A notebook computer intelligent fast charging management control method and system

Abstract

This application proposes a smart fast charging management and control method and system for laptops, relating to the field of laptop power management technology. The method includes acquiring user behavior pattern information, real-time task scheduling, and the charger's maximum power; predicting the charging time based on the user behavior pattern information and real-time task scheduling; determining a first charging power based on the user behavior pattern information and the charging time; determining a second charging power based on the real-time task scheduling and the charging time; determining a desired charging power range based on the first charging power, the second charging power, and the maximum power; generating a charging power-time curve based on the desired charging power range and the charging time; and charging the laptop's battery according to the charging power-time curve. This application enables a fine-grained trade-off and allocation between "maintaining current performance" and "storing energy for future battery life" under limited power input.

Description

Technical Field

[0001] This application relates to the field of notebook computer power management technology, and more specifically, to a method and system for intelligent fast charging management and control of notebook computers. Background Technology

[0002] Traditional portable computing devices primarily rely on real-time monitoring of battery charge, temperature, and system load to dynamically adjust charging power during charging management. While this real-time response mechanism achieves a balance between battery protection and charging efficiency to some extent, its inherent limitation lies in its lack of predictive capability for future usage scenarios. When users frequently switch between high and low load modes, frequent fluctuations in charging power can cause continuous stress damage to the battery, affecting its long-term stability and capacity retention. Furthermore, existing systems struggle to make precise trade-offs and allocations when faced with conflicts between immediate task scheduling and historical behavior predictions, potentially leading to charging strategies that do not align with actual user needs. Summary of the Invention

[0003] This application aims to address at least one of the technical problems existing in the prior art. To this end, this application discloses a smart fast charging management and control method and system for laptops, aiming to solve the problem that existing laptop charging management systems cannot effectively balance the contradiction between user behavior patterns, real-time task scheduling, and the maximum power of the charger, resulting in large fluctuations in charging power, damaged battery life, and inability to meet users' immediate charging needs.

[0004] In a first aspect, this application discloses a smart fast charging management and control method for laptop computers, including: Acquire user behavior pattern information, real-time task scheduling, and the charger's maximum power. Real-time task scheduling includes tasks that require future mobile use of the laptop. User behavior pattern information includes the times when users habitually use the laptop and the computing load. Predict rechargeable time based on user behavior patterns and real-time task scheduling; The first charging power is determined based on user behavior pattern information and available charging time. The second charging power is determined based on the real-time task schedule and available charging time. The desired charging power range is determined based on the first charging power, the second charging power, and the maximum power. Based on the desired charging power range and charging time, a charging power-time curve is generated. The charging power-time curve is a smooth curve to ensure a smooth transition of charging power. Charge the laptop battery according to the charging power-time curve.

[0005] Furthermore, the step of determining the desired charging power range based on the first charging power, the second charging power, and the maximum power includes: The first weight is assigned to the first charging power based on user behavior pattern information; The second weight is assigned to the second charging power according to the real-time task schedule; The desired charging power range is determined based on the first charging power, the second charging power, the first weight, the second weight, and the maximum power.

[0006] Furthermore, the step of determining the desired charging power range based on the first charging power, the second charging power, the first weight, the second weight, and the maximum power includes: The expected charging power is calculated based on the first charging power, the second charging power, the first weight, and the second weight. Expected charging power = first charging power × first weight + second charging power × second weight. The charging power range is determined based on the desired charging power and the maximum power, wherein... When the desired charging power is less than the maximum power, the charging power range is [desired charging power × 80%, maximum power]; When the desired charging power is greater than the maximum power, the charging power range is [maximum power × 80%, maximum power].

[0007] In some preferred embodiments, the step of assigning a first weight to the first charging power based on user behavior pattern information includes: Obtain the computational load from user behavior pattern information; Monitor the operating status of the central processing unit and the graphics processing unit, including operating frequency, utilization, context switching frequency, and I / O queue depth; Predict the actual load based on the operating status; The first weight is assigned to the first charging power based on the calculated load and the actual load.

[0008] Building upon this, this application further proposes that, following the step of allocating a first weight to the first charging power based on the calculated load and the actual load, the following steps are included: Monitor the rate of temperature rise of key heat-generating components in laptops, including the central processing unit, graphics processing unit, and power storage unit; The first weight is adjusted based on the rate of temperature increase, where... When the rate of temperature rise exceeds a preset temperature rise rate threshold, the first weight is reduced. If the rate of temperature rise is less than the preset rate of temperature rise threshold, the first weight is increased.

[0009] As an optional approach, the step of assigning a second weight to the second charging power based on the real-time task schedule includes: Monitor the operation logs of real-time task scheduling, including the number of clicks or views and the number of edits; The reliability index for real-time task scheduling is calculated based on the operation records. The reliability index is calculated as follows: Reliability index = Preset initial value + Preset interaction weight × Number of interactions + Preset edit weight × Number of edits. A second weight is assigned to the second charging power based on a reliability index, wherein the reliability index is positively correlated with the second weight.

[0010] Based on the above, this application further proposes that the step of generating a charging power-time curve according to the desired charging power range and charging time includes: Get the current battery level and the user's preferred charging speed; Input the current battery level, user-defined charging speed preference, desired charging power range, and available charging time into the preset model to generate a charging power-time curve. The preset model is used to generate the charging power-time curve based on the current battery level, user-defined charging speed preference, desired charging power range, and available charging time.

[0011] To refine the solution, the steps for charging a laptop battery based on the charging power-time curve include: While charging the laptop battery according to the charging power-time curve, the real-time battery temperature is continuously monitored. When the real-time battery temperature exceeds the safety threshold, the charging power is reduced in a smooth transition to ensure safe battery operation.

[0012] As a technological improvement, the steps for generating a charging power-time curve based on the desired charging power range and rechargeable time include: Obtain the calculated load fluctuation rate based on the calculated load; Within the sliding time window, when the calculated load volatility is greater than the preset volatility threshold, multiple expected charging power ranges within the sliding time window are aggregated to calculate a unified expected charging power range. A charging power-time curve is generated based on a unified expected charging power range and charging time.

[0013] Secondly, this application also discloses a smart fast charging management and control system for laptop computers, comprising: The information acquisition module is used to acquire user behavior pattern information, real-time task scheduling, and the maximum power of the charger. Real-time task scheduling includes tasks that require future mobile use of the laptop, and user behavior pattern information includes the times when users habitually use the laptop and the computing load. The rechargeable time prediction module is used to predict the rechargeable time based on user behavior pattern information and real-time task scheduling. The first charging power determination module is used to determine the first charging power based on user behavior pattern information and available charging time. The second charging power determination module is used to determine the second charging power based on the real-time task schedule and available charging time. The desired charging power range determination module is used to determine the desired charging power range based on the first charging power, the second charging power, and the maximum power. The charging power-time curve generation module is used to generate a charging power-time curve based on the desired charging power range and the available charging time. The charging power-time curve is a smooth curve to ensure a smooth transition of charging power. The control module is used to charge the laptop battery according to the charging power-time curve.

[0014] Through this technical solution, this application can achieve comprehensive management of user behavior patterns, real-time task scheduling, and charger maximum power through modular design, predict the charging time, and determine the first and second charging power accordingly, thereby generating a smooth charging power-time curve. This effectively solves the problems of large charging power fluctuations, damaged battery life, and inability to meet users' immediate charging needs in the prior art, and realizes intelligent, efficient, and battery-friendly fast charging management.

[0015] This application discloses a smart fast charging management and control method for laptops. By acquiring user behavior pattern information, real-time task scheduling, and the charger's maximum power, and comprehensively considering these factors, it predicts the charging time and determines a first charging power and a second charging power. Based on this, the application determines the desired charging power range according to the first charging power, the second charging power, and the maximum power, and generates a smooth charging power-time curve. Finally, it charges the laptop battery according to this curve. This method effectively solves the technical problems of frequent charging power fluctuations, battery life damage, and the inability to simultaneously meet users' immediate charging needs and device performance optimization caused by purely real-time response mechanisms in existing technologies. By introducing a predictive mechanism based on user behavior patterns and real-time task scheduling, this application can anticipate device load and user needs in advance, thereby generating a more stable and optimized charging strategy. This avoids microscopic damage to the battery caused by drastic fluctuations in charging power, extending battery life. Simultaneously, this method can adjust the charging strategy according to the user's real-time task scheduling, ensuring sufficient power is provided to the user at critical moments, improving user experience and work efficiency. Furthermore, by taking into account the maximum power of the charger, this application is able to make a fine trade-off and allocation between "maintaining current performance" and "storing energy for future battery life" under limited power input, realizing intelligent decision-making under multiple objectives and constraints, and overcoming the limitations of power management systems in the prior art when facing conflicting objectives.

[0016] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0017] The accompanying drawings are used to provide a further understanding of the technical solutions of this application and constitute a part of the specification. They are used together with the embodiments of this application to explain the technical solutions of this application and do not constitute a limitation on the technical solutions of this application.

[0018] Figure 1 This is a flowchart illustrating a smart fast charging management and control method for a laptop computer according to an embodiment of this application. Detailed Implementation

[0019] To make the objectives, technical methods, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0020] It should be noted that the meaning of "multiple" (or "more than") in the description of the embodiments of this application refers to two or more, and "greater than," "less than," "exceeding," etc. are understood to exclude the number itself, while "above," "below," "within," etc. are understood to include the number itself. If "first," "second," etc. are used in the description, they are only for the purpose of distinguishing technical features and should not be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated or the order of the technical features indicated.

[0021] In the description of this application, unless otherwise expressly defined, terms such as "setup," "installation," and "connection" should be interpreted broadly, and those skilled in the art can reasonably determine the specific meaning of the above terms in this application in conjunction with the specific content of the technical solution.

[0022] Based on the above, this application proposes a smart fast charging management and control method and system for laptops, aiming to solve the problem that existing laptop charging management systems cannot effectively balance the contradiction between user behavior patterns, real-time task scheduling and the maximum power of the charger, resulting in large fluctuations in charging power, damage to battery life and inability to meet users' real-time charging needs.

[0023] See Figure 1 , Figure 1 This is a flowchart illustrating a smart fast charging management and control method for a laptop computer according to an embodiment of this application. The smart fast charging management and control method for a laptop computer provided in this embodiment includes, but is not limited to, steps S110 to S170, which will be described in detail below.

[0024] S110. Obtain user behavior pattern information, real-time task scheduling, and the maximum power of the charger. Real-time task scheduling includes tasks that require future mobile use of the laptop. User behavior pattern information includes the times when the user habitually uses the laptop and the computing load. S120. Predict the charging time based on user behavior pattern information and real-time task scheduling. S130. Determine the first charging power based on user behavior pattern information and available charging time; S140. Determine the second charging power based on the real-time task schedule and available charging time; S150. Determine the desired charging power range based on the first charging power, the second charging power, and the maximum power. S160. Generate a charging power-time curve based on the desired charging power range and charging time. The charging power-time curve is a smooth curve to ensure a smooth transition of charging power. S170. Charge the laptop battery according to the charging power-time curve.

[0025] Here, "laptop" refers to a portable personal computer that integrates core components such as a battery, processor, and memory, and can be powered by an external charger. "User behavior pattern information" refers to user habit data acquired through long-term monitoring and learning, such as the time periods when users typically use the device, the types of tasks they perform (e.g., document processing, video editing, gaming), and the computational load generated by these tasks. "Immediate task scheduling" refers to tasks that users need to complete in the short term with specific timeframes or mobile usage requirements, such as an upcoming meeting or working remotely; these tasks may have immediate demands on battery life. "Maximum charger power" refers to the maximum power output capability that the charger currently connected to the laptop can provide, which directly affects the upper limit of charging speed. The implementation environment of this application is typically the power management unit inside the laptop or a separate intelligent charging management chip, which can interact and control the operating system, battery management system, and charging interface.

[0026] The core of the intelligent fast charging management and control method for laptops in this application lies in achieving refined management of the charging process through a series of intelligent steps.

[0027] First, it's necessary to obtain user behavior pattern information, real-time task scheduling, and the charger's maximum power. User behavior pattern information can be obtained in several ways. For example, the system can continuously monitor data such as applications launched by the user at different times, average CPU and GPU utilization, and network activity intensity, and use machine learning algorithms to analyze and model this data to identify when the user habitually uses the laptop and the corresponding computing load. Another way is for users to manually input or select their daily usage habits in the system settings, such as preset modes like "mainly for office work" or "frequently performing graphics processing," and the system will then generate preliminary user behavior pattern information based on these modes. Real-time task scheduling can be obtained through methods such as integrating a calendar application into the system to automatically synchronize user-set calendar events such as meetings and trips, and identifying tasks containing keywords such as "mobile use" or "outing"; or, users can manually input upcoming tasks through a voice assistant or specific applications, specifying their battery life requirements. The charger's maximum power can be obtained through a hardware detection module. When the charger is connected to the laptop, the system will handshake with the charger through the charging interface's communication protocol (such as USB Power Delivery) to read the charger's maximum supported output power.

[0028] Secondly, the system predicts available charging time based on user behavior patterns and real-time task scheduling. Predicting available charging time is a crucial step in intelligent charging management. One approach is for the system to first analyze user behavior patterns, identifying when users typically connect their laptops to power during the day. For example, if a user habitually charges their laptop after returning home in the evening, the system predicts that evening is a good time to charge. Simultaneously, the system considers real-time task scheduling. For instance, if a user has an outing meeting the next morning, the system identifies a period before the meeting that the device needs to be charged to meet mobile usage needs. By comprehensively analyzing this information, the system can predict the charging window and its duration within a future period. For example, if a user habitually charges between 8 PM and 10 PM and has a meeting at 9 AM the next morning, the system might predict a relatively long charging period between 8 PM tonight and 9 AM tomorrow, during which a certain battery level needs to be reached before the meeting.

[0029] Next, the initial charging power is determined based on user behavior patterns and available charging time. The determination of the initial charging power primarily considers the charging needs arising from the user's habitual usage patterns. For example, if user behavior patterns indicate that the user typically performs high-load graphics rendering work at night, the system may tend to reduce the charging power during the predicted charging period to avoid overheating of the device due to simultaneous charging and high-load operation, thereby protecting the battery and maintaining device performance. Alternatively, the system can allocate different charging power priorities to different time periods based on the frequency and duration of different computational loads in the user behavior pattern information. For example, the charging power can be appropriately increased during periods when the user typically engages in light office work, while it may be reduced during periods when the user typically engages in heavy gaming.

[0030] Then, a second charging power is determined based on the immediate task schedule and available charging time. The determination of the second charging power prioritizes meeting the urgent needs of the immediate task schedule. For example, if the immediate task schedule indicates that the user needs to take their laptop out for a presentation in two hours, and the presentation is expected to be lengthy, the system will tend to increase the charging power during the available charging time to ensure the battery is sufficiently charged before the task begins. Alternatively, the system can set different targets for the second charging power based on the urgency and importance of the immediate task schedule. For example, for very urgent and important mobile tasks, the system will prioritize fast charging, even if this may mean sacrificing some battery life for a short period.

[0031] Subsequently, based on the first charging power, the second charging power, and the maximum power, the expected charging power range is determined. Determining the expected charging power range is a trade-off process. One implementation is that the system can simply take a weighted average of the first and second charging powers, using the charger's maximum power as the upper limit, to obtain a preliminary expected charging power. For example, if the suggested first charging power is low and the suggested second charging power is high, the system will make a trade-off based on preset priorities or weights. For instance, if the priority of the immediate task is higher than user habits, the weight of the second charging power will be higher. Then, based on this preliminary expected charging power and the charger's maximum power, the system will set a reasonable charging power range, for example, using the expected charging power as a center value and setting a range that fluctuates upwards and downwards from this center value.

[0032] Next, based on the desired charging power range and charging time, a charging power-time curve is generated. This curve is smooth to ensure a stable transition in charging power. Generating a smooth charging power-time curve is one of the key innovations of this application. One implementation method is that the system can use curve fitting algorithms, such as Bézier curves or spline curves, to connect multiple discrete points within the desired charging power range to form a smooth charging power-time curve. During curve generation, the system ensures that the slope of the curve changes gradually, avoiding sudden increases or decreases in charging power. For example, if the desired charging power range is high within a certain time period, the system will gradually increase the charging power instead of instantaneously reaching the maximum value.

[0033] Finally, the laptop battery is charged according to the charging power-time curve. Once a smooth charging power-time curve is generated, the power management module controls the actual charging current and voltage based on this curve. For example, during charging, the system continuously monitors the battery's real-time status and dynamically adjusts the charger's output according to the charging power-time curve, ensuring that the charging power changes according to the predetermined smooth curve. This method effectively avoids frequent fluctuations in charging power, thereby reducing damage to the battery and extending its lifespan.

[0034] The overall working principle of this application lies in optimizing the charging management process of laptops through forward-looking intelligent prediction and multi-objective trade-offs. Traditional charging methods mainly rely on passive responses to the current system state, resulting in frequent fluctuations in charging power and damage to the battery. This application, by acquiring user behavior pattern information and real-time task scheduling, can predict future usage scenarios and charging needs. User behavior pattern information provides long-term, stable usage habit data, while real-time task scheduling supplements short-term, urgent charging needs. Combined with the charger's maximum power, the system can comprehensively evaluate, within the charging time, whether to meet the user's habitual usage needs (through the first charging power) or the urgent needs of immediate tasks (through the second charging power), while not exceeding the maximum power that the charger can provide.

[0035] After determining the first and second charging powers, the system intelligently weighs them to generate a desired charging power range. This range takes into account user habits, the priority of immediate tasks, and the hardware limitations of the charger. For example, if the user is about to attend an important mobile meeting, the system will assign a higher weight to the second charging power, thus reflecting a faster charging tendency within the desired charging power range.

[0036] Most importantly, this application utilizes the expected charging power range and the predicted charging time to generate a smooth charging power-time curve. This curve is no longer a simple jump adjustment based on real-time load, but a pre-planned, smoothly transitioning charging path. This means that throughout the charging process, the charging power changes in a gradual and continuous manner, avoiding the drastic fluctuations in charging current found in traditional methods. This smooth-transition charging method fundamentally reduces the stress on the internal electrochemical structure of the battery, thereby effectively extending the battery's cycle life and capacity retention.

[0037] Ultimately, the power management module precisely controls the battery charging process based on this pre-generated charging power-time curve. The entire solution forms a closed-loop intelligent management system, where each link is closely connected, from information acquisition, demand forecasting, multi-objective trade-offs to smooth charging control, collectively solving the problems of unstable charging, high battery loss, and inability to meet future needs in existing technologies.

[0038] The core of this application lies in introducing a predictive and trade-off mechanism based on user behavior pattern information and immediate task scheduling. By acquiring information about users' habitual laptop usage times and computing loads, as well as future mobile usage task schedules, this application can proactively predict charging time and determine two key charging powers: the first charging power focuses on meeting the charging needs of users' long-term behavior patterns, while the second charging power focuses on addressing the urgent needs of immediate tasks. This dual-consideration mechanism transforms the charging strategy from a single-dimensional, passive response into a multi-dimensional, intelligent, proactive planning approach.

[0039] Furthermore, this application determines a desired charging power range by comprehensively considering the first charging power, the second charging power, and the charger's maximum power. This step demonstrates the application's capability in multi-objective optimization, enabling a fine-grained trade-off between user habitual needs and immediate task requirements, while ensuring that the charging power does not exceed hardware limitations.

[0040] Most importantly, this application generates a smooth charging power-time curve based on the expected charging power range and charging time. Unlike the frequent jumps in charging power in existing technologies, this smooth curve ensures a stable transition in charging power, fundamentally reducing the electrochemical stress experienced by the battery during charging, thereby significantly extending battery life and capacity retention. For example, in existing technologies, when a user suddenly starts a high-load task, the charging power may drop sharply and then rise sharply again after the task ends. This drastic change causes continuous micro-damage to the battery. This application, however, pre-plans and smoothly adjusts the charging power within the charging time, buffering the impact even with high-load tasks through a smooth transition in the curve.

[0041] In summary, this application effectively solves the problems of unstable charging, large battery loss, and inability to meet future needs in the prior art by introducing innovative technologies such as user behavior pattern prediction, real-time task scheduling consideration, and smooth charging power curve generation, and significantly improves the intelligence level of laptop charging management and user experience.

[0042] In some embodiments, the step of determining the desired charging power range based on the first charging power, the second charging power, and the maximum power includes: The first weight is assigned to the first charging power based on user behavior pattern information; The second weight is assigned to the second charging power according to the real-time task schedule; The desired charging power range is determined based on the first charging power, the second charging power, the first weight, the second weight, and the maximum power.

[0043] Specifically, the first charging power is determined based on user behavior pattern information, reflecting the user's habitual usage needs and computational load on the laptop at a specific time. Assigning a first weight to the first charging power aims to quantify the importance of user behavior pattern information in determining the final expected charging power range. For example, when a user habitually uses the laptop for high-load computation, the first weight might be assigned a higher value to ensure the charging strategy can meet their potential power consumption needs.

[0044] The second charging power is determined based on immediate task scheduling, reflecting the user's future plans for mobile laptop use. Assigning a second weight to the second charging power aims to quantify the importance of immediate task scheduling in determining the final expected charging power range. For example, when a user has an urgent need for mobile use, the second weight might be assigned a higher value to prioritize ensuring the battery reaches sufficient charge in a short time.

[0045] In practical applications, by assigning first and second weights to the first and second charging powers respectively, a dynamic balance between personalized user needs and immediate task scheduling can be achieved. These weights can be dynamically adjusted based on preset rules, machine learning models, or user preferences to ensure that the determination of the desired charging power range more accurately reflects the current usage scenario and user intent.

[0046] This application's solution introduces a first weight and a second weight, enabling differentiated consideration of the first charging power determined based on user behavior patterns and the second charging power determined based on immediate task scheduling when determining the desired charging power range. This weighting mechanism allows the system to dynamically adjust the balance between user habitual needs and immediate task priorities according to actual conditions, thus avoiding the problem of charging strategies that do not meet actual user needs due to single or fixed power combinations. By assigning different importance to different charging power sources, this solution can respond to user needs more flexibly and intelligently, ensuring the rationality and effectiveness of the charging strategy.

[0047] The above technical solution aims to make the determination of charging power range more refined and personalized. This solution effectively balances the priority of users' habitual usage needs with immediate task scheduling, avoiding the problem of charging strategies being out of touch with actual user needs due to simple aggregation or fixed ratio combinations. Therefore, it can generate charging power-time curves that better match actual user scenarios, improving charging efficiency and user experience, while ensuring that the battery is charged reasonably to meet user needs and extend battery life.

[0048] In some embodiments, the step of determining the desired charging power range based on the first charging power, the second charging power, the first weight, the second weight, and the maximum power includes: The expected charging power is calculated based on the first charging power, the second charging power, the first weight, and the second weight, wherein the expected charging power = first charging power × first weight + second charging power × second weight; The charging power range is determined based on the desired charging power and the maximum power, wherein, When the desired charging power is less than the maximum power, the charging power range is [desired charging power × 80%, maximum power]; When the desired charging power is greater than the maximum power, the charging power range is [maximum power × 80%, maximum power].

[0049] Specifically, "expected charging power" refers to the charging power the system expects to provide to the laptop battery after comprehensively considering user behavior patterns and immediate task scheduling. This expected charging power is calculated by multiplying a first charging power by a first weight, and then adding a second charging power multiplied by a second weight. The first charging power is determined based on user behavior pattern information and available charging time, reflecting the charging power requirements of habitual user usage and computational load. The second charging power is determined based on immediate task scheduling and available charging time, reflecting the charging power requirements of future mobile usage. The first and second weights are used to measure the relative importance of user behavior patterns and immediate task scheduling in determining the expected charging power. After calculating the expected charging power, the final charging power range needs to be determined based on this expected charging power and the charger's maximum power. When the expected charging power is less than the charger's maximum power, the charging power range is set to [80% of the expected charging power, maximum power]. This means that, without exceeding the charger's maximum capacity, the charging power is allowed to fluctuate around the expected value, with a certain margin. When the expected charging power is greater than the charger's maximum power, the charging power range is set to [80% of the maximum power, maximum power]. This indicates that even if a higher charging power is desired, it must be limited by the charger's maximum power to ensure the safety of the charging process and device compatibility. By setting a range rather than a single value, flexibility can be provided for generating a smooth charging power-time curve later.

[0050] Through the above technical solution, this application provides a more accurate and flexible method for determining the expected charging power range. By weighting the first charging power and the second charging power, the combined impact of user behavior patterns and real-time task scheduling can be more accurately reflected, allowing the charging strategy to better adapt to the user's personalized needs. Furthermore, by comparing the expected charging power with the charger's maximum power and dynamically adjusting the charging power range, problems such as charger overload or low charging efficiency caused by excessively high expected power are effectively avoided. At the same time, reasonable upper and lower limits are provided for a smooth transition of charging power, thereby improving the intelligence level of charging management and user experience.

[0051] In some embodiments, the step of assigning a first weight to the first charging power based on user behavior pattern information includes: Obtain the computational load from user behavior pattern information; Monitor the operating status of the central processing unit and the graphics processing unit, including operating frequency, utilization, context switching frequency, and I / O queue depth; Predict the actual load based on the described operating status; A first weight is assigned to the first charging power based on the calculated load and the actual load.

[0052] The computational load in user behavior pattern information refers to the typical or expected computational pressure on the system when a user habitually uses a laptop. This computational load can be statistically analyzed based on historical data, such as the types of applications the user frequently runs during a specific time period, and the average utilization rate of the CPU and GPU.

[0053] Furthermore, the operating status of the central processing unit (CPU) and graphics processing unit (GPU) are key indicators that reflect the current workload of a laptop in real time. Operating frequency refers to the processor's current clock frequency, utilization represents the percentage of the processor that is being used, context switching frequency reflects how frequently the operating system switches between different tasks, and I / O queue depth indicates the number of input / output requests waiting to be processed. These operating status parameters can comprehensively and dynamically characterize the actual workload of a laptop.

[0054] Therefore, by monitoring the operating status of the CPU and GPU, the actual load on a laptop can be predicted. Actual load refers to the actual occupancy and stress on the laptop's hardware resources (such as the CPU and GPU) at the current moment or in the short future. This prediction can be achieved using various methods, including machine learning models, statistical analysis methods, or rule-based reasoning, aiming to accurately reflect the system's current performance requirements.

[0055] Ultimately, the allocation of the first weight is based on a comprehensive consideration of both computational load and actual load. Computational load represents the user's habitual needs, while actual load reflects the current real-world demand. By comparing and analyzing these two loads, the first weight can be dynamically adjusted to more accurately reflect the user's behavioral patterns and their priority in charging power demand. For example, when the actual load is significantly higher than the computational load, it may mean that the user is performing a high-intensity task. In this case, the first weight can be appropriately increased to ensure sufficient energy support within the charging time. Conversely, if the actual load is low, the first weight can be appropriately decreased to avoid overcharging or unnecessary power consumption.

[0056] This application's solution obtains computational load from user behavior pattern information and monitors the operating status of the CPU and GPU in real time to predict the actual load, thereby enabling a more refined allocation of the first charging power's weight. Traditionally, the determination of the first charging power may only rely on the macroscopic computational load in user behavior pattern information, ignoring the actual operating status of the laptop at a specific moment. This single-dimensional consideration may lead to inaccurate first weight allocation, thus affecting the rationality of the expected charging power range. Specifically, by introducing the monitoring of the CPU and GPU's operating status and predicting the actual load, this solution can overcome the shortcomings of relying solely on historical computational load. When there is a significant difference between the actual load and the historical computational load, such as when the user suddenly launches a large application or performs a high-intensity computational task, the real-time monitoring of the operating status can capture this change and adjust the weight allocation of the first charging power accordingly. For example, if the actual load is significantly higher than the historical computational load, it indicates that the user has a higher demand for immediate performance. In this case, the first weight will be increased accordingly to ensure that the charging strategy can prioritize meeting the user's potential performance needs. Conversely, if the actual load is lower than the historical computing load, the first weight may be lowered, thereby optimizing charging efficiency or extending battery life while ensuring basic requirements are met. This dynamic adjustment mechanism makes the allocation of the first weight more closely reflect the actual usage scenarios and user needs of laptops, providing a more reliable input for determining the subsequent expected charging power range.

[0057] In some embodiments, the step of assigning a first weight to the first charging power based on the calculated load and the actual load includes the following: Monitor the rate of temperature rise of key heat-generating components in a laptop computer, including a central processing unit, a graphics processing unit, and an energy storage unit; The first weight is adjusted based on the rate of temperature increase, wherein... When the temperature rise rate exceeds a preset temperature rise rate threshold, the first weight is reduced. When the rate of temperature rise is less than a preset temperature rise rate threshold, the first weight is increased.

[0058] Specifically, after initially assigning a first weight based on the computational load and actual load, the system continuously monitors the temperature rise rate of key heat-generating components inside the laptop. These key heat-generating components typically include the central processing unit (CPU), graphics processing unit (GPU), and power storage unit (PSU), which are the main heat sources during laptop operation and charging. Temperature sensors integrated near or inside these components can acquire their temperature data in real time. The temperature rise rate can be obtained by sampling and calculating continuous temperature data; for example, calculating the difference between the current temperature and the previous temperature within a preset time interval and dividing by that time interval.

[0059] Based on the monitored rate of temperature rise, the system dynamically adjusts the previously assigned first weight. Specifically, when the monitored rate of temperature rise exceeds a preset temperature rise rate threshold, it indicates that the laptop's internal temperature is rising rapidly, posing a risk of overheating. To prevent overheating, the system reduces the first weight. Reducing the first weight means that user behavior pattern information has less influence on charging power when determining the desired charging power range, thus tending to select a lower charging power to reduce heat generated during charging. Conversely, when the monitored rate of temperature rise is less than the preset temperature rise rate threshold, it indicates that the laptop's heat dissipation is good, or the temperature rise is slow, with some thermal redundancy. In this case, the system increases the first weight, allowing the charging power to more fully respond to the computational load demands indicated by the user behavior pattern, thereby maximizing charging efficiency while ensuring safety. The preset temperature rise rate threshold can be set according to the laptop's design specifications, battery characteristics, and safety standards to balance charging performance and thermal management requirements.

[0060] This application's solution achieves dynamic thermal management by introducing monitoring of the temperature rise rate of key heat-generating components in a laptop computer. Specifically, when the system detects that the temperature rise rate of the central processing unit, graphics processing unit, or power storage unit exceeds a preset threshold, it indicates that the device is facing a potential overheating risk. At this time, by reducing the first weight allocated to the first charging power, the contribution of user behavior pattern information to the expected charging power range can be effectively reduced, thereby guiding the system to select a lower charging power to reduce the heat generated during the charging process and prevent the device from overheating. Conversely, when the temperature rise rate is below the preset threshold, it indicates that the device has sufficient heat dissipation capacity. In this case, increasing the first weight allows the charging power to more fully respond to the computing load demands indicated by the user behavior pattern, thereby optimizing charging efficiency while ensuring safety. It is precisely because of this dynamic adjustment mechanism based on real-time thermal status that the charging process can better adapt to the actual operating environment of the laptop computer, avoiding the thermal management deficiencies that may result from relying solely on computing load prediction.

[0061] In some embodiments, the step of assigning a second weight to the second charging power according to the real-time task schedule includes: Monitor the operation records of the real-time task arrangement, including the number of clicks or views and the number of edits; The reliability index of the instant task arrangement is calculated based on the operation record, wherein the reliability index = preset initial value + preset interaction weight × number of interactions + preset editing weight × number of edits; A second weight is assigned to the second charging power based on the reliability index, wherein the reliability index is positively correlated with the second weight.

[0062] Specifically, when determining the second weight of the second charging power, it is first necessary to monitor the operation records related to the instant task arrangement. These operation records can be understood as behavioral data of the user's interaction with the instant task arrangement, specifically including the number of times the user clicked or viewed the task arrangement, and the number of times the user edited the task arrangement. These interactions can intuitively reflect the user's level of attention and engagement with the task arrangement. For example, if a user frequently views or modifies a task, it usually means that the task has a high priority or importance to them.

[0063] After acquiring the operation records, a reliability index for instant task scheduling is calculated based on these records. This reliability index is a numerical value that quantifies the user's level of attention to the instant task scheduling, and its calculation method is: Reliability Index = Preset Initial Value + Preset Interaction Weight × Number of Interactions + Preset Edit Weight × Number of Edits. The preset initial value can be a baseline value used to provide a basic reliability assessment without any interaction. The preset interaction weight and preset edit weight are coefficients set based on experience or preset rules, used to measure the contribution of different types of interaction behaviors to reliability. For example, the number of edits is generally considered to reflect the user's actual investment and attention to the task more than simple viewing or clicking; therefore, the preset edit weight can be higher than the preset interaction weight.

[0064] In practical applications, after calculating the reliability index, a second weight is assigned to the second charging power based on this index. This allocation process follows the principle of a positive correlation between the reliability index and the second weight. This means that the higher the reliability index of the immediate task arrangement, the greater the importance the user attaches to the task, and the greater the second weight assigned to the second charging power, and vice versa.

[0065] This application's solution addresses the problem of accurately assessing a user's level of attention to a task based solely on the task assignment itself by introducing monitoring of real-time task scheduling operation records and calculating a reliable index to allocate a second weight. When a user frequently clicks, views, or edits a real-time task assignment, these operation records are considered a direct reflection of the user's effort invested in that task. By quantifying these interactions into interaction and editing counts and combining them with preset weights and initial values ​​to calculate a reliable index, this index can more objectively and precisely reflect the user's actual level of attention to the real-time task assignment. Therefore, a positive correlation is established between this reliable index and the second weight, ensuring that the more important a task is to the user, the greater the weight given to its corresponding second charging power in determining the expected charging power range, thus ensuring that the charging strategy can more accurately respond to the user's actual needs.

[0066] In some embodiments, the step of generating a charging power-time curve based on the desired charging power range and rechargeable time includes: Get the current battery level and the user's preferred charging speed; The current battery level, the user-defined charging speed preference, the desired charging power range, and the available charging time are input into a preset model to generate a charging power-time curve. The preset model is used to generate the charging power-time curve based on the current battery level, the user-defined charging speed preference, the desired charging power range, and the available charging time.

[0067] Specifically, the current battery level refers to the level of charge stored in the laptop battery at the start of charging, usually expressed as a percentage. User-defined charging speed preferences refer to the charging speed preferences selected by the user through the system interface or applications based on their needs. For example, users can choose "fast charging" to charge as much as possible in a short time, "balanced charging" to balance charging speed and battery life, or "slow charging" to maximize battery protection. The preset model can be understood as an intelligent algorithm or a set of rules designed to comprehensively analyze and process multiple parameters, including the current battery level, user-defined charging speed preferences, desired charging power range, and available charging time. The goal of this preset model is to intelligently plan and output an optimal charging power-time curve based on this dynamic information, achieving more refined and personalized charging management.

[0068] This application's solution introduces the battery's current charge level and the user's preferred charging speed as input parameters for generating the charging power-time curve. By utilizing a preset model for comprehensive processing, it effectively addresses the limitations that may exist when generating a curve solely based on the desired charging power range and charging time. Specifically, the preset model can adjust the charging power according to the battery's real-time charge level; for example, it can appropriately increase the charging power to accelerate the charging process when the charge level is low, and decrease the power to protect the battery when the charge level is high. Simultaneously, by considering the user's preferred charging speed, the preset model can adjust the charging strategy to meet the user's personalized needs. For instance, when the user chooses fast charging, it can increase the charging power as much as possible within a safe range, while when the user chooses balanced or slow charging, it may adopt a smoother power curve. Therefore, the generation of the charging power-time curve no longer simply pursues a smooth transition, but combines the actual battery operating conditions and the user's personalized needs, making the charging process more intelligent and efficient.

[0069] In a preferred embodiment, the above-described step of charging the laptop battery according to the charging power-time curve includes: While charging the laptop battery according to the charging power-time curve, the real-time battery temperature is continuously monitored. When the real-time battery temperature exceeds the safety threshold, the charging power is reduced in a smooth transition to ensure safe battery operation.

[0070] Specifically, continuous monitoring of real-time battery temperature refers to acquiring the battery's current temperature data in real time throughout the entire charging cycle using temperature sensors built into the laptop battery pack or charging management system. This temperature data is periodically collected and transmitted to the charging management controller for analysis. A real-time battery temperature exceeding a safety threshold occurs when the monitored battery temperature reaches or exceeds a pre-set upper limit. This safety threshold is typically set based on the battery's chemical characteristics, manufacturer recommendations, and safety standards to prevent dangerous situations such as performance degradation, shortened lifespan, or even thermal runaway due to overheating. In practical applications, charging power is reduced smoothly. For example, when the battery temperature exceeds the safety threshold, the charging management controller does not immediately interrupt charging or drastically reduce the charging power. Instead, it gradually and progressively reduces the charging current or voltage, allowing the charging power to decrease smoothly. This smooth transition strategy avoids system instability or poor user experience caused by a sudden drop in charging power, while giving the battery a buffer time to gradually return its temperature to a safe range. For example, the charging power can be reduced by a certain percentage at preset slopes or in stages, at regular intervals, until the temperature returns to normal or a new safe charging level is reached.

[0071] Through the above technical solution, this application effectively addresses the problem that traditional smart charging solutions may neglect battery thermal management when pursuing charging efficiency. This solution significantly improves the safety and reliability of the charging process by monitoring battery temperature in real time and dynamically adjusting charging power, effectively avoiding potential risks to battery life and user safety caused by battery overheating. Especially in high-load usage or high-temperature environments, this solution intelligently protects the battery, ensuring the long-term stable operation of the laptop, thereby providing users with a safer and longer-lasting battery experience.

[0072] In some embodiments, the step of generating a charging power-time curve based on the desired charging power range and the rechargeable time includes: Obtain the calculated load fluctuation rate based on the calculated load; Within the sliding time window, when the calculated load volatility is greater than the preset volatility threshold, multiple expected charging power ranges within the sliding time window are aggregated to calculate a unified expected charging power range. A charging power-time curve is generated based on a unified expected charging power range and charging time.

[0073] Specifically, computational load volatility refers to the degree of change in a laptop's computational load (such as CPU utilization, GPU load, memory usage, etc.) over a certain period. This volatility can be calculated using statistical methods, such as standard deviation, coefficient of variation, or the maximum-minimum load difference within a specific time window, to quantify the stability of the computational load. The sliding time window refers to a fixed-length period that continuously moves along a time axis. Within this window, the system continuously monitors and evaluates the computational load volatility. When the monitored computational load volatility exceeds a preset volatility threshold, it indicates significant instability or a changing trend in the current or upcoming computational load. In this case, to address this instability, multiple expected charging power ranges within the sliding time window are aggregated. Aggregation can be understood as comprehensively considering these expected charging power ranges calculated during the volatility period, such as taking the average, weighted average, median, or selecting a representative power range according to a certain strategy. Its purpose is to smooth out short-term, drastic fluctuations and obtain a more robust and stable unified expected charging power range. Therefore, based on this aggregated unified expected charging power range and charging time, the final charging power-time curve is generated.

[0074] Through the above technical solution, this application can significantly improve the robustness and adaptability of the intelligent fast charging management and control method for laptops. Especially in scenarios with large fluctuations in user computing load, by dynamically monitoring the computing load fluctuation rate and aggregating the expected charging power range, the problem of unstable charging strategies or decreased efficiency caused by instantaneous load changes can be effectively avoided. As a result, the generated charging power-time curve will be smoother and more stable, not only better matching the user's actual usage needs and reducing unnecessary power adjustments during charging, but also further improving the user experience and extending battery life while ensuring charging efficiency.

[0075] This application also discloses a smart fast charging management and control system for laptop computers, the system comprising: The information acquisition module is used to acquire user behavior pattern information, real-time task scheduling, and the maximum power of the charger. The real-time task scheduling includes tasks that require future mobile use of the laptop. The user behavior pattern information includes the times when the user habitually uses the laptop and the computing load. The rechargeable time prediction module is used to predict the rechargeable time based on the user behavior pattern information and real-time task scheduling. The first charging power determination module is used to determine the first charging power based on the user behavior pattern information and the charging time. The second charging power determination module is used to determine the second charging power based on the real-time task arrangement and the available charging time. The desired charging power range determination module is used to determine the desired charging power range based on the first charging power, the second charging power, and the maximum power. The charging power-time curve generation module is used to generate a charging power-time curve based on the desired charging power range and the charging time, wherein the charging power-time curve is a smooth curve to ensure a smooth transition of charging power. The control module is used to charge the laptop battery according to the charging power-time curve.

[0076] The information acquisition module can be a pure software module, running as part of the operating system service, acquiring user behavior data, calendar information, and charger parameters by calling the system application programming interface. Alternatively, the information acquisition module can be a hardware-software hybrid solution, with an embedded controller handling low-level data acquisition and providing the data to upper-level applications for processing via a software interface.

[0077] The rechargeable time prediction module can be implemented as a standalone computing unit, such as a microcontroller or digital signal processor, dedicated to performing time series analysis and pattern recognition algorithms to identify potential charging windows. In another implementation, the rechargeable time prediction module can be a software library or service running on the laptop's main processor, using statistical or machine learning models to predict charging periods by analyzing user behavior patterns and real-time task scheduling. For example, the module could determine charging times based on historical charging records and calendar events, using simple rule matching or more complex prediction algorithms.

[0078] The first charging power determination module calculates the first charging power based on user behavior patterns and available charging time. This module can be designed as a rule-based decision engine, for example, by pre-setting a series of rules such as "if the user typically performs high-load tasks at night, then set the first charging power to a lower value." Alternatively, it can be a simple lookup table that directly maps different user behavior patterns and available charging time periods to preset first charging power values. The output of this module will serve as an important input for subsequently determining the expected charging power range.

[0079] The second charging power determination module is responsible for calculating the second charging power based on the immediate task schedule and available charging time. This module can employ a similar design to the first charging power determination module; for example, it can assess the urgency of the immediate task using a series of preset priority rules and adjust the second charging power accordingly. For instance, for an upcoming mobile meeting, this module might set a higher target second charging power. Alternatively, it could be a simple algorithm that calculates the charging power required to meet the task's needs based on the remaining time and required battery power. The output of this module is also one of the key inputs for determining the desired charging power range.

[0080] The expected charging power range determination module can be implemented as a simple arithmetic logic unit. For example, it can determine a preliminary expected charging power value by simply averaging the first and second charging powers and considering the charger's maximum power limit. Based on this, the module can set a fixed percentage range, for example, ±10% of the preliminary expected charging power as the expected charging power range. This approach provides a basic trade-off, but may not accurately reflect the priorities in different scenarios.

[0081] The charging power-time curve generation module can employ basic interpolation algorithms, such as linear interpolation, to perform a simple linear transition between the upper and lower limits of the desired charging power range within the charging time, thereby generating a piecewise linear charging power curve. Alternatively, it can be a simple ramp function generator that gradually adjusts the charging power at a preset fixed rate within the charging time to achieve a smooth power change. This approach ensures a basic smooth transition but may lack sufficient flexibility and optimization capabilities in certain complex scenarios.

[0082] The control module's primary responsibility is to control the laptop battery's charging process based on the charging power-time curve. This module can be implemented as a basic feedback control system, for example, using a proportional-integral-derivative (PID) controller to continuously monitor the battery's real-time charging state and compare it to a target value on the charging power-time curve. Based on the comparison result, the controller directly adjusts the charger's output current or voltage to ensure the actual charging power follows the preset curve as closely as possible. In another implementation, the control module can be a simple switching control logic that switches the charger's power output level at preset time points according to the charging power-time curve, achieving segmented power adjustment. This module ensures the final execution of the charging strategy and is the key execution unit for achieving intelligent charging management in the entire system.

[0083] The intelligent fast charging management and control system for laptops disclosed in this application has significant advantages and innovations compared to existing technologies. Traditional charging management systems mainly rely on passive responses to real-time parameters such as current battery level, temperature, and system load, resulting in frequent fluctuations in charging power, which adversely affects battery life and makes it difficult to effectively cope with charging demand conflicts caused by users' immediate tasks.

[0084] The core innovation of this application lies in the introduction of a modular system architecture. Through an information acquisition module, a rechargeable time prediction module, a first charging power determination module, and a second charging power determination module, it achieves forward-looking prediction and multi-dimensional consideration of user behavior patterns and real-time task scheduling. This system design transforms the charging strategy from a single-dimensional, passive response into a multi-dimensional, intelligent, and proactive planning approach.

[0085] Furthermore, the desired charging power range determination module can comprehensively consider the first charging power, the second charging power, and the charger's maximum power to make a fine-grained trade-off, thereby determining an optimized desired charging power range. Most importantly, the charging power-time curve generation module can generate a smooth charging power-time curve based on this desired charging power range and the available charging time. Unlike the frequent jumps in charging power in existing technologies, this smooth curve ensures a stable transition in charging power, fundamentally reducing the electrochemical stress experienced by the battery during charging and significantly extending battery life and capacity retention. The control module precisely executes this smooth curve, ensuring the stability and efficiency of the charging process.

[0086] In summary, the system of this application effectively solves the problems of unstable charging, large battery loss, and inability to meet future needs in the prior art by introducing innovative modules such as user behavior pattern prediction, real-time task scheduling consideration, and smooth charging power curve generation, and significantly improves the intelligence level of laptop charging management and user experience.

[0087] The above provides a detailed description of the preferred embodiments of this application. However, this application is not limited to the above-described embodiments. Those skilled in the art can make various equivalent modifications or substitutions without departing from the spirit of this application. All such equivalent modifications or substitutions are included within the scope defined by the embodiments of this application.

Claims

1. A method for intelligent fast charging management and control of laptop computers, characterized in that, include: The system acquires user behavior pattern information, real-time task scheduling, and the maximum power of the charger. The real-time task scheduling includes tasks that require future mobile use of the laptop. The user behavior pattern information includes the times when the user habitually uses the laptop and the computing load. Predict the charging time based on the user behavior pattern information and real-time task scheduling; The first charging power is determined based on the user behavior pattern information and the available charging time. The second charging power is determined based on the real-time task schedule and the available charging time. The desired charging power range is determined based on the first charging power, the second charging power, and the maximum power. Based on the desired charging power range and the charging time, a charging power-time curve is generated, wherein the charging power-time curve is a smooth curve to ensure a smooth transition of charging power. Charge the laptop battery according to the charging power-time curve.

2. The method according to claim 1, characterized in that, The step of determining the desired charging power range based on the first charging power, the second charging power, and the maximum power includes: A first weight is assigned to the first charging power based on the user behavior pattern information; A second weight is assigned to the second charging power according to the real-time task schedule; The desired charging power range is determined based on the first charging power, the second charging power, the first weight, the second weight, and the maximum power.

3. The method according to claim 2, characterized in that, The step of determining the desired charging power range based on the first charging power, the second charging power, the first weight, the second weight, and the maximum power includes: The expected charging power is calculated based on the first charging power, the second charging power, the first weight, and the second weight, wherein the expected charging power = first charging power × first weight + second charging power × second weight; The charging power range is determined based on the desired charging power and the maximum power, wherein, When the desired charging power is less than the maximum power, the charging power range is [desired charging power × 80%, maximum power]; When the desired charging power is greater than the maximum power, the charging power range is [maximum power × 80%, maximum power].

4. The method according to claim 2, characterized in that, The step of assigning a first weight to the first charging power based on the user behavior pattern information includes: Obtain the computational load from user behavior pattern information; Monitor the operating status of the central processing unit and the graphics processing unit, including operating frequency, utilization, context switching frequency, and I / O queue depth; Predict the actual load based on the described operating status; A first weight is assigned to the first charging power based on the calculated load and the actual load.

5. The method according to claim 4, characterized in that, The step of allocating a first weight to the first charging power based on the calculated load and the actual load includes the following: Monitor the rate of temperature rise of key heat-generating components in a laptop computer, including a central processing unit, a graphics processing unit, and an energy storage unit; The first weight is adjusted based on the rate of temperature increase, wherein... When the temperature rise rate exceeds a preset temperature rise rate threshold, the first weight is reduced. When the rate of temperature rise is less than a preset temperature rise rate threshold, the first weight is increased.

6. The method according to claim 2, characterized in that, The step of allocating a second weight to the second charging power according to the real-time task schedule includes: Monitor the operation records of the real-time task arrangement, including the number of clicks or views and the number of edits; The reliability index of the instant task arrangement is calculated based on the operation record, wherein the reliability index = preset initial value + preset interaction weight × number of interactions + preset editing weight × number of edits; A second weight is assigned to the second charging power based on the reliability index, wherein the reliability index is positively correlated with the second weight.

7. The method according to claim 1, characterized in that, The step of generating a charging power-time curve based on the desired charging power range and the rechargeable time includes: Get the current battery level and the user's preferred charging speed; The current battery level, the user-defined charging speed preference, the desired charging power range, and the available charging time are input into a preset model to generate a charging power-time curve. The preset model is used to generate the charging power-time curve based on the current battery level, the user-defined charging speed preference, the desired charging power range, and the available charging time.

8. The method according to claim 1, characterized in that, The step of charging the laptop battery according to the charging power-time curve includes: While charging the laptop battery according to the charging power-time curve, the real-time battery temperature is continuously monitored. When the real-time temperature of the battery exceeds the safety threshold, the charging power is reduced in a smooth transition manner to ensure safe operation of the battery.

9. The method according to claim 1, characterized in that, The step of generating a charging power-time curve based on the desired charging power range and the rechargeable time includes: The calculated load fluctuation rate is obtained based on the calculated load. Within the sliding time window, when the calculated load volatility is greater than a preset volatility threshold, multiple expected charging power ranges within the sliding time window are aggregated to calculate a unified expected charging power range. A charging power-time curve is generated based on the unified expected charging power range and the charging time.

10. A smart fast charging management and control system for laptop computers, characterized in that: The system includes: The information acquisition module is used to acquire user behavior pattern information, real-time task scheduling, and the maximum power of the charger. The real-time task scheduling includes tasks that require future mobile use of the laptop. The user behavior pattern information includes the times when the user habitually uses the laptop and the computing load. The rechargeable time prediction module is used to predict the rechargeable time based on the user behavior pattern information and real-time task scheduling. The first charging power determination module is used to determine the first charging power based on the user behavior pattern information and the charging time. The second charging power determination module is used to determine the second charging power based on the real-time task arrangement and the available charging time. The desired charging power range determination module is used to determine the desired charging power range based on the first charging power, the second charging power, and the maximum power. The charging power-time curve generation module is used to generate a charging power-time curve based on the desired charging power range and the charging time, wherein the charging power-time curve is a smooth curve to ensure a smooth transition of charging power. The control module is used to charge the laptop battery according to the charging power-time curve.

Images