Device recommendation method, electronic device, readable medium, and program product

Abstract

This application relates to the field of intelligent recommendation technology for outdoor equipment, and provides a method for recommending equipment, electronic devices, readable media, and program products. The method includes: acquiring user usage scenario information; wherein, the usage scenario information includes location information and power demand information of the usage scenario; acquiring predicted environmental characteristic information of the usage scenario based on the location information; determining candidate power supply equipment based on the environmental characteristic information; the candidate power supply equipment includes candidate power generation equipment and / or candidate energy storage equipment; determining multiple combinations of candidate equipment that meet the power demand information based on the power supply parameters of the candidate power supply equipment; wherein, the candidate equipment combination includes at least one power supply equipment; and determining a target recommended equipment combination from the candidate equipment combinations based on a preset priority recommendation algorithm. The solution can accurately recommend outdoor power supply equipment to users by associating it with the actual scenario of outdoor activities.

Description

Technical Field

[0001] This application belongs to the field of intelligent recommendation technology for outdoor equipment, and particularly relates to equipment recommendation methods, electronic devices, computer-readable media, and computer program products. Background Technology

[0002] In recent years, the popularity of outdoor recreational activities has spurred the demand for portable power, and how to recommend suitable outdoor energy storage devices to users has become a key focus of the industry.

[0003] Current equipment recommendation schemes often determine whether the static parameters of a user's outdoor energy storage devices, such as capacity or power generation, meet the user's needs. If they do, the scheme recommends suitable equipment; otherwise, it suggests appropriate static parameters so the user can acquire the appropriate equipment independently. However, this approach often requires users to adjust the recommendations based on the actual outdoor activity scenario, limiting the overall accuracy of the recommendations and making it difficult to adapt to diverse outdoor environments.

[0004] Therefore, how to accurately recommend outdoor power equipment to users based on the actual scenarios of outdoor activities has become an urgent technical problem to be solved. Summary of the Invention

[0005] This application provides a device recommendation method, electronic device, computer-readable medium, and computer program product, which can solve the problem of how to accurately recommend outdoor power equipment to users in relation to the actual scenarios of outdoor activities.

[0006] In a first aspect, embodiments of this application provide a device recommendation method, including: Obtain user usage scenario information; whereby usage scenario information includes location information and electricity demand information of the usage scenario; Based on location information, predictive environmental feature information of the usage scenario is obtained; Candidate power supply equipment is determined based on environmental characteristics; candidate power supply equipment includes candidate power generation equipment and / or candidate energy storage equipment. Based on the power supply parameters of the candidate power supply devices, multiple candidate device combinations that meet the power demand information are determined; wherein, the candidate device combination includes at least one power supply device; Based on a pre-defined priority recommendation algorithm, the target recommended device combination is determined from each candidate device combination.

[0007] In some embodiments, environmental characteristic information includes illumination information and weather information. Determining candidate power supply devices based on environmental characteristic information includes: Determine the scene type based on lighting and weather information; Candidate power supply devices are determined based on scenario type.

[0008] In some embodiments, the electricity demand information includes electrical equipment and the electricity consumption duration of the electrical equipment. Based on the power supply parameters of the candidate power supply equipment, a combination of multiple candidate equipment that meets the electricity demand information is determined, including: The total electricity consumption is determined based on the power information and usage duration of each electrical device. Based on the total power consumption and the power supply parameters of the user's existing power equipment, determine the capacity gap information; Based on the capacity gap information, candidate power supply devices are combined to obtain at least one candidate device combination that satisfies the capacity gap information.

[0009] In some embodiments, candidate power supply devices are combined based on capacity gap information to obtain at least one combination of candidate devices that satisfies the capacity gap information, including: When the capacity gap information is less than or equal to a first preset threshold, at least one combination of candidate devices that meets the capacity gap information is determined based on the power supply parameters of the solar panels in the candidate power generation devices. When the capacity gap information is greater than the first preset threshold and less than or equal to the second preset threshold, at least one combination of candidate devices that meets the capacity gap information is determined based on the power supply parameters of the candidate energy storage devices. If the capacity gap information is greater than the second preset threshold, at least one candidate equipment combination that meets the capacity gap information is determined based on the power supply parameters of the fuel generators among the candidate power generation equipment.

[0010] In some embodiments, a target recommended device combination is determined from each candidate device combination based on a preset priority recommendation algorithm, including: The energy self-sufficiency rate is determined based on the power supply parameters and total power consumption of the power supply equipment included in the candidate equipment combination. Obtain cost information for the power supply equipment included in the candidate equipment portfolio to determine the cost-effectiveness of the equipment; Based on the specifications of the power supply devices included in the candidate device portfolio, determine the device portability score; The recommended weight values ​​for candidate equipment combinations are obtained by weighting and integrating the scores of energy self-sufficiency rate, equipment cost-effectiveness and equipment portability. The candidate device combination with the highest recommendation weight value is taken as the target recommendation device combination.

[0011] In some embodiments, the specification information includes the volume and weight of the power supply devices included in the candidate device portfolio. Based on the specification information of the power supply devices included in the candidate device portfolio, a device portability score is determined, including: The sum of the products of the weight and corresponding volume of the power supply equipment included in the candidate equipment combination is used as the first parameter; The reciprocal of the first parameter is used as the portability score for the candidate device combination.

[0012] In some embodiments, weather information includes temperature and precipitation probability, and illumination information includes irradiance. Determining the scene type based on illumination and weather information includes: When the temperature is greater than or equal to a preset temperature threshold and the duration of the high temperature exceeds a preset duration threshold, the scene type is determined to be a high temperature scene. When the probability of precipitation is greater than or equal to the probability threshold of precipitation, and the irradiance is less than or equal to the irradiance threshold of irradiance, the scene type is determined to be a rainy scene.

[0013] In a second aspect, embodiments of this application provide an electronic device, including a processor, a memory, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it causes the electronic device to perform the method described in any embodiment of the first aspect.

[0014] Thirdly, embodiments of this application provide a computer-readable storage medium storing a computer program that, when executed by a processor, implements the method described in any of the embodiments of the first aspect.

[0015] Fourthly, embodiments of this application provide a computer program product, including a computer program, which, when run, causes the method described in any embodiment of the first aspect to be executed.

[0016] The advantages of the embodiments in this application compared with related technologies are: Based on location information from the usage scenario information, predicted environmental characteristics of the usage scenario are obtained. Combined with real-time weather and other environmental characteristics at the specific location of outdoor activities, candidate power supply devices suitable for the environment are recommended to the user. These candidate power supply devices include at least one of candidate power generation devices or candidate energy storage devices, enabling multi-device collaboration for different environments and reducing the risk of power outages during travel. Furthermore, based on the power supply parameters of the candidate power supply devices, multiple candidate device combinations that meet the power demand information are determined. A pre-defined priority recommendation algorithm is then used to recommend suitable device combinations, achieving a two-dimensional device recommendation that combines environmental factors and specific power demand. This ensures that the recommended device combinations not only meet the user's power needs but also suit the user's actual outdoor activity scenario, reducing the need for user adjustments and improving the accuracy of device recommendations. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of this application, the drawings used in the description of the embodiments or related technologies will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is a schematic flowchart of a device recommendation method provided in an embodiment of this application.

[0019] Figure 2 This is a flowchart illustrating the device recommendation method in an application scenario provided by an embodiment of this application.

[0020] Figure 3 This is a flowchart illustrating another device recommendation method provided in an embodiment of this application.

[0021] Figure 4 This is a schematic diagram of the workflow of a device recommendation system that uses a device recommendation method in an application scenario provided by an embodiment of this application.

[0022] Figure 5 This is another flowchart of a device recommendation method in an application scenario provided by an embodiment of this application.

[0023] Figure 6 This is a schematic diagram illustrating the determination of candidate device combinations based on capacity gap information in an application scenario provided by an embodiment of this application.

[0024] Figure 7 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0025] The technical solutions in the embodiments of this application will be described below with reference to the accompanying drawings.

[0026] Current solutions relying on static parameters for outdoor energy storage device recommendations often have the following drawbacks: 1. Disconnect from environmental prediction: Static parameters fail to consider real-time weather and lighting conditions during outdoor activities, leading to insufficient battery life for recommended energy storage devices. 2. Lack of scenario adaptation: The recommendation logic is not optimized for special usage scenarios such as high temperatures or high altitudes. Recommended energy storage devices in extreme environments may not match actual scenarios, resulting in a high error rate and reliance on user experience, severely impacting the user experience. 3. Limited device combinations: When a user does not have a single compatible device, the lack of multi-device collaboration solutions increases the risk of interrupted energy supply during travel. 4. Disrupted purchase process: Recommended results are not linked to real-time inventory; users must manually check device acquisition channels, with the conversion path taking an average of over 15 minutes.

[0027] To address the aforementioned issues, this application proposes a device recommendation method, an electronic device, a computer-readable medium, and a computer program product. Based on the location of the user's device, the method predicts the environmental characteristics of the usage scenario. Combining this with the user's power consumption and environmental characteristics within the usage scenario, it determines multiple device combinations that match the user's environment and needs. Then, based on a preset priority recommendation algorithm, it recommends at least one of these combinations to the user. This upgrades the device recommendation scheme from traditional static parameter matching to dynamic adaptation to the usage scenario. It not only achieves environmental prediction and adaptation to the actual scenario but also enables multi-device collaboration and allows embedding device acquisition channels within the recommended device combinations.

[0028] Figure 1 This is a flowchart illustrating a device recommendation method provided in an embodiment of this application, such as... Figure 1 The method shown includes the following steps: S101, obtain user usage scenario information.

[0029] The device recommendation method in this application embodiment can be applied to electronic devices such as mobile phones, tablets, wearable devices, in-vehicle devices, laptops, ultra-mobile personal computers (UMPCs), netbooks, and personal digital assistants (PDAs). This application embodiment does not impose any restrictions on the specific type of electronic device.

[0030] The usage scenario information includes the location information of the usage scenario and the electricity demand information.

[0031] Location information is used to indicate the location or area where a user is conducting outdoor activities.

[0032] Electricity demand information is used to indicate the power consumption information and duration of electricity consumption of the equipment required by users when they are engaged in outdoor activities.

[0033] The use case is used to describe the scenario in which a user uses a power supply device during outdoor activities. For example, the use case may be a swamp camping scenario, a mountain hiking scenario on a sunny day, a mountain hiking scenario on a rainy day, a tropical rainforest camping scenario, or an iceberg climbing scenario, etc. The embodiments of this application do not impose any limitations.

[0034] Electronic devices can analyze user-inputted outdoor activity data to obtain usage scenario information, or they can directly retrieve information if they have already analyzed and stored usage scenario information in the same scenario. Outdoor activity data can include at least one of the following: location information, activity requirement information, and equipment information. Activity requirement information indicates the activity type, duration, and time of the outdoor activity; for example, the activity type could be hiking, rock climbing, or camping, and the duration could be several days or several hours. Equipment information indicates the power consumption and power usage time of the user's existing electrical equipment during the outdoor activity. It is understood that users can input outdoor activity data manually, via voice, or by taking photos.

[0035] It should be noted that the outdoor activity data and related basic user information input by users mentioned above can only be obtained with the user's authorization. For example, when a user enters a relevant e-commerce shopping page and the page enters an interactive state, a prompt message will be pushed to the user asking whether to "authorize personal information" or an instruction message indicating whether to fill in relevant outdoor activity data. The user can refuse or authorize personal information, or choose to fill in or not fill in relevant outdoor activity data. Generally, if the user does not authorize or fill in outdoor activity data, the system will make recommendations based on default devices, such as devices with high purchase rates, or devices suitable for the current season.

[0036] The following example illustrates how electronic devices obtain usage scenario information based on outdoor activity data analysis. The outdoor activity data can be categorized into two types based on the specific information included: In the first scenario, when outdoor activity data includes only one or both of location information, activity demand information, or device information, electronic devices can quickly obtain location information and power demand information through multi-round interaction processes, default scene matching processes, or intelligent judgment processes.

[0037] In one implementation, taking the default scene matching process as an example, when the outdoor activity data only includes location information, the electronic device can obtain historical environmental feature information of the area corresponding to the location information and at least one outdoor activity template information. The outdoor activity template information is used to represent the device information of the electrical equipment corresponding to the pre-configured activity demand information. Based on the historical environmental feature information, the target outdoor activity template information corresponding to the user is determined from at least one outdoor activity template information. Based on the activity duration, power information of each electrical equipment and power consumption duration in the target outdoor activity template information, the power demand information is determined, and the power demand information and location information are determined as the usage scene information.

[0038] It is understandable that historical weather information can be meteorological data obtained in the current month, or meteorological data from 15 or 30 days prior to the current time.

[0039] For example, a user inputs only the destination of an outdoor activity—Mount A (an example of location information)—to an electronic device, with the input date being September 15th. The electronic device obtains the average daily temperature of Mount A in September (before the 15th) as 8-12℃ and the probability of precipitation as 65% (an example of historical weather data). The mainstream outdoor activity template information for Mount A includes the activity duration, required power of electrical equipment, and recommended daily usage time for activities such as hiking, camping, and fishing. Taking an outdoor activity where the electronic device determines that a 3-day hiking trip is suitable when the average daily temperature is 8-12℃ and the probability of precipitation is 65% as an example, the default configuration of electrical equipment for this activity is basic lighting (10W×6h / day), mobile phone charging (10Wh×3 times / day), and a small electric cooker (300W×1h / day). The usage scenario information obtained by the electronic device could be the corresponding location coordinates of Mount A, basic lighting (10W×6h / day), mobile phone charging (10Wh×3 times / day), and a small electric cooker (300W×1h / day).

[0040] In one implementation, taking a multi-turn interaction process as an example, if the outdoor activity data only includes activity requirement information (e.g., three-day camping), the electronic device can input a first prompt message to encourage the user to input the location information of the outdoor activity. In response to the user's input location information, a list of electrical devices and a second prompt message are displayed. The list includes device information for multiple electrical devices, and the second prompt message encourages the user to select the desired device from the list or add the required device and its information. In response to the user's selection or addition of devices, the device information for each device is obtained. Based on the power information, power consumption duration, and activity duration in the activity requirement information for each device, the power requirement information is determined, and the user's input location information is used as the location information for the usage scenario to obtain the usage scenario information. If the user does not input location information, the electronic device can respond to the user's refusal to input location information by displaying a list of electrical devices, a second prompt message, and a third prompt message. The third prompt message indicates that location information is missing and the device recommendation results may be biased.

[0041] For example, if the outdoor activity data acquired by the electronic device only includes the user's input of a 3-day camping activity request, without specifying the destination or location information, the electronic device can initiate a multi-round interactive process. The first round pop-up prioritizes the core scenario, asking the user how they intend to camp ("Hiking / Driving / RV?"). Taking hiking as an example, in the second round, the electronic device guides the user to input their destination (supporting automatic location filling or manual search for specific location information), and simultaneously displays weather forecasts for popular campsites, such as the weather for a park for the next 3 days. If the user refuses to provide a destination, the electronic device defaults to displaying the average weather for a certain area for the next 3 days (e.g., daily average irradiance of 4.5 kWh / m²) and prompts "No destination selected; recommendation results may be biased." For device information, electronic devices can use a "frequently used device selection + custom addition" approach to obtain relevant information. For example, the electronic device can display a list of frequently used devices (an example of an electrical device list). If the user selects a portable air conditioner, the electronic device can use the corresponding 800W×8h value from the list as the device information. Alternatively, if the user manually enters "coffee machine 1200W," the electronic device can obtain the 1200W power information. The entire operation involves ≤5 steps and an average time of <90 seconds. Ultimately, the electronic device can obtain information about the user's usage scenarios for electrical devices during outdoor activities, reducing the likelihood of subsequent recommendations failing due to missing information.

[0042] In another implementation, taking the intelligent judgment process as an example, when the outdoor activity data includes location information and activity demand information, the electronic device can obtain the environmental feature information of the location information within the activity duration and the corresponding outdoor activity template information based on the activity duration in the activity demand information; adjust the device information of the electrical equipment in the outdoor activity template information according to the environmental feature information to obtain the device information of at least one electrical equipment that the user ultimately needs; determine the power demand information based on the power information, power consumption duration of each electrical equipment and the activity duration in the activity demand information, and use the location information input by the user as the location information of the usage scenario to obtain the usage scenario information.

[0043] For example, a user inputs information into an electronic device that they will be camping for 3 days from September 20th to 23rd at Mountain A (an example of location information), but does not provide information about the electrical equipment needed. The electronic device learns that Mountain A is a high-altitude area (an example of environmental characteristics) and that the weather from September 20th to 23rd will be mainly cloudy and rainy (another example of environmental characteristics). The electronic device also identifies from at least one pre-stored outdoor activity template that the electrical equipment needed for a 3-day camping trip typically includes lighting (10W x 8h), drone charging (50Wh x 2 times), a heating blanket (60W x 3h), and a small electric cooker (300W x 1h). Based on the environmental characteristics of Mountain A being high-altitude and experiencing cloudy and rainy weather, the electronic device determines that the user's most pressing needs are basic survival and emergency power, suggesting a high-power electrical device like a small electric cooker. Ultimately, the electronic device is determined to require lighting (10W x 8h / day), drone charging (50Wh x 2 times), and a heating blanket (60W x 3h / day), thus obtaining specific power demand information and location information.

[0044] In the second scenario, if the user's input of outdoor activity data includes location information, activity requirements, and equipment information, the electronic device can directly determine the usage scenario information based on the user's input. For example, if a user inputs into the electronic device that they will camp in Park B for 4 days and bring a 110V portable air conditioner (800W×10h / day) and an electric grill (1.2kW×2h / day), the electronic device can determine the location information as the corresponding coordinates of Park B and the power requirements as a portable air conditioner (800W×10h / day) and an electric grill (1.2kW×2h / day).

[0045] In summary, by using multi-round interactions, default scene matching, or intelligent judgment, and combining user-input location information, activity requirement information, and / or device information, and other data related to outdoor activities, the specific scenario in which a user uses electrical equipment and the power demand in that scenario can be quickly determined. This provides an accurate scenario and data foundation for subsequent power equipment recommendations, thereby enabling personalized equipment recommendations for users.

[0046] S102, Obtain predicted environmental feature information of the usage scenario based on location information.

[0047] Environmental feature information is used to represent the weather conditions and renewable energy power generation in the usage scenario. For convenience, this application embodiment takes the change of light as an example. The environmental feature information may include light information and weather information. Light information is used to represent the light conditions in the usage scenario, and weather information is used to represent the weather conditions in the usage scenario.

[0048] For example, illumination information may include at least one of diffuse horizontal irradiance (DHI), direct normal irradiance (DNI), daily average irradiance, and light intensity. Weather information may include at least one of weather conditions (e.g., sunny / cloudy / light rain), temperature (°C), probability of precipitation (%), wind speed (m / s), extreme weather warnings (e.g., typhoon / heavy rain), altitude (m), and humidity (%).

[0049] Electronic devices can obtain environmental characteristic information predicted by weather forecasting platforms, specifically the predicted lighting and weather information for that location within a future time period. For example, an electronic device can obtain the lighting and weather information for the next three days (starting from today) for a given location. Alternatively, the usage scenario information may also include the user's activity time for outdoor activities. The electronic device can obtain the lighting and weather information for that location at that activity time from the weather forecasting platform. For example, if a user is hiking in Park B from September 12th to September 15th, but the current date is September 10th, the electronic device will obtain the predicted lighting and weather information for Park B between September 12th and September 15th.

[0050] S103, determine candidate power supply equipment based on environmental characteristic information.

[0051] Among them, candidate power equipment includes candidate power generation equipment and / or candidate energy storage equipment, which are multiple alternative power generation equipment and / or energy storage equipment that match the user's usage scenario.

[0052] The power generation equipment may include at least one of the following: fuel cell generator, solar panel, wind power generation equipment, piezoelectric generator, thermoelectric generator, hand-cranked generator, or pedal generator. The energy storage equipment may include at least one of the following: fuel cell energy storage equipment, lithium battery energy storage equipment, cryogenic battery pack, or capacitor energy storage equipment. This application embodiment does not limit the type and number of power generation equipment or energy storage equipment. For convenience, this application embodiment uses an example where the power generation equipment includes at least a fuel cell generator and a solar panel, and the energy storage equipment includes at least a lithium battery energy storage equipment and a cryogenic battery pack.

[0053] In one implementation, the electronic device can analyze the power supply demand corresponding to the user's outdoor activities based on environmental characteristic information, and obtain the power generation characteristics of each power generation device and the energy storage characteristics of each energy storage device in the preset device library. Based on the power generation characteristics of each power generation device and the energy storage characteristics of each energy storage device, candidate power generation devices and / or candidate energy storage devices that meet the power supply demand are determined from each power generation device and each energy storage device.

[0054] Among them, power generation characteristics are used to indicate the power generation capacity of power generation equipment and its applicable conditions. For example, fuel generators can generate electricity stably in the absence of stable sunlight or wind, in environments requiring continuous high power output, or in low-temperature environments; solar panels are suitable for generating electricity in environments with high sunlight intensity; wind power generation equipment is suitable for generating electricity in environments with sufficient wind; piezoelectric power generation equipment or thermoelectric power generation equipment is suitable for generating electricity in environments with continuous mechanical vibration or stable temperature differences; hand-cranked / pedal generators are mainly used in emergency scenarios (such as when there is no fuel, no sunlight, and the power consumption is small).

[0055] Energy storage characteristics are used to describe the energy storage capacity of energy storage devices and their applicable conditions. For example, lithium battery energy storage devices are suitable for stable energy storage in normal temperature environments with medium to high energy demands and the need for rapid charging and discharging; low-temperature battery packs are suitable for maintaining stable energy storage capacity at low temperatures with little capacity decay; fuel cell energy storage devices are suitable for long-term low-power supply when fuel is available; capacitor energy storage devices can supply power to high-frequency pulse-type electrical devices and are often suitable for applications requiring instantaneous high power.

[0056] Power demand is used to represent the energy storage characteristics of energy storage devices and the power generation characteristics of power generation devices required in a given usage scenario.

[0057] For example, if the environmental characteristics include cloudy weather, a temperature of 10℃, a wind speed of 3m / s, and a daily average irradiance of 3.5kWh / m², and the electronic device analyzes that the power supply requirement is to be able to provide stable power supply under low light and wind speed conditions, and the power generation characteristics of the fuel generator meet this power supply requirement, and the energy storage characteristics of the lithium battery energy storage device also meet this power supply requirement, then the electronic device can determine that the candidate power generation device is the fuel generator and the candidate energy storage device is the lithium battery energy storage device.

[0058] For example, environmental characteristics include sunny weather, altitude greater than 3000m, precipitation probability of 10%, daily average irradiance of 5.8kWh / m², midday (12:00-14:00) light intensity of 1000W / m², and temperature below -10℃. The electronic equipment analyzes that the power supply requirement is to be able to provide stable power supply under high light intensity and low temperature environment. The power generation characteristics of solar panels and the energy storage characteristics of low temperature battery packs meet the power supply requirements. The electronic equipment determines that the candidate power generation device is solar panels and the candidate energy storage device is low temperature battery packs.

[0059] It should be noted that, to ensure users can successfully conduct outdoor activities, the recommended power supply combination and the user's existing power supply equipment must include at least one power generation device and at least one energy storage device. Therefore, when determining candidate power supply devices, the electronic device can also access the user's existing power supply equipment. If the existing power supply equipment includes a power generation device, the electronic device can determine whether the power generation characteristics of the user's existing power generation device meet the power supply requirements. If they do, it will determine candidate energy storage devices from a pre-set device library based on environmental characteristics, and may also use the user's existing power generation device as a candidate power generation device. If the requirements are not met, it will determine candidate energy storage devices and candidate power generation devices from the pre-set device library based on environmental characteristics.

[0060] It is understandable that when a user's existing power supply includes energy storage devices, the electronic device makes a similar judgment. However, if the existing energy storage devices meet the power supply requirements, the electronic device determines candidate power generation devices from a pre-set device library based on environmental characteristics, and may also consider the user's existing energy storage devices as candidate energy storage devices. Of course, if the user's existing power supply includes both energy storage and power generation devices, and both can meet the power supply requirements, the electronic device may consider the user's existing energy storage and power generation devices as candidate energy storage and candidate power generation devices, respectively. If either does not meet the power supply requirements, the electronic device can perform the judgment and determination process described above for the corresponding situation. When the user has no power supply, the electronic device can determine candidate power generation and candidate energy storage devices from a pre-set device library based on environmental characteristics.

[0061] S104, Based on the power supply parameters of the candidate power supply equipment, determine a combination of multiple candidate equipment that meet the power demand information.

[0062] The candidate device combination includes at least one power supply device.

[0063] When the candidate power supply device is a candidate power generation device, the power supply parameters may include power generation capacity and power generation duration; when the candidate power supply device is a candidate energy storage device, the power supply parameters may include energy storage capacity.

[0064] There can be one or more candidate power devices.

[0065] In one implementation, the electronic device can obtain the power supply parameters of the user's existing power supply equipment and determine the user's total power consumption during outdoor activities based on power demand information. It then calculates the total power supply of the user's existing power supply equipment over the activity duration based on the power supply parameters, and calculates the power difference between this total power supply and the total power consumption. The electronic device will then combine this power difference with the power supply parameters of candidate power supply equipment to generate at least one candidate device combination that satisfies the power difference. When obtaining usage scenario information, the electronic device can output a fourth prompt message, prompting the user to input the power supply parameters of the existing power supply equipment. Responding to the user's power supply parameter input operation, the electronic device obtains the power supply parameters of the user's existing power supply equipment. Alternatively, if the electronic device already stores the power supply parameters of the user's existing power supply equipment, it can directly read the data to obtain these parameters.

[0066] Specifically, for each candidate power generation device, the electronic device will determine whether the total power generation of a single candidate power generation device during the user's outdoor activities is greater than or equal to the power difference based on the power supply parameters of that candidate power generation device. If so, the single candidate power generation device will be combined as a candidate device group. If not, the electronic device will calculate whether the total power generation of multiple candidate power generation devices is greater than or equal to the power difference. If so, the combination of multiple candidate power generation devices will be combined as a candidate device group.

[0067] For each candidate energy storage device, the electronic device determines whether the total energy storage capacity of a single candidate energy storage device during the user's outdoor activities is greater than or equal to the energy difference, based on the device's power supply parameters. If so, the single candidate energy storage device is grouped as a single candidate device. If not, the electronic device calculates whether the total energy storage capacity of multiple candidate energy storage devices connected in parallel is greater than or equal to the energy difference. If so, the combination of multiple candidate power generation devices is grouped as a single candidate device. Furthermore, the electronic device also calculates whether the total power supply of a combination of candidate power generation devices and candidate energy storage devices is greater than or equal to the energy difference, based on the power supply parameters of both candidate power generation devices and candidate energy storage devices. If so, the combination of candidate power generation devices and candidate energy storage devices is grouped as a single candidate device.

[0068] For example, if the electricity demand information includes basic lighting (10W×6h / day), mobile phone charging (10Wh×3 times / day), and a small electric cooker (500W×2h / day), and the activity lasts for 3 days, then the daily electricity consumption of electronic devices can be calculated as 0.01kW×6h + 0.01kWh×3 + 0.5kW×2h = 1.09kWh, and the total electricity consumption for 3 days is 1.09×3 = 3.27kWh. The user's existing power supply equipment is an energy storage device A, with a total energy storage capacity (an example of total power supply) of 1kWh, resulting in a power difference of 2.27kWh. If the electronic device determines that within 3 days, the total power generation of one solar panel Y1 is 3kWh > 2.27kWh, the total power generation of two solar panels Y2 is 2.5kWh > 2.27kWh, and the total energy storage of two energy storage devices B is 3kWh > 2.27kWh, then the electronic device can determine 3 candidate device combinations: the combination of solar panels Y1, the combination of solar panels Y2×2, and the combination of energy storage devices B×2.

[0069] This step combines the power supply parameters of candidate power devices with the user's electricity demand to generate different combinations of power generation and energy storage devices (i.e., candidate device combinations), forming possible power supply solutions. This can provide diverse results for recommending the final device combination and reduce the situation of recommending only a single device.

[0070] S105, based on a preset priority recommendation algorithm, determines the target recommended device combination from each candidate device combination.

[0071] The priority recommendation algorithm includes at least one evaluation index and a priority weight for each evaluation index.

[0072] At least one evaluation metric may include at least one of the following: energy self-sufficiency rate, weight, volume, environmental friendliness rating, and / or cost of the power supply equipment.

[0073] For each candidate device combination, the electronic device can determine the evaluation index of the power supply device within that combination and calculate the product between the evaluation index and its corresponding priority weight to obtain a priority score for the candidate device combination. The electronic device can then select the candidate device combination with the highest priority score as the target recommended device combination and output the power supply device within that target recommended device combination via images, text, or voice, so that the user can obtain a power supply device that meets their needs. Furthermore, the electronic device can also recommend links to obtain the power supply device within that combination while outputting the target recommended device combination, facilitating timely purchase of the corresponding device by the user.

[0074] In one application scenario, such as Figure 2As shown, when a user is engaged in outdoor activities, the destination and device preview list can be automatically filled in through the location of the application (APP) in the mobile phone (an example of an electronic device). The user can input the destination (an example of location information) and the configuration of the required power equipment (an example of power demand information) into the unified recommendation engine in the APP. Alternatively, the user can manually input the power demand (information) and configuration (an example of the user's existing power equipment) through a web browser. Or, the user can click on the scenario-based landing page displayed on the APP or web page (e.g., a special page for essential camping kits in Park B) + one-click purchase advertisement, and input the location information of the outdoor activity, the power information of the power equipment, and the power consumption duration into the unified recommendation engine. The unified recommendation engine can execute S101 to S105 to generate a personalized plan (an example of a target recommended equipment combination) and recommend the personalized plan to the user through the APP, web, or SMS.

[0075] In this embodiment, predicted environmental characteristics of the usage scenario are obtained based on location information from the usage scenario information. Combined with real-time weather and other environmental characteristics at the specific location of the outdoor activity, candidate power supply devices suitable for the environment are recommended to the user. These candidate power supply devices include at least one of candidate power generation devices or candidate energy storage devices, enabling multi-device collaboration for different environments and reducing the risk of energy supply disruptions during travel. Furthermore, based on the power supply parameters of the candidate power supply devices, multiple combinations of candidate devices that meet the power demand information are determined. A suitable device combination is then recommended using a preset priority recommendation algorithm. This achieves a two-dimensional device recommendation that combines environmental factors and specific power demand, ensuring that the recommended device combination not only meets the user's power needs but also satisfies the user's actual outdoor activity scenario, reducing the need for user adjustments and improving the accuracy of device recommendations.

[0076] To improve device recommendation efficiency, in one implementation, such as Figure 3 As shown, candidate power supply devices, including S201 to S202, are determined based on environmental characteristic information.

[0077] S201, determine the scene type based on illumination information and weather information.

[0078] Among them, environmental characteristic information includes illumination information and weather information.

[0079] Scene type is used to indicate the power supply requirements in the usage scenario. For example, the scene type can be a high temperature scene, a rainy scene, a high altitude scene, or a normal scene.

[0080] Electronic devices can determine the scenario type based on the lighting conditions indicated by lighting information and the weather conditions indicated by weather information. Specifically, weather information includes temperature and precipitation probability, and lighting information includes irradiance (i.e., the aforementioned DHI, DNI, daily average irradiance, and / or light intensity). When the temperature is greater than or equal to a preset temperature threshold and the duration of the high temperature is greater than a preset duration threshold, the electronic device determines the scenario type as a high-temperature scenario; when the precipitation probability is greater than or equal to a precipitation probability threshold and the irradiance is less than or equal to a irradiance threshold, the electronic device determines the scenario type as a cloudy / rainy scenario.

[0081] The weather information also includes altitude and extreme weather warning information. When the altitude is greater than or equal to a preset altitude threshold and the air temperature (i.e., temperature) is less than a specific temperature threshold, the electronic device determines the scene type as a high-altitude scene. When the irradiance is greater than or equal to a specific radiation threshold and there is no preset extreme weather in the extreme weather warning information, the electronic device determines the scene type as a normal scene.

[0082] For example, the preset temperature threshold can be set to 30℃, 35℃, etc.; the preset duration threshold can be set to 3 hours, 4 hours, 5 hours, etc.; the precipitation probability threshold can be set to 55%, 60%, 65%, etc.; the solar radiation threshold can be set to 2kWh / m², 2.5kWh / m², 3kWh / m², etc.; the preset altitude threshold can be set to 2000m, 2500m, 3000m, etc.; the specific temperature threshold can be set to -10℃, -15℃, -20℃, etc.; and the specific radiation threshold can be set to 5kWh / m², 5.5kWh / m², 6kWh / m², etc. It is understood that the specific values ​​of the above thresholds are merely examples, and the embodiments of this application are not limited thereto.

[0083] In the above implementation method, by quantifying weather and light information, different scenario types of the usage scenario can be accurately identified, and customized power supply equipment can be provided based on different scenario types in the future.

[0084] S202, determine candidate power supply devices based on scenario type.

[0085] The electronic device stores scene-device mapping relationships, which include power devices and their power supply parameters corresponding to multiple scene types. The electronic device can search for candidate power devices in the scene-device mapping relationships based on a determined scene type.

[0086] For example, after determining the scenario type based on weather and lighting information, the electronic device can identify candidate power supply devices according to Table 1 below. For instance, if the electronic device determines that the scenario is a high-temperature scenario, it can select energy storage device A and solar panel (200W) as candidate energy storage device and candidate power generation device, respectively.

[0087] Table 1 Candidate Power Supply Equipment Selection Form

[0088] In one application scenario, such as Figure 4 The device recommendation system shown is applicable to the device recommendation method in the embodiments of this application (which is a system embedded in an electronic device, and can be regarded as an example of an electronic device), and Figure 5 The diagram illustrates the workflow of the device recommendation method. Users input their outdoor activity destination or needs (an example of outdoor activity data) into the system's destination input layer by taking pictures of electrical appliances or manually setting parameters. For example, inputting "4 days camping in Park B, requiring air conditioning + electric grill". The system can extract location information and activity duration (i.e., activity time) from the outdoor activity data and trigger the dual-engine prediction process in the prediction engine layer, using the solar energy prediction engine (i.e.,... Figure 4 The solar energy forecasting module in the system calls the application programming interface (API) of the weather forecasting platform to obtain information such as daily average irradiance, solar intensity, charging period, and sunshine duration curve (an example of sunshine information), such as 5.8 kWh / m². This information is then processed by the weather forecasting engine (i.e.,... Figure 4 The weather forecast module in the system calls the API interface of the weather forecast platform to obtain information such as temperature, precipitation probability, wind speed and extreme weather warnings (an example of weather information).

[0089] The system will use the scene decision layer to combine environmental feature information (such as lighting and / or weather) obtained from the prediction engine layer with... Figure 4 Table 1 identifies destinations as high-temperature scenarios, rainy scenarios, or normal scenarios, and determines the required energy storage or power generation equipment based on the scenario type. For example, high-capacity energy storage equipment is needed in high-temperature scenarios, generators (an example of fuel generators) and long-endurance energy storage equipment are needed in rainy scenarios, and solar panels and energy storage equipment with appropriate capacity are needed in normal scenarios.

[0090] To enable the recommended power supply to dynamically match the environment, one implementation continues to combine... Figure 3 Based on the power supply parameters of the candidate power supply equipment, a combination of multiple candidate equipment that meets the power demand information is determined, including S203 to S205.

[0091] S203 determines the total power consumption based on the power information and power consumption duration of each electrical device.

[0092] The electricity demand information includes the electrical equipment and the duration of electricity consumption for that equipment.

[0093] Electricity usage time refers to the total time a user uses electrical equipment while engaging in outdoor activities.

[0094] The electronic device calculates the product between the power information and the power consumption time of each electrical device to obtain the power consumption of that device, and then uses the sum of the power consumption of each electrical device as the total power consumption.

[0095] It should be noted that if the electricity usage time refers to the daily usage time of the electrical equipment, the electronic equipment can calculate the product between the activity time and the usage time to obtain the total time the user uses the electrical equipment, and then calculate the product between the total time and the power information to obtain the total electricity consumption.

[0096] For example, electricity demand information includes a portable air conditioner (800W, 10h / day) and an electric oven (1.2kW, 2h / day), with an activity duration of 4 days. The total electricity consumption would then be (0.8×10h + 1.2×2h)×4 = 41.6kWh.

[0097] S204 determines capacity gap information based on total power consumption and power supply parameters of existing power equipment of users.

[0098] The user's existing power supply equipment may include at least one power generation device or at least one energy storage device.

[0099] For each existing power generation device of the user, the electronic device will determine the total power generation of the power generation device based on the product of the power generation capacity and the activity duration of the power generation device; for each existing energy storage device of the user, the electronic device will obtain the energy storage capacity of the energy storage device; the electronic device will take the sum of the total power generation of each power generation device and the energy storage capacity of each energy storage device as the total power supply of the user's existing power equipment, and calculate the power difference between the total power supply and the total power consumption, and take the ratio between the power difference and the total power consumption as the capacity gap information.

[0100] It is understandable that when a user's existing power supply equipment only includes power generation equipment, the electronic device can use the sum of the total power generation of each power generation equipment as the total power supply; when a user's existing power supply equipment only includes energy storage equipment, the electronic device can use the sum of the energy storage capacity of each energy storage equipment as the total power supply.

[0101] To improve the accuracy of device recommendations, precise capacity gap information is needed. Since users often carry solar panels when outdoors, this embodiment uses a user's existing power supply including a solar panel as an example. To obtain accurate capacity gap information, the electronic device can input the solar panel's power generation and environmental characteristics into a trained neural network model. For example, this neural network model can be a Long Short-Term Memory (LSTM) model. This LSTM model can predict the average daily power generation of the solar panel based on environmental characteristics and preset correction conditions. The electronic device can then use this average daily power generation to determine the total power generation of the solar panel, thus obtaining the total power supply.

[0102] The preset correction conditions may include an altitude coefficient and a seasonal factor. The altitude coefficient includes a 2% increase in light intensity for every 1000m increase in altitude, and the seasonal factor includes a summer correction coefficient of 1.1 and a winter correction coefficient of 0.9.

[0103] Daily power generation = Daily irradiance × Solar panel power generation × System efficiency (also known as power generation efficiency).

[0104] Electronic devices can use LSTM models to predict daily average irradiance and system efficiency, and then correct the system efficiency and / or daily average irradiance to finally output the daily average power generation.

[0105] For example, if the solar panel generates 0.11kW, and the electronic device predicts an average daily irradiance of 2.2kWh / m² without preset correction bars using an LSTM model, and the system efficiency is 87%, and the environmental characteristics indicate a low-temperature environment after combining preset correction conditions, the system efficiency needs to be increased by 5% in the low-temperature environment, then the actual average daily power generation = 2.2 × 0.11kW × 0.92 = 0.22kWh.

[0106] In one implementation, the electronic device can also predict the optimal charging period for the solar panels during the activity duration using an LSTM model, and output the optimal charging period to prompt the user to optimize the use of solar panels during outdoor activities and improve the efficiency of solar energy utilization.

[0107] The input and output logic of the LSTM model is explained in detail below.

[0108] Specifically, the LSTM model achieves joint prediction of average daily power generation and optimal charging time through multi-dimensional input. Electronic devices can input environmental characteristic information, solar panel equipment information (including power generation, area, and installation angle), and historical environmental characteristic information into the LSTM model. The LSTM model can extract environmental characteristics such as DHI, DNI, altitude, season, temperature, and precipitation probability from the input data; equipment characteristics such as power generation, area, and installation angle (default 30°); and historical environmental characteristics such as the concurrent irradiance curve and the frequency of extreme weather events. It then determines the corresponding coefficients based on preset correction conditions. For example, altitude corresponds to the altitude coefficient, season corresponds to the correction coefficient, temperature corresponds to the system efficiency decay coefficient, and precipitation probability corresponds to the cloud cover weight. Among these, the preset correction condition states that system efficiency decreases as temperature increases (under standard test conditions, efficiency is 100% at 25℃, and decreases by 0.3% for every 1℃ increase).

[0109] The LSTM model calculates the initial average daily power generation of the solar panels based on extracted features using the integral of a 24-hour irradiance sequence, and identifies the initial optimal charging period by differentiating the irradiance curve. It then dynamically adjusts the initial average daily power generation and the initial optimal charging period based on the determined coefficients, resulting in the final average daily power generation and optimal charging period for the solar panels. For example, if the probability of precipitation is 65%, the optimal charging period is automatically shortened to 2 hours (during rainless intervals), and the system efficiency is reduced to 0.65. If extreme weather conditions such as thunderstorm warnings are detected, charging is paused, with a "prioritize safety" prompt. Furthermore, the model can continuously update its parameters through reinforcement learning to adapt to changing user needs.

[0110] In summary, electronic devices integrate environmental features, equipment information, and historical data through LSTM models. In a single inference process, they simultaneously output the average daily power generation and the optimal charging time that are adapted to the environment. Together, these two factors constitute the "quantity-time" dual-dimensional decision-making basis for solar energy utilization, which can ensure the accuracy and practicality of subsequent equipment combination schemes.

[0111] It should be understood that in other possible implementations, the LSTM model described above can also be replaced with other types of neural network models or combinations of neural network models. For example, in some embodiments, the LSTM model described above can be replaced with a one-dimensional convolutional neural network (CNN) model, a Transformer model, a CNN-LSTM hybrid model, etc. This application does not limit the specific type of the neural network model described above.

[0112] S205, Based on the capacity gap information, combine the candidate power supply devices to obtain at least one candidate device combination that satisfies the capacity gap information.

[0113] In one implementation, candidate power supply devices are combined based on capacity gap information to obtain at least one candidate device combination that satisfies the capacity gap information. This includes: when the capacity gap information is less than or equal to a first preset threshold, determining at least one candidate device combination that satisfies the capacity gap information based on the power supply parameters of the solar panels in the candidate power generation devices; when the capacity gap information is greater than the first preset threshold and less than or equal to a second preset threshold, determining at least one candidate device combination that satisfies the capacity gap information based on the power supply parameters of the candidate energy storage devices; and when the capacity gap information is greater than the second preset threshold, determining at least one candidate device combination that satisfies the capacity gap information based on the power supply parameters of the fuel generators in the candidate power generation devices.

[0114] Wherein, the first preset threshold is less than the second preset threshold. The first preset threshold can be 20%, 25%, 30%, etc.; the second preset threshold can be 50%, 55%, 60%, etc. Of course, the first preset threshold and the second preset threshold can also be other values, and this application embodiment does not impose specific limitations.

[0115] When the capacity gap information is less than or equal to a first preset threshold, the electronic device can determine the average daily power generation of the solar panel based on the power supply parameters of each solar panel and the aforementioned LSTM model. It then determines whether this average daily power generation is greater than or equal to the aforementioned power difference. If so, the solar panel is identified as a candidate device combination. If not, the power generation of the solar panel is increased, or multiple solar panels are connected in series and a similar process is performed to determine the candidate device combination.

[0116] When the capacity gap information is greater than a first preset threshold and less than or equal to a second preset threshold, the electronic device, in addition to determining whether the average daily power generation of the solar panel is greater than or equal to the aforementioned power difference, also determines whether the energy storage capacity of each candidate energy storage device is greater than or equal to the aforementioned power difference. If so, the candidate energy storage device is determined to be a candidate device combination. If not, multiple candidate energy storage devices are connected in parallel, and the sum of the energy storage capacities of the multiple candidate energy storage devices is determined to be greater than or equal to the aforementioned power difference, or the energy storage capacity of the candidate energy storage devices is increased before determining the candidate device combination.

[0117] When the capacity gap information exceeds a second preset threshold, the electronic device, in addition to determining whether the energy storage capacity of each candidate energy storage device is greater than or equal to the aforementioned power difference, also determines the total power generation of the fuel generator based on the power output of each fuel generator in the candidate power generation device, and determines whether the total power generation is greater than or equal to the aforementioned power difference. If so, the candidate energy storage device is determined to be a candidate device combination. If not, at least one candidate energy storage device and at least one fuel generator are combined, and the sum of the total power generation of the fuel generator and the energy storage capacity of the candidate energy storage device is determined. Based on whether the sum is greater than or equal to the aforementioned power difference, it is determined whether to combine the candidate energy storage device and the fuel generator as a candidate device combination.

[0118] It is understandable that when the capacity gap information indicates that there is no power difference, electronic devices can directly determine the user's existing power supply equipment as the target recommended equipment combination.

[0119] In one application scenario, such as Figure 6 As shown, when the electronic device detects that the user's existing power supply equipment cannot meet the user's total power consumption, the electronic device first calculates the gap ratio based on the total power consumption and the total power supply of the existing power supply equipment (an example of gap capacity information: gap ratio = (total power consumption - total power supply of existing power supply equipment) / total power consumption × 100%). If the gap is ≤30%, the device determines to upgrade the solar panel power (e.g., replacing 110W with 200W, an example of candidate equipment combinations); if the gap is between 30% and 50%, the device determines to connect energy storage equipment in parallel (another example of candidate equipment combinations), or if there is still a 30%-50% gap after connecting energy storage equipment in parallel, the device with a larger energy storage capacity is considered as a possible recommended strategy (another example of candidate equipment combinations); if the gap is >50%, the device automatically matches a supplementary solution of generator + energy storage equipment (another example of candidate equipment combinations), and finally outputs the optimal combination strategy (an example of the target recommended equipment combination).

[0120] In the above technical solution, different combinations of power generation equipment such as solar panels and fuel generators and energy storage equipment are determined based on different capacity gap information. Based on the different classification of capacity gap information, equipment combinations of solar panels alone, equipment combinations of fuel generators + energy storage equipment, and equipment combinations of multiple energy storage equipment can be obtained. This not only enables the recommendation and coordination of multiple devices, but also enables the precise matching of equipment combinations with users' electricity demand. It realizes the recommended scheme of using solar panels for small gaps, energy storage equipment for medium gaps, and fuel generators as a backup for large gaps, which can improve the economy and reliability of recommending equipment to users.

[0121] Continue to combine Figures 4 to 6After the device recommendation system determines the device combinations for different scenario types through the scenario decision layer, it can transmit the average daily power generation and charging period of the solar panels (an example of determining the average daily power generation in S204) determined by the solar energy prediction engine to the device recommendation layer through the scenario decision layer. The system determines the gap ratio through the device recommendation layer, and if the determined gap ratio is less than 30% (i.e., ... Figure 5 If the capacity meets the requirements, the system can upgrade the power generation of the solar panels or the energy storage capacity of the energy storage devices determined in the scenario decision layer. If the capacity shortage is met, the system will recommend specific models of the single device. If the shortage is greater than 30%, indicating insufficient capacity, the system can perform parallel operation of energy storage devices or combine generators with energy storage devices. If the shortage is met, the system will recommend specific models of multiple devices. If the system determines that the user's existing power supply equipment does not include the recommended device, it can also recommend purchase links on the official website or offline (i.e., new purchase guidance). Finally, the device recommendation system recommends the necessary device combinations and purchase links to the user through the output layer. It can also optimize the solar prediction engine in the device recommendation system based on user feedback on the recommended device combinations.

[0122] To provide users with more accurate device recommendations, one implementation method continues to combine... Figure 3 Based on a preset priority recommendation algorithm, a target recommended device combination is determined from each candidate device combination, including S206 to S210.

[0123] S206. Determine the energy self-sufficiency rate based on the power supply parameters and total power consumption of the power supply equipment included in the candidate equipment combination.

[0124] For each candidate device combination, the electronic device can calculate the total power supply of the power supply devices included in the candidate device combination based on the power supply parameters of the power supply devices included in the candidate device combination, and take the ratio between the total power supply and the total power consumption as the energy self-sufficiency rate. The process of the electronic device calculating the total power supply of the power supply devices included in the candidate device combination is similar to the process of the electronic device determining the total power supply of the user's existing power supply devices, and will not be described in detail here.

[0125] S207, Obtain cost information of power supply equipment included in the candidate equipment combination, and determine the cost-effectiveness of the equipment.

[0126] Equipment cost-effectiveness is used to describe the cost efficiency of a combination of equipment relative to a single piece of equipment.

[0127] For each candidate device combination, the electronic device can use the sum of the cost information of each power supply device included in the candidate device combination as the combination cost of the device combination, and obtain the cost information of a single power supply device when the gap capacity information (or the power difference determined above) corresponding to the candidate device combination is met. The electronic device calculates the difference between the cost information of the single power supply device and the combination cost, and uses the ratio between the difference and the cost information of the single power supply device as the device cost-effectiveness.

[0128] S208, based on the specifications of the power supply equipment included in the candidate equipment portfolio, determines the equipment portability score.

[0129] The specifications include the volume and weight of the power supply equipment included in the candidate equipment portfolio.

[0130] The electronic device can calculate the sum of the products of the weight and corresponding volume of the power supply equipment included in the candidate device combination as a first parameter; and use the reciprocal of the first parameter as the device portability score of the candidate device combination.

[0131] S209 calculates the recommended weight value for candidate equipment combinations by weighting and integrating the scores for energy self-sufficiency, equipment cost-effectiveness, and equipment portability.

[0132] The recommended weight value for each candidate device combination can be determined using the formula: α × Energy Self-Sufficiency Rate + β × Portability Score + γ × Cost-Effectiveness. Here, α + β + γ = 1. For example, α = 0.5, β = 0.3, and γ = 0.2. In high-temperature or rainy scenarios, α can be increased to 0.7, while β and γ are reduced accordingly.

[0133] S2010: The candidate device combination corresponding to the largest recommendation weight value is taken as the target recommendation device combination.

[0134] For example, assuming a hike on mountain A, the total demand is 7.5 kWh (an example of total electricity consumption), with a deficit of 6.4 kWh (an example of electricity difference), representing a deficit ratio of 85.3%. The candidate equipment combinations identified for electronic devices include combinations 1 and 2. Combination 1 includes one energy storage device A and one 1L generator. Energy self-sufficiency rate = (1.1 + 1.8 × 5) / 7.5 = 1.24 (excess supply), portability score = 1 / (7.2 kg × 35 L) × 100 = 0.408, cost-effectiveness = (1599 (single device cost) - (1599 + 1899 (total combination cost)) / 1599 = -0.188), recommended weight value = 0.7 × 1.24 + 0.3 × 0.408 + 0.2 × (-0.188) = 0.868 + 0.122 - 0.038 = 0.952.

[0135] Combination 2 includes three energy storage devices D connected in parallel (total storage capacity 2304Wh). Energy self-sufficiency rate = (0.22×5+2.304) / 7.5 = (1.1+2.304) / 7.5 = 0.454 (gap 54.6%). Portability score = 1 / (10.8kg×52L)×100 = 0.175. Cost-effectiveness = (1599×3-(1599×3+1797)) / 4797 = 0.62. Recommended weight value = 0.7×0.454+0.3×0.175+0.2×0.62 = 0.318+0.052+0.124 = 0.494. The electronic equipment determined that Combination 1 had the highest recommended weight value. Ultimately, the recommended equipment combination was determined to be one energy storage device A and one 1L generator.

[0136] In one application scenario, combined with Figures 1 to 6 For a 5-day hike in Park B, the location information is Park B (40.2°N, most of the park's altitude exceeds 3000m). The user's existing mobile energy storage device D has a storage capacity of 768Wh. The user's average daily electricity demand is 1.5kWh (lighting + drone charging).

[0137] The electronic device, using a solar energy forecasting engine, estimates an average daily irradiance of 2.2 kWh / m² over a 5-day hike (with mostly cloudy and foggy weather). Effective charging time is only 4 hours, resulting in an average daily solar power generation of 0.24 kWh. The weather forecasting engine predicts daytime temperatures of 8-12°C, a 65% chance of precipitation (light rain turning to snow), and wind speeds of 8-12 m / s over the same period. The electronic device determines an energy deficit of 0.24 - 1.5 = 1.26 kWh / day. The final equipment combination determined is two parallel energy storage devices (D) + a 1L silent generator. The generator's strategy is to operate for 1.5 hours daily (generating 1.8 kWh). Therefore, the target recommended equipment input for the electronic device is the model number of the existing energy storage device D + the model number of the newly added 1L silent generator, along with a purchase link for the 1L silent generator.

[0138] In this way, capacity gap information can be obtained through solar energy forecasting and weather forecasting, and equipment combinations can be determined based on this information to achieve dynamic matching between the environment and the equipment. Customized equipment combinations are provided for scenarios such as high temperatures, rainy weather, and high altitudes, improving the accuracy of adaptation to special scenarios. When no single device can meet the user's needs, a multi-device combination solution is automatically generated, improving energy security and enhancing the user experience. Recommended equipment is also provided to shorten the user's decision-making time.

[0139] Figure 7 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Figure 7 As shown, the electronic device 6 of this embodiment includes: at least one processor 60 ( Figure 7(Only one is shown in the diagram) a processor, a memory 61, and a computer program 62 stored in the memory 61 and executable on the at least one processor 60, wherein the processor 60 executes the computer program 62 to implement the steps in any of the above method embodiments.

[0140] The electronic device 6 can be a desktop computer, laptop, handheld computer, cloud server, or other computing device. This electronic device may include, but is not limited to, a processor 60 and a memory 61. Those skilled in the art will understand that... Figure 6 This is merely an example of electronic device 6 and does not constitute a limitation on electronic device 6. It may include more or fewer components than shown, or combine certain components, or different components, such as input / output devices, network access devices, etc.

[0141] The processor 60 may be a Central Processing Unit (CPU), or it may be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor.

[0142] In some embodiments, the memory 61 may be an internal storage unit of the electronic device 6, such as a hard disk or memory of the electronic device 6. In other embodiments, the memory 61 may be an external storage device of the electronic device 6, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc., equipped on the electronic device 6. Furthermore, the memory 61 may include both internal and external storage units of the electronic device 6. The memory 61 is used to store the operating system, applications, bootloader, data, and other programs, such as the program code of the computer program. The memory 61 can also be used to temporarily store data that has been output or will be output.

[0143] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0144] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps described in the various method embodiments above.

[0145] This application provides a computer program product that, when run on a mobile terminal, enables the mobile terminal to implement the steps described in the above-described method embodiments.

[0146] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments of this application can be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include at least: any entity or device capable of carrying computer program code to a photographing device / terminal device, a recording medium, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium. Examples include USB flash drives, portable hard drives, magnetic disks, or optical disks. In some jurisdictions, according to legislation and patent practice, computer-readable media cannot be electrical carrier signals or telecommunication signals.

[0147] It should be understood that, when used in this application specification and the appended claims, the term "comprising" indicates the presence of the described features, integrals, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or a collection thereof.

[0148] It should also be understood that the term “and / or” as used in this application specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0149] Furthermore, in the description of this application and the appended claims, the terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized. In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0150] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0151] In the embodiments provided in this application, it should be understood that the disclosed apparatus, computer equipment, and methods can be implemented in other ways. For example, the apparatus and computer equipment embodiments described above are merely illustrative. For instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0152] The above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A method for recommending equipment, characterized in that, include: Obtain user usage scenario information; wherein, the usage scenario information includes location information and electricity demand information of the usage scenario; Based on the location information, the predicted environmental feature information of the usage scenario is obtained; Candidate power devices are determined based on the environmental characteristic information; the candidate power devices include candidate power generation devices and / or candidate energy storage devices. Based on the power supply parameters of the candidate power supply devices, multiple candidate device combinations that meet the power demand information are determined; wherein, the candidate device combination includes at least one power supply device; Based on a preset priority recommendation algorithm, a target recommended device combination is determined from each of the candidate device combinations.

2. The method as described in claim 1, characterized in that, The environmental feature information includes illumination information and weather information. The step of determining candidate power supply devices based on the environmental feature information includes: The scene type is determined based on the lighting information and the weather information; The candidate power supply device is determined based on the scenario type.

3. The method as described in claim 1 or 2, characterized in that, The electricity demand information includes the electrical equipment and the power consumption duration of the electrical equipment. The step of determining multiple candidate equipment combinations that meet the electricity demand information based on the power supply parameters of the candidate power supply equipment includes: The total electricity consumption is determined based on the power information of each electrical device and the duration of electricity consumption; Based on the total power consumption and the power supply parameters of the user's existing power equipment, determine the capacity gap information; Based on the capacity gap information, the candidate power supply devices are combined to obtain at least one candidate device combination that satisfies the capacity gap information.

4. The method as described in claim 3, characterized in that, The step of combining the candidate power devices based on the capacity gap information to obtain at least one candidate device combination that satisfies the capacity gap information includes: When the capacity gap information is less than or equal to a first preset threshold, at least one candidate device combination that satisfies the capacity gap information is determined based on the power supply parameters of the solar panels in the candidate power generation equipment. When the capacity gap information is greater than the first preset threshold and less than or equal to the second preset threshold, at least one candidate device combination that satisfies the capacity gap information is determined based on the power supply parameters of the candidate energy storage devices. If the capacity gap information is greater than the second preset threshold, at least one candidate equipment combination that satisfies the capacity gap information is determined based on the power supply parameters of the fuel generators among the candidate power generation equipment.

5. The method as described in claim 3, characterized in that, The preset priority recommendation algorithm determines the target recommended device combination from each candidate device combination, including: Based on the power supply parameters of the power supply equipment included in the candidate equipment combination and the total power consumption, the energy self-sufficiency rate is determined; Obtain cost information of the power supply equipment included in the candidate equipment combination to determine the cost-effectiveness of the equipment; Based on the specifications of the power supply devices included in the candidate device combination, the device portability score is determined. The energy self-sufficiency rate, equipment cost-effectiveness and equipment portability scores are weighted and integrated to obtain the recommended weight value of the candidate equipment combination; The candidate device combination corresponding to the largest recommendation weight value is taken as the target recommendation device combination.

6. The method as described in claim 5, characterized in that, The specifications include the volume and weight of the power supply devices included in the candidate device combination. Determining the device portability score based on the specifications of the power supply devices included in the candidate device combination includes: The sum of the products of the weight and corresponding volume of the power supply equipment included in the candidate device combination is calculated as the first parameter; The reciprocal of the first parameter is used as the device portability score of the candidate device combination.

7. The method as described in claim 2, 4, 5 or 6, characterized in that, Weather information includes temperature and precipitation probability, and illumination information includes irradiance. Determining the scene type based on the illumination information and the weather information includes: When the temperature is greater than or equal to a preset temperature threshold and the duration of the temperature is greater than a preset duration threshold, the scene type is determined to be a high-temperature scene. When the precipitation probability is greater than or equal to the precipitation probability threshold and the irradiance is less than or equal to the irradiance threshold, the scene type is determined to be a rainy scene.

8. An electronic device, characterized in that, The device includes a processor, a memory, and a computer program stored in the memory and executable on the processor, characterized in that, when the processor executes the computer program, the electronic device performs the method as described in any one of claims 1-7.

9. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the method as described in any one of claims 1 to 7.

10. A computer program product, characterized in that, Includes a computer program, which, when run, causes the method as described in any one of claims 1-7 to be performed.

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