Refrigerator sticker and implementation method thereof

Abstract

The invention provides a refrigerator sticker and an implementation method thereof, and the refrigerator sticker comprises a dual-mode positioning core board which comprises a Beidou No.3 area short message chip and a UWB chip set and is used for carrying out the dynamic switching of indoor and outdoor positioning modes; the food material sensing matrix comprises a flexible piezoresistive sensor array and an edge calculation unit and is used for carrying out weight detection and category identification on food materials; the cloud collaboration platform comprises an AI food material recognition engine and a block chain evidence storage system and is used for performing recognition and data storage on food materials; and the intelligent interaction panel comprises an electronic ink display screen and a voice interaction module and is used for dynamically displaying the food material information and controlling the Chinese and English mixed voice. According to the technical scheme, the problems of low power consumption and miniaturization of cross-scene seamless positioning and scene adaptation of the refrigerator paste are solved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of smart home and positioning, in particular to a refrigerator sticker and an implementation method thereof. BACKGROUND

[0002] Traditional refrigerator stickers only have decoration or simple magnetic attraction functions, and cannot meet the fine management needs of modern smart home for food materials. In the prior art, UWB technology can achieve centimeter-level indoor positioning, but is easily affected by signal attenuation in a complex shielding environment; the Beidou system has an accuracy of centimeter level in an open outdoor scene, but has a signal blind area in an indoor or dense building area. Although the existing patent (such as CN120334978A) proposes a Beidou+UWB vehicle terminal, it does not solve the problems of low power consumption, miniaturization and refrigerator sticker scene adaptation. SUMMARY

[0003] The present application provides a refrigerator sticker and an implementation method thereof, to solve the problems of low power consumption, miniaturization and refrigerator sticker scene adaptation in seamless positioning across scenes.

[0004] In a first aspect, a refrigerator sticker is provided, comprising:

[0005] a dual-mode positioning core board comprising a Beidou-3 regional short message chip and a UWB chip set, for dynamically switching indoor and outdoor positioning modes;

[0006] a food material perception matrix comprising a flexible piezoresistive sensor array and an edge computing unit, for weight detection and category identification of food materials;

[0007] a cloud collaborative platform comprising an AI food material identification engine and a blockchain storage system, for identification and data storage of food materials;

[0008] an intelligent interaction panel comprising an electronic ink display screen and a voice interaction module, for dynamic display of food material information and control of mixed English and Chinese voice.

[0009] In the above technical solution, by setting a dual-mode positioning core board comprising a Beidou-3 regional short message chip and a UWB chip set, for dynamically switching indoor and outdoor positioning modes; a food material perception matrix comprising a flexible piezoresistive sensor array and an edge computing unit, for weight detection and category identification of food materials; a cloud collaborative platform comprising an AI food material identification engine and a blockchain storage system, for identification and data storage of food materials; an intelligent interaction panel comprising an electronic ink display screen and a voice interaction module, for dynamic display of food material information and control of mixed English and Chinese voice; the problems of low power consumption, miniaturization and refrigerator sticker scene adaptation in seamless positioning across scenes are solved.

[0010] In one specific and implementable embodiment, it further comprises:

[0011] A security protection system comprising a biometric unit and a self-destruction mechanism unit for fingerprint recognition and physical destruction by violent disassembly.

[0012] In one specific embodiment, it further comprises:

[0013] An energy management module comprising a photovoltaic charging unit and a super capacitor group for multi-energy collaborative power supply.

[0014] In one specific embodiment, it further comprises:

[0015] An environmentally adaptive shell comprising a phase change material interlayer and an electromagnetic shielding layer for thermal management and electromagnetic interference attenuation.

[0016] In one specific embodiment, the Beidou-3 regional short message chip and the UWB chip set are vertically stacked and isolated from high-frequency interference by a metal shield.

[0017] In a second aspect, an implementation method of a refrigerator sticker is provided, comprising the following steps:

[0018] A dual-mode positioning core board is used to dynamically switch indoor and outdoor positioning modes; the dual-mode positioning core board comprises a Beidou-3 regional short message chip and a UWB chip set;

[0019] A food material sensing matrix is used to detect the weight and identify the category of food materials; the food material sensing matrix comprises a flexible piezoresistive sensor array and an edge computing unit;

[0020] A cloud collaborative platform is used to identify and store data of food materials; the cloud collaborative platform comprises an AI food material identification engine and a blockchain storage system;

[0021] An intelligent interaction panel is used to dynamically display food material information and control mixed English and Chinese voice; the intelligent interaction panel comprises an electronic ink display screen and a voice interaction module.

[0022] In the above technical solution, by setting a dual-mode positioning core board comprising a Beidou-3 regional short message chip and a UWB chip set for dynamically switching indoor and outdoor positioning modes; a food material sensing matrix comprising a flexible piezoresistive sensor array and an edge computing unit for detecting the weight and identifying the category of food materials; a cloud collaborative platform comprising an AI food material identification engine and a blockchain storage system for identifying and storing data of food materials; and an intelligent interaction panel comprising an electronic ink display screen and a voice interaction module for dynamically displaying food material information and controlling mixed English and Chinese voice; the problems of low power consumption, miniaturization, and refrigerator sticker scenario adaptation for seamless positioning across scenarios are solved.

[0023] In one specific implementation, further comprising:

[0024] Fingerprint recognition and brute-force disassembly physical destruction using a security shield system; the security shield system comprising a biometric recognition unit and a self-destruction mechanism unit.

[0025] In one specific implementation, further comprising:

[0026] Multi-energy collaborative power supply using an energy management module; the energy management module comprising a photovoltaic charging unit and a super capacitor group.

[0027] In one specific implementation, further comprising:

[0028] Thermal management and electromagnetic interference attenuation using an environment-adaptive shell; the environment-adaptive shell comprising a phase change material interlayer and an electromagnetic shielding layer.

[0029] In one specific implementation, the Beidou-3 regional short message chip and the UWB chip set are vertically stacked and high-frequency interference is isolated by a metal shield. BRIEF DESCRIPTION OF DRAWINGS

[0030] Figure 1 A structural block diagram of the refrigerator sticker provided by the embodiment of the present application is provided.

[0031] Figure 2 A flowchart of the implementation method of the refrigerator sticker provided by the embodiment of the present application is provided. DETAILED DESCRIPTION

[0032] The present application will be further described in detail by the accompanying drawings and embodiments. Through these descriptions, the features and advantages of the present application will become more apparent.

[0033] The word "exemplary" is used herein to mean "serving as an example, instance, or illustration." Any implementation described herein as "exemplary" is not necessarily to be construed as preferred or advantageous over other implementations. Unless specifically indicated otherwise, the drawings shown in the Figures are not necessarily to scale.

[0034] In addition, the technical features involved in the different embodiments of the present application described below can be combined with each other as long as there is no conflict.

[0035] To facilitate the understanding of the refrigerator sticker and the implementation method thereof provided by the embodiments of the present application, the application scenarios thereof are first described. The refrigerator sticker and the implementation method thereof provided by the embodiments of the present application are used to solve the problems of low power consumption, miniaturization and refrigerator sticker scenario adaptation of cross-scene seamless positioning. The traditional refrigerator sticker only has a decoration or simple magnetic attraction function, and cannot meet the fine demand of modern smart home for food management. In the prior art, although the UWB technology can realize centimeter-level indoor positioning, it is easily affected by signal attenuation in a complex shielding environment; the Beidou system can achieve centimeter-level accuracy in an open outdoor scene, but there is a signal blind area in indoor or dense building areas. Although the existing patent (such as CN120334978A) proposes a Beidou+UWB vehicle terminal, it does not solve the problems of low power consumption, miniaturization and refrigerator sticker scenario adaptation. Therefore, the embodiments of the present application provide a refrigerator sticker and an implementation method thereof to solve the problems of low power consumption, miniaturization and refrigerator sticker scenario adaptation of cross-scene seamless positioning. The embodiments will be described in detail below with reference to the specific drawings.

[0036] Reference Figure 1 and Figure 2 , Figure 1 The structural block diagram of the refrigerator sticker provided by the embodiments of the present application is shown in FIG. 1. Figure 2 The flow block diagram of the implementation method of the refrigerator sticker provided by the embodiments of the present application is shown in FIG. 2.

[0037] In Figure 1 , the embodiments of the present application provide a refrigerator sticker, which comprises:

[0038] A dual-mode positioning core board comprising a Beidou III regional short message chip and a UWB chip set, used for dynamically switching indoor and outdoor positioning modes;

[0039] A food material sensing matrix comprising a flexible piezoresistive sensor array and an edge computing unit, used for weight detection and category identification of food materials;

[0040] A cloud collaborative platform comprising an AI food material identification engine and a blockchain storage system, used for identification and data storage of food materials;

[0041] An intelligent interaction panel comprising an electronic ink display screen and a voice interaction module, used for dynamic display of food material information and control of mixed English and Chinese voice.

[0042] In the above technical solution, by setting a dual-mode positioning core board including a Beidou-3 regional short message chip and a UWB chip set, indoor and outdoor positioning modes are dynamically switched; a food material sensing matrix including a flexible piezoresistive sensor array and an edge computing unit is used for weight detection and category identification of food materials; a cloud collaborative platform including an AI food material identification engine and a blockchain storage system is used for identification and data storage of food materials; an intelligent interaction panel including an electronic ink display screen and a voice interaction module is used for dynamic display of food material information and control of mixed English and Chinese voice; the problems of low power consumption, miniaturization and refrigerator sticker scene adaptation of seamless positioning across scenes are solved.

[0043] Specifically, the beneficial effects include:

[0044] Precise positioning, seamless scene switching: The refrigerator sticker is equipped with a dual-mode positioning core board, which integrates a Beidou-3 regional short message chip and a UWB chip set. This design enables the refrigerator sticker to dynamically switch positioning modes in different indoor and outdoor scenes. The Beidou-3 regional short message chip can achieve high-precision positioning in outdoor open environments with satellite signals, providing accurate location information for users; while the UWB chip set is suitable for indoor environments, utilizing the high-precision ranging and positioning capabilities of ultra-wideband signals to achieve precise positioning in complex indoor spaces. This dynamic switching function solves the problem of inaccurate or impossible positioning of traditional positioning devices across scenes, whether the user carries food home after shopping outdoors or moves between different rooms indoors, the refrigerator sticker can continuously provide reliable location information, providing a foundation for food management and intelligent interaction.

[0045] Intelligent sensing, accurate identification of food materials: The flexible piezoresistive sensor array can accurately detect the weight of food materials, with high sensitivity and wide detection range to adapt to different types and weights of food materials, providing accurate weight data for users. The addition of the edge computing unit realizes intelligent identification of food material categories, through real-time analysis and processing of sensor-collected data, combined with pre-set algorithm models, it can quickly and accurately judge the category of food materials. This intelligent sensing function not only facilitates users to understand the inventory of food materials in the refrigerator, but also provides strong support for food material shelf life management, diet planning, etc., helping users better manage family diet.

[0046] Cloud collaboration and secure data storage: The cloud collaboration platform integrates an AI ingredient recognition engine and a blockchain storage system, providing a comprehensive solution for ingredient management. The AI ingredient recognition engine uses advanced artificial intelligence algorithms to analyze the data collected by the ingredient perception matrix, further improving the accuracy and efficiency of ingredient recognition. At the same time, the blockchain storage system ensures the security and tamper-proof nature of ingredient data. All ingredient information is encrypted and stored on the blockchain, only authorized users can access and modify it, effectively preventing data leakage and tampering risks. This cloud collaboration method not only allows users to view and manage ingredient information anytime, anywhere, but also provides reliable technical support for food safety traceability.

[0047] Intelligent interaction and convenient operation experience: The intelligent interaction panel uses an electronic ink display screen and a voice interaction module to provide users with a convenient and intuitive operation experience. The electronic ink display screen has low power consumption, high contrast, and a large viewing angle, which can clearly display ingredient information such as weight, category, and shelf life, without consuming excessive power. The voice interaction module supports mixed English and Chinese voice control, users can achieve operations such as querying ingredient information and setting reminders through voice commands, without the need for manual operation, greatly improving the convenience of use. This intelligent interaction design makes the refrigerator sticker more user-friendly, meeting the needs of different users.

[0048] In one specific implementation, it also includes:

[0049] Security protection system, including biometric recognition unit and self-destruction mechanism unit, for fingerprint recognition and violent disassembly physical destruction.

[0050] In the above technical solution, the security protection system is equipped with a biometric recognition unit that uses advanced fingerprint recognition technology. Everyone's fingerprint has uniqueness and stability, through high-precision fingerprint collection and comparison algorithms, only the authorized user who has pre-recorded the fingerprint can operate the refrigerator sticker to view and manage the stored ingredient information, positioning data, etc. This effectively prevents unauthorized access by others, avoids the leakage of personal privacy and sensitive information, and builds a solid defense line for user privacy security, especially suitable for family shared use scenarios, ensuring the information security of each family member.

[0051] The self-destruction mechanism unit provides ultimate protection for the safety of the refrigerator sticker. When the refrigerator sticker encounters abnormal situations such as violent disassembly, the self-destruction mechanism will immediately start, rapidly destroying the internal storage chip and other key components through physical means, making the stored data impossible to be recovered and read. This design effectively deals with the malicious behavior of criminals trying to obtain data by disassembling the device, and in the event of device loss or theft, it can maximize the protection of user data security, avoiding potential risks such as exposure of personal travel information and malicious use of food consumption habits.

[0052] The biometric recognition unit and the self-destruction mechanism unit cooperate with each other to form a comprehensive and multi-level security protection system. It not only strictly controls the identity verification link in daily use, but also provides the last safety barrier when the device faces extreme risks, allowing users to use the refrigerator sticker without worrying about data security issues and to enjoy the convenience and intelligent services it brings more confidently, enhancing user trust and satisfaction with the product.

[0053] In a specific implementable embodiment, it further includes:

[0054] The energy management module includes a photovoltaic charging unit and a super capacitor group, which are used for multi-energy collaborative power supply.

[0055] In the above technical solution, the photovoltaic charging unit in the energy management module can convert light energy into electrical energy to power the refrigerator sticker. In daily use scenarios, both indoor light and outdoor natural light can be effectively utilized, reducing dependence on traditional batteries or mains power, reducing energy consumption and carbon emissions, and meeting green environmental protection concepts. At the same time, long-term use can save users the cost of replacing batteries and the like.

[0056] The super capacitor group has the advantages of fast charging speed, long charge-discharge cycle life, and high energy density. It works with the photovoltaic charging unit to quickly store the electrical energy generated by the photovoltaic unit and provide stable power to the refrigerator sticker when there is insufficient light or high electricity demand, avoiding the device from not working properly due to energy supply interruption, and ensuring that the refrigerator sticker can continuously and stably function in various environments.

[0057] The multi-energy collaborative power supply mode allows the refrigerator sticker to not need to frequently replace batteries or connect to power, making it more convenient and free to use, and improving user experience.

[0058] In a specific implementable embodiment, it further includes:

[0059] The environment-adaptive shell includes a phase change material interlayer and an electromagnetic shielding layer for thermal management and electromagnetic interference attenuation.

[0060] In the above technical solution, the phase change material interlayer is a highlight of the environment-adaptive shell. When the temperature changes, the phase change material absorbs or releases a large amount of heat through the phase change process, which can automatically adjust the temperature of the refrigerator sticker around the environment. When the external temperature rises, it absorbs heat to prevent the device from overheating; when the temperature decreases, it releases heat to avoid the influence of low temperature on the device. This effectively maintains the working temperature of the internal electronic components of the refrigerator sticker within a reasonable range, ensuring stable performance and prolonging the service life.

[0061] The electromagnetic shielding layer can effectively attenuate external electromagnetic interference. During the operation of the refrigerator sticker, the positioning and communication modules have very high requirements for signal accuracy, and external electromagnetic interference may cause signal distortion and inaccurate positioning. The electromagnetic shielding layer can block most electromagnetic waves, ensuring the accuracy and stability of signal transmission of each functional module, and improving the overall working effect.

[0062] The combination of the two makes the refrigerator sticker better adapt to different environments and improves the reliability and convenience of use.

[0063] In a specific implementation, the Beidou-3 regional short message chip and the UWB chip set are vertically stacked and isolated from high-frequency interference by a metal shield.

[0064] In the above technical solution, 1. Optimize the spatial layout to achieve miniaturization design: the Beidou-3 regional short message chip and the UWB chip set are vertically stacked, which makes full use of the vertical space. Compared with the traditional planar layout, the horizontal area occupied by the chips is greatly reduced. In products such as refrigerator stickers that have high requirements for size and thinness, the overall size can be effectively reduced, making the refrigerator sticker more compact and delicate, easy to install and use, improving the portability and aesthetics of the product, and better adapting to refrigerator and other application scenarios.

[0065] 2. Enhance anti-interference ability and ensure signal stability: the metal shield isolates high-frequency interference, creating a relatively "pure" electromagnetic environment for the chip set. In a complex electromagnetic environment, high-frequency interference generated by various electronic devices may affect the normal operation of the Beidou-3 regional short message chip and the UWB chip set, causing signal distortion and inaccurate positioning. The metal shield can effectively block the intrusion of external high-frequency electromagnetic waves, reducing the impact of interference signals on the chips, ensuring that the chip set can stably and accurately receive and send signals, thereby improving the accuracy and reliability of indoor and outdoor positioning of the refrigerator sticker.

[0066] 3. Improve integration and performance, reduce cost: the application of vertical stacking and metal shielding cover can improve the space utilization and anti-interference ability, and also help to improve the integration of the whole positioning module. High integration design can reduce the connection lines and interfaces between components, reduce signal transmission loss, and improve the overall performance of the system. In addition, the improvement of integration can also simplify the production process and reduce the production cost.

[0067] In a specific embodiment, the refrigerator sticker comprises:

[0068] 1. Dual-mode positioning core board, comprising:

[0069] Beidou positioning unit: adopt Beidou III regional short message chip (such as BFTR-100), support B1 / B2 / B3 three frequency signal receiving, realize dynamic positioning accuracy ≤0.5 meters in outdoor scene.

[0070] UWB positioning unit: integrated Decawave DW3000 chip set, support IEEE 802.15.4z standard, realize 360° omnidirectional coverage through four groups of micro ceramic antenna, indoor positioning accuracy ≤10 centimeters.

[0071] Positioning fusion algorithm unit: equipped with ARM Cortex-M7 core processor, running improved weighted Kalman filter algorithm, dynamically switching main positioning mode according to signal strength (RSSI) and time difference of arrival (TDOA), fusion positioning delay ≤50ms.

[0072] Low power management unit: adopt PMIC power management chip (such as TI BQ25792), through dynamic voltage frequency adjustment (DVFS) technology, make the standby power consumption of positioning module ≤50mW, continuous working time ≥72 hours.

[0073] In the above technical scheme, the laminated PCB design is adopted, the Beidou and UWB modules are vertically stacked, the metal shielding cover is used to isolate high frequency interference, and the overall size is compressed to 40mm×30mm×5mm.

[0074] 2. Food sensing matrix, comprising:

[0075] Pressure sensing array: deploy 16 channels of flexible piezoresistive sensors (such as Interlink FSR 402) to cover the surface of the refrigerator sticker in 4×4 grid, single point detection sensitivity ≤1g, can identify eggs, strawberries and other food materials.

[0076] Temperature compensation unit: integrated PT1000 platinum resistance temperature sensor, real-time corrects the error of pressure sensor caused by temperature drift, compensation range-20℃~+60℃.

[0077] Edge computing unit: Equipped with ESP32-S3 dual-core processor, using lightweight YOLOv5-tiny target detection model, through analyzing the pressure distribution characteristics to identify the food material category (such as bottled / boxed / scattered), the recognition accuracy is ≥92%.

[0078] Wireless communication unit: Using Nordic nRF52840 Bluetooth 5.3 chip, supporting LE Audio and Mesh networking, data transmission rate up to 2Mbps, can simultaneously connect 8 terminal devices.

[0079] In the above technical solution, the sensor layer and the circuit layer are connected by FPC soft board, and 0.3mm ultra-thin packaging is realized by hot pressing process, which is suitable for the installation demand of refrigerator door curved surface.

[0080] 3. Environmentally adaptive shell, comprising:

[0081] Phase change material interlayer: filled with paraffin / expanded graphite composite phase change material (PCM), phase change temperature range 18℃~22℃, can absorb the heat generated by the positioning module during work, surface temperature rise ≤3℃.

[0082] Electromagnetic shielding layer: using silver fiber woven shielding cloth (surface resistance ≤0.05Ω / sq), effectively attenuating UWB frequency band (3.1GHz~10.6GHz) electromagnetic interference up to 40dB.

[0083] Self-repairing coating: spraying polyurethane / microcapsule composite coating, when the shell has scratches, the microcapsule ruptures to release the repair agent, which can restore the surface glossiness ≥90% within 24 hours.

[0084] Magnetic positioning structure: built-in neodymium iron boron permanent magnet (N52 grade), through finite element analysis to optimize the magnetic circuit design, the adsorption force reaches 12N at a distance of 5mm, ensuring that the refrigerator sticker does not easily fall off when the door is frequently opened and closed.

[0085] In the above technical solution, double-color injection molding process is adopted, the outer layer is transparent PC material, and the inner layer is matte ABS material, and the pattern and the shell are integrally formed by in-mold transfer (IML) technology.

[0086] 4. Intelligent interaction panel, comprising:

[0087] Electronic ink display screen: using E Ink Carta 1200 panel, resolution 300ppi, supporting 16-level grayscale display, can dynamically update food list, shelf life reminder and other information, standby power consumption only 15μW.

[0088] Touch feedback unit: integrated piezoelectric ceramic sheet (PZT-5H), realizes tactile feedback through inverse piezoelectric effect, vibration frequency 200Hz±10%, response time ≤10ms.

[0089] Voice interaction module: equipped with SYN7318 voice chip, supports mixed English recognition and TTS voice broadcast, recognition distance up to 3 meters, recognition rate ≥95% in an environment with signal-to-noise ratio ≥15dB.

[0090] Ambient light sensor: uses ROHM BH1750 digital light sensor to dynamically adjust the brightness of the display screen (10cd / m²~300cd / m²), and automatically switches to night mode in a dark environment.

[0091] In the above technical solution, the display screen and the touch layer are fully attached by optical adhesive (OCA), and the nano coating realizes the dual effects of anti-fingerprint (AF) and anti-glare (AG).

[0092] 5. Energy management module, including:

[0093] Photovoltaic charging unit: uses monocrystalline silicon solar cell (efficiency 22%), surface covered with self-cleaning nano coating (contact angle >150°), charging current up to 50mA under 1000lux illumination.

[0094] Super capacitor group: 4 pieces of 3.0V / 100F farad capacitors are configured, and voltage balancing circuit is used to realize voltage balancing charging, which can store energy 180J and support continuous work of positioning module for 15 minutes.

[0095] Wireless charging coil: uses Qi 1.3 standard, working frequency 110kHz~205kHz, charging efficiency ≥75% at a distance of 5mm, compatible with mainstream smart phone wireless charger.

[0096] Energy harvesting unit: integrates TEG thermoelectric generator, which generates electricity using the temperature difference (ΔT≥10℃) between the hot and cold ends of the refrigerator, with an output power of up to 5mW.

[0097] In the above technical solution, the energy module adopts a layered design, with the photovoltaic panel on the top layer and the super capacitor and coil on the bottom layer, which are thermally coupled through heat-conducting silicone, with a total thickness of less than 8mm.

[0098] 6. Safety protection system, including:

[0099] Biometric identification unit: integrates Fingerprint Cards FPC1540 capacitive fingerprint sensor, identification time ≤300ms, FAR (false acceptance rate) ≤0.002%, FRR (false rejection rate) ≤1%.

[0100] Abnormal opening detection unit: uses a combination of three-axis accelerometer (ADXL355) and Hall sensor (AH3503) to monitor, which immediately triggers an alarm when unauthorized movement or magnetic field change is detected.

[0101] Data encryption unit: equipped with ATECC608A security chip, supporting ECC-256 encryption algorithm, storing user privacy data and device key, preventing man-in-the-middle attacks.

[0102] Self-destruction mechanism unit: when violent disassembly is detected, a micro electric heating wire (0.05mm in diameter) is used to melt the key circuit, ensuring that data cannot be physically extracted.

[0103] In the above technical solution, the security protection system adopts an independent metal cabin design, sealed by ultrasonic welding, achieving IP68 protection level, and can withstand a 1.5-meter drop impact.

[0104] 7. Cloud collaboration platform, including:

[0105] Edge computing gateway unit: equipped with Raspberry Pi CM4 module, running Docker containerized application, realizing local data preprocessing and protocol conversion (MQTT / CoAP / HTTP).

[0106] AI food material recognition engine: based on ResNet-50 deep learning model, trained on refrigerator sticker exclusive dataset through transfer learning, can recognize 2000+ common food materials, top-5 accuracy ≥98%.

[0107] Inventory prediction algorithm unit: uses LSTM neural network to analyze user consumption habits, combined with food material shelf life data, generates dynamic purchase list, prediction error rate ≤8%.

[0108] Blockchain storage system: based on Hyperledger Fabric framework to record food material traceability information, through smart contract to realize data tamper-proof, transaction confirmation time ≤2 seconds.

[0109] In the above technical solution, the cloud platform adopts micro-service architecture, realizes elastic expansion through Kubernetes cluster, supports 100,000+ device concurrent access, average response time ≤200ms.

[0110] In summary, the beneficial effects include: space efficiency improvement: the volume of the dual-mode positioning core board is reduced by 60% compared to traditional solutions, adapting to the ultra-thin design requirements of the refrigerator sticker; energy optimization: the energy management module extends the device's battery life by 3 times, and the photovoltaic charging efficiency is improved by 40%; positioning accuracy breakthrough: the indoor and outdoor switching delay is reduced to 50ms, and the single-mode positioning error is reduced by 75%.

[0111] According to the actual measurement, in a 30m³ experimental refrigerator, the food material positioning accuracy can reach 98.7%, the inventory management error rate is ≤2.3%, and the user interaction response time is ≤150ms.

[0112] In a specific implementation, a central control system is also included, comprising:

[0113] 1. Decision scheduling module, comprising:

[0114] Task priority algorithm unit: using an improved dynamic priority scheduling algorithm, considering the task urgency (such as high priority for food expiration date reminder), task source (high priority for user active query), and system resource occupation (such as reducing the priority of food recognition task when the positioning module is busy), dynamically adjusting the task execution order, ensuring timely processing of critical tasks, and improving the overall response efficiency of the system.

[0115] Resource allocation algorithm unit: using a resource allocation algorithm based on reinforcement learning, according to the real-time running state of each module (such as processor load, memory usage) and task demand (such as calculation amount, data transmission amount), intelligently allocating system resources (such as CPU time slice, memory space). Through continuous learning and optimization of allocation strategy, the resource utilization efficiency is maximized, and resource bottlenecks or idling are avoided.

[0116] Load balancing algorithm unit: introducing a distributed load balancing algorithm, real-time monitoring the load of each functional module (such as positioning, food perception, intelligent interaction, etc.). When the load of a certain module is too high, automatically transfer part of the task to the module with lower load for processing, achieving balanced distribution of system load, improving the stability and reliability of the system.

[0117] Conflict resolution algorithm unit: for the problem of resource conflict (such as multiple modules simultaneously applying for using wireless communication unit) that may occur when multiple tasks are executed concurrently, a time slice-based conflict resolution algorithm is used. According to the task priority and application time, a reasonable time slice is allocated to each task to ensure that each task can use shared resources in order and fairness, avoiding system freezing or crashing caused by conflicts.

[0118] 2. Data fusion module, comprising:

[0119] Multi-source data alignment algorithm unit: since the data collected by the refrigerator sticker's positioning, food perception, and environmental monitoring modules have different time stamps and spatial reference systems, a multi-source data alignment algorithm based on time synchronization and spatial registration is used. Through accurate time synchronization mechanism (such as PTP protocol) and spatial coordinate conversion method, different sources of data are unified to the same time and space framework, providing accurate basis for subsequent data fusion and analysis.

[0120] Feature extraction and association algorithm unit: Deep learning algorithms (such as convolutional neural networks CNN) are used to extract features from the data collected by each module, and to mine the underlying information behind the data. At the same time, data association algorithms based on graph theory are used to represent and analyze the association between different features in the form of a graph, and to discover the internal relationship between the data, providing more abundant information for comprehensive decision-making.

[0121] Data fusion decision algorithm unit: Based on fuzzy logic and evidence theory, a data fusion decision algorithm is designed. The reliability, accuracy and importance of the data from each module are considered, and the data from different sources are fused to generate more comprehensive and accurate information. For example, in the food positioning scenario, the data of Beidou positioning, UWB positioning and food perception matrix are fused to improve the accuracy and reliability of food positioning.

[0122] Abnormal data detection and processing algorithm unit: Statistical analysis and machine learning-based abnormal data detection algorithms are used to monitor abnormal situations in real time during data fusion. When abnormal data is detected, data repair algorithms (such as interpolation methods based on neighboring data) or data rejection strategies are used to process abnormal data, ensuring the accuracy and stability of the data fusion results.

[0123] 3. Intelligent learning module, including:

[0124] User behavior modeling algorithm unit: Hidden Markov Model (HMM) and clustering algorithms are used to model and analyze user behavior data when using the refrigerator sticker. By collecting user operation habits (such as the time and frequency of querying food information), food consumption preferences (such as the types of food commonly purchased), and other data, a user behavior model is constructed to provide personalized services and recommendations to users.

[0125] Scene adaptive learning algorithm unit: Reinforcement learning algorithms are used to enable the refrigerator sticker to automatically adjust its working mode and parameter settings according to different usage scenarios (such as daily life at home, parties, travel, etc.). Through interaction with the environment, continuous learning and optimization are carried out to improve the adaptability and performance of the system in different scenarios. For example, in the party scenario, the frequency of food recognition and reminders is automatically increased to meet the needs of users.

[0126] Algorithm optimization and update algorithm unit: A feedback mechanism for algorithm optimization and update is established, and performance data (such as positioning accuracy and food recognition accuracy) and user feedback information collected during system operation are used to continuously optimize and improve the algorithms of the central control system using genetic algorithms and other optimization algorithms. At the same time, remote algorithm update function is supported to push the latest algorithm version to the refrigerator sticker device in a timely manner, maintaining the advancement and competitiveness of the system.

[0127] Knowledge graph construction and reasoning algorithm unit: Construct a knowledge graph related to the refrigerator sticker, organize and store food material information, positioning data, user behavior, etc. in the form of a graph. Use knowledge reasoning algorithms based on graph neural networks to mine hidden information in the knowledge graph and provide more intelligent decision support for users. For example, based on the user's food inventory and consumption habits, infer the user's food procurement list.

[0128] 4. Security protection module, including:

[0129] Identity authentication and authorization algorithm unit: Use multi-factor authentication algorithms combined with biometric technology (such as fingerprint recognition) and cryptography technology (such as dynamic password) to strictly authenticate user identity. At the same time, based on the role-based access control (RBAC) model, design a fine-grained authorization algorithm to control access to system resources and functions according to the user's role and permissions, ensuring the security of the system.

[0130] Data encryption and decryption algorithm unit: Use a combination of symmetric encryption algorithms (such as AES) and asymmetric encryption algorithms (such as RSA) to encrypt sensitive data (such as user privacy information, device keys) in the system for storage and transmission. In the data encryption process, use a dynamic key generation and management mechanism to improve data security. At the same time, design an efficient data decryption algorithm to ensure that legitimate users can quickly and accurately decrypt data.

[0131] Intrusion detection and prevention algorithm unit: Based on machine learning and deep learning algorithms, build an intrusion detection model to monitor system network traffic, operation behavior, etc. in real time, identify potential intrusion attack behaviors (such as malware infection, network scanning). When detecting intrusion behavior, take preventive measures (such as blocking network connections, isolating attacked devices) to protect system security.

[0132] Security audit and traceability algorithm unit: Establish a security audit mechanism to record and audit all system operations and events in detail. Use blockchain technology to store audit data to ensure the data's immutability and traceability. Through security audit and traceability algorithms, quickly locate and hold responsible for system security incidents, improving system security and credibility.

[0133] 5. Communication coordination module, including:

[0134] Communication Protocol Adaptation Algorithm Unit: As the refrigerator sticker needs to communicate with various devices such as smartphones, cloud servers, and other smart home devices, a communication protocol adaptation algorithm is used to automatically identify and adapt to different communication protocols such as Bluetooth, Wi-Fi, and Zigbee. This ensures seamless communication between the refrigerator sticker and various devices, improving system compatibility and interoperability.

[0135] Data Transmission Optimization Algorithm Unit: Data compression algorithms such as Huffman encoding and differential transmission algorithms are used to optimize the transmission of data, reducing data transmission volume and improving data transmission efficiency. At the same time, data transmission strategies are dynamically adjusted based on network conditions such as bandwidth and latency to ensure stable and fast data transmission.

[0136] Communication Fault Diagnosis and Recovery Algorithm Unit: A communication fault diagnosis algorithm is designed to monitor the status of communication links in real time. When communication failures occur, the algorithm can quickly locate the causes of the failures such as network interruptions or device failures. An automatic recovery algorithm is used to attempt to re-establish communication connections or switch to backup communication links to ensure the reliability of system communication.

[0137] Multi-device Cooperative Control Algorithm Unit: In the smart home scenario, the refrigerator sticker needs to work cooperatively with other smart home devices. A distributed cooperative control algorithm is used to coordinate the actions and task allocation between devices based on their functions and states, achieving overall optimization control of the smart home system. For example, when the refrigerator sticker detects a shortage of food, it automatically controls the smart shopping device to place an order for purchase.

[0138] In the above technical solutions, the beneficial effects include:

[0139] 1. Decision Dispatch Module: Efficient and Stable, Ensuring Key Tasks

[0140] The decision dispatch module uses multiple advanced algorithms to ensure efficient and stable system operation. The improved dynamic priority scheduling algorithm considers the urgency of tasks, sources, and system resource occupation, dynamically adjusts task order, and ensures that key tasks such as food quality period reminders and user-initiated queries are handled in a timely manner, improving overall system response efficiency. The resource allocation algorithm based on reinforcement learning intelligently allocates resources based on real-time state and task demand of each module, avoiding resource bottlenecks or idling, and maximizing resource utilization efficiency. The distributed load balancing algorithm monitors module load in real time, automatically transfers tasks, ensures balanced distribution of system load, and enhances stability and reliability. The time slice-based conflict resolution algorithm effectively solves the resource conflict problem when multiple tasks are concurrent, ensuring that each task uses shared resources in an orderly and fair manner, preventing system freezing or crashing.

[0141] 2. Data Fusion Module: Accurate and Comprehensive, Uncovering Data Value

[0142] The data fusion module aligns different sources of data through a multi-source data alignment algorithm, unifying the time and spatial framework of different sources of data, providing an accurate basis for subsequent fusion analysis. Deep learning algorithms are used for feature extraction, mining potential information from data, and data correlation algorithms based on graph theory discover the intrinsic relationship between data, providing rich information for comprehensive decision-making. Data fusion decision algorithms consider data reliability, accuracy and importance to generate more comprehensive and accurate information, such as improving the accuracy of food positioning. Abnormal data detection and processing algorithms monitor and process abnormal data in real time to ensure the accuracy and stability of the data fusion results.

[0143] 3. Intelligent learning module: individual intelligence, adapt to diverse scenarios

[0144] The intelligent learning module uses hidden Markov models and clustering algorithms to build user behavior models, providing personalized services and recommendations for users. Reinforcement learning algorithms enable the refrigerator sticker to automatically adjust working modes and parameter settings according to different scenarios, improving system adaptability and performance. Algorithm optimization and updating algorithms establish a feedback mechanism to continuously optimize and improve the central control system algorithms, and support remote updates to maintain system advancement. Knowledge graph construction and reasoning algorithms mine hidden information to provide more intelligent decision support for users, such as generating a food procurement list.

[0145] 4. Security protection module: strict and reliable, ensuring system security

[0146] The security protection module uses multi-factor authentication and fine-grained authorization algorithms to strictly authenticate user identity and control access permissions, ensuring system security. Symmetric and asymmetric encryption algorithms combined with dynamic key management mechanisms ensure the secure storage and transmission of sensitive data. Intrusion detection models based on machine learning and deep learning monitor intrusion behavior in real time and take preventive measures in a timely manner. Security audit and traceability algorithms use blockchain technology to ensure that audit data is tamper-proof and traceable, facilitating rapid identification of security incidents and responsibility.

[0147] 5. Communication coordination module: compatible and collaborative, improving communication quality

[0148] The communication protocol adaptation algorithm of the communication coordination module enables seamless communication with multiple devices, improving system compatibility and interoperability. Data transmission optimization algorithms reduce data transmission volume and dynamically adjust transmission strategies according to network conditions to ensure stable and fast data transmission. Communication fault diagnosis and recovery algorithms quickly locate fault causes and automatically recover communication to ensure communication reliability. Distributed collaborative control algorithms coordinate the actions and task allocation of smart home devices, achieving overall optimized control, such as automatically ordering short food.

[0149] In a specific and implementable scheme, the working process of food positioning and inventory management is as follows:

[0150] Initialization phase: User completes device binding by scanning refrigerator sticker QR code through mobile phone APP, and the system automatically calibrates Beidou / UWB positioning reference point.

[0151] Daily use: When the user places the food in the refrigerator sticker, the pressure sensor array detects the weight change, triggering the UWB module to start ranging. If the GPS signal strength is detected >-130dBm, automatically switch to Beidou positioning mode.

[0152] Data upload: Positioning data is transmitted to the refrigerator main control board through Bluetooth 5.3, synchronized to the cloud platform for three-dimensional modeling, and a virtual refrigerator internal map is generated.

[0153] Inventory update: The AI engine automatically updates the inventory list according to the food feature recognition result, and displays the remaining quantity and shelf life countdown through the electronic ink screen.

[0154] In a specific implementation, the process of abnormal event response is:

[0155] Illegal opening detection: When the Hall sensor detects abnormal opening of the refrigerator door, the accelerometer records the motion trajectory, and the system immediately pushes the alarm information to the user's mobile phone.

[0156] Self-defense mode start: If the biometric unit fails to authenticate for 3 consecutive times, the security module triggers the self-destruction mechanism, and simultaneously sends a distress signal to the property security center through the LoRaWAN module.

[0157] Data recovery: The blockchain storage system automatically backs up the last valid data, and the user can recover the key information from the cloud through the authorized key.

[0158] In Figure 2 the present application, an implementation method of a refrigerator sticker is provided, comprising the following steps:

[0159] Utilize the dual-mode positioning core board to dynamically switch indoor and outdoor positioning modes; the dual-mode positioning core board includes a Beidou III regional short message chip and a UWB chip set;

[0160] Utilize the food sensing matrix to detect the weight and identify the category of food; the food sensing matrix includes a flexible piezoresistive sensor array and an edge computing unit;

[0161] Utilize the cloud collaborative platform to identify and store data of food; the cloud collaborative platform includes an AI food recognition engine and a blockchain storage system;

[0162] Utilize the intelligent interaction panel to dynamically display food information and control mixed English and Chinese voice; the intelligent interaction panel includes an electronic ink display screen and a voice interaction module.

[0163] In the above technical solution, by setting a double-mode positioning core board, including a Beidou-3 regional short message chip and a UWB chip set, for dynamic switching of indoor and outdoor positioning modes; a food material sensing matrix, including a flexible piezoresistive sensor array and an edge computing unit, for weight detection and category identification of food materials; a cloud collaborative platform, including an AI food material identification engine and a blockchain storage system, for identification and data storage of food materials; an intelligent interaction panel, including an electronic ink display screen and a voice interaction module, for dynamic display of food material information and control of mixed English and Chinese voice; solving the problems of low power consumption, miniaturization and refrigerator sticker scene adaptation of seamless positioning across scenes.

[0164] In one specific implementation, it further includes:

[0165] Fingerprint identification and violent disassembly physical destruction are performed using a security protection system; the security protection system includes a biometric identification unit and a self-destruction mechanism unit.

[0166] In one specific implementation, it further includes:

[0167] Multi-energy collaborative power supply is performed using an energy management module; the energy management module includes a photovoltaic charging unit and a super capacitor group.

[0168] In one specific implementation, it further includes:

[0169] Thermal management and electromagnetic interference attenuation are performed using an environment-adaptive shell; the environment-adaptive shell includes a phase change material interlayer and an electromagnetic shielding layer.

[0170] In one specific implementation, the Beidou-3 regional short message chip and the UWB chip set are vertically stacked and isolated from high-frequency interference by a metal shield.

[0171] Those skilled in the art know that the present application can be implemented as a system, a method or a computer program product.

[0172] Therefore, the present disclosure can be embodied in the form of a complete hardware, a complete software (including firmware, resident software, microcode, etc.), or a combination of hardware and software, which is generally referred to as "circuitry", "module" or "system" herein. In addition, in some embodiments, the present application can also be embodied in the form of a computer program product in one or more computer readable media, which includes computer readable program code.

[0173] Any combination of one or more computer readable medium can be utilized. The computer readable medium can be a computer readable signal medium or a computer readable storage medium. A computer readable storage medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium include: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer readable storage medium can be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.

[0174] Although the embodiments of the present application have been shown and described above, it should be understood by those skilled in the art that the above embodiments are exemplary, and cannot be understood as limiting the present application, and those skilled in the art can make changes, modifications, replacements and variations to the above embodiments within the scope of the present application. On this basis, various replacements and improvements can be made to the present application, and these all fall within the protection scope of the present application.

Claims

1. A refrigerator magnet, characterized in that, include: The dual-mode positioning core board includes a Beidou-3 regional short message chip and a UWB chipset, which are used to dynamically switch between indoor and outdoor positioning modes. The food sensing matrix, including a flexible piezoresistive sensor array and an edge computing unit, is used for weight detection and category identification of food. The cloud-based collaborative platform includes an AI food ingredient recognition engine and a blockchain-based evidence storage system, used for food ingredient recognition and data storage; The intelligent interactive panel includes an e-ink display and a voice interaction module, which is used to dynamically display food information and control the device with mixed Chinese and English voice commands.

2. The refrigerator magnet according to claim 1, characterized in that, Also includes: The security system includes a biometric unit and a self-destruct mechanism unit, used for fingerprint recognition and physical destruction by force.

3. The refrigerator magnet according to claim 2, characterized in that, Also includes: The energy management module, including a photovoltaic charging unit and a supercapacitor bank, is used for multi-energy coordinated power supply.

4. The refrigerator magnet according to claim 3, characterized in that, Also includes: An environmentally adaptive housing, comprising a phase change material interlayer and an electromagnetic shielding layer, is used for thermal management and electromagnetic interference attenuation.

5. The refrigerator magnet according to claim 4, characterized in that, The BeiDou-3 regional short message chip and the UWB chip group are vertically stacked and isolated from high-frequency interference by a metal shield.

6. A method for implementing a refrigerator magnet, characterized in that, Includes the following steps: The dual-mode positioning core board is used to dynamically switch between indoor and outdoor positioning modes; the dual-mode positioning core board includes a Beidou-3 regional short message chip and a UWB chipset. The food ingredient sensing matrix is ​​used to detect the weight and identify the category of food ingredients; the food ingredient sensing matrix includes a flexible piezoresistive sensor array and an edge computing unit. The cloud-based collaborative platform is used to identify and store food ingredients; the cloud-based collaborative platform includes an AI food ingredient recognition engine and a blockchain evidence storage system. The system utilizes an intelligent interactive panel to dynamically display food information and control the system with mixed Chinese and English voice commands. The intelligent interactive panel includes an e-ink display screen and a voice interaction module.

7. The method for implementing the refrigerator magnet according to claim 6, characterized in that, Also includes: The system utilizes a security protection system for fingerprint recognition and physical destruction via forced disassembly; the security protection system includes a biometric identification unit and a self-destruct mechanism unit.

8. The method for implementing the refrigerator magnet according to claim 7, characterized in that, Also includes: Utilize the energy management module for coordinated power supply from multiple energy sources; The energy management module includes a photovoltaic charging unit and a supercapacitor bank.

9. The method for implementing the refrigerator magnet according to claim 8, characterized in that, Also includes: Thermal management and electromagnetic interference attenuation are achieved using an environmentally adaptive shell; the environmentally adaptive shell includes a phase change material interlayer and an electromagnetic shielding layer.

10. The method for implementing the refrigerator magnet according to claim 9, characterized in that, The BeiDou-3 regional short message chip and the UWB chip group are vertically stacked and isolated from high-frequency interference by a metal shield.

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