A Charging Efficiency Optimization Method and System Based on the W Algorithm

Abstract

This invention provides a charging efficiency optimization method and system based on the W algorithm, relating to the field of mobile phone extreme charging efficiency optimization technology. The method includes constructing a charging state-temperature influence topology graph; building a dynamic charging power control model using the W algorithm; real-time acquisition of dynamic data during the extreme charging process to generate an initial extreme charging power command; monitoring battery temperature deviation and charging efficiency deviation after the execution of the initial extreme charging power command to generate a dynamically adjusted extreme charging power control command; and executing the extreme charging power control command. This invention, through precise temperature control and dynamic threshold correction, ensures that the battery is always charged within an appropriate temperature and power range, avoiding battery damage caused by overheating or improper power surges. By correcting algorithm parameters to reduce ineffective energy consumption, it further reduces the additional burden on the battery, effectively extending battery life and reducing the cost and hassle of battery replacement for users.

Description

Technical Field

[0001] This invention relates to the field of mobile phone charging efficiency optimization technology, and in particular to a charging efficiency optimization method and a charging efficiency optimization system based on the W algorithm. Background Technology

[0002] As smartphone functions become increasingly sophisticated, users' demands for longer battery life and faster charging speeds are rising, leading to the emergence of high-speed charging technology. By significantly reducing charging time through increased charging power, high-speed charging has become one of the core competitive advantages of current smartphones. However, during the rapid development of high-speed charging technology, a series of technical pain points have gradually emerged, seriously affecting user experience and battery safety.

[0003] During extreme charging, the high input power generates a significant amount of heat inside the battery. If this heat cannot be dissipated quickly, the battery temperature will rise rapidly. Traditional charging control schemes often adjust power based on fixed temperature thresholds, considering only the battery's own temperature without considering the overall operating status of the phone, thus failing to achieve a dynamic balance between safety and efficiency. Furthermore, users may run background programs during extreme charging, consuming memory and CPU resources and generating additional heat. This makes it difficult for the extreme charging system to accurately determine the cause of the temperature rise, resulting in ineffective power regulation. Simultaneously, as the number of battery cycles increases, the battery's high-temperature resistance gradually decreases. The static safe temperature threshold originally suitable for new batteries is no longer safe for older batteries after multiple cycles, leading to a higher risk of overheating during extreme charging for older batteries. This also prevents new batteries from fully utilizing their charging potential, reducing the safety and efficiency of the extreme charging process. Summary of the Invention

[0004] This invention provides a charging efficiency optimization method and system based on the W algorithm to address the shortcomings of low safety and efficiency in the extreme charging process in existing technologies.

[0005] On the one hand, this invention provides a charging efficiency optimization method based on the W algorithm, including:

[0006] Collect historical data and real-time operating parameters of mobile phone charging, and use a multi-dimensional data association model to construct a charging state-temperature influence topology diagram.

[0007] Based on the charging state-temperature influence topology diagram and combined with the battery safety temperature threshold, a dynamic charging power regulation model is constructed using the W algorithm.

[0008] Real-time collection of dynamic data during the phone's extreme charging process, including battery temperature change rate, memory usage fluctuation value, and background program addition and closure status, is input into the charging power dynamic control model to generate the initial extreme charging power command.

[0009] The battery temperature deviation and charging efficiency deviation are monitored after the initial extreme charging power command is executed. The battery temperature deviation is the difference between the actual temperature and the safety threshold, and the charging efficiency deviation is the difference between the actual charging speed and the theoretical optimal speed. Based on the deviation value, the weight coefficients in the W algorithm are corrected to generate a dynamically adjusted extreme charging power control command.

[0010] Execute the charging power control command until the phone battery is fully charged or the user terminates the charging process.

[0011] According to the charging efficiency optimization method based on the W algorithm provided by the present invention, the historical data of the Extreme Charge includes charging power curves and battery degradation rate records under different temperature ranges, and the real-time operating parameters include real-time battery temperature, memory usage of background programs, and CPU load rate.

[0012] According to the charging efficiency optimization method based on the W algorithm provided by the present invention, the process of constructing the charging state-temperature influence topology diagram includes:

[0013] Data preprocessing is performed on historical data of mobile phone charging to remove outliers and extract four-dimensional features from the valid data: charging power, battery temperature, memory usage, and CPU load, to construct a feature matrix.

[0014] Define the temperature influence correlation tensor and calculate the first-order correlation strength between any two features. The first-order correlation strength represents the weighted fusion of the linear correlation coefficient and mutual information value between the features.

[0015] The multi-level association relationship is extended, and topological edge weights are generated through tensor decomposition. The edge weights represent the degree of influence of multiple features on battery temperature.

[0016] Remove connection edges whose topological edge weights are below a preset threshold to obtain a charging state-temperature influence topology graph. In the graph, nodes represent charging state features and temperature, and edges represent the correlation and influence relationships between features.

[0017] According to the charging efficiency optimization method based on the W algorithm provided by the present invention, the battery safety temperature threshold includes a dynamic threshold and a static threshold. The static threshold is the maximum safe temperature set by the battery manufacturer, and the dynamic threshold is corrected based on the number of battery cycles.

[0018] According to the charging efficiency optimization method based on the W algorithm provided by the present invention, the process of constructing a dynamic charging power control model using the W algorithm includes:

[0019] The objective function of the W algorithm is defined as maximizing charging power while keeping the temperature within a safe threshold.

[0020] The core parameters of the W algorithm were determined, including the temperature sensitivity coefficient, memory usage interference coefficient, and background program heat loss coefficient. Each coefficient was obtained through regression analysis of historical data.

[0021] A charging power calculation model is constructed based on the core parameters, and parameter coupling is achieved based on the W algorithm.

[0022] According to the charging efficiency optimization method based on the W algorithm provided by the present invention, the process of real-time acquisition of dynamic data during the mobile phone's extreme charging process includes:

[0023] Dynamic data includes battery temperature, memory usage, and background program status.

[0024] Battery temperature is collected by a battery thermistor, and the moving average of multiple consecutive collected values ​​is calculated.

[0025] The memory usage rate is obtained through the system memory management interface, which is the ratio of used memory to total memory. The maximum value of multiple consecutive data collections is then calculated.

[0026] The background program status is identified through the application management interface, including newly added and closed background programs. The changes in the number of programs are counted, and heat loss weights are assigned according to program type to generate the heat loss increment of background programs.

[0027] According to the charging efficiency optimization method based on the W algorithm provided by the present invention, the process of monitoring battery temperature deviation and charging efficiency deviation includes:

[0028] Calculate battery temperature deviation The formula is:

[0029]

[0030] In the formula, This is the sliding average of the battery temperature. It has a dynamic safety temperature threshold and adjusts the power according to the battery temperature deviation.

[0031] Calculate charging efficiency deviation The formula is:

[0032]

[0033] In the formula, Theoretical charging efficiency, This represents the actual charging efficiency.

[0034] When the battery temperature deviation or charging efficiency deviation exceeds the corresponding preset threshold, the weight coefficients in the W algorithm are corrected.

[0035] According to the charging efficiency optimization method based on the W algorithm provided by the present invention, the process of correcting the weight coefficients in the W algorithm includes:

[0036] Correction factors are constructed based on battery temperature deviation and charging efficiency deviation. The formula is:

[0037]

[0038] In the formula, e is a coefficient measuring the influence of battery temperature deviation on the correction factor, f is a coefficient measuring the influence of charging efficiency deviation on the correction factor, and g is the maximum acceptable efficiency deviation. This is a dynamic safety temperature threshold.

[0039] Corrected temperature sensitivity coefficient The formula is:

[0040]

[0041] In the formula, This is the corrected temperature sensitivity coefficient.

[0042] Correct memory usage interference factor Heat loss coefficient of background program The formula is:

[0043]

[0044] In the formula, h is the correction coefficient.

[0045] According to the charging efficiency optimization method based on the W algorithm provided by the present invention, the process of generating dynamically adjusted extreme charging power control commands includes:

[0046] The optimal charging power is recalculated based on the corrected W algorithm parameters.

[0047] Set power adjustment step size limit, generate control instructions, including target power value and adjustment duration, and send the instructions through the mobile phone charging management chip interface.

[0048] On the other hand, the present invention also provides a charging efficiency optimization system based on the W algorithm, comprising:

[0049] The topology building module is used to collect historical data and real-time operating parameters of the mobile phone's extreme charging and to build a charging state-temperature influence topology diagram using a multi-dimensional data association model.

[0050] The model calculation module is used to construct a dynamic charging power control model based on the charging state-temperature influence topology diagram and the battery safety temperature threshold, using the W algorithm.

[0051] The instruction generation module is used to collect dynamic data in real time during the phone's extreme charging process. The dynamic data includes battery temperature change rate, memory usage fluctuation value, background program addition and closing status, which are input into the charging power dynamic control model to generate the initial extreme charging power instruction.

[0052] The deviation correction module is used to monitor the battery temperature deviation and charging efficiency deviation after the initial extreme charging power command is executed. The battery temperature deviation is the difference between the actual temperature and the safety threshold, and the charging efficiency deviation is the difference between the actual charging speed and the theoretical optimal speed. Based on the deviation value, the weight coefficients in the W algorithm are corrected to generate dynamically adjusted extreme charging power control commands.

[0053] The instruction execution module is used to execute the charging power control instructions until the mobile phone battery is fully charged or the user terminates the charging process.

[0054] The charging efficiency optimization method and system based on the W algorithm provided by this invention constructs a charging state-temperature influence topology graph, and performs correlation analysis on multi-dimensional parameters such as battery temperature, charging power, memory occupancy, and CPU load to accurately identify the causes of temperature changes. Simultaneously, the solution introduces a dynamic safety temperature threshold, which is corrected based on the battery cycle count, ensuring that the safety threshold matches the battery degradation state. New batteries can achieve their maximum charging potential within a more reasonable temperature range, while older batteries also receive appropriate safety protection, effectively reducing the overheating risk of batteries in different states during extreme charging and fundamentally improving extreme charging safety.

[0055] The W algorithm constructs a multi-parameter coupled dynamic charging power control model through weighted calculations of temperature sensitivity coefficient, memory usage interference coefficient, and background program heat loss coefficient. While ensuring the temperature does not exceed a safe threshold, it can accurately calculate the optimal charging power, avoiding excessive power reduction caused by background program operation and memory usage, while maximizing charging power within a controllable temperature range. By collecting dynamic data in real time, such as battery temperature change rate, memory usage fluctuation value, and the addition and closing status of background programs, and triggering a weight coefficient correction mechanism based on deviation monitoring, the W algorithm can quickly respond to changes in the phone's operating status. When memory usage suddenly increases or the addition of background programs causes additional heat generation, the algorithm adjusts the core coefficients through correction factors to optimize power commands in a timely manner, avoiding control lag. Simultaneously, the power adjustment step size limit ensures smooth power changes, preventing temperature fluctuations due to sudden changes, thus balancing real-time control and system stability. Attached Figure Description

[0056] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0057] Figure 1 This is a flowchart illustrating the charging efficiency optimization method based on the W algorithm provided in an embodiment of the present invention.

[0058] Figure 2 This is a schematic diagram of the charging efficiency optimization system based on the W algorithm provided in an embodiment of the present invention. Detailed Implementation

[0059] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0060] The following is combined with Figures 1-2 This invention describes a charging efficiency optimization method and system based on the W algorithm.

[0061] Figure 1 This is a flowchart illustrating the charging efficiency optimization method based on the W algorithm provided in this embodiment of the invention.

[0062] like Figure 1 As shown, the charging efficiency optimization method and system based on the W algorithm provided in this embodiment of the invention can be executed by a charging efficiency optimization method based on the W algorithm, the method including:

[0063] Collect historical data and real-time operating parameters of mobile phone charging, and use a multi-dimensional data association model to construct a charging state-temperature influence topology diagram.

[0064] Extreme Charge's historical data includes charging power curves and battery degradation rate records under different temperature ranges. Real-time operating parameters include real-time battery temperature, background program memory usage, and CPU load rate.

[0065] Extreme Charge historical data is collected through multiple channels, including mobile phone system background logs, charging management chip records, and laboratory simulation tests, under different usage scenarios. This includes charging power curves and battery degradation rate records at different temperature ranges. Specifically, the charging power curve needs to record continuous data on the change of charging power over time at different ambient temperatures (e.g., low temperature, normal temperature, high temperature) and different remaining battery capacity (e.g., 10%-20%, 50%-60%, 80%-90%).

[0066] Real-time operating parameters are acquired through the phone's built-in sensors and system interface to obtain key operating parameters during the ultra-charging process, including real-time battery temperature, background program memory usage, and CPU load rate. Real-time battery temperature is collected by an integrated thermistor inside the battery. This thermistor must be in close contact with the battery cell to ensure accurate reflection of the cell's actual temperature. For example, placing the thermistor in the center of the battery cell can minimize temperature measurement errors. Background program memory usage is obtained through the system memory management interface. The total memory usage of all currently running background programs (including system processes and third-party application processes) is calculated and compared to the phone's total memory capacity. It's also important to differentiate the memory usage characteristics of different types of programs; for example, large games often consume more memory in the background. CPU load rate is collected through the system process management interface to monitor the CPU's task processing ratio per unit time in real time, preventing excessive heat generated by high CPU load from affecting the ultra-charging process.

[0067] The process of constructing the charging state-temperature effect topology graph includes:

[0068] Data preprocessing was performed on historical charging data of the mobile phone to remove outliers and extract four dimensions of features from the valid data: charging power, battery temperature, memory usage, and CPU load. A feature matrix F was then constructed.

[0069]

[0070] In the formula, The number of valid historical data samples, Let i be the charging power of the i-th sample. Let be the battery temperature of the i-th sample. Let be the memory usage of the i-th sample. Let be the CPU load of the i-th sample.

[0071] Define the temperature effect correlation tensor Let k be the maximum association order. The first-order association strength between any two features is calculated. The first-order association strength represents the weighted fusion of the linear correlation coefficient and mutual information value between the features, as shown in the formula:

[0072]

[0073] In the formula, These are the weighting coefficients. Features and Pearson correlation coefficient, Features and mutual information value, , These represent any two of the following characteristics: charging power, battery temperature, memory usage, and CPU load.

[0074] The linear correlation coefficient is calculated using the Pearson correlation coefficient, which measures the degree of linear correlation between two features. The value ranges from -1 to 1. The closer the absolute value is to 1, the stronger the linear correlation. For example, charging power and battery temperature may show a strong positive linear correlation within a certain range. The mutual information value is used to measure the degree of information sharing between two features. The larger the value, the closer the association between the two features.

[0075] Extending the multi-order correlation, topological edge weights are generated through tensor decomposition. These edge weights represent the degree of influence of multiple features on battery temperature, as shown in the formula:

[0076]

[0077] In the formula, Features and Topological edge weights between For each element of the m-th order correlation tensor, ×3 represents the product of the tensor along the third dimension. Let m be the eigenvector of the m-th order association. It is the Frobenius norm.

[0078] When charging power, memory usage, and CPU load all change simultaneously, their combined impact on battery temperature needs to be analyzed using tensor decomposition to determine multi-level relationships. Tensor decomposition breaks down a high-dimensional correlation tensor into multiple low-dimensional matrices and vectors. Topological edge weights are generated by calculating the product and norm of the decomposed elements. The magnitude of the edge weight represents the degree of influence of the corresponding feature combination on battery temperature; a larger weight indicates a more significant contribution of that feature combination to battery temperature changes during extreme charging. By comparing the edge weights of different feature combinations, it is possible to determine which feature combinations are the key factors affecting battery temperature.

[0079] Remove connection edges whose topological edge weights are below a preset threshold to obtain a charging state-temperature influence topology graph. In the graph, nodes represent charging state features and temperature, and edges represent the correlation and influence relationships between features.

[0080] Based on the charging state-temperature influence topology diagram and combined with the battery safety temperature threshold, a dynamic charging power control model is constructed using the W algorithm. The W algorithm achieves power optimization decision through weighted calculation of temperature sensitivity coefficient, memory usage interference coefficient, and background program heat loss coefficient.

[0081] Battery safety temperature thresholds include dynamic thresholds and static thresholds. The static threshold is the maximum safe temperature set by the battery manufacturer, while the dynamic threshold is adjusted based on the number of battery cycles, using the following formula:

[0082]

[0083] In the formula, For dynamic safety temperature threshold, This is the static safety temperature threshold. Here, N is the degradation coefficient, and N is the number of battery cycles.

[0084] The process of constructing a dynamic charging power control model using the W algorithm includes:

[0085] Under the premise that the temperature does not exceed the safety threshold, maximizing the charging power is defined as the objective function of the W algorithm, and the objective function formula is:

[0086]

[0087] In the formula, Let be the charging power at time t. Let be the battery temperature at time t. Let be the memory usage at time t. Let be the CPU load at time t. To set a safe upper limit for memory usage, This is the safe upper limit for CPU load.

[0088] The core parameters of the W algorithm were determined, including the temperature sensitivity coefficient, memory usage interference coefficient, and background program heat loss coefficient. These coefficients were obtained through regression analysis of historical data, and the formulas are as follows:

[0089]

[0090] In the formula, This represents the temperature sensitivity coefficient, which indicates how sensitive the power is to temperature when the temperature reaches a safe threshold. This represents the partial derivative of charging power with respect to temperature when the temperature reaches a safe threshold, reflecting the degree to which temperature changes affect the power at this point.

[0091]

[0092] In the formula, This is the memory usage coefficient. It is an exponential function of memory usage. This represents the interference coefficient of memory usage, which is the interference coefficient of memory usage on power. It increases as the memory usage rate increases.

[0093]

[0094] In the formula, c is the CPU load factor. It is an exponential function of CPU load. This represents the heat loss coefficient of the background program, which is the power loss coefficient of the CPU load. It increases as the CPU load increases.

[0095] A charging power calculation model is constructed, and parameter coupling is achieved based on the W algorithm. The formula is as follows:

[0096]

[0097] In the formula, Let be the optimal charging power at time t. This refers to the maximum charging power supported by the phone. Target charging temperature, Let t be the battery temperature at time t.

[0098] Real-time collection of dynamic data during the phone's extreme charging process, including battery temperature change rate, memory usage fluctuation value, and background program addition and closure status, is input into the charging power dynamic control model to generate the initial extreme charging power command.

[0099] The process of collecting dynamic data in real time during the phone's extreme charging process includes:

[0100] Dynamic data includes battery temperature, memory usage, and background program status.

[0101] Battery temperature is collected using a battery thermistor, and the moving average of multiple consecutive collected values ​​is calculated. The formula is:

[0102]

[0103] In the formula, s is the size of the moving average window, and s is the index variable for the summation operation.

[0104] The memory usage rate is obtained through the system memory management interface, which is the ratio of used memory to total memory. The maximum value of multiple consecutive data collections is then calculated. The formula is:

[0105]

[0106] In the formula, Calculate the window size for the maximum value.

[0107] Background application status is identified through the application management interface, which also tracks newly added and closed background applications and tracks changes in the number of applications. It assigns heat loss weights based on program type and generates background program heat loss increments. The formula is:

[0108]

[0109] In the formula, Let be the heat loss weight of the i-th program.

[0110] The battery temperature deviation and charging efficiency deviation are monitored after the initial extreme charging power command is executed. The battery temperature deviation is the difference between the actual temperature and the safety threshold, and the charging efficiency deviation is the difference between the actual charging speed and the theoretical optimal speed. Based on the deviation value, the weight coefficients in the W algorithm are corrected to generate a dynamically adjusted extreme charging power control command.

[0111] The process of monitoring battery temperature deviation and charging efficiency deviation includes:

[0112] Calculate battery temperature deviation The formula is:

[0113]

[0114] In the formula, This is a dynamic safety temperature threshold.

[0115] And adjust the power according to the battery temperature deviation, including:

[0116] when This indicates that the temperature has exceeded the threshold and the power needs to be reduced.

[0117] when At that time, the power increase range is adjusted according to the absolute value of the deviation.

[0118] Calculate charging efficiency deviation The formula is:

[0119]

[0120]

[0121]

[0122]

[0123] In the formula, Theoretical charging efficiency, U is the battery voltage. The theoretical charging current, Let be the optimal charging power at time t. This refers to the actual charging efficiency. This is the actual charging current. This is the total power input for the mobile phone.

[0124] When the battery temperature deviates Or charging efficiency deviation When the corresponding preset threshold is exceeded, the weight coefficients in the W algorithm are corrected.

[0125] The process of correcting the weight coefficients in the W algorithm includes:

[0126] Based on battery temperature deviation Deviation from charging efficiency Constructing correction factors The formula is:

[0127]

[0128] In the formula, e is a coefficient measuring the influence of battery temperature deviation on the correction factor, f is a coefficient measuring the influence of charging efficiency deviation on the correction factor, and g is the maximum acceptable efficiency deviation. This is a dynamic safety temperature threshold.

[0129] Corrected temperature sensitivity coefficient The formula is:

[0130]

[0131] In the formula, This is the corrected temperature sensitivity coefficient. When the temperature exceeds the threshold, the sensitivity is increased, resulting in a greater reduction in power.

[0132] Correct memory usage interference factor Heat loss coefficient of background program The formula is:

[0133]

[0134] In the formula, h is a correction coefficient that enhances the power regulation response to memory and CPU load.

[0135] The process of generating dynamically adjusted extreme charge power control commands includes:

[0136] The optimal charging power was recalculated based on the corrected W algorithm parameters. .

[0137] Set a power adjustment step size limit to avoid temperature fluctuations caused by sudden power changes. The step size formula is:

[0138]

[0139] In the formula, i is the step size coefficient, which means that the single power adjustment range does not exceed the corresponding proportion of the maximum charging power.

[0140] The system generates control commands, including target power values ​​and adjustment durations, and sends these commands through the mobile phone charging management chip interface. It also records temperature and efficiency data before and after command execution for future deviation correction.

[0141] The extreme charging power control command is executed until the mobile phone battery is fully charged or the user terminates the extreme charging process.

[0142] After generating a dynamically adjusted high-power charging control command, the command is first structured and parsed to extract key parameters such as the target power value, adjustment duration, and execution priority. The target power value specifies the charging power level to be achieved at the current moment, the adjustment duration defines the time range for the power to transition from the current value to the target value, and the execution priority determines the urgency of the command based on the severity of battery temperature and charging efficiency deviations (e.g., commands for exceeding temperature thresholds have higher priority than regular efficiency optimization commands). After parsing, the command is sent through the phone's built-in charging management chip interface. This interface, acting as the hardware control center, converts the power control signal into an electrical signal recognizable by the charging chip. On one hand, it adjusts the voltage regulation and current control modules within the charging chip to match the corresponding input voltage and current combination according to the target power value. On the other hand, it simultaneously sends a coordination signal to the battery protection board to ensure the battery protection board is in real-time monitoring mode. If overvoltage, overcurrent, or other abnormalities occur during charging, hardware-level protection can be triggered immediately to avoid safety risks during command execution.

[0143] In summary, this embodiment provides a charging efficiency optimization method based on the W algorithm. By constructing a charging state-temperature influence topology graph, it performs correlation analysis on multi-dimensional parameters such as battery temperature, charging power, memory usage, and CPU load, accurately identifying the causes of temperature changes. Simultaneously, the solution introduces a dynamic safety temperature threshold, adjusted based on the battery cycle count, to match the safety threshold with the battery degradation state. This allows new batteries to achieve their maximum charging potential within a more reasonable temperature range, while older batteries receive appropriate safety protection, effectively reducing the overheating risk of batteries in different states during extreme charging and fundamentally improving extreme charging safety.

[0144] The W algorithm constructs a multi-parameter coupled dynamic charging power control model by weighting the temperature sensitivity coefficient, memory usage interference coefficient, and background program heat loss coefficient. While ensuring the temperature does not exceed a safe threshold, it can accurately calculate the optimal charging power, avoiding excessive power reduction due to background program operation and memory usage, and maximizing charging power within a controllable temperature range. For example, when the background program generates only slight additional heat, the algorithm only fine-tunes the power instead of drastically reducing it, effectively maintaining charging efficiency, shortening the overall charging time, and significantly optimizing the user's fast charging experience.

[0145] By collecting dynamic data in real time, such as battery temperature change rate, memory usage fluctuation, and the status of background programs being added and closed, and triggering a weight coefficient correction mechanism based on deviation monitoring, the W algorithm can quickly respond to changes in the phone's operating status. When memory usage suddenly increases or the addition of background programs causes additional heat generation, the algorithm adjusts the core coefficient through a correction factor to optimize power commands in a timely manner and avoid control lag. At the same time, the power adjustment step size limit ensures smooth power changes and prevents temperature fluctuations due to sudden changes, balancing the real-time nature of control with system stability.

[0146] Traditional solutions, due to inaccurate temperature control and fixed safety thresholds, can easily subject batteries to unnecessary high-temperature stress during extreme charging, accelerating battery degradation. This solution, through precise temperature control and dynamic threshold correction, ensures the battery is always charged within an appropriate temperature and power range, avoiding battery damage caused by overheating or improper power surges. Simultaneously, the solution's monitoring of charging efficiency deviations can promptly detect abnormal losses in the charging system, reducing ineffective energy consumption by correcting algorithm parameters, further reducing the additional burden on the battery, effectively extending battery life, and reducing the cost and hassle of battery replacement for users.

[0147] Based on the same general inventive concept, this invention also protects a charging efficiency optimization system based on the W algorithm. The charging efficiency optimization system based on the W algorithm provided by this invention will be described below. The charging efficiency optimization system based on the W algorithm described below can be referred to in correspondence with the charging efficiency optimization method based on the W algorithm described above.

[0148] Figure 2 This is a schematic diagram of the charging efficiency optimization system based on the W algorithm provided in an embodiment of the present invention.

[0149] like Figure 2 As shown, the charging efficiency optimization system based on the W algorithm includes a memory, a processor, and a computer program stored in the memory and capable of running on the processor. The processor includes a topology construction module, a model calculation module, an instruction generation module, a deviation correction module, and an instruction execution module.

[0150] The topology building module is used to collect historical data and real-time operating parameters of mobile phone charging, and to build a charging state-temperature influence topology diagram using a multi-dimensional data association model.

[0151] The model calculation module is used to construct a dynamic charging power control model based on the charging state-temperature influence topology map and the battery safety temperature threshold, using the W algorithm. The W algorithm achieves power optimization decision through weighted calculation of temperature sensitivity coefficient, memory usage interference coefficient, and background program heat loss coefficient.

[0152] The instruction generation module is used to collect dynamic data in real time during the mobile phone's extreme charging process. The dynamic data includes battery temperature change rate, memory usage fluctuation value, background program addition and closing status, which are input into the charging power dynamic control model to generate the initial extreme charging power instruction.

[0153] The deviation correction module is used to monitor the battery temperature deviation and charging efficiency deviation after the initial extreme charging power command is executed. The battery temperature deviation is the difference between the actual temperature and the safety threshold, and the charging efficiency deviation is the difference between the actual charging speed and the theoretical optimal speed. Based on the deviation value, the weight coefficients in the W algorithm are corrected to generate a dynamically adjusted extreme charging power control command.

[0154] The instruction execution module is used to execute the extreme charging power control instruction until the mobile phone battery is fully charged or the user terminates the extreme charging process.

[0155] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0156] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method of optimizing charging efficiency based on an algorithm, characterized in that The application comprises: Collecting mobile phone extreme charging history data and real-time operating parameters, and constructing a charging state-temperature influence topology graph using a multi-dimensional data correlation model; Based on the charging state-temperature influence topology graph, combining a battery safety temperature threshold, and constructing a charging power dynamic regulation model through a W algorithm, the process comprising: Defining the maximum charging power under the premise that the temperature does not exceed the safety threshold as the objective function of the W algorithm, and the objective function formula is: wherein, is the charging power at time t, is the battery temperature at time t, is the memory occupancy at time t, is the CPU load at time t, is the dynamic safety temperature threshold, is the memory occupancy safety upper limit, is the CPU load safety upper limit; Determining the core parameters of the W algorithm, including the temperature sensitivity coefficient, the memory occupation interference coefficient and the background program heat loss coefficient, each coefficient being obtained through historical data regression analysis; According to the core parameters, constructing a charging power calculation model, and realizing parameter coupling based on the W algorithm, the formula is: In the formula, is the optimal charging power at time t, is the maximum extreme charging power supported by the mobile phone, is the target charging temperature, is the battery temperature at time t, represents the temperature sensitivity coefficient, represents the memory occupation interference coefficient, represents the background program heat loss coefficient; Real-time collection of dynamic data in the mobile phone extreme charging process, the dynamic data including battery temperature change rate, memory occupation fluctuation value, background program addition and closing state, input into the charging power dynamic regulation model, and generation of an initial extreme charging power instruction; Monitor the battery temperature deviation and charging efficiency deviation after the initial extreme charging power instruction is executed, the battery temperature deviation is the difference between the actual temperature and the safety threshold, the charging efficiency deviation is the difference between the actual charging speed and the theoretical optimal speed, and the extreme charging power instruction is corrected based on the deviation value The weight coefficient in the algorithm generates a dynamically adjusted extreme charging power control instruction Executing the extreme charging power regulation instruction until the mobile phone battery charging is completed or the user terminates the extreme charging process.

2. The method of claim 1, wherein the method is based on An algorithm-based charging efficiency optimization method, characterized by, The extreme charging history data includes charging power curves and battery attenuation rate records in different temperature intervals, and the real-time operating parameters include real-time battery temperature, background program memory occupation ratio and CPU load rate.

3. The method of claim 1, wherein the method is based on An algorithm-based charging efficiency optimization method, characterized by, The process of constructing the charging state-temperature influence topology graph comprises: Data preprocessing of the mobile phone extreme charging history data, excluding abnormal values, extracting four-dimensional features of charging power, battery temperature, memory occupation rate and CPU load from the effective data, and constructing a feature matrix; Defining a temperature influence correlation tensor, calculating the first-order correlation strength between any two features, and the first-order correlation strength representing the weighted fusion of the linear correlation coefficient and mutual information value between the features; Extending the multi-order correlation relationship, generating a topology edge weight through tensor decomposition, and the edge weight representing the influence degree of multi-feature cooperation on the battery temperature; Removing the connection edges with edge weights lower than a preset threshold, and obtaining the charging state-temperature influence topology graph, in which the nodes represent the charging state features and temperature, and the edges represent the correlation influence relationship between the features.

4. The method of claim 1, wherein the method is based on An algorithmic charging efficiency optimization method, characterized by The battery safety temperature threshold includes a dynamic threshold and a static threshold, and the static threshold is the highest safety temperature set by the battery manufacturer, and the dynamic threshold is corrected based on the battery cycle number.

5. The method of claim 1, wherein the method is based on An algorithmic charging efficiency optimization method, characterized by The process of real-time collection of dynamic data in the mobile phone extreme charging process comprises: The dynamic data includes battery temperature, memory occupation rate and background program state; The battery temperature is collected through a battery thermistor, and a sliding average value of continuous multiple collection values is calculated; The memory occupation rate is obtained through a system memory management interface, and the ratio of used memory to total memory is calculated, and the maximum value of continuous multiple collection values is calculated; The background program state is identified through an application management interface, the number of programs is counted, and a heat loss weight is assigned according to the program type, and a background program heat loss increment is generated. 6.The W-algorithm-based charging efficiency optimization method according to claim 1, characterized in that, The process of monitoring the battery temperature deviation and the charging efficiency deviation comprises: Computing battery temperature deviation The formula is: wherein is a moving average of the battery temperature, is a dynamic safety temperature threshold, and adjusts power according to the battery temperature deviation; Computing a charging efficiency bias with the formula: In the formula, theoretical charging efficiency, actual charging efficiency; When the battery temperature deviation or the charging efficiency deviation exceeds a corresponding preset threshold, the correction weight coefficients in the algorithm.

7. The method of claim 6, wherein the method is based on An algorithmic method for optimizing charging efficiency, characterized by The process of correcting the weight coefficient in the W algorithm comprises: Constructing a correction factor based on battery temperature deviation and charging efficiency deviation The formula is: where e is a coefficient that measures the degree of influence of the battery temperature deviation on the correction factor, f is a coefficient that measures the degree of influence of the charging efficiency deviation on the correction factor, g is the maximum acceptable efficiency deviation, is a dynamic safety temperature threshold; Correcting temperature sensitivity coefficient The formula is: In the formula, is the corrected temperature sensitivity coefficient; Correcting memory occupancy interference coefficients With background program heat loss coefficient The formula is: In the formula, h is the correction coefficient.

8. The method of claim 1, wherein the method is based on An algorithmic charging efficiency optimization method, characterized by, The process of generating a dynamically adjusted extreme charging power regulation instruction comprises: based on the corrected recomputes the optimal charging power based on the algorithm parameters The power adjustment step limit is set, a regulation instruction is generated, the regulation instruction includes a target power value and an adjustment duration, and the instruction is issued through a mobile phone charging management chip interface.

9. A charging efficiency optimization system based on an algorithm, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, The processor implements the computer program to achieve the method of claim 1 to 8 An algorithm-based charging efficiency optimization method.

Images