Multi-device cooperative wireless power transmission system suitable for smart home

Abstract

The invention relates to the technical field of smart home system integration, and discloses a multi-device cooperative wireless electric energy transmission system suitable for smart home, which comprises a whole-house distributed wireless smart electric energy transmission system. The whole-house distributed wireless intelligent electric energy transmission system comprises a cloud intelligent scheduling and AI layer, a home central control and edge collaboration layer, a distributed wireless power supply layer and a terminal equipment layer. According to the multi-device cooperative wireless electric energy transmission system suitable for the smart home, through a multi-level intelligent scheduling architecture of a cloud intelligent scheduling and AI layer and a home central control and edge cooperation layer, and in combination with a UWB centimeter-level positioning technology and a dynamic power distribution algorithm, cooperative power supply management of multiple devices in the whole house is realized; according to the utility model, accurate power supply can be obtained according to the position, the electric quantity and the power requirements for household appliances which are fixedly arranged, mobile cleaning equipment and a portable terminal which is temporarily used, so that the limitation of wired constraint and single-point charging is thoroughly eliminated.

Description

Technical Field

[0001] This invention relates to the field of smart home system integration technology, specifically to a multi-device collaborative wireless power transmission system suitable for smart homes. Background Technology

[0002] With the rapid development of IoT technology and the smart home industry, the number of smart devices in home scenarios has exploded, covering multiple categories such as lighting, security, cleaning, and entertainment, and the power supply needs of these devices are becoming increasingly diversified and decentralized.

[0003] Currently, smart home power supply mainly relies on two methods: wired charging and independent wireless charging for single devices. These methods have many technical bottlenecks and user pain points. For example, traditional wired charging requires sockets and power cords, which not only leads to messy indoor wiring but also affects the aesthetics and safety of the space. Existing wireless charging technologies are mostly "one-to-one" single-point power supply modes. Different brands and models of smart devices often use different wireless charging protocols and power standards, resulting in serious compatibility issues. Moreover, they are mostly fixed deployments (such as desktop wireless chargers and mobile phone wireless charging pads), which can only meet the charging needs of specific locations and specific devices, and cannot adapt to scenarios such as device movement and temporary use. Summary of the Invention

[0004] (a) Technical problems to be solved To address the shortcomings of existing technologies, this invention provides a multi-device collaborative wireless power transmission system suitable for smart homes. It boasts advantages such as multi-device collaborative power supply, cross-protocol compatibility, flexible coverage across all scenarios, efficient energy utilization, and safe intelligent scheduling. This solves the problems of traditional wired charging, which relies on sockets and power cords, leading to cluttered indoor wiring, affecting aesthetics and safety. Furthermore, existing wireless charging technologies are mostly "one-to-one" single-point power supply modes, with different brands and models of smart devices often employing different wireless charging protocols and power standards, resulting in serious compatibility issues. Moreover, these technologies are mostly fixed deployments (such as desktop wireless chargers and mobile phone wireless charging pads), only meeting the charging needs of specific locations and devices, and failing to adapt to scenarios involving device movement or temporary use.

[0005] (II) Technical Solution To achieve the aforementioned goals of multi-device collaborative power supply, cross-protocol compatibility and adaptation, flexible coverage across all scenarios, efficient energy utilization, and safe and intelligent scheduling, this invention provides the following technical solution: a multi-device collaborative wireless power transmission system suitable for smart homes, including a whole-house distributed wireless intelligent power transmission system, wherein the whole-house distributed wireless intelligent power transmission system includes a cloud intelligent scheduling and AI layer, a home central control and edge collaboration layer, a distributed wireless power supply layer, and a terminal device layer; The cloud-based intelligent scheduling and AI layer is deployed on a security and privacy-prioritized cloud platform, responsible for global strategy, deep analysis, and continuous learning; The home central control and edge collaboration layer is a central control host deployed locally in the home. It is the "brain" of the system's real-time response and undertakes core scheduling and collaboration functions. The distributed wireless power supply layer consists of multimodal power supply nodes and related units distributed throughout the walls, floors, and furniture of the house, and is the system's "energy network". The terminal device layer is used to ensure that various smart home devices can be conveniently and securely connected to the power supply network.

[0006] Furthermore, the cloud-based intelligent scheduling and AI layer includes a load intelligent analysis and prediction module, a global strategy optimization engine, a security and ecosystem management platform, and a user habit learning model; The load intelligent analysis and prediction module collects data on device power, electricity, working status and user habits in real time to build a dynamic "device demand profile" and predict short-term and long-term power supply needs (such as pre-activation of power supply for device movement paths). The global strategy optimization engine optimizes the whole-house power allocation strategy and power supply node collaboration logic based on grid peak and valley electricity prices, whole-house energy consumption data, and the status of distributed energy (such as photovoltaics) using machine learning algorithms.

[0007] The security and ecosystem management platform is used to achieve unified identity authentication, secure encrypted power supply and protocol conversion for cross-brand and cross-protocol devices, and to manage device firmware upgrades and system compatibility. The user habit learning model is used to analyze user equipment usage patterns, predict power consumption timing, achieve proactive energy scheduling, and improve user experience and system energy efficiency.

[0008] Furthermore, the home central control and edge collaboration layer includes an AI scheduling engine, a collaborative control core, a regional collaborative controller, and a system management module; The AI ​​scheduling engine is used to run dynamic scheduling algorithms, process the device demand matrix (identity, power, location, power demand) from the "device layer" and the transmitter status matrix (location, capacity, load) from the "power supply layer" in real time, and solve for the optimal real-time energy allocation scheme. The collaborative control core integrates a device positioning and tracking algorithm (processing UWB signals to achieve centimeter-level positioning), and generates precise control commands for the "power supply layer" based on the device's location and movement trajectory. The regional collaborative controller is deployed by room / functional area, manages all power supply nodes in the area, and realizes rapid local collaboration such as automatic activation when the device is close and power sharing between nodes when the load is overloaded, thereby reducing cloud latency. The system management module is responsible for device communication, security authentication (to prevent energy theft), user interface (UI), and data synchronization between local and cloud environments.

[0009] Furthermore, the distributed wireless power supply layer includes a multimodal intelligent power supply node array and a home energy storage and energy recovery unit; The multimodal intelligent power supply node array includes a fixed magnetic resonance emission pad / plate, a steerable radio frequency transmitter, and a mobile power supply node; The home energy storage and energy recovery unit integrates energy storage batteries and energy recovery modules, which can store off-peak electricity from the grid, recover redundant standby power from equipment, and connect to the power generated by the home photovoltaic system. In the event of a power outage or during peak hours, it can provide emergency power to core equipment such as security cameras and routers, thereby improving system resilience, energy efficiency, and economy.

[0010] Furthermore, the terminal device adaptation layer includes a built-in integrated unit, an external adaptation accessory unit, and an adaptive impedance matching unit; The built-in integrated unit integrates a multi-functional receiving module (compatible with magnetic resonance and radio frequency), a device-side communication chip, and a UWB micro tag into newly manufactured smart devices (lighting fixtures, sensors, home appliances), enabling automatic identification, power negotiation, and precise positioning. The external adapter accessory unit provides an external wireless power receiver (such as a charging patch or base) for existing traditional devices (old mobile phones, desk lamps), enabling low-cost upgrades and access; The adaptive impedance matching unit is used so that both the device and the power supply node support the adaptive impedance matching algorithm and dynamically adjust the resonant frequency to cope with scenarios such as device movement and foreign object intervention, and maintain efficient and stable power supply.

[0011] Furthermore, the fixed magnetic resonance emission pad / plate is embedded under desktops, coffee tables, and cabinets to form a stable and efficient "charging hotspot area," supporting 10-100W power, efficiency >85%, and transmission distance of 0.5-2m. The steerable radio frequency transmitter integrates a small phased array antenna and a UWB positioning tag, and can automatically steer to provide precise directional radio frequency charging for moving devices (such as robotic vacuum cleaners and handheld vacuum cleaners). The mobile power supply node is used to power wireless power supply trays and mobile base stations, providing power to temporary or portable devices, and can automatically connect to fixed nodes to replenish its own power.

[0012] Furthermore, the whole-house distributed wireless intelligent power transmission system has the capability of adaptive power supply adjustment in all scenarios, and achieves dynamic closed-loop control between different levels through two-way data interaction.

[0013] Furthermore, the terminal device layer adapts to changes in power supply mode in real time through an adaptive impedance matching unit, ensuring that the power supply efficiency remains stable at over 85% in complex scenarios such as device movement, simultaneous access of multiple devices, and foreign object intervention, and that voltage and current fluctuations do not exceed ±5%, thus ensuring the safe and stable operation of the device.

[0014] (III) Beneficial Effects Compared with existing technologies, the present invention provides a multi-device collaborative wireless power transmission system suitable for smart homes, which has the following advantages: 1. This multi-device collaborative wireless power transmission system for smart homes utilizes a multi-level intelligent scheduling architecture—combining cloud-based intelligent scheduling and AI layer, home central control and edge collaboration layer—with UWB centimeter-level positioning technology and dynamic power distribution algorithms. This enables collaborative power supply management for multiple devices throughout the house. Whether it's fixed appliances, mobile cleaning equipment, or portable terminals used temporarily, they can all receive precise power supply based on their location, power consumption, and power requirements. This completely eliminates the limitations of wired connections and single-point charging, significantly improving the convenience and flexibility of smart home power supply.

[0015] 2. This multi-device collaborative wireless power transmission system for smart homes solves the compatibility issues of devices from different brands and with different protocols through a cross-protocol compatible security and ecosystem management platform, a terminal adaptation solution covering both new and old devices (built-in integrated unit and external adapter accessory unit), and adaptive impedance matching technology. At the same time, it reduces the cost of upgrading existing traditional devices to wireless power supply. The system supports power supply at multiple power levels from 10 to 100W, with a stable transmission efficiency of over 85%, achieving efficient adaptation across all scenarios and devices, and promoting the integrated construction of a whole-house wireless power supply ecosystem.

[0016] 3. This multi-device collaborative wireless power transmission system for smart homes significantly improves energy efficiency through energy storage scheduling of home energy storage and energy recovery units, peak-valley electricity pricing and distributed energy collaborative algorithms of the global strategy optimization engine, and forward-looking energy allocation of user habit learning models. The system can recover redundant standby power from devices, store off-peak electricity from the grid and photovoltaic power, and provide emergency power supply for core devices during peak electricity consumption or grid outages. This not only reduces household electricity costs but also enhances the resilience and energy efficiency of the power supply system, which is in line with the green and low-carbon development trend.

[0017] 4. This multi-device collaborative wireless power transmission system for smart homes effectively prevents risks such as energy theft, unauthorized access, and excessive electromagnetic radiation through multiple security mechanisms, including end-to-end encrypted transmission, device authentication, foreign object detection, and overvoltage, overcurrent, and overheat protection. Simultaneously, by leveraging a collaborative model of continuous cloud learning and real-time local response, it achieves accurate prediction and dynamic adjustment of power demand, reducing manual intervention and allowing users to enjoy an intelligent and safe power supply experience, further enhancing the overall intelligence level of the smart home system. Attached Figure Description

[0018] Figure 1 This is a schematic diagram of the whole-house distributed wireless intelligent power transmission system of the present invention; Figure 2 This is a schematic diagram of the cloud-based intelligent scheduling and AI layer of the present invention; Figure 3 This is a schematic diagram of the home central control and edge collaboration layer of the present invention; Figure 4 This is a schematic diagram of the distributed wireless power supply layer of the present invention; Figure 5 This is a schematic diagram of the terminal device layer of the present invention; Figure 6 This is a flowchart illustrating the operation of the whole-house distributed wireless intelligent power transmission system of the present invention. Detailed Implementation

[0019] 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.

[0020] Please see Figure 1-6 A multi-device collaborative wireless power transmission system suitable for smart homes, including a whole-house distributed wireless smart power transmission system, which includes a cloud-based intelligent scheduling and AI layer, a home central control and edge collaboration layer, a distributed wireless power supply layer, and a terminal device layer. The cloud-based intelligent scheduling and AI layer are deployed on a security and privacy-prioritized cloud platform, responsible for global strategy, deep analysis, and continuous learning; The home central control and edge collaboration layer is a central control host deployed locally in the home. It is the "brain" of the system's real-time response and undertakes core scheduling and collaboration functions. The distributed wireless power supply layer consists of multimodal power supply nodes and related units distributed throughout the walls, floors, and furniture of the house, and is the system's "energy network". The terminal device layer is used to ensure that various smart home devices can be conveniently and securely connected to the power supply network.

[0021] In the case implementation, the cloud-based intelligent scheduling and AI layer includes a load intelligent analysis and prediction module, a global strategy optimization engine, a security and ecosystem management platform, and a user habit learning model; The load intelligent analysis and prediction module collects data on device power, energy consumption, working status and user habits in real time to build a dynamic "device demand profile" and predict short-term and long-term power supply needs (such as pre-activation of power supply for device movement paths). The global strategy optimization engine optimizes the whole-house power allocation strategy and power supply node collaboration logic based on grid peak and valley electricity prices, whole-house energy consumption data, and the status of distributed energy sources (such as photovoltaics).

[0022] The security and ecosystem management platform is used to achieve unified identity authentication, secure encrypted power supply and protocol conversion for cross-brand and cross-protocol devices, and to manage device firmware upgrades and system compatibility. User habit learning models are used to analyze user device usage patterns, predict power consumption timing, achieve proactive energy scheduling, and improve user experience and system energy efficiency.

[0023] Each module adopts a modular design, supports independent operation and dynamic expansion, and can be flexibly configured according to the number of household devices, house structure and user needs. At the same time, with privacy as the core principle, all data collection and transmission processes are encrypted and used only for internal system scheduling and optimization, without leaking user privacy information. Specifically, the load intelligent analysis and prediction module interacts with the home central control and edge collaboration layer in real time, collecting the operating parameters of all terminal devices in the house every 5 seconds, including the power consumption of smart lights (5-30W), the remaining battery power of the robot vacuum cleaner (0%-100%), and the working mode of the smart air conditioner (cooling / heating / standby). Combined with the user's device usage records over the past 30 days (such as high-frequency use of living room entertainment devices from 19:00 to 21:00 every day, and using a handheld vacuum cleaner to clean the whole house every Saturday morning), it constructs a three-dimensional dynamic profile covering "device basic attributes - real-time operating status - user usage preferences". In the short term, it can predict the device movement path within 1 hour (such as activating the power supply node in the bedroom area in advance when the robot vacuum cleaner moves from the living room to the bedroom), and in the long term, it can predict the peak electricity consumption period within 7 days (such as the concentrated charging demand of mobile phones from 7:00 to 8:00 on weekdays). The global strategy optimization engine incorporates a machine learning model that combines random forests and gradient boosting trees. It accesses real-time peak and off-peak electricity price data released by the local power grid (e.g., off-peak electricity price of 0.3 yuan / kWh from 23:00 to 7:00 the next day, and peak electricity price of 0.8 yuan / kWh from 10:00 to 15:00 and 18:00 to 22:00). Combined with the real-time power generation of the household photovoltaic system (e.g., power generation of 500W / h from 12:00 to 14:00 on a sunny day) and whole-house energy consumption statistics, it dynamically adjusts the power allocation scheme. For example, it prioritizes charging the energy storage unit during off-peak hours and prioritizes using energy storage to power high-power devices during peak hours. At the same time, it optimizes the node coordination logic based on the load status of each power supply node (e.g., the current load rate of the living room transmitter node is 60%, and the load rate of the bedroom transmitter node is 30%) to avoid overloading of a single node. The security and ecosystem management platform has built-in conversion interfaces for mainstream wireless power protocols (such as Qi and AirFuel), supporting access for smart devices from different brands such as Huawei, Xiaomi, and Apple. Through a blockchain-based device identity authentication mechanism, each connected device is assigned a unique encrypted identity. Only authenticated devices can obtain power supply permissions, preventing unauthorized devices from stealing energy. At the same time, it regularly scans device firmware versions and pushes security upgrade prompts to users to ensure system compatibility with newly released devices. The user habit learning model uses a time-series analysis algorithm to deeply mine data such as user device usage time, usage duration, and usage scenarios. For example, it identifies patterns such as users placing their mobile phones on their bedside table to charge after 10:00 PM every night and watching movies on a smart projector for 3 hours every Wednesday night. It then adjusts the power output of the power supply nodes in advance during the corresponding time periods (e.g., the bedside power supply node is activated 10 minutes in advance and adjusted to 15W fast charging mode, and the nodes around the projector maintain a stable 30W power supply). Through the coordinated operation of various modules, the cloud-based intelligent scheduling and AI layer can achieve refined and intelligent management of global power supply, ensuring the stability and efficiency of power supply for multiple devices, while also taking into account the economy and security of energy utilization. This provides precise policy support for the home central control and edge collaboration layer, promoting the efficient operation of the whole-house wireless power transmission system.

[0024] In the case implementation, the home central control and edge collaboration layer includes an AI scheduling engine, a collaborative control core, a regional collaborative controller, and a system management module; The AI ​​scheduling engine is used to run dynamic scheduling algorithms, process the device demand matrix (identity, power, location, power demand) from the "device layer" and the transmitter status matrix (location, capacity, load) from the "power supply layer" in real time, and solve for the optimal real-time energy allocation scheme. The collaborative control core integrates equipment positioning and tracking algorithms (processing UWB signals to achieve centimeter-level positioning), and generates precise control commands for the "power supply layer" based on the equipment's location and movement trajectory. The regional collaborative controller is deployed by room / functional area to manage all power supply nodes in the area, enabling rapid local collaboration such as automatic activation when devices are close together and power sharing between nodes when overloaded, thereby reducing cloud latency. The system management module is responsible for device communication, security authentication (to prevent energy theft), user interface (UI), and data synchronization between local and cloud environments.

[0025] Each module adopts a distributed architecture design, supporting parallel computing and fault redundancy backup. When a module experiences a temporary failure, the remaining modules can ensure the continuous operation of core functions through a load-sharing mechanism, ensuring the stability and reliability of the system's local response. In addition, all modules have a low-power operating mode, which is suitable for the long-term continuous working needs of home scenarios. Specifically, the AI ​​scheduling engine incorporates an improved genetic algorithm and a particle swarm optimization fusion algorithm, synchronizing the device demand matrix and transmitter state matrix every 200 milliseconds. For example, when there are three devices waiting to be charged in the living room (a mobile phone needs 15W, a smart speaker needs 10W, and a laptop needs 65W), and the remaining capacity of the three power supply nodes in the living room area is 80W, 50W, and 100W respectively, the algorithm can complete the calculation within 10 milliseconds and output the optimal allocation scheme of "mobile phone accessing node 1, smart speaker accessing node 2, and laptop accessing node 3", while reserving 20% ​​of the node capacity to cope with sudden device access. The collaborative control core is equipped with a high-precision UWB positioning chip. By receiving signals sent by UWB micro tags at the terminal device layer, it achieves a positioning accuracy of ±2 cm. Combined with the Kalman filter algorithm, it predicts the device's movement trajectory. For example, when the robot vacuum cleaner moves from the living room to the bedroom at a speed of 0.3 m / s, the system can predict its path 1.5 seconds in advance and send a control command to the steerable radio frequency transmitter in the bedroom area to "turn 30° and activate 15W power supply" to ensure that the power supply is not interrupted during the device's movement. The regional collaborative controllers are deployed independently according to functional areas such as "living room, bedroom, kitchen, and bathroom". Each controller manages 4-8 power supply nodes and has a built-in local collaborative rule library. When the load rate of a node exceeds 90% (overload threshold), the power sharing mechanism is automatically triggered. For example, if the current load rate of the fixed magnetic resonance emission pad in the bedroom reaches 95% (connected to 2 high-power devices), the regional collaborative controller can instruct idle mobile power supply nodes in the same area to connect and share 30% of the power supply. The entire collaborative process takes no more than 50 milliseconds, which is far lower than the cloud scheduling latency. The system management module supports multi-protocol communication adaptation including Wi-Fi, Ethernet, and UWB. It ensures device communication security through AES-256 encryption. During device access, a three-step authentication process of "identity verification - power supply permission verification - communication key negotiation" must be completed to prevent unauthorized devices from accessing and stealing energy. It also provides a visual user interface that supports functions such as device power supply status query, power supply priority setting, and fault alarm viewing. Local data is synchronized with the cloud every 5 minutes, and the synchronization process adopts an incremental transmission mode to reduce network bandwidth consumption. Through deep collaboration among various modules, the home central control and edge collaboration layer can achieve rapid response and precise scheduling in local scenarios. This avoids excessive reliance on the cloud, reduces network latency and communication costs, and ensures the stability and efficiency of power supply for multiple devices through refined positioning, allocation and collaboration mechanisms. It becomes the core bridge connecting cloud strategies and terminal execution, promoting the real-time and localized operation of the whole-house wireless power transmission system.

[0026] In the case implementation, the distributed wireless power supply layer includes a multimodal intelligent power supply node array and a home energy storage and energy recovery unit; The multimodal intelligent power supply node array includes a fixed magnetic resonance emission pad / plate, a steerable radio frequency transmitter, and a mobile power supply node. The fixed magnetic resonance emission pad / plate is embedded under desktops, coffee tables, and cabinets to form a stable and efficient "charging hotspot area," supporting 10-100W power, with an efficiency of >85% and a transmission distance of 0.5-2m. The steerable radio frequency transmitter integrates a small phased array antenna and a UWB positioning tag, and can automatically steer to provide precise directional radio frequency charging for moving devices (such as robot vacuum cleaners and handheld vacuum cleaners). The mobile power supply node is used to power wireless power supply trays and mobile base stations, providing power to temporary or portable devices, and can automatically dock with fixed nodes to replenish its own power. The home energy storage and energy recovery unit integrates energy storage batteries and energy recovery modules. It can store off-peak electricity from the grid, recover redundant standby energy from equipment, and connect to the electricity generated by the home photovoltaic system. In the event of a power outage or during peak hours, it can provide emergency power to core equipment such as security cameras and routers, thereby improving system resilience, energy efficiency, and economy.

[0027] Among them, the multimodal intelligent power supply node array adopts a three-dimensional deployment mode of "fixed + mobile + directional", covering the power supply needs of all scenarios such as static use, dynamic movement, and temporary emergency in the home. Each node supports dynamic power adjustment and collaborative work. The home energy storage and energy recovery unit has intelligent charging and discharging switching function and is linked with the cloud strategy optimization engine in real time to realize on-demand energy scheduling. Specifically, fixed magnetic resonance emission pads / plates are configured according to the principle of "dense deployment in high-frequency use areas and reasonable coverage in secondary areas". For example, two pads are embedded under the coffee table in the living room and one pad is embedded under each bedside table in the bedroom. A single emission pad / plate can simultaneously power 2-3 low-power devices (such as mobile phones and smartwatches) or 1 high-power device (such as laptops). Its resonant frequency can be automatically adapted according to the connected devices. When the transmission distance is 1m, the power supply efficiency remains above 90%, meeting the stable charging needs of the devices. The steerable RF transmitter can be deployed in a corner of the room ceiling or high on the wall. It is equipped with a 360° rotation drive module and a power adaptive adjustment unit. By receiving the device position and movement trajectory data sent by the collaborative control core, it can complete the direction adjustment within 0.5 seconds, with a positioning accuracy of ±5cm. For devices with a movement speed ≤1m / s, it can achieve continuous tracking and power supply, with a power supply range of 5-30W, which is suitable for uninterrupted power supply of low-power mobile devices. The mobile power supply node has a built-in high-capacity lithium battery and a wireless receiving module. A single full charge can provide 50Wh of power and supports 5-20W power output. It can be fixed to the wireless power supply tray by magnetic attraction or used as an independent mobile power source. When its own power is lower than 20%, it will automatically move to the nearest fixed magnetic resonance emission pad / plate area to replenish power in a non-contact manner with a replenishment efficiency of over 88% without manual intervention. The home energy storage and energy recovery unit is equipped with a 1kWh high-capacity lithium battery pack, supporting 220V AC input / output and a 12V DC interface. It identifies redundant standby power of devices (such as 1.5W for a smart TV and 0.8W for a router) through a current detection module and converts it into DC power for storage through an energy recovery module, achieving a recovery efficiency of ≥75%. Simultaneously, it connects to the home photovoltaic system. When photovoltaic power generation exceeds the real-time electricity demand of the entire house, excess power is automatically stored in the energy storage unit. During peak grid periods (such as 18:00-22:00), the system prioritizes power from the energy storage unit, reducing grid power consumption. In the event of a sudden power outage, the energy storage unit switches to emergency power mode within 0.3 seconds, continuously powering core devices such as security cameras, routers, and smart locks, ensuring the normal operation of basic smart home functions. Through the full-scene coverage and flexible power supply of the multimodal intelligent power supply node array, combined with the energy optimization management of home energy storage and energy recovery units, the distributed wireless power supply layer not only breaks the location limitations and power constraints of the traditional power supply mode, achieving the power supply effect of "stable supply to static devices, tracking supply to mobile devices, and flexible supply to temporary devices", but also significantly improves energy utilization efficiency and reduces household electricity costs through energy recovery and peak-valley scheduling. At the same time, the emergency power supply function enhances the system's ability to cope with emergencies, laying a solid energy foundation for the efficient and reliable operation of the whole-house wireless power transmission system.

[0028] In the implementation of the case, the terminal device adaptation layer includes a built-in integrated unit, an external adaptation accessory unit, and an adaptive impedance matching unit. The built-in integrated unit integrates a multi-functional receiving module (compatible with magnetic resonance and radio frequency), device-side communication chip and UWB micro tag into newly manufactured smart devices (lighting fixtures, sensors, home appliances), enabling automatic identification, power negotiation and precise positioning; External adapter accessory units provide external wireless power receivers (such as charging pads and bases) for existing traditional devices (old mobile phones, desk lamps), enabling low-cost upgrades and access; The adaptive impedance matching unit is used so that both the device and the power supply node support the adaptive impedance matching algorithm, dynamically adjust the resonant frequency to cope with scenarios such as device movement and foreign object intervention, and maintain efficient and stable power supply.

[0029] Each unit adopts a modular and standardized design, supports bidirectional data interaction with the distributed wireless power supply layer, home central control and edge collaboration layer, adapts to the full power range of 10-100W power supply needs, and has the characteristics of convenient installation, strong compatibility and low energy consumption, ensuring that different devices can obtain a consistent and efficient power supply experience after being connected. Specifically, the multi-functional receiver module with a built-in integrated unit adopts a miniaturized and integrated design, with a size controlled within 5cm×3cm×1cm. It can be directly embedded inside smart lamp holders, sensor housings, home appliance control panels, etc., without occupying additional space. Its device-side communication chip supports Wi-Fi and UWB dual-mode communication. When the device is connected to the power supply network, it can automatically send information such as device identity and power requirements to the home central control and edge collaboration layer, completing rapid authentication and power negotiation within 1 second. The UWB micro tag has a power consumption of less than 1mW and a continuous working time of up to 3 years, providing stable signal support for accurate device positioning. For example, after a newly manufactured smart desk lamp is connected to the system, it can automatically report its location information, and the system will allocate the nearest power supply node to activate power supply. The external adapter accessory unit offers a variety of compatibility solutions. For older portable devices such as mobile phones, an ultra-thin charging patch (thickness ≤2mm) is available, which can be fixed to the back of the device by magnetic attraction or adhesive. The patch has a built-in receiving coil and conversion chip, which can convert wireless power into the device's compatible voltage (5V / 9V). For traditional home appliances such as table lamps and small fans, a wireless power supply base is provided. After the base is connected to the mains power, it powers the device through magnetic resonance technology. The device only needs to replace the original power cord plug and connect it to the base. The upgrade cost is controlled in the range of 50-200 yuan, and no modification to the device itself is required. The adaptive impedance matching unit has a built-in high-precision impedance detection chip and a fast adjustment module. The device and the power supply node exchange impedance data in real time (the detection frequency is 100Hz). When the device moves, causing a change in transmission distance (such as moving from 0.5m to 1.5m), foreign objects are introduced (such as metal objects approaching the power supply area), or multiple devices are connected at the same time, causing a change in load, the algorithm can calculate the optimal resonant frequency within 50 milliseconds. By adjusting the impedance matching state of the receiving coil and the transmitting coil, the power supply efficiency fluctuation is controlled within ±3%. For example, when a robot vacuum cleaner moves and cleans, even if the relative position with the power supply node keeps changing, it can maintain a stable power supply efficiency of more than 85%. Through the native adaptation capabilities of the built-in integrated unit, the low-cost upgrade solution of the external adapter accessory unit, and the dynamic optimization mechanism of the adaptive impedance matching unit, the terminal device adaptation layer completely breaks down the wireless power supply barriers between different brands, types, and old and new devices, achieving the goal of "all devices can be accessed and all scenarios can be stably powered". This not only reduces the cost of replacing devices for users, but also ensures the efficiency and stability of system operation, laying a solid foundation for the large-scale application of whole-house multi-device collaborative wireless power transmission systems.

[0030] In the case implementation, the whole-house distributed wireless intelligent power transmission system has the ability to adaptively adjust power supply in all scenarios, and achieves dynamic closed-loop control between different levels through two-way data interaction.

[0031] Among them, the whole-house distributed wireless intelligent power transmission system takes "cloud strategy guidance - local real-time execution - terminal feedback optimization" as its core logic. The cloud intelligent scheduling and AI layer, home central control and edge collaboration layer, distributed wireless power supply layer and terminal device adaptation layer form a seamless collaborative system. Each layer adjusts its operating status autonomously based on preset rules and real-time data, while supporting cross-level dynamic response and fault redundancy processing to ensure the stability, efficiency and safety of power supply in all scenarios. Specifically, after the system starts, the terminal device adaptation layer first completes device identification and status reporting (including device type, remaining power, current location, power requirements, etc.) through built-in UWB micro tags or external adapter accessories. The data is transmitted in real time to the home central control and edge collaboration layer via the UWB communication link. The collaborative control core of the home central control and edge collaboration layer locks the device location through a centimeter-level positioning algorithm. The AI ​​scheduling engine, combined with the transmitter status matrix (location, capacity, load) reported by the distributed wireless power supply layer, generates a preliminary energy allocation plan within 200 milliseconds. At the same time, the device demand data and local scheduling results are synchronized to the cloud intelligent scheduling and AI layer. The cloud-based intelligent scheduling and AI-layer load intelligent analysis and prediction module constructs a dynamic "device demand profile" based on historical data, predicting short-term device movement paths and power supply needs. The global strategy optimization engine combines information such as grid peak and valley electricity prices and photovoltaic power generation to optimize the global power supply strategy, which is then sent to the home central control and edge collaboration layer through a secure encrypted link. The home central control and edge collaboration layer adjusts the local scheduling scheme according to the cloud strategy and sends precise control commands to the distributed wireless power supply layer to activate fixed magnetic resonance transmitter pads / plates, steerable radio frequency transmitters, or mobile power supply nodes in the corresponding areas to achieve targeted power supply. During power supply, the adaptive impedance matching unit of the terminal device adaptation layer monitors the transmission status in real time. When the impedance changes due to device movement, foreign object intervention, or simultaneous access of multiple devices, it immediately coordinates with the power supply node to adjust the resonant frequency, keeping the power supply efficiency fluctuation within ±3%. The home energy storage and energy recovery unit of the distributed wireless power supply layer intelligently switches charging and discharging modes according to the grid load and device power consumption, storing energy during off-peak hours and releasing energy during peak hours, while recovering redundant standby energy from the devices. If a power supply node experiences overload or failure, the regional collaborative controller can trigger power sharing from surrounding nodes within 50 milliseconds to ensure uninterrupted power supply. Through bidirectional data interaction and dynamic collaboration at all levels, the system forms a closed-loop control process of "device demand collection - scheduling scheme generation - power supply execution - status feedback - strategy optimization". For example, when a user moves compatible devices from the living room to the bedroom, the terminal devices report the location change in real time. The home central control and edge collaboration layer quickly adjust the positioning data and activate the bedroom power supply node. The distributed wireless power supply layer switches the power supply mode to adapt to the device's movement status, and the cloud synchronously updates the power supply strategy to match the scene change. The entire process requires no manual intervention. The system autonomously completes the entire process of adaptive adjustment, which not only meets the flexible power supply needs of multiple devices and multiple scenarios, but also achieves a dual improvement in energy utilization efficiency and power supply reliability through the combination of global optimization and local collaboration, fully demonstrating the intelligent and integrated advantages of the whole-house wireless power transmission system.

[0032] In the implementation of the case, the terminal equipment layer adapts to changes in power supply mode in real time through an adaptive impedance matching unit, ensuring that the power supply efficiency remains stable at over 85% in complex scenarios such as equipment movement, simultaneous access of multiple devices, and foreign object intervention, and that voltage and current fluctuations do not exceed ±5%, thus ensuring the safe and stable operation of the equipment.

[0033] Among them, the adaptive impedance matching unit adopts a bidirectional collaborative adjustment architecture of "device end + power supply node". It has a built-in high-precision detection module and fast response algorithm, which can capture impedance changes in the wireless power transmission link in real time and optimize the link matching by dynamically adjusting the resonance parameters. At the same time, it is linked with the scheduling commands of the home central control and edge collaboration layer to form a closed-loop control of "detection-analysis-adjustment-feedback" to ensure the stability of power supply parameters in complex scenarios. Specifically, the adaptive impedance matching unit is equipped with a high-frequency impedance detection chip with a detection frequency of up to 100Hz. It can accurately identify scenarios such as changes in transmission distance caused by device movement (e.g., dynamic adjustment from 0.5m to 2m), load superposition caused by multiple devices being connected simultaneously (e.g., 3 mobile phones + 1 laptop connected to the same power supply node at the same time), and impedance abrupt changes caused by the intervention of metal foreign objects. It outputs impedance change data in real time. After receiving the data, the adjustment module built into the device and the power supply node calculates the optimal resonant frequency and impedance matching parameters within 50 milliseconds through an adaptive optimization model based on a genetic algorithm. It dynamically adjusts the equivalent impedance of the receiving coil and the transmitting coil. For example, when a metal key approaches the power supply area and causes the impedance to drop by 30%, the adjustment module can quickly increase the resonant frequency to compensate the impedance to the optimal range and maintain stable transmission efficiency. The adaptation logic for different scenarios is as follows: In the scenario of device movement, the UWB micro tag of the terminal device reports the location data in real time. The adaptive impedance matching unit predicts the trend of transmission distance change in advance and adjusts the matching parameters in advance to avoid efficiency fluctuations caused by sudden changes in distance. In the scenario of multiple devices accessing at the same time, the unit dynamically allocates impedance matching resources according to the power demand priority of each device to ensure that the power supply efficiency of high-power devices (such as laptops) is not less than 88% and the voltage fluctuation of low-power devices (such as smartwatches) is controlled within ±3%. In the scenario of foreign object intervention, the unit identifies the presence of foreign objects through abnormal impedance changes. While maintaining power supply stability, it sends an alarm signal to the system management module to remind the user to remove the foreign object. If the foreign object continues to exist and affects power supply safety, it will link the power supply node to reduce the output power until the foreign object is removed. Through the real-time detection and rapid adjustment capabilities of the adaptive impedance matching unit, combined with the three-party collaborative mechanism of "device-power supply node-control layer", the terminal device layer can effectively offset impedance interference in complex scenarios, ensuring that the wireless power transmission link is always in the optimal matching state. Whether it is a dynamically moving robot vacuum cleaner, a living room scenario with multiple devices densely connected, or a sudden situation where an unexpected foreign object intervenes, it can achieve a stable power supply efficiency of over 85%, and the voltage and current fluctuations are strictly controlled within the safe range of ±5%. This not only avoids device failure and shutdown due to unstable power supply, but also prevents damage to the device battery or circuit due to overvoltage and overcurrent, providing a core guarantee for the long-term safe operation of various smart home devices.

[0034] When implementing this procedure, please follow these steps: 1) First, complete the system deployment and device access: embed fixed magnetic resonance emission pads / boards in designated locations throughout the house (such as desktops, coffee tables, and under cabinets), deploy steerable radio frequency transmitters on walls or ceilings, configure mobile power supply nodes and home energy storage and energy recovery units, and complete the hardware installation and network construction of cloud intelligent scheduling and AI layer, home central control and edge collaboration layer (achieve secure cloud connection through Wi-Fi / Ethernet, and achieve local precise positioning and communication through UWB deployment); at the same time, enable the built-in integrated unit for newly manufactured smart devices, adapt external wireless power supply receivers (charging patches, bases, etc.) for existing traditional devices, and complete the identity registration and initial configuration of all terminal devices; 2) Then start the system and complete device identification and status reporting: Turn on the home central control host and cloud platform. The system automatically enters the initialization mode. The terminal device sends information such as identity, power demand, remaining power, and current location to the home central control and edge collaboration layer through the built-in UWB micro tag or external adapter accessories. The home central control and edge collaboration layer performs security authentication on the device (based on blockchain-based unique identity verification). After successful authentication, the device status data is synchronized to the cloud intelligent scheduling and AI layer. The cloud builds an initial "device demand profile" and feeds back the basic power supply strategy to the local device. 3) Further intelligent scheduling and precise power supply: The AI ​​scheduling engine of the home central control and edge collaboration layer combines terminal device status data with the transmitter status matrix (location, capacity, load) reported by the distributed wireless power supply layer to generate a real-time energy distribution scheme; the collaborative control core locks the device location through the UWB centimeter-level positioning algorithm, sends control commands to the distributed wireless power supply layer, and activates the corresponding power supply node (static devices activate fixed magnetic resonance transmitter pads / plates, mobile devices activate steerable radio frequency transmitters for tracking power supply, and temporary devices call mobile power supply nodes); during the power supply process, the adaptive impedance matching unit of the terminal device adaptation layer and the power supply node work together in real time to dynamically adjust the resonant frequency to cope with scenarios such as device movement and foreign object intervention, and maintain efficient and stable power supply; 4) Finally, status monitoring, feedback optimization, and mode switching are performed: During system operation, data is exchanged in real time at each level. The home energy storage and energy recovery unit of the distributed wireless power supply layer intelligently switches charging and discharging modes (energy storage during off-peak hours, discharge during peak hours, and recovery of redundant standby energy) based on the grid peak and off-peak electricity prices, photovoltaic power generation, and equipment power consumption. The cloud-based intelligent scheduling and AI layer continuously analyze equipment usage data, optimize global strategies, and issue updates. When the equipment is fully charged or stopped, the system automatically stops supplying power or switches to a low-power maintenance mode. If equipment failure, unauthorized access, or other abnormalities are detected, the safety protection mechanism is immediately triggered (cutting off the corresponding power supply node and sending alarm information) to ensure the safe and reliable operation of the system.

[0035] In summary, this multi-device collaborative wireless power transmission system for smart homes, through a multi-level intelligent scheduling architecture encompassing cloud-based intelligent scheduling and AI layers, home central control, and edge collaboration layers, combined with UWB centimeter-level positioning technology and dynamic power allocation algorithms, achieves collaborative power management for multiple devices throughout the house. Whether it's fixed appliances, mobile cleaning equipment, or portable terminals used temporarily, they can all receive precise power supply based on location, power consumption, and power requirements, completely eliminating the limitations of wired connections and single-point charging. This significantly improves the convenience and flexibility of smart home power supply. Through a cross-protocol compatible security and ecosystem management platform, a terminal adaptation solution covering both new and old devices (built-in integrated units and external adapter accessory units), and adaptive impedance matching technology, it solves the compatibility issues of devices from different brands and protocols, while reducing the cost of upgrading existing traditional devices to wireless power supply. The system supports multiple power levels from 10-100W, with a stable transmission efficiency of over 85%, achieving efficient adaptation across all scenarios and devices, and promoting the integrated construction of a whole-house wireless power supply ecosystem.

[0036] Furthermore, through energy storage scheduling of home energy storage and energy recovery units, peak-valley electricity pricing and distributed energy collaborative algorithms of the global strategy optimization engine, and forward-looking energy allocation of user habit learning models, energy utilization efficiency is significantly improved. The system can recover standby redundant power from devices, store off-peak electricity from the grid and photovoltaic power, and provide emergency power to core devices during peak electricity consumption or grid outages. This not only reduces household electricity costs but also enhances the resilience and energy efficiency of the power supply system, aligning with the green and low-carbon development trend. Through multiple security mechanisms such as end-to-end encrypted transmission, device authentication, foreign object detection, and overvoltage, overcurrent, and overheat protection, risks such as energy theft, unauthorized access, and excessive electromagnetic radiation are effectively prevented. At the same time, by leveraging the collaborative mode of continuous cloud learning and local real-time response, accurate prediction and dynamic adjustment of power supply demand are achieved, reducing manual intervention and allowing users to enjoy an intelligent and safe power supply experience, further enhancing the overall intelligence level of the smart home system.

[0037] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0038] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A multi-device collaborative wireless power transmission system suitable for smart homes, including a whole-house distributed wireless smart power transmission system, characterized in that: The whole-house distributed wireless intelligent power transmission system includes a cloud-based intelligent scheduling and AI layer, a home central control and edge collaboration layer, a distributed wireless power supply layer, and a terminal device layer. The cloud-based intelligent scheduling and AI layer is deployed on a security and privacy-prioritized cloud platform, responsible for global strategy, deep analysis, and continuous learning; The home central control and edge collaboration layer is a central control host deployed locally in the home. It is the "brain" of the system's real-time response and undertakes core scheduling and collaboration functions. The distributed wireless power supply layer consists of multimodal power supply nodes and related units distributed throughout the walls, floors, and furniture of the house, and is the system's "energy network". The terminal device layer is used to ensure that various smart home devices can be conveniently and securely connected to the power supply network.

2. The multi-device collaborative wireless power transmission system for smart homes according to claim 1, characterized in that: The cloud-based intelligent scheduling and AI layer includes a load intelligent analysis and prediction module, a global strategy optimization engine, a security and ecosystem management platform, and a user habit learning model. The load intelligent analysis and prediction module collects data on device power, electricity, working status and user habits in real time to build a dynamic "device demand profile" and predict short-term and long-term power supply needs (such as pre-activation of power supply for device movement paths). The global strategy optimization engine optimizes the whole-house power allocation strategy and power supply node collaboration logic based on the grid peak and valley electricity prices, whole-house energy consumption data, and the status of distributed energy (such as photovoltaics) using machine learning algorithms. The security and ecosystem management platform is used to achieve unified identity authentication, secure encrypted power supply and protocol conversion for cross-brand and cross-protocol devices, and to manage device firmware upgrades and system compatibility. The user habit learning model is used to analyze user equipment usage patterns, predict power consumption timing, achieve proactive energy scheduling, and improve user experience and system energy efficiency.

3. The multi-device collaborative wireless power transmission system for smart homes according to claim 1, characterized in that: The home central control and edge collaboration layer includes an AI scheduling engine, a collaborative control core, a regional collaborative controller, and a system management module; The AI ​​scheduling engine is used to run dynamic scheduling algorithms, process the device demand matrix (identity, power, location, power demand) from the "device layer" and the transmitter status matrix (location, capacity, load) from the "power supply layer" in real time, and solve for the optimal real-time energy allocation scheme. The collaborative control core integrates a device positioning and tracking algorithm (processing UWB signals to achieve centimeter-level positioning), and generates precise control commands for the "power supply layer" based on the device's location and movement trajectory. The regional collaborative controller is deployed by room / functional area, manages all power supply nodes in the area, and realizes rapid local collaboration such as automatic activation when the device is close and power sharing between nodes when the load is overloaded, thereby reducing cloud latency. The system management module is responsible for device communication, security authentication (to prevent energy theft), user interface (UI), and data synchronization between local and cloud environments.

4. The multi-device collaborative wireless power transmission system for smart homes according to claim 1, characterized in that: The distributed wireless power supply layer includes a multimodal intelligent power supply node array and a home energy storage and energy recovery unit; The multimodal intelligent power supply node array includes a fixed magnetic resonance emission pad / plate, a steerable radio frequency transmitter, and a mobile power supply node; The home energy storage and energy recovery unit integrates energy storage batteries and energy recovery modules, which can store off-peak electricity from the grid, recover redundant standby power from equipment, and connect to the power generated by the home photovoltaic system. In the event of a power outage or during peak hours, it can provide emergency power to core equipment such as security cameras and routers, thereby improving system resilience, energy efficiency, and economy.

5. The multi-device collaborative wireless power transmission system for smart homes according to claim 1, characterized in that: The terminal device adaptation layer includes a built-in integrated unit, an external adaptation accessory unit, and an adaptive impedance matching unit. The built-in integrated unit integrates a multi-functional receiving module (compatible with magnetic resonance and radio frequency), a device-side communication chip, and a UWB micro tag into newly manufactured smart devices (lighting fixtures, sensors, home appliances), enabling automatic identification, power negotiation, and precise positioning. The external adapter accessory unit provides an external wireless power receiver (such as a charging patch or base) for existing traditional devices (old mobile phones, desk lamps), enabling low-cost upgrades and access; The adaptive impedance matching unit is used so that both the device and the power supply node support the adaptive impedance matching algorithm and dynamically adjust the resonant frequency to cope with scenarios such as device movement and foreign object intervention, and maintain efficient and stable power supply.

6. The multi-device collaborative wireless power transmission system for smart homes according to claim 4, characterized in that: The fixed magnetic resonance emission pad / plate is embedded under desktops, coffee tables, and cabinets to form a stable and efficient "charging hotspot area," supporting 10-100W power, efficiency >85%, and transmission distance of 0.5-2m. The steerable radio frequency transmitter integrates a small phased array antenna and a UWB positioning tag, and can automatically steer to provide precise directional radio frequency charging for moving devices (such as robotic vacuum cleaners and handheld vacuum cleaners). The mobile power supply node is used to power wireless power supply trays and mobile base stations, providing power to temporary or portable devices, and can automatically connect to fixed nodes to replenish its own power.

7. The multi-device collaborative wireless power transmission system for smart homes according to claim 1, characterized in that: The whole-house distributed wireless intelligent power transmission system has the ability to adaptively adjust power supply in all scenarios, and achieves dynamic closed-loop control between different levels through two-way data interaction.

8. The multi-device collaborative wireless power transmission system for smart homes according to claim 1, characterized in that: The terminal device layer adapts to changes in power supply mode in real time through an adaptive impedance matching unit, ensuring that the power supply efficiency remains stable at over 85% in complex scenarios such as device movement, simultaneous access of multiple devices, and foreign object intervention, and that voltage and current fluctuations do not exceed ±5%, thus ensuring the safe and stable operation of the device.

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