Method for predicting service life of solid state disk

Abstract

The invention relates to the technical field of hard disk life prediction, and discloses a solid state disk life prediction method, which comprises the following steps: collecting multi-source data of a solid state disk, the multi-source data comprising health state data, I / O load data and environmental data of the solid state disk; preprocessing the multi-source data; calculating a health score of the solid state disk based on the preprocessed multi-source data, and establishing a prediction model by using the health score and the time sequence data; based on comparison between the prediction result and a set threshold value, an early warning signal is triggered according to a comparison result; and optimizing the prediction model in real time based on the comparison between the prediction result and the actual service life. By collecting the multi-source data, including the health state, the I / O load and the environment data, of the solid state disk, the problem that tiny differences of the hard disk under different operation conditions are ignored in an existing method is solved. Compared with a traditional method, the method has the advantage that the prediction accuracy is greatly improved.

Description

Technical Field

[0001] The present invention relates to the technical field of hard disk life prediction, and in particular to a method for predicting the life of a solid state hard disk. Background Art

[0002] At present, the common solid-state drive (SSD) life prediction method mainly monitors the working status parameters of the hard drive to indirectly infer the health of the hard drive. For example, the hard drive's error log, temperature changes, power status, etc. can be monitored, and these data are used as the basis for evaluating the health of the hard drive. In addition, another common practice is to establish a mathematical model to analyze the trend of hard drive performance degradation and then predict possible problems in the future. However, these methods still have some limitations.

[0003] First, traditional methods often ignore the subtle differences in SSDs under different working conditions. This neglect makes it difficult to achieve a high level of prediction accuracy, and it is difficult to accurately assess the health of hard disks under specific conditions. Second, when dealing with large amounts of complex data, traditional mathematical models show obvious limitations in existing prediction methods. Due to the large amount of data and complex changes, the simplified processing of traditional methods seriously affects the accuracy and adaptability of predictions. Summary of the invention

[0004] In view of the deficiencies in the prior art, the present invention provides a method for predicting the life of a solid-state drive, which solves the problems in the prior art of ignoring subtle differences under different working conditions, failing to fully utilize complex data, and insufficient processing capabilities of traditional models.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: A method for predicting the life of a solid state drive comprises the following steps: Collecting multi-source data of the solid-state drive, wherein the multi-source data includes health status data, I / O load data, and environmental data of the solid-state drive; Preprocessing the multi-source data, wherein the preprocessing includes removing abnormal data; Calculate the health score of the solid state drive based on the preprocessed multi-source data; Use the health score and time series data to build a prediction model, and output the prediction results of the health status of the solid-state drive through the prediction model; Based on the comparison between the prediction results and the set threshold, an early warning signal is triggered according to the comparison results; Based on the comparison between the prediction results and the actual service life, the prediction model is optimized in real time, and the output results are improved by updating the prediction model parameters.

[0006] Preferably, the health status data includes: write amplification, number of bad blocks, number of erases and writes, read error rate and write error rate; I / O load data includes: read and write times, data transfer rate, average response time, and random read and write ratio; Environmental data includes: operating temperature, operating humidity, vibration, and power supply fluctuations.

[0007] Preferably, the preprocessing also includes data denoising, outlier detection, data standardization, data padding and time alignment.

[0008] Preferably, the health score is calculated by a fuzzy logic algorithm, and the fuzzy logic performs weighted fusion on the health indicators.

[0009] Preferably, the time series data includes: Health status data changes over time, including the change of SSD health score over time, write amplification, number of bad blocks and number of erase and write cycles; Changes in I / O load data over time: including changes in the number of read and write times of the SSD, data transfer rate, and I / O request response time over time; Changes in environmental data over time: including changes in temperature, humidity, and vibration of the SSD working environment over time; Fault history data: including the fault types and fault times that occurred on the SSD during the historical period; Performance degradation data: includes the performance degradation data of the solid-state drive, including the decrease in write speed and the increase in read and write response time.

[0010] Preferably, the prediction model includes a Bayesian reasoning model, and the Bayesian reasoning model generates prediction results through a Markov Chain Monte Carlo algorithm.

[0011] Preferably, the set thresholds include: a health score threshold, a write amplification threshold, a bad block quantity threshold and an environmental factor threshold.

[0012] Preferably, the set threshold includes three levels of thresholds: Level 1 threshold: When the health score is lower than the level 1 threshold, a level 1 warning is output; Secondary threshold: When the health score is higher than the primary threshold and lower than the secondary threshold, a secondary warning is output; Level 3 threshold: When the health score is higher than the level 2 threshold and lower than the level 3 threshold, a level 3 warning is output; Level 4 threshold: When the health score is higher than the level 3 threshold, a level 4 warning is output.

[0013] The present invention provides a method for predicting the life of a solid state drive. The method has the following beneficial effects: 1. The present invention collects multi-source data of SSDs, including health status, I / O load and environmental data, to solve the problem that existing methods ignore the subtle differences of hard disks under different operating conditions. Through comprehensive data collection and preprocessing, the present invention can more accurately assess the health status of hard disks, and the accuracy of prediction is greatly improved compared with traditional methods.

[0014] 2. The present invention can effectively avoid the prediction bias caused by model simplification in traditional methods by using the prediction model constructed by health score and time series data. Compared with the existing hard disk life prediction technology, the model of the present invention can combine the time series trend of hard disk health changes to achieve more accurate future health prediction, especially when facing complex data, it is more stable and accurate.

[0015] 3. The present invention calculates the health score of the solid-state drive by using multi-source data, and optimizes the prediction model in real time based on the comparison between the prediction result and the actual service life. Compared with the traditional method, the present invention can automatically adapt to the changes in different working environments and hard disk loads, significantly improving the intelligence level and accuracy of solid-state drive life prediction. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 It is a schematic diagram of the method flow of the present invention. DETAILED DESCRIPTION

[0017] The following will be combined with the drawings in the specification of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0018] In order to better understand the present invention, the above contents are described in detail below in conjunction with specific embodiments.

[0019] Please see attached Figure 1 The embodiment of the present invention provides a method for predicting the life of a solid state drive, comprising the following steps: Collecting multi-source data of the solid-state drive, wherein the multi-source data includes health status data, I / O load data, and environmental data of the solid-state drive; In this embodiment, by collecting multi-source data of the solid-state drive, basic data support is provided for subsequent health score calculation, prediction model establishment and early warning mechanism. The collection of multi-source data includes health status data, I / O load data and environmental data of the solid-state drive. These data reflect the important characteristics and working conditions of the hard disk during use, and through the full analysis of these data, the remaining life of the hard disk can be effectively evaluated.

[0020] Specifically, health status data is mainly used to reflect the physical health of the SSD, such as write amplification, number of bad blocks, number of erases and writes, and other indicators. I / O load data mainly reflects the amount and speed of data processed by the hard disk during use, helping to evaluate the performance of the hard disk under actual workload. Environmental data takes into account the impact of factors such as temperature, humidity, and vibration on the health of the hard disk, further improving the evaluation of the hard disk status.

[0021] These include: Health status data As one of the core data in the life prediction of solid-state drives, health status data can directly affect the remaining life assessment of the drive. Specifically, health status data includes the following aspects: Write-Amplification (WA) Write amplification is a measure of the ratio between the actual amount of data written to the SSD and the amount of user data written. A larger write amplification value means that more data needs to be written repeatedly, causing the hard drive to wear out faster, thus affecting its service life. In general, write amplification is closely related to the flash memory structure, garbage collection mechanism and write load of the SSD.

[0022] Bad-Blocks-BB The number of bad blocks reflects the number of damaged units in the SSD during use. As the hard disk is used for a longer time, bad blocks will inevitably appear in the SSD. The appearance of bad blocks directly affects the health of the hard disk. The more bad blocks there are, the more serious the hard disk wear is. Generally speaking, the life of the SSD will be shortened as the number of bad blocks increases.

[0023] Erase-Cycle-EC The number of times each storage unit is erased and written is a key health indicator of the SSD. The NAND flash memory unit of the SSD has an erase and write life limit. After the erase and write times are exceeded, the unit will fail, resulting in data storage being unavailable. In one possible implementation, the more erase and write times, the worse the health of the SSD, thus affecting its remaining life. The erase and write times are calculated based on the total erase and write amount of each storage unit during the use of the SSD.

[0024] Read-Error-Rate and Write-Error-Rate Read and write error rates are indicators of the success rate of hard drive operations. A high error rate usually indicates that the hard drive has problems processing data, which may be caused by physical damage to the hard drive itself or excessive use.

[0025] I / O load data I / O load data can reflect the pressure on the SSD during use and its performance. Generally speaking, I / O load data includes the following important indicators: Read / Write-Count The read / write times indicate the number of read / write operations performed by the SSD in a unit of time. A higher read / write times may mean that the SSD is processing data frequently, resulting in increased wear and tear on the hard drive. This data helps analyze the load of the hard drive during long-term use.

[0026] Data-Transfer-Rate The data transfer rate measures the speed at which the SSD completes data reading and writing in a unit of time. A low transfer rate may be a precursor to hard drive failure, affecting the normal use of the hard drive.

[0027] Average-Response-Time The average response time measures how quickly the drive responds to I / O requests. Under heavy loads, the response time may be longer, affecting the overall performance of the drive.

[0028] Random-Read / Write-Ratio The random read / write ratio indicates the frequency of random read / write operations on the SSD. Compared with sequential read / write, random read / write has a greater impact on the wear of the hard disk. Therefore, a higher random read / write ratio may accelerate the life decay of the hard disk.

[0029] Environmental data Environmental data is mainly used to evaluate the impact of external factors on SSDs. Different working environment conditions will have different degrees of impact on the health of the hard disk. Environmental data usually includes the following aspects: Operating-Temperature The operating temperature of a solid-state drive has a significant impact on its lifespan. Too high a temperature will accelerate the aging of the drive and cause performance degradation. Therefore, real-time temperature monitoring is an important factor in ensuring the lifespan of the drive. Normally, when the temperature exceeds the safe operating range of the drive, an early warning may be triggered.

[0030] Operating-Humidity Humidity is another environmental factor that affects the health of a hard drive. High humidity can cause circuits within the hard drive to short or oxidize, affecting its normal operation. Monitoring operating humidity is critical to protecting the hard drive from environmental damage.

[0031] Vibration Solid-state drives are sensitive to vibration, especially during high-speed read and write operations. The electronic components inside the hard drive may be affected by mechanical vibration, increasing the risk of failure. Therefore, vibration monitoring is also an important indicator to ensure the life of the hard drive.

[0032] Power-Fluctuations The stability of the power supply directly affects the normal operation of the hard disk. Voltage fluctuations may cause the hard disk to not work stably, or even cause data loss. Monitoring power fluctuations can help prevent hard disk damage caused by power problems.

[0033] Therefore, since the source data covers health status data, I / O load data and environmental data, these data provide essential information support for subsequent health score calculation, time series prediction and optimization model. By preprocessing these multi-source data, calculating health scores, establishing prediction models and performing real-time optimization, the present invention can accurately predict the remaining life of the SSD and provide early warning signals to help users understand the health status of the hard disk in a timely manner and take corresponding measures.

[0034] Preprocessing the multi-source data, wherein the preprocessing includes removing abnormal data; In this embodiment, by removing abnormal data, standardizing and processing missing values, the accuracy and completeness of the data are ensured, thereby providing reliable basic data for the subsequent calculation of health scores and the establishment of prediction models. Specifically, the goal of the preprocessing process is to eliminate any data interference factors that may affect the calculation of health scores and the prediction results of the model.

[0035] Generally speaking, raw data may be affected by various factors such as measurement errors, sensor failures, and environmental changes, resulting in incomplete or inaccurate data. If these bad data are not processed, they will seriously affect the subsequent analysis results. Therefore, data preprocessing is crucial to improving the accuracy of subsequent models. This includes: Remove abnormal data In this embodiment, removing abnormal data is the first step of data preprocessing. Abnormal data generally refers to data that exceeds the normal range, which may be caused by equipment failure, operating errors or external factors. In some embodiments, the detection of abnormal data can be carried out by the following methods: Based on statistical methods: for example using Scoring or box plot method to detect outliers. The basic idea of ​​the scoring method is that for a given raw data , calculate its average value with the data set and standard deviation The difference is as follows: ; in, is the mean of the data set; is the standard deviation of the data set. Generally, when the absolute value of the Z score is greater than 3, the data point is considered to be abnormal data; is the standardized value.

[0036] Method based on data range: For certain data with clearly defined ranges (such as environmental data such as temperature and humidity), upper and lower thresholds can be set. If the data exceeds the range, it is considered to be abnormal data.

[0037] Model-based prediction method: If the available data set is complex and the distribution of abnormal data is difficult to capture by simple statistical methods, abnormal data can be identified through machine learning methods (such as isolation forest and support vector machine).

[0038] After the abnormal data is removed, the cleanliness and validity of the data set are ensured, providing a reliable data source for the next step of health score calculation.

[0039] Data standardization and normalization In the process of multi-source data preprocessing, standardization and normalization steps are crucial to ensure that features of different dimensions can be compared and analyzed in the same model. Generally speaking, health status data (such as write amplification, number of bad blocks) and environmental data (such as temperature, humidity) often have different dimensions and units. Therefore, data standardization will help eliminate these differences so that various types of data can contribute their information in a balanced manner during the calculation process.

[0040] Specifically, data normalization can be processed using the following formula: ; in, is the original data; is the mean of the data; is the standard deviation of the data; is the standardized data.

[0041] After standardization, the mean of each data is 0 and the standard deviation is 1, which can eliminate the dimensional differences between different data features. As an option, some specific models may be more suitable for using normalization methods to scale the data to the [0,1] interval, especially when processing images or certain types of input data.

[0042] Data filling Since some missing data will inevitably appear during the data collection process, data filling becomes another important step in data preprocessing. In some embodiments, missing data can be filled by the following common methods: Mean filling: For missing data points, use the mean of the feature to fill in. Mean filling is suitable for situations where the proportion of missing data is small and the data distribution is relatively uniform.

[0043] Interpolation filling: For time series data, common interpolation methods include linear interpolation, spline interpolation, etc. Interpolation methods can be used to estimate missing values ​​based on the relationship between previous and subsequent data points.

[0044] Regression filling: When the missing data is complex, statistical methods such as regression analysis can be used to predict missing values ​​based on other relevant features. For example, multivariate regression analysis can be used to predict missing data points.

[0045] Nearest neighbor filling: When data is missing, fill it in with the values ​​of other data points adjacent to the data point. The specific method can be to use the K nearest neighbor algorithm (KNN) to fill in.

[0046] Data filling ensures the integrity of the data set by effectively supplementing missing data, making subsequent model training and prediction more stable and reliable.

[0047] Time Alignment For multi-source time series data, time alignment is a key step to ensure data consistency and accuracy. In this embodiment, the health status data, I / O load data, and environmental data of the solid-state drive may come from different devices or sensors, and the timestamps may not be completely consistent. Therefore, the data needs to be time aligned before analysis.

[0048] Generally, time alignment can be achieved through the following methods: Synchronize timestamps: For different data sources, set a unified time window, such as aligning the timestamps of various data sources at the minute or second level. Interpolation methods can be used to uniformly map data at different time points.

[0049] Time window merging: Merge data from different time periods into a certain time window. Common window sizes are 5 minutes, 10 minutes, 30 minutes, etc., to ensure that all data sets have the same time step.

[0050] Therefore, through the implementation of the above steps, the multi-source data of the SSD can provide accurate and complete data input for the subsequent health score calculation and prediction model after preprocessing. In this process, by removing abnormal data, standardizing, filling missing data, and time alignment, the data quality is effectively guaranteed and the accuracy of subsequent analysis and prediction is improved. These preprocessing steps provide a strong guarantee for the establishment of subsequent models, so that the health status of the SSD can be accurately evaluated and its remaining life can be predicted.

[0051] Calculate the health score of the solid state drive based on the preprocessed multi-source data; In this embodiment, the health score calculation of the solid state drive is a key step in the in-depth analysis of the pre-processed multi-source data. By combining the health status data, I / O load data and environmental data, a comprehensive health score is generated to accurately reflect the current health status of the hard drive. This health score provides an important basis for subsequent life prediction.

[0052] Specifically, the calculation of the health score relies on data from multiple different sources, which respectively represent the physical health of the hard disk, the operating load, and the impact of the external environment. Due to the different importance of each data source, it is usually necessary to weight these data to reflect the contribution of each data source to the health status.

[0053] Specifically, the calculation of the health score includes the following steps: first, determine the weight of each data source; then, weighted synthesis of the values ​​of each data source based on the weight, and finally generate a unified health score value. Through this score, the health status of the hard disk can be comprehensively evaluated and provide necessary support for subsequent life prediction.

[0054] Steps to calculate health score In this embodiment, based on the preprocessed multi-source data, the health score calculation can be divided into the following specific steps: Data weighting The core of health score calculation is to weight various health indicators. Generally speaking, the health status of a hard disk is affected by multiple factors, including health status data such as write amplification, number of bad blocks, number of erases, and external data such as I / O load and environmental factors. The contribution of these data is usually different, so weighting is needed to ensure that the proportion of each data source's influence on the health score is reasonable.

[0055] Specifically, the health score It can be calculated by weighted average method, the formula is as follows: ; in, , , , …, are the values ​​of each health indicator; , , , …, are the corresponding weights, and the sum of all weights is 1; that is: ; in, is the total number of data sources; The weight of each data; is the number of weights. If the number of weights is 5, the 5 weights are , , , , , and the sum of these 5 weights is equal to 1.

[0056] In some embodiments, the weight can be dynamically adjusted based on factors such as hard disk model, usage environment, hard disk load, etc. to more accurately reflect the impact of different indicators. For example, in certain high-load working environments, I / O load data may have a greater impact on hard disk life, so the weight of I / O load data can be appropriately increased.

[0057] Processing of health status data Health status data such as write amplification, number of bad blocks, and number of erases and writes are direct indicators of the health of the hard disk. These data are usually provided directly by the hard disk's SMART system or sensors. In the specific implementation process, each health indicator value will be calculated according to its weight. For example, the write amplification (WA) value usually indicates the erase efficiency of the SSD, while the number of bad blocks is an important sign of physical damage to the hard disk, and the erase and write times are directly related to the durability of the SSD.

[0058] In some embodiments, the actual write amount refers to the amount of data actually written to the hard disk during use, while the user write amount refers to the amount of data specified by the hard disk user. The higher this ratio is, the greater the wear of the hard disk is, and therefore its health status score is lower.

[0059] I / O load data processing I / O load data mainly reflects the performance pressure of the hard disk during the reading and writing process. By counting the hard disk's read and write times, data transfer rate, average response time, random read and write ratio and other indicators, the load of the hard disk during actual use can be evaluated. In some embodiments, the processing of I / O load data can be performed using the following formula: ; in, It is the I / O load data; , , , is the weight of each indicator, indicating the influence of each indicator on the I / O load. The sum of the weights should be 1, indicating that the contribution of all factors to the I / O load is distributed proportionally; The number of read operations performed by the hard disk per unit time, indicating the hard disk read load. It is the number of write operations performed by the hard disk per unit time, indicating the write load of the hard disk; The data transfer rate of the hard disk, which indicates the amount of data successfully read or written by the hard disk per unit time; It is the average response time of the hard disk when performing I / O operations. It indicates the time the hard disk spends in request processing and is usually used to measure the performance of the hard disk.

[0060] Processing of environmental data Environmental data mainly considers the impact of the SSD working environment on the hard drive life, especially factors such as temperature, humidity and vibration. High temperature, humidity or excessive vibration environment will accelerate the aging process of the hard drive, causing its performance to deteriorate. Therefore, environmental data needs to have a certain negative impact on the hard drive health score. Environmental data Through real-time data collection by sensors, the following formula can be used for weighted calculation: ; in, , , They are the ambient temperature, humidity and vibration values ​​respectively; , , are the weights of temperature, humidity and vibration values ​​respectively.

[0061] Calculation of comprehensive score The final value of the health score is a weighted composite value of the health status data, I / O load data, and environmental data. In some embodiments, the composite health score may be refined based on specific needs. Ultimately, the health score will be used as input for subsequent life prediction models.

[0062] Therefore, based on the pre-processed multi-source data, the health score obtained through weighted calculation reflects the overall health status of the hard disk. The calculation of the health score not only takes into account the physical health status of the hard disk, but also integrates the influence of I / O load and environmental factors. Through this multi-dimensional weighted fusion of data, the health score can accurately reflect the health status of the hard disk in the actual working environment, providing a reliable basis for subsequent life prediction and fault warning.

[0063] Use the health score and time series data to build a prediction model, and output the prediction results of the health status of the solid-state drive through the prediction model; In this embodiment, a prediction model for the health status of the solid state drive is established by combining the health score and time series data, and the prediction result of the future health status of the solid state drive is output through the model to predict the remaining life of the hard drive. This step is the core part of the solid state drive life prediction method, which uses multi-dimensional data to accurately predict the health status of the hard drive.

[0064] Generally speaking, the health status of a solid-state drive depends not only on the current health score, but also on the historical health status and workload of the drive during past use. Therefore, the combination of health score and time series data can more comprehensively and accurately predict the remaining life of the drive. To accomplish this task, we first need to collect and process health score data and related time series data to establish a multivariate dynamic prediction model.

[0065] Specifically, the health score reflects the current physical state of the SSD, while the time series data describes the evolution of the health state over time. By combining these two types of data and inputting them into the prediction model, the future health state of the hard drive can be predicted. In order to ensure the accuracy of the prediction results, a variety of factors that may affect the change of health state must be considered, including I / O load, environmental conditions, etc.

[0066] Combining health scores with time series data In this embodiment, the health score is calculated by weighted fusion of multiple source data (such as write amplification, number of bad blocks, number of erases, etc.). Combining the health score with time series data helps capture the trend of hard disk health status over time. In some embodiments, the time series data includes the change of health score over time, the historical records of write load, number of bad blocks, etc., and the impact of environmental factors on hard disk health.

[0067] After combining the health score with the time series data, the following prediction model is used to estimate the future health of the hard drive: ; in, To predict the time health status; Score your health at the current moment; It is time series data, indicating the historical health data of the hard disk; It is the I / O load data; For environmental data.

[0068] Building and training prediction models In this embodiment, the prediction model can adopt various types of models, including but not limited to regression models, neural network models, support vector machines (SVM), etc. Specifically, a dynamic regression model based on health scores and time series data is used, or a deep learning model (such as a long short-term memory network, LSTM) is used for training to achieve accurate prediction of the future health status of the hard disk.

[0069] In some embodiments, the LSTM model in deep learning is used to analyze time series data. This type of model can capture long-term time dependencies through its recurrent neural network structure, thereby accurately predicting the changing trend of health status. Specifically, the LSTM network can predict future health status based on historical data and can handle nonlinear relationships in time series data. This type of model can be described by the following formula: ; in, Indicates at time The predicted status of The input data at the current moment (including health score, I / O load, environmental data, etc.); and Separate moments The hidden state and cell state of .

[0070] Model optimization and result output The output of the prediction model is the predicted health status of the SSD in the future. By optimizing the model training process, the model can adapt to the usage and environmental conditions of different hard disks, thereby achieving high-precision life prediction in practical applications. The model training data can be optimized through multi-dimensional information such as historical usage data and hard disk failure records.

[0071] In actual implementation, the prediction results are usually further analyzed to adjust the use of the hard disk according to the health status prediction results. For example, if the prediction results show that the health status of the hard disk will decline rapidly, an early warning mechanism may be triggered to notify the user to back up or replace the hard disk.

[0072] Therefore, by combining the health score with the time series data and analyzing it through an appropriate prediction model, an accurate prediction of the remaining life of the SSD is achieved. This method can not only predict the current health status of the hard drive, but also consider the impact of its historical usage data and environmental changes on the future life of the hard drive, thereby providing users with a more accurate and reliable hard drive life prediction service. This prediction model provides strong support for hard drive failure warning, health management and optimization strategies.

[0073] Based on the comparison between the prediction results and the set threshold, an early warning signal is triggered according to the comparison results; In this embodiment, after comparing the health status prediction result of the solid state drive with the set threshold, the system will trigger a corresponding warning signal based on the comparison result. This warning mechanism plays a vital role in ensuring the stability of the hard drive during use, timely discovering potential failures, and reducing the risk of data loss.

[0074] In general, the health status of a hard disk is calculated by combining a health score and a time series data prediction model. In order to avoid the hard disk being discovered only when its performance has degraded to an irreversible level, the present invention sets multiple health score thresholds. When the predicted health score is lower than a certain threshold, the system triggers an early warning signal to promptly remind the user to take measures, such as backing up data or replacing the hard disk. The early warning mechanism can provide different warning levels according to different threshold levels, so that the user can respond according to the actual situation.

[0075] Specifically, the comparison between the health score and the set threshold determines the triggering of the warning signal. When the predicted health score is lower than the set threshold, the system will determine whether the health of the hard disk has entered the predetermined warning interval. The warning signal can be divided into multiple levels, and the system will trigger different levels of warning signals for different health states. In this way, the system can provide timely feedback based on the actual condition of the hard disk, thereby improving data security and the service life of the hard disk.

[0076] Triggering of early warning signals In this embodiment, the triggering mechanism of the early warning signal is generally based on the following steps: Comparison of health score with set threshold: The system first outputs the health status prediction result of the SSD through the prediction model, which is based on the analysis of the comprehensive health score and time series data. Then, the system compares the prediction result with multiple pre-set health score thresholds. The set health score thresholds usually include multiple levels, such as the first threshold, the second threshold, the third threshold, etc. Each threshold corresponds to a different health condition and possible risks.

[0077] When the health score falls below the threshold, an alert is triggered: In the specific implementation process, when the health score is lower than the set threshold, the system will activate the early warning mechanism. Generally speaking, when the health score is close to the first-level threshold, the system will issue a slight warning, reminding the user that the hard disk is at risk and that data backup is required. If the health score is lower than the second-level threshold, the system will issue a moderate warning, reminding the user that the hard disk may experience performance degradation, and it is recommended to consider replacing the hard disk or performing other maintenance operations. If the health score is lower than the third-level threshold, the system will trigger a serious warning, notifying the user that the hard disk is about to fail and that the data needs to be backed up and the hard disk needs to be replaced immediately.

[0078] Level of warning signal: Depending on the comparison between the predicted health score and the set threshold, the system can trigger the following different levels of warning signals: Level 1 warning: When the health score approaches the set level 1 threshold, the system outputs a level 1 warning signal to prompt the user that the hard disk health status has deteriorated and recommends data backup.

[0079] Level 2 warning: When the health score is lower than the level 1 threshold but higher than the level 2 threshold, the system outputs a level 2 warning signal, prompting the user that the hard disk is in poor health and recommends backing up data and considering replacing the hard disk.

[0080] Level 3 warning: When the health score is lower than the level 2 threshold but higher than the level 3 threshold, the system outputs a level 3 warning signal, prompting the user that the hard drive is about to fail and that the user needs to back up data immediately and prepare to replace the hard drive.

[0081] Level 4 warning: In extreme cases, when the health score is lower than the level 3 threshold, the system will trigger a level 4 warning to remind the user that the hard drive is close to complete failure and data may be lost.

[0082] In one possible implementation, the triggering of the warning signal is not only based on a single health score threshold, but also can consider the influence of other factors such as environmental data and I / O load for comprehensive evaluation and judgment.

[0083] Notification method of early warning signal: After the warning signal is triggered, the system will notify the user in various ways. Generally, the warning signal can be notified in the following ways: Visual prompts: For example, a warning box pops up through the system interface to remind users of the health status of the hard disk.

[0084] Email or SMS notification: Send detailed hard disk health status report to users via email or SMS, and remind users to take corresponding actions.

[0085] Sound warning: In some embodiments, the system can also draw the user's attention by issuing a warning sound.

[0086] And the setting of the health score threshold is a key factor to ensure that the early warning signal is accurately triggered. In this embodiment, the threshold setting can be dynamically adjusted according to factors such as different hard disk models, working environments, and usage loads. Generally speaking, the health score threshold should be personalized according to the working environment and usage frequency of the hard disk. For example, for hard disks in high-load or high-temperature environments, the health score threshold may need to be set more strictly to facilitate early detection of potential failure risks.

[0087] In some embodiments, the threshold value can be adaptively adjusted based on historical data and the specific usage of the hard disk. For example, the health change pattern of the hard disk under different usage conditions is analyzed by a machine learning algorithm, and the threshold value is automatically adjusted to improve the accuracy and sensitivity of the early warning.

[0088] Therefore, by comparing the health score with the set threshold, the early warning signal is successfully triggered. When the health status of the SSD is close to or below the set threshold, the system will promptly issue an early warning signal and provide different early warning notifications based on different levels of health status. This mechanism can not only effectively improve the real-time performance of hard disk health management, but also remind users to perform necessary data backup and hard disk replacement operations in advance to avoid the adverse effects of data loss or hard disk failure.

[0089] Based on the comparison between the prediction results and the actual service life, the prediction model is optimized in real time, and the output results are improved by updating the prediction model parameters.

[0090] In this embodiment, real-time optimization is performed by comparing with the actual service life. By comparing the predicted results with the actual service life of the hard disk, the system can identify the prediction deviation and adjust the parameters of the model in time to improve the future output results. This optimization process is achieved through a feedback mechanism to ensure the accuracy and adaptability of the prediction model.

[0091] Generally, the prediction model may not be able to predict the health of the hard disk completely accurately in the early stage because the model relies on historical data and fixed parameters. Over time, the use of the hard disk, load changes, and environmental factors will affect the health of the hard disk, so the prediction model must be optimized regularly. By comparing the prediction results with the actual lifespan in real time, the optimization process can correct the model parameters to adapt to new data patterns and environmental changes.

[0092] Specifically, the optimization method in this embodiment includes comparing the predicted output of the model with the actual service life of the hard disk. Based on the comparison results, the system dynamically adjusts the relevant parameters in the model, such as weights, biases, or model structures, to improve the accuracy of the prediction. Through this optimization process, the system can continuously learn by itself and improve its ability to predict the service life of the hard disk under different usage environments and loads.

[0093] Real-time optimization process: Comparison of predicted results with actual service life After each prediction, the system compares the predicted health status with the actual service life of the hard disk. Generally, the actual service life of the hard disk can be obtained by monitoring the real-time status of the hard disk or through historical failure data. If the difference between the predicted result and the actual service life is large, it indicates that there is a problem with the accuracy of the prediction model and it needs to be optimized.

[0094] Error calculation and feedback mechanism When there is a difference between the predicted and actual useful life, the system calculates the forecast error. The calculation formula can be expressed as: ; in, The health status predicted by the model; is the actual health status or service life. By calculating the error, the system can evaluate the deviation of the model and provide a reference for subsequent optimization.

[0095] Model parameter tuning Based on the calculated error, the system automatically adjusts the parameters of the prediction model. Specifically, methods such as gradient descent or least squares can be used to adjust the weights and biases of the model based on the error. For example, in some embodiments, the system may adjust the weights of various indicators in the health score calculation, or update the parameters of the time series data model. This optimization process can be described by the following formula: ; in, For the The weight at the next moment The updated weight value will be adjusted according to the gradient descent rule; For the The weight at the current moment The value of is the learning rate, which determines the magnitude of the weight change at each update; Error For The partial derivative of a weight indicates the sensitivity of the error to changes in the weight; Adaptive Adjustment and Retraining In some embodiments, in order to better adapt to the actual usage of the hard disk, the model is not only optimized for the existing data, but also may need to be retrained. The retraining process further improves the prediction ability of the model by introducing new historical data and real-time feedback. In this way, the prediction model can continuously learn new data patterns and avoid prediction distortion caused by environmental changes or load changes.

[0096] Moreover, the dynamic optimization process can significantly improve the adaptability and accuracy of the prediction model. As the hard drive is used under different working conditions, the system can continuously track its health status and continuously adjust the model to make the prediction results more accurate. In some embodiments, by using advanced machine learning algorithms such as neural networks, support vector machines, etc., the model can automatically learn from historical data and self-adjust during the use of the hard drive to improve future prediction results.

[0097] In addition, the optimized model can not only more accurately predict the remaining life of the hard drive, but also perform more stably under different load and environmental conditions. Through the real-time feedback mechanism, the system can identify potential problems during the use of the hard drive and provide early warning by optimizing the parameters of the prediction model, helping users take measures to prevent hard drive failure and data loss.

[0098] Therefore, by optimizing the prediction model in real time, the accuracy and reliability of the SSD life prediction are ensured. By comparing the prediction results with the actual service life, the system can continuously adjust the model parameters to adapt to different hard disk usage environments and load conditions. This optimization process not only improves the accuracy of the prediction, but also enhances the system's ability to warn of hard disk failures, thereby providing users with a more intelligent and accurate hard disk health management service.

[0099] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for predicting the life of a solid state drive, characterized in that: The following steps are involved: Collecting multi-source data of the solid-state drive, wherein the multi-source data includes health status data, I / O load data, and environmental data of the solid-state drive; Preprocessing the multi-source data, wherein the preprocessing includes removing abnormal data; Calculate the health score of the solid state drive based on the preprocessed multi-source data; Use the health score and time series data to build a prediction model, and output the prediction results of the health status of the solid-state drive through the prediction model; Based on the comparison between the prediction results and the set threshold, an early warning signal is triggered according to the comparison results; Based on the comparison between the prediction results and the actual service life, the prediction model is optimized in real time, and the output results are improved by updating the prediction model parameters.

2. The method for predicting the life of a solid state drive according to claim 1, characterized in that: The health status data includes: write amplification, number of bad blocks, number of erases and writes, read error rate and write error rate; I / O load data includes: read and write times, data transfer rate, average response time, and random read and write ratio; Environmental data includes: operating temperature, operating humidity, vibration, and power supply fluctuations.

3. The method for predicting the life of a solid state drive according to claim 1, characterized in that: The preprocessing also includes data denoising, outlier detection, data normalization, data padding and time alignment.

4. The method for predicting the life of a solid state drive according to claim 1, characterized in that: The health score is calculated using a fuzzy logic algorithm, which performs weighted fusion on health indicators.

5. The method for predicting the life of a solid state drive according to claim 1, characterized in that: The time series data includes: Health status data changes over time, including the change of SSD health score over time, write amplification, number of bad blocks and number of erase and write cycles; Changes in I / O load data over time: including changes in the number of read and write times of the SSD, data transfer rate, and I / O request response time over time; Changes in environmental data over time: including changes in temperature, humidity, and vibration of the SSD working environment over time; Fault history data: including the fault types and fault times that occurred on the SSD during the historical period; Performance degradation data: includes the performance degradation data of the solid-state drive, including the decrease in write speed and the increase in read and write response time.

6. The method for predicting the life of a solid state drive according to claim 5, characterized in that: The prediction model includes a Bayesian reasoning model, and the Bayesian reasoning model generates a prediction result through a Markov chain Monte Carlo algorithm.

7. The method for predicting the life of a solid state drive according to claim 1, characterized in that: The set thresholds include: a health score threshold, a write amplification threshold, a bad block quantity threshold and an environmental factor threshold.

8. The method for predicting the life of a solid state drive according to claim 7, characterized in that: The set threshold includes three levels of thresholds: Level 1 threshold: When the health score is lower than the level 1 threshold, a level 1 warning is output; Secondary threshold: When the health score is higher than the primary threshold and lower than the secondary threshold, a secondary warning is output; Level 3 threshold: When the health score is higher than the level 2 threshold and lower than the level 3 threshold, a level 3 warning is output; Level 4 threshold: When the health score is higher than the level 3 threshold, a level 4 warning is output.

Images