Smart home-oriented multi-device power sharing and robot scheduling optimization method

Abstract

This application relates to the fields of smart home and wireless power transmission technology, and discloses a method for multi-device power sharing and robot scheduling optimization for smart homes. The method includes: a terminal device calculating a service interruption risk index based on the estimated peak current of the service task and the DC internal resistance of the power supply battery; when the risk index exceeds a safety threshold and the task is latency-tolerant, the task is suspended and external power is requested; otherwise, a degraded parameter set is invoked for operation. A server schedules a mobile robot to the target area based on a global energy efficiency topology. The mobile robot performs fine-tuning alignment using the reflected impedance gradient and provides accompanying power to the terminal device through a magnetic coupling channel, while simultaneously monitoring changes in the transmitter's input impedance to identify the task completion status. This invention solves the undervoltage lockout problem in battery aging scenarios through proactive risk prediction and multi-modal handling strategies, and achieves high-precision physical alignment and non-intrusive power supply control without visual assistance.

Description

Technical Field

[0001] This invention relates to the field of smart home and wireless power transmission technology, specifically to a method for multi-device power sharing and robot scheduling optimization in smart homes. Background Technology

[0002] With the popularization of IoT technology, distributed terminal devices such as smart locks, wireless sensors, and security cameras are widely used in home scenarios. These devices typically rely on chemical batteries for power to maintain the flexibility of their wireless deployment. However, as the number of battery cycles increases and ambient temperature changes, the degradation of the electrochemical active materials inside the battery leads to a significant increase in DC internal resistance. Most existing power management strategies only detect the static terminal voltage of the battery, that is, determine whether the power is sufficient under low load or idle conditions. This approach ignores the voltage division effect of high-resistance batteries when outputting large currents. In practical applications, when the device performs high-power operations such as motor driving or radio frequency transmission, the aging battery will generate a huge transient voltage drop across its internal resistance, causing the power rail voltage to drop instantly below the hardware undervoltage lockout threshold, thereby triggering an unexpected device reset or task failure, even if the battery's static remaining power appears sufficient at this time.

[0003] To address the battery life issues of distributed devices, mobile robot-based automatic charging technology has become a research hotspot. However, existing mobile charging scheduling schemes typically employ path planning algorithms based on the shortest distance or first-in-first-out principles, lacking a comprehensive assessment of business urgency and task value. This may lead to mobile robots prioritizing nearby but non-critical power requests, causing critical business nodes in high-risk situations (such as door locks about to fail) to go offline due to lack of timely power replenishment.

[0004] Furthermore, the efficiency of wireless power transmission is highly dependent on the physical alignment accuracy of the transmitting and receiving coils. Existing mobile robots mainly rely on visual sensors to recognize QR codes or feature points for positioning and alignment. This method is prone to recognition failures or positioning drift in poorly lit indoor corners or complex home environments with obstructed views. Meanwhile, the start and stop control of the power supply process typically relies on upper-layer communication protocols such as Bluetooth or Wi-Fi. The establishment of the communication link and the parsing of data packets introduce significant latency, making it difficult to meet the real-time requirements of rapid response in accompanying power supply systems. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides a method for multi-device power sharing and robot scheduling optimization for smart homes. This method solves the technical problems of existing smart home devices easily triggering undervoltage lockout and causing service failure when performing high-power tasks in a state of battery aging or low power, and the inability of fixed charging facilities to flexibly respond to the emergency power supply needs of distributed devices.

[0006] To achieve the above objectives, the first aspect of this invention provides a method for multi-device power sharing and robot scheduling optimization in smart homes, comprising the following steps: The terminal device responds to the detected service task request by identifying the estimated peak current and task latency type corresponding to the service task request.

[0007] Task latency types include latency-tolerant tasks and latency-sensitive tasks. The terminal device synchronously collects the current open-circuit voltage and DC internal resistance data of the power supply battery and calculates the service interruption risk index.

[0008] The service interruption risk index represents the probability that the terminal voltage of the power supply battery will drop below the cutoff voltage when it outputs the estimated peak current.

[0009] The system executes differentiated handling strategies based on the comparison results between the service interruption risk index and the preset security threshold, combined with the task latency type.

[0010] When the service interruption risk index is greater than or equal to the safety threshold and the task latency type is latency-tolerant, the terminal device suspends the service task request, enters a low-power listening state, and sends a power supply cooperation request to the server through the communication module.

[0011] The server receives a power supply cooperation request, calculates the scheduling path based on the global energy efficiency topology, constructs a system total cost objective function that includes mobile energy consumption cost and business value recovery benefit, determines the assignment relationship between the mobile robot and the terminal device by solving for the minimum value of the objective function, and controls the mobile robot to move to the preset coordinate area where the terminal device is located.

[0012] After the mobile robot reaches the preset coordinate area, it performs physical fine-tuning alignment based on the reflection impedance gradient. The mobile robot's launch module enters a low-power detection mode and controls the chassis module to perform trial displacement in the horizontal plane.

[0013] The impedance detection circuit acquires the real part of the input impedance of the transmitting coil in real time, calculates the rate of change of eigenvalues ​​between adjacent sampling points to obtain the reflected impedance gradient, and controls the chassis module to move in the direction of gradient rise according to the direction of the reflected impedance gradient until the magnitude of the reflected impedance gradient is less than the preset convergence threshold or the coupling coefficient meets the transmission threshold.

[0014] After alignment is completed, the mobile robot sends a power-ready signal through the magnetic field coupling channel. The mobile robot modulates the frequency or amplitude of the magnetic field generated by the transmitting coil. The passive analog front-end circuit of the terminal device senses the signal and triggers a hardware interrupt to wake up the processing module and sends back an acknowledgment signal.

[0015] The mobile robot then starts power output to provide power to the terminal device. At this time, the mobile robot adjusts the transmission power so that the rectified output voltage of the receiving module is higher than the current terminal voltage of the power supply battery. By using line logic or automatically blocking the discharge circuit of the power supply battery, the receiving module takes over the power supply to the load.

[0016] During the accompanying power supply, the mobile robot performs non-intrusive task status monitoring. The mobile robot monitors the real part change of the input impedance of the transmitting coil in real time, and estimates the equivalent load resistance of the terminal equipment based on the transformer impedance transformation principle and the pre-locked mutual inductance value.

[0017] When the equivalent load resistance is detected to jump from the low impedance range to the high impedance range and the duration exceeds the judgment time window, the task is determined to be completed and the mobile robot stops high power output.

[0018] In one possible implementation, when the service interruption risk index is greater than or equal to the safety threshold and the task latency type is latency-sensitive, if it is determined that the mobile robot is in an unaccessible range, the terminal device calls the degraded operation parameter set to execute the business task request.

[0019] Degraded operating parameter sets prevent the terminal voltage from dropping below the cutoff voltage by limiting the duty cycle or gain of the hardware driver.

[0020] The second aspect of the present invention provides a multi-device power sharing and robot scheduling optimization system for smart homes, the system including terminal devices, mobile robots and servers.

[0021] The terminal device is equipped with a processing module, a power module, and a power receiving module. The processing module is used to identify task characteristics and perform risk assessment. The power module is used to collect the battery's open-circuit voltage and DC internal resistance. The power receiving module includes a receiving coil and a rectifier circuit, and is equipped with a load modulation circuit for feedback signals.

[0022] The mobile robot is equipped with a chassis module, a launch module, and a navigation module. The chassis module is used to perform physical movement and attitude adjustment. The transmitting module includes an inverter drive circuit and an impedance detection circuit, which are used to output wireless power and perform physical alignment according to the reflected impedance gradient; The navigation module is used to obtain absolute coordinates.

[0023] The server is configured with a global energy efficiency scheduling algorithm, which is used to plan the scheduling path of the mobile robot based on the distribution topology of the terminal devices and the power supply requests.

[0024] This invention provides a method for multi-device power sharing and robot scheduling optimization in smart homes. It offers the following advantages: 1. This invention calculates the service interruption risk index by real-time acquisition of the DC internal resistance and open-circuit voltage of the power supply battery and combined with the estimated peak current of the business task. It quantifies the risk of voltage drop caused by the voltage division effect of internal resistance in advance. For tasks with different latency types, the system adopts differentiated handling strategies such as suspending and waiting for external power supply or calling downgrade parameter sets. This avoids system reset caused by hardware undervoltage lockout triggered by transient large current at the end of battery aging, and extends the effective service time of the device in non-ideal battery conditions.

[0025] 2. This invention utilizes the physical mapping relationship between mutual inductance and input impedance in a magnetically coupled resonant system, and employs a search algorithm based on the reflection impedance gradient to control a mobile robot for fine-tuning alignment. This method does not require an additional visual recognition module and can achieve precise coupling between the transmitting coil and the receiving coil in complex environments with no light or visual obstruction, thereby improving the robustness of the system in unstructured environments.

[0026] 3. This invention utilizes the sensitivity of the transmitter's input impedance to changes in the receiver's load, and infers the service status of the terminal device based on the transformer impedance transformation principle. The transmitter module can directly identify the task completion time and stop power output through the physical layer impedance characteristics without relying on upper-layer software communication protocol interaction, thus eliminating the impact of communication delay on the control logic and simplifying the control timing of the accompanying power supply for mobile robots. Attached Figure Description

[0027] Figure 1 This is a system architecture diagram of the present invention; Figure 2 This is a flowchart of the method of the present invention.

[0028] Among them, 100 is the terminal device; 110 is the processing module; 120 is the power supply module; 130 is the power receiving module; 140 is the communication module; 200 is the mobile robot; 210 is the chassis module; 220 is the transmitting module; 230 is the navigation module; and 300 is the server. Detailed Implementation

[0029] 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. Example

[0030] Please see the appendix Figure 1This invention provides a multi-device power sharing and robot scheduling optimization system for smart homes, including: a terminal device 100, a mobile robot 200, and a server 300.

[0031] The terminal device 100 includes a processing module 110, a power supply module 120, a power receiving module 130, and a communication module 140.

[0032] The processing module 110 is connected to the power module 120 and the power receiving module 130. The processing module 110 stores an energy consumption feature library, which records the current waveform feature parameters and voltage sensitivity threshold of the terminal device 100 when performing different business operations.

[0033] The power module 120 is physically connected to the power supply battery of the terminal device 100. The power module 120 is equipped with a voltage sampling circuit and an impedance tracking circuit to collect the open circuit voltage data and DC internal resistance data of the power supply battery in real time.

[0034] The power receiving module 130 includes a receiving coil and a rectifier and voltage regulator circuit. The power receiving module 130 is equipped with a load modulation circuit, which is used to feed back digital signals to an external transmitter by changing the equivalent impedance of the receiving coil terminal.

[0035] The mobile robot 200 includes a chassis module 210, a launch module 220, and a navigation module 230. The chassis module 210 performs physical movement and attitude adjustment, and the navigation module 230 is used to obtain the absolute coordinates of the mobile robot 200 in the environment.

[0036] The transmitting module 220 includes a transmitting coil, an inverter drive circuit, and an impedance detection circuit. The impedance detection circuit is connected to the transmitting coil and is used to acquire the voltage and current signals at the transmitting coil end and calculate the reflected impedance value.

[0037] The transmitting module 220 is equipped with a frequency modulation circuit or an amplitude modulation circuit for loading control signals into the alternating magnetic field generated by the transmitting coil.

[0038] Server 300 establishes data connections with terminal device 100 and mobile robot 200 via wireless network.

[0039] Server 300 is equipped with a global energy efficiency scheduling algorithm, which is used to receive power supply requests and generate scheduling instructions for mobile robot 200.

[0040] See attached document Figure 2 This invention provides a method for multi-device power sharing and robot scheduling optimization in smart homes, comprising the following steps: S100 and processing module 110 detect the business task request to be executed, and identify the estimated peak current and task delay type corresponding to the business task request according to the energy consumption feature library. The task delay type includes delay-tolerant tasks and delay-sensitive tasks. S200 and power module 120 collect the open-circuit voltage and DC internal resistance of the current power supply battery. Processing module 110 calculates the service interruption risk index based on the estimated peak current, open-circuit voltage and DC internal resistance. The service interruption risk index represents the probability that the terminal voltage of the power supply battery drops below the cutoff voltage when outputting the estimated peak current. S300 and processing module 110 compare the service interruption risk index with the preset security threshold and execute a multimodal handling strategy in combination with the task latency type. When the service interruption risk index is greater than or equal to the safety threshold and the task latency type is a latency-tolerant task, the processing module 110 suspends the business task request, controls the terminal device 100 to enter a low-power listening state, and sends a power supply cooperation request to the server 300 through the communication module 140. When the service interruption risk index is greater than or equal to the safety threshold and the task latency type is a latency-sensitive task, the processing module 110 determines whether the mobile robot 200 is within the access range. If the determination result is no, the processing module 110 calls the preset degraded operation parameter set to execute the business task request. The degraded operation parameter set limits the peak power of the hardware driver. S400 and server 300 receive power supply cooperation requests, calculate scheduling paths based on the current position of mobile robot 200 and the distribution topology of terminal devices 100 with the goal of minimizing the total energy efficiency cost of the system, and issue navigation instructions to mobile robot 200. S500 and mobile robot 200 move to the preset coordinate area where terminal device 100 is located. Transmission module 220 turns on low power detection mode and controls chassis module 210 to make a slight displacement. Impedance detection circuit monitors the change of reflected impedance gradient at the transmitting coil end in real time until the coupling coefficient meets the preset transmission threshold. S600, the transmitting module 220 modulates the magnetic field frequency or amplitude of the transmitting coil and sends a power-ready signal. After the power module 120 or analog front-end circuit of the terminal device 100 senses the power-ready signal, it triggers a hardware interrupt signal to wake up the processing module 110. The processing module 110 controls the power receiving module 130 to send back an acknowledgment signal. After receiving the acknowledgment signal, the transmitting module 220 increases the transmitting power and connects in parallel with the power supply battery to power the processing module 110. S700, the processing module 110 executes the previously suspended service task request. During this period, the transmitting module 220 continuously monitors the characteristic waveform of the reflected impedance through the impedance detection circuit. When the characteristic waveform of the reflected impedance matches the preset task end characteristic, the transmitting module 220 stops high-power output.

[0041] In this embodiment, the energy efficiency modeling and risk index calculation process executed by the processing module 110 specifically includes the following steps: S110, the processing module 110 monitors the interrupt vector table of the terminal device's operating system or task scheduler. When a call instruction for a specific function or a hardware interrupt request is detected, the processing module 110 obtains the unique identifier of the business task to be executed.

[0042] S120, the processing module 110 accesses the energy consumption feature library stored in the non-volatile memory of the terminal device based on the unique identifier. The energy consumption feature library is constructed in the form of a lookup table or key-value pair data structure, and each unique identifier is associated with a set of pre-calibrated physical parameters.

[0043] Specifically, the physical parameters stored in the energy consumption characteristic database include at least the estimated peak current and task latency type. The estimated peak current is the maximum instantaneous current value that the service task may generate during hardware execution. This value is obtained in the following ways: During the production testing phase before the terminal equipment leaves the factory, the current waveforms of each functional module are tested using a precision current probe and the maximum values ​​are recorded. Alternatively, during the operation of the terminal device, the power module 120 records the maximum current during the execution of historical tasks and updates it with a weighted average. The task latency type is a binary or enumerated variable used to distinguish the real-time requirements of business tasks. For example, for tasks such as firmware upgrade, log upload, and timed environmental sampling, the task latency type is marked as latency-tolerant. For tasks such as biometric unlocking, fire alarm, and real-time video streaming, the task latency type is marked as latency-sensitive. In addition, the energy consumption feature library also stores business criticality weights to quantify the priority of different business tasks in the system logic.

[0044] The value range of the business criticality weight is preset to an integer from 1 to 10, where the larger the value, the higher the priority. For example, the weight of the fire alarm task is set to 10, and the weight of the log upload task is set to 1.

[0045] S130. After obtaining the parameters of the business task to be executed, the processing module 110 reads the status data of the internal register of the power module 120 through the I2C bus or SPI bus. The status data includes the current open circuit voltage value and DC internal resistance value of the power supply battery.

[0046] To obtain the open-circuit voltage and DC internal resistance values, the power module 120 in this embodiment integrates a fuel gauge chip. The fuel gauge chip uses the open-circuit voltage method combined with the coulomb method, or uses an impedance tracking algorithm to estimate the battery status in real time.

[0047] The DC internal resistance value is a parameter that dynamically changes with the battery aging degree and the current ambient temperature. The specific sampling circuit and internal algorithm implementation of the fuel gauge chip are well known in the field and will not be described in detail here. It should be noted that the open circuit voltage value in this invention refers to the battery electromotive force after removing the load effect. When the system is running under load, this value is usually estimated by the fuel gauge chip based on the current terminal voltage and current through the internal model.

[0048] S140 and processing module 110 calculate the service interruption risk index based on the acquired physical parameters and battery status data. The service interruption risk index is a dimensionless value, and its construction principle is based on the non-ideal power characteristics of the battery. In actual power supply circuits, batteries have internal resistance. When the load current increases, the voltage drop across the internal resistance increases, causing the actual terminal voltage output to the load terminal of the terminal device to decrease. If the actual terminal voltage is lower than the chip's minimum operating voltage, it will trigger a forced system reset. Therefore, this step aims to quantify the possibility that performing a specific high-current task under the current battery condition will cause the terminal voltage to drop below the safety threshold.

[0049] Processing module 110 uses the following calculation model to derive the service interruption risk index: ; In the formula, the symbols are defined as follows: Indicates the first The terminal device executes the first Service interruption risk index for each business task; This indicates the first [item] read from the energy consumption feature library. The business criticality weight of each business task; This indicates the first [item] read from the energy consumption feature library. The estimated peak current for each business task, in amperes; Indicates the current The DC internal resistance value of the power supply battery is collected at all times, in ohms; Indicates the current The open-circuit voltage of the power supply battery is collected at all times, in volts; Indicates the first The hardware undervoltage lockout threshold or minimum operating cutoff voltage of a terminal device is a fixed hardware parameter in volts. The risk sensitivity index is defined as [2,5]. This index is used to amplify the risk value under nonlinear amplification critical conditions. That is, when the internal resistance voltage drop is close to the voltage margin, the calculation result is significantly increased through exponential operation, thereby improving the system's sensitivity to low charge or high internal resistance conditions.

[0050] In this computational model, the numerator term This represents the voltage drop that occurs inside the battery due to its internal resistance at the moment the task is executed. denominator This represents the current battery voltage margin before the system shuts down and resets due to undervoltage. When the internal resistance voltage drop approaches or exceeds this margin, the ratio within the parentheses will approach or exceed 1, after exponential... After magnification, An increase in the value indicates a greater risk of power outage.

[0051] S150, the processing module 110 temporarily stores the calculated service interruption risk index in random access memory as the basis for determining the multimodal handling strategy in subsequent steps.

[0052] Through the above steps, the system establishes a correlation between the physical health status of the battery and the characteristics of specific business loads, enabling it to predict the risk of service interruption due to voltage drops before task execution.

[0053] After calculating the service interruption risk index, the processing module 110 needs to execute specific control logic based on the index and task attributes. This can balance business continuity and system stability in the event of insufficient power or excessive internal resistance. The process specifically includes the following steps: S310, Processing module 110 reads the security threshold preset in read-only memory The service interruption risk index calculated in step S140 is then compared numerically with the security threshold. It is a scalar value set based on the fault tolerance capability of the system hardware, and its value usually ranges from 1.0 to 1.5.

[0054] For example, setting This indicates that the system allows the estimated internal resistance voltage drop to reach 83% of the available voltage margin, while reserving approximately 17% of the voltage margin as a safety buffer.

[0055] When the service interruption risk index is less than the safety threshold, it indicates that the current battery state is sufficient to support the peak current generated by the business task without triggering an undervoltage reset. The processing module 110 directly calls the underlying driver interface to execute the business task.

[0056] S320. When the service interruption risk index is greater than or equal to the safety threshold, the processing module 110 further determines the task delay type identified in step S120. If the task delay type is delay-tolerant, the processing module 110 performs a suspension operation.

[0057] The specific suspension operation is not simply stopping the program, but includes: Processing module 110 writes the context data of the current business task into the reserved area of ​​random access memory; Subsequently, the processing module 110 configures the interrupt mask register of the power module 120 to retain only the wake-up interrupt permissions of the wireless power receiving module 130 and the communication module 140. Finally, the processing module 110 controls the system to enter sleep mode or stop mode to reduce static power consumption.

[0058] Meanwhile, the communication module 140 assembles a power supply cooperation request data packet containing the terminal device's unique identifier, current GPS coordinates or relative position coordinates, and a calculated service interruption risk index, and sends it to the server via a wireless network.

[0059] S330. If the service interruption risk index is greater than or equal to the safety threshold, and the task latency type is latency-sensitive, the processing module 110 detects whether the mobile robot is within the accessible range. This detection is performed by reading local flag bits. The setting logic for this flag is as follows: when the communication module 140 receives a wireless beacon signal from the mobile robot within a preset time window, and the signal strength indicator is greater than a preset access threshold, or when the processing module 110 receives a message from the server indicating that the robot's estimated arrival time is less than the current system time, the flag will be set to... If true, If true, proceed to the subsequent handshake and power supply process.

[0060] S340. If the service interruption risk index is greater than or equal to the safety threshold, the task latency type is latency-sensitive, and the mobile robot is not detected to be present, the processing module 110 activates the degraded operation mode. The degraded operation mode refers to limiting the hardware output power through software means, thereby forcibly reducing the peak current of the load.

[0061] In practice, the degraded operation mode calls different preset degrade parameter sets depending on the hardware peripherals driven by the business task: For tasks involving driving motors, processing module 110 modifies the pulse width modulation (PWM) control register of the motor drive circuit to reduce the PWM duty cycle. For example, the duty cycle is adjusted from 100% full speed to 50% or lower, utilizing the inductive characteristics of the motor coil to smooth the current waveform. Although this extends the operating time, it effectively reduces the instantaneous peak current.

[0062] For radio frequency transmission tasks, the processing module 110 modifies the gain configuration register of the radio frequency front-end power amplifier to reduce the transmit power and correspondingly reduce the communication rate to ensure the link budget.

[0063] S350. When executing the degraded operation mode, in order to ensure that the degraded current does not pull down the voltage to the crash line, the processing module 110 dynamically calculates the maximum allowable target current according to the following constraints. .

[0064] The basic principle is that, to prevent system power loss, the current flowing through the battery's internal resistance must be limited so that the sum of the voltage drop across the internal resistance and the cutoff voltage does not exceed the current battery open-circuit voltage. The calculation formula is as follows: ; In the formula, the symbols are defined as follows: This indicates the maximum current limit allowed by the hardware driver in degraded mode, in amperes. This indicates the open-circuit voltage of the battery at the current moment, measured in volts. This represents the hardware undervoltage lockout threshold of the terminal device, expressed in volts. This indicates the current DC internal resistance of the battery, expressed in ohms. This represents the safety margin factor, with a value ranging from 1.1 to 1.3. This factor is used to introduce additional safety margin to offset the effects of sensor measurement errors, temperature fluctuations, and transient noise in the circuit.

[0065] In the calculation Next, the processing module 110 queries the current-drive parameter mapping table pre-stored in the energy consumption characteristic database. This mapping table records the typical current consumption values ​​corresponding to different PWM duty cycles or different PA gain levels. The processing module 110 selects values ​​less than or equal to... The driving parameters corresponding to the maximum current level are written into the hardware register for execution.

[0066] Through the above steps, the system, under extreme conditions of no external power supply and critical battery status, actively sacrificed performance indicators to ensure the completion of core business functions, thus avoiding system power failure and restart caused by forced full-power operation.

[0067] When the mobile robot 200 reaches the coarse positioning coordinate area of ​​the distributed terminal 100, the positioning error of the Global Positioning System or SLAM mapping is usually on the order of centimeters to decimeters, which cannot meet the high precision requirements of coil coaxiality for magnetically coupled resonant wireless power transmission. Therefore, this invention does not rely on visual sensors, but uses the electromagnetic coupling characteristics of the wireless power transmission system itself to perform physical fine-tuning and alignment. This process specifically includes the following steps: After reaching the target coordinates, the S510 and mobile robot 200 control the chassis module 210 to stop large-scale movements and enter the fine-tuning mode. In the fine-tuning mode, the mobile chassis module 210 uses omnidirectional wheels or Mecanum wheels to perform small stepping movements in the plane.

[0068] The transmit control module 220 controls the inverter drive circuit to enter a low-power detection mode. In this mode, the inverter's switching frequency is locked at the system's resonant frequency, but the duty cycle is limited to an extremely low level, so that the transmit current is maintained between 1 / 10 and 1 / 20 of the rated current, ensuring that the generated magnetic field strength is sufficient to excite the passive response of the receiver, but not enough to generate a large current or electromagnetic interference.

[0069] The S520 and transmit control module 220 initiate an impedance scanning sequence, and the impedance detection circuit acquires the high-frequency voltage signal at the transmit coil port in real time. and high-frequency current signals The acquired analog signal is quantized by a high-speed analog-to-digital converter and then input into a digital signal processor (DSP). The DSP performs a fast Fourier transform on the electric and current signals to extract the amplitude and phase of the fundamental component and calculate the equivalent input impedance of the transmitting coil. .

[0070] The S530 processing module calculates the magnitude or characteristic parameters of the reflection impedance based on the principle of electromagnetic field coupling. The basic principle is that in a magnetically coupled resonant system, when two coils are magnetically coupled, the loop impedance on the receiving side is mapped to the transmitting side through mutual inductance, manifesting as the reflection impedance. The magnitude of this reflection impedance is related to the mutual inductance between the coils. Closely related, and mutually inductive Since the relative distance between the coils is a monotonic function (exhibiting a single-peak characteristic near the coaxial axis), the spatial alignment state of the coils can be directly deduced by monitoring the change in the transmitter impedance.

[0071] The physical mapping relationship is as follows: ; in The inherent impedance of the transmitting coil, The total impedance of the receiving circuit. ω is the angular frequency.

[0072] Due to the load at the receiving end during the alignment process Keep constant, input impedance The changes are mainly due to mutual inductance The decision is made when the coils are fully aligned, the mutual inductance... Reaching its maximum value results in increased reflection impedance. It reached its extreme value.

[0073] S540 and mobile robot 200 execute a gradient hill-climbing algorithm based on extreme value search to find the optimal alignment point, while the mobile chassis module 210 controls the robot to perform small, tentative displacements within the horizontal plane. After each displacement, the current coordinate position is recorded. and the corresponding reflection impedance characteristic value .

[0074] For a series-series compensated topology, eigenvalues The real part of the input impedance is chosen. This component directly reflects the equivalent resistance characteristic of energy transfer from the transmitter to the receiver.

[0075] The controller calculates the gradient vector based on the impedance change rate of adjacent sampling points and updates the target position for the next time step. The position update control law is as follows: ; ; In the formula, the symbols are defined as follows: This represents the target position vector for the next fine-tuning move; Represents the position vector at the current moment; Indicates the location The characteristic value of the reflection impedance was measured and calculated at the location; This represents the step gain coefficient, with a value range of 0.5 to 2.0, to balance search speed and steady-state accuracy. A variable step size strategy is adopted: when the gradient magnitude is large, a larger value is taken to quickly approach the peak; when the gradient magnitude is small, a smaller value is taken to avoid oscillations near the peak point. This represents a small increment of tentative displacement, typically set to 5 mm to 10 mm.

[0076] S550. The system continues to execute the above gradient update process until a preset termination condition is met. The termination condition includes: Calculated gradient magnitude Less than the preset convergence threshold The threshold is set to 5% of the peak gradient, indicating that the top flat region of the impedance curve has been reached. Alternatively, the calculated coupling coefficient Exceeding the preset transmission threshold (e.g.) ).

[0077] When the termination condition is met, the mobile robot 200 locks the chassis braking mechanism, the transmission control module 220 records the current optimal impedance parameters as the reference for subsequent power transmission, and prepares to enter the next stage of the handshake process. Through the above closed-loop search based on physical field characteristics, the system can overcome uneven ground and accumulated positioning errors to achieve millimeter-level coil alignment accuracy.

[0078] After the mobile robot 200 completes physical alignment, the system does not immediately output high-power electrical energy. Instead, it executes a physical layer handshake protocol based on a magnetic field coupling channel. The handshake data frame contains hardware characteristic parameters of the terminal device (such as the internal resistance of the receiving coil). (e.g., rated voltage, etc.) to ensure power supply safety and synchronize control timing. This process specifically includes the following steps: S610, the transmitting module 220 executes the wake-up sequence transmission. The modulation circuit in the transmitting module 220 loads a specific wake-up data frame on the alternating magnetic field generated by the transmitting coil. The wake-up data frame is modulated using amplitude shift keying or frequency shift keying.

[0079] For example, the transmitter module 220 sets the inverter's operating frequency at the center frequency. With frequency shift The frequency shifts between the preset bit sequences to achieve signal demodulation while ensuring energy transmission efficiency. The signal-to-noise ratio is set to 2% to 5% of the center frequency, and the data transmission rate is controlled at a low level to ensure a low coupling coefficient.

[0080] S620, the terminal device 100 performs hardware-level wake-up and feedback. At this time, the main processing module 110 of the terminal device may be in deep sleep or power-off state. Therefore, wake-up depends on the passive analog front-end circuit in the power receiving module 130, which includes a passive resonant network composed of inductors and capacitors and a detector diode.

[0081] The modulated magnetic field signal induced by the receiving coil is restored into digital pulses by an envelope detector or frequency discriminator. When the pulse sequence matches the preset hardware wake-up pattern, the trigger circuit outputs a high-level signal to the external interrupt pin of the processing module 110 to force the wake-up of the processing module 110.

[0082] After the processing module 110 is awakened, it controls the load modulation circuit in the power receiving module 130 to respond. The load modulation circuit includes a modulation resistor connected in parallel or in series with the receiving coil. The processing module 110 controls the switch to periodically change the equivalent impedance of the receiving side by switching the control switch on and off at a specific frequency, thereby sending an acknowledgment signal back to the transmitting side.

[0083] S630 and transmitter module 220 identify the feedback signal and initiate power transmission. The impedance detection circuit on the transmitter side monitors the envelope change of the transmitter coil current in real time and demodulates the confirmation signal fed back by the terminal equipment. Once the handshake is successful, transmitter module 220 adjusts the phase shift angle or duty cycle of the inverter, executes a soft-start strategy, and linearly increases the transmitter power to the rated operating point within a set time.

[0084] At this time, the output voltage after rectification by the power receiving module 130 Adjusted to a voltage higher than the current terminal voltage of the power supply battery At a specific level, to ensure smooth power supply switching and avoid overvoltage damage to the battery, a preset voltage difference is set. Keep it between 0.5V and 1.0V.

[0085] According to circuit principles, since the output terminals of the power receiving module and the battery output terminal form a wired-OR logic through an ideal diode controller and a MOSFET, when... When the ideal diode is forward-biased, the power receiving module automatically takes over the power supply to the load, and the power supply battery automatically stops discharging due to the low potential, with the current dropping to zero or switching to a charging state.

[0086] S640. During the power supply period, the transmitter module 220 performs non-intrusive task status monitoring. This is because the load current of the terminal device increases when it performs service tasks (such as motor rotation or radio frequency transmission). It will present a specific time-domain waveform.

[0087] Based on the impedance transformation principle of transformers, the change in the load resistance of the receiving side will be directly reflected as the change in the real part of the input impedance of the transmitting side through mutual inductance coupling. When the load current of the receiving side increases, in order to maintain magnetic flux balance, the transmitting side must provide a larger excitation current, which is manifested as a decrease in the real part of the input impedance.

[0088] To determine whether the mission has ended, the transmitting module 220 bases its determination on the detected real part of the input impedance. Estimate the equivalent load impedance on the receiving side. The estimation model is as follows: ; In the formula, the symbols are defined as follows: Indicates in The estimated equivalent load resistance of the terminal equipment at any given time, in ohms; Represents the angular frequency of the system ( ); This represents the mutual inductance value between the coils, which is locked as a constant after alignment is completed in step S550. Indicates in The real part of the transmitter input impedance measured at each moment is calculated by synchronous sampling of voltage and current and discrete Fourier transform. This represents the high-frequency AC parasitic internal resistance of the transmitting coil and its matching circuit. This represents the high-frequency AC parasitic internal resistance of the receiving coil and its rectifier circuit.

[0089] The S650 and the transmission module 220 will calculate in real time. Alternatively, the corresponding load power is matched with a preset task characteristic template. When the terminal device starts executing the task, the load current increases sharply, and the equivalent resistance... Significantly reduced; When the task continues to run It fluctuates within a low impedance range; When the task is completed (e.g., the door lock is closed, data has been sent), the processing module 110 shuts down the peripherals, and the load current drops back to the standby level, resulting in... It rises in a step to the high impedance range.

[0090] Transmission module 220 is set to monitor sliding time windows. Changes. When detected. Continuously higher than the preset idle impedance threshold Exceeding the judgment time At that point, it is determined that the business task has been completed.

[0091] Among them, the idle impedance threshold It is the reference impedance value measured and recorded by the system during the idle standby phase after the handshake is successful in step S630; Judgment Time The time is set to 50ms to 200ms to filter out short-term impedance spikes caused by dynamic load fluctuations. The transmitting module 220 then stops high-power output and reports the task completion status to the server. After the task is completed, the mobile robot removes its accompanying state, updates its own status to idle, and reports to the scheduling server, waiting for the next scheduling instruction.

[0092] This step enables the power supply end to physically perceive the service status of the power consumption end, eliminating the need for frequent task progress interactions in the upper-layer software protocol, thus reducing communication latency and system complexity.

[0093] 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 method for optimizing multi-device power sharing and robot scheduling in smart homes, characterized in that, Includes the following steps: Terminal devices monitor service task requests and identify estimated peak current and task latency types. Collect current open-circuit voltage and DC internal resistance data of the power supply battery, and assess the service interruption risk index based on the estimated peak current; When the service interruption risk index exceeds the preset safety threshold and the task latency type is latency-tolerant, the business task request is suspended and a power supply cooperation request is sent to the server. The server calculates and schedules paths based on global energy efficiency topology, controlling the mobile robot to reach the preset coordinate area where the terminal device is located. The mobile robot performs physical fine-tuning alignment based on the reflection impedance gradient and sends a power-ready signal through the magnetic field coupling channel. The terminal device responds to the power-ready signal feedback confirmation signal, and the mobile robot starts power output to provide power to the terminal device until the business task request is detected to be completed.

2. The method for multi-device power sharing and robot scheduling optimization for smart homes according to claim 1, characterized in that, The steps for assessing the service interruption risk index specifically include: Calculate the product of the estimated peak current and the DC internal resistance to obtain the estimated voltage drop value; Calculate the difference between the current open-circuit voltage and the estimated voltage drop value to obtain the estimated terminal voltage; The service interruption risk index is determined based on the probability that the estimated terminal voltage is lower than the terminal device's cutoff voltage.

3. The method for multi-device power sharing and robot scheduling optimization for smart homes according to claim 1, characterized in that, Also includes: When the service interruption risk index exceeds the security threshold and the task latency type is latency-sensitive, determine whether the mobile robot is within the accessible range. If the judgment result is negative, then the downgraded running parameter set is invoked to execute the business task request; The degraded operating parameter set limits the duty cycle or gain of the hardware driver so that the voltage drop caused by the actual operating current does not cause the terminal voltage to fall below the cutoff voltage.

4. The method for multi-device power sharing and robot scheduling optimization for smart homes according to claim 1, characterized in that, The steps for calculating and scheduling paths based on global energy efficiency topology specifically include: Construct a system total cost objective function, which includes a mobile energy consumption cost item and a business value recovery revenue item; The mobile energy consumption cost item is determined based on the product of the mobile robot's travel distance and the energy consumption coefficient per unit distance; The business value recovery revenue item is determined based on the product of the service interruption risk index of the terminal device and the penalty cost factor; The assignment relationship between the mobile robot and the terminal device is determined by solving for the minimum value of the objective function.

5. The method for multi-device power sharing and robot scheduling optimization for smart homes according to claim 1, characterized in that, The step of physical fine-tuning alignment based on reflection impedance gradient specifically includes: The mobile robot's launch module is controlled to enter a low-power detection mode, and the chassis module is controlled to perform exploratory displacement in the horizontal plane. The real part of the input impedance of the transmitting coil is acquired in real time as an eigenvalue, and the rate of change of the eigenvalue between adjacent sampling points is calculated to obtain the reflection impedance gradient. The chassis module is controlled to move in the direction of the gradient increase according to the direction of the reflection impedance gradient in order to find the extreme point of the reflection impedance.

6. The method for multi-device power sharing and robot scheduling optimization for smart homes according to claim 5, characterized in that, The termination condition for the physical fine-tuning alignment is: The calculated magnitude of the reflection impedance gradient is less than the preset convergence threshold, or the coupling coefficient calculated based on the input impedance is greater than the preset transmission threshold. When the termination condition is met, the chassis module is locked and the current impedance parameters are recorded as a reference for subsequent power output.

7. The method for multi-device power sharing and robot scheduling optimization for smart homes according to claim 1, characterized in that, The step of sending a power-ready signal through a magnetic field coupling channel specifically includes: The mobile robot modulates the frequency or amplitude of the magnetic field generated by the transmitting coil and sends a magnetic field signal containing a wake-up preamble. The passive analog front-end circuit of the terminal device senses the magnetic field signal and triggers a hardware interrupt after demodulating the wake-up preamble to wake up the processing module in a dormant state.

8. The method for multi-device power sharing and robot scheduling optimization for smart homes according to claim 1, characterized in that, The step of providing power to the terminal device specifically includes: The mobile robot adjusts its transmission power so that the rectified output voltage of the terminal device's power receiving module is higher than the current terminal voltage of the power supply battery. By utilizing the wired-OR logic formed by the output terminal of the power receiving module and the output terminal of the power supply battery, the discharge circuit of the power supply battery is automatically blocked and the power receiving module takes over the power supply to the load.

9. The method for multi-device power sharing and robot scheduling optimization for smart homes according to claim 1, characterized in that, The step of detecting the completion of the business task request specifically includes: During the accompanying power supply, the mobile robot monitors the real part change of the input impedance of the transmitting coil in real time; Based on the principle of transformer impedance transformation, the equivalent load resistance of the terminal equipment is estimated by using the real part of the monitored input impedance and the pre-locked mutual inductance value. The estimated equivalent load resistance is matched with a preset task feature template to identify the business task status.

10. The method for multi-device power sharing and robot scheduling optimization for smart homes according to claim 9, characterized in that, The step of identifying the status of the business task specifically includes: When the equivalent load resistance is detected to jump from the low impedance range to the high impedance range and the duration exceeds the preset judgment time window, it is determined that the service task request has been completed. The mobile robot stops high-power output and reports the task completion status to the server.

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