A mobile power supply charging method, a charging base, a storage medium and a program product

Abstract

A mobile power supply charging method, a charging base, a storage medium and a program product, relate to the field of secondary batteries, and the method comprises: acquiring ventilation layout information and electric appliance position data of a room; inputting the ventilation layout information and the electric appliance position data into an airflow dynamics model to obtain indoor airflow field distribution and airflow direction; acquiring temperature distribution data when the mobile power supply is charging, and determining a temperature hot spot area according to the temperature distribution data; calculating an optimal heat dissipation direction based on the indoor airflow field distribution and the temperature distribution data; recording an initial orientation angle of the charging base, and adjusting the orientation of the charging base to the optimal heat dissipation direction; when the charging progress of the mobile power supply reaches a preset completion threshold and the real-time temperature of the temperature hot spot area is lower than a preset temperature threshold, adjusting the support orientation of the charging base back to the initial orientation angle. By implementing the present application, a better heat dissipation mode can be provided for the mobile power supply during the charging process, thereby prolonging the service life of the mobile power supply.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of secondary batteries, and in particular to a mobile power supply charging method, a charging base, a storage medium and a program product. BACKGROUND

[0002] With the popularity of mobile devices, mobile power supplies have become an indispensable power supply device in users' daily life. In order to improve the charging experience of users, new mobile power supply products integrated with charging bases have appeared on the market, and users can place the mobile power supply on the dedicated charging base for charging, realizing the random placement and taking of the mobile power supply.

[0003] In related technologies, the charging control of the mobile power supply mainly adopts a constant current and constant voltage charging method, and the charging process is controlled by detecting the charging current and the battery voltage; with the development of technology, some products are also equipped with a fast charging mode, which shortens the charging time by increasing the charging power, and automatically reduces the charging power when the temperature is detected to be out of limit, thereby ensuring the charging safety.

[0004] However, although the charging method in the related art can improve the charging speed, the higher charging power during fast charging will generate more heat, and the long-term heat accumulation will affect the service life of the mobile power supply. SUMMARY

[0005] The present application provides a mobile power supply charging method, a charging base, a storage medium and a program product, which are used to provide a better heat dissipation mode for the mobile power supply during the charging process and prolong the service life thereof.

[0006] In a first aspect, the present application provides a mobile power supply charging method applied to a charging base, which comprises: acquiring ventilation layout information and electrical appliance position data of a room; inputting the ventilation layout information and the electrical appliance position data into an airflow dynamics model to obtain indoor airflow field distribution and airflow direction; acquiring temperature distribution data when the mobile power supply is charging, and determining a temperature hot spot area according to the temperature distribution data; calculating a best heat dissipation direction based on the indoor airflow field distribution and the temperature distribution data; the best heat dissipation direction is an orientation angle parameter for making the temperature hot spot area of the mobile power supply face the airflow direction; recording an initial orientation angle of the charging base, and adjusting the orientation of the charging base to the best heat dissipation direction; when the charging progress of the mobile power supply reaches a preset completion threshold and the real-time temperature of the temperature hot spot area is lower than a preset temperature threshold, adjusting the support orientation of the charging base back to the initial orientation angle.

[0007] In the above embodiment, the charging base determines the hot spot area based on the temperature distribution of the mobile power supply during charging, analyzes the indoor airflow field distribution by obtaining the room ventilation layout and the electrical appliance position information in combination with the airflow dynamics model, calculates the optimal heat dissipation direction, and adjusts the orientation of the charging base to align the hot spot area with the airflow direction, thereby fully utilizing the room ventilation or the electrical appliance for heat dissipation, ensuring the heat dissipation effect and prolonging the service life of the mobile power supply.

[0008] In combination with some embodiments of the first aspect, in some embodiments, based on the indoor airflow field distribution and the temperature distribution data, the step of calculating the optimal heat dissipation direction specifically comprises: calculating the temperature gradient distribution and the heat flow direction of the temperature hot spot area based on the temperature distribution data, constructing a heat conduction model; inputting the indoor airflow field distribution as a boundary condition into the heat conduction model to obtain multiple heat dissipation efficiencies of the mobile power supply under different orientation angles; selecting the highest heat dissipation efficiency as a target heat dissipation efficiency, and determining the orientation angle corresponding to the target heat dissipation efficiency as the optimal heat dissipation direction.

[0009] In the above embodiment, the charging base analyzes the temperature gradient distribution, the heat flow direction, and the indoor airflow field distribution by constructing a heat conduction model, selects the optimal heat dissipation direction by comparing the heat dissipation efficiencies under different orientation angles, and uses the accurate calculation method based on the physical model to make the heat dissipation direction more scientific and reliable.

[0010] In combination with some embodiments of the first aspect, in some embodiments, after the steps of recording the initial orientation angle of the charging base and adjusting the orientation of the charging base to the optimal heat dissipation direction, the method further comprises: monitoring the real-time temperature of the temperature hot spot area, and increasing the charging power from the current charging power to a target charging power when the real-time temperature is lower than the lower limit value of the preset temperature range; the target charging power is calculated based on the temperature variation trend of the temperature hot spot area and the heat dissipation efficiency corresponding to the optimal heat dissipation direction; and reducing the charging power to the current charging power when the real-time temperature exceeds the upper limit value of the preset temperature range.

[0011] In the above embodiment, the charging base dynamically adjusts the charging power based on the real-time temperature monitoring and the preset temperature range, increases the target power to speed up the charging when the temperature is low, and reduces the power in time when the temperature is too high, thereby achieving the intelligent balance between the charging speed and safety.

[0012] In some embodiments of the first aspect, after the step of recording the initial orientation angle of the charging base and adjusting the orientation of the charging base to the optimal heat dissipation direction, the method further comprises: determining a target charging amount according to the initial remaining power and the actual capacity of the target power bank; when the current scenario is identified as a preset charging scenario, obtaining alarm information of a user terminal connected to the charging base, and determining the available charging time; calculating the estimated charging time required for charging in the slow charging strategy according to the target charging amount, and determining the fast charging compensation time for charging in the fast charging strategy when the estimated charging time is greater than the available charging time; when the fast charging compensation time is greater than a preset time threshold, generating an initial charging plan based on the fast charging compensation time; the initial charging plan comprises a plurality of charging time intervals, and each charging time interval is bound with a charging power and a charging strategy; and charging is performed according to the initial charging plan.

[0013] In the above embodiments, the charging base calculates the charging time requirement, automatically plans the time allocation of the fast charging and slow charging strategies, and accurately controls the plurality of charging time intervals to ensure the charging requirement while minimizing the fast charging time and reducing the heat generation.

[0014] In some embodiments of the first aspect, after the step of charging according to the initial charging plan, the method further comprises: monitoring the battery temperature of the target power bank in real time, and calculating the temperature rise rate according to the battery temperature; when the temperature rise rate exceeds a preset temperature rise threshold, adjusting the initial charging plan to a second charging plan based on the temperature rise rate and the real-time remaining power; charging is performed according to the second charging plan, and charging is stopped when the real-time remaining power reaches the target charging amount.

[0015] In the above embodiments, the charging base dynamically adjusts the charging plan when the temperature rise is abnormal by monitoring the battery temperature and the temperature rise rate in real time, thereby ensuring the safety of the charging process.

[0016] In some embodiments of the first aspect, the step of obtaining the ventilation layout information and the electric appliance position data of the room specifically comprises: extracting room structure features, furniture position features, and electric appliance placement features according to a plurality of room images uploaded by a user; constructing a polygon mesh model based on the room structure features, and performing three-dimensional space reconstruction according to the furniture position features and the electric appliance placement features to generate a room three-dimensional model; labeling target heat dissipation electric appliances and target ventilation areas in the room three-dimensional model to generate the ventilation layout information and the electric appliance position data.

[0017] In the above embodiments, the charging base extracts environmental features based on room images and reconstructs a three-dimensional model, and accurately labels heat dissipation electric appliances and ventilation areas, thereby improving the accuracy of ventilation layout analysis.

[0018] In some embodiments of the first aspect, in some embodiments, after the step of labeling the target heat-dissipating electrical appliance and the target ventilation area in the room three-dimensional model, determining the ventilation layout information and the electrical appliance position data, the method further comprises: establishing a communication connection with the target heat-dissipating electrical appliance, and acquiring the working parameters of the target heat-dissipating electrical appliance; and calculating a plurality of preset ventilation layouts when each target heat-dissipating electrical appliance is in different working parameters and the target ventilation area is in different switch states.

[0019] In the above embodiments, the charging base acquires the working parameters by establishing a communication connection with the heat-dissipating electrical appliance, and calculates the preset ventilation layouts in different working states, so that the heat dissipation direction selection is more real-time and adaptive.

[0020] In the second aspect, the embodiments of the present application provide a charging base, comprising: one or more processors and a memory; the memory is coupled with the one or more processors, and the memory is used to store computer program codes, the computer program codes comprising computer instructions, and the one or more processors invoke the computer instructions to make the charging base execute the method as described in the first aspect and any possible implementation manner of the first aspect.

[0021] In the third aspect, the embodiments of the present application provide a computer program product comprising instructions, when the above computer program product runs on the charging base, the above charging base executes the method as described in the first aspect and any possible implementation manner of the first aspect.

[0022] In the fourth aspect, the embodiments of the present application provide a computer readable storage medium comprising instructions, when the above instructions run on the charging base, the above charging base executes the method as described in the first aspect and any possible implementation manner of the first aspect.

[0023] It can be understood that the charging base provided in the above second aspect, the computer program product provided in the third aspect and the computer storage medium provided in the fourth aspect are all used to execute the method provided in the embodiments of the present application. Therefore, the beneficial effects that can be achieved are referred to the beneficial effects in the corresponding method, which will not be repeated here.

[0024] The one or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages:

[0025] 1. By employing airflow dynamics modeling and analysis based on room ventilation layout information and appliance location data, as well as hotspot area identification and optimal heat dissipation direction calculation based on power bank temperature distribution data, the system can accurately grasp the indoor airflow distribution characteristics and adjust the charging base orientation to the optimal heat dissipation position. This effectively solves the problem of low charging efficiency caused by relying solely on reducing charging power or passive heat dissipation in existing technologies. It then achieves active heat dissipation optimization by making full use of natural ventilation, reducing heat accumulation while ensuring charging efficiency and extending the service life of the power bank.

[0026] 2. By adopting a dynamic charging power adjustment mechanism based on real-time temperature monitoring of hot spots, and calculating the target charging power by combining temperature change trends and the heat dissipation efficiency corresponding to the optimal heat dissipation direction, intelligent adjustment of charging power can be achieved while ensuring safety. This effectively solves the problem that the charging power adjustment in the existing technology is too mechanical and cannot make full use of heat dissipation conditions, and achieves a dynamic balance between charging power and heat dissipation capacity, maximizing charging efficiency while ensuring charging safety.

[0027] 3. By employing environmental feature extraction and 3D spatial reconstruction technology based on multiple room images, as well as a labeling mechanism for target heat dissipation appliances and ventilation areas, it can automatically acquire and analyze room environmental information, effectively solving the problems of difficulty in acquiring ventilation layout information and low accuracy in existing technologies. This enables rapid and accurate collection of environmental information, improving the scientificity and reliability of the heat dissipation solution. Attached Figure Description

[0028] Figure 1 This is a flowchart illustrating a mobile power bank charging method in an embodiment of this application.

[0029] Figure 2 This is another schematic flowchart of the mobile power bank charging method in the embodiments of this application;

[0030] Figure 3 This is a schematic diagram of the physical device structure of a charging base in the embodiments of this application. Detailed Implementation

[0031] The terminology used in the following embodiments of this application is for the purpose of describing particular embodiments only and is not intended to be limiting of this application. As used in the specification of this application, the singular expressions “a,” “an,” “the,” “the,” and “this” are intended to include the plural expressions as well, unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used in this application refers to any or all possible combinations including one or more of the listed items.

[0032] Hereinafter, the terms "first", "second", "third", etc. are used only for the purpose of description, and cannot be understood as implying or suggesting relative importance or implicitly indicating the number of the technical features indicated. Therefore, the features defined with "first", "second", etc. can explicitly or implicitly include one or more of the features, and in the description of the embodiments of the present application, the meaning of "a plurality of" is two or more, unless otherwise specified.

[0033] For ease of understanding, the application scenarios of the embodiments of the present application are introduced as follows.

[0034] With the popularity of mobile devices, users are increasingly dependent on power banks. Especially in the scenarios of business trips, travel, etc., charging at night is often needed to ensure the use of the next day. However, a large amount of heat will be generated during the charging process of the power bank, and long-term temperature accumulation will significantly reduce the battery life. A user's power bank has a capacity drop of nearly 10% after continuous fast charging for several times, and the heat is serious during charging.

[0035] In the related art, temperature control of the power bank can be achieved by reducing the charging power or increasing the cooling fan. The following introduces the scenario of using the power bank charging method in the related art.

[0036] The existing technology mainly adopts two schemes to solve the charging and heat dissipation problem: one is to reduce the heat by reducing the charging power, such as a certain brand of power bank which automatically reduces to 5W charging when the temperature exceeds 45℃; the other is to increase the cooling fan for forced cooling, such as a certain charging base equipped with a small fan. But these schemes either seriously affect the charging efficiency or increase the additional power consumption.

[0037] And by using the power bank charging method in the embodiments of the present application, through environmental perception and orientation optimization, active cooling by fully utilizing natural ventilation conditions is achieved, which not only improves the cooling efficiency. The following introduces the scenario of using the power bank charging method in the present application.

[0038] The charging base using the present scheme first acquires the room ventilation layout through the environmental perception module, such as the air conditioner outlet position, wind speed and other parameters. Then the temperature distribution of the power bank is monitored in real time, and the hot spot area near the charging interface is identified. Based on the airflow field analysis, the charging base is automatically turned to the best cooling direction, so that the hot spot area is directly opposite to the airflow direction, maximizing the charging efficiency under the premise of safety. In this way, not only the natural ventilation conditions are fully utilized, but also the additional cooling energy consumption is avoided.

[0039] As can be seen, by using the power bank charging method in the embodiments of the present application, the problem of low charging efficiency can be effectively solved while achieving temperature control, thereby achieving a balance between charging speed and battery life.

[0040] For ease of understanding, the method provided by the embodiment is described in the following flow. Please refer to Figure 1 A flowchart of a method for charging a mobile power supply in an embodiment of the present application.

[0041] S101, obtain ventilation layout information and electrical appliance position data of a room.

[0042] The ventilation layout information represents a data set of the positions, types and working states of natural or mechanical ventilation devices in the room, including window position and opening / closing state, air conditioner outlet position and air speed, ventilation fan installation position and the like; the electrical appliance position data refers to the spatial distribution coordinates and heat dissipation characteristic parameters of electrical appliances in the room, including three-dimensional spatial position, volume size, heating power and the like of the electrical appliances; the room represents an indoor environment with a relatively closed space, which can be a living room, an office or other indoor place.

[0043] When the charging base is first installed or detects a change in the room layout, the charging base needs to obtain the latest environmental information. Specifically, the charging base can scan the surrounding environment through an environment perception module built-in or interconnected with it, and collect the spatial distribution information of the ventilation devices and electrical appliances in the room. The environment perception module can also be realized through a designated application program in the user's mobile terminal such as a mobile phone; for the ventilation devices, the charging base records parameters such as their types, position coordinates, orientation angles, and monitors their working states in real time through an interconnection program; for the electrical appliances, the charging base establishes a device position mapping table to record the three-dimensional spatial coordinates and volume information of each device. The charging base standardizes and structures all the collected data for storage, forming a complete environmental information database.

[0044] In some embodiments, the automatic acquisition of room environmental information can be realized in various ways: optionally, the charging base can extract and analyze the camera data of the room camera after obtaining the camera reading permission, construct a room three-dimensional point cloud model using camera recognition technology or laser scanning technology, identify the ventilation devices and electrical appliances through deep learning algorithm, extract their spatial position and shape characteristics, obtain temperature distribution through infrared thermal imaging, and finally fuse and process to obtain complete environmental information; optionally, the charging base can directly obtain the device position information and working state data of the devices connected to the smart home system by interfacing with the smart home system, and simultaneously collect the environmental temperature field distribution in real time using multiple distributed temperature sensors, and generate an environmental information model through a data fusion algorithm. It can be understood that other ways based on computer vision or Internet of Things technology can also be used to realize the automatic collection and processing of environmental information, which are not limited here.

[0045] S102, input the ventilation layout information and electrical appliance position data into an airflow dynamics model to obtain indoor airflow field distribution and airflow direction.

[0046] wherein, the airflow dynamics model represents a mathematical model describing the flow characteristics of fluid in an enclosed space, including continuity equation, momentum equation and energy equation, etc. control equation group; the indoor airflow field distribution refers to the three-dimensional distribution state of the velocity, pressure and temperature, etc. physical quantities of air in the indoor space; the airflow direction represents the vector direction of the airflow movement at a specific position, which is calculated by the velocity field.

[0047] After obtaining the environmental information, the charging base needs to analyze the indoor airflow field characteristics to optimize the heat dissipation scheme. Specifically, the charging base first establishes a room geometric model, sets the ventilation device in the ventilation layout information as the inlet boundary condition, and sets the appliance position as the heat source boundary condition; then based on the Navier-Stokes equation group, a calculation grid is constructed, and a finite volume method is used for discretization processing; then the velocity field, pressure field and temperature field distribution of the whole space are obtained by iterative solution; finally, post-processing analysis is performed to extract the airflow direction and velocity of the key position, and generate the airflow field characteristic map. The charging base visualizes the calculation results, which is convenient for subsequent heat dissipation optimization analysis.

[0048] In some embodiments, airflow field analysis can be achieved in various ways: optionally, the charging base can use modal decomposition method to decompose the complex flow field into several characteristic modes, and reconstruct the flow field by superimposing the characteristic modes, which not only ensures the calculation accuracy but also improves the calculation efficiency; optionally, the charging base can use deep learning method, train neural network model based on a large amount of CFD simulation data, and realize fast and accurate flow field prediction. It can be understood that other numerical simulation or machine learning methods can also be used for airflow field analysis, which is not limited here.

[0049] S103, obtain temperature distribution data of the power bank during charging, and determine a temperature hot spot area according to the temperature distribution data.

[0050] wherein, the temperature distribution data represents the temperature spatial distribution of each part of the power bank during charging, including the real-time temperature values of multiple measurement points such as battery surface temperature, charging interface temperature and shell temperature; the temperature hot spot area refers to the local position where the temperature is obviously higher than the surrounding area, which usually appears in the position where the heat generating elements are concentrated and the heat dissipation is poor; the real-time temperature value is used to represent the temperature measurement result of a specific position at a certain time, and the unit is Celsius.

[0051] When the mobile power supply starts charging, the charging base needs to accurately grasp its temperature distribution characteristics. Specifically, the charging base performs temperature scanning sampling on the mobile power supply through the multi-point temperature sensor array built-in the mobile power supply, and the sampling interval is 0.1 seconds; at the same time, the charging base performs spatial interpolation processing on the collected temperature data to generate a continuous temperature distribution surface; then, the charging base uses a temperature gradient analysis algorithm to identify the area with a sharp temperature change, and determines the spatial range and center position of the temperature hot spot through cluster analysis; finally, the charging base establishes a dynamic monitoring model of the temperature distribution to update the position of the hot spot area of the mobile power supply and the temperature change trend in real time.

[0052] In some embodiments, temperature distribution analysis and hot spot identification can be achieved in various ways: optionally, the charging base can be interconnected with an infrared camera, adopt thermal image processing technology, acquire temperature distribution through infrared imaging, identify hot spot areas combined with image segmentation and edge detection algorithms, and calculate the area, peak temperature and temperature gradient of the hot spot; optionally, the charging base can establish a three-dimensional heat conduction model of the mobile power supply based on the finite element heat analysis method, predict the internal temperature distribution by solving the heat conduction equation, and correct and optimize the model combined with the measured data. It can be understood that other heat analysis or image processing methods can also be used to realize temperature distribution feature extraction, which is not limited here.

[0053] It should be noted that the temperature distribution data acquisition process first establishes a hot spot detection matrix M. The temperature sensor array of the charging base is arranged in 4x4, and the original temperature data set T={tij} is collected. For each measurement point, calculate its spatial distance weight matrix W, and the weight coefficient wij=exp(-dij² / 2σ²), where dij is the spatial distance, and σ is the Gaussian kernel parameter. The sampling points are expanded by bilinear interpolation to generate a high-resolution temperature field T'. The hot spot area identification algorithm is executed as follows: first, calculate the temperature gradient field ∇T=(∂T / ∂x,∂T / ∂y)to get the direction with the largest temperature change rate. For each point (x, y), calculate its local temperature eigenvalue λ(x, y)=trace(H), where H is the Hessian matrix of the temperature field. Set the threshold θ, when λ(x, y)>θ, mark the point as a hot spot candidate area.

[0054] For example, assuming that the temperature matrix of the mobile power supply at a certain moment when charging is [[35, 36, 37, 38], [36, 39, 41, 39], [37, 42, 43, 40], [36, 38, 39, 37]], it is expanded to a high-resolution temperature field of 16x16 through interpolation. After calculating the temperature gradient, it is found that there is a temperature gradient near the (2, 2) position, and this area is marked as a hot spot area, and the system will continuously monitor the temperature change trend of this area.

[0055] S104, calculate the optimal heat dissipation direction based on the indoor airflow field distribution and temperature distribution data.

[0056] wherein the optimal heat dissipation direction represents a charging base support orientation capable of achieving maximum heat dissipation efficiency, which is usually determined by the direction from the temperature hot spot area to the airflow direction; the heat dissipation efficiency refers to the ratio of the heat transferred by the mobile power supply to the environment per unit time to its heat generation power; the orientation angle parameter is used to quantify the rotation position of the charging base, and the angle is calibrated based on the initial position.

[0057] After obtaining the indoor airflow field and temperature distribution information, the charging base needs to determine the optimal heat dissipation direction. Specifically, the charging base first establishes a mathematical model of heat transfer, taking the airflow field distribution as the boundary condition of forced convection heat transfer; then calculates the angle between the hot spot area and the airflow under different orientation angles, and combines the airflow velocity and temperature gradient to calculate the local heat transfer coefficient; then, the charging base searches for the orientation angle corresponding to the maximum heat dissipation efficiency through numerical optimization method, and comprehensively considers the influence of multiple hot spot areas; finally, the charging base converts the optimization result into specific orientation adjustment parameters, including rotation angle and adjustment sequence.

[0058] In some embodiments, the calculation of the optimal heat dissipation direction can be achieved in various ways: optionally, the charging base can use genetic algorithm, taking the orientation angle as the optimization variable and the heat dissipation efficiency as the fitness function, to find the global optimal solution through population iteration and refine the optimization result through local search; optionally, the charging base can use reinforcement learning method to learn the optimal orientation adjustment strategy through interaction with the environment, establish the state-action mapping relationship, and realize adaptive heat dissipation optimization. It can be understood that other optimization algorithms or intelligent control methods can also be used to determine the optimal heat dissipation direction, which is not limited here.

[0059] It should be noted that the calculation of the optimal heat dissipation direction first constructs a heat transfer model. For each hot spot area Hi, a heat conservation equation is established: Q = hA (Ts-Ta), where h is the convection heat transfer coefficient, which is calculated from the airflow velocity v and the characteristic length L: h = k (v, L). According to the airflow field data, the velocity component v (α) = v · cos (θ-α) under different orientation angles α is calculated, where θ is the airflow direction.

[0060] wherein the optimization algorithm execution process is as follows:

[0061] 1. Initialize the angle search space Α=[0, 2π], which is divided into N discrete points;

[0062] 2. For each angle αi, calculate the comprehensive heat dissipation efficiency η (αi);

[0063] 3. Obtain the temperature Ti of all hot spots and the airflow velocity vi (αi) at the corresponding position;

[0064] 4. Calculate local heat transfer coefficient hi = f(vi, Ti);

[0065] 5. Comprehensive efficiency η(αi) =∑(wi·hi), wi is the weight of hot spot;

[0066] 6. Update the search direction using gradient ascent method: αk+1=αk+γ∇η(αk), until the formula: |η(αk+1)-η(αk)|<ε, or the algorithm reaches the maximum number of iterations.

[0067] S105, record the initial orientation angle of the charging base, and adjust the orientation of the charging base to the optimal heat dissipation direction.

[0068] Wherein, the initial orientation angle represents the support direction of the charging base before starting charging, as the reference position for subsequent angle adjustment; the charging base orientation refers to the angle position of the support structure relative to the fixed reference direction; the orientation adjustment means changing the rotation angle of the charging base support structure through the driving mechanism, including the rotation direction and the angle size.

[0069] After determining the optimal heat dissipation direction, the charging base needs to perform orientation adjustment operation. Specifically, the charging base first obtains the absolute angle value of the current support structure through the angle sensor, and records it as the initial orientation angle; then, the charging base calculates the rotation angle and direction needed from the initial orientation to the optimal heat dissipation direction; next, the charging base starts the stepper motor and rotates smoothly to the target position according to the preset angular velocity, while ensuring the rotation accuracy through closed-loop control; during the adjustment process, the charging base monitors the stability of the power bank in real time to ensure reliable charging contact; finally, the charging base locks the current orientation and continuously monitors the heat dissipation effect.

[0070] In some embodiments, the orientation adjustment control can be achieved in various ways: optionally, the charging base can use a PID control algorithm to adjust the motor speed and direction in real time according to the angle deviation, to achieve precise positioning through proportional-integral-derivative action, while adding acceleration limit to avoid violent motion; optionally, the charging base can use an adaptive control method to dynamically adjust the control parameters according to the load weight and friction, establish a disturbance observer to compensate for external disturbances, and improve the stability and reliability of the adjustment process. It can be understood that other motion control or servo systems can also be used to achieve accurate orientation adjustment, which is not limited here.

[0071] In some embodiments, the orientation adjustment control system first reads the initial value of the angle sensor a0, which is recorded as the reference orientation. The motor control uses a speed planning algorithm to generate an angle-time curve θ(t). To ensure smoothness, the speed curve v(t) uses an S-shaped planning: the acceleration section uses a cosine function for smooth transition, the constant speed section maintains a constant angular velocity, and the deceleration section uses a sine function for soft landing.

[0072] The specific execution process is: set the target angle at, calculate the angle difference a = at-a0. According to the maximum angular velocity wmax and the acceleration amax, the motion is divided into three sections: acceleration, constant speed and deceleration:

[0073] 1. Acceleration section [0, t1]: θ(t) = a0+0.5amax(1-cos(πt / t1))·t²;

[0074] 2. Constant speed section [t1, t2]: θ(t) = θ(t1)+wmax·(t-t1);

[0075] 3. Deceleration section [t2, t3]: θ(t) = θ(t2)+wmax·(t-t2)-0.5amax·sin(π(t-t2) / (t3-t2))·(t-t2)².

[0076] S106, when the charging progress of the mobile power supply reaches the preset completion threshold and the real-time temperature of the temperature hotspot area is lower than the preset temperature threshold, the support orientation of the charging base is adjusted back to the initial orientation angle.

[0077] Wherein, the charging progress represents the ratio of the charged amount of the mobile power supply to the target charging amount; the preset completion threshold refers to the progress percentage triggering the charging completion judgment, usually set to 95% or more; the preset temperature threshold represents the highest temperature limit allowed to end charging; the support orientation represents the direction in which the charging base provides support for the mobile power supply.

[0078] When the charging is close to completion and the temperature drops to a safe range, the charging base needs to restore the initial orientation. Specifically, the charging base first continuously monitors the charging progress and the temperature of the hotspot area, and eliminates transient fluctuations through data smoothing processing; when it is detected that the charging progress exceeds the preset completion threshold, and the temperature of the continuous multiple sampling periods is lower than the preset threshold, the orientation restoration process is triggered; then, the charging base calls the initial orientation angle stored before, calculates the return path; then, the charging base controls the motor to rotate smoothly at a low speed, ensuring the stability of the mobile power supply; finally, after reaching the initial position, the charging base locks the support structure, completing the entire charging process.

[0079] In some embodiments, the orientation recovery control can be achieved in various ways: optionally, the charging base can use a trajectory planning algorithm to generate a continuous speed and smooth acceleration motion curve, achieve stable rotation through trapezoidal speed planning, and decelerate in advance based on the inertia characteristics of the power bank; optionally, the charging base can use a fuzzy control method to adaptively adjust the motion parameters according to the current angle deviation and the state of the power bank, make intelligent decisions through a fuzzy rule base, and ensure the safety and stability of the rotation process. It can be understood that other intelligent control or motion planning methods can also be used to achieve stable orientation recovery, which is not limited here.

[0080] It should be noted that the orientation recovery process first establishes a judgment matrix D, which includes two key indicators of charging progress P and hotspot temperature T. The data is processed using the sliding window method: set the window size w, and perform weighted average on the last w sampling points, with the weight coefficient using exponential decay λi=exp(-i / w). The system continuously calculates the judgment index: J=α·(P / Pth)+β·((Tmax-T) / (Tmax-Tth)); where Pth is the progress threshold, Tth is the temperature threshold, and α, β are weight coefficients. When the judgment index J is greater than 1 for n consecutive periods, the orientation recovery process is triggered.

[0081] The scene of the present embodiment is supplemented as follows.

[0082] Further, the present scheme can also be deeply combined with a smart home system. For example, the charging base can obtain the user's work and rest time and automatically plan the optimal charging strategy during nighttime charging. When a low temperature is detected, the charging power is appropriately increased to speed up charging; when the temperature rises, the orientation of the charging base is adjusted or the power is reduced. Historical charging data is recorded to continuously optimize the heat dissipation model parameters. This intelligent charging management not only prolongs the life of the power bank, but also provides better user experience.

[0083] After combining the above scenarios, the method provided by the present embodiment is further described in more detail. Please refer to Figure 2 , which is another flowchart of the power bank charging method in the present embodiment.

[0084] S201, extracting room structure features, furniture position features, and appliance placement features according to a plurality of room images uploaded by a user.

[0085] Among them, the room image represents the room interior photo taken by the user through the mobile terminal, including panoramic and local detail pictures; the room structure feature refers to the spatial layout and size information of architectural components such as walls, floors, doors, and windows; the furniture position feature represents the placement position and occupied space of furniture such as cabinets and tables and chairs in the room; and the appliance placement feature is used to represent the installation position and working state of electrical equipment such as air conditioners and fans.

[0086] After the user completes the room image upload, the charging base needs to perform feature extraction on the image. Specifically, the charging base first pre-processes the uploaded image, including illumination equalization, noise filtering, and distortion correction; then the charging base uses a deep learning model to perform semantic segmentation on the image, marking the room structure, furniture, and appliances as different categories; then the charging base extracts the geometric features of the room structure, calculates the wall angle and room size; then the charging base identifies the outline and position of the furniture, establishes a furniture space occupancy map; finally, the charging base locates the electrical equipment, records its installation height and orientation angle, and detects whether it is in working condition.

[0087] In some embodiments, feature extraction can be achieved in various ways: optionally, the charging base can be based on monocular depth estimation technology, first recover depth information from a single image through a pre-trained deep neural network, then use the RANSAC algorithm to fit the main plane, combine edge detection algorithm to extract the wall profile, recover the real size through perspective transformation, and finally fuse multi-view information to construct a consistent spatial structure representation; optionally, the charging base can use instance segmentation and three-dimensional reconstruction method, first use Mask R-CNN network to detect and segment each object, then recover the three-dimensional position and attitude of the object based on the principle of multi-view geometry, eliminate errors between different views through graph optimization method, and finally generate an accurate spatial layout description. It can be understood that other computer vision or three-dimensional reconstruction methods can also be used to realize room feature extraction, which is not limited here.

[0088] It should be noted that in the image feature extraction process, the charging base adopts a continuous image processing flow. In the image preprocessing stage, the contrast is enhanced by calculating the cumulative distribution function of the image block, and the size of each image block is adaptively determined according to the image resolution, and a smooth transition is realized between adjacent blocks by using bilinear interpolation. The noise suppression adopts a double-weighted Gaussian filter based on the pixel spatial distance and the gray value difference, and the filter kernel size is adaptively adjusted according to the noise level. The semantic segmentation model adopts a codec structure, wherein the encoder extracts multi-level features through residual connection, and expands the receptive field through hole convolution during feature extraction, and the convolution kernel inflation rate increases exponentially. The decoder fuses information of different scales through a feature pyramid structure, and balances the contribution of features at each layer by using weighted summation. For example, for a room image of 1920x1080, the encoder down-samples to form 5 scale feature maps, and the decoder restores to the original resolution through layer-by-layer up-sampling, and finally outputs the class probability distribution of each pixel. In the geometric feature extraction process, the charging base first constructs a scale space in the image gradient space, detects edge points through a Gaussian difference operator, and the edge intensity threshold is adaptively determined according to the image gradient histogram. The line detection adopts a parameter estimation method based on voting, accumulates the contribution of edge points in the Hough space, and extracts the main straight line segment through non-maximum suppression. The plane fitting process uses a random sample consensus algorithm, and minimizes the projection error through iterative optimization, and combines geometric constraints to ensure the orthogonality of the plane. Finally, based on the known reference object, a perspective transformation matrix is established to restore the true size.

[0089] S202, construct a polygon mesh model based on the room structure features, and perform three-dimensional space reconstruction according to the furniture position features and the electric appliance placement features to generate a room three-dimensional model.

[0090] Among them, the polygon mesh model represents a spatial geometric structure composed of multiple interconnected polygons, used to describe the walls, floor and ceiling of the room; three-dimensional space reconstruction refers to the process of converting two-dimensional image information into a three-dimensional model with depth and volume information; the room three-dimensional model is used to represent the complete indoor environment spatial structure, including the three-dimensional representation of building components, furniture placement and electric appliance arrangement.

[0091] After completing the room feature extraction, the charging base needs to build a complete room three-dimensional model. Specifically, the charging base first generates a basic mesh model according to the room structure characteristics, decomposes the wall surface into regular polygonal units, and sets appropriate mesh density to balance accuracy and computing efficiency; then the charging base calculates the connection relationship and boundary conditions of the polygonal units based on the spatial topological relationship; then the charging base places the furniture model to the corresponding position, adjusts its orientation and size parameters; then the charging base integrates the three-dimensional information of the electrical equipment, including the installation height, heat dissipation space and working range; finally, the charging base optimizes the overall model to eliminate overlaps and interference, and ensures the rationality of the space layout.

[0092] In some embodiments, three-dimensional reconstruction can be achieved in various ways: optionally, the charging base can use a parameterized modeling method, first build a parameterized geometric model of the room, define the shape parameters and position parameters of the wall, door and window components, then determine the optimal parameter configuration through constraint solving, generate a smooth surface model using subdivision surface technology, and finally add material and texture information to enhance the visual effect; optionally, the charging base can use point cloud-based reconstruction technology to obtain dense point clouds through multi-view reconstruction, generate curved surface meshes using Poisson reconstruction algorithm, extract main geometric features combining with plane detection and structure simplification algorithm, and finally obtain a room model that retains details and has clear structure. It can be understood that other three-dimensional modeling or reconstruction methods can also be used to realize room space reconstruction, which is not limited here. Figure Three

[0093] S203, label the target heat dissipation electrical appliances and the target ventilation area in the room three-dimensional model, and generate ventilation layout information and electrical appliance position data.

[0094] Among them, the target heat dissipation electrical appliances represent electronic devices for heat dissipation management, such as air conditioner indoor units, electric fans, etc.; the target ventilation area refers to the spatial position with air exchange function, including windows, ventilation openings, etc.; the ventilation layout information is used to represent the spatial distribution and working parameters of the ventilation facilities; the electrical appliance position data represents the accurate spatial coordinates and orientation information of the heat dissipation electrical appliances.

[0095] After obtaining the room three-dimensional model, the charging base needs to label the key heat management information. Specifically, the charging base first identifies the electrical equipment with heat dissipation demand in the model, records its position coordinates, heat dissipation area and heat power; then the charging base marks all the ventilation areas, including natural ventilation and mechanical ventilation facilities, measures their effective opening area and ventilation capacity; then the charging base establishes the spatial association relationship between the ventilation area and the heat dissipation electrical appliances, analyzes the air flow propagation path and the influence of the block; then the charging base calculates the ventilation efficiency index of each area, evaluates the degree of heat dissipation condition; finally, the charging base generates a complete heat management information database, providing basic data for subsequent air flow analysis.​

[0096] It should be noted that in the heat management information labeling process, the charging base establishes a spatial relationship representation model based on graph theory. Each node in the model represents a heat source or a ventilation point, and the node attributes include position, type and parameter information; the edge represents the heat transfer channel between the nodes, and the edge weight is determined by calculating the comprehensive heat transfer coefficient of distance, obstruction and air flow characteristics. Based on this model, the heat transfer efficiency is analyzed by the shortest path algorithm, and the overall heat dissipation capacity is evaluated by the network flow algorithm. The charging base uses a hierarchical parameter representation method to decompose the thermal characteristics of each device into static parameters and dynamic parameters. Static parameters describe the inherent properties of the device, such as heat dissipation area, rated power, etc.; dynamic parameters represent characteristics related to the operating state, such as temperature rise rate, heat dissipation efficiency, etc. The state estimation algorithm is used to update the dynamic parameters in real time to ensure that the model can accurately reflect the current working state. For example, for an electric fan, the system will establish an accurate heat source model based on its installation position, air outlet angle and operating power for subsequent air flow optimization analysis.

[0097] In some embodiments, the charging base will pre-collect dynamic parameters of the ventilation layout information, that is, the charging base will establish a communication connection with the target heat dissipation electrical appliance to obtain the working parameters of the target heat dissipation electrical appliance; and calculate a plurality of preset ventilation layouts when each target heat dissipation electrical appliance is in different working parameters and the target ventilation area is in different switch states.

[0098] Among them, dynamic parameter collection represents the process of real-time acquisition of device operating state data; working parameters refer to the current operating power, air speed and other variables of the heat dissipation electrical appliance; the preset ventilation layout is used to represent the air flow distribution state under specific working conditions; the communication connection represents the data exchange channel between the charging base and the heat dissipation electrical appliance; and the ventilation area switch state refers to the opening and closing of ventilation facilities such as doors, windows and ventilation fans.

[0099] After the heat dissipation electrical appliance is connected to the home network, the charging base needs to establish a ventilation layout database. Specifically, the charging base first establishes a stable data connection with each heat dissipation electrical appliance through WiFi or Bluetooth wireless communication protocol; then the charging base continuously collects the real-time working state of each device, including the operating mode, power level and air supply parameter; then the charging base identifies the state combination of all controllable ventilation areas to generate a complete list of ventilation scenes; then the charging base analyzes the air flow distribution under each scene, calculates the air flow speed and direction at key positions; finally, the charging base stores the analysis results in the database for subsequent heat dissipation optimization.

[0100] In some embodiments, the ventilation layout analysis can be implemented in various ways: optionally, the charging base can use a neural network prediction method, first establish a multi-layer perceptron model, the input layer contains device parameters and ventilation state, the hidden layer extracts features through the ReLU activation function, the output layer predicts the airflow field distribution, and the network weight is optimized through the back propagation algorithm to realize fast airflow prediction; optionally, the charging base can use computational fluid dynamics method, solve Navier-Stokes equation set to obtain accurate airflow field, based on viscous effect and turbulence influence, while using adaptive grid refinement to improve calculation accuracy. It can be understood that other flow field prediction or numerical simulation methods can also be used to implement the ventilation layout analysis, which is not limited here.

[0101] S204, input the ventilation layout information and the electric appliance position data into the airflow dynamics model to obtain the indoor airflow field distribution and the airflow direction.

[0102] Referring to step S102, the charging base calculates the indoor airflow field distribution and the airflow direction.

[0103] S205, obtain temperature distribution data when the power bank is charging, and determine the temperature hot spot area according to the temperature distribution data.

[0104] Referring to step S103, the charging base determines the temperature distribution data and the temperature hot spot area.

[0105] S206, based on the indoor airflow field distribution and the temperature distribution data, calculate the best heat dissipation direction.

[0106] Referring to step S104, the charging base calculates the best heat dissipation direction.

[0107] In some embodiments, the charging base will perform heat dissipation simulation based on a heat conduction model, that is, the charging base will calculate the temperature gradient distribution and heat flow direction of the temperature hot spot area based on the temperature distribution data, and construct a heat conduction model; input the indoor airflow field distribution as a boundary condition into the heat conduction model to obtain multiple heat dissipation efficiencies of the power bank under different orientation angles; select the highest heat dissipation efficiency as the target heat dissipation efficiency, and determine the orientation angle corresponding to the target heat dissipation efficiency as the best heat dissipation direction.

[0108] Wherein, the heat conduction model represents a mathematical equation set describing the heat transfer process; the temperature gradient distribution refers to the rate of change of temperature in space; the heat flow direction is used to represent the main path of heat transfer; the heat dissipation efficiency represents the ratio of the heat transferred by the power bank to the environment per unit time to its heat power; the orientation angle refers to the rotation angle of the charging base support structure relative to the reference direction.

[0109] After obtaining the temperature distribution and airflow field data, the charging base needs to optimize the heat dissipation direction. Specifically, the charging base first calculates the three-dimensional temperature gradient according to the temperature field data to determine the main direction of heat diffusion; then the charging base establishes a coupled heat transfer model including conduction, convection and radiation, where the conduction coefficient varies with temperature and the convection heat transfer coefficient is determined by the airflow field; then the charging base scans different orientations in the polar coordinate system with a step of 10 degrees, calculates the total heat dissipation at each angle under steady state, and then evaluates the heat dissipation efficiency of each direction through the energy balance equation, combined with the constraint conditions of temperature uniformity and hot spot control; finally, the charging base determines the optimal orientation angle and converts the result into motor control instructions.

[0110] In some embodiments, heat dissipation optimization can be achieved in various ways: optionally, the charging base can use a multi-objective genetic algorithm to encode the orientation angle as a chromosome, generate new candidate solutions through crossover and mutation operations, select the optimal individual using non-dominated sorting, and obtain the Pareto optimal solution set after multiple generations of evolution; optionally, the charging base can use a reinforcement learning method to model the heat dissipation control as a Markov decision process, learn the optimal angle adjustment strategy through a deep Q network, and improve training efficiency using experience replay. It can be understood that other optimization algorithms or intelligent control methods can also be used to optimize the heat dissipation direction, which are not limited here.

[0111] S207, record the initial orientation angle of the charging base, and adjust the orientation of the charging base to the optimal heat dissipation direction.

[0112] Referring to step S105, the charging base adjusts the orientation of the charging base.

[0113] S208, determine the target charging power according to the initial remaining power of the target mobile power supply and the actual capacity.

[0114] Wherein, the target mobile power supply represents a portable power supply device that needs to be charged; the initial remaining power refers to the actual power value before the mobile power supply starts charging; the actual capacity represents the maximum power that the mobile power supply can store; the target charging power is used to represent the power value that needs to be supplemented this time.

[0115] Before the mobile power supply starts charging, the charging base needs to determine the charging target. Specifically, the charging base first collects the current voltage and current parameters of the mobile power supply through the power detection circuit, calculates the initial remaining power in combination with the built-in power estimation model; then the charging base reads the specification parameters of the mobile power supply to obtain the rated capacity and actual capacity information, wherein the actual capacity will gradually decay with the use time; then the charging base analyzes the battery health status to evaluate the charging efficiency and safety margin; then the charging base calculates the specific power value that needs to be supplemented according to the target power ratio set by the user; finally, the charging base stores the target charging power into the temporary cache for subsequent charging strategy planning.

[0116] In some embodiments, the power determination can be achieved in various ways: optionally, the charging base can use the coulomb counting method to continuously monitor the charging and discharging current through a high-precision current sampling circuit, integrate the current to obtain the power change, combine the temperature compensation and aging correction coefficient to achieve a power estimation accuracy of ±2%, and establish a battery equivalent circuit model to predict the voltage response during charging; optionally, the charging base can use a power estimation method based on machine learning to collect voltage, current, temperature and other feature data of the battery under different working conditions, train a neural network model to learn the mapping relationship between power and features, continuously optimize the model parameters through online learning, and improve the accuracy and adaptability of power estimation. It can be understood that other power estimation or battery management methods can also be used to determine the charging target, which is not limited here.

[0117] S209, when identifying that the current scene is a preset charging scene, obtaining alarm information of a user terminal interconnected with the charging base, and determining available charging time.

[0118] Among them, the preset charging scene represents a use environment that meets certain charging conditions, such as night charging, timed charging, etc.; the alarm information refers to the timed reminder data set by the user on the terminal device; the available charging time is used to represent the time interval from the start of charging to the user's expected use time.

[0119] After the mobile power supply is connected to the charging base, the charging base needs to obtain time constraint information. Specifically, the charging base first establishes a data connection with the user terminal through the wireless communication module to obtain the time management information of the authorized user; then the charging base identifies whether it is currently in a preset charging scene, such as detecting ambient light, analyzing historical charging rules, etc.; then the charging base reads the alarm settings of the user terminal, including the latest alarm time and repetition rules; then the charging base calculates the interval from the current time to the next expected use time as the available charging time window; finally, the charging base associates the time constraint information with the charging target to provide a decision basis for formulating the charging strategy.

[0120] In some embodiments, the time constraint acquisition can be achieved in various ways: optionally, the charging base can collect user charging habit data including daily charging time period, usage time distribution, etc. based on user behavior analysis, establish a time series model to predict the next usage time, dynamically adjust the prediction result combined with real-time feedback, and achieve personalized time management; optionally, the charging base can use a multi-source information fusion method to comprehensively analyze the user's schedule, location information and device usage state, build a probabilistic graph model to infer the most likely usage time point, and ensure the reliability of the prediction through confidence evaluation. It can be understood that other time prediction or scene recognition methods can also be used to achieve charging time planning, which is not limited here.

[0121] It should be noted that the scene recognition and time planning use a reasoning method based on a probabilistic graph model. The charging base first builds a dynamic Bayesian network, with nodes including time characteristics, environmental characteristics and user behavior characteristics. Through the EM algorithm, the model parameters are learned to establish the conditional probability relationship between the characteristics. Scene judgment calculates the posterior probability distribution of the scene type through backward reasoning, and triggers the corresponding processing flow when the probability of a certain scene exceeds the threshold. The time constraint analysis uses a prediction method based on time series. The system extracts the periodic pattern of user behavior through seasonal decomposition, and combines the ARIMA model to predict the short-term behavior trend. After obtaining the alarm information, the dynamic programming algorithm is used to integrate multi-source time constraints to generate the optimal charging time window. For example, the system detects that the user usually charges from 11 pm to 7 am, and sets a 7 am alarm, so the available charging time is determined as the current time to 5 am the next day.

[0122] S210, calculating the expected charging time required for charging in a slow charging strategy according to the target charging capacity, and determining a fast charging compensation time for charging in a fast charging strategy when the expected charging time is greater than the available charging time.

[0123] Wherein, the slow charging strategy represents a charging method using lower charging power, which can prolong the battery life; the expected charging time refers to the time required to complete the target charging capacity according to the current charging strategy; the fast charging strategy is used to represent a charging method using higher charging power, which can shorten the charging time; the fast charging compensation time represents the length of the time period that needs to use fast charging to meet the time constraint.

[0124] After determining the charging target and time constraints, the charging base needs to plan the charging strategy. Specifically, the charging base first determines the slow charging power level according to the battery specification parameters, which is usually set to 0.3-0.5 times the nominal power; then the charging base calculates the time required for the complete slow charging process based on the target charging capacity and slow charging power, and combines the influence of charging efficiency with the change of capacity; then the charging base compares the expected charging time with the available charging time to determine whether fast charging compensation is needed; after that, the charging base calculates the required fast charging compensation time according to the time difference and fast charging power when fast charging is needed; finally, the charging base evaluates the rationality of the fast charging compensation time to ensure that it does not exceed the safety limit of the battery.

[0125] In some embodiments, the charging time calculation can be achieved in various ways: optionally, the charging base can use a segmented charging model to divide the charging process into two stages of constant current charging and constant voltage charging, respectively, to establish a mathematical relationship between charging time and capacity change, and to achieve accurate time estimation by integrating the charging time, while considering the influence of temperature, aging and other factors on charging efficiency; optionally, the charging base can use an adaptive charging control method to dynamically adjust the charging power according to the real-time monitored battery state, to establish a mapping relationship between the charging time and the power curve, and to solve the optimal power configuration that meets the time constraints through an optimization algorithm. It can be understood that other charging control or time optimization methods can also be used to implement the charging strategy planning, which is not limited here.

[0126] S211、When the fast charging compensation time is greater than a preset time threshold, an initial charging plan is generated based on the fast charging compensation time.

[0127] The initial charging plan includes a plurality of charging time intervals, each of which is bound with a charging power and a charging strategy. The preset time threshold represents the minimum fast charging compensation time that triggers the generation of the charging plan; the charging time interval refers to a continuous time period with the same charging parameters; the charging power is used to represent the power output size of the power supply in each time interval; and the charging strategy represents the specific execution scheme for controlling the charging process.

[0128] After the fast charging compensation time is determined, the charging base needs to generate an initial charging plan. Specifically, the charging base first compares the fast charging compensation time with a preset threshold, and starts the planning process when the threshold is exceeded; then the charging base determines the maximum allowed power for each charging time interval according to the battery characteristics and temperature limits; then the charging base optimizes the division of the charging time interval to minimize the fast charging time while ensuring the charging target; after that, the charging base assigns specific charging parameters to each time interval, including charging voltage, current limit and protection threshold; finally, the charging base generates a complete charging execution timing table to guide the subsequent charging process.

[0129] In some embodiments, the charging planning can be implemented in various ways: optionally, the charging base can use a dynamic planning algorithm to model the charging process as a multi-stage decision problem, with each stage corresponding to a charging time interval, and solve the optimal power allocation scheme through state transition equations, while combining constraints such as battery temperature rise, efficiency change, etc., to achieve globally optimal charging control; optionally, the charging base can use a model predictive control method to establish a prediction model of the battery charging process, optimize the charging power sequence in a rolling time domain, and correct the prediction error through real-time feedback to ensure the safety and efficiency of the charging process. It can be understood that other optimization control or scheduling algorithms can also be used to generate the charging planning, which is not limited here.

[0130] It should be noted that the generation of the charging planning is based on a mixed integer programming method. The charging base first discretizes the time axis to construct a decision variable matrix, including the charging mode selection and power level of each time period. The objective function design combines the comprehensive effects of battery temperature rise, charging efficiency and life loss, and balances each target through weight coefficients. The constraint conditions include power continuity constraint, temperature safety constraint and charging quantity satisfaction constraint. The solution of the objective function uses a branch and bound algorithm to obtain the lower bound of the problem through linear relaxation, and constructs a feasible solution through a heuristic method. In the search process, the cut plane method is used to accelerate convergence, and the feasible region is reduced by adding effective inequalities. For example, for a charging task that needs to be compensated by fast charging, the system will prioritize the fast charging period to the time period when the battery temperature is low, and set a reasonable power transition between adjacent time periods.

[0131] S212, performing charging according to the initial charging planning.

[0132] Wherein, the initial charging planning represents a charging control scheme generated according to the charging target and time constraint; performing charging refers to the specific operation of implementing the charging process according to the planning scheme; the charging power represents the real-time output power in the charging process; the charging strategy is used to represent the charging control mode adopted in different time intervals.

[0133] After completing the charging planning, the charging base needs to perform specific charging operations. Specifically, the charging base first initializes the charging control circuit and sets the output parameters to the initial state specified by the planning; then the charging base starts the real-time monitoring system, including the sampling module of multiple parameters such as voltage, current, temperature; then the charging base executes the charging process according to the time sequence table, and smoothly transitions the charging parameters at the time interval switching point; then the charging base continuously evaluates the charging state, including the charge rate, temperature rise trend and charging efficiency; when an abnormal state is detected, the charging base immediately adjusts the charging parameters or starts the protection mechanism; finally, the charging base records the execution data of the entire charging process for subsequent optimization and analysis.

[0134] In some embodiments, the charging base can make charging plan changes based on temperature changes, that is, the charging base can monitor the battery temperature of the target mobile power supply in real time, and calculate the temperature rise rate according to the battery temperature; when the temperature rise rate exceeds the preset temperature rise threshold, the initial charging plan is adjusted to a second charging plan based on the temperature rise rate and the real-time remaining power; the charging is performed according to the second charging plan, and the charging is stopped when the real-time remaining power reaches the target charging power.

[0135] In some embodiments, the charging base can make charging plan changes based on temperature changes, that is, the charging base can monitor the battery temperature of the target mobile power supply in real time, and calculate the temperature rise rate according to the battery temperature; when the temperature rise rate exceeds the preset temperature rise threshold, the initial charging plan is adjusted to a second charging plan based on the temperature rise rate and the real-time remaining power; the charging is performed according to the second charging plan, and the charging is stopped when the real-time remaining power reaches the target charging power.

[0136] In some embodiments, the charging base can make charging plan changes based on temperature changes, that is, the charging base can monitor the battery temperature of the target mobile power supply in real time, and calculate the temperature rise rate according to the battery temperature; when the temperature rise rate exceeds the preset temperature rise threshold, the initial charging plan is adjusted to a second charging plan based on the temperature rise rate and the real-time remaining power; the charging is performed according to the second charging plan, and the charging is stopped when the real-time remaining power reaches the target charging power.

[0137] In some embodiments, the charging plan adjustment can be achieved in various ways: optionally, the charging base can use model predictive control method, establish battery thermal-electric coupling model, predict state evolution in rolling time domain, obtain optimal power sequence by solving quadratic programming problem, and compensate model error and external disturbance in real time; optionally, the charging base can use fuzzy control method, take temperature rise rate and remaining power as input variables, determine power adjustment amount through fuzzy reasoning, and dynamically adjust control rules using adaptive mechanism. It can be understood that other adaptive control or intelligent planning methods can also be used to adjust the charging strategy, which is not limited here.

[0138] In some embodiments, the charging base can perform high-power charging attempts when heat dissipation is sufficient, that is, the charging base can monitor the real-time temperature of the temperature hotspot area, and when the real-time temperature is lower than the lower limit value of the preset temperature range, the charging power is increased from the current charging power to the target charging power; the target charging power is calculated based on the temperature change trend of the temperature hotspot area and the heat dissipation efficiency corresponding to the best heat dissipation direction; when the real-time temperature exceeds the upper limit value of the preset temperature range, the charging power is reduced to the current charging power.

[0139] wherein the sufficient heat dissipation state indicates that the temperature is below the safety range; the high-power charging refers to a fast charging mode exceeding the standard power; the preset temperature range is used to indicate the temperature interval for safe operation; the current charging power indicates the power level before adjustment; the target charging power refers to the maximum safe power that is attempted to be raised to; and the temperature change trend is used to indicate the direction of heat accumulation or dissipation.

[0140] Upon confirming that the heat dissipation condition is good, the charging base needs to attempt to improve the charging efficiency. Specifically, the charging base first confirms through real-time temperature monitoring that all hotspot temperatures are below the lower limit of the safety range; then the charging base calculates the possible maximum charging power according to the current heat dissipation efficiency and temperature margin; then the charging base adopts a stepwise power raising strategy, increasing by 10% each time and maintaining an observation period; then the charging base comprehensively analyzes the temperature response characteristics, including the instantaneous change rate and the long-term cumulative effect; at the same time, the charging base continuously evaluates the effectiveness of the heat dissipation channel to ensure that heat is timely discharged; upon finding any temperature abnormalities, the charging base immediately executes power rollback and reenters the standard charging mode; throughout the process, the charging base always ensures that all parameters are strictly limited within the safety range.

[0141] In some embodiments, high-power charging control can be achieved in various ways: optionally, the charging base can use an adaptive PID control method, taking temperature deviation as input, adjusting controller gain through online parameter identification, achieving accurate tracking of temperature, and introducing feedforward compensation to improve system response speed; optionally, the charging base can use optimal state feedback control, constructing a state space model containing battery state of charge and temperature state, obtaining state feedback gain matrix by solving Lyapunov equation to ensure system stability. It can be understood that other feedback control or optimization control methods can also be used to achieve high-power charging management, which is not limited here.

[0142] S213、In the case that the charging progress of the mobile power supply reaches the preset completion threshold and the real-time temperature of the temperature hotspot area is lower than the preset temperature threshold, the support orientation of the charging base is adjusted back to the initial orientation angle.

[0143] Referring to step S106, the charging base initializes the orientation of the charging base.

[0144] In the embodiments of the present application, by using the intelligent heat dissipation control method based on environment perception, the natural ventilation condition is fully utilized for active heat dissipation optimization by acquiring room ventilation layout information, analyzing air flow field distribution, identifying temperature hot spots and dynamically adjusting the orientation of the charging base, thereby effectively solving the problem of low charging efficiency caused by only relying on reducing charging power or increasing heat dissipation fans in the prior art, and further realizing the effect of prolonging the service life of the mobile power supply while ensuring the charging efficiency. At the same time, the present scheme also integrates the charging strategy planning based on time constraints, the temperature control of multi-physical field coupling and the adaptive power regulation mechanism, which can provide more intelligent and efficient charging services according to the actual scene requirements.

[0145] The charging base in the embodiments of the present application will be described from the perspective of hardware processing. Please refer to Figure 3 , which is a schematic diagram of an entity device structure of the charging base in the embodiments of the present application.

[0146] It should be noted that Figure 3 The structure of the charging base shown is only an example and should not impose any limitation on the functions and use range of the embodiments of the present application.

[0147] As Figure 3 shown, the charging base includes a CPU 301, which can perform various appropriate actions and processes according to programs stored in a ROM 302 or loaded from a storage portion 308 to a RAM 303, such as performing the methods described in the above embodiments. In the RAM 303, various programs and data required for system operation are also stored. The CPU 301, the ROM 302 and the RAM 303 are connected to each other through a bus 304. An I / O interface 305 is also connected to the bus 304.

[0148] The following components are connected to the I / O interface 305: an input portion 306 including an audio input device, a button switch, etc.; an output portion 307 including a Liquid Crystal Display (LCD) and an audio output device, an indicator light, etc.; a storage portion 308 including a hard disk, etc.; and a communication portion 309 including a network interface card such as a LAN (Local Area Network) card, a modem, etc. The communication portion 309 performs communication processing via a network such as the Internet. A drive 310 is also connected to the I / O interface 305 as needed. A removable medium 311 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc. is mounted on the drive 310 as needed, so that a computer program read therefrom is installed in the storage portion 308 as needed.

[0149] In particular, the processes described above with reference to the flow charts can be implemented as a computer software program according to embodiments of the present application. For example, embodiments of the present application include a computer program product comprising a computer program carried on a computer readable medium, the computer program comprising computer programs for executing the methods illustrated by the flow charts. In such embodiments, the computer program can be downloaded and installed from a network via the communication section 309, and / or installed from the removable medium 311. When the computer program is executed by the CPU 301, various functions defined in the present application are executed.

[0150] The flow charts and block diagrams in the drawings are schematic illustrations of possible architectures, functions and operations of systems, methods and computer program products in accordance with various embodiments of the present application. In this regard, each block in the flow charts or block diagrams can represent a module, a segment, or a portion of code, which comprises one or more executable instructions for implementing the specified logical functions. It should also be noted that in some alternative implementations, the functions noted in the blocks can occur out of the order noted in the figures.

[0151] In particular, the charging base of the embodiment includes a processor and a memory, and the memory stores a computer program, which, when executed by the processor, implements the mobile power supply charging method provided by the above-mentioned embodiments.

[0152] As another aspect, the present application also provides a computer readable storage medium, which can be included in the charging base described in the above-mentioned embodiments, or can exist separately without being assembled into the charging base. The storage medium carries one or more computer programs, which, when executed by a processor of the charging base, enable the charging base to implement the mobile power supply charging method provided in the above-mentioned embodiments.

[0153] The above-described embodiments are only used to illustrate the technical solutions of the present application, but not limit the present application; even though the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: they can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacements to some technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present application.

[0154] In the above embodiments, the term "when" can be interpreted to mean "if" or "after" or "in response to determining" or "in response to detecting" depending on the context. Similarly, the phrase "on determining" or "if detecting (a stated condition or event)" can be interpreted to mean "if determining" or "in response to determining" or "on detecting (a stated condition or event)" or "in response to detecting (a stated condition or event)" depending on the context.

[0155] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be implemented by a computer program instructing the relevant hardware to complete, and the program can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the above-mentioned embodiments. The aforementioned storage medium includes ROM 302 or random storage memory RAM 303, magnetic disc or optical disc, and various storage medium capable of storing program codes.

Claims

1. A method for charging a mobile power bank, characterized in that, Applied to a charging dock, the method includes: Obtain information on the room's ventilation layout and the location of electrical appliances; The ventilation layout information and the electrical appliance location data are input into the airflow dynamics model to obtain the indoor airflow field distribution and airflow direction. Acquire temperature distribution data during power bank charging, and determine temperature hotspot areas based on the temperature distribution data; Based on the indoor airflow distribution and the temperature distribution data, the optimal heat dissipation direction is calculated. The optimal heat dissipation direction is the azimuth angle parameter that aligns the hot spot area of ​​the power bank with the airflow direction. The step of calculating the optimal heat dissipation direction based on the indoor airflow distribution and the temperature distribution data specifically includes: calculating the temperature gradient distribution and heat flow direction of the hot spot area based on the temperature distribution data, and constructing a heat conduction model; inputting the indoor airflow distribution as a boundary condition into the heat conduction model to obtain multiple heat dissipation efficiencies of the power bank at different azimuth angles; selecting the highest heat dissipation efficiency as the target heat dissipation efficiency, and determining the azimuth angle corresponding to the target heat dissipation efficiency as the optimal heat dissipation direction. Record the initial orientation angle of the charging base, and adjust the orientation of the charging base to the optimal heat dissipation direction; When the charging progress of the power bank reaches a preset completion threshold and the real-time temperature of the hot spot area is lower than the preset temperature threshold, the support orientation of the charging base is adjusted back to the initial orientation angle.

2. The method according to claim 1, characterized in that, After the steps of recording the initial orientation angle of the charging dock and adjusting the orientation of the charging dock to the optimal heat dissipation direction, the method further includes: The real-time temperature of the hot spot area is monitored, and when the real-time temperature is lower than the lower limit of the preset temperature range, the charging power is increased from the current charging power to the target charging power; the target charging power is calculated based on the temperature change trend of the hot spot area and the heat dissipation efficiency corresponding to the optimal heat dissipation direction. When the real-time temperature exceeds the upper limit of the preset temperature range, the charging power is reduced to the current charging power.

3. The method according to claim 1, characterized in that, After the steps of recording the initial orientation angle of the charging dock and adjusting the orientation of the charging dock to the optimal heat dissipation direction, the method further includes: Determine the target charging capacity based on the initial remaining power and actual capacity of the target power bank; When the current scenario is identified as a preset charging scenario, the alarm clock information of the user terminal connected to the charging base is obtained to determine the available charging time; Calculate the estimated charging time required for slow charging based on the target charging capacity, and determine the fast charging compensation time for fast charging if the estimated charging time is greater than the available charging time. When the fast charging compensation time is greater than a preset time threshold, an initial charging plan is generated based on the fast charging compensation time; the initial charging plan includes multiple charging time intervals, and each charging time interval is bound to a charging power and a charging strategy. Charge is performed according to the initial charging plan.

4. The method according to claim 3, characterized in that, After the step of performing charging according to the initial charging plan, the method further includes: The battery temperature of the target power bank is monitored in real time, and the temperature rise rate is calculated based on the battery temperature. When the temperature rise rate exceeds a preset temperature rise threshold, the initial charging plan is adjusted to a second charging plan based on the temperature rise rate and the real-time remaining power. Charging is performed according to the second charging plan, and charging stops when the remaining power reaches the target charging level in real time.

5. The method according to claim 1, characterized in that, The steps of obtaining room ventilation layout information and appliance location data specifically include: Based on multiple room images uploaded by users, extract room structure features, furniture location features, and appliance placement features; A polygonal mesh model is constructed based on the room's structural features, and a three-dimensional spatial reconstruction is performed based on the furniture's location features and the appliance's placement features to generate a three-dimensional room model. The target heat dissipation appliances and target ventilation areas in the 3D model of the room are labeled to generate ventilation layout information and appliance location data.

6. The method according to claim 5, characterized in that, After the step of annotating the target heat dissipation appliances and target ventilation areas in the three-dimensional model of the room, and determining the ventilation layout information and appliance location data, the method further includes: Establish a communication connection with the target heat dissipation appliance and obtain the operating parameters of the target heat dissipation appliance; Calculate multiple preset ventilation layouts when each of the target heat dissipation appliances is under different operating parameters and the target ventilation area is under different on / off states.

7. A charging dock, characterized in that, The charging dock includes: one or more processors and a memory; the memory is coupled to the one or more processors, the memory is used to store computer program code, the computer program code including computer instructions, and the one or more processors call the computer instructions to cause the charging dock to perform the method as described in any one of claims 1-6.

8. A computer-readable storage medium comprising instructions, characterized in that, When the instruction is executed on the charging dock, the charging dock performs the method as described in any one of claims 1-6.

9. A computer program product, characterized in that, When the computer program product is run on the charging dock, the charging dock performs the method as described in any one of claims 1-6.

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