A method for detecting battery degradation according to endurance time

Abstract

The application provides a method for detecting battery attenuation according to endurance time, and relates to the field of data processing, and comprises the following steps: obtaining reported data of a plurality of sample satellite positioning terminals, the battery attenuation states of the plurality of sample satellite positioning terminals being different, and the reported data comprising at least battery capacity and a time stamp; determining a plurality of key percentile differences based on the reported data of the plurality of sample satellite positioning terminals; obtaining reported data of a current satellite positioning terminal; determining optimal interpolation parameters according to the reported data of the plurality of sample satellite positioning terminals; interpolating the reported data of the current satellite positioning terminal according to the optimal interpolation parameters to generate an interpolated battery capacity array; and judging whether the current satellite positioning terminal has abnormal battery attenuation based on the plurality of key percentile differences and the interpolated battery capacity array, and if so, determining the battery attenuation state of the current satellite positioning terminal based on the plurality of key percentile differences and the interpolated battery capacity array, thereby improving the accuracy of battery attenuation detection.

Description

Technical Field

[0001] This invention relates to the field of the Internet of Things, and in particular to a method for detecting battery degradation based on battery life. Background Technology

[0002] In numerous fields such as the Internet of Things and intelligent tracking, satellite positioning terminals are widely used as key equipment. Their battery life directly affects the user experience and functional stability, and battery performance is the core factor determining battery life. As usage time increases, battery degradation inevitably occurs. Accurately detecting battery degradation is crucial for ensuring the normal operation of satellite positioning terminals and for timely maintenance and replacement.

[0003] Traditional methods for detecting battery degradation largely rely on precise measurement equipment and specific testing procedures in a laboratory environment. These methods require professional operation, making them not only costly and time-consuming but also difficult to implement on a large scale in real-world applications. While some methods based on simple parameter thresholds can identify battery anomalies to a certain extent, they lack a comprehensive and accurate analysis of the battery degradation process and are easily affected by environmental factors, usage habits, and other interferences, leading to misjudgments or missed detections.

[0004] For example, simply comparing the current remaining battery capacity with the initial remaining capacity, or assessing the battery status solely based on usage time, without considering the actual discharge situation, cannot accurately determine whether the battery has experienced abnormal degradation.

[0005] Therefore, there is a need to provide a method for detecting battery degradation based on battery life, in order to improve the accuracy of battery degradation detection. Summary of the Invention

[0006] This invention provides a method for detecting battery degradation based on battery life, comprising: acquiring reported data from multiple sample satellite positioning terminals, wherein the battery degradation status of the multiple sample satellite positioning terminals differs, and the reported data includes at least battery remaining capacity and a timestamp; determining multiple key percentile differences based on the reported data from the multiple sample satellite positioning terminals; acquiring the reported data from the current satellite positioning terminal; determining optimal interpolation parameters based on the reported data from the multiple sample satellite positioning terminals; interpolating the reported data from the current satellite positioning terminal based on the optimal interpolation parameters to generate an interpolated battery remaining capacity array; and determining whether the current satellite positioning terminal has abnormal battery degradation based on the multiple key percentile differences and the interpolated battery remaining capacity array, and if so, determining the battery degradation status of the current satellite positioning terminal based on the multiple key percentile differences and the interpolated battery remaining capacity array.

[0007] Furthermore, based on the reported data from multiple sample satellite positioning terminals, several key percentile differences are determined, including: determining multiple percentile differences; for each percentile difference and each sample satellite positioning terminal, calculating the time interval at which the battery capacity of the sample satellite positioning terminal decreases by one percentile based on the reported data from the sample satellite positioning terminals; for each percentile difference, calculating the key coefficient of the percentile difference based on the time interval at which the battery capacity of each sample satellite positioning terminal decreases by one percentile; and determining multiple key percentile differences based on the key coefficient of each percentile difference.

[0008] Furthermore, based on the time interval for each percentile decrease in battery capacity of each sample satellite positioning terminal, the key coefficient of the percentile difference is calculated, including: for each sample satellite positioning terminal, calculating the time interval difference value corresponding to the percentile difference for each sample satellite positioning terminal based on the time interval for each percentile decrease in battery capacity; calculating the mean of the time interval difference of the percentile difference based on the time interval difference value corresponding to each sample satellite positioning terminal; calculating the global time interval difference value of the percentile difference based on the time interval for each percentile decrease in battery capacity; and calculating the key coefficient of the percentile difference based on the mean of the time interval difference of the percentile difference and the global time interval difference value.

[0009] Furthermore, based on the time interval for each percentile decrease in the remaining battery capacity of the sample satellite positioning terminal, the time interval difference value corresponding to the percentile difference is calculated, including: calculating the standard deviation of the time interval for each percentile decrease in the remaining battery capacity of the sample satellite positioning terminal, as the time interval difference value corresponding to the percentile difference.

[0010] Furthermore, based on the time interval for each percentile decrease in the battery capacity of each sample satellite positioning terminal, the global time interval difference of the percentile difference is calculated, including: for each sample satellite positioning terminal, based on the time interval for each percentile decrease in the battery capacity of the sample satellite positioning terminal, the mean of the time interval corresponding to the percentile difference is calculated; the standard deviation of the mean of the time interval corresponding to the percentile difference is calculated as the global time interval difference of the percentile difference.

[0011] Furthermore, based on the data reported by multiple sample satellite positioning terminals, the optimal interpolation parameters are determined, including: using a genetic algorithm to determine the optimal interpolation parameters based on the data reported by multiple sample satellite positioning terminals.

[0012] Furthermore, the optimal interpolation parameters include the optimal interpolation slope for each battery capacity interval.

[0013] Furthermore, based on multiple key percentile differences and the interpolated battery balance array, it is determined whether the current satellite positioning terminal has abnormal battery degradation. This includes: for each key percentile difference, calculating the time interval at which the battery balance of the current satellite positioning terminal decreases by a key percentile difference based on the interpolated battery balance array; determining the outlier value of the corresponding key percentile difference based on the time interval threshold corresponding to the key percentile difference and the time interval at which the battery balance of the current satellite positioning terminal decreases by a key percentile difference; and determining whether the current satellite positioning terminal has abnormal battery degradation based on the outlier value of each key percentile difference corresponding to the current satellite positioning terminal.

[0014] Furthermore, based on multiple key percentile differences and the interpolated battery balance array, the current battery degradation state of the satellite positioning terminal is determined, including: for each key percentile difference, calculating the time interval at which the battery balance of the current satellite positioning terminal decreases by each key percentile difference according to the interpolated battery balance array, generating a time interval sequence corresponding to the key percentile difference for the current satellite positioning terminal; generating a time interval matrix corresponding to the current satellite positioning terminal according to the time interval sequence corresponding to each key percentile difference; and determining the current battery degradation state of the satellite positioning terminal based on the time interval matrix corresponding to the current satellite positioning terminal.

[0015] Furthermore, based on the time interval matrix corresponding to the current satellite positioning terminal, the battery degradation state of the current satellite positioning terminal is determined, including: based on the time interval matrix corresponding to the current satellite positioning terminal and the time interval matrix corresponding to each sample satellite positioning terminal, similar sample satellite positioning terminals are identified; based on the battery degradation state of the similar sample satellite positioning terminals, the battery degradation state of the current satellite positioning terminal is determined.

[0016] Compared with existing technologies, the method for detecting battery degradation based on battery life provided by this invention has at least the following beneficial effects: 1. By acquiring data reported by multiple satellite positioning terminals with varying battery degradation states, several key percentile differences were determined. First, multiple percentile differences were identified. Then, the time interval for each percentile decrease in battery capacity for each sample terminal was calculated, leading to the calculation of key coefficients and ultimately determining the key percentile differences. Simultaneously, a genetic algorithm was used to determine optimal interpolation parameters from the sample data, such as the optimal interpolation slope for each battery capacity interval. The determination of these key parameters, based on a large amount of sample data, fully considers different battery degradation states and actual usage conditions, accurately reflecting battery degradation characteristics and avoiding biases from single data points or subjective judgments. This provides a scientific basis for subsequent accurate battery degradation detection, improving the reliability and accuracy of the detection results.

[0017] 2. After acquiring the data reported by the current satellite positioning terminal and interpolating to generate a battery remaining quantity array, for each key percentile difference, the time interval for each decrease in battery remaining quantity by that percentile difference is calculated. Outliers are identified by combining this with the corresponding time interval threshold, and then the abnormal values ​​of each key percentile difference are used to determine whether battery degradation is abnormal. This method analyzes battery data from multiple key perspectives, capturing subtle changes in the battery degradation process and promptly identifying potential anomalies. Even if battery degradation is not obvious at certain stages, accurate judgment can be made through comprehensive analysis of multiple key percentile differences, effectively avoiding missed and false detections, and providing strong support for timely measures to maintain the battery.

[0018] 3. If the current terminal's battery degradation is determined to be abnormal, this method generates a time interval sequence for each key percentile difference, constructs a time interval matrix, and then identifies similar samples by comparing it with the sample terminal matrix. Finally, the current terminal status is determined based on the battery degradation status of the similar samples. Since similar samples share similarities in usage environment and operating mode, their battery degradation patterns are also similar. This method can fully utilize sample information and comprehensively consider the impact of multiple factors on battery degradation, making the evaluation results more consistent with actual conditions. This provides a reliable basis for decisions regarding battery maintenance and replacement of satellite positioning terminals, ensuring the stable operation of the terminals. Attached Figure Description

[0019] This specification will be further described by way of exemplary embodiments, which will be described in detail with reference to the accompanying drawings. These embodiments are not limiting; in these embodiments, the same reference numerals denote the same structures, wherein: Figure 1 This is a schematic diagram of the battery remaining curve of a satellite positioning terminal according to some embodiments of this specification; Figure 2 This is a flowchart illustrating a method for detecting battery degradation based on battery life, according to some embodiments of this specification. Figure 3 This is a flowchart illustrating the determination of multiple key percentile differences according to some embodiments of this specification; Figure 4 This is a flowchart illustrating the calculation of the key coefficients of percentile difference according to some embodiments of this specification. Detailed Implementation

[0020] To more clearly illustrate the technical solutions of the embodiments in this specification, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are merely some examples or embodiments of this specification. For those skilled in the art, these drawings can be applied to other similar scenarios without creative effort. Unless obvious from the context or otherwise specified, the same reference numerals in the drawings represent the same structures or operations.

[0021] Satellite positioning terminals can be applied to many fields such as the Internet of Things and intelligent tracking.

[0022] As an example only, the satellite positioning terminal does not report any data while charging; both the satellite locator and communication module are in a silent state. After being removed from the charging dock, the satellite positioning terminal automatically enters working mode, searching for satellite signals for positioning. Once a satellite signal is found, it calculates its own position coordinates based on the signal, with a positioning accuracy generally within 5 meters. If the terminal is indoors or underground and cannot find a satellite signal, it searches for signals from nearby cellular mobile communication base stations and uses these signals to calculate its own position coordinates, with a positioning accuracy generally within 50 meters. If the terminal remains completely stationary for more than 5 minutes, it will enter sleep mode. In sleep mode, the terminal will not perform positioning calculations or report positioning messages to the platform via the mobile communication network until motion or vibration is detected, waking the terminal up. In sleep mode, the terminal maintains the low power consumption required for motion detection. Under normal operating conditions, the terminal performs a positioning calculation and records the result every 15 seconds, and sends a message every minute containing 4 positioning records and 1 battery level record. When the network is offline, the terminal continues to calculate its own location coordinates and record the data in a cache. Once the network is restored, the cached data is reported to the platform. Because the cache size is limited, outdated data is discarded. Therefore, prolonged network outages can lead to the loss of some location data.

[0023] Figure 1 This is a schematic diagram of the battery remaining curve of a satellite positioning terminal according to some embodiments shown in this specification, such as... Figure 1 As shown, after the satellite positioning terminal leaves its charging dock and enters working mode, the battery begins to consume, and the remaining capacity gradually decreases. Under normal operating conditions, the satellite positioning terminal frequently performs positioning calculations and transmits information, causing the battery capacity to decrease steadily. The "sleep period" marked in the figure represents a low-power state entered when the satellite positioning terminal is stationary for more than 5 minutes. During this time, positioning calculations and information reporting cease, battery consumption decreases significantly, and the change in remaining capacity slows down, corresponding to the flattened portion of the curve in the figure. As the satellite positioning terminal is reawakened or resumes operation after the sleep period ends, the battery continues to consume, and the remaining capacity decreases more rapidly.

[0024] Figure 2 This is a flowchart illustrating a method for detecting battery degradation based on battery life, according to some embodiments of this specification. Figure 2 As shown, a method for detecting battery degradation based on battery life may include the following steps.

[0025] Step 110: Obtain the reported data from multiple sample satellite positioning terminals.

[0026] Among them, the battery degradation status of multiple sample satellite positioning terminals differs. The reported data includes at least the remaining battery level and timestamp, and may also include data such as location and terminal status.

[0027] For example, the data reported by the satellite positioning terminal can be: [( , ), ( , ), …, ( , )] in, It represents the remaining battery capacity, expressed as a percentage, for example... , . It is the timestamp of the report.

[0028] Step 120: Based on the reported data from multiple sample satellite positioning terminals, determine several key percentile differences.

[0029] Figure 3 This is a flowchart illustrating the determination of multiple key percentile differences according to some embodiments of this specification, such as... Figure 3 As shown, it specifically includes: Determine multiple percentile differences, such as 99%, 98%, 97%, 96%, etc. For each percentile difference and each sample satellite positioning terminal, calculate the time interval at which the remaining battery capacity of the sample satellite positioning terminal decreases by each percentile difference, based on the data reported by the sample satellite positioning terminal. For each percentile difference, the key coefficient of the percentile difference is calculated based on the time interval at which the battery remaining capacity of each sample satellite positioning terminal decreases by a percentile difference. Based on the key coefficients for each percentile, multiple key percentiles are determined.

[0030] Specifically, the data reported by the sample satellite positioning terminals records the correlation information between battery remaining capacity and corresponding timestamps in time series format. For a given percentile difference and each sample terminal, it is necessary to accurately locate adjacent battery remaining capacity data points that cross that percentile difference and their corresponding timestamps from the reported data. Specifically, it is necessary to find the two time points corresponding to when the battery remaining capacity decreases from a certain percentile value to another percentile value separated by a specific percentile difference. By calculating the time difference between these two time points and taking its negative (to ensure that the time interval is consistently positive, representing the time taken for the remaining capacity to decrease), the time interval for each percentile difference decrease in battery remaining capacity can be obtained.

[0031] For example, calculating the time interval for each ten-percentage-point decrease in battery capacity, i.e. in, This is the timestamp when the battery level is 100%. The timestamp when the battery level is 90% is displayed. This is the timestamp when the battery is at 80% capacity, and so on.

[0032] For example, calculating the time interval at which the remaining battery capacity decreases by twenty percent each time, i.e. For example, calculating the time interval at which the remaining battery capacity decreases by 30 percentage points each time, i.e. For example, calculating the time interval at which the remaining battery capacity decreases by forty percentage points each time, i.e. .

[0033] For example, calculating the time interval at which the remaining battery capacity decreases by 50 percentage points each time, i.e. .

[0034] For example, calculating the time interval at which the remaining battery capacity decreases by 60 percentage points each time, i.e. For example, calculating the time interval at which the remaining battery capacity decreases by seventy percent each time, i.e. .

[0035] For example, calculating the time interval at which the remaining battery capacity decreases by eighty percent each time, i.e. .

[0036] For example, calculating the time interval at which the remaining battery capacity decreases by 90 percentage points each time, i.e. .

[0037] Figure 4 This is a flowchart illustrating the calculation of the key coefficients of percentile difference according to some embodiments of this specification, such as... Figure 4 As shown, in some embodiments, the key coefficient of percentile difference is calculated based on the time interval of each percentile decrease in the remaining battery capacity of each sample satellite positioning terminal, including: For each sample satellite positioning terminal, the time interval difference corresponding to the percentile difference is calculated based on the time interval of each percentile difference in the remaining battery capacity of the sample satellite positioning terminal. The mean value of the time interval difference of the percentile difference is calculated based on the time interval difference value corresponding to each sample satellite positioning terminal. The global time interval difference value of the percentile difference is calculated based on the time interval of each sample satellite positioning terminal's battery remaining amount decreasing by a percentile difference. The critical coefficient of percentile difference is calculated based on the mean difference between percentile time intervals and the global difference between percentile time intervals.

[0038] In some embodiments, the time interval difference value corresponding to each percentile difference in the remaining battery capacity of the sample satellite positioning terminal is calculated based on the time interval of each percentile difference in the remaining battery capacity of the sample satellite positioning terminal, including: The standard deviation of the time interval for each percentile decrease in the remaining battery capacity of the sample satellite positioning terminal is calculated, and this standard deviation is used as the time interval difference value corresponding to the percentile difference of the sample satellite positioning terminal.

[0039] Specifically, for a given sample satellite positioning terminal, a series of time intervals are obtained during its use, representing different percentile decreases in battery capacity. Due to factors such as the unevenness of internal chemical reactions and instantaneous changes in external operating conditions during actual battery degradation, the time intervals between adjacent percentile decrease stages are not entirely stable. By calculating the standard deviation of multiple time intervals for each percentile decrease (e.g., ten percentage points) in the sample satellite positioning terminal's battery capacity, the dispersion of time intervals around the mean during that percentage decrease can be accurately characterized. This standard deviation, as the time interval difference value, reflects the battery degradation stability of the sample satellite positioning terminal during the corresponding percentile decrease stage. A small difference value indicates a relatively stable degradation rate during that stage, while a large difference value means a larger fluctuation in the degradation rate.

[0040] The mean of the time interval differences for each sample satellite positioning terminal corresponding to the percentile difference can be calculated and used as the mean of the time interval differences of the percentile difference.

[0041] The mean percentile difference time interval is the average of the time interval differences among multiple sample satellite positioning terminals at the same percentile. It comprehensively considers the battery degradation stability of different sample terminals at a specific percentile from a macroscopic and holistic perspective. This mean eliminates the bias caused by accidental factors in individual samples, presenting a more objective and comprehensive average level of battery degradation stability at that percentile.

[0042] When batteries at different degradation states decrease according to a certain percentile difference, if the degradation process is unstable at that percentile, it means that the time interval calculated for each sample terminal has a large difference. Averaging these large differences will result in a larger mean of the time interval difference for the percentile difference. This indicates that at this percentile difference stage, the battery degradation rate fluctuates drastically, and the degradation characteristics have changed significantly.

[0043] Conversely, if the mean is small, it indicates that the battery degradation is relatively stable and the degradation rate fluctuates less across different sample terminals during this percentile decrease phase. This means that the battery degradation characteristics are relatively uniform during this percentile phase, and its ability to reveal the overall battery degradation pattern is relatively limited.

[0044] In some embodiments, the global time interval difference value of the percentile difference is calculated based on the time interval of each percentile decrease in the remaining battery capacity of each sample satellite positioning terminal, including: For each sample satellite positioning terminal, the mean of the time interval corresponding to the percentile difference is calculated based on the time interval of each percentile difference in the remaining battery capacity of the sample satellite positioning terminal. Calculate the standard deviation of the mean time interval for each sample satellite positioning terminal corresponding to the percentile difference, and use it as the global time interval difference value of the percentile difference.

[0045] Specifically, for each sample satellite positioning terminal, the mean time interval corresponding to each percentile decrease in battery capacity is calculated based on the time interval data for each specific percentile decrease in battery capacity. Since the time interval of each decrease may fluctuate due to various factors during the process of battery capacity decreasing by the same percentile for a single terminal, calculating the mean can obtain the average time characteristic of battery degradation for that terminal at the corresponding percentile, eliminating the influence of individual abnormal time intervals and more accurately reflecting its overall degradation rate.

[0046] Because the battery degradation states of multiple sample satellite positioning terminals differ, the battery performance and degradation patterns of sample satellite positioning terminals with different degradation states vary. After calculating the mean of the time interval for each terminal, the standard deviation of the mean of the time interval for each sample satellite positioning terminal corresponding to the percentile difference is calculated.

[0047] For sample satellite positioning terminals in different degradation states, the mean time interval for the remaining battery capacity to decrease at the same percentile will vary due to factors such as the internal chemical characteristics and aging degree of the battery. Calculating the standard deviation of these mean time intervals for different terminals can quantify the dispersion of their battery degradation rate at the corresponding percentile. If the differences in battery degradation states are small, the degradation characteristics of each sample satellite positioning terminal are similar, and the standard deviation of the mean time interval may be small, indicating that the overall degradation is relatively concentrated. If the differences in battery degradation states are large, the degradation rates of different sample satellite positioning terminals vary significantly, and the standard deviation of the mean time interval will be large, reflecting a high degree of overall dispersion.

[0048] The mean difference of percentile time intervals and the global difference of time intervals can be normalized. The weighted sum of the normalized mean difference of percentile time intervals and the global difference of time intervals can be obtained to obtain the critical coefficient of percentile difference. The larger the mean difference of percentile time intervals and the global difference of time intervals after normalization, the larger the critical coefficient of percentile difference.

[0049] The calculation of the key coefficient for each percentile difference is based on a comprehensive consideration of the mean of the time interval difference and the global time interval difference. The mean of the time interval difference reflects the dispersion of the time interval of battery capacity reduction in different sample terminals around the mean at a certain percentile difference, reflecting the local stability of battery degradation at that stage.

[0050] The global time interval difference value is obtained by calculating the standard deviation of the mean of the time intervals of different sample satellite positioning terminals with respect to the same percentile difference. Since the battery degradation status of different sample terminals is different, this value quantifies the overall dispersion of the battery degradation rate of different terminals at the corresponding percentile difference.

[0051] The critical coefficient is calculated by comparing the mean difference in time intervals of percentile differences with the global difference in time intervals. Essentially, this measures the "abnormality" or "criticality" of that percentile difference relative to the overall degradation process. If the mean difference in time intervals of a certain percentile difference is significantly greater than the global difference in time intervals, it indicates that the battery degradation rate fluctuates drastically at that stage, differing significantly from the overall degradation trend. Its critical coefficient will be high, indicating that this percentile difference is a critical node in the battery degradation process. Conversely, if the two are close, it indicates that the percentile difference contributes little to the overall degradation characteristics, and its critical coefficient is low.

[0052] Battery degradation is a complex process, and different percentiles reflect the degradation status of the battery across different remaining charge ranges. Key percentiles can capture characteristic changes during battery degradation, such as percentiles where the degradation rate changes significantly. By focusing on these key percentiles, we can more accurately analyze the degradation patterns at different stages, avoiding information redundancy and a lack of focus that can result from uniform analysis of all percentiles. Furthermore, combining information from multiple key percentiles allows for a comprehensive assessment of the overall battery degradation status, identifying any abnormal degradation, and providing strong support for timely maintenance measures, ensuring the normal operation of satellite positioning terminals, and effectively preventing service interruptions due to battery life issues.

[0053] Step 130: Obtain the current reported data from the satellite positioning terminal.

[0054] Step 140: Determine the optimal interpolation parameters based on the data reported by multiple sample satellite positioning terminals.

[0055] Specifically, the reported data should include at least the remaining battery level and timestamp.

[0056] Specifically, it includes: Using a genetic algorithm, the optimal interpolation parameters are determined based on data reported by multiple sample satellite positioning terminals. The optimal interpolation parameters include the optimal interpolation slope for each battery remaining range.

[0057] Specifically, in real-world scenarios, satellite positioning terminals continuously collect and report data. Battery level data visually represents the remaining battery power, while timestamps precisely mark the exact moment the data was collected. Together, these form the foundational dataset for analyzing dynamic battery changes. However, this raw reported data is often discrete and irregularly distributed.

[0058] To address this issue, interpolation is needed to normalize the data points into integer percentile values, thereby constructing a unified and ordered data sequence to facilitate further analysis of battery degradation characteristics. However, within different battery remaining capacity ranges, factors such as the battery's chemical properties and operating conditions can lead to varying degradation rates and trends. Using uniform interpolation parameters cannot accurately reflect the actual changes in each range, resulting in significant deviations in the interpolation results and affecting the accurate assessment of battery status.

[0059] Genetic algorithms offer an effective solution to this problem. They are optimization search algorithms that simulate the mechanisms of natural selection and genetics in biological evolution, possessing powerful global search capabilities and adaptive adjustment characteristics. In determining the optimal interpolation parameters, the genetic algorithm first randomly generates a set of initial interpolation parameters. These parameters cover key information such as the interpolation slope corresponding to each battery capacity range. Each parameter combination can be considered an "individual," representing a possible interpolation scheme.

[0060] Next, the algorithm evaluates these "individuals" based on a specific fitness function. The fitness function is designed around the goal of normalizing data points to integer percentile correspondences and accurately reflecting changes in battery capacity. For example, it may consider factors such as the fit between the interpolated data and the original data, the accuracy of characterizing battery degradation trends, and the rationality of data normalization. By calculating the fitness value of each "individual," its quality can be determined.

[0061] Subsequently, a genetic algorithm simulates the natural selection process, retaining individuals with higher fitness values—those with interpolation parameter combinations that make the interpolation results more accurate and better reflect the actual changes in battery capacity—while eliminating individuals with lower fitness values. Simultaneously, through crossover, some parameters of the retained individuals are exchanged and combined to generate new individuals; through mutation, certain parameters of the individuals are randomly changed to increase population diversity. Through multiple generations of iterative evolution, the interpolation parameter combinations are continuously optimized, ultimately determining the optimal interpolation slope and other optimal interpolation parameters for each battery capacity interval. Using these optimal interpolation parameters for interpolation processing, the reported data points from multiple sample satellite positioning terminals can be accurately normalized to points corresponding to integer percentiles, providing a high-quality, normalized data foundation for subsequent in-depth analysis of battery degradation status.

[0062] Step 150: Based on the optimal interpolation parameters, interpolate the data reported by the current satellite positioning terminal to generate an interpolated battery balance array.

[0063] Specifically, the interpolation process makes reasonable estimates between the original data points based on these parameters. For two adjacent original data points, the remaining battery capacity at other locations between these two points is calculated based on their respective battery capacity ranges and the corresponding optimal interpolation slope. In this way, a large number of intermediate values ​​are added to the original data points, making the data denser and more continuous.

[0064] After interpolation, the data points are further normalized to correspond to integer percentiles. Since battery capacity typically varies between 0% and 100%, the entire range is divided into 100 equal parts, with each 1% corresponding to a value. Based on the continuous battery capacity array obtained after interpolation, the battery capacity value corresponding to each integer percentile is found. If an integer percentile happens to fall on a data point after interpolation, the value of that data point is directly taken; otherwise, precise calculation is performed based on the values ​​of adjacent data points and the interpolation rule to ensure that each integer percentile has a unique battery capacity value corresponding to it.

[0065] Step 160: Based on multiple key percentile differences and the interpolated battery balance array, determine whether the current satellite positioning terminal has abnormal battery degradation. If so, determine the current battery degradation status of the satellite positioning terminal based on multiple key percentile differences and the interpolated battery balance array.

[0066] In some embodiments, determining whether the current satellite positioning terminal has abnormal battery degradation is based on multiple key percentile differences and an interpolated battery balance array, including: For each critical percentile difference, based on the interpolated battery balance array, calculate the time interval at which the battery balance of the current satellite positioning terminal decreases by a critical percentile difference. Based on the time interval threshold corresponding to the critical percentile difference and the time interval at which the battery balance of the current satellite positioning terminal decreases by a critical percentile difference, determine the outlier value of the critical percentile difference corresponding to the current satellite positioning terminal. Based on the abnormal values ​​of each key percentile difference corresponding to the current satellite positioning terminal, determine whether the current satellite positioning terminal has abnormal battery degradation.

[0067] Specifically, for each key percentile difference, the system first records the time points at which the battery balance drops to the corresponding value of that key percentile difference each time, based on the interpolated battery balance array. Then, it calculates the time interval for each decrease in battery balance to that key percentile difference. These time intervals reflect the rate of change in battery performance during that specific degradation phase. Simultaneously, each key percentile difference has a pre-set time interval threshold. This threshold is derived from statistical analysis of the time intervals at that percentile difference using a large number of normal battery samples in operation, representing the time range boundary under normal degradation conditions.

[0068] The actual time interval for each critical percentile decrease in the remaining battery power of the current satellite positioning terminal is compared with the corresponding time interval threshold. When the time interval is less than the threshold for the critical percentile, it means that the battery degradation rate is faster than normal during that critical percentile stage. This situation may be caused by potential problems such as accelerated consumption of internal battery chemicals or abnormal changes in electrode structure, so such time intervals are identified as abnormal time intervals.

[0069] To quantify the severity of this anomaly, the ratio of the number of abnormal time intervals to the total number of time intervals corresponding to that critical percentile is calculated. This ratio represents the anomaly value for the current satellite positioning terminal at that critical percentile. The magnitude of the anomaly value directly reflects the frequency and severity of battery degradation anomalies at that critical percentile. A larger anomaly value indicates more frequent battery degradation anomalies during that phase, and a higher probability of battery failure or severe performance degradation.

[0070] If there is an outlier in at least one critical percentile difference for the current satellite positioning terminal that is greater than the outlier threshold (e.g., 60%), then the current satellite positioning terminal is determined to have an abnormal battery degradation.

[0071] In some embodiments, the current battery degradation state of the satellite positioning terminal is determined based on multiple key percentile differences and an interpolated battery balance array, including: For each critical percentile difference, based on the interpolated battery balance array, calculate the time interval at which the current satellite positioning terminal's battery balance decreases by a critical percentile difference, and generate the time interval sequence for the current satellite positioning terminal corresponding to the critical percentile difference. Based on the time interval sequence of each key percentile difference corresponding to the current satellite positioning terminal, generate the time interval matrix corresponding to the current satellite positioning terminal. Based on the time interval matrix corresponding to the current satellite positioning terminal, the battery degradation status of the current satellite positioning terminal is determined.

[0072] Specifically, due to the existence of multiple key percentile differences, each percentile difference generates a corresponding time interval sequence. These time interval sequences with different key percentile differences are integrated, with the key percentile differences as rows and the order of the time intervals as columns, to construct a matrix, which is the time interval matrix corresponding to the current satellite positioning terminal.

[0073] In some embodiments, determining the battery degradation state of the current satellite positioning terminal based on the time interval matrix corresponding to the current satellite positioning terminal includes: Based on the time interval matrix corresponding to the current satellite positioning terminal and the time interval matrix corresponding to each sample satellite positioning terminal, similar sample satellite positioning terminals are identified. Based on the battery degradation status of similar sample satellite positioning terminals, the current battery degradation status of the satellite positioning terminal is determined.

[0074] Specifically, similar sample satellite positioning terminals are determined based on the time interval matrix corresponding to the current satellite positioning terminal and the time interval matrix corresponding to each sample satellite positioning terminal. This can be achieved using a similarity algorithm, such as the cosine similarity algorithm. The similarity is measured by calculating the cosine of the angle between the time interval matrix of the current satellite positioning terminal and the time interval matrix of each sample satellite positioning terminal. The closer the cosine value is to 1, the higher the similarity between the two time interval matrices. During the calculation, the time interval matrix of the current terminal is compared one by one with the time interval matrix of each sample terminal to obtain their similarity values. Then, based on a pre-set similarity threshold (e.g., 80%), sample satellite positioning terminals with similarity higher than this threshold are selected; these terminals are considered similar to the current terminal.

[0075] Similar sample satellite positioning terminals may share similarities with current satellite positioning terminals in terms of battery usage environment and operating mode, and their battery degradation process will also exhibit similar patterns. By referring to the battery degradation of these similar sample satellite positioning terminals, the battery degradation status of the current terminal can be inferred more accurately.

[0076] The battery degradation state of the current satellite positioning terminal can be determined in any way based on the battery degradation state of similar sample satellite positioning terminals. For example, the battery degradation state of the most similar sample satellite positioning terminal can be used as the battery degradation state of the current satellite positioning terminal.

[0077] For example, the cosine similarity between the time interval matrix corresponding to the current satellite positioning terminal and the time interval matrix corresponding to each similar sample satellite positioning terminal can be used as a weight to perform a weighted summation of the battery degradation status of similar sample satellite positioning terminals, thus obtaining the battery degradation status of the current satellite positioning terminal.

[0078] Based on multiple key percentile differences and an interpolated battery remaining quantity array, the time interval for each key percentile decrease in battery remaining quantity is calculated and a sequence is generated, thus constructing a time interval matrix. This process fully utilizes the battery remaining quantity data, transforming discrete information into an ordered matrix, comprehensively and meticulously reflecting the time interval characteristics of the battery at different critical stages of degradation, providing a rich and accurate data foundation for subsequent analysis. The current terminal's time interval matrix is ​​compared with the sample terminal matrix to identify similar samples, and the current terminal state is inferred from the battery degradation state of these similar samples. Since similar samples share similarities in usage environment, operating mode, and other aspects, their battery degradation patterns are also similar. This method avoids the limitations of judging based on single data points, comprehensively considering the influence of multiple factors on battery degradation, making the judgment results more consistent with reality.

[0079] Finally, it should be understood that the embodiments described in this specification are merely illustrative of the principles of the embodiments described herein. Other variations may also fall within the scope of this specification. Therefore, alternative configurations of the embodiments described herein are intended to be illustrative rather than limiting, and should be considered consistent with the teachings of this specification. Accordingly, the embodiments described herein are not limited to those explicitly introduced and described herein.

Claims

1. A method for detecting battery degradation based on battery life, characterized in that, include: Acquire reported data from multiple sample satellite positioning terminals, wherein the battery degradation status of the multiple sample satellite positioning terminals differs, and the reported data includes at least the remaining battery level and timestamp; Based on the reported data from multiple sample satellite positioning terminals, several key percentile differences were determined; Obtain the currently reported data from satellite positioning terminals; The optimal interpolation parameters are determined based on the data reported by multiple sample satellite positioning terminals. Based on the optimal interpolation parameters, the reported data from the current satellite positioning terminal is interpolated to generate an interpolated battery balance array. Based on multiple key percentile differences and the interpolated battery balance array, determine whether the current satellite positioning terminal has abnormal battery degradation. If so, determine the current battery degradation status of the satellite positioning terminal based on multiple key percentile differences and the interpolated battery balance array.

2. The method for detecting battery degradation based on battery life according to claim 1, characterized in that, Based on data reported by multiple sample satellite positioning terminals, several key percentile differences were determined, including: Determine multiple percentile differences; For each percentile difference and each sample satellite positioning terminal, calculate the time interval at which the remaining battery capacity of the sample satellite positioning terminal decreases by each percentile difference, based on the data reported by the sample satellite positioning terminal. For each percentile difference, the key coefficient of the percentile difference is calculated based on the time interval at which the battery remaining capacity of each sample satellite positioning terminal decreases by a percentile difference. Based on the key coefficients for each percentile, multiple key percentiles are determined.

3. The method for detecting battery degradation based on battery life according to claim 2, characterized in that, Based on the time interval for each sample satellite positioning terminal's battery remaining capacity to decrease by a percentile, the key coefficients of the percentile difference are calculated, including: For each sample satellite positioning terminal, the time interval difference corresponding to the percentile difference is calculated based on the time interval of each percentile difference in the remaining battery capacity of the sample satellite positioning terminal. The mean value of the time interval difference of the percentile difference is calculated based on the time interval difference value corresponding to each sample satellite positioning terminal. The global time interval difference value of the percentile difference is calculated based on the time interval of each sample satellite positioning terminal's battery remaining amount decreasing by a percentile difference. The critical coefficient of percentile difference is calculated based on the mean difference between percentile time intervals and the global difference between percentile time intervals.

4. The method for detecting battery degradation based on battery life according to claim 3, characterized in that, Based on the time interval of each percentile decrease in the remaining battery capacity of the sample satellite positioning terminals, calculate the time interval difference corresponding to each percentile difference, including: The standard deviation of the time interval for each percentile decrease in the remaining battery capacity of the sample satellite positioning terminal is calculated, and this standard deviation is used as the time interval difference value corresponding to the percentile difference of the sample satellite positioning terminal.

5. The method for detecting battery degradation based on battery life according to claim 3, characterized in that, Based on the time interval for each percentile decrease in battery capacity of each sample satellite positioning terminal, calculate the global time interval difference value of the percentile difference, including: For each sample satellite positioning terminal, the mean of the time interval corresponding to the percentile difference is calculated based on the time interval of each percentile difference in the remaining battery capacity of the sample satellite positioning terminal. Calculate the standard deviation of the mean time interval for each sample satellite positioning terminal corresponding to the percentile difference, and use it as the global time interval difference value of the percentile difference.

6. A method for detecting battery degradation based on battery life according to any one of claims 1-5, characterized in that, Based on data reported by multiple sample satellite positioning terminals, the optimal interpolation parameters are determined, including: The optimal interpolation parameters are determined using a genetic algorithm based on data reported by multiple sample satellite positioning terminals.

7. The method for detecting battery degradation based on battery life according to claim 6, characterized in that, The optimal interpolation parameters include the optimal interpolation slope for each battery capacity interval.

8. A method for detecting battery degradation based on battery life according to any one of claims 1-5, characterized in that, Based on multiple key percentile differences and the interpolated battery remaining quantity array, it is determined whether the current satellite positioning terminal has abnormal battery degradation, including: For each critical percentile difference, based on the interpolated battery balance array, calculate the time interval at which the battery balance of the current satellite positioning terminal decreases by a critical percentile difference. Based on the time interval threshold corresponding to the critical percentile difference and the time interval at which the battery balance of the current satellite positioning terminal decreases by a critical percentile difference, determine the outlier value of the critical percentile difference corresponding to the current satellite positioning terminal. Based on the abnormal values ​​of each key percentile difference corresponding to the current satellite positioning terminal, determine whether the current satellite positioning terminal has abnormal battery degradation.

9. A method for detecting battery degradation based on battery life according to any one of claims 1-5, characterized in that, Based on multiple key percentile differences and interpolated battery remaining quantity arrays, the current battery degradation status of the satellite positioning terminal is determined, including: For each critical percentile difference, based on the interpolated battery balance array, calculate the time interval at which the current satellite positioning terminal's battery balance decreases by a critical percentile difference, and generate the time interval sequence for the current satellite positioning terminal corresponding to the critical percentile difference. Based on the time interval sequence of each key percentile difference corresponding to the current satellite positioning terminal, generate the time interval matrix corresponding to the current satellite positioning terminal. Based on the time interval matrix corresponding to the current satellite positioning terminal, the battery degradation status of the current satellite positioning terminal is determined.

10. A method for detecting battery degradation based on battery life according to claim 9, characterized in that, Based on the time interval matrix corresponding to the current satellite positioning terminal, determine the current battery degradation status of the satellite positioning terminal, including: Based on the time interval matrix corresponding to the current satellite positioning terminal and the time interval matrix corresponding to each sample satellite positioning terminal, similar sample satellite positioning terminals are identified. Based on the battery degradation status of similar sample satellite positioning terminals, the current battery degradation status of the satellite positioning terminal is determined.

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