Multi-equipment energy distribution method and device, equipment, storage medium and product

Abstract

The invention discloses a multi-device energy distribution method and device, equipment, a storage medium and a product, and relates to the technical field of energy distribution.The multi-device energy distribution method comprises the steps that when energy supplementation needs to be conducted on intelligent equipment, historical behavior data of a user and historical energy data of all the intelligent equipment are obtained, and determining an energy collection plan and an equipment operation plan of each intelligent equipment in a preset future time based on historical behavior data and historical energy data, and determining an energy distribution plan of each intelligent equipment based on the energy collection plan, the equipment operation plan and the energy storage capacity of each intelligent equipment. Based on planning of energy collection and energy consumption, the energy of each device in the future time can be planned, so that each device has enough energy in the future time, the situation that the electric quantity of the other part of the device is insufficient when the electric quantity of part of the device is sufficient is prevented, and the energy supplementing effect is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of energy distribution, and particularly relates to a multi-device energy distribution method, device, equipment, storage medium and product. BACKGROUND

[0002] With the popularity of smart devices, the dependence on electric energy in outdoor sports, exploration, camping and emergency rescue scenes is significantly enhanced. If the smart device of a user is powered off during travel, it will have a great impact on the user.

[0003] The current method usually increases the energy collection system of multiple smart devices of each user, so that the user can supplement the energy of the smart device during travel. However, the energy collection efficiency and energy consumption of each smart device are different, so that sometimes when the power of part of the devices is sufficient, the power of another part of the devices will be insufficient, resulting in poor energy supplement effect. SUMMARY

[0004] The main purpose of the present application is to provide a multi-device energy distribution method, device, equipment, storage medium and product, which aims to solve the technical problem of poor energy supplement effect.

[0005] To achieve the above purpose, the present application provides a multi-device energy distribution method, which comprises: When it is necessary to supplement the energy of the smart device, the historical behavior data of the user and the historical energy data of each smart device in a preset historical time are acquired; Based on the historical behavior data and the historical energy data, the energy collection plan and the device operation plan of each smart device in a preset future time are determined; Based on the energy collection plan, the device operation plan and the energy storage capacity of each smart device, the energy distribution plan of each smart device in the future time is determined.

[0006] In an embodiment, the step of determining the energy collection plan and the device operation plan of each smart device in a preset future time based on the historical behavior data and the historical energy data comprises: Based on the historical behavior data and the historical energy data, the future energy collection data and the future energy consumption data of each smart device in the future time are determined; Based on the future energy collection data and the future energy consumption data, the energy collection plan and the device operation plan of each smart device in a preset future time are determined.

[0007] In one embodiment, the historical behavior data includes user travel status data, travel environment data, and travel route data; the historical energy data includes historical energy collection data and historical energy consumption data; and the step of determining the future energy collection data and future energy consumption data of each of the smart devices within the future time period based on the historical behavior data and the historical energy data includes: Based on the travel status data and the travel route data, determine the user's future behavior data within the future time period, and obtain future environmental data; Based on the future behavior data, the future environment data, and the historical energy collection data, the future energy collection data of each of the smart devices is determined within the future time period; Based on the future behavior data and the historical energy consumption data, the future energy consumption data of each of the smart devices within the future time period is determined.

[0008] In one embodiment, the future energy harvesting data includes future energy harvesting efficiency, and the future energy consumption data includes future energy consumption efficiency. The step of determining the energy harvesting plan and device operation plan for each of the smart devices within the preset future time period based on the future energy harvesting data and the future energy consumption data includes: Based on the future energy harvesting efficiency, the energy harvesting priority of each of the aforementioned smart devices is determined; Based on the energy harvesting priority, the energy harvesting time period and energy harvesting mode of each of the smart devices in the future time are determined to obtain the energy harvesting plan; Based on the future energy consumption efficiency, determine the device operation priority of each of the smart devices; Based on the device operation priority, the device operation time period and device operation mode of each of the smart devices in the future time are determined to obtain the device operation plan.

[0009] In one embodiment, after the steps of determining the user's future behavior data within the future time period based on the travel status data and the travel route data, and obtaining future environmental data, the method further includes: Based on the future behavior data, determine the user's energy collection needs within the future time period; Based on the future behavior data, the energy harvesting demand, and the future environmental data, energy harvesting recommendations are determined for the future time period. The energy harvesting recommendations include the recommended energy harvesting behavior, the energy value that can be harvested, and the energy harvesting increase corresponding to the energy harvesting behavior. The energy harvesting activity is communicated to the user via voice and visualized on the corresponding smart device.

[0010] In one embodiment, the step of determining the energy allocation plan for each of the smart devices within the future timeframe based on the energy harvesting plan, the device operation plan, and the energy storage capacity of each of the smart devices includes: Based on the energy harvesting plan and the equipment operation plan, the energy storage value of each of the smart devices is determined within the future time period; Based on the energy storage capacity, determine the energy storage overflow threshold and energy storage insufficiency threshold for each of the smart devices; Based on the energy storage value of each of the smart devices within the future time period, a first target smart device with an energy storage value higher than the energy storage overflow threshold and a second smart target device with an energy storage value lower than the energy storage insufficiency threshold are determined. Based on the first target device and the second target device at various points in time in the future, the energy allocation plan is determined, wherein the energy allocation plan includes allocating the energy of the first target device to the smart devices other than the first target device, and allocating the energy of the smart devices other than the second target device to the second target device.

[0011] Furthermore, to achieve the above objectives, this application also proposes a multi-device energy distribution device, which includes: The data acquisition module is used to acquire the user's historical behavior data and the historical energy data of each smart device within a preset historical time period when the smart device needs to be recharged. The planning and determination module is used to determine the energy harvesting plan and equipment operation plan of each of the smart devices within a preset future time period based on the historical behavior data and the historical energy data. An energy allocation module is used to determine the energy allocation plan for each of the smart devices within the future time period based on the energy harvesting plan, the equipment operation plan, and the energy storage capacity of each of the smart devices.

[0012] In addition, to achieve the above objectives, this application also proposes a multi-device energy distribution device, the device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the multi-device energy distribution method as described above.

[0013] In addition, to achieve the above objectives, this application also proposes a storage medium, which is a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, it implements the steps of the multi-device energy distribution method described above.

[0014] In addition, to achieve the above objectives, this application also provides a computer program product, which includes a computer program that, when executed by a processor, implements the steps of the multi-device energy distribution method described above.

[0015] One or more technical solutions proposed in this application have at least the following technical effects: When a smart device needs to be recharged, this application acquires historical behavior data of the user and historical energy data of each smart device within a preset historical time period. Based on the historical behavior data and the historical energy data, it determines the energy collection plan and device operation plan of each smart device within a preset future time period. Based on the energy collection plan, the device operation plan, and the energy storage capacity of each smart device, it determines the energy allocation plan of each smart device within the future time period.

[0016] To address the issue of varying energy harvesting efficiency and consumption among smart devices, which sometimes results in some devices having sufficient power while others have insufficient power, leading to poor energy replenishment, this application uses historical data to determine future energy harvesting and device operation plans, and then allocates energy to each device based on these plans. Since the energy harvesting and operation plans are determined from historical user and device data, future energy harvesting and consumption can be planned more rationally. Based on this energy harvesting and consumption planning, the energy availability of each device in the future can be determined, allowing for energy planning that ensures each device has sufficient energy in the future, preventing situations where some devices have sufficient power while others have insufficient power, and improving the effectiveness of energy replenishment. Attached Figure Description

[0017] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0018] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 This is a flowchart illustrating an embodiment of the multi-device energy distribution method of this application. Figure 2 This is a schematic diagram of a first scenario provided for an embodiment of the multi-device energy distribution method of this application; Figure 3 This is a flowchart illustrating Embodiment 2 of the multi-device energy distribution method of this application; Figure 4 This is a schematic diagram of the module structure of the multi-device energy distribution device according to an embodiment of this application; Figure 5 This is a schematic diagram of the device structure of the hardware operating environment involved in the multi-device energy distribution method in the embodiments of this application; Figure 6 This is a schematic diagram illustrating the data acquisition consent process involved in the multi-device energy distribution method in this application embodiment.

[0020] The purpose, features, and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0021] It should be understood that the specific embodiments described herein are merely illustrative of the technical solutions of this application and are not intended to limit this application.

[0022] To better understand the technical solution of this application, a detailed description will be provided below in conjunction with the accompanying drawings and specific implementation methods.

[0023] It should be noted that the executing entity in this embodiment can be a computing service device with data processing, network communication, and program execution functions, such as a tablet computer, personal computer, or mobile phone, or an electronic device or multi-device energy distribution device capable of performing the above functions. The following description uses a multi-device energy distribution device as an example to illustrate this embodiment and the subsequent embodiments.

[0024] With the widespread use of smart devices, the reliance on electricity in outdoor sports, adventure, camping and emergency rescue scenarios has increased significantly. If a user's smart device loses power, it will have a significant impact on the user's experience.

[0025] Current methods typically involve adding energy harvesting systems to multiple smart devices for each user, allowing them to replenish the devices' power during travel. However, the energy harvesting efficiency and energy consumption of each smart device vary, meaning that sometimes some devices have sufficient power while others are low on power, resulting in ineffective power replenishment.

[0026] In addition, current smart devices only support light or mechanical energy, which cannot cope with complex environments and are prone to overcharging, over-discharging and electrolyte leakage risks. Their energy storage safety is poor, and their materials lack flexibility, waterproof, fireproof and environmental protection properties, resulting in insufficient adaptability and durability.

[0027] Based on this, embodiments of this application provide a method for energy distribution among multiple devices, referring to... Figure 1 , Figure 1 This is a flowchart illustrating the first embodiment of the multi-device energy distribution method of this application.

[0028] In this embodiment, the multi-device energy distribution method includes steps S10 to S30: Step S10: When it is necessary to replenish the energy of the smart devices, obtain the user's historical behavior data and the historical energy data of each smart device within a preset historical time period. It should be noted that smart devices refer to wearable or portable devices that integrate energy harvesting, energy storage, and communication functions, such as smart sports shoes, smart hats, smart backpacks, and smart tents, which can independently or collaboratively power users or other electronic devices. Energy replenishment refers to the system's action of charging or injecting energy into a smart device through internal energy scheduling or external energy input when the device's energy storage module is low or when the user has external power needs. Preset historical time refers to a time window (such as the past 24 hours, the past 7 days, etc.) pre-set by the system to limit the scope of historical data collection and ensure the timeliness and representativeness of the data. User historical behavior data refers to user activity information collected through sensors and user interaction within the preset historical time period, including exercise status, travel routes, daily routines, and usage habits. Sensors include accelerometers, GPS (Global Positioning System), and heart rate monitoring modules; exercise status includes walking, running, and standing still. The historical energy data of each smart device refers to the energy-related data recorded by each smart device within the preset historical time period, including energy collection amount, changes in energy storage capacity, energy output records, number of charge and discharge cycles, energy loss, etc. Among them, energy collection amount includes, for example, light energy, piezoelectric energy, and thermal energy.

[0029] It should also be noted that in this embodiment, the smart sports shoes / insoles use piezoelectric ceramics + triboelectric nanogenerators to harvest mechanical energy during walking / running; the smart hat / clothing integrates a flexible photovoltaic film on its surface for efficient light energy harvesting; the smart tent's outer layer uses composite photovoltaic materials to harvest solar energy, its frame is embedded with a flexible wind energy film, and its bottom is equipped with a thermoelectric element to harvest energy from the diurnal temperature difference; the emergency backpack combines a photovoltaic film with a foldable wind energy unit to power walkie-talkies and medical equipment. The relationship between the various devices in this embodiment can be referred to... Figure 2Each energy harvesting module uses a USB-C Type-C 3.1 interface, supports 24V / 5A power supply, and is compatible with smartphones and outdoor equipment. The wireless charging modules between devices comply with the Qi 1.5 (Wireless Power Consortium Qi Specification Version 1.5) standard and support 5W / 10W power output.

[0030] The energy storage unit in this embodiment uses a flexible graphene supercapacitor or a solid-state battery with a self-healing electrolyte, which has a long lifespan and safety. The battery management system (BMS) has overvoltage, overcurrent, overtemperature and short-circuit protection. The energy storage substrate material has waterproof, fireproof, impact-resistant and biodegradable properties, meeting the requirements for long-term outdoor use. The flexible graphene supercapacitor has a thickness ≤0.5mm, energy density ≥15Wh / kg, cycle life ≥10000 cycles, and the self-healing electrolyte is based on a polyurethane matrix, which restores conductivity within 5 minutes after breakage (conductivity ≥100S / cm).

[0031] Understandably, when the system determines that a smart device needs energy replenishment, this embodiment will first retrieve two types of key historical data within a preset time window from local storage or the cloud platform: one is the user's behavioral pattern data during this period, such as whether they often run in the afternoon or prefer to camp at night; the other is the energy income and expenditure records of each smart device in the same time period, such as how much light energy the hat collected on a sunny day or how much mechanical energy the shoes generated during exercise. The above data will be used as the basic input for the subsequent AI prediction model and scheduling strategy formulation.

[0032] This embodiment systematically acquires historical data on user behavior and equipment energy, providing highly relevant input for subsequent intelligent prediction and dynamic scheduling. Since user behavior exhibits certain regularities, and equipment energy harvesting efficiency is highly dependent on the environment and user status, analysis based on real historical data can significantly improve the accuracy of the prediction model.

[0033] Step S20: Based on the historical behavior data and the historical energy data, determine the energy harvesting plan and equipment operation plan for each of the smart devices within a preset future time period; It should be noted that the preset future time refers to a future time window set by the system, such as the next 6 hours or the next 24 hours, used for energy supply and demand forecasting and scheduling planning. Energy harvesting planning refers to the optimal energy harvesting strategy formulated for each smart device for a future time period, including which harvesting modes to use, such as solar priority or piezoelectric activation; when to start / sleep the harvesting module; and the expected harvesting volume. Device operation planning refers to the arrangement of the working status of each smart device in the future time period, including whether to participate in energy sharing, whether to supply power externally, and whether to enter energy-saving or standby modes.

[0034] Understandably, this embodiment utilizes a built-in AI prediction model to forecast future energy supply and demand, and generates specific operational strategies accordingly. Specifically, the system inputs historical behavioral data and historical energy data into the AI ​​model, combines current environmental information and device status, and predicts how much energy each smart device can collect within a preset future time period, as well as the potential electricity demand from users. Based on this prediction result, the system formulates energy collection plans and device operation plans for each device, thereby forming a collaborative, efficient, and forward-looking energy management solution.

[0035] The AI ​​predictive control module receives data from environmental sensors, including temperature, light intensity, wind speed, and humidity; user behavior sensors, including GPS trajectory, motion acceleration, and device usage frequency; and historical database data, including energy collection volume and device energy consumption records for the past 7 days. The AI ​​algorithm uses an LSTM neural network model with an input dimension of 128 and an output dimension of 64. The training objective is to minimize the energy supply and demand error over the next 24 hours. When the predicted energy overflow rate is ≥15%, cross-device energy allocation is automatically initiated; when the predicted under-energy rate is ≥20%, user behavior suggestions are triggered.

[0036] In one feasible implementation, the specific implementation of determining the energy harvesting plan and equipment operation plan for each of the smart devices within a preset future time period based on the historical behavior data and the historical energy data can also be: Based on the historical behavior data and the historical energy data, the future energy collection data and future energy consumption data of each of the smart devices are determined within the future time period. Based on the future energy collection data and the future energy consumption data, the energy collection plan and device operation plan of each of the smart devices within the preset future time period are determined.

[0037] It should be noted that future energy harvesting data refers to the amount and type of energy that each smart device can harvest from the environment within a preset future time period, predicted by an AI model. This prediction is based on historical energy data, user behavior patterns, and environmental forecast information. Future energy consumption data refers to the total energy consumption required for each smart device to operate itself or supply power to external loads within the same future time period, predicted by the system. This prediction is based on users' historical electricity consumption habits, the current status of the devices, and task plans.

[0038] Understandably, the system first utilizes historical behavioral and energy data, combined with environmental forecasts such as weather and temperature, and user schedules, to use AI models to deduce the future energy collection and consumption data for each smart device in the future time period. Subsequently, the system compares these two sets of predicted data. If a device's projected collection volume exceeds its consumption, it is included as an energy supplier, and its collection module is scheduled to operate efficiently. If the projected volume is insufficient, it is marked as an energy consumer, and other devices are planned to provide supplementary energy through wireless sharing. Ultimately, this generates a specific energy collection plan and operation plan for each device, thereby achieving refined and differentiated energy collaborative scheduling.

[0039] This embodiment makes the energy planning process more structured and quantifiable by predicting future energy harvesting and consumption data. Compared to directly outputting scheduling strategies, this embodiment first predicts supply and demand and then formulates plans, significantly improving the scientific nature and accuracy of scheduling decisions. Specifically, because the system clearly predicts whether each device will have sufficient or insufficient energy in the future, it can arrange energy flow paths across devices in advance. For example, it can transfer electricity from a hat to a backpack, avoiding delays or conflicts caused by ad-hoc scheduling.

[0040] In one feasible implementation, the historical behavior data includes user travel status data, travel environment data, and travel route data; the historical energy data includes historical energy collection data and historical energy consumption data; and the specific implementation of determining the future energy collection data and future energy consumption data of each of the smart devices within the future time period based on the historical behavior data and the historical energy data can also be: Based on the travel status data and the travel route data, the user's future behavior data within the future time period is determined, and future environmental data is obtained. Based on the future behavior data, the future environmental data, and the historical energy collection data, the future energy collection data of each of the smart devices within the future time period is determined. Based on the future behavior data and the historical energy consumption data, the future energy consumption data of each of the smart devices within the future time period is determined.

[0041] It should be noted that travel status data refers to the user's movement status information within a historical time period, including whether they are walking, running, cycling, stationary, or traveling by vehicle, and is usually derived from accelerometer, gyroscope, or GPS speed data. Travel environment data refers to the environmental parameters recorded during the user's historical travels, including light intensity, temperature, humidity, wind speed, and altitude, and can be obtained from built-in sensors on the device or third-party environmental databases. Travel route data refers to the user's geographical location trajectory information within a historical time period, including origin, destination, points along the way, areas of stay, and duration of stay, and is usually collected by GPS or BeiDou positioning modules.

[0042] Future behavioral data refers to user activity plans over a future time period inferred through behavioral pattern recognition or trajectory prediction models based on historical travel status and route data, such as jogging along a park trail from 9:00 to 10:00 AM tomorrow. Future environmental data refers to forecast information obtained from meteorological services or environmental databases for the aforementioned future time period and predicted travel route, such as future light intensity, weather conditions, and temperature changes.

[0043] Understandably, this embodiment utilizes user travel status data and route data, employing time-series modeling or trajectory prediction algorithms to infer future user behavior data within a future timeframe. Simultaneously, the system combines this predicted route with corresponding future environmental data obtained from an external environmental service platform. On the energy harvesting side, the system performs multi-dimensional matching and extrapolation of future behavior data, future environmental data, and historical energy harvesting data to predict the future energy harvesting capacity of each device. On the energy consumption side, the system primarily relies on future behavior data and historical energy consumption data to calculate the potential future energy consumption demands of each device, ultimately outputting two sets of key predicted values ​​for subsequent planning.

[0044] This embodiment significantly improves the spatiotemporal accuracy of future energy supply and demand estimation by segmenting user behavior into three dimensions: state, route, and environment, and using each dimension for data collection and consumption prediction. For example, knowing only that a user runs is insufficient to accurately predict power generation, but knowing that they run for 30 minutes on a sunny lakeside track in the morning allows for a precise estimation of the combined output of photovoltaic and piezoelectric modules. This embodiment, through the above method, can distinguish the energy collection potential under different scenarios, reducing prediction errors.

[0045] Furthermore, this embodiment directly incorporates future environmental data into the data acquisition and prediction process, enabling the system to perceive and adapt to changes in external conditions. For example, if rain is forecast for the next day, even if the user travels as planned, the system will automatically lower the expected solar energy collection and schedule other energy sources in advance. This not only improves the reliability of energy planning but also provides a more reliable input basis for subsequent dynamic scheduling, thereby improving the overall energy acquisition efficiency of the system.

[0046] In one feasible implementation, the future energy harvesting data includes future energy harvesting efficiency, and the future energy consumption data includes future energy consumption efficiency. A further implementation of determining the energy harvesting plan and device operation plan for each smart device within a preset future time period based on the future energy harvesting data and the future energy consumption data can also be: Based on the future energy harvesting efficiency, the energy harvesting priority of each smart device is determined. Based on the energy harvesting priority, the energy harvesting time period and energy harvesting mode of each smart device in the future time period are determined to obtain the energy harvesting plan. Based on the future energy consumption efficiency, the device operation priority of each smart device is determined. Based on the device operation priority, the device operation time period and device operation mode of each smart device in the future time period are determined to obtain the device operation plan.

[0047] It should be noted that future energy harvesting efficiency refers to the predicted energy harvesting efficiency of each smart device per unit time or per unit of activity within a preset future time period, reflecting its energy conversion capability under specific environmental and behavioral conditions. Future energy consumption efficiency refers to the predicted energy consumption efficiency of each smart device in completing a unit of task within a future time period. Energy harvesting priority is a priority obtained by ranking each smart device according to its future energy harvesting efficiency; the higher the harvesting efficiency, the higher the priority, and the more frequently it will be scheduled for energy harvesting.

[0048] Energy harvesting time period refers to the specific time interval allocated to each smart device in the future to perform energy harvesting. Energy harvesting mode refers to the specific energy harvesting method activated by the device within the harvesting time period, such as activating only photovoltaic mode, or activating both piezoelectric and thermoelectric modes simultaneously. Device operation priority is the priority ranking of each device's operation tasks based on future energy consumption efficiency and task importance; devices with higher efficiency or more critical tasks receive higher operation priority. Device operation time period refers to the specific time interval in the future during which the device is allowed or required to perform operational tasks such as power supply, communication, and data processing. Device operation mode refers to the operating state of the device within the operation time period, such as high-power output mode, energy-saving standby mode, or energy-sharing mode.

[0049] Understandably, in this embodiment, the system sets energy harvesting priorities for each smart device based on predicted future energy harvesting efficiency. Subsequently, based on these priorities, higher-priority devices are allocated better energy harvesting time periods and matching energy harvesting modes, forming an energy harvesting plan. Simultaneously, the system determines the device operation priority for each device based on future energy consumption efficiency and task urgency. Based on this, higher-priority devices are scheduled to perform tasks in high-efficiency operation modes during critical periods, while lower-priority devices enter energy-saving or standby states, thereby generating a device operation plan.

[0050] Through the above steps, this embodiment can effectively improve the efficiency of energy harvesting and energy use. Specifically, since high-harvest-efficiency devices are prioritized for use, the system can acquire more usable energy under the same environmental conditions, reducing power consumption waste caused by ineffective harvesting; at the same time, high-consumption-efficiency devices are prioritized for operation, which means that a unit of energy can support more user value, thereby improving the overall economy and practicality of energy utilization.

[0051] Furthermore, by mapping priorities to specific time periods and modes, this embodiment enables precise scheduling of energy harvesting and usage across the spatiotemporal dimensions. For example, during periods of ample sunlight but when users are stationary, the photovoltaic mode of hats is activated first, rather than shoes that rely on exercise for power generation; during periods of high load demand at night, backpacks with high energy storage efficiency and stable output are activated first, rather than small-capacity accessories. Through the aforementioned dynamic resource allocation mechanism, this embodiment maximizes the overall energy gain of the system and extends the availability of key equipment, significantly enhancing the system's adaptability and energy harvesting and usage capabilities in complex and ever-changing scenarios.

[0052] In one feasible implementation, the specific implementation following the step of determining the user's future behavior data within the future time period based on the travel status data and the travel route data, and obtaining future environmental data, may also be: Based on the future behavior data, the user's energy collection needs in the future time period are determined. Based on the future behavior data, the energy collection needs, and the future environmental data, energy collection suggestions for the future time period are determined. The energy collection suggestions include the suggested energy collection behavior, the energy value that can be collected, and the energy collection increase corresponding to the energy collection behavior. The energy collection behavior is notified to the user via voice and visualized in the corresponding smart device.

[0053] It should be noted that energy harvesting requirements refer to the minimum energy replenishment needed to support a user's activities or equipment operation, predicted based on future user behavior. For example, camping for 2 hours requires at least 200 mAh of electricity. The energy harvesting recommendation system generates proactive guidance information based on future behavior, environment, and equipment capabilities to instruct users to take specific actions to improve energy harvesting efficiency. Recommended energy harvesting behaviors refer to specific actions or behavioral adjustments recommended for the user, such as walking in sunlight for 30 minutes between 10:00 and 11:00 AM, or unfolding a hat to increase the area exposed to sunlight.

[0054] The energy value that can be collected is the total energy value that the system predicts each smart device can actually collect under the current environmental conditions and the suggested actions. The energy collection improvement refers to the additional energy gained compared to not performing the suggested actions, used to quantify the effectiveness of the suggestion. Visualization in this embodiment refers to the intuitive presentation of the energy collection suggestion and related data on the smart device's display screen, LED indicator, or accompanying APP interface in the form of graphics, text, or progress bars.

[0055] Understandably, after completing the prediction of future behavior data and the acquisition of future environmental data, this embodiment further conducts user interaction and behavior guidance. The system in this embodiment first derives the user's energy collection needs during this period based on the future behavior data. Next, combining the future environmental data and the collection capability models of each device, it calculates several feasible energy collection behavior schemes and evaluates the energy value that can be collected under each scheme and its energy collection improvement relative to the baseline scenario. Finally, the system proactively reminds the user of the optimal or multiple candidate schemes via voice notification, while simultaneously displaying them visually on relevant smart devices, including suggested content, expected benefits, and operation instructions.

[0056] This embodiment significantly enhances the overall energy harvesting potential of the system through the steps described above. Specifically, since some energy harvesting is highly dependent on the user's location and posture, passive harvesting by the device alone may not achieve optimal efficiency. However, by providing users with specific and actionable energy harvesting behavior suggestions and quantifying their benefits, it is possible to effectively incentivize users to actively cooperate, thereby overcoming environmental limitations and maximizing energy capture. Furthermore, the voice notifications and visual presentation in this embodiment allow users to quickly obtain key action suggestions without having to view complex data. The clear display of the energy harvesting improvement enhances the credibility and persuasiveness of the suggestions, encouraging user adoption and thus improving energy harvesting efficiency.

[0057] Step S30: Based on the energy harvesting plan, the equipment operation plan, and the energy storage capacity of each of the smart devices, determine the energy allocation plan for each of the smart devices in the future time period.

[0058] It should be noted that energy storage capacity refers to the current available power and maximum storage limit of the energy storage modules in each smart device, reflecting its energy buffering and supply capabilities. Energy allocation planning refers to the detailed energy flow arrangement formulated by the system for each smart device within a preset future time period, including: which devices act as energy suppliers to output electrical energy, which act as receivers to receive replenished energy, when wireless energy transmission will take place, the transmission power and duration, etc., aiming to achieve supply and demand balance and efficient utilization.

[0059] Understandably, based on the energy harvesting plan and equipment operation plan of each device, this embodiment further optimizes the global energy flow by combining the current energy storage capacity of each device. Specifically, the system first assesses the future net energy surplus or deficit of each device, identifying devices with energy surplus and those with energy deficit. Then, based on wireless energy transmission capabilities, the topology between devices, and energy storage safety boundaries, a specific energy allocation plan is formulated, including when, from which device, how much energy to which device, and what transmission mode to use, thereby ensuring that all devices meet their own operational needs while achieving dynamic balance and efficient sharing of the overall system energy.

[0060] Because the energy allocation decision considers not only future energy production and utilization but also the current energy storage status, the system can effectively avoid the forced abandonment of energy by high-power devices due to their inability to output power, and the shutdown of low-power devices due to failure to replenish energy in time. Furthermore, this embodiment uses energy storage capacity as a condition for energy allocation, ensuring the safety and sustainability of energy distribution. For example, the system will prioritize energy transfer to critical devices with low energy storage capacity, while limiting input to devices near full charge to prevent overcharging; for devices with large energy storage capacity but low harvesting efficiency, they may be arranged as energy transfer stations to temporarily store and forward energy. This allows energy allocation to balance efficiency, safety, and topological feasibility, significantly improving the robustness and collaborative performance of multi-device energy networks, especially in emergency scenarios involving prolonged outdoor use or limited communication, ensuring continuous power supply to critical loads.

[0061] In summary, when the smart devices need to be recharged, this embodiment acquires the user's historical behavior data and the historical energy data of each smart device within a preset historical time period. Based on the historical behavior data and the historical energy data, it determines the energy collection plan and device operation plan for each smart device within a preset future time period. Based on the energy collection plan, the device operation plan, and the energy storage capacity of each smart device, it determines the energy allocation plan for each smart device within the future time period.

[0062] To address the issue of varying energy harvesting efficiency and consumption among smart devices, which sometimes results in some devices having sufficient power while others have insufficient power, leading to poor energy replenishment, this embodiment uses historical data to determine future energy harvesting and device operation plans, and then allocates energy to each device based on these plans. Since the energy harvesting and operation plans are determined from historical user and device data, future energy harvesting and consumption can be planned more rationally. Based on this planning, the energy availability of each device in the future can be determined, allowing for energy planning to ensure that each device has sufficient energy in the future, preventing situations where some devices have sufficient power while others have insufficient power, and improving the effectiveness of energy replenishment.

[0063] Based on the first embodiment of this application, in the second embodiment of this application, the content that is the same as or similar to that in Embodiment 1 above can be referred to the above description, and will not be repeated hereafter. Based on this, please refer to... Figure 3 The multi-device energy distribution method further includes steps S31 to S34 in step S30: Step S31: Based on the energy harvesting plan and the equipment operation plan, determine the energy storage value of each of the smart devices in the future time period; It should be noted that the energy storage value refers to the dynamic power status sequence predicted by each smart device within a preset future time period after considering its energy harvesting plan and equipment operation plan. It is usually expressed in terms of timestamps, representing the expected remaining power at each point in time.

[0064] Understandably, based on the known energy harvesting plans of each smart device, the system uses the current actual energy storage capacity as the initial value, performs power estimation for each time period, and calculates the expected energy storage value of each device at each moment in the future if no cross-device energy sharing is carried out.

[0065] For example, if a smart hat currently has 60% battery, and according to the data collection plan, it can collect 100 mAh (milliampere-hours) between 10 AM and 12 PM; according to the operation plan, there are no external power supply tasks during this period, then its predicted energy storage value at 12 PM = current battery level + 100 mAh. This process is executed in parallel for all devices, ultimately generating a set of energy storage curves that change over time, serving as the basis for subsequent judgments of energy surplus or deficit.

[0066] Because the prediction is strictly based on the established data collection and operation plan, the results truly reflect the energy of the equipment in the absence of coordination, thus accurately identifying which equipment will have surplus power and which will face insufficient power, providing an objective and quantitative decision-making benchmark for subsequent energy allocation.

[0067] Step S32: Based on the energy storage capacity, determine the energy storage overflow threshold and energy storage insufficiency threshold for each of the smart devices; It should be noted that the energy storage overflow threshold refers to the upper limit of the energy capacity set to prevent the energy storage module from overcharging. It is usually slightly lower than 100% of the energy storage capacity. When the predicted energy storage value reaches or exceeds this threshold, the system determines that the device has a risk of energy overflow and needs to stop collecting or outputting excess energy. The energy storage insufficient threshold refers to the lower limit of the energy capacity set to prevent the energy storage module from over-discharging. When the predicted energy storage value drops below this threshold, the system determines that the device has a risk of power outage and needs to prioritize receiving external energy replenishment.

[0068] It is understood that this embodiment sets energy overflow thresholds and energy shortage thresholds for each smart device based on the known energy storage capacity. These two thresholds are not fixed percentages, but can be adaptively adjusted according to device type, current ambient temperature, battery aging status, or task importance. For example, a backpack used for emergency communication may have a higher shortage threshold to ensure reliability, while a hat, lacking critical load, can have a threshold of 10%; in low-temperature environments, to protect the solid-state battery, the overflow threshold may be lowered from 95% to 90%.

[0069] Because the thresholds can be dynamically adjusted according to the device role and environment, the system can prevent high-value devices from failing due to over-discharge and avoid high-efficiency acquisition devices from being forced to abandon energy due to full charge, thereby maximizing energy utilization within the safety boundary. Therefore, this embodiment can effectively balance system efficiency and device reliability by calculating the energy storage overflow threshold and the energy storage insufficiency threshold.

[0070] Step S33: Based on the energy storage value of each of the smart devices in the future time period, determine a first target smart device whose energy storage value is higher than the energy storage overflow threshold, and a second smart target device whose energy storage value is lower than the energy storage insufficiency threshold; It should be noted that the first target smart device refers to a smart device whose predicted energy storage value exceeds its own energy storage overflow threshold within a preset future time period, and is identified by the system as an energy supplier with energy output capability. The second target smart device refers to a smart device whose predicted energy storage value is lower than its own energy storage insufficiency threshold within the same future time period, and is identified by the system as an energy consumer with a risk of power shortage.

[0071] Understandably, this embodiment compares the predicted energy storage value of each smart device with its corresponding energy overflow threshold and energy shortage threshold on a time-period basis. If the energy storage value of a device in a future time period is greater than or equal to the overflow threshold, it is marked as the first target smart device, i.e., a potential energy supply source. If the energy storage value of a device in a future time period is less than or equal to the shortage threshold, it is marked as the second target smart device, i.e., an object requiring external energy replenishment. The identification process described above in this embodiment can be dynamically updated at the time granularity to ensure that the energy allocation decision can respond to real-time changes in power status, and finally output two sets of device lists as input for subsequent energy allocation path planning.

[0072] This embodiment compares the predicted energy storage value with a dynamically set safety threshold, thereby achieving accurate and automated identification of energy suppliers and consumers in the system and avoiding misjudgments that may occur if the supply and demand relationship is judged solely based on the absolute value of the electricity.

[0073] Furthermore, since the identification results are directly related to the safe operating boundary of the device, this embodiment can proactively trigger energy scheduling before overcharging or over-discharging occurs, achieving preventative energy management. For example, if the hat's battery level is expected to reach 96% at 11:30, while the backpack's battery level is expected to drop to 12% at 13:00, the system can arrange for wireless energy transfer from the hat to the backpack in advance at 12:00. This frees up the hat's storage space to continue collecting light energy while ensuring the backpack's power supply security for subsequent tasks. Therefore, through the above steps, this embodiment can significantly improve the system's proactive protection capabilities and collaborative efficiency in complex scenarios.

[0074] Step S34: Based on the first target device and the second target device at each time point in the future time, determine the energy allocation plan, wherein the energy allocation plan includes allocating the energy of the first target device to the smart devices other than the first target device, and allocating the energy of the smart devices other than the second target device to the second target device.

[0075] It should be noted that the smart devices other than the first target device refer to all other smart devices besides those identified as having surplus energy, including the second target device and neutral devices whose power is within a safe range, neither overflowing nor lacking. The smart devices other than the second target device refer to all other smart devices besides those identified as having insufficient power, including the first target device and neutral devices. In this embodiment, power allocation is achieved through wireless power transmission to facilitate cross-device energy flow, where wireless power transmission includes methods such as magnetic resonance coupling or ultrasonic coupling.

[0076] It is understood that this embodiment actively distributes excess energy exceeding the safety limit in the first target device to other smart devices outside the first target device. Furthermore, to ensure the power supply security of the second target device, the system allocates energy from all available devices other than the second target device, prioritizing devices with close proximity, high transmission efficiency, and sufficient remaining power for wireless power replenishment.

[0077] The above allocation process in this embodiment takes into account the wireless power transmission topology, transmission efficiency, energy storage status and task priority between devices, and finally generates a detailed energy allocation plan that includes time, source device, target device, transmission power and duration.

[0078] This embodiment, through the aforementioned steps, allocates energy based on surplus output and deficit replenishment, preventing energy waste and ensuring uninterrupted power supply to critical equipment. Specifically, energy from the primary target device is allocated to non-primary target devices, effectively freeing up storage space for high-capacity devices, allowing them to continue efficiently harvesting environmental energy and avoiding forced shutdown of harvesting modules due to full charge. Simultaneously, power is supplied from non-secondary target devices to the secondary target device, establishing a multi-source replenishment channel. Even if a single power supply device fails, the system can still receive support from other neutral devices, improving robustness. Furthermore, the neutral devices in this embodiment can temporarily receive and store excess energy, then forward it to more urgently needed devices in subsequent periods, thus smoothing energy supply and demand fluctuations in the spatiotemporal dimension. Through this energy allocation mechanism, this embodiment enables the entire system to possess stronger energy retention capabilities, fault tolerance, and service continuity in outdoor or emergency scenarios without external power grid support.

[0079] In summary, this embodiment determines the energy storage value of each smart device within the future timeframe based on the energy harvesting plan and the device operation plan. Based on the energy storage capacity, it determines the energy storage overflow threshold and energy storage shortage threshold for each smart device. Based on the energy storage value of each smart device within the future timeframe, it identifies a first target smart device with an energy storage value higher than the energy storage overflow threshold and a second target smart device with an energy storage value lower than the energy storage shortage threshold. Based on the first target device and the second target device at each time point within the future timeframe, it determines the energy allocation plan. The energy allocation plan includes allocating the energy of the first target device to smart devices other than the first target device, and allocating the energy of smart devices other than the second target device to the second target device.

[0080] This embodiment enables the system to predict power risks in various time periods through dynamic calculation of energy storage values, thereby adjusting energy before overcharging or over-discharging occurs. By setting personalized thresholds for energy storage capacity, it balances the safety margins of different devices with task importance, avoiding insufficient protection or resource idleness caused by a uniform strategy. Furthermore, by identifying the energy supply and demand devices corresponding to different power data from the first / second target devices, energy supply and demand planning is performed based on these devices. Finally, through energy allocation, the system realizes the potential for high-power device acquisition, and through energy supply between multiple devices, even if some devices are offline, the system can still achieve energy flow through neutral device relay.

[0081] It should be noted that the above examples are only for understanding this application and do not constitute a limitation on the multi-device energy distribution method of this application. Any simple modifications based on this technical concept are within the protection scope of this application.

[0082] This application also provides a multi-device energy distribution device; please refer to... Figure 4 The multi-device energy distribution device includes: The data acquisition module 10 is used to acquire the user's historical behavior data and the historical energy data of each smart device within a preset historical time period when the smart device needs to be recharged. The planning and determination module 20 is used to determine the energy harvesting plan and equipment operation plan of each of the smart devices within a preset future time period based on the historical behavior data and the historical energy data. The energy distribution module 30 is used to determine the energy distribution plan for each of the smart devices in the future time period based on the energy harvesting plan, the equipment operation plan, and the energy storage capacity of each of the smart devices.

[0083] In one embodiment, the planning determination module includes: The energy data determination submodule is used to determine the future energy collection data and future energy consumption data of each of the smart devices in the future time period based on the historical behavior data and the historical energy data. The planning and determination submodule is used to determine the energy collection plan and equipment operation plan of each of the smart devices within the preset future time period based on the future energy collection data and the future energy consumption data.

[0084] In one embodiment, the energy data determination submodule includes: The behavior prediction unit is used to determine the user's future behavior data within the future time period based on the travel status data and the travel route data, and to obtain future environmental data. The data acquisition and prediction unit is used to determine the future energy acquisition data of each of the smart devices within the future time period based on the future behavior data, the future environment data, and the historical energy acquisition data. The energy consumption prediction unit is used to determine the future energy consumption data of each of the smart devices within the future time period based on the future behavior data and the historical energy consumption data.

[0085] In one embodiment, the planning determination submodule includes: The energy collection priority determination unit is used to determine the energy collection priority of each of the smart devices based on the future energy collection efficiency. The energy acquisition planning unit is used to determine the energy acquisition time period and energy acquisition mode of each of the smart devices in the future time based on the energy acquisition priority, so as to obtain the energy acquisition plan; The device priority determination unit is used to determine the device operation priority of each of the smart devices based on the future energy consumption efficiency. The operation planning unit is used to determine the operating time period and operating mode of each of the smart devices in the future time based on the device operation priority, so as to obtain the device operation plan.

[0086] In one embodiment, the energy data determination submodule includes: A demand determination unit is used to determine the user's energy collection demand in the future time period based on the future behavior data. The energy collection suggestion determination unit is used to determine energy collection suggestions for the future time period based on the future behavior data, the energy collection demand, and the future environmental data. The energy collection suggestions include the suggested energy collection behavior, the energy value that can be collected, and the energy collection increase corresponding to the energy collection behavior. A visualization unit is used to notify the user of the energy harvesting behavior via voice and to visualize it in the corresponding smart device.

[0087] In one embodiment, the energy distribution module includes: The energy storage value determination submodule is used to determine the energy storage value of each of the smart devices in the future time period based on the energy harvesting plan and the equipment operation plan. The threshold determination submodule is used to determine the energy storage overflow threshold and energy storage insufficiency threshold of each of the smart devices based on the energy storage capacity. The target device determination submodule is used to determine, based on the energy storage value of each of the smart devices in the future time period, a first target smart device whose energy storage value is higher than the energy storage overflow threshold, and a second smart target device whose energy storage value is lower than the energy storage insufficiency threshold; An energy allocation submodule is used to determine the energy allocation plan based on the first target device and the second target device at various points in time in the future time period. The energy allocation plan includes allocating the energy of the first target device to the smart device other than the first target device, and allocating the energy of the smart device other than the second target device to the second target device.

[0088] The multi-device energy distribution device provided in this application, employing the multi-device energy distribution method described in the above embodiments, can solve the technical problem of poor energy replenishment effect. Compared with the prior art, the beneficial effects of the multi-device energy distribution device provided in this application are the same as those of the multi-device energy distribution method described in the above embodiments, and other technical features in the multi-device energy distribution device are the same as those disclosed in the methods of the above embodiments, and will not be repeated here.

[0089] This application provides a multi-device energy distribution device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, which are executed by the at least one processor to enable the at least one processor to perform the multi-device energy distribution method in Embodiment 1 above.

[0090] The following is for reference. Figure 5 The diagram illustrates a structural schematic of a multi-device energy distribution device suitable for implementing embodiments of this application. The multi-device energy distribution device in the embodiments of this application may include, but is not limited to, mobile terminals such as mobile phones, tablets, laptops, digital broadcast receivers, PDAs (Personal Digital Assistants), PMPs (Portable Media Players), in-vehicle terminals (e.g., in-vehicle navigation terminals), and fixed terminals such as digital TVs and desktop computers. Figure 5 The multi-device energy distribution device shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of this application.

[0091] like Figure 5As shown, the multi-device power distribution device may include a processing unit 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 1002 or a program loaded from a storage device 1003 into a random access memory (RAM) 1004. The RAM 1004 also stores various programs and data required for the operation of the multi-device power distribution device. The processing unit 1001, ROM 1002, and RAM 1004 are interconnected via a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Typically, the following systems can be connected to the I / O interface 1006: input devices 1007 including, for example, a touchscreen, touchpad, keyboard, mouse, image sensor, microphone, accelerometer, gyroscope, etc.; output devices 1008 including, for example, a liquid crystal display (LCD), speaker, vibrator, etc.; storage devices 1003 including, for example, magnetic tape, hard disk, etc.; and communication devices 1009. Communication device 1009 allows the multi-device power distribution device to communicate wirelessly or wiredly with other devices to exchange data. While the figure shows a multi-device power distribution device with various systems, it should be understood that implementation or possession of all the systems shown is not required. More or fewer systems may be implemented alternatively.

[0092] Specifically, according to the embodiments disclosed in this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments disclosed in this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device, or installed from storage device 1003, or installed from ROM 1002. When the computer program is executed by processing device 1001, it performs the functions defined in the methods of the embodiments disclosed in this application.

[0093] The multi-device energy distribution device provided in this application, employing the multi-device energy distribution method described in the above embodiments, can solve the technical problem of poor energy replenishment effect. Compared with the prior art, the beneficial effects of the multi-device energy distribution device provided in this application are the same as those of the multi-device energy distribution method described in the above embodiments, and other technical features of this multi-device energy distribution device are the same as those disclosed in the previous embodiment method, and will not be repeated here.

[0094] It should be understood that the various parts disclosed in this application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any suitable manner in one or more embodiments or examples.

[0095] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0096] This application provides a computer-readable storage medium having computer-readable program instructions (i.e., a computer program) stored thereon, the computer-readable program instructions being used to execute the multi-device energy distribution method described in the above embodiments.

[0097] The computer-readable storage medium provided in this application may be, for example, a USB flash drive, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, system, or device. The program code contained on the computer-readable storage medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination thereof.

[0098] The aforementioned computer-readable storage medium may be included in a multi-device power distribution device; or it may exist independently and not assembled into a multi-device power distribution device.

[0099] The aforementioned computer-readable storage medium carries one or more programs, which, when executed by the multi-device energy distribution device, cause the multi-device energy distribution device to perform the aforementioned multi-device energy distribution method.

[0100] Computer program code for performing the operations of this application can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a Local Area Network (LAN) or a Wide Area Network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0101] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0102] The modules described in the embodiments of this application can be implemented in software or hardware. The names of the modules do not necessarily limit the functionality of the unit itself.

[0103] The readable storage medium provided in this application is a computer-readable storage medium that stores computer-readable program instructions (i.e., a computer program) for executing the above-described multi-device energy distribution method, thereby solving the technical problem of poor energy replenishment effect. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in this application are the same as the beneficial effects of the multi-device energy distribution method provided in the above embodiments, and will not be repeated here.

[0104] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the multi-device energy distribution method described above.

[0105] The computer program product provided in this application can solve the technical problem of poor energy replenishment effect. Compared with the prior art, the beneficial effects of the computer program product provided in this application are the same as the beneficial effects of the multi-device energy distribution method provided in the above embodiments, and will not be repeated here.

[0106] All user-related data involved in this application was obtained with the user's permission or consent, as per [reference]. Figure 6 In other words, when this application is applied to a specific product or technology, user permission is required to acquire and process the relevant data, and the processing of the relevant data must comply with the relevant laws, regulations and regulatory standards of the relevant countries and regions.

[0107] The above description is only a part of the embodiments of this application and does not limit the scope of protection of this application. All equivalent structural transformations made under the technical concept of this application and using the contents of the specification and drawings of this application, or direct / indirect applications in other related technical fields, are included in the patent protection scope of this application.

Claims

1. A method for energy distribution across multiple devices, characterized in that, The method includes: When the smart devices need to be recharged, the user's historical behavior data and the historical energy data of each smart device are obtained within a preset historical time period. Based on the historical behavior data and the historical energy data, determine the energy harvesting plan and equipment operation plan for each of the smart devices within a preset future time period; Based on the energy harvesting plan, the equipment operation plan, and the energy storage capacity of each of the smart devices, the energy allocation plan for each of the smart devices is determined for the future time period.

2. The method as described in claim 1, characterized in that, The steps of determining the energy harvesting plan and equipment operation plan for each smart device within a preset future time period based on the historical behavior data and the historical energy data include: Based on the historical behavior data and the historical energy data, the future energy collection data and future energy consumption data of each of the smart devices in the future time period are determined; Based on the future energy collection data and the future energy consumption data, the energy collection plan and equipment operation plan of each smart device are determined within the preset future time period.

3. The method as described in claim 2, characterized in that, The historical behavior data includes user travel status data, travel environment data, and travel route data; the historical energy data includes historical energy collection data and historical energy consumption data; and the step of determining the future energy collection data and future energy consumption data of each of the smart devices within the future time period based on the historical behavior data and the historical energy data includes: Based on the travel status data and the travel route data, determine the user's future behavior data within the future time period, and obtain future environmental data; Based on the future behavior data, the future environment data, and the historical energy collection data, the future energy collection data of each of the smart devices is determined within the future time period; Based on the future behavior data and the historical energy consumption data, the future energy consumption data of each of the smart devices within the future time period is determined.

4. The method as described in claim 2, characterized in that, The future energy harvesting data includes future energy harvesting efficiency, and the future energy consumption data includes future energy consumption efficiency. The step of determining the energy harvesting plan and device operation plan for each of the smart devices within a preset future time period based on the future energy harvesting data and the future energy consumption data includes: Based on the future energy harvesting efficiency, the energy harvesting priority of each of the aforementioned smart devices is determined; Based on the energy harvesting priority, the energy harvesting time period and energy harvesting mode of each of the smart devices in the future time are determined to obtain the energy harvesting plan; Based on the future energy consumption efficiency, determine the device operation priority of each of the smart devices; Based on the device operation priority, the device operation time period and device operation mode of each of the smart devices in the future time are determined to obtain the device operation plan.

5. The method as described in claim 3, characterized in that, After the steps of determining the user's future behavior data within the future time period based on the travel status data and the travel route data, and obtaining future environmental data, the method further includes: Based on the future behavior data, determine the user's energy collection needs within the future timeframe; Based on the future behavior data, the energy harvesting demand, and the future environmental data, energy harvesting recommendations are determined for the future time period. The energy harvesting recommendations include the recommended energy harvesting behavior, the energy value that can be harvested, and the energy harvesting increase corresponding to the energy harvesting behavior. The energy harvesting activity is communicated to the user via voice and visualized on the corresponding smart device.

6. The method as described in claim 1, characterized in that, The step of determining the energy allocation plan for each of the smart devices within the future time period based on the energy harvesting plan, the equipment operation plan, and the energy storage capacity of each of the smart devices includes: Based on the energy harvesting plan and the equipment operation plan, the energy storage value of each of the smart devices is determined within the future time period; Based on the energy storage capacity, determine the energy storage overflow threshold and energy storage insufficiency threshold for each of the smart devices; Based on the energy storage value of each of the smart devices within the future time period, a first target smart device with an energy storage value higher than the energy storage overflow threshold and a second smart target device with an energy storage value lower than the energy storage insufficiency threshold are determined. Based on the first target device and the second target device at various points in time in the future, the energy allocation plan is determined, wherein the energy allocation plan includes allocating the energy of the first target device to the smart devices other than the first target device, and allocating the energy of the smart devices other than the second target device to the second target device.

7. A multi-device energy distribution device, characterized in that, The device includes: The data acquisition module is used to acquire the user's historical behavior data and the historical energy data of each smart device within a preset historical time period when the smart device needs to be recharged. The planning and determination module is used to determine the energy harvesting plan and equipment operation plan of each of the smart devices within a preset future time period based on the historical behavior data and the historical energy data. An energy allocation module is used to determine the energy allocation plan for each of the smart devices within the future time period based on the energy harvesting plan, the equipment operation plan, and the energy storage capacity of each of the smart devices.

8. A multi-device energy distribution device, characterized in that, The device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the multi-device energy distribution method as described in any one of claims 1 to 6.

9. A storage medium, characterized in that, The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, it implements the steps of the multi-device energy distribution method as described in any one of claims 1 to 6.

10. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements the steps of the multi-device energy distribution method as described in any one of claims 1 to 6.

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