Multifunctional hanging rope type power management system based on intelligent scheduling algorithm

Abstract

The invention relates to the technical field of intelligent power management, in particular to a multifunctional hanging rope type power management system based on an intelligent scheduling algorithm, which comprises a U-shaped titanium alloy frame, a two-way charging and discharging unit, a lithium polymer battery and a built-in TIBQ25895 main control chip, and is characterized in that the main control chip dynamically generates a strategy through a multi-target optimization model (charging time / loss / efficiency ternary target); the charging habit of a user is predicted in combination with an equipment feature library (more than or equal to 200 kinds of parameters, cloud updating) and an LSTM neural network, an intelligent protocol identification circuit (supporting PD3.1 / QC4 +) is arranged in a Type-C output line, a 0.1-0.5 A low-current mode is automatically started for equipment less than or equal to 500mAh, the system realizes quintuple protection through a sampling resistor and a thermistor, a touch control screen displays the electric quantity, the current and the battery health degree in real time, and the charging habit of the user is predicted through the LSTM neural network. The charging and discharging efficiency of the device is greater than or equal to 85%, and the problems of short endurance, low charging efficiency and poor compatibility of wearable equipment are solved.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent power management, and in particular to a multifunctional rope-type power management system based on an intelligent scheduling algorithm. Background Art

[0002] With the popularity of wearable devices such as AR / AI smart glasses and Bluetooth headsets, the technical bottlenecks of portable power management systems are becoming increasingly prominent. The portable power solutions currently on the market have significant shortcomings in terms of wearing comfort, energy scheduling efficiency, device compatibility, and intelligent management:

[0003] Inefficiencies in energy scheduling algorithms

[0004] Existing power supplies mostly use a fixed current charging strategy:

[0005] Single-objective optimization flaws: Focusing only on charging speed while ignoring battery loss. For example, a 500mAh smart glasses battery is charged with a 500mA current (twice the safe rate), shortening the battery life by more than 50%.

[0006] Lack of dynamic adaptation capability: Unable to adjust strategies based on device status (such as battery health and ambient temperature). The intelligent charging algorithm disclosed in CN113595784A does not build a multi-objective model, resulting in a charging efficiency of ≤70% and large energy consumption fluctuations.

[0007] Insufficient device compatibility and protocol support

[0008] Incomplete protocol coverage: Most power banks only support one or two fast charging protocols, such as QC3.0 or PD2.0, and are not compatible with proprietary protocols such as Huawei FCP and Samsung AFC, resulting in a charging failure rate of ≥15%;

[0009] Lack of real-time updates: The device feature library is fixed and cannot adapt to the charging needs of new device models (such as the new generation of AR glasses). The protocol identification circuit of CN112803115A does not support cloud updates, and the protocol matching success rate decreases year by year as new devices are launched.

[0010] Lack of intelligent prediction and security protection

[0011] Passive management mode: protection is activated only after a fault occurs. For example, the overcurrent protection response time is ≥500μs, far exceeding the battery cell safety critical value (200μs);

[0012] Lack of user behavior awareness: Charging needs cannot be predicted. For example, users need to pre-charge frequently used devices at night. Traditional power supplies require manual operation. The power management system of CN211508548U does not involve behavioral learning functions, and the energy utilization rate is ≤60%. Summary of the Invention

[0013] (1) Technical problems solved

[0014] In view of the deficiencies of the existing technology, the present invention provides a multifunctional rope-type power management system based on an intelligent scheduling algorithm.

[0015] (2) Technical solution

[0016] To achieve the above-mentioned objectives, the present invention provides the following technical solutions: The multifunctional lanyard-type power management system based on the intelligent scheduling algorithm of the present invention includes a wearable structural unit, a dual-channel charging and discharging unit, an energy unit and a main control chip. The dual-channel charging and discharging unit and the wearable structural unit are an integrated structure, the energy unit and the main control chip are built into the wearable structural unit, and the main control chip is configured with an intelligent scheduling algorithm module, which dynamically generates a charging strategy based on a multi-objective optimization model.

[0017] Preferably, the multi-objective optimization model takes minimizing charging time, minimizing battery loss and maximizing energy efficiency as objective functions, and solves the optimal solution through the constraints 0.1A≤output current≤2A, 3.0V≤output voltage≤20V, and temperature≤60℃.

[0018] Further preferably, the intelligent scheduling algorithm module includes a device feature library that stores charging parameters of ≥200 types of devices, and the main control chip is configured with a wireless communication module to support online updates through the cloud.

[0019] Preferably again, the main control chip is configured with a user behavior learning module, which analyzes the user's device usage time patterns and charging habits based on the LSTM neural network to predict future charging needs.

[0020] Preferably, the dual-channel charging and discharging unit includes a Type-C output line and a Micro-USB input line, and the Type-C output line has a built-in intelligent protocol identification circuit and supports the PD3.1 / QC4+ protocol.

[0021] Further preferably, the energy unit adopts a lithium polymer battery, and the energy unit is electrically connected to the main control chip through a thermistor.

[0022] Again preferably, the Type-C output line and the Micro-USB input line are electrically connected to the main control chip through a sampling resistor.

[0023] Preferably, the wearable structural unit adopts a U-shaped titanium alloy frame, and the wearable structural unit is covered with a skin-friendly silicone layer.

[0024] Further preferably, the wearable structural unit is equipped with a touch control screen, which displays the power level, current, predicted charging time and health of the device energy unit in real time.

[0025] Again preferably, the main control chip adopts TIBQ25895 chip, and the main control chip is configured with a power switch. The main control chip cooperates with the intelligent scheduling algorithm module to dynamically adjust the output current based on device requirements, battery status, and ambient temperature, and automatically enables 0.1-0.5A low current mode for small-capacity devices ≤500mAh.

[0026] (3) Beneficial effects

[0027] Compared with the existing technology, the present invention provides a multifunctional rope-type power management system based on an intelligent scheduling algorithm, which has the following beneficial effects:

[0028] Breakthrough in wearing comfort and portability: U-shaped titanium alloy memory frame with skin-friendly silicone layer, adaptive to the neck curvature, achieving seamless wearing.

[0029] Intelligent scheduling and improved charging efficiency: A multi-objective optimization algorithm dynamically generates strategies, shortening charging time by 35% compared to traditional solutions (for example, charging a 500mAh device is reduced from 2 hours to 75 minutes), reducing battery loss by 40% (extending cycle life to over 1,000 times), and improving energy efficiency to 92%. The device feature library (≥200 parameters) is combined with cloud updates and is compatible with 10+ protocols including PD3.1 / QC4+, with a protocol recognition time of ≤100ms, resolving compatibility issues.

[0030] Predictive management and safety assurance: The LSTM neural network predicts charging needs (with an accuracy of ≥85%) and adjusts the battery SOC to the optimal range in advance (e.g., pre-charging to 70% for predicted nighttime use), minimizing loss at full charge. A five-fold protection mechanism (overcurrent response ≤ 100μs, overtemperature current reduction ≤ 60°C) combined with sampling resistors and thermistors ensures charging safety, reducing the risk of cell bulging by 90%.

[0031] Interactive experience and energy efficiency work together: The touch control screen displays the power level, current and battery health in real time (accuracy ±1%). The lithium polymer battery is equipped with simultaneous charging and discharging, allowing charging while in use, meeting all-weather usage needs. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 This is a schematic diagram of device access and protocol identification in the present invention;

[0033] Figure 2 This is a schematic diagram of the user behavior prediction process of the present invention;

[0034] Figure 3 This is a schematic diagram of the overall device structure of the present invention;

[0035] Figure 4 Schematic cross-sectional view of a wearable structural unit of the present invention;

[0036] In the figure: 1. Wearable structural unit; 2. Energy unit; 3. Main control chip; 4. Type-C output line; 5. Micro-USB input line; 6. Sampling resistor; 7. Thermistor; 8. Touch control screen. DETAILED DESCRIPTION

[0037] 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.

[0038] See also Figure 1-3 The multifunctional lanyard-type power management system based on the intelligent scheduling algorithm of the present invention includes a wearable structural unit 1, a dual-channel charging and discharging unit, an energy unit 2 and a main control chip 3. The dual-channel charging and discharging unit and the wearable structural unit 1 are an integrated structure. The energy unit 2 and the main control chip 3 are built into the wearable structural unit 1. The main control chip 3 is configured with an intelligent scheduling algorithm module, and the intelligent scheduling algorithm module dynamically generates a charging strategy based on a multi-objective optimization model.

[0039] Intelligent scheduling algorithm operation mechanism

[0040] Solving multi-objective optimization models:

[0041] The intelligent scheduling algorithm built into the main control chip 3 is based on the ternary objective function

[0042] F(x)=[f1(x),f2(x),f3(x)] is the core, where

[0043] f1(x) optimizes the charging time based on the improved Dijkstra algorithm and determines the optimal current-time path through dynamic programming;

[0044] f2(x) uses the battery equivalent circuit model (TheveninModel) to calculate the internal resistance loss and combines it with the Arrhenius equation to predict the cell aging rate

[0045] f3(x) maximizes energy conversion efficiency through the MPPT (maximum power point tracking) algorithm based on the principle of power flow conservation.

[0046] The constraints are verified in real time through hardware sampling: the current sensor monitors the 0.1-2A range, the voltage divider network feedback is in the 3.0-20V range, and the thermistor 7 uploads temperature data in real time (≤60°C).

[0047] Device feature library collaboration:

[0048] Built-in 200+ device feature database (stores VID / PID, battery capacity, charging rate and other parameters). When a new device is connected:

[0049] The intelligent protocol identification circuit obtains basic parameters through PD3.1 / QC4+ protocol handshake;

[0050] The algorithm matches the feature library data. If the match fails, adaptive learning is initiated to build a device model through 5 charge and discharge cycles.

[0051] Dual-channel charging and discharging synergistic principle

[0052] Type-C output line 4:

[0053] Integrated intelligent protocol recognition circuit (parallel 4-channel detection of PD / QC / AFC / FCP protocols), completes protocol recognition within 100ms, and adjusts output according to algorithm instructions:

[0054] For devices with a capacity of ≤500mAh, the Buck circuit is forced to output a low current of 0.1-0.5A (error ±5%).

[0055] For mobile phones and other devices, the PD3.1 protocol is activated to output 20W fast charging, and the battery health (SOH) is read through the I2C bus. If the SOH is less than 80%, the power is limited to 10W.

[0056] Micro-USB input line 5:

[0057] Supports 5V / 2A fast charging input, and the main control chip 3 dynamically distributes input power through the power switch:

[0058] When input power ≥ output power, 80% of the power is used to power the device and 20% is used to charge the battery;

[0059] When the input power is less than the output power, the battery supplements the difference power, and the algorithm adjusts the output current to a safe range.

[0060] Energy Unit 2 Management Mechanism

[0061] Lithium polymer battery dynamic management:

[0062] The 2000mAh battery uses a thermistor 7 (accuracy ±1°C) to monitor the temperature in real time, combined with the algorithm to achieve:

[0063] Pre-charge stage: charge at 0.1C (200mA) when the voltage is <3V;

[0064] Constant current stage: after the voltage is ≥3V, charge at 1C (2A) until it reaches 4.2V;

[0065] Trickle stage: ends when the current decays to 0.05C (100mA) to avoid overcharging.

[0066] Safety redundancy design:

[0067] The sampling resistor 6 (0.01Ω, ±1%) monitors the current in real time. When the overcurrent reaches 2.5A, the power switch is cut off within 100μs; when the overvoltage reaches 5.5V, the TVS diode is clamped, and when the overtemperature reaches 60℃, the current is reduced to 0.1A.

[0068] User behavior learning module

[0069] LSTM neural network training:

[0070] Collect charging data (time point, device type, charging duration) from users for more than 7 days and construct a three-dimensional input sequence Xt = [Tt, Dt, Lt], where:

[0071] Tt is the charging timestamp (accurate to 15 minutes);

[0072] Dt is the device type code (One-Hot code);

[0073] Lt is the usage time since the last charge.

[0074] Through 128-dimensional hidden layer training, the charging probability in the next 24 hours is predicted with an accuracy of ≥85%.

[0075] Predictive scheduling execution:

[0076] When the predicted charging probability is >70%, the algorithm adjusts the battery SOC to 60-80% in advance (to avoid loss during full-charge storage). For example, if it is predicted that the user will charge at 10 pm, the battery will be automatically charged to 70% at 9 pm.

[0077] Intelligent protocol identification circuit

[0078] Parallel detection architecture:

[0079] 4 independent channels working simultaneously:

[0080] PD channel: Send PDO request through CC line and detect response within 10ms;

[0081] QC channel: D+ / D- lines send a voltage sequence of 0.6V→3.3V→5V, and identify characteristic voltages within 20ms;

[0082] AFC / FCP channel: Sends vendor-specific pulse sequences (such as Huawei FCP's 1.8V / 3.3V transitions) and matches within 30ms.

[0083] Once any channel detects a response, it immediately interrupts other channels, and the total recognition time is ≤100ms.

[0084] Adaptive learning algorithm:

[0085] After each successful identification, the device protocol characteristics (such as PD's PPS parameters and QC's handshake timing) are stored in EEPROM. The historical data will be called first during the next connection, and the matching time is shortened to 50ms.

[0086] Wearable structure optimization

[0087] Titanium alloy frame mechanical design:

[0088] The yield strength of the memory metal frame is ≥800MPa, and the recovery deviation after bending 180° and releasing is ≤1°. The curvature is optimized through finite element analysis to ensure that the neck contact pressure is ≤20g / cm 2 .

[0089] Interactive interface intelligent display:

[0090] The touch control screen 8 (1.2-inch OLED) communicates with the main control chip 3 via the SPI protocol and displays in real time:

[0091] Dynamic data: current current / voltage, remaining capacity (accuracy ±1%), predicted charging completion time;

[0092] Health data: device battery SOH, system battery cycle life (number of times).

[0093] Detailed workflow summary

[0094] Step 1: Device preparation and wearing

[0095] Users wear the U-shaped titanium alloy frame around their neck, which is light and comfortable;

[0096] The touch control screen 8 lights up, displaying the current remaining power and system status.

[0097] Step 2: Recharging process (charging the device)

[0098] Use Micro-USB input line 5 to connect external power supply;

[0099] The main control chip 3 starts the charging management program and identifies the charging protocol;

[0100] Thermistor 7 monitors battery temperature in real time to ensure safe charging;

[0101] The touch control screen 8 displays the charging progress and the estimated completion time.

[0102] Step 3: Charging / power transmission process (powering peripheral devices)

[0103] Insert Type-C output line 4 into the device to be charged;

[0104] The main control chip 3 identifies the device type and battery status;

[0105] The intelligent scheduling algorithm module selects the optimal charging mode based on a multi-objective optimization model;

[0106] The sampling resistor 6 monitors the output current in real time, and the high-speed comparator performs feedback regulation;

[0107] The touch control screen 8 displays the output current, voltage and device battery health.

[0108] Step 4: Enable the intelligent scheduling algorithm module

[0109] Automatically match charging protocols (such as PD3.1 / QC4+) based on the parameters in the device feature library;

[0110] The user behavior learning module analyzes historical data and predicts future charging needs;

[0111] Automatically enable 0.1-0.5A low current mode for small-capacity devices ≤500mAh to protect the device battery.

[0112] Step 5: Real-time status monitoring and adjustment

[0113] Thermistor 7 monitors the battery temperature in real time to prevent overheating;

[0114] Sampling resistor 6 monitors the output current in real time to ensure the safety and efficiency of the charging process;

[0115] The touch control screen 8 displays the power level, current, predicted charging time and device battery health in real time;

[0116] Supports users to manually adjust the charging mode or view detailed information.

[0117] Step 6: Cloud Update and Self-Learning

[0118] Download the latest device feature library from the cloud via the wireless communication module;

[0119] The user behavior learning module continuously accumulates charging data and optimizes charging strategies;

[0120] Improve charging efficiency and user experience, and extend device life.

[0121] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A multifunctional rope-type power management system based on intelligent scheduling algorithm, characterized in that: The invention comprises a wearable structural unit (1), a dual-path charging and discharging unit, an energy unit (2) and a main control chip (3); the dual-path charging and discharging unit and the wearable structural unit (1) are an integrated structure; the energy unit (2) and the main control chip (3) are built into the wearable structural unit (1); the main control chip (3) is configured with an intelligent scheduling algorithm module; and the intelligent scheduling algorithm module dynamically generates a charging strategy based on a multi-objective optimization model.

2. The multifunctional rope-type power management system based on the intelligent scheduling algorithm according to claim 1 is characterized in that: The multi-objective optimization model takes minimizing charging time, minimizing battery loss and maximizing energy efficiency as objective functions, and solves the optimal solution through the constraints of 0.1A≤output current≤2A, 3.0V≤output voltage≤20V, and temperature≤60℃.

3. The multifunctional rope-type power management system based on intelligent scheduling algorithm according to claim 1 is characterized in that: The intelligent scheduling algorithm module includes a device feature library that stores charging parameters of ≥200 types of devices. The main control chip (3) is equipped with a wireless communication module to support online updates through the cloud.

4. The multifunctional rope-type power management system based on intelligent scheduling algorithm according to claim 1 is characterized in that: The main control chip (3) is configured with a user behavior learning module, which analyzes the time pattern of user device usage and charging habits based on the LSTM neural network and predicts future charging needs.

5. The multifunctional rope-type power management system based on intelligent scheduling algorithm according to claim 1 is characterized in that: The dual-channel charging and discharging unit comprises a Type-C output line (4) and a Micro-USB input line (5); the Type-C output line (4) has a built-in intelligent protocol identification circuit and supports the PD3.1 / QC4+ protocol.

6. The multifunctional rope-type power management system based on intelligent scheduling algorithm according to claim 1 is characterized in that: The energy unit (2) adopts a lithium polymer battery, and the energy unit (2) is electrically connected to the main control chip (3) via a thermistor (7).

7. The multifunctional rope-type power management system based on intelligent scheduling algorithm according to claim 5 is characterized in that: The Type-C output line (4) and the Micro-USB input line (5) are electrically connected to the main control chip (3) via a sampling resistor (6).

8. The multifunctional rope-type power management system based on intelligent scheduling algorithm according to claim 1 is characterized in that: The wearable structural unit (1) adopts a U-shaped titanium alloy frame, and the wearable structural unit (1) is covered with a skin-friendly silicone layer.

9. The multifunctional rope-type power management system based on intelligent scheduling algorithm according to claim 1 is characterized in that: The wearable structural unit (1) is equipped with a touch control screen, and the touch control screen (8) displays the power level, current, predicted charging time and health of the device energy unit (2) in real time.

10. The multifunctional rope-type power management system based on intelligent scheduling algorithm according to claim 1 is characterized in that: The main control chip (3) adopts a TIBQ25895 chip, and the main control chip (3) is configured with a power switch. The main control chip (3) cooperates with an intelligent scheduling algorithm module to dynamically adjust the output current based on device requirements, battery status, and ambient temperature, and automatically enables a 0.1-0.5A low current mode for small-capacity devices ≤500mAh.

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