A Real-Time Charging Optimization Method and System for Mobile Phones Based on the W Algorithm in Dynamic Environments

Abstract

This invention provides a method and system for real-time mobile phone charging optimization using the W algorithm in dynamic environments, relating to the field of mobile phone charging optimization technology. The method includes: establishing a unique identifier association between the device to be charged and available charging resources; acquiring device usage status, subsequent schedule information, device relationships, and dynamic environmental variables; calculating a comprehensive priority score based on the W algorithm; outputting resource scheduling decisions for different resource scenarios; continuously tracking changes in device parameters, battery level, and resource status; and optimizing the basic weights of priority evaluation indicators. This invention achieves precise matching of charging resources by combining the comprehensive priority evaluation of the W algorithm with scenario-based scheduling strategies. A dynamic recalculation mechanism based on triggering conditions such as battery level changes, schedule adjustments, and resource anomalies, along with continuous iterative optimization of weight factors, improves the real-time performance, adaptability, and resource utilization of charging scheduling in dynamic environments, effectively reducing device waiting time and enhancing the user charging experience.

Description

Technical Field

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

[0002] Early charging technologies used linear chargers as their core architecture, relying on basic charging standards such as USB BC1.2 to achieve a standard 5V / 1A charging output. The core technology involved using a linear voltage regulator circuit to reduce the input voltage to the battery's operating range, while simultaneously employing a simple current limiting module to prevent overcurrent. As lithium-ion batteries became the mainstream energy storage medium for mobile phones, their nominal voltage range of 3.0-4.2V and constant current-constant voltage charging curve characteristics drove the adjustment of charging technology towards adapting to these electrochemical characteristics. This led to the development of dedicated charging management chips for lithium-ion batteries, capable of real-time monitoring of battery voltage and current to match the parameter requirements of each charging stage.

[0003] Charging optimization technology, centered on the Battery Management System (BMS), integrates hardware modules such as cell voltage acquisition, temperature monitoring, and current measurement. Through algorithms, it calculates the battery's remaining capacity (SOC) and state of health (SOH) in real time, providing data support for adjusting charging parameters. Furthermore, charging strategies and hardware coordination mechanisms at the mobile phone system level are gradually improving. For example, through operating system scheduling, dynamic power allocation to power-consuming modules such as the CPU and screen is achieved during charging. Simultaneously, combined with cell balancing technology, the voltage of each cell in a multi-cell series battery pack is balanced to ensure consistent cell status during charging.

[0004] Typical real-time charging optimization methods for mobile phones do not fully collect dynamic environmental variables and user-related information, relying only on static or single-dimensional data to evaluate priorities. This leads to rigid scheduling strategies that cannot adapt to environmental changes and fluctuations in user demand. Traditional scheduling algorithms struggle to handle single-resource multi-device conflicts and multi-resource distributed scenarios simultaneously, easily causing resource contention or idleness, reducing overall utilization. Furthermore, they lack dynamic triggering recalculation mechanisms, failing to update scheduling strategies in a timely manner when device battery levels change abruptly, user schedules are adjusted, or resources become abnormal, often resulting in long device waiting times or resource overload.

[0005] To address the shortcomings of the existing technology, this technical solution proposes a W-algorithm-based real-time charging optimization method for mobile phones under dynamic environments. Summary of the Invention

[0006] This invention provides a method for optimizing real-time mobile phone charging using the W algorithm under dynamic environments, in order to overcome the shortcomings of existing technologies.

[0007] On the one hand, this invention provides a method for optimizing real-time mobile phone charging using the W algorithm under dynamic environments, including:

[0008] S1: Establish a unique identifier association between the device to be charged and the available charging resources;

[0009] S2: Based on unique identifier association, obtain device usage status, subsequent schedule information, device association relationships and dynamic environmental variables, and output real-time status data;

[0010] S3: Preset the basic weights of the priority evaluation indicators, and calculate the comprehensive priority score based on the W algorithm, according to real-time status data and basic weights;

[0011] S4: For single-resource multi-device conflict scenarios and multi-resource distributed scenarios, output resource scheduling decisions based on comprehensive priority scores;

[0012] S5: Based on resource scheduling decisions, continuously track changes in equipment parameters, power consumption and resource status. When preset trigger conditions are met, trigger priority recalculation and scheduling strategy update, and output scheduling evaluation results.

[0013] S6: Based on the scheduling evaluation results, optimize the basic weights of the priority evaluation indicators.

[0014] The real-time charging optimization method for mobile phones using the W algorithm in a dynamic environment provided by the present invention includes the following step S1: Establishing a unique identifier association between the device to be charged and the available charging resources.

[0015] S11: Assign a unique device identifier to the device to be charged, including the model, the user to which it belongs, and the hardware characteristics;

[0016] S12: Assign a unique resource identifier containing type, deployment location, and device number to available charging resources, and establish an identifier management system in conjunction with the unique device identifier;

[0017] S13: Based on the identification management system, the device to be charged is associated with the available charging resources through the identification mapping mechanism, and the types of charging resources that the device can be matched with and the historical matching records are recorded.

[0018] According to the W-algorithm-based real-time charging optimization method for mobile phones under dynamic environments provided by the present invention, step S2, the step of outputting real-time status data, includes:

[0019] S21: Obtain the usage status data of the device to be charged through the device system interface;

[0020] S22: Extract the schedule information of the associated user of the device to be charged through a user-authorized schedule management application;

[0021] S23: Based on the identification management system, query the linkage relationship between the device to be charged and other devices, and record the dependency logic of charging needs between each device;

[0022] S24: Collect dynamic environmental variable data, including ambient temperature, power supply voltage stability and current load of the charging resource in the area where the charging resource is located;

[0023] S25: The usage status data, associated user schedule information, the dependency logic of charging needs between various devices, and environmental variable data are structurally integrated according to the preset output format to output standardized real-time status data.

[0024] According to the W-algorithm-based real-time charging optimization method for mobile phones under dynamic environments provided by the present invention, step S3, which involves presetting the basic weights of the priority evaluation indicators, includes:

[0025] S31: Determine the priority evaluation indicator system;

[0026] S32: The initial weights of each indicator are calculated using the historical charging data statistical method.

[0027] S33: Adjust the initial weights for typical application scenarios to form scenario-based initial weights;

[0028] S34: Verify the rationality of the initial scenario-based weights through simulated scheduling tests and output the simulation test results; based on the simulation test results, readjust the corresponding indicator weights until the preset optimization target is met, and obtain the basic weights.

[0029] According to the W-algorithm-based real-time charging optimization method for mobile phones under dynamic environments provided by the present invention, in step S3, the W-algorithm formula is expressed as:

[0030]

[0031] In the formula, W is the dynamic weighting factor, SOC is the battery state of charge, T is the ambient temperature, and P is the weighting factor. user G is used to set priority. load Let α, β, γ, and δ represent the current load of the charging resources, and let SOC, T, and P be the load values, respectively. user G load The dynamic weighting coefficients.

[0032] According to the W-algorithm-based real-time charging optimization method for mobile phones under dynamic environments provided by the present invention, step S3, calculating the comprehensive priority score, includes:

[0033] S35: Extract the feature values ​​of the priority evaluation indicators corresponding to each device to be charged from the real-time status data.

[0034] S36: Standardize the feature values ​​of each priority evaluation indicator and output the standardized feature values;

[0035] S37: Based on the W algorithm, the standardized feature values ​​are multiplied by the corresponding basic weights to obtain the individual scores of each indicator;

[0036] S38: The scores of all individual indicators are summed to obtain the comprehensive priority score of the device to be charged. If there are multiple devices, they are sorted from high to low scores to form a priority sequence.

[0037] According to the W-algorithm-based real-time charging optimization method for mobile phones under dynamic environments provided by the present invention, step S4, the step of outputting resource scheduling decisions, includes:

[0038] S41: For scenarios involving multiple devices conflicting with a single resource, retrieve the maximum output power, current idle time period, and compatible device type of the charging resource; compare the comprehensive priority scores of the conflicting devices, prioritize allocating charging resources to the device with the highest comprehensive priority score, determine the charging start time and charging duration, and generate a single resource scheduling decision table.

[0039] S42: For multi-resource distributed scenarios, construct a device resource matching matrix; use the Hungarian algorithm to solve for the maximum weight matching scheme of the matching matrix and generate a multi-resource scheduling decision table;

[0040] S43: Synchronize the single-resource scheduling decision table and the multi-resource scheduling decision table to the charging resource management module and the user terminal to trigger the pre-start preparation of charging resources.

[0041] According to the W-algorithm-based real-time charging optimization method for mobile phones under dynamic environments provided by the present invention, in step S5, the preset triggering conditions include: the rate of change of the battery level of the device to be charged exceeds a preset threshold; the user modifies the schedule information, resulting in an increase in the urgency of the time period when the device needs to be used; the charging resources are in an abnormal state, including output power fluctuation exceeding 20%, temperature exceeding a safety threshold, and sudden failure; the number of newly added devices to be charged exceeds 120% of the current resource capacity; the power supply voltage fluctuation in the dynamic environmental variables exceeds the rated value; the change in ambient temperature causes the charging efficiency to decrease beyond a preset range; the time since the last priority calculation exceeds a preset period, and there are devices that have not completed charging.

[0042] According to the W-algorithm-based real-time charging optimization method for mobile phones under dynamic environments provided by the present invention, step S6, which optimizes the basic weights of the priority evaluation index, includes:

[0043] S61: Extract optimization data from scheduling evaluation results;

[0044] S62: Establish a weight optimization objective function, use the particle swarm optimization algorithm to iteratively optimize the basic weights, and find the optimal weight combination;

[0045] S63: Verify and test the optimal weight combination by selecting actual data from different scenarios in a preset number of groups for scheduling simulation and outputting the simulation results. If the simulation results meet all optimization objectives, they are determined as the new basic weights. If they do not meet the objectives, the algorithm inertia weights are adjusted and iterated again to output the optimal basic weights.

[0046] S64: Store the optimal basic weights in the weight database and set the weight update cycle.

[0047] The present invention also provides a real-time charging optimization system for mobile phones using the W algorithm in dynamic environments, including: an identification association module, used to establish a unique identification association between the device to be charged and the available charging resources, and to mark the physical characteristics and scheduling execution boundaries of each charging resource;

[0048] The data acquisition and processing module is used to obtain equipment usage status, subsequent schedule information, equipment association relationships and dynamic environmental variables based on unique identifier association, and output real-time status data.

[0049] The priority calculation module is used to preset the basic weights of priority evaluation indicators and, based on the W algorithm, calculate the comprehensive priority score according to real-time status data and basic weights.

[0050] The resource scheduling module is used to output resource scheduling decisions based on comprehensive priority scores for single-resource multi-device conflict scenarios and multi-resource distributed scenarios.

[0051] The strategy update module is used to continuously track changes in device parameters, power consumption, and resource status based on resource scheduling decisions. When preset trigger conditions are met, priority recalculation and scheduling strategy update are triggered, and scheduling evaluation results are output.

[0052] The weight optimization module is used to optimize the basic weights of priority evaluation indicators based on the scheduling evaluation results.

[0053] The present invention provides a method and system for real-time mobile phone charging optimization under dynamic environments using the W algorithm. By establishing a unique identifier association between the device to be charged and the charging resource and collecting dynamic environmental variables, multi-dimensional real-time status data is constructed. Combined with the W algorithm's comprehensive priority evaluation and scenario-based scheduling strategy, accurate matching of charging resources is achieved. At the same time, a dynamic recalculation mechanism based on triggering conditions such as power changes, schedule adjustments, and resource anomalies, combined with the particle swarm optimization algorithm for continuous iterative optimization of weight factors, significantly improves the real-time performance, adaptability, and resource utilization of charging scheduling under dynamic environments. It takes into account both user needs and environmental constraints, effectively reduces device waiting time, and improves the user charging experience. Attached Figure Description

[0054] 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.

[0055] Figure 1 This is a flowchart of the W-algorithm-based real-time charging optimization method for mobile phones under dynamic environments provided in this embodiment of the invention.

[0056] Figure 2 This is a flowchart of optimizing the basic weights of priority evaluation indicators provided in an embodiment of the present invention;

[0057] Figure 3 This is a schematic diagram of the structure of the W algorithm-based real-time charging optimization system for mobile phones under dynamic environments provided in an embodiment of the present invention. Detailed Implementation

[0058] 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.

[0059] Example 1:

[0060] The following is combined with Figures 1-3 This invention describes a method and system for optimizing real-time mobile phone charging under dynamic environments using the W algorithm.

[0061] like Figures 1-2 As shown in the embodiment of the present invention, the W-algorithm-based real-time charging optimization method for mobile phones under dynamic environments includes:

[0062] S1: Establish a unique identifier association between the device to be charged and the available charging resources, and mark the physical characteristics and scheduling execution boundaries of each charging resource.

[0063] Step S1, the step of establishing a unique identifier association between the device to be charged and the available charging resources includes:

[0064] S11: Assign a unique device identifier to the device to be charged, including model number, user, and hardware characteristics. Specifically, the unique device identifier adopts a composite coding rule of "user ID, device model, and hardware hash value". The hardware characteristics cover key parameters such as battery capacity, charging interface type, and supported fast charging protocols to ensure the global uniqueness and traceability of the device identity.

[0065] S12: Assign a unique resource identifier to available charging resources, including type, deployment location, and device number. Combine this unique device identifier with the identifier to establish an identifier management system. The resource identifier adopts the format of "regional code, resource type, deployment serial number" and also marks the physical characteristics of the resource, including maximum output power (e.g., 65W / 120W), supported charging protocols, number of interfaces, and heat dissipation capacity, forming a resource metadata file.

[0066] S13: Based on the identifier management system, an identifier mapping mechanism is used to associate the device to be charged with available charging resources, recording the types of charging resources that the device can adapt to and historical matching records. The identifier mapping mechanism uses a bidirectional hash table for storage, with the key being the device identifier and the value being a list of compatible resource identifiers; at the same time, a historical matching log is constructed to record the past pairing results (success / failure) between the device and the resource, charging efficiency, and user feedback, which are used for subsequent compatibility prediction.

[0067] S2: Based on unique identifier association, obtain device usage status, subsequent schedule information, device association relationships and dynamic environmental variables, and output real-time status data.

[0068] Step S2, the steps for outputting real-time status data, include:

[0069] S21: Obtain the usage status data of the device to be charged through the device system interface. Specifically, this is done by calling Android's BatteryManager API or iOS's UIDevice framework to collect real-time operating parameters such as battery level (SOC), charging current / voltage, device temperature, currently running background processes, and screen brightness.

[0070] S22: Extract the calendar information of the associated user of the device to be charged through a user-authorized calendar management application. Connect to the open API of mainstream calendar applications to obtain the user's schedule (meetings, travel, to-do items) for the next 24 hours, analyze key time nodes (such as needing to be fully charged 30 minutes before the meeting starts) and mark the urgency of the device's use.

[0071] S23: Based on the identifier management system, query the linkage relationship between the device to be charged and other devices, and record the dependency logic of charging needs between each device. For example, user A's mobile phone and tablet have a "primary device priority charging" logic (when the mobile phone battery is below 20%, the tablet charging task is delayed), or in a family scenario, a child's watch needs to be started after the parent's mobile phone is charged. Such dependency relationships are stored through the device association graph.

[0072] S24: Collect dynamic environmental variable data, including ambient temperature, power supply voltage stability, and current load of the charging resource. Ambient temperature is collected in real time by temperature and humidity sensors deployed near the charging resource; power supply voltage stability is measured by the power quality monitoring module to measure voltage fluctuation range (e.g., 220V±5%) and frequency (50Hz±0.2Hz); load statistics show the number of devices currently connected to the resource and the total power percentage (e.g., "Current load 60%, remaining capacity can support 2 65W devices").

[0073] S25: This function integrates usage status data, associated user schedule information, charging requirement dependencies between devices, and environmental variable data into a structured format according to a preset output format, outputting standardized real-time status data. JSON format is used for encapsulation to ensure consistency and efficiency in cross-module data interaction.

[0074] S3: Presets the basic weights of priority evaluation indicators, and calculates the comprehensive priority score based on the W algorithm, according to real-time status data and basic weights.

[0075] Step S3, which involves setting the basic weights of the priority evaluation indicators, includes:

[0076] S31: Determine the priority evaluation index system, which includes current power level, ambient temperature, user priority, and current load of charging resources.

[0077] S32: The initial weights of each indicator are calculated using historical charging data statistics. Based on the charging logs of the past 6 months, the correlation between each indicator and charging efficiency is analyzed using Pearson correlation coefficients. For example, the correlation coefficient between SOC and efficiency is 0.72, and the correlation coefficient between ambient temperature and efficiency is -0.65. Initial weights are assigned accordingly.

[0078] S33: Adjust the initial weights for typical application scenarios to form scenario-based initial weights. Divide the scenarios into three categories: office, home, and public transportation. For office scenarios, increase the weight of schedule urgency (from 0.15 to 0.22), for home scenarios, strengthen the weight of related device dependence (from 0.1 to 0.18), and for public transportation scenarios, increase the weight of resource adaptability (from 0.1 to 0.15).

[0079] S34: Verify the rationality of the initial weights in the scenario through simulated scheduling tests and output the simulation test results. Construct a virtual charging scenario (including 50 devices and 20 resources) based on the AnyLogic simulation platform, input historical data to drive the simulation, and evaluate the impact of indicator weights on average charging waiting time and user complaint rate. Based on the simulation test results, readjust the corresponding indicator weights until the preset optimization objective is met, obtaining the basic weights. The AnyLogic simulation platform is a multi-paradigm simulation platform widely used for modeling, analyzing, and optimizing complex systems. Its core advantage lies in supporting the integration of three mainstream methods: Discrete Event Simulation (DES), System Dynamics (SD), and Multi-Agent Simulation (MAS), allowing users to flexibly select or combine modeling methods according to problem requirements, making it particularly suitable for solving complex system problems involving dynamic, stochastic, and multi-factor interactions.

[0080] In step S3, the W algorithm formula is expressed as:

[0081]

[0082] In the formula, W is the dynamic weighting factor, SOC is the battery state of charge, T is the ambient temperature, and P is the weighting factor. user G is used to set priority. load Let α, β, γ, and δ represent the current load of the charging resources, and let SOC, T, and P be the load values, respectively. user G load The dynamic weighting coefficients are typically set as follows: α=0.4, β=0.2, γ=0.3, δ=0.1.

[0083] When the battery temperature is detected to be above 40°C, the β coefficient automatically increases, and the charging power is reduced. During off-peak hours at night, the weight of the γ coefficient is automatically increased.

[0084] Step S3, the steps for calculating the overall priority score, include:

[0085] S35: Extract feature values ​​of priority evaluation indicators for each device to be charged from real-time status data. For example, device A has a SOC of 0.3 (30% battery remaining), an ambient temperature of T of 0.5 (30℃), and a user priority P. user =0.8 (recharge required 1 hour before the meeting), resource load G load =0.6 (current load 60%).

[0086] S36: Standardize the feature values ​​of each priority evaluation indicator and output standardized feature values. The min-max normalization method is used for standardization; for example, a SOC original value of 30% corresponds to a standardized value of 0.3 (assuming the SOC range is 0%-100%).

[0087] S37: Based on the W algorithm, the standardized feature values ​​are multiplied by the corresponding basic weights to obtain the individual scores of each indicator.

[0088] S38: Accumulate the individual scores of all indicators to obtain the comprehensive priority score of the device to be charged. If there are multiple devices, sort them from highest to lowest score to form a priority sequence. The score range is [0,1], with higher scores indicating higher priority scheduling.

[0089] S4: For single-resource multi-device conflict scenarios and multi-resource distributed scenarios, output resource scheduling decisions based on comprehensive priority scores.

[0090] Step S4, the steps for outputting resource scheduling decisions, include:

[0091] S41: For scenarios involving multiple devices conflicting on a single resource, retrieve the maximum output power, current idle time period, and compatible device type of the charging resource. Compare the comprehensive priority scores of the conflicting devices, prioritize allocating charging resources to the device with the highest comprehensive priority score, determine the charging start time and charging duration, and generate a single resource scheduling decision table.

[0092] S42: For multi-resource distributed scenarios, construct a device resource matching matrix. Rows in the matrix represent devices to be charged, columns represent available charging resources, and element values ​​are the combined priority score of the device and resource multiplied by the remaining capacity of the resource. The Hungarian algorithm is used to solve for the maximum weighted matching scheme in the matching matrix, ensuring the global total score is maximized, and generating a multi-resource scheduling decision table that includes device-resource pairings, charging duration, and expected completion time.

[0093] S43: Synchronize the single-resource scheduling decision table and the multi-resource scheduling decision table to the charging resource management module and the user terminal, triggering the pre-start preparation of charging resources. Push decision instructions through the MQTT message queue, the resource management module controls the pre-insertion and uninsertion of the charging gun and adjusts the output voltage, and the user terminal APP pushes a notification ("Your mobile phone has been scheduled to start charging at 14:00").

[0094] S5: Based on resource scheduling decisions, continuously track changes in equipment parameters, power consumption, and resource status. When preset trigger conditions are met, trigger priority recalculation and scheduling strategy update, and output scheduling evaluation results.

[0095] In step S5, the preset trigger conditions include: the rate of change of the power level of the device to be charged exceeds a preset threshold; the user modifies the schedule information, causing the urgency of the device's usage time to increase; the charging resources are in an abnormal state, including output power fluctuations exceeding 20%, temperature exceeding a safety threshold, and sudden failures; the number of newly added devices to be charged exceeds 120% of the current resource capacity; the power supply voltage fluctuation in the dynamic environmental variables exceeds ±10% of the rated value; changes in ambient temperature cause a decrease in charging efficiency exceeding 15%; and the time since the last priority calculation exceeds a preset period, with some devices still not fully charged.

[0096] S6: Based on the scheduling evaluation results, optimize the basic weights of the priority evaluation indicators.

[0097] Step S6, which involves optimizing the basic weights of the priority evaluation indicators, includes:

[0098] S61: Extract optimization data from the scheduling evaluation results. The evaluation results include charging completion rate (target ≥ 95%), average user waiting time (target ≤ 10 minutes), resource utilization rate (target ≥ 80%), and user complaint rate (target ≤ 0.5%), forming a multi-objective optimization dataset.

[0099] S62: Establish a weight optimization objective function, and use the particle swarm optimization algorithm to iteratively optimize the basic weights to find the optimal weight combination. The particle swarm optimization algorithm is set with a population size of 50, 100 iterations, and the inertia weight decreasing linearly from 0.9 to 0.4.

[0100] S63: Verify and test the optimal weight combination. Select a preset number of sets of actual data from different scenarios for scheduling simulation and output the simulation results. If the simulation results meet all optimization objectives, they are determined as the new basic weights. If not, adjust the algorithm's inertia weights and iterate again to output the optimal basic weights. Select 10 sets of historical data from each of the three scenarios (office, home, and travel) to simulate the scheduling process under the new weights and statistically analyze indicators such as completion rate and waiting time. If the completion rate for all scenarios is ≥96% and the waiting time is ≤8 minutes, the new weights are confirmed; otherwise, adjust the algorithm's learning factors and iterate again.

[0101] S64: Store the optimal base weights in the weight database and set the weight update cycle (once a week by default, or triggered after major scenario changes (such as holidays or new device launches)). The database uses Redis to cache the latest weights, ensuring that the real-time scheduling module can read them quickly.

[0102] In summary, the W-algorithm-based real-time charging optimization method for mobile phones in dynamic environments establishes a unique identifier association between the device to be charged and the charging resource, collects dynamic environmental variables, constructs multi-dimensional real-time status data, and combines the W-algorithm's comprehensive priority evaluation and scenario-based scheduling strategy to achieve precise adaptation of charging resources. Simultaneously, the dynamic recalculation mechanism based on triggering conditions such as power changes, schedule adjustments, and resource anomalies, combined with the particle swarm optimization algorithm for continuous iterative optimization of weight factors, significantly improves the real-time performance, adaptability, and resource utilization of charging scheduling in dynamic environments, balancing user needs and environmental constraints, effectively reducing device waiting time and improving the user charging experience.

[0103] like Figure 3 As shown, this invention also provides a real-time charging optimization system for mobile phones using the W algorithm under dynamic environments, including:

[0104] The identification association module is used to establish a unique identification association between the device to be charged and the available charging resources, and to mark the physical characteristics and scheduling execution boundaries of each charging resource.

[0105] The data acquisition and processing module is used to obtain equipment usage status, subsequent schedule information, equipment association relationships and dynamic environmental variables based on unique identifier association, and output real-time status data.

[0106] The priority calculation module is used to preset the basic weights of priority evaluation indicators and, based on the W algorithm, calculate the comprehensive priority score according to real-time status data and basic weights.

[0107] The resource scheduling module is used to output resource scheduling decisions based on comprehensive priority scores for single-resource multi-device conflict scenarios and multi-resource distributed scenarios.

[0108] The strategy update module is used to continuously track changes in device parameters, power consumption, and resource status based on resource scheduling decisions. When preset trigger conditions are met, priority recalculation and scheduling strategy updates are triggered, and scheduling evaluation results are output.

[0109] The weight optimization module is used to optimize the basic weights of priority evaluation indicators based on the scheduling evaluation results.

[0110] 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.

[0111] 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 mobile phone real-time charging optimization method of W algorithm in a dynamic environment, characterized in that, Comprise: S1: establish the unique identification association of the to-be-charged device and the available charging resource; S2: based on the unique identification association, obtain the device usage state, subsequent schedule information, device association relationship and dynamic environment variable, and output real-time state data; S3: preset the basic weight of the priority evaluation index, and based on the W algorithm, calculate the comprehensive priority score according to the real-time state data and the basic weight; In step S3, the W algorithm formula is represented as: In the formula, W is a dynamic weight factor, SOC is the battery state of charge, T is the ambient temperature, P user is the user priority, G load is the current load of available charging resources, and α, β, γ, δ are dynamic weight coefficients of SOC, T, P user , and G load , respectively. In step S3, the step of calculating the comprehensive priority score comprises: S35: extracting the characteristic value of each priority evaluation index corresponding to the to-be-charged device from the real-time state data; S36: standardizing the characteristic value of each priority evaluation index, and outputting the standardized characteristic value; S37: based on the W algorithm, multiplying the standardized characteristic value by the corresponding basic weight to obtain each index single score; S38: all the index single scores are added to obtain the comprehensive priority score of the to-be-charged device, if there are multiple devices, then the scores are sorted from high to low to form a priority sequence; S4: for single-resource multi-device conflict scenarios and multi-resource distributed scenarios, output resource scheduling decisions according to the comprehensive priority score; In step S4, the step of outputting the resource scheduling decision comprises: S41: for the single-resource multi-device conflict scenario, the maximum output power, the current idle period and the adaptive device type of the available charging resource are called; the comprehensive priority score of the conflict device is compared, and the charging resource is preferentially allocated to the device with the highest comprehensive priority score to determine the charging start time and charging duration, and a single-resource scheduling decision table is generated; S42: for the multi-resource distributed scenario, a device resource matching matrix is constructed; the Hungarian algorithm is used to solve the maximum weight matching scheme of the matching matrix to generate a multi-resource scheduling decision table; S43: the single-resource scheduling decision table and the multi-resource scheduling decision table are synchronized to the charging resource management module and the user terminal to trigger the pre-start preparation of the charging resource; S5: based on the resource scheduling decision, continuously track the device parameters, power and resource state changes, and trigger priority recalculation and scheduling strategy update when the preset trigger condition is met, and output the scheduling evaluation result; S6: based on the scheduling evaluation result, the basic weight of the priority evaluation index is optimized.

2. The mobile phone real-time charging optimization method of W-algorithm in dynamic environment according to claim 1, characterized in that, In step S1, the step of establishing the unique identification association of the to-be-charged device and the available charging resource comprises: S11: allocating a unique device identification containing model, user, and hardware features to the to-be-charged device; S12: allocating a unique resource identification containing type, deployment location, and device number to the available charging resource, and establishing an identification management system in combination with the unique device identification; S13: based on the identification management system, the to-be-charged device and the available charging resource are associated through an identification mapping mechanism, and the device-adaptable charging resource type and historical matching record are recorded.

3. The method of claim 2, wherein, In step S2, the step of outputting the real-time state data comprises: S21: obtaining the usage state data of the to-be-charged device through a device system interface; S22: Extracting schedule information of the associated user of the to-be-charged device through a user-authorized schedule management application; S23: Querying a linkage relationship between the to-be-charged device and other devices based on an identification management system, and recording a dependency logic of charging demand between devices; S24: Collecting dynamic environmental variable data, including environmental temperature of an area where available charging resources are located, power supply voltage stability, and current load of the available charging resources; S25: Structurally integrating the use state data, the schedule information of the associated user, the dependency logic of charging demand between devices, and the dynamic environmental variable data according to a preset output format, and outputting standardized real-time state data.

4. The method of claim 1, wherein, In step S3, the step of determining the basic weight of the preset priority evaluation index includes: S31: Determining a priority evaluation index system; S32: Calculating initial weights of each index by using a historical charging data statistical method; S33: Adjusting the initial weights for a typical application scenario to form a scenario-based initial weight; S34: Verifying the rationality of the scenario-based initial weight by simulation scheduling test, outputting a simulation test result, readjusting the weight of the corresponding index according to the simulation test result until a preset optimization target is met, and obtaining the basic weight.

5. The method of claim 1, wherein, In step S5, the preset trigger condition includes: a power change rate of the to-be-charged device exceeds a preset threshold; a user modifies schedule information, resulting in an emergency level of device use period being improved; an available charging resource appears an abnormal state, including an output power fluctuation exceeding 20%, a temperature exceeding a safety threshold, and a sudden failure; a number of newly added to-be-charged devices exceeds 120% of a current resource carrying capacity; a power supply voltage fluctuation in a dynamic environmental variable exceeds a rated value; an environmental temperature change causes a charging efficiency to decrease by more than a preset range; a distance from a last priority calculation time exceeds a preset period, and there is a device with incomplete charging.

6. The method of claim 1, wherein, In step S6, the step of optimizing the basic weight of the priority evaluation index includes: S61: Extracting optimization basis data from the scheduling evaluation result; S62: Establishing a weight optimization objective function, iteratively optimizing the basic weight by using a particle swarm optimization algorithm, and finding an optimal weight combination; S63: Verifying the optimal weight combination, selecting actual data of different scenes of a preset group number for scheduling simulation, outputting a simulation result, determining a new basic weight if the simulation result meets all optimization targets, adjusting an algorithm inertia weight to reiterate if the simulation result does not meet all optimization targets, and outputting an optimal basic weight; S64: Storing the optimal basic weight to a weight database, and setting a weight update period.

7. A mobile phone real-time charging optimization system under dynamic environment using the mobile phone real-time charging optimization method under dynamic environment according to any one of claims 1 to 6, characterized in that, The identification association module is configured to establish a unique identification association between the to-be-charged device and the available charging resource, and mark physical characteristics and scheduling execution boundaries of each available charging resource; The data acquisition and processing module is configured to acquire device use state, subsequent schedule information, device association relationship, and dynamic environmental variable based on the unique identification association, and output real-time state data. ​ A priority calculation module is configured to preset a basic weight of a priority evaluation index, and calculate a comprehensive priority score based on a W algorithm and the real-time state data and the basic weight; A resource scheduling module is configured to output a resource scheduling decision according to the comprehensive priority score for a single-resource multi-device conflict scenario and a multi-resource distributed scenario; A strategy updating module is configured to continuously track changes in device parameters, power and resource states based on the resource scheduling decision, trigger priority recalculation and scheduling strategy updating when a preset triggering condition is met, and output a scheduling evaluation result; A weight optimization module is configured to optimize the basic weight of the priority evaluation index based on the scheduling evaluation result.

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