Mobile phone real-time charging optimization method and system based on W algorithm in dynamic environment

Abstract

The invention provides a W-algorithm-based mobile phone real-time charging optimization method and system in a dynamic environment, and relates to the technical field of mobile phone charging optimization, and the method comprises the steps: building a unique identifier association between a to-be-charged device and an available charging resource; acquiring an equipment use state, subsequent schedule information, an equipment association relationship and a dynamic environment variable; calculating a comprehensive priority score based on a W algorithm; outputting resource scheduling decisions for different resource scenes; continuously tracking equipment parameters, electric quantity and resource state changes; and optimizing the basic weight of the priority evaluation index. According to the invention, by combining the W algorithm, the priority evaluation and the scenarized scheduling strategy are integrated, so that the accurate adaptation of the charging resources is realized; based on a dynamic recalculation mechanism of triggering conditions such as electric quantity change, schedule adjustment and resource abnormity, and continuous iterative optimization of weight factors, the real-time performance, the adaptability and the resource utilization rate of charging scheduling in a dynamic environment are improved, the equipment waiting time is effectively shortened, and the charging experience of a user is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of mobile phone charging optimization, and particularly relates to a mobile phone real-time charging optimization method and system of W algorithm in a dynamic environment. BACKGROUND

[0002] Early linear chargers were the core architecture, relying on USB BC1.2 and other basic charging specifications to achieve 5V / 1A standard charging output. The technical core is to reduce the input voltage to the battery adaptation range through a linear voltage regulator circuit, while cooperating with a simple current limiting module to prevent overcurrent. With the emergence of lithium-ion batteries as the mainstream energy storage carrier of mobile phones, their 3.0-4.2V nominal voltage range and constant current-constant voltage charging curve characteristics have driven the charging technology to adapt to these electrochemical characteristics, giving rise to specialized charging management chips for lithium-ion batteries that can monitor battery terminal voltage and current in real time and match the parameter requirements of the battery charging phase.

[0003] Charging optimization technology takes the battery management system (BMS) as the core, integrating 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 charging parameter adjustment. In addition, charging strategies at the mobile phone system level and hardware coordination mechanisms have gradually improved, such as through operating system scheduling to achieve dynamic power distribution to CPU, screen, and other power consumption modules during charging, and combining cell balancing technology to balance and adjust the voltage of each cell in a multi-cell series battery pack, ensuring consistent state of each cell during charging.

[0004] General mobile phone real-time charging optimization methods do not fully collect dynamic environmental variables and user-related information, relying only on static or single-dimensional data to evaluate priority, resulting in rigid scheduling strategies that cannot adapt to environmental changes and user demand fluctuations. Traditional scheduling algorithms have difficulty handling single-resource multi-device conflicts and multi-resource distributed scenarios simultaneously, which can lead to resource contention or idling, reducing overall utilization. Moreover, there is a lack of dynamic trigger re-computation mechanism, which cannot update the scheduling strategy in a timely manner when device power suddenly changes, user schedules are adjusted, or resources are abnormal, often resulting in long waiting times for devices or resource overloading.

[0005] To solve the above-mentioned deficiencies in the prior art, the present technical solution provides a mobile phone real-time charging optimization method of W algorithm in a dynamic environment. SUMMARY

[0006] The present application provides a mobile phone real-time charging optimization method of W algorithm in a dynamic environment to solve the deficiencies in the prior art.

[0007] In one aspect, the present application provides a mobile phone real-time charging optimization method of W algorithm in a dynamic environment, comprising: S1: Establishing an association between a unique identifier of a device to be charged and an available charging resource; S2: Based on the association of the unique identifier, obtaining a device usage state, subsequent schedule information, device association relationship and dynamic environment variable, and outputting real-time state data; S3: Predefining a basic weight of a priority evaluation index, and based on the W algorithm, calculating a comprehensive priority score according to the real-time state data and the basic weight; S4: For a single-resource multi-device conflict scenario and a multi-resource distributed scenario, outputting a resource scheduling decision according to the comprehensive priority score; S5: Based on the resource scheduling decision, continuously tracking changes in device parameters, power and resource states, triggering priority recalculation and scheduling strategy updating when a preset triggering condition is met, and outputting a scheduling evaluation result; S6: Based on the scheduling evaluation result, optimizing the basic weight of the priority evaluation index.

[0008] According to the mobile phone real-time charging optimization method of the W algorithm in a dynamic environment provided by the application, the step S1 of establishing an association between a unique identifier of a device to be charged and an available charging resource comprises: S11: Assigning a unique device identifier containing a model, a user to which the device belongs and a hardware feature to the device to be charged; S12: Assigning a unique resource identifier containing a type, a deployment location and a device number to the available charging resource, and establishing an identifier management system in combination with the unique device identifier; S13: Based on the identifier management system, associating the device to be charged with the available charging resource through an identifier mapping mechanism, and recording a charging resource type to which the device is adaptable and a historical matching record.

[0009] According to the mobile phone real-time charging optimization method of the W algorithm in a dynamic environment provided by the application, the step S2 of outputting real-time state data comprises: S21: Obtaining usage state data of the device to be charged through a device system interface; S22: Extracting schedule information of an associated user of the device to be charged through a user-authorized schedule management application; S23: Based on the identifier management system, querying a linkage relationship between the device to be charged and other devices, and recording a charging demand dependency logic between the devices; S24: Collecting dynamic environment variable data, including an environmental temperature of an area where the charging resource is located, a power supply voltage stability and a current load of the charging resource; S25: Structurally integrating the usage state data, the schedule information of the associated user, the charging demand dependency logic between the devices and the environmental variable data according to a preset output format, and outputting standardized real-time state data.

[0010] The mobile phone real-time charging optimization method of the W algorithm in a dynamic environment provided by the application comprises the following steps of S1-S4. 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 typical application scenarios to form scenario-based initial weights; S34: verifying the rationality of the scenario-based initial weights by simulation scheduling test, outputting simulation test results; according to the simulation test results, the weights of corresponding indexes are readjusted until a preset optimization target is met, and the basic weights are obtained.

[0011] The mobile phone real-time charging optimization method of the W algorithm in a dynamic environment provided by the application comprises the following steps of S1-S4. In the formula, W is a dynamic weight factor, SOC is a battery power state, T is an environmental temperature, P is a power consumption of a device, and G is a current load of a charging resource. user is used for setting a priority, G load is a current load of a charging resource, and α, β, γ and δ are dynamic weight coefficients of SOC, T, P user and G load respectively.

[0012] The mobile phone real-time charging optimization method of the W algorithm in a dynamic environment provided by the application comprises the following steps of S1-S4. S35: extracting feature values of priority evaluation indexes corresponding to each device to be charged from real-time state data; S36: performing standardization processing on the feature values of each priority evaluation index, and outputting standardized feature values; S37: multiplying the standardized feature values and corresponding basic weights based on the W algorithm to obtain single-item scores of each index; S38: accumulating all single-item scores of indexes to obtain a comprehensive priority score of the device to be charged; if there are multiple devices, the devices are sorted in descending order of the scores to form a priority sequence.

[0013] The mobile phone real-time charging optimization method of the W algorithm in a dynamic environment provided by the application comprises the following steps of S1-S4. S41: for a single-resource and multiple-device conflict scenario, the maximum output power, the current idle period and the adaptive device type of the charging resource are called; the comprehensive priority scores of conflict devices are compared, and the charging resource is preferentially allocated to the device with the highest comprehensive priority score to determine a charging start time and a charging duration, and a single-resource scheduling decision table is generated; S42: For a multi-resource distributed scenario, a device resource matching matrix is constructed; a Hungarian algorithm is used to solve the maximum weight matching scheme of the matching matrix, and a multi-resource scheduling decision table is generated; 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, triggering the pre-start preparation of the charging resource.

[0014] According to the mobile phone real-time charging optimization method of the W algorithm in a dynamic environment provided by the application, the preset trigger conditions in step S5 include: the power change rate of the device to be charged exceeds the preset threshold; the user modifies the schedule information, resulting in an emergency level increase of the device use period; the charging resource appears an abnormal state, including an output power fluctuation exceeding 20%, a temperature exceeding a safety threshold, and a sudden failure; the number of newly added devices to be charged exceeds 120% of the current resource carrying capacity; the power supply voltage fluctuation in the dynamic environment variable exceeds the rated value; the environmental temperature change causes the charging efficiency to decrease by more than a preset range; the distance from the last priority calculation time exceeds a preset period, and there is a device that has not completed charging.

[0015] According to the mobile phone real-time charging optimization method of the W algorithm in a dynamic environment provided by the application, the step of optimizing the basic weight of the priority evaluation index in step S6 includes: S61: Extract optimization basis data from the scheduling evaluation results; S62: Establish a weight optimization objective function, and use a particle swarm optimization algorithm to iteratively optimize the basic weight to find the optimal weight combination; S63: Verify the optimal weight combination, select actual data of different scenes of a preset group number for scheduling simulation, and output the simulation results; if the simulation results meet all optimization objectives, the new basic weight is determined; if not, adjust the algorithm inertia weight and reiterate to output the optimal basic weight; S64: Store the optimal basic weight in the weight database and set a weight update period.

[0016] The application also provides a mobile phone real-time charging optimization system of the W algorithm in a dynamic environment, which includes: an identification association module for establishing a unique identification association between the device to be charged and the available charging resource, and labeling the physical characteristics and scheduling execution boundaries of each charging resource; A data acquisition and processing module is used to obtain the device usage state, subsequent schedule information, device association relationship and dynamic environment variables based on the unique identification association, and output real-time state data; A priority calculation module is used to preset the basic weight of the priority evaluation index, and calculate the comprehensive priority score based on the W algorithm and the real-time state data and the basic weight; The 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. The policy updating module is configured to continuously track device parameters, power and resource state changes based on the resource scheduling decision, trigger priority recalculation and scheduling policy updating when a preset trigger condition is met, and output a scheduling evaluation result. The weight optimization module is configured to optimize the basic weight of the priority evaluation index based on the scheduling evaluation result.

[0017] The mobile phone real-time charging optimization method and system of the W algorithm in a dynamic environment provided by the application realize accurate adaptation of charging resources by establishing a unique identifier association between the device to be charged and the charging resource and collecting dynamic environmental variables, constructing multi-dimensional real-time state data, and combining W algorithm comprehensive priority evaluation and scenario-based scheduling strategy. At the same time, based on the dynamic recalculation mechanism of power change, schedule adjustment, resource anomaly and other trigger conditions, the particle swarm optimization algorithm is used for continuous iterative optimization of the weight factor, which significantly improves the real-time performance, adaptability and resource utilization rate of charging scheduling in a dynamic environment, takes into account user demand and environmental constraints, effectively reduces device waiting time and improves user charging experience. BRIEF DESCRIPTION OF DRAWINGS

[0018] In order to more clearly illustrate the technical solutions in the application or prior art, the following will briefly introduce the drawings needed to be used in the embodiments or prior art description. Obviously, the drawings in the following description are some embodiments of the application, and those skilled in the art can also obtain other drawings according to these drawings without creative labor.

[0019] Fig. 1 is a flowchart of the mobile phone real-time charging optimization method of the W algorithm in a dynamic environment provided by the embodiment of the application; Fig. 2 is a flowchart of the optimization of the basic weight of the priority evaluation index provided by the embodiment of the application; Fig. 3 is a structural schematic diagram of the mobile phone real-time charging optimization system of the W algorithm in a dynamic environment provided by the embodiment of the application. DETAILED DESCRIPTION

[0020] In order to make the purpose, technical scheme and advantages of the application more clear, the technical scheme in the application will be described clearly and completely in the following combined with the drawings in the application. Obviously, the described embodiments are part of the embodiments of the application, not all the embodiments. Based on the embodiments in the application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the application.

[0021] Embodiment one: The mobile phone real-time charging optimization method and system of the W algorithm in a dynamic environment are described below. Figs. 1-3 The mobile phone real-time charging optimization method and system of the W algorithm in a dynamic environment are described below.

[0022] As Figs. 1-2 The mobile phone real-time charging optimization method and system of the W algorithm in a dynamic environment are described below. S1: Establish the association of the unique identification of the device to be charged and the available charging resource, and mark the physical characteristics and scheduling execution boundary of each charging resource.

[0023] In step S1, the step of establishing the association of the unique identification of the device to be charged and the available charging resource includes: S11: Assign a unique device identification containing the model, the user to which it belongs, and the hardware features to the device to be charged. Specifically, the unique device identification adopts a composite coding rule of "user ID device model hardware hash value", wherein the hardware features cover the battery capacity, the type of charging interface, and the supported fast charging protocol and other key parameters, to ensure the global uniqueness and traceability of the device identity.

[0024] S12: Assign a unique resource identification containing the type, deployment location, and device number to the available charging resource, and establish an identification management system in combination with the unique device identification. The resource identification adopts a format of "region code resource type deployment serial number", and simultaneously marks the resource physical characteristics, including the maximum output power (such as 65W / 120W), the supported charging protocol, the number of interfaces, and the heat dissipation capacity, etc., to form a resource metadata archive.

[0025] S13: Based on the identification management system, associate the device to be charged with the available charging resource through an identification mapping mechanism, and record the charging resource type that the device can adapt, and the historical matching record. The identification mapping mechanism adopts a bidirectional hash table storage, with the device identification as the key and the adaptive resource identification list as the value; and a historical matching log is constructed to record the past pairing results (success / failure) of the device and the resource, the charging efficiency, and the user feedback, for subsequent adaptability prediction.

[0026] S2: Based on the unique identification association, obtain the device usage state, subsequent schedule information, device association relationship, and dynamic environment variables, and output real-time state data.

[0027] In step S2, the step of outputting real-time state data includes: S21: Obtain the usage state data of the device to be charged through a device system interface. Specifically, the real-time running parameters such as the battery capacity (SOC), the charging current / voltage, the device temperature, the currently running background process, and the screen brightness are collected by calling the BatteryManager API of Android or the UIDevice framework of iOS.

[0028] S22: Extract the schedule information of the associated user of the device to be charged through the user-authorized schedule management application. Connect to the open API of the mainstream schedule application to obtain the user's schedule within the next 24 hours (meetings, travel, to-do lists), analyze the key time nodes (such as needing to be fully charged 30 minutes before the meeting starts), and mark the emergency level of the device.

[0029] S23: Based on the identity management system, query the linkage relationship between the device to be charged and other devices, and record the dependency logic of the charging demand between each device. For example, there is a "main device priority charging" logic between user A's mobile phone and tablet (the mobile phone's power is lower than 20%, and the tablet charging task is delayed), or the child watch needs to start after the parent's mobile phone is fully charged in the home scenario. Store such dependency relationships through the device association graph.

[0030] S24: Collect dynamic environmental variable data, including the environmental temperature of the area where the charging resource is located, the stability of the power supply voltage, and the current load of the charging resource. The environmental temperature is collected in real time by deploying a temperature and humidity sensor near the charging resource; the power supply voltage stability is measured by the power quality monitoring module to measure the voltage fluctuation range (such as 220V±5%) and frequency (50Hz±0.2Hz); the load capacity is the number of devices currently connected to the resource and the total power ratio (such as "current load 60%, remaining can support 2 65W devices").

[0031] S25: Structure and integrate the usage status data, the schedule information of the associated user, the dependency logic of the charging demand between each device, and the environmental variable data according to the preset output format, and output standardized real-time status data. Encapsulate in JSON format to ensure consistency and efficiency of cross-module data interaction.

[0032] S3: Pre-set the base weight of the priority evaluation index, and based on the W algorithm, calculate the comprehensive priority score according to the real-time status data and the base weight.

[0033] In step S3, the step of pre-setting the base weight of the priority evaluation index includes: S31: Determine the priority evaluation index system, which includes the current power, environmental temperature, user priority, and current load of the charging resource.

[0034] S32: Calculate the initial weight of each index using the historical charging data statistical method. Based on the charging log of the past 6 months, analyze the correlation between each index and charging efficiency through the Pearson correlation coefficient, for example, the correlation coefficient between SOC and efficiency is 0.72, and the correlation coefficient of environmental temperature is -0.65, based on which the initial weight is assigned.

[0035] S33: Adjust the initial weights for typical application scenarios to form scenario-based initial weights. Divide the office, home, and public travel scenarios into three categories. The office scenario increases the weight of the schedule urgency (from 0.15 to 0.22), the home scenario strengthens the dependence of associated devices (from 0.1 to 0.18), and the public travel scenario increases the resource adaptability weight (from 0.1 to 0.15).

[0036] S34: Verify the rationality of the scenario-based initial weights through simulation scheduling tests and output the simulation test results. Based on the AnyLogic simulation platform, a virtual charging scenario (including 50 devices and 20 resources) is constructed, historical data is input to drive the simulation, and the impact of index weight on average charging waiting time and user complaint rate is evaluated. According to the simulation test results, the corresponding index weight is adjusted again until the preset optimization goal is met, and the basic weight is obtained. The AnyLogic simulation platform is a multi-paradigm simulation platform widely used in modeling, analysis and optimization of complex systems. Its core advantage is to support the integration of 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, especially suitable for solving dynamic, random and multi-factor interactive complex system problems.

[0037] In step S3, the W algorithm formula is represented as: In the formula, W is the dynamic weight factor, SOC is the battery state of charge, T is the environmental temperature, P user is used to set the priority, G load is the current load of the charging resource, and α, β, γ, δ are the dynamic weight coefficients of SOC, T, P user , G load , respectively, which are usually set as: α = 0.4, β = 0.2, γ = 0.3, δ = 0.1.

[0038] When the battery temperature is detected to be > 40℃, the β coefficient automatically increases to reduce the charging power. The γ coefficient weight is automatically increased during the night valley electricity period.

[0039] In step S3, the steps for calculating the comprehensive priority score include: S35: Extract the feature values of the priority evaluation indicators corresponding to each device to be charged from the real-time state data. For example, the SOC of device A is 0.3 (30% remaining power), the environmental temperature T is 0.5 (30℃), the user priority P user is 0.8 (charging 1 hour before the meeting), and the resource load G load is 0.6 (current load 60%).

[0040] S36: Standardize the characteristic values of each priority evaluation indicator and output the standardized characteristic values. The minimum-maximum normalization method is used for standardization, for example, the original value of SOC 30% corresponds to the standardized value 0.3 (assuming the SOC range is 0%-100%).

[0041] S37: Based on the W algorithm, multiply the standardized characteristic values by the corresponding basic weight to obtain the individual scores of each indicator.

[0042] S38: Add up all the individual scores of the indicators to obtain the comprehensive priority score of the device to be charged. If there are multiple devices, sort them in descending order of score to form a priority sequence. The score range is [0, 1], and the higher the score, the more need for priority scheduling.

[0043] S4: For single-resource multi-device conflict scenarios and multi-resource distributed scenarios, output resource scheduling decisions based on the comprehensive priority score.

[0044] In step S4, the step of outputting the resource scheduling decision includes: S41: For single-resource multi-device conflict scenarios, retrieve the maximum output power of the charging resource, the current idle period, and the adaptive device type. Compare the comprehensive priority scores of the conflict devices and prioritize the allocation of charging resources to the device with the highest comprehensive priority score to determine the charging start time and charging duration, and generate a single-resource scheduling decision table.

[0045] S42: For multi-resource distributed scenarios, construct a device-resource matching matrix. The rows represent the devices to be charged, the columns represent the available charging resources, and the element values are the comprehensive priority scores of the devices and resources multiplied by the remaining capacity of the resources. Use the Hungarian algorithm to solve the maximum weight matching scheme of the matching matrix to maximize the global total score and generate a multi-resource scheduling decision table containing device-resource pairs, charging duration, and expected completion time.

[0046] 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 the charging resource. Push the decision instructions through the MQTT message queue, and the resource management module controls the pre-plugging and unplugging of the charging gun and adjusts the output voltage. The user terminal APP pushes a notification ("Your phone has been reserved for charging at 14:00").

[0047] S5: Based on the resource scheduling decision, continuously track the changes in device parameters, power, and resource status, and trigger priority recalculation and scheduling strategy update when the preset trigger conditions are met to output the scheduling evaluation results.

[0048] In step S5, the preset trigger condition includes that the power change rate of the device to be charged exceeds the preset threshold. The user modifies the schedule information, causing the emergency level of the device use period to increase. The charging resource appears an abnormal state, including that the output power fluctuation exceeds 20%, the temperature exceeds the safety threshold, and a sudden failure occurs. The number of newly added devices to be charged exceeds 120% of the current resource carrying capacity. In the dynamic environment variable, the power supply voltage fluctuation exceeds ±10% of the rated value. The environmental temperature change causes the charging efficiency to decrease by more than 15%. The time since the last priority calculation exceeds the preset period, and there is a device that has not completed charging.

[0049] S6: Based on the scheduling evaluation result, the base weight of the priority evaluation index is optimized.

[0050] In step S6, the step of optimizing the base weight of the priority evaluation index includes: S61: Extract optimization basis data from the scheduling evaluation result. The evaluation result includes the charging completion rate (target ≥ 95%), the average user waiting time (target ≤ 10 minutes), the resource utilization rate (target ≥ 80%), and the user complaint rate (target ≤ 0.5%), forming a multi-objective optimization data set.

[0051] S62: Establish a weight optimization objective function, and use a particle swarm optimization algorithm to iteratively optimize the base weight to find the optimal weight combination. The particle swarm optimization algorithm sets the population size to 50, the iteration number to 100, and the inertia weight to linearly decrease from 0.9 to 0.4.

[0052] S63: Verify the optimal weight combination, select actual data of different scenes in a preset group number for scheduling simulation, and output the simulation result. If the simulation result meets all optimization objectives, it is determined as the new base weight. If not, adjust the algorithm inertia weight and reiterate to output the optimal base weight. Select 10 groups of historical data for each of the office, home, and travel scenes to simulate the scheduling process under the new weight, and calculate the completion rate, waiting time, and other indicators. If the completion rate of all scenes is ≥ 96% and the waiting time is ≤ 8 minutes, the new weight is confirmed; otherwise, adjust the algorithm learning factor and reiterate.

[0053] S64: Store the optimal base weight in the weight database and set the weight update period (default is once a week, or trigger an update after a major scene change (such as a holiday or new device online)). The database uses Redis to cache the latest weight to ensure that the real-time scheduling module can quickly read it.

[0054] In summary, the mobile phone real-time charging optimization method of the W algorithm in a dynamic environment, by establishing the unique identifier association of the to-be-charged device and the charging resource and dynamic environment variable collection, constructs multi-dimensional real-time state data, combines the W algorithm comprehensive priority evaluation and the scenario-based scheduling strategy, realizes the accurate adaptation of the charging resource; at the same time, based on the dynamic recalculation mechanism of the trigger conditions such as power change, schedule adjustment, resource anomaly, and the continuous iterative optimization of the weight factor by the particle swarm optimization algorithm, the real-time performance, adaptability and resource utilization rate of the charging scheduling in a dynamic environment are significantly improved, the user demand and environmental constraints are considered, the device waiting time is effectively reduced and the user charging experience is improved.

[0055] As shown in Fig. 3 The application also provides a mobile phone real-time charging optimization system of the W algorithm in a dynamic environment, comprising: An identifier association module is configured to establish the unique identifier association of the to-be-charged device and the available charging resource, and label the physical characteristics and scheduling execution boundary of each charging resource.

[0056] A data collection and processing module is configured to acquire the device usage state, subsequent schedule information, device association relationship and dynamic environment variable based on the unique identifier association, and output real-time state data.

[0057] A priority calculation module is configured to preset the basic weight of the priority evaluation index, and calculate the comprehensive priority score based on the real-time state data and the basic weight based on the W algorithm.

[0058] A resource scheduling module is configured to output the resource scheduling decision according to the comprehensive priority score for the single-resource multi-device conflict scene and the multi-resource distributed scene.

[0059] A strategy updating module is configured to continuously track the device parameter, power and resource state change based on the resource scheduling decision, trigger the priority recalculation and scheduling strategy updating when the preset trigger condition is met, and output the scheduling evaluation result.

[0060] A weight optimization module is configured to optimize the basic weight of the priority evaluation index based on the scheduling evaluation result.

[0061] Through the description of the above embodiments, those skilled in the art can clearly understand that the embodiments can be realized by means of software and the necessary general hardware platform, and of course, can also be realized by hardware. Based on such understanding, the above technical solutions can be embodied in the form of a software product, which can be stored in a computer readable storage medium such as ROM / RAM, magnetic disk, optical disk, etc., and includes a plurality of instructions for making a computer device (which can be a personal computer, server, or network device, etc.) execute the method described in each embodiment or some part of the embodiment.

[0062] Finally, it should be noted that the above examples are only used to illustrate the technical solutions of the present application, and are not intended to limit the same; although the present application has been described in detail with reference to the foregoing examples, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing examples, or make equivalent replacements for some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

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 of the device to be charged and the available charging resources associated; S2: based on the unique identification association, obtain the device usage state, subsequent schedule information, device association relationship and dynamic environment variable, output real-time state data; S3: preset priority evaluation index based weight, and based on W algorithm, according to the real-time state data and the basic weight, calculate the comprehensive priority score; S4: for single resource and multiple device conflict scene and multi resource distributed scene, according to the comprehensive priority score, output resource scheduling decision; S5: based on the resource scheduling decision, continuously track the device parameter, power and resource state change, when the preset trigger condition is met, trigger priority recalculation and scheduling strategy update, output scheduling evaluation result; S6: based on the scheduling evaluation result, the basic weight of 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 of the device to be charged and the available charging resources associated includes: S11: allocate a unique device identification containing model, user, hardware features for the device to be charged; S12: allocate a unique resource identification containing type, deployment location, device number for the available charging resource, combine the unique device identification, establish an identification management system; S13: based on the identification management system, the device to be charged and the available charging resource are associated through the identification mapping mechanism, and the device adaptable charging resource type, historical matching record are recorded.

3. The method of claim 2, wherein, In step S2, the step of outputting the real-time state data includes: S21: obtain the usage state data of the device to be charged through the device system interface; S22: through the user authorized schedule management application, extract the schedule information of the associated user of the device to be charged; S23: based on the identification management system, query the linkage relationship of the device to be charged and other devices, record the dependence logic of the charging demand among the devices; S24: collect dynamic environment variable data, including the environmental temperature of the area where the charging resource is located, the power supply voltage stability and the current load of the charging resource; S25: the usage state data, the schedule information of the associated user, the dependence logic of the charging demand among the devices and the environmental variable data are structured and integrated according to the preset output format, and the standardized real-time state data is output.

4. The method of claim 1, wherein, In step S3, the step of presetting the basic weight of priority evaluation index includes: S31: determine the priority evaluation index system; S32: calculate the initial weight of each index by using the historical charging data statistical method; S33: for typical application scene, adjust the initial weight to form the scene initial weight; S34: verify the rationality of the scene initial weight by simulation scheduling test, output simulation test result; according to the simulation test result, adjust the weight of the corresponding index again, until the preset optimization target is met, and the basic weight is obtained.

5. The mobile phone real-time charging optimization method of W-algorithm in dynamic environment according to claim 4, characterized in that, In step S3, the W algorithm formula is represented as: In the formula, W is a dynamic weight factor, SOC is the state of charge of the battery, T is the ambient temperature, P user is used to set the priority, G load is the current load of the charging resource, and α, β, γ, δ are the dynamic weight coefficients of SOC, T, P user , and G load , respectively.

6. The mobile phone real-time charging optimization method of W-algorithm in dynamic environment according to claim 5, characterized in that, In step S3, the step of calculating the comprehensive priority score includes: S35: extract the feature value of the priority evaluation index corresponding to each of the devices to be charged from the real-time state data; S36: standardize the characteristic values of each priority evaluation index, and output standardized characteristic values; S37: multiply the standardized characteristic values by corresponding basic weights based on the W algorithm to obtain individual index scores; S38: accumulate all the individual index scores to obtain a comprehensive priority score of the device to be charged, and if there are multiple devices, sort them in descending order of the score to form a priority sequence.

7. The method of claim 1, wherein, In step S4, the step of outputting the resource scheduling decision includes: S41: for the single-resource multi-device conflict scenario, retrieve the maximum output power, current idle period, and adaptive device type of the charging resource; compare the comprehensive priority scores of the conflict devices, and preferentially allocate the charging resource to the device with the highest comprehensive priority score to determine the charging start time and charging duration, and generate a single-resource scheduling decision table; S42: for the multi-resource distributed scenario, construct a device resource matching matrix; use the Hungarian algorithm to solve the maximum weight matching scheme of the matching matrix to generate a multi-resource scheduling decision table; 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 the charging resource.

8. The method of claim 1, wherein, In step S5, the preset trigger condition includes: the power change rate of the device to be charged exceeds a preset threshold; the user modifies the schedule information, resulting in an urgent increase in the device usage period; the charging resource appears an abnormal state, including an output power fluctuation exceeding 20%, a temperature exceeding a safety threshold, and a sudden failure; the number of newly added devices to be charged exceeds 120% of the current resource carrying capacity; the power supply voltage fluctuation in the dynamic environment variable exceeds the rated value; the change in ambient temperature causes the charging efficiency to decrease by more than a preset range; the time since the last priority calculation exceeds a preset period, and there is a device that has not completed charging.

9. The method of claim 1, wherein, In step S6, the step of optimizing the basic weight of the priority evaluation index includes: S61: extract optimization data from the scheduling evaluation results; S62: establish a weight optimization objective function, and use a particle swarm optimization algorithm to iteratively optimize the basic weight to find the optimal weight combination; S63: verify the optimal weight combination, select actual data of different scenes in a preset group for scheduling simulation, and output the simulation results; if the simulation results meet all optimization objectives, the new basic weight is determined; if not, adjust the algorithm inertia weight and reiterate to output the optimal basic weight; S64: store the optimal basic weight in the weight database and set a weight update period.

10. A mobile phone real-time charging optimization system of W-algorithm under dynamic environment, which adopts the mobile phone real-time charging optimization method of W-algorithm under dynamic environment as claimed in any one of claims 1 to 9, characterized in that, includes: An identification association module for establishing a unique identification association between a device to be charged and an available charging resource, and labeling the physical characteristics and scheduling execution boundaries of each charging resource; A data acquisition and processing module for obtaining device usage status, subsequent schedule information, device association relationship, and dynamic environment variables based on the unique identification association, and outputting real-time state data; The priority calculation module is configured to preset a basic weight of a priority evaluation index, and calculate a comprehensive priority score based on the real-time state data and the basic weight according to a W algorithm. The 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. The 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. The 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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