Dynamic power consumption management method and system of portable brain-computer interface device and storage medium

Abstract

The invention provides a dynamic power consumption management method and system for portable brain-computer interface equipment and a storage medium, and the method at least comprises the following steps: S1, after the system is electrified and initialized, controlling a main control chip, an electroencephalogram acquisition chip, a first wireless communication module and a second wireless communication module to enter a deep sleep mode; s2, in response to occurrence of at least one preset awakening event, awakening the main control chip; s3, the main control chip generates at least one task to be executed according to the type of the wake-up event and adds the task to be executed into a task queue; s4, the main control chip schedules and executes the tasks in the task queue; and S5, after all tasks in the task queue are executed, the main control chip returns to the deep sleep mode. The system comprises a main control chip, an electroencephalogram acquisition chip, a first wireless communication module and a second wireless communication module. The method has the advantages of obviously reducing the average power consumption of the system, intelligently selecting a wireless transmission path, improving the response efficiency of the system and the like.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of brain-computer interface, in particular to a dynamic power management method and system of a portable brain-computer interface device and a storage medium. BACKGROUND

[0002] With the development of wearable medical devices and brain-computer interface technology, portable electroencephalogram devices have been widely used in medical monitoring, cognitive science research and consumer electronics. Such devices are usually composed of an analog front-end, a main controller and a wireless communication module, and need to work continuously for a long time to monitor the user's electroencephalogram signals. Therefore, the power consumption of the device directly determines its endurance and user experience.

[0003] The portable electroencephalogram devices on the market usually adopt a continuous working mode, that is, the analog front-end chip continuously acquires signals, the main control chip continuously processes data, and the data is continuously transmitted through a single wireless module. Some devices may be able to provide multiple wireless transmission modes (such as Wi-Fi and Bluetooth), but the wireless transmission mode is generally manually switched by the user, lacking intelligent management.

[0004] The prior art has the following defects: 1. Continuous high power consumption: each chip (main control, ADC, wireless module) in the system is in a working or standby state when there is no effective task, resulting in a large amount of static and dynamic power consumption being wasted; 2. Lack of coordinated scheduling: each functional unit works independently, and the main control chip needs to run continuously to coordinate all parties, which cannot enter deep sleep, resulting in high overall system power consumption; 3. Coarse management of wireless module power consumption: the power consumption of a high-bandwidth Wi-Fi module in establishing a connection and transmitting data is much higher than that of a Bluetooth module when transmitting a small amount of data, and the existing scheme cannot intelligently select the optimal wireless transmission path according to the data volume, real-time requirements and network environment; 4. Single wake-up mechanism: the system can usually only be awakened by an external button or a fixed timer, and cannot respond flexibly and with low latency according to actual and diversified task requirements.

[0005] In summary, the existing portable electroencephalogram devices have the problems of high system power consumption and short endurance time, which limits their clinical application and popularization. SUMMARY

[0006] The present application aims to overcome the deficiencies of the prior art and provide a dynamic power management method and system of a portable brain-computer interface device and a storage medium. The core idea is to change the running mode of the system from "continuous activity" to "event-driven", and through multi-chip coordinated deep sleep and intelligent wake-up scheduling, the average power consumption of the system is significantly reduced while ensuring the integrity of the electroencephalogram data and the real-time transmission of the data.

[0007] To achieve the above-mentioned purposes, the application adopts the following technical solutions: In a first aspect, the application provides a dynamic power management method for a portable brain-computer interface device, comprising at least the following steps: S1, after system power-on initialization, controlling the master control chip, the electroencephalogram acquisition chip, the first wireless communication module and the second wireless communication module to enter a deep sleep mode; S2, in response to occurrence of at least one preset wake-up event, waking up the master control chip; S3, the master control chip generates at least one to-be-executed task according to the type of the wake-up event and adds the task to a task queue; S4, the master control chip schedules the tasks in the task queue, wherein: when executing a data collection task, waking up the electroencephalogram acquisition chip to collect electroencephalogram signals, and controlling the electroencephalogram acquisition chip to return to the deep sleep mode after the collection is completed; when executing a data transmission task, selecting a target module from the first wireless communication module and the second wireless communication module based on a preset path selection strategy, waking up the selected target module to transmit data, and controlling the selected target module to return to the deep sleep mode after the transmission is completed; S5, after all the tasks in the task queue are executed, the master control chip returns to the deep sleep mode.

[0008] In some embodiments, the preset wake-up event in step S2 includes at least one of the following events: a timing interrupt generated by a timer inside the master control chip; an external interrupt generated by a user operating an external input device; a wireless wake-up interrupt generated by the second wireless communication module in a listening state when receiving an external instruction.

[0009] In some embodiments, the preset wake-up event in step S2 further includes: a motion wake-up interrupt generated by a motion sensor connected to the master control chip when detecting movement of the device.

[0010] Therefore, when the motion sensor detects that the user wears the device or starts an activity, the master control chip is woken up by the interrupt signal generated by the motion sensor, which can improve the intelligent degree of the electroencephalogram device and realize more intelligent context-aware wake-up.

[0011] In some embodiments, the second wireless communication module is a Bluetooth Low Energy (BLE) module, which maintains a listening function for a specific connection request or instruction in the deep sleep mode and generates the wireless wake-up interrupt when listening to it.

[0012] In some implementations, step S4, which selects the target module from the first wireless communication module and the second wireless communication module based on a preset path selection strategy, specifically includes: Determine whether the amount of data to be transmitted exceeds a preset threshold; If the data volume exceeds the preset threshold, the first wireless communication module is selected as the target module. If the data volume is not greater than the preset threshold, then the second wireless communication module is selected as the target module; The first wireless communication module has higher transmission bandwidth and power consumption than the second wireless communication module.

[0013] In some implementations, the path selection strategy in step S4 is also based on the strength of the wireless signal; the step of selecting the target module further includes: After selecting the first wireless communication module, it is further determined whether its wireless signal strength is lower than the strength threshold. If the strength is below the threshold, then the second wireless communication module is selected as the target module.

[0014] In some implementations, after the EEG acquisition chip completes the data acquisition task in step S4, the following steps are also included: The collected EEG data is stored in a cache; Based on the attributes of the EEG data, corresponding data transmission tasks are automatically generated and added to the task queue.

[0015] In a second aspect, the present invention provides a dynamic power management system for a portable brain-computer interface device, which is capable of running the dynamic power management method for the portable brain-computer interface device as described above. The system includes a main control chip, an EEG acquisition chip, a first wireless communication module, and a second wireless communication module. The main control chip is connected to the EEG acquisition chip, the first wireless communication module, and the second wireless communication module, and is configured as follows: Controls itself and all connected chips to enter or exit deep sleep mode; Wake up in response to a preset wake-up event; Generate and schedule a task queue based on the wake-up event; When performing data acquisition tasks, the EEG acquisition chip is woken up and controlled to acquire EEG signals. When performing data transmission tasks, a target wireless communication module is selected and woken up based on a path selection strategy to perform data transmission. After the task is completed, the control chip and itself return to deep sleep mode.

[0016] In some implementations, the dynamic power management system of the portable brain-computer interface device further includes: A real-time clock (RTC) or a low-power timer is used to generate a timed interrupt in a preset wake-up event; An external interrupt interface is used to connect an input device to receive external interrupts in a preset wake-up event; The second wireless communication module is configured to generate a wireless wake-up interrupt in deep sleep mode and notify the main control chip via an interrupt pin.

[0017] In some implementations, the main control chip is a microcontroller, the EEG acquisition chip is a multi-channel high-precision ADC that supports entering a sleep mode via digital commands, the first wireless communication module is a Wi-Fi module, and the second wireless communication module is a Bluetooth Low Energy (BLE) module.

[0018] Thirdly, the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the dynamic power management method for the portable brain-computer interface device described above.

[0019] The beneficial effects of this invention include: 1. Significantly reduced average system power consumption: By keeping each chip in deep sleep mode for most of the idle time, static power consumption and ineffective dynamic power consumption are greatly reduced. Tests show that overall power consumption can be reduced by more than 60% compared to continuous operation mode.

[0020] 2. Intelligent wireless transmission path selection: The main control chip dynamically schedules high-power Wi-Fi modules or low-power BLE modules for data transmission based on the data volume, urgency of transmission, and available network environment, achieving an optimal balance between transmission performance and power consumption.

[0021] 3. Improve system response efficiency: The task queue-based scheduling mechanism enables the system to process concurrent or continuous task requests in an orderly and efficient manner, avoiding resource conflicts and invalid waiting, and ensuring the real-time performance of the system while reducing power consumption. Attached Figure Description

[0022] Figure 1 This is a hardware architecture block diagram of the dynamic power management system of a portable brain-computer interface device provided in an embodiment of the present invention.

[0023] Figure 2 A schematic diagram of a portable brain-computer interface device that integrates the dynamic power management system of the present invention and is capable of running the dynamic power management method of the present invention; Figure 3 This is an overall flowchart of the dynamic power consumption management method for a portable brain-computer interface device as shown in Embodiment 1 of the present invention.

[0024] Figure 4 yesFigure 3 The flowchart shown is a sub-process flowchart of the data acquisition process for the dynamic power consumption management method. Figure 5 yes Figure 3 The flowchart illustrates the intelligent wireless transmission path selection method of the dynamic power consumption management approach.

[0025] Figure 6 This is an overall flowchart of the dynamic power consumption management method for a portable brain-computer interface device as shown in Embodiment 2 of the present invention.

[0026] Figure reference numerals: 101-Main control chip; 102-EEG acquisition chip; 103-First wireless communication module; 104-Second wireless communication module; 105-Motion sensor. Detailed Implementation

[0027] The following specific examples illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that, unless otherwise specified, the following embodiments and features described therein can be combined with each other.

[0028] It should be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of the present invention. Therefore, the drawings only show the components related to the present invention and are not drawn according to the actual number, shape and size of the components in the actual implementation. In the actual implementation, the form, quantity and proportion of each component can be arbitrarily changed, and the layout of the components may also be more complex.

[0029] Example 1 Please see Figures 1-5 The present invention provides a dynamic power consumption management method, system and storage medium for portable brain-computer interface devices according to the following embodiments. It belongs to the field of brain-computer interface technology and is applicable to application scenarios of brain signal acquisition, processing and wireless transmission. It can overcome the problems of excessive power consumption and short battery life caused by the continuous operation of each functional unit and lack of intelligent power consumption scheduling in existing brain-computer devices.

[0030] Please see Figure 1 The diagram shown is a system hardware architecture block diagram of the dynamic power management system of a portable brain-computer interface device according to a preferred embodiment of the present invention.

[0031] This dynamic power management system can be integrated into portable brain-computer interface devices (such as EEG acquisition devices). For specific implementation details, please refer to [link / reference needed]. Figure 2 .

[0032] like Figure 1 As shown, the dynamic power management system of this portable brain-computer interface device is based on a main control chip 101 (e.g., STM32F405). The main control chip 101 is connected to an EEG acquisition chip 102 (e.g., ADS1299) via an SPI bus for acquiring EEG signals. The main control chip 101 is also connected to a first wireless communication module 103 (e.g., a BW16 Wi-Fi module) via an SPI / USART bus, and to a second wireless communication module 104 (e.g., an NRF52840 BLE module) via an SPI bus. The main control chip 101 has a built-in real-time clock (RTC) and multiple low-power timers. The system also has an external interrupt interface for connecting user input devices such as buttons. The interrupt pin of the second wireless communication module 104 (BLE module) is connected to the external interrupt pin of the main control chip 101 for wireless wake-up. After system initialization, all chips (main control chip 101, EEG acquisition chip 102, first wireless communication module 103, and second wireless communication module 104) enter deep sleep state, with only the RTC, low-power timer, and part of the monitoring circuit of the second wireless communication module 104 maintaining extremely low power operation.

[0033] The main control chip 101 is configured as follows: Controls itself and all connected chips to enter or exit deep sleep mode; Wake up in response to a preset wake-up event; Generate and schedule a task queue based on the wake-up event; When performing data acquisition tasks, the EEG acquisition chip 102 is woken up and controlled to acquire EEG signals; When performing data transmission tasks, a target wireless communication module is selected and woken up based on a path selection strategy to perform data transmission. After the task is completed, the control chip and itself return to deep sleep mode.

[0034] As a preferred embodiment, the dynamic power management system for portable brain-computer interface devices also includes: A real-time clock (RTC) or a low-power timer is used to generate a timed interrupt in a preset wake-up event; An external interrupt interface is used to connect an input device to receive external interrupts in a preset wake-up event; The second wireless communication module 104 is configured to generate a wireless wake-up interrupt in deep sleep mode and notify the main control chip 101 through the interrupt pin.

[0035] Preferably, the system also includes a motion sensor 105 connected to the main control chip 101 via an I2C bus. The motion sensor 105 is preferably an inertial measurement unit (IMU chip). Thus, when the motion sensor 105 detects that the user is wearing the device or begins to move, the motion sensor 105 generates an interrupt signal to wake up the main control chip 101, which can improve the intelligence level of the brainwave device and achieve more intelligent context-aware wake-up.

[0036] Preferably, the main control chip 101 is a microcontroller, the EEG acquisition chip 102 is a multi-channel high-precision ADC that supports entering a sleep mode via digital commands, the first wireless communication module 103 is a Wi-Fi module, and the second wireless communication module 104 is a low-power Bluetooth BLE module.

[0037] Please see Figure 3 The diagram shows the overall workflow of the dynamic power management method of the present invention, and the specific steps are as follows: Step S301: System power-on initialization.

[0038] Step S302: Entering a collaborative deep sleep state. The main control chip 101 sends specific instructions to control the EEG acquisition chip 102, Wi-Fi module, BLE module, and itself to enter a deep sleep mode. At this time, the overall system current can be reduced to the microamp level.

[0039] Step 303: Determine whether a wake-up event has occurred (i.e., the main control chip 101 is waiting for a wake-up event). The preset wake-up events of this invention include, but are not limited to: Timer wake-up: An interrupt is generated by the RTC or a low-power timer to indicate that a preset acquisition cycle has been reached.

[0040] External event wake-up: When a user presses a device button, an external interrupt is generated.

[0041] Wireless command wake-up: When the BLE module detects a specific connection request or command packet in deep sleep monitoring mode, it will generate an interrupt signal to wake up the main control chip 101.

[0042] Motion wake-up: A motion wake-up interrupt is generated by the motion sensor 105 connected to the main control chip 101 when it detects movement of the device.

[0043] Once a wake-up event is detected, the process proceeds to step S304.

[0044] Step S304: The main control chip 101 is woken up and resumes normal operation mode from sleep mode.

[0045] Step S305: The main control chip 101 reads the current wake-up source and generates a corresponding task according to the preset mapping relationship, adding it to the task queue. For example, timer wake-up corresponds to "data acquisition task", and wireless command wake-up may correspond to "configuration update task" or "data request task".

[0046] Step S306: The main control chip 101 retrieves the next task to be processed from the task queue.

[0047] Step S307: Determine the task type. If it is a data acquisition task, proceed to step S308; if it is a data transmission task, proceed to step S309.

[0048] Step S308: Execute the data acquisition sub-process (see below for details) Figure 4 describe).

[0049] Step S309: Execute the data transmission sub-process. At this time, the main control chip 101 calls the intelligent wireless transmission path selection algorithm (see below for details). Figure 5 (Description) to determine which wireless module to use.

[0050] Step S310: Based on the selection result, the main control chip 101 wakes up the corresponding wireless module (Wi-Fi or BLE), establishes a connection, and transmits data. After the transmission is completed, a command is immediately sent to put the wireless module into deep sleep mode.

[0051] Step S311: Determine if the task queue is empty. If the queue is not empty, return to step S306 to continue processing the next task; if the queue is empty, it means that all scheduled tasks have been completed, and the process proceeds to step S312. (It should be noted that this step can be integrated into step S307, or it can exist independently after step S310.) Step S312: Return to cooperative deep sleep state. After confirming that all peripherals are in sleep mode, the main control chip 101 also configures itself into deep sleep mode, and the system re-enters the ultra-low power state, waiting for the next wake-up event.

[0052] Please refer to Figure 4 The diagram shows a flowchart of the data acquisition sub-process in this invention, which corresponds to step S308, as follows: S401: The main control chip 101 wakes up the EEG acquisition chip 102 via the SPI bus; S402: Send the acquisition command and read the converted EEG data; S403: After the data reading is complete, the main control chip 101 immediately sends a hibernation command to cause the EEG acquisition chip 102 to return to deep hibernation; S404: Store the read data in the cache, and generate a "data transfer task" and add it to the task queue as needed (e.g., send after each collection, or send after caching multiple times); S405: After completion, return to step S306.

[0053] Please see Figure 5 The diagram shows a flowchart of the intelligent wireless transmission path selection algorithm in this invention. This flowchart corresponds to step S309, and is as follows: Step S501: Begin path selection decision.

[0054] Step S502: Obtain the size of the data packet to be transmitted.

[0055] Step S503: Determine whether the data volume exceeds a preset threshold. This threshold can be adjusted according to the actual application scenario.

[0056] Step S504: If the data volume is greater than the threshold, a high-bandwidth Wi-Fi module is initially selected as the target.

[0057] Step S505 (Optional Optimization Step): Before making a final decision to use Wi-Fi, further check the received signal strength indicator (RSSI) of the current Wi-Fi signal. Determine if the RSSI is below a strength threshold (e.g., -75 dBm).

[0058] Step S506: If the Wi-Fi signal strength is sufficient, then it is finally determined to use the Wi-Fi module for transmission.

[0059] Step S507: If the data volume is not greater than the threshold (No in step S503), or the Wi-Fi signal is too weak (Yes in step S505), then select the low-power BLE module as the target transmission path.

[0060] Step S508: Output the final selected target wireless module decision.

[0061] Example 2 Please see Figure 6 The diagram shows the overall workflow of another embodiment of the dynamic power management method of the present invention. It is consistent with the above... Figures 3-5 The processes running in the two are basically the same, the difference being: (1) the flowchart is more detailed and complete; (2) the signal strength (RSSI) is not introduced as a decision factor.

[0062] Furthermore, it should be noted that the present invention also has the following alternatives: 1. The wake-up source is not limited to the three types mentioned above, and can also integrate a motion sensor 105 (such as an accelerometer, ICM20948, or MPU6050). When the motion sensor 105 detects that the user is wearing the device or has started a specific activity, it generates an interrupt signal to wake up the system.

[0063] 2. In addition to data volume and signal strength, wireless path selection strategies can also incorporate data urgency (such as abnormal EEG alarms) as a decision factor, prioritizing the use of channels with lower latency.

[0064] 3. The main control chip 101 is not limited to the STM32F405. Any microcontroller (such as other ARM Cortex-M series chips) with multiple low-power modes, rich peripheral interfaces, and multiple interrupt sources can implement this solution. The EEG acquisition chip 102 is not limited to the ADS1299. Any multi-channel, high-precision ADC that supports controlling its working mode (including sleep mode) through digital instructions can be used.

[0065] The dynamic power consumption management method for the portable brain-computer interface device of the present invention has at least the following advantages over the prior art: 1. Significantly reduced average system power consumption: By keeping each chip in deep sleep mode for most of the idle time, static power consumption and ineffective dynamic power consumption are greatly reduced. Tests show that overall power consumption can be reduced by more than 60% compared to continuous operation mode.

[0066] 2. Intelligent wireless transmission path selection: The main control chip 101 dynamically schedules high-power Wi-Fi modules or low-power BLE modules for data transmission based on the data volume, urgency of transmission, and available network environment, achieving an optimal balance between transmission performance and power consumption.

[0067] 3. Improve system response efficiency: The task queue-based scheduling mechanism enables the system to process concurrent or continuous task requests in an orderly and efficient manner, avoiding resource conflicts and invalid waiting, and ensuring the real-time performance of the system while reducing power consumption.

[0068] This invention employs a dynamic, task-triggered collaborative sleep and wake-up mechanism that significantly reduces the average power consumption of the system while ensuring the integrity and real-time transmission of EEG data, thereby extending the device's usage time on a single charge and improving the user experience.

[0069] The above embodiments are merely illustrative of the principles and effects of the present invention and are not intended to limit the invention. Any person skilled in the art can modify or alter the above embodiments without departing from the spirit and scope of the present invention. Therefore, all equivalent modifications or alterations made by those skilled in the art without departing from the spirit and technical concept disclosed in the present invention should still be covered by the claims of the present invention.

Claims

1. A method for dynamic power management of a portable brain-computer interface device, characterized in that, At least comprising the following steps: S1, after system power-on initialization, control the main control chip (101), the electroencephalogram acquisition chip (102), the first wireless communication module (103) and the second wireless communication module (104) to enter a deep sleep mode; S2, in response to the occurrence of at least one preset wake-up event, wake up the main control chip (101); S3, the main control chip (101) generates at least one task to be executed according to the type of the wake-up event and adds it to the task queue; S4, the main control chip (101) schedules the tasks in the task queue, wherein: when executing a data collection task, wake up the electroencephalogram acquisition chip (102) to collect electroencephalogram signals, and after the collection is completed, control the electroencephalogram acquisition chip (102) to return to the deep sleep mode; when executing a data transmission task, select a target module from the first wireless communication module (103) and the second wireless communication module (104) based on a preset path selection strategy, wake up the selected target module to transmit data, and after the transmission is completed, control the selected target module to return to the deep sleep mode; S5, after all the tasks in the task queue are executed, the main control chip (101) returns to the deep sleep mode.

2. The method of dynamic power management for a portable brain-computer interface device of claim 1, wherein, The preset wake-up event in step S2 includes at least one of the following events: a timing interrupt generated by a timer inside the main control chip (101); an external interrupt generated by a user operating an external input device; a wireless wake-up interrupt generated by the second wireless communication module (104) in a listening state when receiving an external instruction; a motion wake-up interrupt generated by a motion sensor (105) connected to the main control chip (101) when detecting device movement.

3. The method of dynamic power management for a portable brain-computer interface device of claim 2, wherein, The second wireless communication module (104) is a Bluetooth Low Energy (BLE) module that maintains a listening function for specific connection requests or instructions in the deep sleep mode and generates the wireless wake-up interrupt when listening.

4. The method of dynamic power management for a portable brain-computer interface device of claim 1, wherein, The path selection strategy in step S4 includes: determining whether the data volume of the data to be transmitted is greater than a preset threshold; if the data volume is greater than the preset threshold, selecting the first wireless communication module (103) as the target module; if the data volume is not greater than the preset threshold, selecting the second wireless communication module (104) as the target module; wherein the transmission bandwidth and power consumption of the first wireless communication module (103) are higher than those of the second wireless communication module (104).

5. The method for dynamic power management of a portable brain-computer interface device according to claim 4, wherein, The path selection strategy in step S4 is also based on the strength of the wireless signal; the step of selecting the target module further includes: after selecting the first wireless communication module (103), further determining whether its wireless signal strength is lower than a strength threshold; if it is lower than the strength threshold, then select the second wireless communication module (104) as the target module.

6. The method of dynamic power management for a portable brain-computer interface device according to any one of claims 1-5, wherein, In step S4, after the electroencephalogram acquisition chip (102) executes the data collection task, it further includes: storing the collected electroencephalogram data into a cache; According to the attribute of the electroencephalogram data, a corresponding data transmission task is automatically generated and added to the task queue.

7. A dynamic power management system of a portable brain-computer interface device, capable of running the dynamic power management method according to any one of claims 1 to 6, characterized in that, The system comprises a master chip (101), an electroencephalogram acquisition chip (102), a first wireless communication module (103) and a second wireless communication module (104). The master chip (101) is connected with the electroencephalogram acquisition chip (102), the first wireless communication module (103) and the second wireless communication module (104) respectively, and is configured to: Control itself and each connected chip to enter or exit the deep sleep mode; Be awakened in response to a preset wake-up event; Generate and schedule a task queue according to the wake-up event; When executing a data acquisition task, wake up and control the electroencephalogram acquisition chip (102) to acquire electroencephalogram signals; When executing a data transmission task, select and wake up a target wireless communication module based on a path selection strategy to perform data transmission; After the task is executed, control the related chips and itself to return to the deep sleep mode.

8. The dynamic power management system of a portable brain-computer interface device according to claim 7, wherein, Further comprising: A real-time clock (RTC) or a low-power timer for generating a timing interrupt in the preset wake-up event; An external interrupt interface for connecting an input device to receive an external interrupt in the preset wake-up event; The second wireless communication module (104) is configured to generate a wireless wake-up interrupt in the deep sleep mode and notify the master chip (101) through an interrupt pin.

9. The dynamic power management system of a portable brain-computer interface device according to claim 8, wherein, The master chip (101) is a microcontroller, the electroencephalogram acquisition chip (102) is a multi-channel high-precision ADC that supports entering a sleep mode through digital instructions, the first wireless communication module (103) is a Wi-Fi module, and the second wireless communication module (104) is a Bluetooth low energy (BLE) module.

10. A computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program, when executed by a processor, implements the steps of the dynamic power management method of the portable brain-computer interface device according to any one of claims 1-6.

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