Intelligent household multi-device linkage power utilization control method based on MOS tube

Abstract

The invention discloses a smart home multi-device linkage power utilization control method based on MOS tubes, and belongs to the technical field of smart home control. The digital processor is used as a core, the built-in numerical control module and the data processing unit are matched, the on-off state of the MOS tube is accurately regulated and controlled in combination with a program control algorithm, fine management of power utilization parameters of intelligent household equipment is achieved, the limitation that traditional control response is lagged and poor in adaptability is broken through, rated parameters and real-time requirements of the equipment are dynamically matched, and the power utilization efficiency is improved. The optimal operation of equipment is guaranteed to prolong the service life, invalid energy consumption is reduced, a high-precision foundation is laid for multi-equipment cooperative linkage, the stability and energy efficiency of a system are improved, multi-scene linkage logic is preset by means of a logic state machine, and rapid switching and reliable execution of multi-scene linkage are realized by matching with an application selection switch of an MOS tube array structure. Scene requirements are automatically adapted, and through independent loop control, fault detection and standby loop design, the problems that linkage switching is tedious and the fault influence is large are solved.

Description

Technical Field

[0001] This invention relates to the field of smart home control technology, and in particular to a method for controlling the power consumption of multiple smart home devices in a coordinated manner based on MOSFETs. Background Technology

[0002] Currently, most smart home power control solutions on the market use traditional relays or ordinary switching elements to control the on / off state of devices. These solutions suffer from low control precision and slow response speed, making it difficult to meet the collaborative operation needs of various types of devices such as lighting, home appliances, and security equipment. Furthermore, they have poor adaptability to the load characteristics of different devices, which can easily lead to energy waste or abnormal device operation. At the same time, existing solutions mostly only have single overload or short circuit protection functions, lacking comprehensive monitoring of multiple risks such as leakage current, abnormal voltage, temperature rise, and surge. Moreover, most of them are passive power supply cut-off protection modes, which cannot predict risks in advance and take pre-treatment measures, which can easily lead to safety hazards or affect the continuity of use. The linkage logic of the control system is fixed, and the scene switching flexibility is insufficient, making it difficult to adapt to users' personalized usage habits. Summary of the Invention

[0003] The purpose of this invention is to provide a method for controlling the power consumption of multiple smart home devices based on MOSFETs, so as to solve the problems mentioned in the background art.

[0004] To achieve the above objectives, the present invention provides the following technical solution: a smart home multi-device linkage power control method based on MOSFETs, comprising: A smart home multi-device interconnection power control architecture is built using a digital processor as the core control unit. The system uses program control to uniformly schedule and coordinate the operation of smart home electrical devices, which include lighting, home appliances, and security equipment. An integrated monitoring and security mechanism provides real-time protection for the entire electricity consumption process; By leveraging the collaborative operation of application selection switches and logic state machines, the switching and execution of device linkage logic in multiple scenarios can be accomplished. The power parameters are adjusted using numerical control technology, and the equipment can be expanded and the control program upgraded.

[0005] Furthermore, the digital processor integrates a high-performance numerical control module and a data processing unit: The numerical control module is configured to receive power status signals from each smart home electrical device in real time through the communication interface. The power status signals include the device operating voltage, operating current, actual power, and temperature parameters. The digital signal is analyzed and optimized by a preset program control algorithm. Combined with the rated power consumption parameters of the equipment, the preset operating mode and the real-time power consumption requirements, the output accuracy, frequency and amplitude of the digital control command are dynamically adjusted, and the optimized control command is transmitted to the MOS tube drive circuit. Controlling the conduction angle, conduction time, and turn-off timing of MOSFETs to adjust the power consumption and operating status of equipment; The data processing unit is configured to convert the received analog signal into a digital signal and then transmit it to the numerical control module.

[0006] Furthermore, the program control algorithm includes a numerical control parameter adaptive calibration module and a device operation learning module: The numerical control parameter adaptive calibration module is configured to automatically identify the differences in power demand of different smart home devices based on their load type, rated power, power consumption characteristics and operating scenarios, and dynamically adjust the output parameters of the digital control commands. The device operation learning module is configured to continuously collect historical operation data of the device, establish a device operation database, and analyze and mine the device operation patterns and user habits based on data analysis. The historical operation data includes power consumption, start-stop frequency and running time at different times. The CNC parameter adaptive calibration module combines equipment operating rules with user habits to continuously optimize calibration strategies, so that the conduction and turn-off states of the MOSFETs are adapted to the power requirements of the equipment under different operating conditions.

[0007] Furthermore, the monitoring safety mechanism includes safety protection functions such as overload monitoring, leakage current monitoring, voltage anomaly monitoring, temperature monitoring, and surge protection; Dedicated sensors and detection elements are deployed in the power supply circuits, main lines and key nodes of various smart home electrical devices to collect operating parameters in real time during the power consumption process; The digital processor performs real-time analysis and comparison of the collected operating parameters to determine whether smart home electrical devices are within the preset safety threshold range, and at the same time predicts the trend of parameter changes. When a parameter is detected to exceed a safety threshold or exhibit an abnormal trend, a graded protection instruction is triggered, wherein the graded protection instruction includes: For minor anomalies, the control application selector switch adjusts the operating power of the corresponding device or switches the power supply circuit; for serious anomalies, the application selector switch quickly cuts off the power supply circuit of the corresponding device or the entire area, and sends a safety warning message to the user terminal through the wireless communication module, providing detailed information on the anomaly type, location, and handling suggestions.

[0008] Furthermore, the monitoring safety mechanism also includes an abnormal early warning processing unit, configured to send an early warning message to the user terminal through a digital processor when the power consumption parameter is detected to be close to the safety threshold but has not reached the condition for triggering the protection command, informing the user of the potential power consumption risk, and the logic state machine automatically switches to the early warning linkage state to start the preprocessing mechanism.

[0009] Furthermore, the logic state machine predefines multiple device linkage logic states, including daily home scene linkage state, energy-saving scene linkage state, sleep scene linkage state, home security scene linkage state, guest meeting scene linkage state, and custom scene linkage state. Each device linkage logic state corresponds to a unique device operation combination scheme, power parameter configuration, and triggering condition. The logic state machine automatically identifies the current scene requirements and switches to the corresponding target state based on the control instructions issued by the digital processor, the manual operation signals issued by the user through the terminal APP or control panel, and the light, temperature and human body sensing scene trigger signals collected by the environmental sensors. After the state transition, the logic state machine sends a control signal to the application selection switch to execute the corresponding power supply circuit switching action and carry out the coordinated operation of multiple devices.

[0010] Furthermore, the application selection switch adopts a MOS transistor array structure design, wherein each MOS transistor controls a power supply circuit of a smart home device, and each MOS transistor is equipped with an independent drive circuit, fault detection port and protection element; The logic state machine controls the on and off states of the MOSFET by outputting high and low level signals, selectively connecting and disconnecting the power supply circuits of multiple devices to meet the device combination control requirements in different linkage scenarios. The fault detection port monitors the conduction state, voltage drop, and current value of the MOSFET in real time and feeds back the detection results to the digital processor. The digital processor analyzes the feedback detection results, detects MOSFET faults, and marks the fault location. When a MOSFET fault is detected, the system switches to a backup MOSFET or backup power supply circuit and sends a fault alarm message to the user terminal.

[0011] Furthermore, it also includes a program update extension module and a device access registration module: The program update extension module is configured to upload new linkage control programs, scene configuration schemes or function upgrade packages to the digital processor via mobile terminal APP, cloud server or local control panel, thereby expanding the linkage scene types and control functions of the logic state machine. The device access registration module is configured to quickly connect new smart home devices. After connecting the new device to the system, it automatically identifies the device type, rated parameters and communication protocol, quickly completes device information registration and linkage logic configuration, and generates corresponding control commands and linkage schemes.

[0012] Furthermore, the digital processor is also configured to perform dynamic thermal stress assessment and active suppression steps: Real-time monitoring of the drain-source voltage of each MOS transistor in the MOS transistor array and the current flowing through Calculate instantaneous power loss ; Combining the pre-defined Cauer thermal impedance network model and real-time ambient temperature Estimate the real-time junction temperature of each MOSFET. ; Based on the junction temperature fluctuation characteristics under specific equipment linkage scenarios, the real-time degradation rate (MDR) of the MOSFET is calculated to quantify the performance degradation rate of the MOSFET due to thermal cycling stress. The calculation formula is as follows: in, The degradation rate of the MOSFET; and These are empirical constants related to MOSFET materials and packaging, determined through accelerated aging experiments. The amplitude (K) of the junction temperature fluctuation during the current linkage operation; The thermal cycling frequency (Hz) corresponding to this linkage operation; The activation energy (eV) that leads to the failure mechanism of a MOSFET; is the Boltzmann constant (eV / K); The average junction temperature (K) during thermal cycling. is the base of the natural logarithm; When calculated Exceeding the preset reliability threshold At the same time, the digital processor dynamically adjusts the gate drive voltage slope of the corresponding MOS transistor drive circuit or fine-tunes its switching frequency to reduce the junction temperature fluctuation while ensuring normal equipment operation. This reduces the accumulation of thermal stress in the MOSFET.

[0013] Furthermore, when the logic state machine switches between device linkage logic states, if the target state involves a preset time window... If the power supply circuits of multiple devices are simultaneously switched on and off, a spectrum dispersion switching strategy is implemented to suppress electromagnetic interference (EMI). The digital processor analyzes the device combination and its load characteristics corresponding to the target state, and identifies potential synchronous switching events and their estimated aggregate EMI intensity. If the estimated aggregate EMI intensity exceeds the scenario compatibility threshold, the single linkage switching command will be decomposed into N interleaved switching sub-commands, where N is the number of devices involved in the switching. Allocate an independent micro-delay time to each switch sub-instruction ,in The micro-delay time Based on pseudo-random sequence generation or preset interleaved time pattern determination, to ensure the non-correlation of switching timing; The sum of the micro-delay times satisfies ,in To ensure that the user is unaware of the maximum linkage delay threshold; By driving the MOS transistor array in sequence with interleaved switching sub-instructions that include micro-delay time, the switching noise energy generated by the switching of multiple devices is dispersed in the time domain, thereby broadening the spectrum in the frequency domain and reducing the peak EMI amplitude generated by the synchronous switching of multiple devices.

[0014] Compared with the prior art, the beneficial effects of the present invention are: 1. This invention utilizes a digital processor paired with a built-in numerical control module and data processing unit, combined with a program control algorithm to precisely regulate the switching state of MOSFETs, thereby achieving refined management of the power parameters of smart home devices. This overcomes the limitations of traditional control methods, such as slow response and poor adaptability, and can dynamically match the rated parameters of the devices with real-time power demands. This ensures that the devices operate under optimal conditions to extend their service life, while reducing ineffective losses through precise energy consumption regulation. At the same time, it lays a high-precision control foundation for the collaborative linkage of multiple devices, improving the stability and energy efficiency of the power system.

[0015] 2. This invention relies on a logic state machine to preset multi-scenario linkage logic, and in conjunction with an application selection switch of a MOS transistor array structure, to achieve rapid switching and reliable execution of device linkage in multiple scenarios. The logic state machine can automatically identify scenario requirements and switch the corresponding control scheme. The application selection switch ensures continuous operation through independent loop control and fault detection mechanism, solving the problems of cumbersome scenario switching and large fault impact range in traditional linkage control. Users can enjoy a smart power consumption experience adapted to the scenario without frequent manual operation. Furthermore, the backup circuit design avoids functional failure due to a single fault, improving the convenience and reliability of the system. Attached Figure Description

[0016] Figure 1 This is a schematic diagram of the power control method for multi-device linkage in smart homes according to the present invention. Detailed Implementation

[0017] 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.

[0018] Please see Figure 1 The present invention provides the following technical solutions: A method for controlling the power consumption of multiple smart home devices based on MOSFETs includes: A smart home multi-device interconnection power control architecture is built using a digital processor as the core control unit. The system uses program control to uniformly schedule and coordinate the operation of smart home electrical devices, which include lighting, home appliances, and security equipment. An integrated monitoring and security mechanism provides real-time protection for the entire electricity consumption process; By leveraging the collaborative operation of application selection switches and logic state machines, the switching and execution of device linkage logic in multiple scenarios can be accomplished. The power parameters are adjusted using numerical control technology, and the equipment can be expanded and the control program upgraded.

[0019] The digital processor integrates a high-performance numerical control module and a data processing unit. The numerical control module is configured to receive power status signals from each smart home electrical device in real time through the communication interface. The power status signals include the device operating voltage, operating current, actual power, and temperature parameters. The digital signal is analyzed and optimized by a preset program control algorithm. Combined with the rated power consumption parameters of the equipment, the preset operating mode and the real-time power consumption requirements, the output accuracy, frequency and amplitude of the digital control command are dynamically adjusted, and the optimized control command is transmitted to the MOS tube drive circuit. Controlling the conduction angle, conduction time, and turn-off timing of MOSFETs to adjust the power consumption and operating status of equipment; The data processing unit is configured to convert the received analog signal into a digital signal and then transmit it to the numerical control module.

[0020] The program control algorithm includes a numerical control parameter adaptive calibration module and a device operation learning module: The numerical control parameter adaptive calibration module is configured to automatically identify the differences in power demand of different smart home devices based on their load type, rated power, power consumption characteristics and operating scenarios, and dynamically adjust the output parameters of the digital control commands. The device operation learning module is configured to continuously collect historical operation data of the device, establish a device operation database, and analyze and mine the device operation patterns and user habits based on data analysis. The historical operation data includes power consumption, start-stop frequency and running time at different times. The CNC parameter adaptive calibration module combines equipment operating rules with user habits to continuously optimize calibration strategies, so that the conduction and turn-off states of the MOSFETs are adapted to the power requirements of the equipment under different operating conditions.

[0021] In the above embodiments, by integrating the CNC parameter adaptive calibration module and the equipment operation learning module, intelligent and personalized optimization of the control strategy is achieved. This automatically identifies differences in load type, rated power, and power consumption characteristics of different devices, dynamically adjusting the output parameters of control commands. This solves the problems of poor adaptability and insufficient versatility of traditional control methods for different devices. By continuously collecting historical operating data from the equipment, a comprehensive operating database is established, deeply mining equipment operating patterns and user habits to provide data support for control strategy optimization. The two modules work together to enable continuous iterative optimization of the CNC parameter calibration strategy, allowing the switching state of the MOSFET to accurately adapt to the power consumption needs of the equipment under different operating conditions. This achieves dual optimization of equipment self-adaptation and user habit adaptation, improving the flexibility and adaptability of the control strategy. Predictive control based on user habits further enhances the comfort and convenience of the power experience, while minimizing ineffective energy consumption.

[0022] The monitoring safety mechanism covers safety protection functions including overload monitoring, leakage current monitoring, voltage anomaly monitoring, temperature monitoring, and surge protection; Dedicated sensors and detection elements are deployed in the power supply circuits, main lines and key nodes of various smart home electrical devices to collect operating parameters in real time during the power consumption process; The digital processor performs real-time analysis and comparison of the collected operating parameters to determine whether smart home electrical devices are within the preset safety threshold range, and at the same time predicts the trend of parameter changes. When a parameter is detected to exceed a safety threshold or exhibit an abnormal trend, a graded protection instruction is triggered, wherein the graded protection instruction includes: For minor anomalies, the control application selector switch adjusts the operating power of the corresponding device or switches the power supply circuit; for serious anomalies, such as leakage, overload, surge, etc., the application selector switch quickly cuts off the power supply circuit of the corresponding device or the entire area, and sends safety warning information to the user terminal through the wireless communication module, providing detailed information on the type of anomaly, the location of the occurrence, and handling suggestions.

[0023] The monitoring safety mechanism also includes an anomaly early warning processing unit, configured to send an early warning message to the user terminal via a digital processor when the detected power consumption parameter is close to the safety threshold but has not reached the conditions for triggering a protection command. This message informs the user of potential power consumption risks, such as excessive line load or large voltage fluctuations. The logic state machine automatically switches to the early warning linkage state and initiates a preprocessing mechanism, which includes: For situations where the line load is too high, reduce the operating power of unnecessary equipment or adjust the equipment start-up and shutdown sequence to prevent further load increases; for situations with large voltage fluctuations, switch to the backup power supply circuit or activate the voltage regulator to stabilize the supply voltage; for situations where the equipment temperature rises abnormally, adjust the equipment operating mode or activate the cooling device. Through a series of pre-processing measures, risks are mitigated, ensuring the continuous and stable operation of the power system, reducing the frequency of protection command triggering, and improving the user experience.

[0024] In the above embodiments, a comprehensive safety protection system is constructed by covering multiple safety protection functions such as overload, leakage, and voltage abnormality. By deploying dedicated sensors and detection elements in the power supply circuit, main line, and key nodes, comprehensive and real-time collection of power parameters is achieved. The digital processor performs real-time analysis, comparison, and trend prediction of the parameters to ensure early detection and handling of risks. The graded protection commands respond differently according to the severity of the abnormality. Minor abnormalities are handled flexibly by adjusting power and switching circuits, while severe abnormalities are quickly cut off and warnings are sent, balancing safety and continuity of use. When parameters approach the safety threshold, a warning prompt and pre-processing mechanism are triggered. By adjusting the equipment operating status and switching power supply circuits, the risk expansion is avoided. This achieves an upgrade from passive protection to active warning and from simple disconnection to graded handling, effectively eliminating potential electrical safety hazards and reducing unnecessary power outages.

[0025] The logic state machine predefines multiple device linkage logic states, including daily home scene linkage state, energy-saving scene linkage state, sleep scene linkage state, home security scene linkage state, guest meeting scene linkage state, and custom scene linkage state. Each device linkage logic state corresponds to a unique device operation combination scheme, power parameter configuration, and triggering condition. The logic state machine automatically identifies the current scene requirements and switches to the corresponding target state based on the control instructions issued by the digital processor, the manual operation signals issued by the user through the terminal APP or control panel, and the light, temperature and human body sensing scene trigger signals collected by the environmental sensors. After the state transition, the logic state machine sends a control signal to the application selection switch to execute the corresponding power supply circuit switching action and carry out the coordinated operation of multiple devices. For example, in the scenario of leaving home, it automatically turns off all lights and home appliances and starts security equipment; in the scenario of sleeping, it turns off the main light, turns on the night light, and reduces the operating power of the air conditioner.

[0026] The application selection switch adopts a MOSFET array structure design, in which each MOSFET controls a power supply circuit for one smart home device, and each MOSFET is equipped with an independent drive circuit, fault detection port and protection components. The logic state machine controls the on and off states of the MOSFET by outputting high and low level signals, selectively connecting and disconnecting the power supply circuits of multiple devices to meet the device combination control requirements in different linkage scenarios. The fault detection port monitors the conduction state, voltage drop, and current value of the MOSFET in real time and feeds back the detection results to the digital processor. The digital processor analyzes the feedback detection results, detects MOSFET faults, and marks the fault location. When a MOSFET failure is detected, the system switches to a backup MOSFET or backup power supply circuit to ensure the continuity of the linkage control. At the same time, it sends a fault alarm message to the user terminal to prompt the user to repair or replace it in time, thus ensuring the reliability of the power system.

[0027] In the above embodiments, the application selection switch, which combines a logic state machine with a MOSFET array structure, enables rapid switching and reliable execution of device linkage logic across multiple scenarios. The logic state machine predefines various linkage logic states, each corresponding to a unique device operation combination, parameter configuration, and triggering condition. It can automatically identify scenario requirements based on control commands, manual operation signals, and environmental trigger signals, achieving seamless state switching. The application selection switch employs a MOSFET array design, with each MOSFET independently controlling a device power supply circuit. Equipped with a dedicated drive circuit and fault detection port, it ensures the independence and reliability of circuit control, solving problems such as delayed scenario switching in linkage control, insufficient flexibility in device combination control, and a wide range of fault impact. It can quickly respond to device linkage needs in different scenarios, such as automatically turning off home appliances and activating security systems when leaving home, and adjusting light and air conditioning power during sleep, improving the convenience and accuracy of scenario switching. Simultaneously, the fault detection port provides real-time feedback on the MOSFET's operating status, and in conjunction with a backup circuit switching mechanism, effectively prevents the failure of a single MOSFET from causing the entire linkage function to fail.

[0028] The smart home multi-device linkage power control method based on MOSFETs also includes a program update extension module and a device access registration module: The program update extension module is configured to upload new linkage control programs, scene configuration schemes, or function upgrade packages to the digital processor via a mobile terminal APP, cloud server, or local control panel, thereby expanding the linkage scene types and control functions of the logic state machine. The CNC module can automatically identify the control parameters, logic rules, and data formats of the new program, and complete the adaptation and integration with the original system, achieving function upgrades without modifying the hardware structure. The device access registration module is configured to quickly connect new smart home devices. After connecting the new device to the system via scanning a code or Bluetooth pairing, it automatically identifies the device type, rated parameters, and communication protocol, quickly completes device information registration and linkage logic configuration, and generates corresponding control commands and linkage schemes.

[0029] In the above embodiments, new programs and upgrade packages are uploaded through various means such as mobile terminal APPs and cloud servers. The CNC module can automatically identify and complete the adaptation and integration with the original system. It can expand scene types and control functions without modifying the hardware structure, ensuring that the system can continuously adapt to new control requirements and technology upgrades. The device access registration module realizes the rapid access of new devices through convenient methods such as scanning codes and Bluetooth pairing. It automatically identifies the device type, rated parameters and communication protocols, and quickly completes registration and linkage logic configuration without complex manual debugging. This lowers the threshold for system function upgrades and device expansion, improves the system's versatility and life cycle, and allows users to flexibly add new devices and expand new scenes according to their needs. This avoids the waste of replacing the entire control system due to device updates or changes in needs. At the same time, it ensures seamless collaboration between new devices and the original system, maintains the consistency and stability of multi-device linkage control, and provides strong support for the long-term use and iterative upgrade of the smart home system.

[0030] Furthermore, the digital processor is also configured to perform dynamic thermal stress assessment and active suppression steps: Real-time monitoring of the drain-source voltage of each MOS transistor in the MOS transistor array and the current flowing through Calculate instantaneous power loss ; Combining the pre-defined Cauer thermal impedance network model and real-time ambient temperature Estimate the real-time junction temperature of each MOSFET. ; Based on the junction temperature fluctuation characteristics under specific equipment linkage scenarios, the real-time degradation rate (MDR) of the MOSFET is calculated to quantify the performance degradation rate of the MOSFET due to thermal cycling stress. The calculation formula is as follows: in, is the degradation rate of the MOS transistor; and is an empirical constant related to the MOS transistor material and packaging determined through an accelerated aging experiment; is the amplitude (K) of the junction temperature fluctuation during the current linked operation; is the thermal cycle frequency (Hz) corresponding to this linked operation; is the activation energy (eV) that causes the failure mechanism of the MOS transistor; is the Boltzmann constant (eV / K); is the average junction temperature (K) during the thermal cycle, is the base of the natural logarithm; When the calculated exceeds the preset reliability threshold the digital processor dynamically adjusts the gate drive voltage slope of the corresponding MOS transistor drive circuit or finely tunes its switching frequency to reduce the amplitude of the junction temperature fluctuation while ensuring the normal linked operation of the device, and reduce the accumulation of thermal stress of the MOS transistor.

[0031] In a further optimized embodiment of the present invention, in order to address the unique challenges brought by the multi-device linked scenario in smart home, especially to solve the problem of long-term reliability degradation of power switching devices caused by frequent and drastic changes in load, and the cumulative electromagnetic interference problem caused by synchronous switching of multiple devices, the present invention proposes a control method integrating dynamic thermal stress assessment and active suppression, as well as a spectral dispersion switching strategy. These methods significantly improve the long-term working life and electromagnetic compatibility of the system through precise calculations of the digital processor and intelligent adjustment of the control logic without significantly increasing the hardware cost.

[0032] In a smart home environment, device linked operations are highly dynamic and random. For example, switching from the "sleep mode" to the "entertaining mode" may involve large-scale power adjustments of the lighting system, start and stop of the air conditioning system, and activation of audio-visual equipment. These operations will cause the MOS transistor array in the control system to withstand severe power shocks and frequent working state transitions. Although the safety monitoring mechanism mentioned in the basic embodiment can prevent immediate damage caused by overheating (i.e., "anti-burning" protection), they ignore a hidden problem crucial for long-term reliability: thermal cycle stress accumulation.

[0033] As a core power switching device, the MOSFET has a complex internal structure composed of various materials with different physical properties, including silicon chips, solder layers, bonding wires, lead frames, and molding compounds. When a MOSFET is operating, its power loss is converted into heat, causing the internal temperature to rise. During device switching, power loss changes drastically in a short period, leading to rapid fluctuations in the temperature of the core region (junction temperature) of the MOSFET. Because the various materials constituting the MOSFET have different coefficients of thermal expansion, this rapid junction temperature fluctuation causes uneven expansion and contraction between different material layers. This mismatched deformation generates thermomechanical stress at the material interfaces. Under long-term repeated thermal cycling, this stress leads to the accumulation of material fatigue damage. For example, it may cause microcracks in the solder layer to gradually propagate, or cause the bonding wires to loosen or even detach from the chip or lead frame. This microstructural degradation ultimately leads to a decrease in MOSFET performance or even complete failure. This embodiment aims to elevate system reliability management to an "anti-aging" level by quantifying this degradation process in real time and proactively intervening.

[0034] The core of this method lies in a series of precise monitoring, complex calculations, and intelligent decision-making processes executed within the digital processor. The entire process can be divided into four main stages: precise monitoring of instantaneous power consumption, dynamic estimation of real-time junction temperature, quantitative assessment of degradation rate, and active suppression of thermal stress.

[0035] The first stage is precise monitoring of instantaneous power consumption. This is the foundation for all subsequent thermal analysis. The heat of a MOSFET originates from its power loss during operation. The digital processor needs to accurately know how much electrical energy the MOSFET consumes and converts into heat at every moment. To this end, the system is equipped with high-precision voltage and current sensors on each loop of the MOSFET array. The digital processor monitors the drain-source voltage and drain current flowing through each MOSFET in real time through a high-frequency sampling circuit. The product of these two parameters is the instantaneous power loss of that MOSFET. In smart home interconnection scenarios, device switching speeds are extremely fast, with current and voltage fluctuating at the microsecond level. Therefore, it is essential to ensure a sufficiently high sampling frequency and wide bandwidth to accurately capture these rapidly changing power spikes, especially the switching losses generated by the MOSFET during turn-on and turn-off. This real-time power loss data stream serves as the heat source input, driving subsequent thermal model calculations.

[0036] The second stage is real-time dynamic estimation of the junction temperature. The lifespan and reliability of a MOSFET directly depend on its internal hottest spot, namely the chip junction temperature. However, due to physical packaging limitations, it is impossible to directly place a temperature sensor inside the chip for measurement. The casing temperature or heatsink temperature measured by external sensors has a significant temperature difference and time delay compared to the junction temperature, failing to accurately reflect the dynamic changes in the junction temperature. Therefore, this invention employs a high-precision thermal impedance network model based on electrothermal analogy, specifically the Cauer thermal impedance network model.

[0037] The Cauer model is a physical model that accurately describes the conduction of heat from the junction region of a semiconductor chip to the external environment. It abstracts the physical structure of a MOSFET and its heat dissipation path as a series of network nodes with specific thermal resistance and thermal capacity parameters. Thermal resistance represents the resistance to heat conduction, and thermal capacity represents the material's ability to absorb heat. This model accurately simulates how heat is transferred layer by layer, such as from the chip to the solder layer, then to the lead frame, and finally dissipated into the environment through the package. During the system design phase, detailed thermal characteristic calibration of the selected MOSFET is required. This is typically done through transient thermal testing experiments or sophisticated finite element simulations. By analyzing the temperature rise curve of the MOSFET under specific power excitation, the thermal resistance and thermal capacity parameters of each layer in the Cauer model can be extracted. These parameters are pre-stored in the database of the digital processor.

[0038] In real-time operation, the digital processor uses the instantaneous power loss calculated in the first stage as input excitation (in the electrothermal analogy, power loss is equivalent to a current source, and temperature is equivalent to voltage) and injects it into the Cauer thermal impedance network model. Simultaneously, the system incorporates real-time ambient temperature obtained from environmental sensors as boundary conditions. The digital processor, through a high-performance floating-point unit, solves the differential equations of this thermal network model in real time (in discrete implementations, this is typically represented by digital filtering or iterative calculations of a state-space model). This process accurately simulates the generation, absorption, and conduction of heat within the MOSFET. Its output is a precise estimate of the real-time junction temperature of the MOSFET. Crucially, the Cauer model accurately reflects the dynamic characteristic that junction temperature changes lag behind power changes due to the material's heat capacity, enabling the system to capture microsecond-level junction temperature fluctuations, which is essential for analyzing thermal stress.

[0039] The third stage is the quantitative assessment of degradation rate. Based on the ability to track the junction temperature of the MOSFET in real time, the core innovation of this invention lies in introducing the key indicator of "MOSFET degradation rate" to quantify the impact of thermal cycling stress under the current workload on the performance degradation rate of the MOSFET. This is a predictive indicator based on physical failure mechanisms.

[0040] To calculate the degradation rate, the digital processor first needs to extract key parameters characterizing the thermal cycling properties from the real-time junction temperature data stream. Due to the randomness of load changes in smart homes, junction temperature fluctuations are often irregular. Therefore, advanced signal processing algorithms are required to identify and quantify effective thermal cycles. For example, rainflow counting, a method widely used in mechanical engineering for fatigue life analysis, can be employed. Rainflow counting decomposes the complex junction temperature fluctuation history into a series of standard thermal cycles with different amplitudes and average values. From these standard thermal cycles, three core influencing factors can be extracted: the amplitude of the junction temperature fluctuation, the frequency of thermal cycles, and the average junction temperature during the thermal cycle.

[0041] Subsequently, the digital processor utilizes a comprehensive physical failure model to calculate the degradation rate. This model integrates fatigue damage theory from materials science and accelerated aging theory from semiconductor reliability physics. Its core idea is: First, there is a strong nonlinear relationship between the degradation rate and the amplitude of junction temperature fluctuations, typically exhibiting a power-law relationship. This means that the greater the junction temperature fluctuation amplitude, the greater the thermomechanical stress generated per thermal cycle, leading to an exponential increase in device lifetime loss. This relationship reflects the fatigue characteristics of materials under high stress (similar to the principle described by the Coffin-Manson model).

[0042] Secondly, the degradation rate is approximately linearly related to the frequency of thermal cycling. The more frequent the coordinated operations, the more damage accumulates per unit time, and the faster the degradation rate.

[0043] Furthermore, there is an exponentially accelerating relationship between degradation rate and average junction temperature. At higher average temperatures, the creep rate of materials increases, and the chemical reaction rate also rises, making failure mechanisms more easily activated. To scientifically describe this temperature dependence, the model introduces the activation energy, a parameter that leads to specific failure mechanisms in MOSFETs. The activation energy represents the energy barrier for a specific failure mechanism (such as solder aging, electromigration, etc.), and is typically obtained through accelerated aging experiments. Simultaneously, the calculation implicitly utilizes the physical relationship between temperature and molecular kinetic energy (e.g., through the Arrhenius law principle embodied in the Boltzmann constant) to quantify the accelerating effect of average temperature on failure rate.

[0044] Finally, the model also includes several empirical constants related to MOSFET materials, packaging structures, and manufacturing processes. These constants are used to calibrate the model to match the actual aging characteristics of a specific MOSFET model. These empirical constants and activation energy parameters also need to be derived during the product design phase through fitting extensive accelerated aging experimental data and pre-stored in a digital processor.

[0045] By comprehensively considering all the above factors, the digital processor can calculate the real-time degradation rate of the MOS transistor under the current working conditions in real time. This calculation result intuitively reflects the consumption speed of the remaining life of the MOS transistor by the current linkage operation.

[0046] The fourth stage is active suppression of thermal stress. After obtaining the real-time degradation rate, the system enters the decision-making and execution stage. A reliability threshold, that is, the maximum allowable degradation rate of the MOS transistor, is preset in the digital processor. This threshold is deduced inversely according to the overall life goal of the smart home control system design (for example, ensuring reliable operation for 15 years) and reliability indicators.

[0047] The digital processor continuously compares the real-time calculated degradation rate with this reliability threshold. When it is detected that the real-time degradation rate of a certain MOS transistor exceeds the threshold, it means that the current thermal stress level is too high, accelerating the aging of the device, and if no intervention is taken, the long-term reliability requirements cannot be met. At this time, the digital processor will immediately activate the active suppression strategy.

[0048] Different from the traditional rough methods of directly cutting off the circuit or globally reducing power under over-temperature protection, the active suppression strategy aims to flexibly and precisely reduce the accumulation of thermal stress in the MOS transistor on the premise of ensuring that the normal linkage function of the device and the user experience are not significantly affected. Its core means is to dynamically adjust the driving circuit parameters of the MOS transistor.

[0049] One of the most important adjustment means is to control the slope of the gate driving voltage, that is, to realize the dynamic adjustment of the switching speed of the MOS transistor. The switching speed of the MOS transistor depends on the change speed of the gate voltage. By controlling the interface between the digital processor and the gate driving circuit (for example, using a digital gate driver with adjustable driving current, or dynamically switching different gate series resistors), the charging and discharging current of the driving circuit to the gate capacitance of the MOS transistor can be precisely changed, thereby changing the rising and falling slopes of the gate voltage.

[0050] When thermal stress needs to be suppressed, the digital processor will instruct to reduce the slope of the gate driving voltage, making the on and off processes of the MOS transistor slower (i.e., soft switching technology). The mechanism of slowing down the switching speed is that it makes the power loss change more smoothly during the on and off processes of the MOS transistor, reducing the instantaneous rate of power change. Although the extension of the switching process may slightly increase the energy loss of a single switch, it effectively smooths the change process of the junction temperature, thereby significantly reducing the amplitude of the junction temperature fluctuation. Due to the power-law relationship between the degradation rate and the amplitude of the junction temperature fluctuation, the life gain brought by significantly reducing the amplitude of the junction temperature fluctuation is much greater than the cost of slightly increasing the switching loss. The digital processor will adaptively find an optimal gate driving slope within the constraint of ensuring the normal linkage response speed of the device, so that the degradation rate falls back within the safe range.

[0051] Another auxiliary adjustment method is to fine-tune the switching frequency in applications involving pulse width modulation (PWM) control (such as light dimming or motor speed control). While keeping the duty cycle constant (i.e., keeping the average output power of the device constant), slightly changing the switching frequency can change the distribution characteristics of power loss over time, affecting the balance between heat generation and dissipation, thereby changing the characteristics of junction temperature fluctuations to some extent and helping to reduce thermal stress.

[0052] Through this closed-loop control, the present invention realizes an intelligent MOSFET reliability management mechanism. It can adaptively adjust the control strategy according to different linkage scenarios, equipment load characteristics, and the aging state of the devices themselves, ensuring that while achieving fast and accurate smart home control, the aging rate of the core power devices is kept within an acceptable range, greatly improving the long-term stability and durability of the entire system.

[0053] When the logic state machine switches the device linkage logic state, if the target state involves a preset time window... If the power supply circuits of multiple devices are simultaneously switched on and off, a spectrum dispersion switching strategy is implemented to suppress electromagnetic interference (EMI). The digital processor analyzes the device combination and its load characteristics corresponding to the target state, and identifies potential synchronous switching events and their estimated aggregate EMI intensity. If the estimated aggregate EMI intensity exceeds the scenario compatibility threshold, the single linkage switching command will be decomposed into N interleaved switching sub-commands, where N is the number of devices involved in the switching. Allocate an independent micro-delay time to each switch sub-instruction ,in The micro-delay time Based on pseudo-random sequence generation or preset interleaved time pattern determination, to ensure the non-correlation of switching timing; The sum of the micro-delay times satisfies ,in To ensure that the user is unaware of the maximum linkage delay threshold; By driving the MOS transistor array in sequence with interleaved switching sub-instructions that include micro-delay time, the switching noise energy generated by the switching of multiple devices is dispersed in the time domain, thereby broadening the spectrum in the frequency domain and reducing the peak EMI amplitude generated by the synchronous switching of multiple devices.

[0054] In a smart home control system, using a MOS transistor array as an application selection switch can achieve fast and contactless circuit on and off. At the moment when the MOS transistor conducts and turns off, the voltage across its two ends and the current flowing through it will undergo drastic jumps. This fast voltage change rate and current change rate will emit electromagnetic interference outward through conduction paths (such as power lines) and radiation paths (such as space electromagnetic waves).

[0055] In single-device control, this interference can usually be suppressed by reasonable circuit layout design and adding simple filtering measures. However, in the multi-device linkage scenario of smart homes, the problem becomes particularly prominent. When a user triggers a scenario switch, for example, starting the "movie viewing mode", the logic state machine may instruct to simultaneously turn off the main lighting system in the living room and simultaneously turn on multiple devices such as a projector, an amplifier, a subwoofer, and an ambient light. If the control system performs strict synchronous switching operations on these devices, then multiple MOS transistors will act simultaneously within a very short time window. At this time, the switching noises generated by each loop will coherently superimpose in the time domain. This superimposition effect will result in a huge cumulative electromagnetic interference spike.

[0056] This high-peak electromagnetic interference energy is usually concentrated in a relatively narrow frequency band and is very likely to exceed the requirements of electromagnetic compatibility standards. More seriously, it will pollute the electromagnetic environment inside the home and is sufficient to interfere with the normal operation of other sensitive electronic devices. For example, it may cause the communication rate of a wireless router to decrease or even disconnect; it may penetrate into a high-fidelity audio system, resulting in an increase in background noise or popping sounds. The traditional solution is to add more complex and larger electromagnetic interference filters, but this will significantly increase the hardware cost and volume and is not conducive to the miniaturization and integration of smart home devices.

[0057] The spectrum dispersion switch strategy proposed in this invention provides an intelligent and low-cost solution. Its core idea is to utilize the precise timing control ability of a digital processor to artificially break its synchronization by finely tuning the switching timings of multiple devices in the time domain, thereby dispersing the concentrated electromagnetic interference energy into a wider frequency spectrum range in the frequency domain and reducing the peak interference amplitude. This is a typical application of the "peak shaving and valley filling" strategy in the field of electromagnetic interference suppression.

[0058] The execution process of this strategy can be divided into the following stages: synchronous event recognition and risk assessment, staggered timing generation and constraint satisfaction, and precise timing execution.

[0059] The first stage is synchronous event identification and risk assessment. When the logic state machine is preparing to switch the device linkage logic state, the digital processor first evaluates whether the switching operation involves simultaneously switching on and off the power supply circuits of multiple devices within a preset very short time window. If it is determined to be so, it is identified as a potential synchronous switching event, and the subsequent risk assessment process needs to be initiated.

[0060] The purpose of the risk assessment is to predict the intensity of electromagnetic interference (EMI) that may be generated by this synchronous switching event. The digital processor analyzes the device combination and its load characteristics corresponding to the target state. The system has already obtained detailed information about each connected device through the device access registration module, including device type (e.g., inductive, capacitive, or resistive load) and rated power. Different types of loads generate EMI characteristics that vary greatly during switching. For example, inductive loads (such as motors and transformers) are prone to generating high voltage spikes when turned off, while capacitive loads (such as LED drivers and switching power supplies) are prone to generating large inrush currents when turned on. Based on this information and a preset interference model, the digital processor quickly estimates the aggregate EMI intensity that will be generated if synchronous switching is performed.

[0061] The digital processor then compares the estimated aggregate electromagnetic interference intensity with the electromagnetic compatibility threshold for the current scenario. This threshold can be dynamically set based on the electromagnetic susceptibility of the home environment. For example, the threshold can be set more stringent when high-precision measuring equipment is operating or when important wireless communications are in progress.

[0062] The second stage involves staggered timing generation and constraint fulfillment. If the estimated aggregated electromagnetic interference intensity exceeds the scene compatibility threshold, the digital processor determines that synchronous switching cannot be performed and a spectral dispersion strategy must be adopted. At this time, the digital processor decomposes the original single linkage switching instruction into N staggered switching sub-instructions, where N is the number of devices involved in the switching operation in this linkage scenario.

[0063] The key step lies in allocating an independent micro-delay time for each switching sub-instruction. This micro-delay time determines the execution delay of the corresponding MOSFET relative to the trigger moment. The setting of these micro-delay times is crucial and directly determines the effectiveness of electromagnetic interference suppression. If the delay time is set too regularly (e.g., equal-interval delays), although it can reduce the interference peak at the original frequency, it will generate strong harmonic components at the new frequency corresponding to the delay interval, causing the interference energy to shift from one frequency band to another, which is only a temporary solution.

[0064] Therefore, this invention employs a specific algorithm to ensure the uncorrelatedness of the switching timing, thereby achieving optimal spectral dispersion. There are two main implementation mechanisms: The first one is the generation method based on pseudo-random sequences. Inside the digital processor, a pseudo-random number generator can be implemented using hardware logic circuits or software algorithms, for example, by a linear feedback shift register. The system generates N micro-delay values randomly distributed within a specific time range according to the number N of devices to be dispersed. Since these delay values have good randomness and uniformity, the occurrence times of multiple device switching events are also randomly distributed. This random jitter in timing spreads the energy of the switching noise fully in the frequency domain, presenting characteristics similar to white noise without obvious peaks. This effect is usually referred to as the "whitening" of the spectrum.

[0065] The second one is to adopt a preset staggered time pattern. For some common device combinations and interlock scenarios, specific delay combination patterns that can achieve the best electromagnetic interference suppression effect can be pre-designed through offline simulation and experimental measurement. These patterns are stored in the digital processor. During operation, the digital processor selects the most suitable staggered time pattern according to the current device combination to allocate micro-delay times. For example, according to the power level and load type of the devices, low-power devices can be switched first, and the switching times of high-power and strong-interference devices can be dispersed as much as possible.

[0066] When generating micro-delay times, a crucial constraint must be met: the imperceptibility of the user experience. The core advantage of smart home interlock lies in fast response and coordination consistency. If obvious order in device switching occurs for electromagnetic interference suppression, it will seriously affect the user experience. Therefore, this invention stipulates a maximum interlock delay threshold to ensure user imperceptibility. This threshold defines the maximum time length allowed from when the user issues an instruction to when all devices complete state switching, usually determined according to the human perception ability, for example, set to 100 milliseconds or 200 milliseconds. When generating the micro-delay time sequence, the digital processor must ensure that the sum of all micro-delay times, that is, the total duration of the entire staggered switching process, is strictly less than this maximum interlock delay threshold. The system needs to find a delicate balance between the electromagnetic interference suppression effect and the interlock response speed.

[0067] The third stage is precise timing execution. After determining the staggered switch sub-instruction sequence containing micro-delay times, the digital processor uses its internal high-resolution timer and fast interrupt response mechanism to drive the MOS transistor array precisely according to the predetermined timing. For example, for an interlock scenario involving 3 devices, originally planned to switch simultaneously at T0, now it may become device 1 switches at T0 + 5 milliseconds, device 2 switches at T0 + 18 milliseconds, and device 3 switches at T0 + 11 milliseconds.

[0068] The technical benefits of this operation are significant. In the time domain, the massive energy pulse that was originally concentrated at a single moment is dispersed into a series of smaller pulses. Because the time intervals between these pulses are small and non-uniform, the switching noise energy they generate is effectively distributed over time. According to the basic principles of the Fourier transform, the broadening of the time-domain signal corresponds to the broadening of its frequency-domain spectrum. Although the overall electromagnetic interference energy may not change much, the peak energy that was originally concentrated at a specific frequency point is redistributed over a wider frequency range. As a result, the amplitude of electromagnetic interference at any given frequency point is greatly reduced. This makes the system more likely to meet electromagnetic compatibility standards and significantly reduces the risk of interference to surrounding sensitive electronic equipment.

[0069] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A method for controlling the power consumption of multiple smart home devices based on MOSFETs, characterized in that, include: A smart home multi-device interconnection power control architecture is built using a digital processor as the core control unit. The system uses program control to uniformly schedule and coordinate the operation of smart home electrical devices, which include lighting, home appliances, and security equipment. An integrated monitoring and security mechanism provides real-time protection for the entire electricity consumption process; By leveraging the collaborative operation of application selection switches and logic state machines, the switching and execution of device linkage logic in multiple scenarios can be accomplished. The power parameters are adjusted using numerical control technology, and the equipment can be expanded and the control program upgraded.

2. The smart home multi-device linkage power control method based on MOSFET as described in claim 1, characterized in that, The digital processor integrates a high-performance numerical control module and a data processing unit. The numerical control module is configured to receive power status signals from each smart home electrical device in real time through the communication interface. The power status signals include the device operating voltage, operating current, actual power, and temperature parameters. The digital signal is analyzed and optimized by a preset program control algorithm. Combined with the rated power consumption parameters of the equipment, the preset operating mode and the real-time power consumption requirements, the output accuracy, frequency and amplitude of the digital control command are dynamically adjusted, and the optimized control command is transmitted to the MOS tube drive circuit. Controlling the conduction angle, conduction time, and turn-off timing of MOSFETs to adjust the power consumption and operating status of equipment; The data processing unit is configured to convert the received analog signal into a digital signal and then transmit it to the numerical control module.

3. The smart home multi-device linkage power control method based on MOSFET as described in claim 2, characterized in that, The program control algorithm includes a numerical control parameter adaptive calibration module and a device operation learning module: The numerical control parameter adaptive calibration module is configured to automatically identify the differences in power demand of different smart home devices based on their load type, rated power, power consumption characteristics and operating scenarios, and dynamically adjust the output parameters of the digital control commands. The device operation learning module is configured to continuously collect historical operation data of the device, establish a device operation database, and analyze and mine the device operation patterns and user habits based on data analysis. The historical operation data includes power consumption, start-stop frequency and running time at different times. The CNC parameter adaptive calibration module combines equipment operating rules with user habits to continuously optimize calibration strategies, so that the conduction and turn-off states of the MOSFETs are adapted to the power requirements of the equipment under different operating conditions.

4. The smart home multi-device linkage power control method based on MOSFET as described in claim 1, characterized in that, The monitoring safety mechanism includes safety protection functions such as overload monitoring, leakage current monitoring, voltage anomaly monitoring, temperature monitoring, and surge protection. Dedicated sensors and detection elements are deployed in the power supply circuits, main lines and key nodes of various smart home electrical devices to collect operating parameters in real time during the power consumption process; The digital processor performs real-time analysis and comparison of the collected operating parameters to determine whether smart home electrical devices are within the preset safety threshold range, and at the same time predicts the trend of parameter changes. When a parameter is detected to exceed a safety threshold or exhibit an abnormal trend, a graded protection instruction is triggered, wherein the graded protection instruction includes: For minor anomalies, the control application selector switch adjusts the operating power of the corresponding device or switches the power supply circuit; for serious anomalies, the application selector switch quickly cuts off the power supply circuit of the corresponding device or the entire area, and sends a safety warning message to the user terminal through the wireless communication module, providing detailed information on the anomaly type, location, and handling suggestions.

5. The smart home multi-device linkage power control method based on MOSFET as described in claim 4, characterized in that, The monitoring safety mechanism also includes an abnormal early warning processing unit, which is configured to send an early warning message to the user terminal through a digital processor when the power consumption parameter is detected to be close to the safety threshold but has not reached the condition for triggering the protection command, informing the user of the potential power consumption risk, and the logic state machine automatically switches to the early warning linkage state and starts the preprocessing mechanism.

6. The smart home multi-device linkage power control method based on MOSFET as described in claim 1, characterized in that, The logic state machine predefines multiple device linkage logic states, including daily home scene linkage state, energy-saving scene linkage state, sleep scene linkage state, home security scene linkage state, guest meeting scene linkage state, and custom scene linkage state. Each device linkage logic state corresponds to a unique device operation combination scheme, power parameter configuration, and triggering condition. The logic state machine automatically identifies the current scene requirements and switches to the corresponding target state based on the control instructions issued by the digital processor, the manual operation signals issued by the user through the terminal APP or control panel, and the light, temperature and human body sensing scene trigger signals collected by the environmental sensors. After the state transition, the logic state machine sends a control signal to the application selection switch to execute the corresponding power supply circuit switching action and carry out the coordinated operation of multiple devices.

7. The smart home multi-device linkage power control method based on MOSFET as described in claim 6, characterized in that, The application selection switch adopts a MOS transistor array structure design, wherein each MOS transistor controls a power supply circuit of a smart home device, and each MOS transistor is equipped with an independent drive circuit, fault detection port and protection element. The logic state machine controls the on and off states of the MOSFET by outputting high and low level signals, selectively connecting and disconnecting the power supply circuits of multiple devices to meet the device combination control requirements in different linkage scenarios. The fault detection port monitors the conduction state, voltage drop, and current value of the MOSFET in real time and feeds back the detection results to the digital processor. The digital processor analyzes the feedback detection results, detects MOSFET faults, and marks the fault location. When a MOSFET fault is detected, the system switches to a backup MOSFET or backup power supply circuit and sends a fault alarm message to the user terminal.

8. The smart home multi-device linkage power control method based on MOSFET as described in claim 1, characterized in that, It also includes a program update extension module and a device access registration module: The program update extension module is configured to upload new linkage control programs, scene configuration schemes or function upgrade packages to the digital processor via mobile terminal APP, cloud server or local control panel, thereby expanding the linkage scene types and control functions of the logic state machine. The device access registration module is configured to quickly connect new smart home devices. After connecting the new device to the system, it automatically identifies the device type, rated parameters and communication protocol, quickly completes device information registration and linkage logic configuration, and generates corresponding control commands and linkage schemes.

9. The smart home multi-device linkage power control method based on MOSFET as described in claim 7, characterized in that, The digital processor is also configured to perform dynamic thermal stress assessment and active suppression steps: Real-time monitoring of the drain-source voltage of each MOS transistor in the MOS transistor array and the current flowing through Calculate instantaneous power loss ; Combining the pre-defined Cauer thermal impedance network model and real-time ambient temperature Estimate the real-time junction temperature of each MOSFET. ; Based on the junction temperature fluctuation characteristics under specific equipment linkage scenarios, the real-time degradation rate (MDR) of the MOSFET is calculated to quantify the performance degradation rate of the MOSFET due to thermal cycling stress. The calculation formula is as follows: in, The degradation rate of the MOSFET; and These are empirical constants related to MOSFET materials and packaging, determined through accelerated aging experiments. This represents the amplitude of the junction temperature fluctuation during the current coordinated operation. This refers to the thermal cycling frequency corresponding to this linkage operation; The activation energy that leads to the failure mechanism of a MOSFET; Boltzmann's constant; The average junction temperature during thermal cycling. is the base of the natural logarithm; When calculated Exceeding the preset reliability threshold At the same time, the digital processor dynamically adjusts the gate drive voltage slope of the corresponding MOS transistor drive circuit or fine-tunes its switching frequency to reduce the junction temperature fluctuation while ensuring normal equipment operation. This reduces the accumulation of thermal stress in the MOSFET.

10. The smart home multi-device linkage power control method based on MOSFET as described in claim 6, characterized in that, When the logic state machine switches the device linkage logic state, if the target state involves a preset time window... If the power supply circuits of multiple devices are simultaneously switched on and off, a spectrum dispersion switching strategy is implemented to suppress electromagnetic interference. The digital processor analyzes the device combination and its load characteristics corresponding to the target state, and identifies potential synchronous switching events and their estimated aggregate EMI intensity. If the estimated aggregate EMI intensity exceeds the scenario compatibility threshold, the single linkage switching command will be decomposed into N interleaved switching sub-commands, where N is the number of devices involved in the switching. Allocate an independent micro-delay time to each switch sub-instruction ,in The micro-delay time Based on pseudo-random sequence generation or preset interleaved time pattern determination, to ensure the non-correlation of switching timing; The sum of the micro-delay times satisfies ,in To ensure that the user is unaware of the maximum linkage delay threshold; By driving the MOS transistor array in sequence with interleaved switching sub-instructions that include micro-delay time, the switching noise energy generated by the switching of multiple devices is dispersed in the time domain, thereby broadening the spectrum in the frequency domain and reducing the peak EMI amplitude generated by the synchronous switching of multiple devices.

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