Power bank control system based on AI voice

Abstract

The invention relates to the technical field of power bank control systems, in particular to an AI voice-based power bank control system, which comprises a voice interaction module, an equipment recognition module, an environment sensing module, a dynamic charging management module and a control center module, the voice interaction module is used for receiving a voice instruction of a user and analyzing the instruction content through a natural language processing technology; the device identification module is used for detecting the type of a connected device and a charging protocol, and obtaining the real-time charging demand of the device. The environment sensing module is used for acquiring an environment noise value # imgabs0 # and a temperature value # imgabs1 # of a charging environment; and the dynamic charging management module is used for dynamically adjusting the output power according to the equipment type, the environmental parameters and the historical charging data of the user. According to the invention, through environment perception and dynamic threshold determination, the voice interaction robustness is optimized, real-time adaptive adjustment of the charging strategy is realized, and the method is especially suitable for complex scenes such as travel, outdoor and the like.

Description

Technical Field

[0001] The present invention relates to the technical field of power bank control systems, and in particular to a power bank control system based on AI voice. Background Art

[0002] A power bank, also known as a mobile power bank, is a portable charger that can be carried around by individuals and stores energy. It is primarily used to charge handheld mobile devices and other consumer electronics (e.g., wireless phones and laptops), particularly in environments without an external power supply. Currently, the fastest-growing segments on the market are high-power and magnetic power banks, both of which are still in their developmental stages. High-power power banks primarily serve high-end business users, who demand extremely high charging efficiency. Applying artificial intelligence (AI) technology to high-power power banks can significantly enhance the user charging experience.

[0003] While existing smart power banks support fast-charging protocol recognition and basic voice interaction, they suffer from significant user experience flaws in complex environments. For example, in noisy environments, the recognition rate of voice commands drops significantly, forcing users to repeat operations. Electromagnetic noise interference can also affect the stability of the power bank's internal circuits. Furthermore, the power bank's inability to dynamically adjust its charging strategy based on ambient temperature, device type, and user habits can lead to overheating risks or inefficient charging. Furthermore, existing technologies lack intelligent priority management for the coordinated charging of multiple devices, often resulting in charging delays for critical devices.

[0004] In response to the above problems, the present invention proposes an AI voice-based power bank control system, which optimizes the robustness of voice interaction through environmental perception and dynamic threshold judgment, and realizes real-time adaptive adjustment of charging strategies. It is particularly suitable for complex scenarios such as travel and outdoor activities. Summary of the Invention

[0005] The purpose of the present invention is to provide an AI voice-based power bank control system to solve the problems raised in the above-mentioned background technology, such as the significant drop in the recognition rate of voice commands in a noisy environment, which requires users to repeat operations. At the same time, the power bank cannot dynamically adjust the charging strategy according to the ambient temperature, device type and user habits, which may cause overheating risks or low charging efficiency.

[0006] To achieve the above objectives, the present invention provides an AI voice-based power bank control system, which includes a voice interaction module, a device identification module, an environment perception module, a dynamic charging management module, and a control center module;

[0007] The voice interaction module is used to receive the user's voice instructions and analyze the instruction content through natural language processing technology;

[0008] The device identification module is used to detect the type of connected device and the charging protocol, and obtain the real-time charging requirements of the device;

[0009] The environmental perception module is used to collect the environmental noise value of the charging environment and temperature values ;

[0010] The dynamic charging management module is used to dynamically adjust the output power according to the device type, environmental parameters and user historical charging data;

[0011] The control center module is used to integrate voice commands, device requirements and environmental data to generate a charging strategy through preset threshold judgment logic;

[0012] The specific judgment logic of the control center module is:

[0013] Get the current charging efficiency , and according to the formula , calculate the environmental interference value ;

[0014] Preset threshold ,like Exceeding the preset threshold , then the noise reduction mode is triggered and the maximum output power is limited;

[0015] like Not exceeding the threshold , then match the optimal charging mode according to the device charging protocol.

[0016] As a further improvement of this technical solution, the specific method for the device identification module to detect the charging protocol of the connected device is:

[0017] Get the voltage requirements of the connected device and current requirements ;

[0018] According to the formula , calculate the protocol matching degree ,in, Is the rated voltage of the power bank, is the rated current of the power bank;

[0019] If the agreement matches ≥0.9, then start fast charging mode;

[0020] If 0.6≤ <0.9, then start the balance mode;

[0021] like <0.6, then start the safe mode.

[0022] As a further improvement of this technical solution, the specific method for the dynamic charging management module to dynamically adjust the output power is:

[0023] Extract high-frequency usage periods and commonly used device types based on users' historical charging data;

[0024] Assign weight values to high-frequency time periods , weight value Dynamic adjustment based on frequency of use;

[0025] Assigning type priorities to devices , type priority Based on manual settings by the user;

[0026] By formula ,calculate value, dynamically optimize power allocation, if If the value is ≥1.0, the output power will be increased during the preset high-frequency usage period and the fast charging mode will be maintained. If the value is less than 1.0, the balanced mode is maintained. If the value is less than 0.5, the output power is limited, the system switches to safe mode, and a voice prompt is triggered: "The environment is highly disturbed and the system has switched to safe mode."

[0027] As a further improvement of this technical solution, the specific method for the voice interaction module to parse the instruction content is:

[0028] Recognize basic commands through the localized voice library, including starting fast charging, switching modes, checking battery level, and finding location;

[0029] Preset ambient noise threshold , if the ambient noise value Exceed , the anti-noise algorithm is enabled and high-frequency instruction keywords are matched first.

[0030] As a further improvement of this technical solution, the specific implementation of the anti-noise algorithm is as follows:

[0031] Get the voice signal strength through the voice interaction module and ambient noise intensity ;

[0032] According to the formula , calculate the signal-to-noise ratio ;

[0033] If the signal-to-noise ratio <15 , it will only respond to preset emergency instructions.

[0034] As a further improvement of this technical solution, the control center module is also used to predict the battery life of the power bank, specifically:

[0035] Get the remaining power of the power bank , Current output power and equipment energy consumption coefficient ;

[0036] According to the formula , calculate the remaining battery life ;

[0037] If the remaining battery time If the power consumption is less than the minimum threshold set by the user, the voice interaction module will give a voice prompt suggesting to switch to power saving mode.

[0038] As a further improvement of this technical solution, the dynamic charging management module is also used to calculate the device priority , and combined with the remaining power of the control center module Data, dynamically allocate output power, specifically:

[0039] Get the current power demand of the charging device and the user usage frequency of the current device ;

[0040] According to the formula , calculate the current device charging priority ;

[0041] Priority is The device with the highest value is allocated the most power.

[0042] As a further improvement of this technical solution, the system also includes a data storage module, which is used to store the user's historical charging data, including high-frequency usage periods, common device types, and device type priorities manually set by the user. , and is also used to record the environmental noise value collected by the environmental perception module , temperature value And the corresponding charging efficiency , and is also used to save the data generated by the dynamic charging management module Value calculation log and power adjustment record.

[0043] As a further improvement of this technical solution, the system also includes a safety protection module, which is used to monitor the temperature value of the charging environment in real time. And collect circuit data in real time, and according to the temperature value of the charging environment And the circuit data to make corresponding control strategies, specifically:

[0044] Real-time monitoring of the temperature of the charging environment ,like If the safety threshold is exceeded, the power will be forced to be reduced and a voice alarm will be issued through the voice interaction module;

[0045] Circuit data is collected in real time. If a sudden current surge is detected, that is, exceeding 200% of the rated current or the voltage exceeds the device protocol range, the output is immediately cut off and a voice prompt of the fault information is given through the voice interaction module.

[0046] Compared with the prior art, the present invention has the following beneficial effects:

[0047] 1. In the AI voice-based power bank control system, the temperature value is collected in real time through the environmental perception module , combined with the device charging protocol and real-time demand obtained by the device identification module, the dynamic charging management module calculates the environmental interference value ,when When the preset threshold is exceeded, the system automatically triggers noise reduction mode and limits the maximum output power to avoid the risk of overheating in high-temperature environments. At the same time, it extracts high-frequency usage periods and device types based on the user's historical charging data, and dynamically optimizes power distribution through formulas to achieve a balance between charging efficiency and safety, significantly reducing overheating risks and improving energy utilization.

[0048] 2. In the AI voice-based power bank control system, the device identification module detects the connection status of multiple devices, and the dynamic charging management module calculates the device charging priority according to the formula , and combined with the remaining power of the control center module Data is collected and output power is dynamically allocated. When users connect devices with high usage frequency and low power demand at the same time, the system prioritizes allocating maximum power to devices with high usage frequency, ensuring that key devices are quickly charged. At the same time, by adjusting the power allocation strategy in real time, charging delays caused by competition among multiple devices are avoided, making the power bank better suited for travel, outdoor activities and other scenarios, significantly improving user satisfaction with the battery life of key devices. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 This is the overall principle block diagram of the power bank control system based on AI voice of the present invention. DETAILED DESCRIPTION

[0050] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0051] This embodiment is applied to the portable charging scenario of traveling users. The system is deployed as follows: Figure 1 As shown in the figure, a power bank control system based on AI voice has the following specific module configurations:

[0052] Voice interaction module: used to receive user voice commands and parse the command content through natural language processing technology.

[0053] The specific method for parsing the instruction content is:

[0054] Recognize basic commands through the localized voice library, including starting fast charging, switching modes, checking battery level, and finding location;

[0055] Preset ambient noise threshold , if the ambient noise value Exceed , the anti-noise algorithm is enabled and high-frequency instruction keywords are matched first.

[0056] Through the above settings, the system can accurately recognize user commands even in noisy environments, thereby improving the practicality of the system.

[0057] The specific implementation of the anti-noise algorithm is as follows:

[0058] Get the voice signal strength through the voice interaction module and ambient noise intensity ;

[0059] According to the formula , calculate the signal-to-noise ratio ;

[0060] If the signal-to-noise ratio <15 , it will only respond to preset emergency instructions.

[0061] Ensure that the system can still respond to key instructions in an extremely low signal-to-noise ratio environment, thereby improving safety.

[0062] Device Identification Module: Detects the connected device type and charging protocol, and obtains the device's real-time charging requirements. Automatically identifies the device type and charging protocol to ensure compatibility and efficiency during the charging process.

[0063] The specific method for detecting the charging protocol of the connected device is:

[0064] Get the voltage requirements of the connected device and current requirements ;

[0065] According to the formula , calculate the protocol matching degree ,in, Is the rated voltage of the power bank, is the rated current of the power bank;

[0066] If the agreement matches ≥0.9, then start fast charging mode;

[0067] If 0.6≤ <0.9, then start the balance mode;

[0068] like <0.6, then start the safe mode.

[0069] Through the above settings, the charging mode can be intelligently adjusted according to the device needs to protect the device battery and extend its service life.

[0070] The operating parameters in fast charging mode are:

[0071] Output voltage: Based on the voltage requirements of the connected device 98%-100% output (such as the device requires 20V, the output is 19.6V-20V).

[0072] Output current: The current requirement of the connected device 95%-100% output (e.g. if the device requires 5A, the output will be 4.75A-5A).

[0073] Power limit: The maximum power does not exceed the rated power of the power bank (e.g. 100W).

[0074] Protection mechanism: Real-time monitoring of the temperature of the charging environment ,like If the temperature is ≥55℃, the power will be reduced to balanced mode. If the voltage fluctuation is greater than or equal to 5%, the system will automatically switch to safe mode.

[0075] The operating parameters in balanced mode are:

[0076] Output voltage: Based on the voltage requirements of the connected device 85%-95% output (such as the device requires 20V, the output is 17V-19V).

[0077] Output current: The current requirement of the connected device 80%-90% output (such as the device requires 5A, the output is 4A-4.5A).

[0078] Power limit: The maximum power is 70%-90% of the rated power (e.g. 70W-90W).

[0079] Dynamic adjustment: according to the environmental interference value Dynamically adjust the current, the formula is: ,in, is the adjusted current, is the maximum environmental interference value, =1000. At the same time, if the protocol matching degree Because the device protocol has been updated to ≥0.9, it automatically switches to fast charging mode.

[0080] The working parameters in safe mode are:

[0081] Output voltage: Fixed at 5V (compatible with general protocols) and does not respond to high voltage requirements of the device.

[0082] Output current: limited to 1A-2A to avoid overload risk.

[0083] Power limit: Maximum power 10W (5V×2A).

[0084] Protection mechanism: If the device impedance is detected to be abnormal (≥200 ), immediately cut off the output and issue a voice prompt indicating that the device is incompatible. Also, in safety mode, manually switching to fast charge or equalization mode is prohibited.

[0085] Environmental perception module: used to collect environmental noise values in the charging environment and temperature values . Real-time monitoring of the charging environment provides a basis for dynamic adjustment of charging strategies.

[0086] In addition, mode switching logic and collaborative control are adopted, and environmental parameters trigger switching: if the temperature value ≥60℃, regardless of value, forced to switch to safe mode; if the ambient noise value ≥85 , the current output in balanced mode is reduced by 10%.

[0087] Dynamic priority management: When multiple devices are connected, high The value device is allocated power first, low The value device automatically degrades mode.

[0088] User intervention: Users can use voice commands to force the fast charge to temporarily increase the mode, but must meet <50℃ and ≥0.8.

[0089] Dynamic charging management module: Dynamically adjusts output power based on device type, environmental parameters, and user charging history. This enables intelligent charging management and improves charging efficiency and safety.

[0090] The specific method of adjusting the output power is:

[0091] Extract high-frequency usage periods and commonly used device types based on users' historical charging data;

[0092] Assign weight values to high-frequency time periods , weight value Dynamic adjustment based on frequency of use;

[0093] Assigning type priorities to devices , type priority Based on manual settings by the user;

[0094] By formula ,calculate value, dynamically optimize power allocation, if If the value is ≥1.0, the output power will be increased during the preset high-frequency usage period and the fast charging mode will be maintained. If the value is less than 1.0, the balanced mode is maintained. If the value is less than 0.5, the output power is limited, the system switches to safe mode, and a voice prompt is triggered: "The environment is highly disturbed and the system has switched to safe mode."

[0095] Through the above settings, power is intelligently allocated according to user habits and device requirements to ensure charging efficiency and device safety.

[0096] The dynamic charging management module is also used to calculate device priority , and combined with the remaining power of the control center module Data, dynamically allocate output power, specifically:

[0097] Get the current power demand of the charging device and the user usage frequency of the current device ;

[0098] According to the formula , calculate the current device charging priority ;

[0099] Priority is The device with the highest value is allocated the most power.

[0100] Through the above settings, key equipment can be quickly recharged and the user experience can be improved.

[0101] Control Center Module: This module integrates voice commands, device requirements, and environmental data to generate a charging strategy using pre-set threshold judgment logic. This multifaceted information helps develop an optimal charging strategy, improving charging efficiency and safety.

[0102] The specific judgment logic is:

[0103] Get the current charging efficiency , and according to the formula , calculate the environmental interference value ;

[0104] Preset threshold ,like Exceeding the preset threshold , then the noise reduction mode is triggered and the maximum output power is limited;

[0105] like Not exceeding the threshold , then match the optimal charging mode according to the device charging protocol.

[0106] The above settings can avoid overheating and interference in high temperature and high noise environments, ensuring charging safety.

[0107] The control center module is also used to predict the battery life of the power bank, specifically:

[0108] Get the remaining power of the power bank , Current output power and equipment energy consumption coefficient ;

[0109] According to the formula , calculate the remaining battery life ;

[0110] If the remaining battery time If the battery level is lower than the minimum threshold set by the user, the voice interaction module will prompt the user to switch to power saving mode. This will promptly remind the user of the battery level of the power bank to avoid the embarrassment of power outages.

[0111] Data storage module: used to store user historical charging data, including high-frequency usage periods, commonly used device types, and device type priorities manually set by users , and is also used to record the environmental noise value collected by the environmental perception module , temperature value And the corresponding charging efficiency , and is also used to save the data generated by the dynamic charging management module Value calculation log and power adjustment record.

[0112] Safety protection module: used to monitor the temperature of the charging environment in real time And collect circuit data in real time, and according to the temperature value of the charging environment And the circuit data to make corresponding control strategies, the specific operations are:

[0113] Real-time monitoring of the temperature of the charging environment ,like If the safety threshold is exceeded, the power will be forced to be reduced and a voice alarm will be issued through the voice interaction module;

[0114] Circuit data is collected in real time. If a sudden current surge is detected, that is, exceeding 200% of the rated current or the voltage exceeds the device protocol range, the output is immediately cut off and a voice prompt of the fault information is given through the voice interaction module.

[0115] Through the above settings, the safety of the charging process is ensured, potential safety hazards are avoided, and a quick response is made in emergency situations to protect the safety of equipment and users.

[0116] The following is an example of a real-world use case in a business trip scenario: a user is simultaneously charging a mobile phone (supporting PD fast charging) and headphones (normal charging) in a high-speed rail carriage. The carriage is subject to constant noise (75dB) and high temperatures at the air conditioning outlet (ambient temperature 45°C). The system workflow is as follows:

[0117] Step 1: Environmental perception and initial detection.

[0118] Noise detection: The noise value measured by the environmental perception module =75dB (main frequency band is concentrated in 1kHz-4kHz);

[0119] Temperature monitoring: power bank temperature value =52℃;

[0120] Device identification: A mobile phone (PD protocol, requiring 20V / 3A) and headphones (5V / 1A) were detected.

[0121] Step 2: Control center global strategy formulation.

[0122] Collect charging efficiency =85%, and preset the threshold =500;

[0123] Calculate environmental interference value ;

[0124] Determine the environmental interference value =585>Preset threshold =500, triggering the safety policy: limiting the maximum output power to 70W (70% of the rated power).

[0125] Step 3: Dynamic charging management module power optimization.

[0126] Extract high-frequency usage periods and commonly used device types based on users' historical charging data;

[0127] Assign weight values to high-frequency time periods , weight value Dynamically adjust the weight of high-speed rail travel time based on the frequency of use =1.2, mobile phone type priority =1.5;

[0128] calculate ;

[0129] Dynamically optimize power allocation: If the value is less than 0.5, the output power is limited (power allocated to the phone: 20V × 2.5A = 50W (original demand: 60W), power allocated to the headset: 5V × 1A = 5W, total power 55W, below the limit of 70W), and the device switches to safe mode. A voice prompt is triggered: "Environmental interference is high, and the device has switched to safe mode."

[0130] At the same time, the control center module can predict the battery life of the power bank and obtain the remaining power of the power bank. , Current output power and equipment energy consumption coefficient , and according to the formula , calculate the remaining battery life If the remaining battery time If the battery level is lower than the minimum threshold set by the user, the voice interaction module will prompt the user to switch to power saving mode. This will promptly remind the user of the battery level of the power bank to avoid the embarrassment of power outages.

[0131] In addition, when the user uses a power bank to charge a mobile phone and headphones at the same time, the dynamic charging management module can calculate the device priority , and combined with the remaining power of the control center module Data, dynamically allocate output power. Get the power demand of the current charging device (mobile phone or headset) and the user usage frequency of the current device , according to the formula , calculate the current device charging priority , thereby allocating maximum power to the devices with the highest value first, ensuring rapid power replenishment of key devices and improving user experience.

[0132] And in the actual application process, the system presets the environmental noise threshold , when the user is in the high-speed rail carriage, the ambient noise value Exceed When the anti-noise algorithm is enabled, high-frequency command keywords are matched first, and the voice signal strength is obtained through the voice interaction module. and ambient noise intensity , and according to the formula , calculate the signal-to-noise ratio , when the signal-to-noise ratio <15 In this way, the system can still respond to critical commands in an extremely low signal-to-noise ratio environment, thus improving safety.

[0133] When the user uses the device for a long time, the system monitors the temperature of the charging environment in real time through the safety protection module. And collect circuit data in real time, and according to the temperature value of the charging environment And circuit data to make corresponding control strategies. Real-time monitoring of the temperature value of the charging environment ,like If the safety threshold is exceeded, power is forcibly reduced and a voice alarm is issued through the voice interaction module. Circuit data is also collected in real time. If a sudden current surge is detected, exceeding 200% of the rated current or the voltage exceeds the device's protocol range, the output is immediately cut off and a voice prompt is issued through the voice interaction module to indicate the fault.

[0134] In summary, the present invention collects temperature values in real time through the environment perception module , combined with the device charging protocol and real-time demand obtained by the device identification module, the dynamic charging management module calculates the environmental interference value ,when When the preset threshold is exceeded, the system automatically triggers the noise reduction mode and limits the maximum output power to avoid the risk of overheating in high-temperature environments. At the same time, based on the user's historical charging data, the high-frequency usage period and device type are extracted, and the power allocation is dynamically optimized through the formula to achieve a balance between charging efficiency and safety, significantly reducing the risk of overheating and improving energy utilization. At the same time, the present invention detects the connection status of multiple devices through the device identification module, and the dynamic charging management module calculates the device charging priority according to the formula , and combined with the remaining power of the control center module Data is collected and output power is dynamically allocated. When users connect devices with high usage frequency and low power demand at the same time, the system prioritizes allocating maximum power to devices with high usage frequency, ensuring that key devices are quickly charged. At the same time, by adjusting the power allocation strategy in real time, charging delays caused by competition among multiple devices are avoided, making the power bank better suited for travel, outdoor activities and other scenarios, significantly improving user satisfaction with the battery life of key devices.

[0135] The above shows and describes the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions are merely preferred examples of the present invention and are not intended to limit the present invention. Various changes and improvements may be made to the present invention without departing from the spirit and scope of the present invention. Such changes and improvements fall within the scope of the present invention. The scope of protection claimed in the present invention is defined by the appended claims and their equivalents.

Claims

1. A power bank control system based on AI voice, characterized in that: The system includes a voice interaction module, a device identification module, an environment perception module, a dynamic charging management module and a control center module; The voice interaction module is used to receive the user's voice instructions and analyze the instruction content through natural language processing technology; The device identification module is used to detect the type of connected device and the charging protocol, and obtain the real-time charging requirements of the device; The environmental perception module is used to collect the environmental noise value of the charging environment and temperature values ; The dynamic charging management module is used to dynamically adjust the output power according to the device type, environmental parameters and user historical charging data; The control center module is used to integrate voice commands, device requirements and environmental data to generate a charging strategy through preset threshold judgment logic; The specific judgment logic of the control center module is: Get the current charging efficiency , and according to the formula , calculate the environmental interference value ; Preset threshold ,like Exceeding the preset threshold , then the noise reduction mode is triggered and the maximum output power is limited; like Not exceeding the threshold , then match the optimal charging mode according to the device charging protocol.

2. The AI voice-based power bank control system according to claim 1 is characterized in that: The specific method for the device identification module to detect the charging protocol of the connected device is: Get the voltage requirements of the connected device and current requirements ; According to the formula , calculate the protocol matching degree ,in, Is the rated voltage of the power bank, is the rated current of the power bank; If the agreement matches ≥0.9, then start fast charging mode; If 0.6≤ <0.9, then start the balance mode; like <0.6, then start the safe mode.

3. The AI voice-based power bank control system according to claim 2 is characterized in that: The specific method for the dynamic charging management module to dynamically adjust the output power is: Extract high-frequency usage periods and commonly used device types based on users' historical charging data; Assign weight values to high-frequency time periods , weight value Dynamic adjustment based on frequency of use; Assigning type priorities to devices , type priority Based on manual settings by the user; By formula ,calculate value, dynamically optimize power allocation, if If the value is ≥1.0, the output power will be increased during the preset high-frequency usage period and the fast charging mode will be maintained. If the value is less than 1.0, the balanced mode is maintained. If the value is less than 0.5, the output power is limited, the system switches to safe mode, and a voice prompt is triggered: "The environment is highly disturbed and the system has switched to safe mode." 4. The AI voice-based power bank control system according to claim 1 is characterized in that: The specific method for the voice interaction module to parse the instruction content is: Recognize basic commands through the localized voice library, including starting fast charging, switching modes, checking battery level, and finding location; Preset ambient noise threshold , if the ambient noise value Exceed , the anti-noise algorithm is enabled and high-frequency instruction keywords are matched first.

5. The AI voice-based power bank control system according to claim 4 is characterized in that: The specific implementation of the anti-noise algorithm is as follows: Get the voice signal strength through the voice interaction module and ambient noise intensity ; According to the formula , calculate the signal-to-noise ratio ; If the signal-to-noise ratio <15 , it will only respond to preset emergency instructions.

6. The AI voice-based power bank control system according to claim 1 is characterized in that: The control center module is also used to predict the battery life of the power bank, specifically: Get the remaining power of the power bank , Current output power and equipment energy consumption coefficient ; According to the formula , calculate the remaining battery life ; If the remaining battery time If the power consumption is less than the minimum threshold set by the user, the voice interaction module will give a voice prompt suggesting to switch to power saving mode.

7. The AI voice-based power bank control system according to claim 6 is characterized in that: The dynamic charging management module is also used to calculate device priority , and combined with the remaining power of the control center module Data, dynamically allocate output power, specifically: Get the current power demand of the charging device and the user usage frequency of the current device ; According to the formula , calculate the current device charging priority ; Priority is The device with the highest value is allocated the most power.

8. The AI voice-based power bank control system according to claim 3 is characterized in that: The system also includes a data storage module, which is used to store the user's historical charging data, including high-frequency usage periods, common device types, and device type priorities manually set by the user. , and is also used to record the environmental noise value collected by the environmental perception module , temperature value And the corresponding charging efficiency , and is also used to save the data generated by the dynamic charging management module Value calculation log and power adjustment record.

9. The AI voice-based power bank control system according to claim 1, characterized in that: The system also includes a safety protection module, which is used to monitor the temperature value of the charging environment in real time. And collect circuit data in real time, and according to the temperature value of the charging environment And the circuit data to make corresponding control strategies, specifically: Real-time monitoring of the temperature of the charging environment ,like If the safety threshold is exceeded, the power will be forced to be reduced and a voice alarm will be issued through the voice interaction module; Circuit data is collected in real time. If a sudden current surge is detected, that is, exceeding 200% of the rated current or the voltage exceeds the device protocol range, the output is immediately cut off and a voice prompt of the fault information is given through the voice interaction module.

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