (e.g., "The maple leaf boardwalk would perfectly complement your orange scarf").

[0143] Dynamic content generation system

[0144] The composite feature algorithm "30-year-old woman + yellow dress + cheerful expression" = "A flower arranging workshop is being held on the sunny terrace; your bright attire will make your arrangements stand out."

[0145] Real-time scene overlay is achieved through AR glasses: "Raincoat detected → Indoor exhibition hall navigation route pushed + Friendly reminder: The locker on your right is available for free use."

[0146] Heat map scheduling module: Updates the passenger flow heat map every 5 seconds. If the density of a certain area is >80 people / 100㎡, triggers the "shuttle bus increase" command (pushed to the scene scheduling platform API);

[0147] Inventory warning module: Monitors the daily sales of "Cai Mian" fragrance cards. If the sales drop is greater than 30% in 24 hours, it automatically sends a replenishment request to the supplier system (pre-built API integration).

[0148] Experimental data and effect verification

[0149] To verify the technical effects of this invention, we conducted the following experiments:

[0150] 1. Personalized prompt generation accuracy test

[0151] Test sample: 1000 users of different genders, ages and clothing characteristics.

[0152] Testing method: After the system generates personalized prompts, three professionals score them to determine whether the personalized prompts match the user's characteristics.

[0153] Results: The average accuracy rate reached 90%, significantly higher than that of traditional personalized prompt systems (approximately 60%).

[0154] 2. User satisfaction survey

[0155] Survey sample: 500 users of this system and traditional personalized prompt systems.

[0156] Survey Methodology: A questionnaire survey was conducted to assess user satisfaction with personalized prompts.

[0157] Results: The user satisfaction rate of this system reached 85%, while that of traditional personalized prompt systems was only 70%.

[0158] 3. System response time test

[0159] Test scenario: Different numbers of people using the system simultaneously.

[0160] Test method: Measure the total time from user trigger to the completion of the personalized prompt display.

[0161] Results: The response time is approximately 2 seconds when used by a single user and approximately 4 seconds when used by 5 users simultaneously, meeting the requirements of real-time applications.

[0162] 4. Risk control engine performance test

[0163] Test scenario: Passenger flow data during peak hours at a certain 5A venue.

[0164] Test method: Compare the response time of the shuttle bus and the toilet queuing time of this system with those of manually dispatched systems.

[0165] Result: Shuttle bus response time was reduced by 40%;

[0166] Toilet waiting time reduced by 35%;

[0167] The average user stay time increased from 45 minutes to 65 minutes; surrounding business revenue increased by 18%.

[0168] Experimental Data Table

[0169] Experimental Project Test samples Test Results Comparison results Signature generation accuracy 1000 users 90％ Traditional systems 60% User satisfaction 500 users 85％ Traditional systems 70% System response time Single-person use 2 seconds Traditional system 3 seconds 5 people use at the same time 4 seconds Traditional system 6 seconds Shuttle bus response time A certain 5A scenario shorten by 40% Manual dispatch takes 1-2 hours toilet waiting time A certain 5A scenario Reduce by 35% Manual dispatching is delayed Average user dwell time A certain 5A scenario 65 minutes Traditional system 45 minutes Surrounding commercial revenue A certain 5A scenario Increase by 18% Traditional system is stable

[0170] 2. Formula Comparison Table

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[0172]

[0173] This invention achieves an intelligent upgrade of the blessing device through true solar time algorithm, AI personalized signature, blockchain NFT ownership verification, and multimodal sensor technology. Its innovation lies in the deep integration of traditional culture and modern digital technology, preserving the cultural core while enhancing user experience and commercial value through technological means.

[0174] The core value of this format lies in "amplifying the sense of ritual through technology"—the physical hexagonal facet provides a tangible ritual carrier, simplifies the interaction threshold, and the large model transforms "praying for blessings" from "one-way wishing" into "two-way dialogue." As long as attention is paid to details such as hardware stability, content accuracy, and lightweight subsequent conversion, it has the potential to become a differentiated and explosive scenario, especially with strong applicability in the fields of cultural tourism, commercial traffic generation, and emotional consumption.

[0175] The system utilizes multi-modal data acquisition, spatiotemporal computation, intelligent generation, and data closed-loop technologies to achieve a personalized blessing experience for users, while blockchain-based notarization ensures data security and copyright ownership. This system not only enhances the user's cultural experience but also provides strong data support for scene management.

[0176] Based on the core logic of the hexagonal prayer pillar, and combined with the three goals of "deepening the sense of ritual, enhancing user stickiness, and extending consumption value", innovation points can be designed from four dimensions: process, experience, product, and consumption. This will not only retain the essence of prayer, but also give it a new form that is youthful, interactive, and long-term.

[0177] The hexagonal series of products successfully transforms traditional prayer culture into a modern emotional consumption vehicle through the design logic of "structure as content." Its market acceptance hinges on precisely matching scenario needs (such as commercial traffic generation and psychological healing) and the dual empowerment of technology and culture (AR, environmentally friendly materials). In the future, with the deepening of the experience economy and the upgrading of users' demand for ritualistic experiences, hexagonal products are expected to further penetrate fields such as cultural tourism, health, and education, becoming an innovative category with both commercial value and social significance.

[0178] From the perspective of product logic and scenario adaptation, the hexagonal series products (prayer pillars, wishing pillars, energy stations, etc.) are indeed an innovation in experiential markets and scenarios, which is mainly reflected in three aspects:

[0179] 1. Innovation through the integration of form and function

[0180] Breaking away from the stereotype of traditional blessing products that only offer "single wishes," the "hexagonal" structure naturally divides multiple emotional dimensions, allowing users to precisely address their specific needs (such as stress relief, personal growth, and blessings). This transforms abstract emotional needs into tangible and interactive experiences. For example, the "hexagonal gas station" breaks down the vague encouragement of "refueling" into specific, scenario-based needs like "focus" and "breakthrough," making it more ritualistic than simple slogans or catchphrases and better meeting modern people's need for "lightweight psychological support."

[0181] 2. Flexibility and innovation in scene adaptation

[0182] No longer confined to traditional prayer settings like temples and fairs, these products are expanded through conceptual extensions (energy stations, growth pillars, healing pillars, etc.) to permeate diverse scenarios such as workplaces, campuses, commercial centers, and family spaces. They can even be made into different forms like desktop ornaments and public installations. This "scenario-customized" approach transforms products from "specific traditional cultural symbols" into "reusable emotional interaction carriers." For example, hexagonal prisms on campus emphasize "academic growth," while those in the workplace emphasize "stress release." Essentially, they use the same structural framework to adapt to the emotional pain points of different scenarios.

[0183] 3. Innovation in user participation methods

[0184] Traditional prayers are often "one-way wishes," while the hexagonal series allows users to participate more actively through "multi-faceted choices" and "interactive rituals" (such as touching, sticking stickers, and opening blind boxes). For example, the "Wish Blind Box Pillar" combines wishing with entertainment, while the "Energy Station" transforms prayers into daily psychological cues, lowering the barrier to participation and making the experience more in line with young people's preference for "light rituals."

[0185] In summary, this innovation lies in grasping the visual recognizability of the "hexagonal structure" and the core demand for "emotional attachment." Through conceptual transformation and scenario segmentation, it allows traditional blessing products to move from "niche traditional cultural scenarios" to "mass emotional consumption scenarios," achieving both differentiation and coverage of a wider range of user needs.

[0186] The core of these innovations is to "transform prayer from a 'one-off ritual' into a 'perceptible, participatory, and sustainable part of life'": extending the lifecycle through time and social interaction in the process; deepening memories through multi-sensory experiences and empathy in the experience; adapting products to various scenarios through modularity and environmental friendliness; and enhancing value through memberships and digital collectibles in the consumption process. Ultimately, this preserves the core traditional cultural values ​​of "fortune, prosperity, longevity, happiness, wealth, and peace" while making young people feel that "this is a prayer method that understands me," achieving a win-win situation for cultural heritage and commercial value.

[0187] 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 within the protection scope of the present invention.

Claims

1. An intelligent interaction method based on spatiotemporal coupling matrix and multimodal perception, characterized in that, Includes the following steps: Step 1. Set up an interactive device with multiple domain options in a fixed scene. When the user selects a domain option, the interactive device detects the triggering behavior through sensors and captures user information through a camera. Step 2. Obtain the precise time of user triggering and current GPS coordinates, acquire latitude and longitude data, import the spatiotemporal coupling matrix, and deduce the approximate basic information through the dynamic grid decision system; Step 3. Combine the user's feature information captured by the camera with the true solar time data for embedding, fusion and analysis, and correspond to the specific information in the AI ​​model database of the selected field to generate a simplified version of the personalized signature content; Step 4. If the user interacts by clicking / scanning the code, the system will proceed to the next step of analysis, importing the AI ​​large model database to generate detailed interpretation of the divination text; Step 5. Build user profile models using user interaction and consumption behavior data to provide data support for scenario marketing and route planning; Step 6. The digital twin passenger flow heat map generated in real time at the edge can dynamically adjust the configuration of scene resources, improving user experience and operational efficiency.

2. The intelligent interaction method based on spatiotemporal coupling matrix and multimodal perception according to claim 1, characterized in that: The specific steps for calculating the spatiotemporal coupling matrix event based on the GPS coordinates in step two are as follows: Step 1: Calculate the sexagenary cycle matrix G based on the current system time T and geographical coordinates (L, B); Step 2: Calculate the energy value Ei of each palace based on G within the nine-square grid coordinate system Ω; Step 3: Output the event probability distribution P based on Ei and the preset gate weight W.

3. The intelligent interaction method based on spatiotemporal coupling matrix and multimodal perception according to claim 1, characterized in that: The sensors in step one include infrared / TOF / capacitive sensors, which capture user trigger signals in real time, including proximity, touch, and pressing actions.

4. The intelligent interaction method based on spatiotemporal coupling matrix and multimodal perception according to claim 1, characterized in that: In step three, the camera captures user feature information including gender, age, expression, and clothing color. This information is then captured and imported into the database.

5. The intelligent interaction method based on spatiotemporal coupling matrix and multimodal perception according to claim 1, characterized in that: In steps five and six, a user profile model is constructed using user interaction and consumption behavior data. The total number of people participating in the interaction and the gender and age ratio are statistically analyzed. Furthermore, the gender and age ratio of people participating in the interaction and consumption are statistically analyzed and imported into the database, which can be used to recommend the next tour route.

6. An intelligent interaction system based on spatiotemporal coupling matrix and multimodal perception, characterized in that: It includes a device housing, a trigger module, a data acquisition module, a control processing module, a network module, and a printing module. All modules are housed inside the device housing, which is a multi-faceted prism.

7. The intelligent interaction system based on spatiotemporal coupling matrix and multimodal perception according to claim 6, characterized in that: The device casing is a hexagonal prism, with each face independently corresponding to a field option, and is equipped with text and graphic labels for the corresponding option and a corresponding trigger module.

8. The intelligent interaction system based on spatiotemporal coupling matrix and multimodal perception according to claim 7, characterized in that: Each face of the hexagonal prism corresponds to career / studies / wealth / marriage / health / peace.

9. The intelligent interaction system based on spatiotemporal coupling matrix and multimodal perception according to claim 8, characterized in that: The data acquisition module has a camera installed on each side. The camera is connected to the control processing module and the network module, and transmits the data to the control processing module.

10. The intelligent interaction system based on spatiotemporal coupling matrix and multimodal perception according to claim 6, characterized in that: The triggering module includes, but is not limited to, an infrared sensor, a TOF sensor, and a capacitive sensor, which are connected to the control processing module.

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