Sports camera battery charging bin operation management method and system

Abstract

The invention relates to the technical field of sports camera battery lease operation management, and discloses a sports camera battery charging bin operation management method and system, which can obtain use data such as discharge intensity data and internal temperature data of a battery and dynamically update the performance record of the battery according to the use data. The real power supply capability of the battery at different temperatures and discharge intensities can be accurately reflected, and the limitation that the battery performance is evaluated only according to the cycle index or the normal temperature state in the prior art is overcome. Meanwhile, the real-time environment information and the historical use data of the charging bin are combined, the station demand feature of the charging bin is pre-judged, the feature describes the battery capacity possibly needed by the next user, and therefore the user demand can be matched in a targeted mode. When a battery renting request is received, the system compares pre-judged site demand characteristics with performance records of available batteries and recommends the most matched battery.

Description

Technical Field

[0001] This invention relates to the field of action camera battery rental operation management technology, and in particular to an operation management method and system for action camera battery charging compartments. Background Technology

[0002] Action camera battery rental services are becoming increasingly popular in outdoor activity areas, providing users with convenient battery rental and return. However, in actual operation, especially in harsh outdoor environments such as high altitudes and frigid zones, existing battery status assessment and scheduling methods struggle to accurately reflect the true performance of batteries and effectively match users' personalized usage needs, leading to poor user experience and low operational efficiency. Specifically, battery performance degrades significantly in low-temperature environments, and the intensity of use and environmental exposure vary greatly among different users. Furthermore, the operating system cannot directly obtain users' activity plans, posing challenges to intelligent battery matching and scheduling.

[0003] Existing action camera battery rental services typically deploy battery charging stations in areas such as ski resorts and tourist attractions, allowing users to rent fully charged batteries and return depleted ones. The backend management system monitors the charging station status, including battery charging status and the number of fully charged batteries, and allocates batteries based on this information to balance resources. However, as operating time increases, batteries naturally degrade during charge-discharge cycles, leading to a decrease in maximum usable capacity. Even if new and old batteries show "100% fully charged," the actual shooting time can differ significantly, resulting in inconsistent user experiences. To address this issue, operators have upgraded their management systems, introducing battery condition assessment. This involves recording the number of cycles, internal resistance, and other data, and calculating a condition score, prioritizing batteries in good condition for allocation to popular locations.

[0004] However, in harsh environments such as alpine ski resorts, lithium batteries experience a significant performance drop at low temperatures, causing the battery state score assessed at room temperature to fail to accurately reflect their true performance under extreme conditions. Users complained that even with rented "high-quality batteries," they quickly ran out of power on the slopes. The operations team upgraded the system again, installing ambient temperature sensors in the charging compartments and making initial adjustments to battery life based on site temperature. However, charging compartments are typically deployed indoors, while users' actual usage environments are outdoors, where battery temperature continues to drop, affecting performance. The system's prediction of battery life based solely on charging compartment temperature has limited accuracy.

[0005] Furthermore, the vastly different skiing behaviors of various users mean that even batteries rented at the same location with identical initial conditions and temperatures can exhibit drastically different final battery life. Existing systems cannot precisely match a battery to a user's individual shooting needs, resulting in low user satisfaction.

[0006] To address the aforementioned issues, existing technologies urgently need improvement. Summary of the Invention

[0007] This invention provides a method and system for the operation and management of charging cases for action cameras, aiming to solve the problems of inaccurate battery status assessment and inability to effectively match users' personalized needs in existing action camera battery rental services, resulting in poor user experience and low operational efficiency.

[0008] In a first aspect, to solve the above-mentioned technical problems, the present invention provides an operation and management method for a charging case for an action camera, comprising: Obtain battery usage data, including battery discharge intensity data and internal temperature data; Based on this usage data, update the battery performance record, which describes the battery's power supply capability under different temperature environments and different discharge intensities; Obtain real-time environmental information and historical usage data of the charging compartment; Based on the real-time environmental information and the historical usage data, the site demand characteristics of the charging station are predicted. These site demand characteristics describe the battery capacity characteristics required by the next user. Upon receiving a battery rental request, retrieve a list of available batteries in the charging compartment, which contains the performance record of each available battery; The site's demand characteristics are compared with the performance records of each battery in the available battery list; Based on the comparison results, batteries that match the needs of the site are selected for recommendation.

[0009] Through this technical solution, this application can dynamically update the performance record of the battery based on the actual usage data, and predict the site demand by combining the real-time environment of the charging compartment and historical usage data. Thus, when a user rents a battery, it can intelligently match and recommend the battery that best meets the user's needs, effectively solving the problems of inaccurate battery performance evaluation and low matching degree of user needs in the prior art, and significantly improving user experience and operational efficiency.

[0010] Furthermore, the battery performance record is updated based on usage data, which describes the battery's power supply capability under different temperature environments and different discharge intensities, including: The validity assessment of the usage data includes: determining whether there are any cases in the usage data where the readings of the battery internal temperature sensor exceed a preset reasonable range; and determining whether the battery usage time recorded in the usage data is lower than a preset time threshold. If the data used passes the validity assessment, the battery performance record is updated based on the data used.

[0011] By employing this technical solution, this application conducts a validity assessment of the usage data before updating the battery performance record, eliminating the interference of abnormal or invalid data on the accuracy of the performance record, ensuring the reliability of the performance record, and thus making subsequent battery matching and recommendations more accurate.

[0012] Building upon the above, this application further proposes updating the battery's performance record based on usage data. This performance record describes the battery's power supply capability under different temperature environments and different discharge intensities, including: Establish a battery reference voltage performance curve; Collect instantaneous discharge current, output voltage, and internal core temperature during the battery's usage cycle; Based on the battery model, internal core temperature, and instantaneous discharge current, consult the battery reference voltage performance curve to obtain the expected output voltage; Calculate the voltage deviation between the actual output voltage and the expected output voltage; The effective internal resistance increase factor can be inferred from this voltage deviation. The effective internal resistance increase factor is combined with the average internal core temperature and average discharge intensity during the battery's service life to update the performance record.

[0013] By establishing a battery reference voltage performance curve and combining it with instantaneous data from actual use, this application calculates the voltage deviation and infers the effective internal resistance increase factor, enabling a more refined assessment of the actual performance degradation of the battery under different operating conditions. This results in more accurate and comprehensive performance recording, providing a more reliable data foundation for subsequent intelligent matching.

[0014] Preferably, based on real-time environmental information and historical usage data, the site demand characteristics of the charging station are predicted. These site demand characteristics describe the battery capability characteristics that the next user may need, including: Collect temperature data of the internal core area of ​​the battery and battery model information during one usage cycle; Store the thermal response characteristics of each battery model under different external ambient temperatures; Aggregate the internal core temperature of multiple batteries returned to the same charging compartment within a short period of time and the average temperature drop rate of the batteries during one usage cycle, and filter out abnormal data; Based on the filtered battery group data and the thermal response characteristics of each battery, the consensus ambient temperature of the battery group in the charging compartment area is estimated. Compare the consensus ambient temperature of the battery pack with the real-time temperature reported by the ambient temperature sensor in the real-time environmental information of the charging compartment; If the temperature exceeds the limit, it is determined that there is a calibration deviation in the ambient temperature sensor in the real-time environmental information of the charging compartment, and the consensus ambient temperature of the battery group is used as the site ambient temperature. By combining the calibrated site ambient temperature and historical user behavior data of the charging compartment, site demand characteristics are generated.

[0015] This technical solution calculates the consensus ambient temperature of the battery group by aggregating the internal core temperature and temperature drop rate of multiple batteries, and uses this to calibrate the deviation of the charging compartment ambient temperature sensor. This allows for a more accurate acquisition of the real ambient temperature in the charging compartment area, making the prediction of site demand characteristics closer to reality and improving the accuracy of battery matching.

[0016] In some preferred embodiments, based on real-time environmental information and historical usage data, the site demand characteristics of the charging station are predicted. These site demand characteristics describe the battery capacity characteristics required by the next user, including: Timeliness assessment of historical usage data, including identifying historical data that is more than a preset threshold apart from the current time period; Analyze recent usage data of batteries returned within the current time period, including average power consumption, average usage duration, and average temperature drop rate. Compare the recent usage behavior data with the historical data after the timeliness assessment to identify differences in behavior patterns; If the number of users exceeds the limit, then the site demand characteristics will be generated based on the recent usage behavior data and the current real-time environment information.

[0017] This technical solution enables the dynamic identification of changes in user behavior patterns by evaluating the timeliness of historical data and comparing it with recent usage behavior data. This avoids using outdated data for prediction, thereby making the generation of site demand characteristics more timely and accurate, and better adapting to changes in user needs.

[0018] More specifically, the site demand characteristics are compared with the performance records of each battery in the available battery list, including: Based on the characteristics of site requirements, identify the priority ranking and dynamic weight of battery capability dimension in the current scenario; Each battery capability dimension in the site demand characteristics is compared one by one with the corresponding capability dimension in the performance record of each battery in the available battery list. The comparison results of each dimension are weighted and fused based on priority ranking and dynamic weights. Based on the weighted fusion results, a comprehensive matching score is generated for each battery.

[0019] This technical solution identifies the priority ranking and dynamic weight of battery capabilities in the current scenario, and performs weighted fusion of the comparison results. This enables a more comprehensive and flexible assessment of the matching degree between the battery and the site's demand characteristics, generating a comprehensive matching score, thereby achieving a more intelligent and personalized battery recommendation.

[0020] Furthermore, based on the characteristics of site requirements, the priority ranking and dynamic weight of battery capability dimensions are identified in the current scenario, including: Obtain real-time environmental information of the charging compartment, including real-time wind speed data and real-time humidity data; The historical user behavior patterns of the charging compartment were analyzed under similar wind and humidity conditions in the past. These historical user behavior patterns included the average demand intensity of users for low-temperature discharge capability, high-intensity discharge capability, and fast charging capability. Based on real-time wind and humidity data, as well as historical user behavior patterns, the correlation strength between low-temperature discharge capability, high-intensity discharge capability, and fast charging capability is dynamically adjusted. Based on the adjusted correlation strength and the preliminary demand values ​​for each battery capability dimension in the site demand characteristics, the priority ranking and dynamic weight of each battery capability dimension are calculated.

[0021] This technical solution combines real-time wind and humidity data with historical user behavior patterns to dynamically adjust the correlation strength between different battery capability dimensions. This allows for more accurate identification of user needs and preferences in the current environment, thereby calculating more realistic priority rankings and dynamic weights, and further optimizing the accuracy of battery matching.

[0022] Building upon the above, this application further proposes calculating the priority ranking and dynamic weight of each battery capability dimension based on the adjusted correlation strength and the preliminary demand values ​​for each battery capability dimension in the site demand characteristics, including: Identify the dimensions of uncertainty in the initial demand values; Identify dimensions with uncertainties and transform initial demand values ​​into demand ranges; Based on the adjusted correlation strength and the demand range of each dimension, calculate the weight fluctuation range of each dimension within the demand range. Based on the weight fluctuation range of each dimension, the dimension with the smallest weight change throughout the entire demand range is identified as the core dimension. Based on the core dimensions, and taking into account the average weight within the demand range, the priority ranking and dynamic weight of each battery capability dimension are calculated, taking into account the weight fluctuation range of other dimensions.

[0023] This technical solution identifies the uncertainty in demand values ​​and transforms it into demand ranges, thereby calculating the weight fluctuation range. This enables more robust handling of demand uncertainty and, based on the core dimension with the smallest weight change, calculates a more stable and reliable priority ranking and dynamic weight, improving the adaptability and accuracy of battery matching.

[0024] Furthermore, based on the adjusted correlation strength and the preliminary demand values ​​for each battery capability dimension in the site demand characteristics, the priority ranking and dynamic weight of each battery capability dimension are calculated, including: Obtain real-time altitude and air pressure data of the charging compartment; The historical user behavior patterns of the charging compartment were analyzed under similar altitude and air pressure conditions in the past. These historical user behavior patterns included the average demand intensity of users for low-temperature discharge capability, high-intensity discharge capability, and fast charging capability. Based on real-time altitude data, real-time air pressure data, and historical user behavior patterns, the correlation strength between low-temperature discharge capability, high-intensity discharge capability, and fast charging capability is dynamically adjusted. Calculate the weight sensitivity of each battery capability dimension under different combinations of altitude and air pressure; Identify the dimension with the highest weight sensitivity as the key sensitive dimension; Based on the key sensitive dimensions, and according to the weight change trends under different altitude and air pressure combinations, combined with the weight sensitivity of other dimensions, the priority ranking and dynamic weight of each battery capability dimension are calculated.

[0025] This technical solution combines real-time altitude and air pressure data to analyze historical user behavior patterns and calculate the weight sensitivity of each battery capability dimension under different combinations of altitude and air pressure. It can identify key sensitive dimensions and use them as a benchmark to calculate priority ranking and dynamic weights, thereby enabling the battery matching solution to better adapt to user needs in special environments such as high altitude and low air pressure, and further improve the accuracy of matching.

[0026] Secondly, this application also discloses an operation and management system for an action camera battery charging case, comprising: The detection end is used to acquire battery usage data, including battery discharge intensity data and internal temperature data; based on the usage data, the battery performance record is updated, which describes the battery's power supply capability under different temperature environments and different discharge intensities; real-time environmental information and historical usage data of the charging compartment are acquired. The prediction end is used to predict the site demand characteristics of the charging station based on the real-time environmental information and the historical usage data. The site demand characteristics describe the battery capacity characteristics required by the next user. The recommendation module, upon receiving a battery rental request, retrieves a list of available batteries in the charging compartment, which includes the performance record of each available battery. It then compares the site's demand characteristics with the performance record of each battery in the list of available batteries. Based on the comparison results, it selects and recommends batteries that match the site's demand characteristics.

[0027] This technical solution modularizes the operation management method into detection, prediction, and recommendation modules, achieving systematic battery operation management. It can efficiently acquire battery data, predict site needs, and intelligently recommend batteries, thereby effectively solving the problems of low system integration and low management efficiency in existing technologies and improving the overall operation management level. Beneficial effects

[0028] The action camera battery charging case operation and management method disclosed in this application acquires battery usage data such as discharge intensity and internal temperature data, and dynamically updates battery performance records accordingly. This accurately reflects the battery's true power supply capacity under different temperatures and discharge intensities, overcoming the limitations of existing technologies that assess battery performance solely based on cycle count or room temperature conditions. Furthermore, this application combines real-time environmental information and historical usage data of the charging case to predict the site demand characteristics. These characteristics describe the battery capacity the next user might require, enabling targeted matching of user needs. Upon receiving a battery rental request, the system compares the predicted site demand characteristics with the performance records of available batteries and recommends the most suitable battery.

[0029] Through the aforementioned technical solution, this application effectively addresses the problems of inaccurate battery status assessment and inability to effectively match users' personalized needs in existing action camera battery rental services. Specifically, this application can accurately assess the actual performance of batteries in harsh outdoor environments, avoiding the frustration of users renting "high-quality batteries" only to find them running out of power quickly. Simultaneously, by predicting the characteristics of station needs, the system can precisely match a battery that meets the personalized shooting requirements of users about to depart, significantly improving user satisfaction. In summary, this application, through refined battery performance assessment and intelligent demand matching, achieves optimized scheduling of battery resources, improves operational efficiency, and provides users with a higher-quality and more reliable battery rental service. Attached Figure Description

[0030] Figure 1 This is a flowchart of an operation and management method for a battery charging compartment of an action camera provided by an embodiment of the present invention; Figure 2 This is a schematic diagram of a process for updating battery performance records based on usage data, provided by an embodiment of the present invention; Figure 3This is a structural diagram of an operation and management system for a battery charging compartment of an action camera provided in an embodiment of the present invention. Detailed Implementation

[0031] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0032] Reference Figure 1 , Figure 1 This is a flowchart of an operation and management method for a battery charging case of an action camera provided by an embodiment of the present invention, including the following steps: S1, Obtain battery usage data, including battery discharge intensity data and internal temperature data; S2, based on the usage data, update the battery performance record, which describes the battery's power supply capability under different temperature environments and different discharge intensities; S3, Obtain real-time environmental information of the charging compartment and historical usage data of the charging compartment; S4. Based on the real-time environmental information and the historical usage data, predict the site demand characteristics of the charging station, wherein the site demand characteristics describe the battery capacity characteristics required by the next user. S5, upon receiving a battery rental request, obtain a list of available batteries in the charging compartment, the list containing the performance record of each available battery; S6, compare the site demand characteristics with the performance record of each battery in the available battery list; S7. Based on the comparison results, select and recommend batteries that match the site's demand characteristics.

[0033] Traditional action camera battery rental services are becoming increasingly popular in outdoor activity areas. However, in actual operation, especially in harsh outdoor environments such as high altitudes and frigid zones, existing battery status assessment and scheduling methods struggle to accurately reflect the true performance of batteries and effectively match users' personalized usage needs, leading to poor user experience and low operational efficiency. Specifically, battery performance degrades significantly in low-temperature environments, and the usage intensity and environmental exposure of different users vary greatly. Furthermore, the operating system cannot directly obtain users' activity plans, posing a challenge to intelligent battery matching and scheduling.

[0034] To address this issue, this application proposes an operation and management method for action camera battery charging cases. By acquiring battery usage data and updating battery performance records based on this data, the method more accurately reflects the battery's true power supply capacity under different environments and usage intensities. Simultaneously, by combining real-time environmental information and historical usage data of the charging case, the method predicts the site demand characteristics of the charging case, i.e., the battery capability characteristics that the next user might need. Upon receiving a battery rental request, the method compares the site demand characteristics with the performance records of available batteries and recommends the most suitable battery based on the comparison results. Therefore, this application effectively solves the problems of inaccurate battery performance evaluation and low user demand matching in existing technologies, significantly improving user experience and operational efficiency.

[0035] To better understand the technical solution proposed in this application, some key terms involved will be explained first.

[0036] "Battery usage data" refers to various operating parameters collected during actual battery use, such as discharge intensity data and internal temperature data. This data reflects the battery's performance under real-world operating conditions.

[0037] "Discharge intensity data" refers to the amount of current output by the battery per unit time, which directly affects the battery's energy consumption rate and internal heat generation.

[0038] "Internal temperature data" refers to the temperature of the core area inside the battery, which has a significant impact on the battery's chemical reaction rate, internal resistance, and overall performance, especially in low-temperature environments.

[0039] "Performance record" refers to a data set that quantitatively describes the battery's power supply capability under different temperature environments and discharge intensities. This record comprehensively reflects the battery's actual performance under various usage conditions, rather than just its nominal capacity.

[0040] "Real-time environmental information of the charging compartment" refers to the environmental parameters currently present in the charging compartment, such as ambient temperature, humidity, and wind speed. This information helps assess the battery's standby status within the charging compartment and predict its performance in upcoming usage environments.

[0041] "Historical usage data of charging stations" refers to the operational records of charging stations over a period of time, including battery rental patterns, return times, user feedback, etc. This data helps to analyze the demand patterns of charging station sites.

[0042] "Site demand characteristics" refers to the battery capability characteristics that the next user may need, predicted based on the real-time environmental information and historical usage data of the charging station. These characteristics may include requirements for low-temperature discharge capability, high-intensity discharge capability, or long-range battery life.

[0043] The “List of Available Batteries” refers to the set of batteries currently available for rent in the charging compartment, which includes detailed performance records for each battery.

[0044] The action camera battery charging case operation management method of this application first requires acquiring battery usage data. This usage data can include battery discharge intensity data and internal temperature data. For example, this can be achieved by integrating a miniature sensor module inside the action camera battery. This module can include a current sensor and a temperature sensor. The current sensor can monitor the battery's discharge current in real time and convert it into discharge intensity data. The temperature sensor can measure the temperature of the core area inside the battery in real time and use it as internal temperature data. This data can be periodically transmitted to the charging case or a cloud server for storage and processing via a wireless communication module (such as Bluetooth or Wi-Fi). Alternatively, data acquisition functionality can be integrated into the action camera itself. When the battery is inserted into the action camera and used, the action camera can record the battery's discharge current and internal temperature, and upload this usage data through the charging case's data interface when the battery is returned to the charging case.

[0045] Based on the acquired usage data, the battery's performance record needs to be updated. This performance record describes the battery's power supply capability under different temperature environments and discharge intensities. For example, a multi-dimensional performance model can be established, using temperature and discharge intensity as input variables and the battery's actual usable capacity, internal resistance, voltage decay rate, etc., as output variables. When new usage data is acquired, this data can be input into the performance model, and the model's parameters can be iteratively optimized using machine learning algorithms (such as regression analysis or neural networks) to update the battery's performance record. Specifically, if a battery exhibits a rapid voltage drop under low-temperature, high-intensity discharge, the corresponding low-temperature, high-intensity discharge capability parameter in its performance record will be lowered. As another implementation method, a rule-based update strategy can be adopted. For example, a series of performance thresholds and decay rules can be preset. When the battery's usage data meets specific conditions (such as the discharge intensity exceeding a threshold within a certain temperature range), the corresponding capability parameters in its performance record are directly adjusted according to the preset rules.

[0046] Next, it's necessary to obtain real-time environmental information and historical usage data for the charging compartment. Real-time environmental information can be obtained by deploying environmental sensors outside the charging compartment, such as temperature, humidity, and wind speed sensors. These sensors can collect environmental parameters of the area where the charging compartment is located in real time and transmit them to the charging compartment's control unit or cloud server via wired or wireless means. Historical usage data can be obtained from a backend database. This database stores all battery rental, return, and charging records, as well as user behavior patterns, since the charging compartment began operation. For example, it can record the number of batteries rented in each time period, the average usage time per user, and user feedback on battery performance.

[0047] Based on real-time environmental information and historical usage data, the system predicts the station demand characteristics of charging stations. These station demand characteristics describe the battery capability characteristics required by the next user. For example, predictive models can be used to analyze this data. This predictive model can be a time series analysis or machine learning-based model. Model inputs include real-time environmental information such as current temperature, humidity, and wind speed, as well as user behavior data (e.g., average rental duration, average discharge intensity, and preference for low-temperature performance) under similar environmental conditions over a past period (e.g., the past 24 hours, the past week). Through comprehensive analysis of this data, the model can predict the battery capability characteristics most likely to be needed by the next user. For example, in cold and windy winters, the model might predict a higher demand for low-temperature discharge capability and high-intensity discharge capability.

[0048] Upon receiving a battery rental request, the system needs to retrieve a list of available batteries within the charging compartment. This list contains the performance record of each available battery. When a user initiates a battery rental request via a mobile application or the charging compartment's interface, the charging compartment's control system first queries its internal battery management module to obtain the unique identifiers of all batteries currently in an available state (e.g., fully charged and not rented). For each available battery, the system retrieves its latest performance record from the battery performance database and integrates this information into the available battery list. For example, each item in the list may include the battery ID, current charge level, and its power supply capabilities under different temperatures and discharge intensities.

[0049] Next, the site demand characteristics are compared with the performance records of each battery in the available battery list. For example, a matching score algorithm can be defined. This algorithm compares each requirement in the site demand characteristics (such as the requirement for low-temperature discharge capability, the requirement for high-intensity discharge capability) with the corresponding capability parameters in the available battery performance records one by one. For example, if the site demand characteristics show that the user has a high demand for low-temperature discharge capability, the algorithm will prioritize batteries that have less performance degradation in low-temperature environments. Various methods such as weighted averaging and fuzzy matching can be used during the comparison process to quantify the degree of matching between each battery and the site demand characteristics.

[0050] Finally, based on the comparison results, the system recommends batteries that match the site's needs. For example, the system can calculate the overall matching score for each battery using a matching score algorithm and recommend the battery with the highest score as the first choice to the user. If multiple batteries have the same or very similar scores, the system can further filter them based on other auxiliary factors (such as battery cycle count, health status, etc.). The recommendation results can be displayed to the user through the charging case's display screen or the user's mobile application interface, allowing the user to select a battery to rent based on the recommendations.

[0051] This application's method for operating and managing the charging case of an action camera, by acquiring usage data such as battery discharge intensity and internal temperature, can more accurately update battery performance records, thus overcoming the limitations of existing technologies that only evaluate battery performance based on cycle count or room temperature conditions. In traditional solutions, battery performance significantly degrades in low-temperature environments, and the battery state score evaluated at room temperature cannot accurately reflect its true performance under harsh conditions, causing users to experience rapid battery depletion even when renting "high-quality batteries." This application, by considering power supply capabilities under different temperature environments and discharge intensities, makes battery performance evaluation more refined and realistic.

[0052] Furthermore, this application addresses the problem of existing systems failing to accurately match batteries for users about to depart by acquiring real-time environmental information and historical usage data of the charging compartment and predicting the site demand characteristics accordingly. Traditional systems predict range solely based on charging compartment temperature, resulting in limited accuracy and failing to consider the differences in skiing behavior patterns among different users. This application, through comprehensive analysis of environmental information and historical user behavior, can predict the battery capability characteristics that the next user may require, such as the need for low-temperature discharge capability, high-intensity discharge capability, or long range, thereby achieving personalized matching.

[0053] Upon receiving a battery rental request, this application compares the predicted site demand characteristics with the performance records of each battery in the available battery list, and recommends a matching battery based on the comparison results. This is significantly different from the existing technology that simply assigns batteries in good condition to popular sites. This application's matching process is more intelligent and refined, ensuring that the batteries rented by users not only have good performance but also best meet their personalized usage needs and the challenges of their environment. For example, in a cold alpine ski resort, the system will prioritize recommending batteries that perform well in low-temperature environments, even if their overall rating at room temperature is not the highest.

[0054] In summary, this application significantly improves the user experience and operational efficiency of action camera battery rental services by introducing refined battery performance evaluation, intelligent site demand prediction, and a personalized battery matching recommendation mechanism. Compared with existing technologies, this application can more accurately reflect the real performance of batteries under harsh environments and more effectively match users' personalized usage needs, thereby solving the core problems of inaccurate battery performance evaluation and low user demand matching in traditional solutions, demonstrating significant technological progress and practical value.

[0055] In some embodiments described above in this application, a method is proposed to update battery performance records based on battery usage data, wherein the performance records describe the battery's power supply capability under different temperature environments and different discharge intensities. However, in practice, if unfiltered or unverified usage data is directly used to update the performance records, inaccuracies may be introduced, leading to biases in battery performance evaluation and consequently affecting subsequent battery recommendation decisions.

[0056] In this regard, refer to Figure 2 , Figure 2 This is a schematic diagram of a process for updating battery performance records based on usage data, provided by an embodiment of the present invention, including: S21, perform a validity assessment on the usage data, the validity assessment including: determining whether there is a situation in the usage data where the reading of the battery internal temperature sensor exceeds a preset reasonable range; S22, determine whether the battery usage time recorded in the usage data is lower than a preset time threshold; S23, if the usage data passes the validity assessment, then update the battery performance record based on the usage data.

[0057] Specifically, the validity assessment aims to ensure the quality and reliability of the usage data used to update battery performance records. Determining whether the usage data contains readings from the battery's internal temperature sensor that exceed a preset reasonable range involves verifying the data collected by the battery's internal temperature sensor. The preset reasonable range can be determined based on the battery's physical characteristics, operating environment limitations, and the sensor's own measurement range; for example, it can be set to -20°C to 80°C. If the sensor reading exceeds this range, it may indicate a sensor malfunction or data anomaly, and such data should not be used to update performance records.

[0058] Furthermore, determining whether the recorded battery usage time is below a preset threshold is to exclude data that is too short to fully reflect the battery's true performance. The preset threshold can be set based on experience or experimental data; for example, it could be set to 5 minutes. If the battery usage time is too short, its discharge curve and temperature changes may not provide meaningful performance evaluation information.

[0059] The usage data will only be considered valid and used to update the battery's performance record if it passes the two validity assessments mentioned above: the internal temperature sensor reading is within a reasonable range and the battery usage time reaches a preset threshold.

[0060] This application's solution ensures the quality of data used to update battery performance records from the outset by introducing a validity assessment of the usage data. By determining whether the readings of the battery's internal temperature sensor exceed a preset reasonable range, data deviations caused by sensor malfunctions or extreme abnormal operating conditions can be effectively identified and eliminated. Simultaneously, by determining whether the battery usage time is below a preset time threshold, data that cannot fully reflect the battery's true performance due to short-term use can be avoided. It is precisely because of this rigorous data screening and verification that the subsequent performance record update process can be based on more accurate and representative data, thereby improving the accuracy and reliability of performance records.

[0061] The above technical solution effectively avoids performance record update deviations caused by inaccurate or unrepresentative battery usage data. This allows battery performance records to more realistically and accurately reflect their power supply capabilities under different environments and discharge intensities, thus providing more reliable basic data for subsequent site demand characteristic prediction and battery recommendation. Consequently, it can significantly improve the overall decision-making accuracy and user satisfaction of the action camera battery charging case operation management system, and reduce operational risks caused by inaccurate battery performance assessments.

[0062] In some preferred embodiments, it is assumed that an action camera battery is rented and used by a user and then returned to the charging case. The system first acquires the battery's usage data, including its discharge intensity data, internal temperature data, and usage duration. Before updating the performance record, the system evaluates the validity of this usage data. Specifically, if the system detects that the battery's internal temperature sensor consistently reports a reading of -50°C during the current usage cycle, while the preset reasonable temperature range is -20°C to 80°C, the data is considered invalid because it exceeds the preset reasonable range, potentially indicating a sensor malfunction. Alternatively, if the battery's usage duration is only 30 seconds, while the preset duration threshold is 5 minutes, the data is also considered invalid because it is insufficient to reflect the battery's stable performance. Only when the battery's internal temperature reading remains within a reasonable range and the usage duration exceeds the preset threshold—for example, the internal temperature fluctuates around 25°C for 2 hours—is the usage data considered valid and used to accurately update the battery's performance record. In this way, the accuracy of the battery performance record can be ensured, avoiding misjudgments caused by abnormal data.

[0063] Traditional operational management methods for action camera battery charging cases, while providing a macroscopic indication of battery health when updating battery performance records based on battery usage data, often struggle to accurately capture subtle performance changes caused by internal aging and wear under varying temperature environments and discharge intensities. For example, simply updating performance records based on battery discharge intensity and internal temperature data may fail to effectively identify increases in the battery's effective internal resistance, leading to discrepancies between the performance record and the battery's actual power supply capacity. Failure to address these issues can result in misjudgments of actual battery performance, affecting the accuracy of battery recommendations and ultimately reducing user experience and operational efficiency. To address this, this application proposes a more refined and accurate method for updating battery performance records. By establishing a reference voltage performance curve and combining it with actual operational data, the method infers the effective internal resistance increase factor of the battery, thereby more accurately updating the battery performance record.

[0064] According to the above-mentioned operation and management method for the action camera battery charging case, updating the battery performance record based on the usage data, wherein the performance record describes the battery's power supply capability under different temperature environments and different discharge intensities, specifically includes the following steps: Establish a battery reference voltage performance curve; The instantaneous discharge current, output voltage, and internal core temperature of the battery are collected during its usage cycle. Based on the battery model, the internal core temperature, and the instantaneous discharge current, the battery reference voltage performance curve is consulted to obtain the expected output voltage; Calculate the voltage deviation between the actual output voltage and the expected output voltage; The effective internal resistance increase factor is inferred based on the voltage deviation. The performance record is updated by combining the effective internal resistance increase factor with the average internal core temperature and average discharge intensity during the battery's service life.

[0065] Specifically, establishing a battery reference voltage performance curve involves conducting a series of standard tests on the battery under controlled temperature environments and discharge intensities before it leaves the factory or is first put into use, recording its voltage response characteristics under various typical operating conditions. This test data is used to construct a queryable database or mathematical model. The curve describes the expected output voltage of a specific battery model under ideal or healthy conditions, at a given internal core temperature and instantaneous discharge current. Its purpose is to provide a reliable reference benchmark for subsequent actual battery performance evaluation.

[0066] The acquisition of instantaneous discharge current, output voltage, and internal core temperature during the battery's usage cycle can be understood as the real-time monitoring and recording of key battery operating parameters by the Battery Management System (BMS) or sensors integrated within the battery during the actual period the battery is leased and used by the user. Instantaneous discharge current reflects the battery's current load, output voltage directly reflects the battery's ability to supply power, and internal core temperature reveals the battery's internal thermal state. This real-time collected data forms the basis for evaluating the battery's actual performance.

[0067] In practical applications, the expected output voltage is obtained by querying the battery's reference voltage performance curve based on the battery model, internal core temperature, and instantaneous discharge current. For example, the theoretical output voltage of the battery under current operating conditions can be obtained by searching a pre-established reference voltage performance curve database using real-time collected battery model, internal core temperature, and instantaneous discharge current data, or by calculating using a pre-trained model. The purpose is to establish a comparative reference between actual and theoretical performance.

[0068] Furthermore, calculating the voltage deviation between the actual output voltage and the expected output voltage involves comparing the real-time acquired actual battery output voltage with the expected output voltage obtained by querying a benchmark curve, and determining the difference between the two. This deviation can be an absolute value or a percentage, directly reflecting the gap between the battery's actual performance and its ideal performance.

[0069] Therefore, the effective internal resistance increase factor is inferred based on the voltage deviation, with the aim of quantifying the voltage deviation as a change in the battery's internal impedance. An increase in battery internal resistance is a significant indicator of battery aging, leading to a decrease in output voltage at the same discharge current. The degree of increase in the battery's effective internal resistance can be calculated in reverse using the voltage deviation, thus more accurately reflecting the battery's health and aging level.

[0070] Finally, the effective internal resistance increase factor is combined with the average internal core temperature and average discharge intensity during the battery's usage cycle to update the performance record. This means that the inferred internal resistance increase factor is used as a key indicator of battery performance degradation, and combined with environmental factors such as the battery's average temperature and average discharge intensity throughout its usage cycle, the battery's performance record is corrected and supplemented. The performance record can contain detailed information such as the battery's internal resistance and capacity decay rate at different temperatures and discharge intensities, thereby providing a comprehensive, dynamic, and accurate battery health assessment.

[0071] This application's solution introduces a battery reference voltage performance curve and collects the battery's instantaneous discharge current, output voltage, and internal core temperature in real time, enabling a precise comparison between the battery's actual operating performance and its theoretical performance under ideal conditions. By calculating the voltage deviation between the actual and expected output voltage, the actual degree of battery performance degradation can be quantified. Furthermore, this voltage deviation is converted into an effective internal resistance increase factor, directly reflecting the key physical mechanisms of battery aging and loss. This method, based on a physical model and real-time data calibration, allows battery performance record updates to no longer rely solely on macroscopic usage data but delve into changes in the battery's internal electrochemical characteristics, thus more accurately capturing the changes in the battery's true power supply capacity under different temperatures and discharge intensities. By combining this increase factor with the average internal core temperature and average discharge intensity, it ensures that the performance record comprehensively reflects the battery's aging characteristics under various complex operating conditions, providing a more reliable data foundation for subsequent battery matching and recommendations.

[0072] The above technical solution significantly improves the accuracy and precision of battery performance records. Compared to methods that update data solely based on macroscopic usage data, this solution establishes a reference voltage performance curve and calculates voltage deviation to infer the effective internal resistance increase factor, enabling a more precise quantification of the actual aging and performance degradation within the battery. This refined performance evaluation allows for a more realistic description of the battery's power supply capability, especially under complex environments with varying temperatures and discharge intensities, more accurately reflecting the battery's usability. Consequently, in subsequent battery rental request processing, the system can match and recommend batteries based on more reliable performance records, effectively avoiding battery recommendation errors caused by inaccurate performance records. This improves user experience, extends battery life, and optimizes charging station operational efficiency.

[0073] In some preferred embodiments, a specific example is given below. Assume that a certain model of action camera battery, at the time of manufacture, has undergone standard testing to establish its reference voltage performance curves under different internal core temperatures (-10℃, 0℃, 25℃, 40℃) and different discharge intensities (0.5C, 1C, 2C). When a user rents this model of battery, its battery management system (BMS) continuously collects instantaneous discharge current, output voltage, and internal core temperature. For example, during a certain usage cycle, the battery operates under conditions of an average internal core temperature of 15℃ and an average discharge intensity of 1.5C. At a certain instant, the instantaneous discharge current is collected as 1.2A, the output voltage as 3.6V, and the internal core temperature as 20℃. At this time, the system will look up the preset reference voltage performance curve based on the battery model, the internal core temperature of 20℃, and the instantaneous discharge current of 1.2A, and obtain the expected output voltage of 3.7V under these conditions. There is a voltage deviation of 0.1V between the actual output voltage of 3.6V and the expected output voltage of 3.7V. Based on this 0.1V voltage deviation, the system infers, using a preset model or algorithm, that the battery's effective internal resistance has increased by 5 milliohms. Subsequently, the system updates the battery's performance record with this 5 milliohm increase factor, combined with the average internal core temperature of 15°C and the average discharge intensity of 1.5C during the usage cycle. This performance record will detail the 5 milliohm increase in internal resistance at 15°C and 1.5C discharge intensity, thus more accurately reflecting its current power supply capacity and health status.

[0074] In some of the above embodiments, the site demand characteristics of the charging compartment are predicted by acquiring real-time environmental information and historical usage data. However, in practical applications, the ambient temperature sensor of the charging compartment may have calibration deviations due to long-term use, environmental influences, or manufacturing defects, resulting in inaccurate real-time environmental information and affecting the accuracy of site demand characteristic prediction. If the above problems are not addressed, the system may misjudge the battery capacity characteristics required by the next user, thereby reducing user experience and operational efficiency. To address this, this application further proposes a more accurate method for predicting charging compartment site demand characteristics by introducing a consensus ambient temperature of the battery group to calibrate the ambient temperature sensor, thereby improving the accuracy of the prediction.

[0075] Based on the real-time environmental information and historical usage data, the above-mentioned site demand characteristics of the charging station are predicted. These site demand characteristics describe the battery capability characteristics that the next user may need, including: Collect temperature data of the internal core area of ​​the battery and the battery model information during one use cycle; Store the thermal response characteristics of each battery model under different external ambient temperatures; Aggregate the internal core temperature of multiple batteries returned to the same charging compartment within a short period of time and the average temperature drop rate of the batteries during one usage cycle, and filter out abnormal data; Based on the filtered battery group data and the thermal response characteristic parameters of each battery, the consensus ambient temperature of the battery group in the charging compartment area is estimated. Compare the consensus ambient temperature of the battery pack with the real-time temperature reported by the ambient temperature sensor in the real-time environmental information of the charging compartment; If the temperature exceeds the limit, it is determined that there is a calibration deviation in the ambient temperature sensor in the real-time environmental information of the charging compartment, and the consensus ambient temperature of the battery group is used as the ambient temperature of the site. By combining the calibrated ambient temperature of the site with historical user behavior data of the charging compartment, the site demand characteristics are generated.

[0076] Specifically, during the period the battery is rented and used by the user, temperature data of the battery's internal core area and the battery's model information are continuously collected during each usage cycle. The temperature data of the internal core area reflects the changes in the battery's thermal state under actual usage conditions, while the battery's model information is used to match corresponding thermal response characteristic parameters. These thermal response characteristic parameters refer to the pattern or model of how the internal core temperature of each battery model changes over time under different external ambient temperatures. These parameters can be obtained and stored in advance through experiments or simulations; for example, they can be a lookup table, a mathematical model, or a set of empirical curves. The purpose is to be able to deduce the external ambient temperature based on the changes in the battery's internal temperature.

[0077] In practical applications, to improve the accuracy and robustness of the estimation, the system aggregates the internal core temperatures of multiple batteries returned to the same charging compartment within a short period and the average temperature drop rate of those batteries during a single usage cycle. The aggregation operation can employ the average, median, or other statistical methods. Simultaneously, to avoid the influence of outlier data on the estimation results, the aggregated data is filtered; for example, data points that significantly deviate from the group average are removed.

[0078] Furthermore, based on the filtered battery group data and the thermal response characteristic parameters of each battery, the consensus ambient temperature of the battery group in the charging compartment area can be calculated. This consensus ambient temperature is based on the internal temperature change patterns of multiple batteries after actual use, and is calculated in reverse to obtain the actual ambient temperature of the charging compartment area. It can more accurately reflect the true ambient temperature of the charging compartment.

[0079] Subsequently, the consensus ambient temperature of the battery pack is compared with the real-time temperature reported by the ambient temperature sensor in the real-time environmental information of the charging compartment. If the difference between the two exceeds a preset threshold, it can be determined that there is a calibration deviation in the ambient temperature sensor in the real-time environmental information of the charging compartment. In this case, to ensure the accuracy of the site demand characteristic prediction, the consensus ambient temperature of the battery pack will be used as the calibrated site ambient temperature.

[0080] Finally, by combining the calibrated site ambient temperature and historical user behavior data from the charging compartment, the site demand characteristics are generated. The historical user behavior data can include user preferences and usage patterns regarding battery capacity, discharge rate, and low-temperature performance under different ambient temperatures. This approach yields more accurate and reliable site demand characteristics, providing a solid foundation for subsequent battery recommendations.

[0081] The solution proposed in this application improves the accuracy of site demand characteristic prediction because it introduces a mechanism for environmental temperature calibration based on battery pack data. Traditional methods rely directly on the real-time temperature reported by the environmental temperature sensors in the charging compartment. However, these sensors may develop calibration deviations due to long-term use, environmental influences, or manufacturing defects, resulting in a discrepancy between the reported temperature and the actual ambient temperature. This solution collects the internal core temperature data and average temperature drop rate of multiple batteries during their actual usage cycle, and combines this with pre-stored thermal response characteristic parameters for each battery model. This allows for the reverse calculation of the actual external ambient temperature—the battery pack consensus ambient temperature—from the perspective of the battery's physical response. Since the aggregated data from multiple batteries has higher robustness, and the internal temperature change of a battery is a direct physical reflection of its surrounding environment, the battery pack consensus ambient temperature more accurately represents the actual ambient temperature of the charging compartment area. By comparing this consensus ambient temperature with the real-time temperature reported by the sensors, a significant deviation can be detected, indicating a calibration error in the sensors. The more reliable battery pack consensus ambient temperature is then used as the calibrated site ambient temperature. Therefore, the subsequent process of generating site demand characteristics will be based on more accurate ambient temperature information, thereby effectively avoiding the problem of inaccurate predictions caused by sensor errors.

[0082] Through the above technical solution, this application effectively solves the calibration deviation problem that may exist in the ambient temperature sensor of the charging compartment in the prior art, and significantly improves the accuracy of real-time environmental information. Since the prediction of site demand characteristics is based on the calibrated and more realistic site ambient temperature, the generated site demand characteristics can more accurately reflect the actual needs of the next user for battery capacity characteristics. This not only avoids battery recommendation errors caused by sensor errors and improves the matching degree of battery recommendations, but also helps to optimize battery scheduling and management, thereby improving the overall efficiency of action camera battery charging compartment operation and management and user satisfaction.

[0083] In some preferred embodiments, suppose a charging compartment for action camera batteries is located in a mountainous area, and its ambient temperature sensor reports a real-time temperature of 5 degrees Celsius. However, within a short period, 10 batteries of the same model are returned to the charging compartment. The system collects the internal core temperature data and average temperature drop rate of these 10 batteries during their respective usage cycles. By querying the pre-stored thermal response characteristic parameters of this battery model and aggregating and filtering the data of these 10 batteries, the system calculates the consensus ambient temperature of the battery group in the charging compartment area to be 0 degrees Celsius. Since the difference between 5 degrees Celsius and 0 degrees Celsius (5 degrees Celsius) exceeds a preset calibration deviation threshold (e.g., 2 degrees Celsius), the system determines that the ambient temperature sensor of the charging compartment has a calibration deviation. Therefore, the system uses 0 degrees Celsius as the calibrated site ambient temperature. Subsequently, combining the site ambient temperature of 0 degrees Celsius with the historical user behavior data of the charging compartment (e.g., in an environment of 0 degrees Celsius, users typically require batteries with stronger low-temperature discharge capabilities), the system generates more accurate site demand characteristics, for example, emphasizing a higher weight for the demand for low-temperature discharge capabilities. Based on this accurate site demand characteristics, when the system receives a rental request, it can more accurately recommend batteries that are suitable for the current low-temperature environment and user needs, thereby ensuring that users can have a good user experience even in low-temperature mountain environments.

[0084] In some of the embodiments described above in this application, when predicting the site demand characteristics of charging stations based on real-time environmental information and historical usage data, relying solely on all unfiltered historical usage data may fail to accurately capture dynamic changes in user behavior patterns or the seasonal or sudden impacts of environmental factors. For example, when the user group or their usage habits in the area where the charging station is located change significantly, past historical data may no longer be sufficiently representative, leading to a deviation between the predicted site demand characteristics and actual demand. If this problem is not addressed, it may result in recommended batteries that do not match actual user needs, affecting user experience and operational efficiency. Therefore, this application further proposes a method to optimize the prediction of site demand characteristics by evaluating the timeliness of historical data and combining it with recent usage behavior data to more accurately reflect current demand.

[0085] Based on real-time environmental information and historical usage data, the above-mentioned system predicts the site demand characteristics of charging stations. These site demand characteristics describe the battery capability characteristics required by the next user, specifically including: The timeliness of the historical usage data is evaluated, including identifying historical data that is more than a preset threshold apart from the current time period. Analyze recent usage behavior data of batteries returned within the current time period, including average power consumption, average usage duration, and average temperature drop rate; Compare the recent usage behavior data with the historical data after the timeliness assessment to identify differences in behavior patterns; If the number of cases exceeds the limit, the site demand characteristics are generated based on the recent usage behavior data and the current real-time environment information.

[0086] Specifically, the timeliness assessment of the historical usage data refers to filtering the stored historical usage data of the charging stations to ensure that the data used for analysis has current reference value. This timeliness assessment can be understood as a data cleaning process, the purpose of which is to identify and exclude historical data that is too far removed from the current time period and may no longer reflect current user behavior patterns or environmental conditions. For example, a preset threshold, such as three months or six months, can be set; any data earlier than this threshold will be marked as outdated data and will not participate in subsequent direct comparative analysis.

[0087] The analysis of recent battery usage data within the current time period refers to the system collecting and summarizing the usage information of batteries returned to the charging case in the most recent period (e.g., the past 24 hours, week, or month). This recent usage data specifically includes the user's average power consumption, average usage time, and average battery temperature drop rate during the most recent usage period. This data directly reflects the user's actual needs for battery performance, such as high-intensity discharge needs (reflected by average power consumption), long-term usage needs (reflected by average usage time), and thermal management needs under specific environments (reflected by average temperature drop rate).

[0088] In practical applications, comparing the recent user behavior data with the historical data after timeliness assessment aims to determine whether the current user behavior pattern differs significantly from the relatively recent historical behavior pattern that has undergone timeliness screening. This comparison can be achieved through various statistical methods, such as calculating the mean, variance, or distribution difference between the two sets of data, or using machine learning models for pattern recognition. If the comparison results show that the difference exceeds a preset threshold, it indicates that the current user needs or environmental conditions may have changed significantly, and the traditional historical pattern is no longer applicable.

[0089] Furthermore, if the difference exceeds a preset threshold, the site demand characteristics are generated based on the recent usage behavior data and the current real-time environmental information. This means that when a significant change in user behavior patterns is detected, the system will prioritize recent usage behavior data that better reflects the current situation, and combine it with real-time environmental information of the charging station (such as real-time temperature and humidity) to generate more accurate and timely site demand characteristics. This aims to ensure that the prediction of site demand characteristics can quickly respond to market changes and avoid prediction bias caused by relying on outdated data.

[0090] This application's solution effectively addresses the problem of decreased accuracy in predicting site demand characteristics when user behavior patterns or environmental conditions change, by introducing a timeliness assessment of historical usage data and an analysis of recent usage behavior data. Specifically, the timeliness assessment mechanism proactively identifies and excludes outdated historical data that may no longer be representative, thereby preventing this data from interfering with current demand predictions.

[0091] Meanwhile, by conducting in-depth analysis of recent battery return usage data, the system can promptly capture the latest user demand trends and usage habits. When recent behavior patterns differ significantly from historical patterns after timeliness evaluation, it indicates that market demand or user preferences may have changed. In this case, the system will prioritize adopting recent usage behavior data that better reflects the current situation and combine it with real-time environmental information to generate site demand characteristics that are closer to the current reality. This dynamic adjustment and prioritization ensures that the prediction of site demand characteristics no longer statically relies on historical averages but can flexibly adapt to the ever-changing market environment and user needs.

[0092] Through the above technical solution, this application can significantly improve the accuracy and real-time performance of predicting the demand characteristics of action camera battery charging station sites. Especially when user behavior patterns or external environmental conditions change rapidly, this solution can effectively avoid prediction biases caused by relying on outdated historical data, thereby ensuring that the recommended batteries more accurately match the actual needs of the next user. This not only improves the user experience and reduces operating costs caused by battery mismatches, but also enhances the intelligence and adaptability of the entire charging station operation and management system, enabling it to better respond to dynamic market changes and optimize the allocation efficiency of battery resources.

[0093] In some preferred embodiments, a specific example is given below. Suppose an action camera battery charging compartment is located near a ski resort. During the peak winter skiing season, users' demand for the battery's low-temperature discharge capability and high-intensity discharge capability increases significantly. If the system relies solely on historical usage data including summer data for prediction, it may underestimate the demand for these special capabilities.

[0094] The specific implementation of the scheme in this application is as follows: First, the system assesses the timeliness of historical usage data and identifies historical summer data that is more than 6 months away from the current winter period, excluding them from the main reference range.

[0095] Secondly, the system analyzed recent usage data of batteries returned during the current winter season and found that the average power consumption was high, the average usage time was long, and the average temperature drop rate of the batteries was fast. This indicates that users have carried out high-intensity and long-term shooting activities in low-temperature environments.

[0096] Next, the system compares these recent usage behavior data with relatively recent historical data (e.g., data from last winter) that has undergone timeliness assessment to determine the differences in behavioral patterns. If it finds that user behavior patterns this winter (e.g., the need for high-intensity power-ups) have significantly increased compared to last year, and the difference exceeds a preset threshold, the system will take action.

[0097] Therefore, the system will no longer rely entirely on last year's historical patterns, but will generate new site demand characteristics based on recent usage behavior data and real-time environmental information (e.g., low real-time temperature) during the current winter, clearly indicating that the next user is very likely to need a battery with excellent low-temperature discharge capability and high-intensity discharge capability.

[0098] In this way, the system can respond promptly to seasonal or sudden changes in demand, ensuring that the recommended batteries can meet the user's actual needs to the greatest extent.

[0099] In some embodiments described above, this application proposes comparing site demand characteristics with the performance records of each battery in the available battery list. However, in its implementation, if only a simple, indiscriminate comparison is performed, it may not fully consider the differentiated needs of users for battery capabilities in different usage scenarios, resulting in a low degree of match between the recommended battery and actual needs. For example, in low-temperature environments, users may be more concerned about the battery's low-temperature discharge capability; while in high-intensity usage scenarios, users may be more concerned about the battery's high-intensity discharge capability. If the comparison mechanism fails to dynamically adjust the importance of each capability dimension, it may fail to provide users with the optimal battery selection.

[0100] To address this, this application further proposes a method to optimize the aforementioned comparison process. By introducing dynamic weights and priority ranking mechanisms, the battery recommendations can more accurately match the user's actual needs. Specifically, the steps of comparing the site's demand characteristics with the performance records of each battery in the available battery list include: Based on the site demand characteristics, identify the priority ranking and dynamic weight of battery capability dimension in the current scenario; Each battery capability dimension in the site demand characteristics is compared one by one with the corresponding capability dimension in the performance record of each battery in the available battery list. The comparison results of each dimension are weighted and fused according to the priority ranking and the dynamic weights. Based on the weighted fusion results, a comprehensive matching score is generated for each battery.

[0101] Specifically, identifying the priority ranking and dynamic weighting of battery capabilities in the current scenario means that the system intelligently determines which battery capability dimensions (such as low-temperature discharge capability, high-intensity discharge capability, and battery life) the user values ​​most in a specific scenario, based on real-time environmental information of the charging station, historical usage data, and predicted site demand characteristics. For example, in cold mountainous areas, low-temperature discharge capability may be given higher priority and weight; while in scenarios requiring long-term recording, battery life may be more important. Dynamic weighting means that these priorities and weights are not fixed but are adjusted according to the real-time context.

[0102] Comparing each battery capability dimension in the site requirement characteristics with the corresponding capability dimension in the performance record of each battery in the available battery list means that the system will conduct a detailed comparison of each battery capability requirement described in the site requirement characteristics with the corresponding actual capability value in the performance record of the available batteries, and evaluate the performance of each battery in each single capability dimension.

[0103] The weighted fusion of comparison results for each dimension based on priority ranking and dynamic weights means that after completing the comparison of a single dimension, the system will perform a weighted calculation on the comparison results of these single dimensions according to the pre-identified priorities and dynamic weights, so that the more important capability dimensions in the current scenario will have a greater weight in the final evaluation.

[0104] Therefore, based on the weighted fusion results, a comprehensive matching score is generated for each battery. This score is a quantitative indicator that comprehensively reflects the overall matching degree between each available battery and the current site's demand characteristics. The higher the score, the better the matching degree.

[0105] This application's solution introduces a priority ranking and dynamic weighting of battery capabilities, transforming the comparison between batteries and site requirements from a simple linear match to an intelligent adjustment based on the characteristics of the actual application scenario. When the system identifies a higher requirement for specific battery capabilities in the current scenario, such as under extreme temperature or high-intensity discharge conditions, the corresponding capability dimension will be assigned a higher weight. This weighted and fused comparison method can more accurately reflect the actual applicability of each battery in a specific scenario, thus avoiding recommendation bias caused by simple comparisons. By generating a comprehensive matching score, the system can provide users with a quantitative and intuitive basis for battery selection, ensuring that the recommended battery is not only usable but also the most suitable for the current needs.

[0106] Through the above technical solution, this application can significantly improve the accuracy of battery recommendations and user satisfaction. This solution enables the system to dynamically adapt to changing usage environments and user needs, ensuring that the most suitable battery is recommended in any specific scenario. This not only optimizes the user experience but also improves the utilization efficiency of battery resources and reduces operational problems that may arise from battery performance mismatch.

[0107] In some preferred embodiments, suppose a charging case for an action camera is located near a ski resort, where the current real-time environmental information shows an external temperature of -10°C, and historical usage data indicates that users in this area typically engage in high-intensity skiing and filming, placing high demands on the battery's low-temperature discharge capability and high-intensity discharge capability. When a user initiates a battery rental request, the system first identifies, based on the site's demand characteristics, that "low-temperature discharge capability" and "high-intensity discharge capability" should have higher priority and dynamic weight in the current scenario. Subsequently, the system compares the performance records of each battery in the available battery list (including its performance under low-temperature and high-intensity discharge conditions) with the site's demand characteristics one by one.

[0108] For example, battery 100 exhibits excellent discharge performance at -10℃ but only average performance under high-intensity discharge; battery 200 performs exceptionally well under high-intensity discharge, but its performance degrades at -10℃. The system weights and fuses the comparison results of these two batteries across both low-temperature discharge and high-intensity discharge dimensions based on preset priorities and dynamic weights. Ultimately, the system generates a comprehensive matching score for both battery 100 and battery 200 based on the weighted fusion result and recommends the battery with the highest score to the user. For instance, if low-temperature environments are the primary challenge, battery 100 might score higher and thus be prioritized.

[0109] In some of the embodiments described above in this application, priority ranking and dynamic weighting of battery capability dimension are proposed based on site demand characteristics to identify the current scenario. However, in practical applications, if only a preliminary judgment based on site demand characteristics is made, it may not be possible to fully consider the impact of real-time environmental factors on the user's actual battery demand, resulting in inaccurate priority ranking and dynamic weighting, thereby affecting the matching degree of battery recommendation.

[0110] In response, this application further proposes an operation and management method for action camera battery charging cases, wherein the method identifies the priority ranking and dynamic weight of battery capacity dimensions in the current scenario based on site demand characteristics, including: The real-time environmental information of the charging compartment is obtained, including real-time wind data and real-time humidity data. The historical user behavior patterns of the charging compartment under similar wind and humidity conditions were analyzed. The historical user behavior patterns included the average demand intensity of users for low-temperature discharge capability, high-intensity discharge capability, and fast charging capability. Based on the real-time wind data, the real-time humidity data, and the historical user behavior patterns, the correlation strength between the low-temperature discharge capability, the high-intensity discharge capability, and the fast charging capability is dynamically adjusted. Based on the adjusted correlation strength and the preliminary demand values ​​for each battery capability dimension in the site demand characteristics, the priority ranking and dynamic weight of each battery capability dimension are calculated.

[0111] Specifically, obtaining real-time environmental information about the charging compartment refers to collecting and acquiring current wind and humidity data in real time through environmental sensors deployed in or near the charging compartment. Real-time wind data can include wind speed and direction, while real-time humidity data can include relative humidity. This data is used to reflect the immediate climatic conditions of the environment in which the charging compartment is located.

[0112] The analysis of historical user behavior patterns under similar wind and humidity conditions refers to the system querying and analyzing user behavior patterns when renting batteries from charging stations under environmental conditions similar to the current real-time wind and humidity data. These historical user behavior patterns specifically include the average intensity of user demand for battery low-temperature discharge capability, high-intensity discharge capability, and fast charging capability. For example, in windy and humid weather conditions, users may be more inclined to engage in outdoor water sports or high-intensity activities, which typically place higher demands on battery discharge intensity and performance at specific temperatures.

[0113] In practical applications, dynamically adjusting the correlation between low-temperature discharge capability, high-intensity discharge capability, and fast charging capability based on real-time wind and humidity data, as well as historical user behavior patterns, means that the system intelligently adjusts the importance or correlation between different battery capability dimensions based on current environmental conditions and historical experience. For example, if historical data shows a significant increase in user demand for high-intensity discharge capability under current wind and humidity conditions, the system will increase the correlation between high-intensity discharge capability and other capability dimensions, giving it a more important position in subsequent priority calculations.

[0114] Furthermore, based on the adjusted association strength and the initial demand values ​​for each battery capability dimension in the site demand characteristics, the priority ranking and dynamic weight of each battery capability dimension are calculated. This means that after the association strength is dynamically adjusted, the system combines the preset or initially assessed demand values ​​for each battery capability dimension in the site demand characteristics with a weighted algorithm or machine learning model to calculate the final priority ranking and dynamic weight of each battery capability dimension in the current scenario. These weights directly affect the accuracy of battery matching recommendations and user satisfaction.

[0115] This application's solution, by incorporating real-time wind and humidity data and combining them with historical user behavior patterns in similar environments, enables a more refined understanding and prediction of users' potential needs regarding battery capabilities. It is precisely this in-depth analysis of environmental factors and user behavior patterns that allows the system to dynamically adjust the correlation strength between different battery capability dimensions. This, in turn, allows for a more accurate reflection of users' true battery needs in the current scenario when calculating priority ranking and dynamic weights, avoiding potential biases caused by relying solely on preliminary demand values.

[0116] The above technical solutions significantly improve the accuracy of battery recommendations and user satisfaction. By combining real-time environmental information with historical user behavior patterns, the system can more intelligently identify user preferences for battery performance under specific climatic conditions. For example, in windy and humid environments, users may require batteries with superior high-intensity discharge capabilities or low-temperature discharge capabilities. Therefore, the recommended batteries will better match the user's actual usage scenarios, thereby improving operational efficiency and user experience.

[0117] In some preferred embodiments, a specific example is given below. Suppose an action camera battery charging station is located in a seaside area, and the initial demand characteristics of the site indicate that users have a high demand for the average battery life. On a certain day, the system obtains real-time environmental information, showing that the current wind speed is moderate to strong (e.g., wind speed 15 m / s) and the real-time humidity is high (e.g., relative humidity 80%). The system analyzes the historical user behavior patterns of the charging station under similar wind and humidity conditions. Historical data shows that under these weather conditions, users typically engage in water sports such as surfing and windsurfing. These activities place high demands on the battery's high-intensity discharge capacity and stability in humid environments, while the demand for fast charging capabilities is relatively low.

[0118] Based on this analysis, the system dynamically adjusts the correlation strength between low-temperature discharge capability, high-intensity discharge capability, and fast charging capability. For example, it increases the correlation strength between high-intensity discharge capability and the current environment, while decreasing the correlation strength of fast charging capability. Subsequently, combining the preliminary demand values ​​for each battery capability dimension in the site's demand characteristics, the system calculates that the priority ranking and dynamic weight of high-intensity discharge capability are significantly increased in the current scenario, while the weight of fast charging capability is relatively decreased. Finally, when receiving a battery rental request, the system will prioritize recommending batteries that perform well in high-intensity discharge capability, even if they are not optimal in fast charging, as they better meet the user's actual needs in the current environment.

[0119] In some embodiments described above, this application proposes calculating the priority ranking and dynamic weights of each battery capability dimension based on the adjusted association strength and the initial demand values ​​for each battery capability dimension in the site demand characteristics. However, in practical applications, the initial demand values ​​for each battery capability dimension in the site demand characteristics may not always be precise values, but are often affected by various uncertain factors, such as the randomness of user behavior and the complexity of environmental changes. If these potentially uncertain initial demand values ​​are directly used as fixed inputs for weight calculation, the calculated priority ranking and dynamic weights may be unstable or inaccurate, thus affecting the matching degree of battery recommendations. To address this, this application further proposes an optimization scheme aimed at identifying and quantifying these uncertainties to calculate more robust and accurate priority ranking and dynamic weights for battery capability dimensions.

[0120] Based on the adjusted correlation strength and the preliminary demand values ​​for each battery capability dimension in the site demand characteristics, the priority ranking and dynamic weight of each battery capability dimension are calculated, specifically including: Identify the dimensions of uncertainty in the preliminary demand values; Identify dimensions with uncertainties and transform the initial demand values ​​into demand ranges; Based on the adjusted correlation strength and the demand range of each dimension, calculate the weight fluctuation range of each dimension within the demand range; Based on the weight fluctuation range of each dimension, the dimension with the smallest weight change throughout the entire demand interval is identified as the core dimension. Based on the core dimension, and according to the average weight within the demand range, combined with the weight fluctuation range of other dimensions, the priority ranking and dynamic weight of each battery capability dimension are calculated.

[0121] Specifically, identifying dimensions with uncertainty in the preliminary demand values ​​means that the system analyzes historical data, user feedback, or preset rules to determine whether the preliminary demand values ​​for each battery capability dimension (e.g., low-temperature discharge capability, high-intensity discharge capability, fast charging capability, etc.) in the site demand characteristics have high volatility or prediction errors. For example, if the historical demand values ​​for a certain dimension are widely distributed, or if the prediction model has low confidence in its predictions, then that dimension is identified as having uncertainty.

[0122] In this context, identifying dimensions with inherent uncertainty and converting the initial demand value into a demand range means that for dimensions identified as having uncertainty, the initial demand value is no longer considered a single, precise numerical value, but rather represented as a demand range encompassing possible values. This demand range can be determined using statistical methods (such as confidence intervals), expert experience, or a preset error range to reflect the possible variation range of the demand value. For example, if the initial demand value for high-intensity discharge capability is "high," but there is uncertainty, it can be converted into a demand range of "medium-high to extremely high."

[0123] In practical applications, calculating the weight fluctuation range of each dimension within the demand range, based on the adjusted correlation strength and the demand range of each dimension, means that, after considering the correlation strength dynamically adjusted by real-time environmental information (such as real-time wind data and real-time humidity data), multiple representative points are selected within the demand range for each battery capability dimension to perform weight calculations, and the distribution of these calculation results is analyzed to determine the maximum and minimum range of possible weight fluctuations for that dimension within the entire demand range.

[0124] Furthermore, identifying the dimension with the smallest weight change throughout the entire demand range as the core dimension, based on the weight fluctuation range of each dimension, means comparing the weight fluctuation range of different battery capability dimensions within their respective demand ranges and selecting the dimension with the smallest fluctuation range as the core dimension. This core dimension is considered the most stable demand dimension, and its weight is least affected by the uncertainty of the initial demand value; therefore, it can serve as a reliable benchmark for subsequent weight calculations.

[0125] Therefore, using the core dimension as a benchmark, and based on the average weight within the demand interval, combined with the weight fluctuation range of other dimensions, calculating the priority ranking and dynamic weight of each battery capability dimension involves first determining the average weight of the core dimension, and then using this as a reference, combined with the average weight of other non-core dimensions within their respective demand intervals and their weight fluctuation range, to comprehensively calculate the final priority ranking and dynamic weight of all battery capability dimensions through weighted averaging, fuzzy logic, or other optimization algorithms. This method ensures that the weight allocation remains highly stable and reasonable even under uncertain environments.

[0126] This application's solution effectively addresses the instability in weight calculation caused by inaccurate initial demand values ​​in traditional methods by introducing a mechanism for identifying and handling uncertainty in initial demand values. Specifically, when the initial demand value in a site's demand characteristics is uncertain, it is no longer treated as a fixed point value but rather transformed into a demand range, thus more comprehensively reflecting the possible range of actual demand. Based on this, by calculating the weight fluctuation range of each dimension within the demand range, the impact of uncertainty on weight calculation can be quantified. The core dimension with the smallest weight change is identified, providing a stable benchmark for subsequent weight calculations and avoiding excessive interference with the overall weight allocation due to fluctuations in a dimension with high uncertainty. Finally, using the core dimension as a benchmark, combined with the average weight and fluctuation range of other dimensions, a comprehensive calculation is performed. This ensures that the final priority ranking and dynamic weights not only consider the demand intensity of each dimension but also their stability under uncertain environments, thereby improving the robustness and accuracy of weight allocation.

[0127] Through the above technical solution, this application can effectively address the uncertainty of preliminary demand values ​​in site demand characteristics, avoiding weight calculation deviations caused by inaccurate data. Therefore, the calculated priority ranking and dynamic weights of battery capabilities are more stable and reliable, ensuring the accuracy and matching degree of battery recommendations even when there is some ambiguity in demand forecasting. This not only improves the intelligence level of charging station operation and management but also significantly enhances the user experience, enabling users to obtain batteries that better meet their actual usage needs, thereby extending battery life and improving resource utilization efficiency.

[0128] In some preferred embodiments, suppose the initial demand characteristics of a charging station show that users' demand for "low-temperature discharge capability" is 80 (out of 100) and their demand for "high-intensity discharge capability" is 70. However, due to the variable weather in the area and the diverse types of user activities, the system identifies that both of these initial demand values ​​have a certain degree of uncertainty.

[0129] Specifically, the system converts the initial demand value of 80 for "low-temperature discharge capability" into a demand range of [75, 85], and the initial demand value of 70 for "high-intensity discharge capability" into a demand range of [65, 75].

[0130] Next, the system calculates the weight fluctuation range of these two dimensions within their respective demand intervals based on the adjusted correlation strength (for example, in the current cold weather, the correlation strength between low-temperature discharge capability and high-intensity discharge capability is relatively high). Assume that after calculation, the weight fluctuation range of "low-temperature discharge capability" is [0.4, 0.5], while the weight fluctuation range of "high-intensity discharge capability" is [0.3, 0.45].

[0131] By comparison, the system found that the weight fluctuation range of "low temperature discharge capability" (0.1) is smaller than that of "high intensity discharge capability" (0.15), so "low temperature discharge capability" is identified as the core dimension.

[0132] Finally, using "low-temperature discharge capability" as the benchmark, combined with its average weight within the demand range (e.g., 0.45), and the average weight of "high-intensity discharge capability" within the demand range (e.g., 0.375) and its fluctuation range, the system calculates the final priority ranking and dynamic weights. For example, the final weight of "low-temperature discharge capability" might be determined to be 0.55, and the weight of "high-intensity discharge capability" to be 0.45. This ensures that when recommending batteries, priority is given to batteries with better low-temperature discharge performance, while also considering high-intensity discharge requirements, thus providing the optimal battery matching solution even under uncertain environments.

[0133] In some existing implementations, when calculating the priority ranking and dynamic weights of each battery capability dimension, real-time wind and humidity data are primarily considered, and historical user behavior patterns under similar wind and humidity conditions are analyzed. However, in practical applications, especially under varying geographical environments or extreme climate conditions, relying solely on wind and humidity data may not fully capture the impact of the environment on battery performance and user needs. For example, in high-altitude areas or environments with drastic air pressure changes, the battery's discharge characteristics, heat dissipation efficiency, and actual user habits may be significantly affected, and these effects cannot be accurately predicted using simple wind and humidity models. Failure to address these issues may lead to a decrease in the matching accuracy of battery recommendations, impacting user experience and operational efficiency. To address this, this application proposes a more refined method that incorporates real-time altitude and air pressure data, combined with an analysis of their sensitivity to battery capability dimension weights, to more accurately calculate the priority ranking and dynamic weights of each battery capability dimension.

[0134] In response, this application further proposes a method for calculating the priority ranking and dynamic weight of each battery capability dimension based on the adjusted correlation strength and the preliminary demand values ​​for each battery capability dimension in the site demand characteristics, including: Obtain real-time altitude and air pressure data of the charging compartment; The historical user behavior patterns of the charging compartment were analyzed under similar altitude and air pressure conditions in the past. The historical user behavior patterns included the average demand intensity of users for low-temperature discharge capability, high-intensity discharge capability and fast charging capability. Based on real-time altitude data, real-time air pressure data, and historical user behavior patterns, the correlation strength between low-temperature discharge capability, high-intensity discharge capability, and fast charging capability is dynamically adjusted. Calculate the weight sensitivity of each battery capability dimension under different combinations of altitude and air pressure; Identify the dimension with the highest weight sensitivity as the key sensitive dimension; Based on the key sensitive dimensions, and according to the weight change trends under different altitude and air pressure combinations, combined with the weight sensitivity of other dimensions, the priority ranking and dynamic weight of each battery capability dimension are calculated.

[0135] Specifically, obtaining real-time altitude and atmospheric pressure data for the charging compartment refers to acquiring the altitude and atmospheric pressure information of the current location of the charging compartment through environmental sensors integrated inside the charging compartment or through an interface with an external meteorological service. This data can be updated in real time to reflect instantaneous changes in the environment in which the charging compartment is located.

[0136] Analyzing historical user behavior patterns under similar altitude and air pressure conditions can be understood as the system querying a historical database to identify the average demand intensity for battery low-temperature discharge capability, high-intensity discharge capability, and fast charging capability under environmental conditions similar to the current real-time altitude and air pressure. For example, in high-altitude areas, users may prefer batteries with strong low-temperature discharge capability because low-temperature environments are more common.

[0137] In practical applications, the correlation between low-temperature discharge capability, high-intensity discharge capability, and fast charging capability is dynamically adjusted based on real-time altitude and air pressure data, as well as historical user behavior patterns. This can be understood as using machine learning models or pre-set rule engines to adjust the degree of interaction between these capability dimensions based on current altitude and air pressure conditions, and historical data on user preferences for different battery capabilities under these conditions. For example, in high-altitude, low-pressure environments, the battery's heat dissipation efficiency may decrease, leading to a stronger correlation between high-intensity discharge capability and increased internal temperature.

[0138] Furthermore, calculating the weight sensitivity of each battery capability dimension under different combinations of altitude and air pressure refers to assessing the degree to which the weight values ​​of each battery capability dimension (such as low-temperature discharge capability, high-intensity discharge capability, and fast charging capability) are affected when altitude and air pressure change. This can be achieved through regression analysis or sensitivity analysis on historical data to quantify the strength of the influence of environmental factors on the weights of each capability dimension.

[0139] Among these, the dimension with the highest weight sensitivity is identified as the key sensitive dimension. This means that among all battery capability dimensions, the dimension whose weight value is most sensitive to changes in altitude and air pressure is determined. This dimension is considered the most critical battery performance indicator under current environmental conditions.

[0140] Therefore, based on key sensitive dimensions, and considering the weight change trends under different altitude and air pressure combinations, combined with the weight sensitivity of other dimensions, the priority ranking and dynamic weight of each battery capability dimension are calculated. This means that the system will take the dimension most sensitive to environmental changes as the core, and combine the sensitivity of other dimensions and their weight change patterns under different environmental combinations to comprehensively determine the final priority ranking and dynamic weight, thereby ensuring the accuracy and adaptability of battery recommendations.

[0141] This application's solution, by incorporating real-time altitude and air pressure data, enables a more comprehensive understanding of the charging compartment's physical environment. Given the significant impact of altitude and air pressure on battery electrochemical performance, heat dissipation characteristics, and user habits under specific environmental conditions, this solution analyzes historical user behavior patterns under similar altitude and air pressure conditions to more accurately capture users' actual preferences for different battery capabilities (such as low-temperature discharge capability, high-intensity discharge capability, and fast charging capability). Based on this, the system dynamically adjusts the correlation strength between these capability dimensions, allowing it to flexibly adjust the interrelationships between these dimensions according to the unique characteristics of the current environment. Furthermore, by calculating the weight sensitivity of each battery capability dimension under different combinations of altitude and air pressure, this solution identifies the key sensitive dimensions that have the most significant impact on battery performance under specific environmental changes. Using this key sensitive dimension as a benchmark, combined with the weight sensitivity and trends of other dimensions, the system can generate more refined and environmentally adaptable priority rankings and dynamic weights, thereby overcoming the limitations that may arise from relying solely on wind and humidity data and ensuring the accuracy of battery recommendations in complex and variable geographical environments.

[0142] Through the aforementioned technical solution, this application significantly improves the adaptability and accuracy of the action camera battery charging case operation and management method in complex geographical environments. Specifically, by considering real-time altitude and air pressure data, and combining this with an analysis of their sensitivity to battery capacity dimensions, the system can more accurately predict site demand characteristics. Especially in special environments such as high altitude and low air pressure, it can effectively avoid battery recommendation biases caused by insufficient consideration of environmental factors. As a result, users will obtain batteries that are more closely matched to their actual usage scenarios and environmental conditions, thereby significantly improving user experience and satisfaction. Furthermore, this refined weighting mechanism helps optimize battery resource allocation, reduces the rental of mismatched batteries, and thus improves the operational efficiency of the charging case and battery turnover rate.

[0143] In some preferred embodiments, assume an action camera battery charging case is deployed in a mountainous area at an altitude of 3000 meters, with integrated sensors reporting the current altitude as 3050 meters and the air pressure as 700 hPa in real time. The system first acquires this real-time environmental data. Subsequently, the system queries a historical database to analyze past user behavior patterns within the range of 2800-3200 meters in altitude and 680-720 hPa in air pressure. For example, historical data shows that in such environments, users' demand for low-temperature discharge capability is significantly higher than in plains areas because mountain temperatures are generally lower. Simultaneously, due to the thin air at high altitudes, battery heat dissipation efficiency may decrease, and users' demand for high-intensity discharge capability may be accompanied by higher requirements for battery heat dissipation performance. Based on these historical patterns and real-time environmental data, the system dynamically adjusts the correlation strength between low-temperature discharge capability, high-intensity discharge capability, and fast charging capability. For example, the correlation between low-temperature discharge capability and high-intensity discharge capability may be strengthened because in high-altitude, low-temperature environments, users may need the battery to function normally at low temperatures while also performing high-intensity action shooting.

[0144] Next, the system calculates the weight sensitivity of each battery capability dimension under different combinations of altitude and air pressure. For example, analysis reveals that the weight of low-temperature discharge capability is most sensitive to changes in altitude and air pressure, with its weight significantly increasing in high-altitude, low-pressure environments, thus identifying it as a key sensitive dimension. Using low-temperature discharge capability as a benchmark, the system combines the weight sensitivity of other dimensions (such as high-intensity discharge capability and fast charging capability) and their changing trends under different combinations of altitude and air pressure to ultimately calculate the priority ranking and dynamic weight of each battery capability dimension. For example, in the current scenario of an altitude of 3050 meters and an air pressure of 700 hPa, the weight of low-temperature discharge capability might be set to the highest, followed by high-intensity discharge capability, while the weight of fast charging capability is relatively low. When a user rents a battery, the system will prioritize recommending batteries with excellent low-temperature and high-intensity discharge capabilities to better meet the user's actual needs in mountainous environments.

[0145] refer to Figure 3 , Figure 3 This is a structural diagram of an operation and management system for a battery charging compartment of an action camera provided in an embodiment of the present invention, including: The detection end is used to acquire battery usage data, including battery discharge intensity data and internal temperature data; based on the usage data, the battery performance record is updated, the performance record describing the battery's power supply capability under different temperature environments and different discharge intensities; real-time environmental information of the charging compartment and historical usage data of the charging compartment are acquired; The prediction end is used to predict the site demand characteristics of the charging compartment based on the real-time environmental information and the historical usage data. The site demand characteristics describe the battery capacity characteristics required by the next user. The recommendation module, upon receiving a battery rental request, retrieves a list of available batteries in the charging compartment, the list containing the performance record of each available battery; compares the site demand characteristics with the performance record of each battery in the list of available batteries; and, based on the comparison result, selects and recommends a battery that matches the site demand characteristics.

[0146] Traditional action camera battery rental services are becoming increasingly popular in outdoor activity areas. However, in actual operation, especially in harsh outdoor environments such as high altitudes and frigid zones, existing battery status assessment and scheduling methods struggle to accurately reflect the true performance of batteries and effectively match users' personalized usage needs, leading to poor user experience and low operational efficiency. Specifically, battery performance degrades significantly in low-temperature environments, and the usage intensity and environmental exposure of different users vary greatly. Furthermore, the operating system cannot directly obtain users' activity plans, posing a challenge to intelligent battery matching and scheduling.

[0147] To address this issue, this application proposes an operation and management system for action camera battery charging cases. By setting up a detection end, a prediction end, and a recommendation end, it achieves accurate evaluation of battery performance, intelligent prediction of site demand, and personalized battery matching and recommendation. The detection end is responsible for comprehensively acquiring battery usage data, real-time environmental information of the charging case, and historical usage data, providing foundational data for subsequent performance updates and demand prediction. The prediction end intelligently analyzes this data and predicts the battery capability characteristics that the next user might need. The recommendation end compares the predicted site demand characteristics with the performance records of available batteries when a user initiates a rental request and recommends the most suitable battery. Therefore, this application effectively solves the problems of inaccurate battery performance evaluation and low user demand matching in existing technologies, significantly improving user experience and operational efficiency.

[0148] The core of the action camera battery charging case operation management system proposed in this application lies in the intelligent management and matching of batteries through the collaborative work of the detection end, prediction end, and recommendation end.

[0149] The detection terminal is used to acquire battery usage data, including battery discharge intensity data and internal temperature data. Based on this usage data, it updates the battery's performance record, which describes the battery's power supply capability under different temperature environments and discharge intensities. It also acquires real-time environmental information of the charging compartment and historical usage data of the charging compartment. The specific methods for acquiring battery usage data, updating battery performance records, and acquiring real-time environmental information and historical usage data of the charging compartment have been described in the above embodiments and will not be repeated here. It is important to emphasize that the detection terminal can be implemented as one or more processing modules. For example, it can be a data acquisition and processing module integrated into the charging compartment control unit, or an independent server-side data processing service. The detection terminal can include various sensor interfaces (e.g., for connecting internal battery sensors and charging compartment environmental sensors), a data storage module (for storing raw data and performance records), and a data processing module (for executing performance record update algorithms). As one implementation, the detection terminal can be a distributed system, where some functions (such as battery usage data acquisition) are performed by the microcontroller inside the battery, while other functions (such as performance record updates and charging compartment environmental information acquisition) are performed by the local controller of the charging compartment or a cloud server.

[0150] The prediction terminal is used to predict the site demand characteristics of the charging station based on the real-time environmental information and the historical usage data. These site demand characteristics describe the battery capacity characteristics required by the next user. The specific method for predicting site demand characteristics has already been described in the above embodiments and will not be repeated here. It is important to emphasize that the prediction terminal can be implemented as an independent software module or service, running on a cloud server or a local edge computing device within the charging station. This prediction terminal can integrate machine learning models or expert systems to analyze complex environmental and historical usage data, thereby generating accurate site demand characteristics. For example, a neural network-based prediction model can be used to learn the complex relationship between environmental factors and user needs by training historical data. As another implementation, the prediction terminal can be a rule engine-based system that determines the current site demand characteristics based on preset rules and thresholds.

[0151] The recommendation mechanism, upon receiving a battery rental request, retrieves a list of available batteries in the charging compartment, the list containing the performance record of each available battery; compares the site demand characteristics with the performance record of each battery in the list of available batteries; and, based on the comparison result, selects a battery that matches the site demand characteristics for recommendation. The specific methods for retrieving the list of available batteries, comparing the site demand characteristics with the battery performance records, and selecting a matching battery for recommendation have already been described in the above embodiments, and will not be repeated here.

[0152] It's important to emphasize that the recommendation engine can be implemented as a decision engine running on the charging compartment control unit or a cloud server. This engine can include a matching algorithm module that compares site demand characteristics with battery performance records and calculates a matching score. For example, a multi-attribute decision analysis (MADM) method can be used, combining different weights and priorities to score the battery. Alternatively, the recommendation engine can be a module based on an optimization algorithm designed to maximize user satisfaction or operational efficiency. The recommendation results can be displayed to the user through the charging compartment's screen or the user's mobile application interface.

[0153] This application's action camera battery charging case operation management system, by introducing a detection, prediction, and recommendation terminal, achieves an intelligent upgrade to action camera battery rental services, significantly surpassing existing technologies. Traditional systems often rely solely on cycle count or state score at room temperature for battery performance evaluation. In harsh outdoor environments such as extreme cold, this fails to accurately reflect the battery's true performance, leading to users renting "high-quality batteries" yet still experiencing insufficient battery life. This application's detection terminal acquires battery discharge intensity and internal temperature data, updating battery performance records under different temperature environments and discharge intensities accordingly. This makes battery performance evaluation more refined and accurate, overcoming the limitations of traditional solutions.

[0154] Furthermore, existing systems typically rely solely on the charging case temperature for rough predictions regarding user needs, failing to consider individualized usage patterns and environmental differences among users, resulting in low battery matching accuracy. This application's predictive mechanism, however, intelligently predicts the battery capabilities a user might require by comprehensively analyzing real-time environmental information and historical usage data from the charging case. This includes specific needs such as low-temperature discharge capability, high-intensity discharge capability, or long-range battery life, thereby achieving precise insight into user needs.

[0155] Furthermore, upon receiving a battery rental request, the recommendation system in this application can perform a refined comparison between the anticipated site demand characteristics and the performance records of available batteries, and recommend the most suitable battery. This is fundamentally different from the existing technology that simply allocates batteries in good condition to popular sites. The system in this application ensures that the batteries rented by users not only have good performance but also best meet their personalized usage needs and the challenges of their environment. For example, in a cold alpine ski resort, the system will prioritize recommending batteries that perform well in low-temperature environments, even if their overall rating at room temperature is not the highest.

[0156] In summary, the action camera battery charging case operation management system of this application provides a more intelligent, accurate, and personalized battery management solution through the collaborative work of its detection, prediction, and recommendation ends. It effectively solves the core problems of inaccurate battery performance evaluation and low matching degree of user needs in the prior art, and has significant technological progress and practical value.

[0157] The above description is merely an embodiment of this application and is not intended to limit the scope of protection of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A method for operating and managing the charging case of an action camera battery, characterized in that, include: Obtain battery usage data, including battery discharge intensity data and internal temperature data; Based on the usage data, update the battery performance record, which describes the battery's power supply capability under different temperature environments and different discharge intensities; Obtain real-time environmental information of the charging compartment and historical usage data of the charging compartment; Based on the real-time environmental information and the historical usage data, the site demand characteristics of the charging station are predicted, and the site demand characteristics describe the battery capacity characteristics required by the next user. Upon receiving a battery rental request, a list of available batteries in the charging compartment is obtained, the list containing the performance record of each available battery; The site demand characteristics are compared with the performance records of each battery in the list of available batteries; Based on the comparison results, batteries that match the site's demand characteristics are selected for recommendation.

2. The method for operating and managing the charging case of an action camera battery according to claim 1, characterized in that, The battery performance record is updated based on the usage data. The performance record describes the battery's power supply capability under different temperature environments and different discharge intensities, including: The validity of the usage data is evaluated, including: determining whether there are any cases in the usage data where the reading of the battery internal temperature sensor exceeds a preset reasonable range; and determining whether the battery usage time recorded in the usage data is lower than a preset time threshold. If the usage data passes the validity assessment, the battery performance record is updated based on the usage data.

3. The method for operating and managing the charging case of an action camera battery according to claim 1, characterized in that, The battery performance record is updated based on the usage data. The performance record describes the battery's power supply capability under different temperature environments and different discharge intensities, including: Establish a battery reference voltage performance curve; The instantaneous discharge current, output voltage, and internal core temperature of the battery are collected during its usage cycle. Based on the battery model, the internal core temperature, and the instantaneous discharge current, the battery reference voltage performance curve is consulted to obtain the expected output voltage; Calculate the voltage deviation between the actual output voltage and the expected output voltage; The effective internal resistance increase factor is inferred based on the voltage deviation. The performance record is updated by combining the effective internal resistance increase factor with the average internal core temperature and average discharge intensity during the battery's service life.

4. The method for operating and managing the charging case of an action camera battery according to claim 1, characterized in that, The method of predicting the site demand characteristics of the charging station based on the real-time environmental information and the historical usage data, wherein the site demand characteristics describe the battery capability characteristics that the next user may need, including: Collect temperature data of the internal core area of ​​the battery and the battery model information during one use cycle; Store the thermal response characteristics of each battery model under different external ambient temperatures; Aggregate the internal core temperature of multiple batteries returned to the same charging compartment within a short period of time and the average temperature drop rate of the batteries during one usage cycle, and filter out abnormal data; Based on the filtered battery group data and the thermal response characteristic parameters of each battery, the consensus ambient temperature of the battery group in the charging compartment area is estimated. Compare the consensus ambient temperature of the battery pack with the real-time temperature reported by the ambient temperature sensor in the real-time environmental information of the charging compartment; If the temperature exceeds the limit, it is determined that there is a calibration deviation in the ambient temperature sensor in the real-time environmental information of the charging compartment, and the consensus ambient temperature of the battery group is used as the ambient temperature of the site. By combining the calibrated ambient temperature of the site with historical user behavior data of the charging compartment, the site demand characteristics are generated.

5. The method for operating and managing the charging case of an action camera battery according to claim 1, characterized in that, The step of predicting the site demand characteristics of the charging station based on the real-time environmental information and the historical usage data, wherein the site demand characteristics describe the battery capacity characteristics required by the next user, includes: The timeliness of the historical usage data is evaluated, including identifying historical data that is more than a preset threshold apart from the current time period. Analyze recent usage behavior data of batteries returned within the current time period, including average power consumption, average usage duration, and average temperature drop rate. Compare the recent usage behavior data with the historical data after the timeliness assessment to identify differences in behavior patterns; If the number of cases exceeds the limit, the site demand characteristics are generated based on the recent usage behavior data and the current real-time environment information.

6. The method for operating and managing the charging case of an action camera battery according to claim 1, characterized in that, The step of comparing the site demand characteristics with the performance records of each battery in the available battery list includes: Based on the site demand characteristics, identify the priority ranking and dynamic weight of battery capability dimension in the current scenario; Each battery capability dimension in the site demand characteristics is compared one by one with the corresponding capability dimension in the performance record of each battery in the available battery list. The comparison results of each dimension are weighted and fused according to the priority ranking and the dynamic weights. Based on the weighted fusion results, a comprehensive matching score is generated for each battery.

7. The method for operating and managing the charging case of an action camera battery according to claim 6, characterized in that, The step of identifying the priority ranking and dynamic weight of battery capability dimension in the current scenario based on the site demand characteristics includes: The real-time environmental information of the charging compartment is obtained, including real-time wind data and real-time humidity data. The historical user behavior patterns of the charging compartment under similar wind and humidity conditions were analyzed. The historical user behavior patterns included the average demand intensity of users for low-temperature discharge capability, high-intensity discharge capability, and fast charging capability. Based on the real-time wind data, the real-time humidity data, and the historical user behavior patterns, the correlation strength between the low-temperature discharge capability, the high-intensity discharge capability, and the fast charging capability is dynamically adjusted. Based on the adjusted correlation strength and the preliminary demand values ​​for each battery capability dimension in the site demand characteristics, the priority ranking and dynamic weight of each battery capability dimension are calculated.

8. The method for operating and managing the battery charging compartment of an action camera according to claim 7, characterized in that, The step of calculating the priority ranking and dynamic weight of each battery capability dimension based on the adjusted correlation strength and the preliminary demand values ​​for each battery capability dimension in the site demand characteristics includes: Identify the dimensions of uncertainty in the preliminary demand values; Identify dimensions with uncertainties and transform the initial demand values ​​into demand ranges; Based on the adjusted correlation strength and the demand range of each dimension, calculate the weight fluctuation range of each dimension within the demand range; Based on the weight fluctuation range of each dimension, the dimension with the smallest weight change throughout the entire demand interval is identified as the core dimension. Based on the core dimension, and according to the average weight within the demand range, combined with the weight fluctuation range of other dimensions, the priority ranking and dynamic weight of each battery capability dimension are calculated.

9. The method for operating and managing the charging case of an action camera battery according to claim 7, characterized in that, The step of calculating the priority ranking and dynamic weight of each battery capability dimension based on the adjusted correlation strength and the preliminary demand values ​​for each battery capability dimension in the site demand characteristics includes: Obtain real-time altitude and air pressure data of the charging compartment; The historical user behavior patterns of the charging compartment under similar altitude and air pressure conditions were analyzed. The historical user behavior patterns included the average demand intensity of users for low-temperature discharge capability, high-intensity discharge capability, and fast charging capability. Based on the real-time altitude data, the real-time air pressure data, and the historical user behavior patterns, the correlation strength between the low-temperature discharge capability, the high-intensity discharge capability, and the fast charging capability is dynamically adjusted. Calculate the weight sensitivity of each battery capability dimension under different combinations of altitude and air pressure; Identify the dimension with the highest weight sensitivity as the key sensitive dimension; Based on the aforementioned key sensitive dimensions, and according to the weight change trends under different altitude and air pressure combinations, combined with the weight sensitivity of other dimensions, the priority ranking and dynamic weight of each battery capability dimension are calculated.

10. An operation and management system for a charging case of an action camera battery, characterized in that, include: The detection end is used to acquire battery usage data, including battery discharge intensity data and internal temperature data; based on the usage data, the battery performance record is updated, the performance record describing the battery's power supply capability under different temperature environments and different discharge intensities; Obtain real-time environmental information of the charging compartment and historical usage data of the charging compartment; The prediction end is used to predict the site demand characteristics of the charging compartment based on the real-time environmental information and the historical usage data. The site demand characteristics describe the battery capacity characteristics required by the next user. The recommendation module, upon receiving a battery rental request, retrieves a list of available batteries in the charging compartment, the list containing the performance record of each available battery; and compares the site demand characteristics with the performance record of each battery in the list of available batteries. Based on the comparison results, batteries that match the site's demand characteristics are selected for recommendation.

Images