WO2021143133A1 - 非易失内存器件剩余寿命预测方法、装置、设备及介质 - Google Patents
非易失内存器件剩余寿命预测方法、装置、设备及介质 Download PDFInfo
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F11/00—Error detection; Error correction; Monitoring
- G06F11/008—Reliability or availability analysis
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F11/00—Error detection; Error correction; Monitoring
- G06F11/30—Monitoring
- G06F11/3003—Monitoring arrangements specially adapted to the computing system or computing system component being monitored
- G06F11/3037—Monitoring arrangements specially adapted to the computing system or computing system component being monitored where the computing system component is a memory, e.g. virtual memory, cache
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F11/00—Error detection; Error correction; Monitoring
- G06F11/30—Monitoring
- G06F11/3055—Monitoring arrangements for monitoring the status of the computing system or of the computing system component, e.g. monitoring if the computing system is on, off, available, not available
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F3/00—Input arrangements for transferring data to be processed into a form capable of being handled by the computer; Output arrangements for transferring data from processing unit to output unit, e.g. interface arrangements
- G06F3/06—Digital input from, or digital output to, record carriers, e.g. RAID, emulated record carriers or networked record carriers
- G06F3/0601—Interfaces specially adapted for storage systems
- G06F3/0602—Interfaces specially adapted for storage systems specifically adapted to achieve a particular effect
- G06F3/0614—Improving the reliability of storage systems
- G06F3/0616—Improving the reliability of storage systems in relation to life time, e.g. increasing Mean Time Between Failures [MTBF]
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F3/00—Input arrangements for transferring data to be processed into a form capable of being handled by the computer; Output arrangements for transferring data from processing unit to output unit, e.g. interface arrangements
- G06F3/06—Digital input from, or digital output to, record carriers, e.g. RAID, emulated record carriers or networked record carriers
- G06F3/0601—Interfaces specially adapted for storage systems
- G06F3/0668—Interfaces specially adapted for storage systems adopting a particular infrastructure
- G06F3/0671—In-line storage system
- G06F3/0673—Single storage device
- G06F3/0679—Non-volatile semiconductor memory device, e.g. flash memory, one time programmable memory [OTP]
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- G—PHYSICS
- G11—INFORMATION STORAGE
- G11C—STATIC STORES
- G11C16/00—Erasable programmable read-only memories
- G11C16/02—Erasable programmable read-only memories electrically programmable
- G11C16/06—Auxiliary circuits, e.g. for writing into memory
- G11C16/34—Determination of programming status, e.g. threshold voltage, overprogramming or underprogramming, retention
- G11C16/349—Arrangements for evaluating degradation, retention or wearout, e.g. by counting erase cycles
Definitions
- This application relates to the technical field of storage devices, and more specifically, to a method, apparatus, equipment, and computer-readable storage medium for predicting the remaining life of a non-volatile memory device.
- Non-volatile memory devices are widely used in storage devices because they have high reliability and are not prone to data loss.
- Intel's Optane DCPMM device (which is a persistent storage device that uses DIMM (dual in-line memory module) memory module physical scale) has large width, fast read and write speed, large capacity, long life, and byte-oriented Etc.
- the purpose of this application is to provide a method, apparatus, equipment, and computer-readable storage medium for predicting the remaining life of a non-volatile memory device, which are used to reduce the data loss rate in the non-volatile memory device and improve the difficulty Reliability of lossy memory devices.
- a method for predicting the remaining life of a non-volatile memory device including:
- the remaining life prediction strategy corresponding to the current business mode of the non-volatile memory device is used to calculate the non-volatile memory device.
- Carry out remaining life prediction including:
- the first time point, the first remaining percentage life, the second remaining percentage life different from the first remaining percentage life, and the second time point The remaining life of the non-volatile memory device in the fixed service mode.
- the initial value of the remaining percentage life, the first time point, the first remaining percentage life, the second remaining percentage life different from the first remaining percentage life, the second time Pointing to calculate the remaining life of the non-volatile memory device in the fixed service mode includes:
- p0 is the initial value of the remaining percentage life
- t2 is the second time point
- t1 is the first time point
- p2 is the second remaining percentage life different from the first remaining percentage life
- p1 is the first remaining percentage life.
- the remaining life prediction strategy corresponding to the current business mode of the non-volatile memory device is used to calculate the non-volatile memory device.
- the remaining life prediction of the device includes:
- the calculation of the non-easy price is based on the total amount of data written, the monthly average data written, the third remaining percentage life, and the fourth remaining percentage life that is different from the third remaining percentage life.
- the remaining life of the memory device under the non-fixed service mode includes:
- the write endurance of the nonvolatile memory device is calculated based on the total amount of data written, the third remaining percentage life, and the fourth remaining percentage life different from the third remaining percentage life Sex, including:
- the write tolerance of the non-volatile memory device the monthly average data write volume, and the third remaining percentage life, it is calculated that the non-volatile memory device is in the non-fixed business mode
- the remaining life including:
- Data write is the total amount of data written
- p4 is the fourth remaining percentage life different from the third remaining percentage life
- p3 is the third remaining percentage life
- month_avarage write is the monthly average The amount of data written.
- the method further includes:
- the health level of the non-volatile memory device is pre-divided, including four health levels that are reduced in sequence: normal, non-critical, important, and fatal.
- a device for predicting the remaining life of a non-volatile memory device including:
- a pre-setting module configured to pre-set the remaining life prediction strategy corresponding to the business mode according to different business modes in the non-volatile memory device
- a determining mode module configured to collect operating parameters of the non-volatile memory device, and determine the current business mode of the non-volatile memory device according to the operating parameters
- a life prediction module configured to use a remaining life prediction strategy corresponding to the current business mode of the non-volatile memory device to predict the remaining life of the non-volatile memory device;
- the prompt module is used to determine whether the predicted remaining life is greater than the set value, and if so, a prompt is issued.
- a device for predicting the remaining life of a non-volatile memory device including:
- Memory used to store computer programs
- the processor is used to implement the steps of the method for predicting the remaining life of a non-volatile memory device as described in any one of the above when the computer program is executed.
- a computer-readable storage medium having a computer program stored on the computer-readable storage medium, and when the computer program is executed by a processor, the steps of the method for predicting the remaining life of a non-volatile memory device as described in any of the above are implemented .
- This application provides a method, apparatus, equipment and computer-readable storage medium for predicting the remaining life of a non-volatile memory device, wherein the method includes: pre-setting the corresponding business mode according to different business modes in the non-volatile memory device The remaining life prediction strategy of the non-volatile memory device; collect the operating parameters of the non-volatile memory device, and determine the current business mode of the non-volatile memory device according to the operating parameters; use the remaining life prediction strategy corresponding to the current business model of the non-volatile memory device to be difficult The remaining life of the missing memory device is predicted; it is judged whether the predicted remaining life is greater than the set value, and if so, a prompt is issued.
- the above-mentioned technical solutions disclosed in this application pre-set the remaining life prediction strategy corresponding to the business mode of the non-volatile memory device, and use the remaining life prediction strategy corresponding to the current business mode of the non-volatile memory device to predict the non-volatile memory device.
- the memory device performs the remaining life prediction, and judges whether the predicted remaining life is greater than the set value, and if so, a prompt is issued so that the user can replace the non-volatile memory device in time, so as to prevent the occurrence of bad blocks as much as possible to reduce the data loss Loss rate, thereby improving the reliability of non-volatile memory devices.
- FIG. 1 is a flowchart of a method for predicting the remaining life of a non-volatile memory device according to an embodiment of the application
- Fig. 2 is a flowchart of remaining life prediction in a fixed service mode provided by an embodiment of the application
- Fig. 3 is a flowchart of remaining life prediction in a non-fixed service mode according to an embodiment of the application
- FIG. 4 is a schematic structural diagram of an apparatus for predicting remaining life of a non-volatile memory device according to an embodiment of the application;
- FIG. 5 is a schematic structural diagram of a device for predicting the remaining life of a nonvolatile memory device according to an embodiment of the application.
- FIG. 1 shows a flowchart of a method for predicting the remaining life of a non-volatile memory device according to an embodiment of the present application.
- the method for predicting remaining life of a non-volatile memory device according to an embodiment of the present application may include:
- S11 Pre-set a remaining life prediction strategy corresponding to the business model according to different business models in the non-volatile memory device.
- the remaining life prediction strategy corresponding to each business mode can be set in advance according to different business modes in the non-volatile memory device.
- the business model mentioned here can specifically refer to the fixed business model (that is, the customer's access to non-volatile memory devices, the ratio of read and write, and the daily throughput are relatively stable), the non-fixed business model (that is, the customer's The access, read/write ratio, and daily throughput of memory-losing devices all have large changes).
- the non-volatile memory device mentioned in this application may be a DCPMM device or other types of non-volatile memory devices, which is not limited in this application.
- S12 Collect operating parameters of the non-volatile memory device, and determine the current business mode of the non-volatile memory device according to the operating parameters.
- the operating parameters of the non-volatile memory device are collected, and the current business mode of the non-volatile memory device is determined according to the operating parameters.
- S13 Use the remaining life prediction strategy corresponding to the current business model of the non-volatile memory device to predict the remaining life of the non-volatile memory device.
- the remaining life prediction strategy corresponding to the current business model of the non-volatile memory device can be obtained from the preset remaining prediction strategy, and the remaining life prediction strategy can be used to The non-volatile memory device performs a remaining life prediction to obtain the remaining life of the non-volatile memory device.
- the non-volatile memory devices are replaced in time, so as to prevent the currently used non-volatile memory devices from generating a large number of bad blocks, so as to reduce the data loss rate.
- predicting the remaining life of the non-volatile memory device also facilitates the staff to know the health status of the non-volatile memory device in time, so that it is convenient for the staff to purchase and prepare replacement devices in advance, so that the remaining life is longer than the design.
- the setting value mentioned here can be specifically set according to the performance of the non-volatile memory device.
- the above-mentioned technical solutions disclosed in this application pre-set the remaining life prediction strategy corresponding to the business mode of the non-volatile memory device, and use the remaining life prediction strategy corresponding to the current business mode of the non-volatile memory device to predict the non-volatile memory device.
- the memory device performs the remaining life prediction, and judges whether the predicted remaining life is greater than the set value, and if so, a prompt is issued so that the user can replace the non-volatile memory device in time, so as to prevent the occurrence of bad blocks as much as possible to reduce the data loss Loss rate, thereby improving the reliability of non-volatile memory devices.
- FIG. 2 shows a flowchart of remaining life prediction in a fixed service mode provided by an embodiment of the present application.
- a method for predicting remaining life of a non-volatile memory device provided by an embodiment of the present application, when it is determined that a non-volatile memory device is
- the remaining life prediction strategy corresponding to the current business model of the non-volatile memory device is used to predict the remaining life of the non-volatile memory device, which may include:
- S1211 Obtain the initial value of the remaining percentage life of the non-volatile memory device when it enters the fixed service mode, and obtain the first remaining percentage life of the non-volatile memory device at the first time point, and every first preset time interval Obtain the second remaining percentage life of the non-volatile memory device once;
- S1212 Determine whether the second remaining percentage life is the same as the first remaining percentage life, if not, record the second remaining percentage life different from the first remaining percentage life, and record the second remaining percentage life different from the first remaining percentage life The second time point corresponding to the life;
- S1213 Use the initial value of the remaining percentage life, the first time point, the first remaining percentage life, the second remaining percentage life that is different from the first remaining percentage life, and the second time point to calculate the non-volatile memory device in the fixed service mode. Remaining life.
- the present application predicts the remaining life of the non-volatile memory device from the remaining percentage life (Percentage Remaining) in the interface provided by the non-volatile memory device firmware.
- the specific process of using the remaining life prediction strategy corresponding to the current business mode of the non-volatile memory device to predict the remaining life of the non-volatile memory device is as follows:
- the initial value of the remaining percentage life of the non-volatile memory device when it enters the fixed service mode (denoted as p0).
- the remaining life prediction starts from the first time point (denoted as t1)
- the first time point can be specifically recorded in hours as the time unit, and the first preset time interval can be specifically 5 days, of course, it can also be other time intervals;
- the remaining life expectancy of the non-volatile memory device in the fixed service mode is realized, so as to understand the health status of the non-volatile memory device in time.
- An embodiment of the present application provides a method for predicting the remaining life of a nonvolatile memory device, which uses an initial value of remaining percentage life, a first time point, a first remaining percentage life, a second remaining percentage life different from the first remaining percentage life, The second point in time is to calculate the remaining life of the non-volatile memory device in the fixed service mode, which can include:
- p0 is the initial value of the remaining percentage life
- t2 is the second time point
- t1 is the first time point
- p2 is the second remaining percentage life different from the first remaining percentage life
- p1 is the first remaining percentage life.
- the specific process of using p0, t1, p1, p2, and t2 to calculate the remaining life of the non-volatile memory device in the fixed service mode can be:
- FIG. 3 shows a flow chart for predicting the remaining life of a non-fixed service mode provided by an embodiment of the present application.
- the method for predicting the remaining life of a non-volatile memory device provided by an embodiment of the present application is not easy to determine.
- the remaining life prediction strategy corresponding to the current business model of the non-volatile memory device is used to predict the remaining life of the non-volatile memory device, which may include:
- S1223 Determine whether the fourth remaining percentage life is the same as the third remaining percentage life, if not, record the fourth remaining percentage life different from the third remaining percentage life, and end the recording of the data write volume of the nonvolatile memory device step;
- S1224 Obtain the total amount of data written by the non-volatile memory device from the beginning of the recording to the end of the recording, and obtain the monthly average data write amount of the non-volatile memory device;
- S1225 Calculate the remaining life of the non-volatile memory device in the non-fixed business mode based on the total amount of data written, the average monthly data written, the third remaining percentage life, and the fourth remaining percentage life different from the third remaining percentage life .
- the specific process of using the remaining life prediction strategy corresponding to the current business mode of the non-volatile memory device to predict the remaining life of the non-volatile memory device is as follows:
- the second preset time interval may specifically be 5 days, of course, it may also be other time intervals;
- Use Data write , month_avarage write , p3, and p4 to calculate the remaining life of non-volatile memory devices in non-fixed business mode.
- An embodiment of the present application provides a method for predicting the remaining life of a non-volatile memory device, which is based on the total amount of data written, the average monthly data write, the third remaining percentage life, and the fourth remaining percentage life that is different from the third remaining percentage life. Calculate the remaining life of non-volatile memory devices in non-fixed business mode, which can include:
- the remaining life of the non-volatile memory device in the non-fixed business mode is calculated according to the write tolerance of the non-volatile memory device, the monthly average data write volume, and the third remaining percentage life.
- the decrease in the data storage capacity of the non-volatile memory device is mainly caused by data writing.
- Data write , month_avarage write , p3, and p4 to calculate the remaining life of the non-volatile memory device in the non-fixed business mode.
- endurance write , month_avarage write , and p3 can be used to calculate the remaining life of the non-volatile memory device in the non-fixed business mode.
- An embodiment of the present application provides a method for predicting the remaining life of a non-volatile memory device, which calculates the non-volatile memory device according to the total amount of data written, the third remaining percentage life, and the fourth remaining percentage life different from the third remaining percentage life.
- the write tolerance can include:
- the remaining life of the non-volatile memory device in the non-fixed service mode is calculated according to the write tolerance of the non-volatile memory device, the average monthly data write volume, and the third remaining percentage life, which may include:
- Data write is the total amount of data written
- p4 is the fourth remaining percentage life different from the third remaining percentage life
- p3 is the third remaining percentage life
- month_avarage write is the monthly average data write volume.
- the method for predicting the remaining life of a non-volatile memory device can also predict the remaining life of the non-volatile memory device by using a remaining life prediction strategy corresponding to the current business mode of the non-volatile memory device.
- the health level of the non-volatile memory device is pre-divided, and may include normal, non-critical, important, and fatal with successively lower health levels.
- the remaining life prediction strategy corresponding to the current business model of the non-volatile memory device When using the remaining life prediction strategy corresponding to the current business model of the non-volatile memory device to predict the remaining life of the non-volatile memory device, it can also determine whether the health level of the non-volatile memory device is degraded, if not, continue Use the remaining life prediction strategy corresponding to the current business model of the non-volatile memory device to predict the remaining life of the non-volatile memory device.
- the remaining life prediction strategy corresponding to the current business model is the step of predicting the remaining life of the non-volatile memory device to prevent the degradation of the health level of the non-volatile memory device from affecting the remaining life prediction of the non-volatile memory device. Improve the accuracy of remaining life prediction.
- the health level of the non-volatile memory device is pre-divided, and may include four health levels of normal, non-critical, important, and fatal, and the four health levels are sequentially reduced.
- the current status of the non-volatile memory device can be used when the health level is normal or non-critical.
- the remaining life prediction strategy corresponding to the business model predicts the remaining life of the non-volatile memory device.
- the embodiment of the present application also provides a device for predicting the remaining life of a non-volatile memory device. See FIG. 4, which shows a schematic structural diagram of the device for predicting the remaining life of a non-volatile memory device provided by an embodiment of the present application, which may include :
- the pre-setting module 41 is configured to pre-set the remaining life prediction strategy corresponding to the business mode according to different business modes in the non-volatile memory device;
- the determining mode module 42 is used to collect operating parameters of the non-volatile memory device, and determine the current business mode of the non-volatile memory device according to the operating parameters;
- the life prediction module 43 is used to predict the remaining life of the non-volatile memory device by using the remaining life prediction strategy corresponding to the current business mode of the non-volatile memory device;
- the prompt module 44 is used to determine whether the predicted remaining life is greater than the set value, and if so, a prompt is issued.
- the determining mode module 42 may include:
- the first obtaining unit is used to obtain the initial value of the remaining percentage life of the non-volatile memory device when it enters the fixed service mode, and obtain the first remaining percentage life of the non-volatile memory device at the first time point, and every second Acquire the second remaining percentage life of the non-volatile memory device once at a preset time interval;
- the first judging unit is used to judge whether the second remaining percentage life is the same as the first remaining percentage life; if not, record the second remaining percentage life different from the first remaining percentage life, and record the difference from the first remaining percentage life The second time point corresponding to the second remaining percentage life of
- the first calculation unit is used to calculate the nonvolatile memory device's current value by using the initial value of the remaining percentage life, the first time point, the first remaining percentage life, the second remaining percentage life different from the first remaining percentage life, and the second time point The remaining life in the fixed business model.
- the first calculation unit may include:
- the first calculation subunit is used to utilize Calculate the remaining life of non-volatile memory devices in fixed service mode
- p0 is the initial value of the remaining percentage life
- t2 is the second time point
- t1 is the first time point
- p2 is the second remaining percentage life different from the first remaining percentage life
- p1 is the first remaining percentage life.
- the determining mode module 42 may include:
- the second acquiring unit is configured to acquire the third remaining percentage life of the non-volatile memory at the third time point, and start to record the data writing amount of the non-volatile memory device;
- the third obtaining unit is configured to obtain the fourth remaining percentage life of the non-volatile memory device every second preset time interval;
- the second judging unit is used to judge whether the fourth remaining percentage life is the same as the third remaining percentage life, if not, then recording the fourth remaining percentage life different from the third remaining percentage life, and ending the recording of the non-volatile memory device Steps of the amount of data written;
- the fourth obtaining unit is used to obtain the total amount of data written by the non-volatile memory device from the beginning of the recording to the end of the recording, and obtain the monthly average data writing amount of the non-volatile memory device;
- the second calculation unit is used to calculate the non-fixed business of the non-volatile memory device based on the total amount of data written, the average monthly data write volume, the third remaining percentage life, and the fourth remaining percentage life different from the third remaining percentage life. The remaining life in the mode.
- the second calculation unit may include:
- the second calculation subunit is used to calculate the write endurance of the nonvolatile memory device according to the total amount of data written, the third remaining percentage life, and the fourth remaining percentage life different from the third remaining percentage life;
- the third calculation subunit is used to calculate the remaining life of the non-volatile memory device in the non-fixed business mode according to the write tolerance of the non-volatile memory device, the monthly average data write volume, and the third remaining percentage life.
- the second calculation subunit may include:
- the fourth calculation subunit is used to utilize Calculate the endurance write of non-volatile memory devices
- the fifth calculation subunit is used to utilize Calculate the remaining life of non-volatile memory devices in non-fixed business mode
- Data write is the total amount of data written
- p4 is the fourth remaining percentage life different from the third remaining percentage life
- p3 is the third remaining percentage life
- month_avarage write is the monthly average data write volume.
- the judgment module is used to judge whether the health level of the non-volatile memory device is degraded when the remaining life prediction strategy corresponding to the current business mode of the non-volatile memory device is used to predict the remaining life of the non-volatile memory device, and if so, Then re-execute the step of predicting the remaining life of the non-volatile memory device using the remaining life prediction strategy corresponding to the current business model of the non-volatile memory device; where the health level of the non-volatile memory device is pre-divided and can be Including normal, non-serious, important, and fatal with decreasing health levels.
- FIG. 5 shows a schematic structural diagram of a device for predicting the remaining life of a non-volatile memory device provided by an embodiment of the present application, which may include:
- the memory 51 is used to store computer programs
- the processor 52 is configured to execute the computer program stored in the memory 51 and can implement the following steps:
- Pre-set the remaining life prediction strategy corresponding to the business model according to the different business models in the non-volatile memory device collect the operating parameters of the non-volatile memory device, and determine the current business mode of the non-volatile memory device according to the operating parameters;
- the remaining life prediction strategy corresponding to the current business model of the non-volatile memory device predicts the remaining life of the non-volatile memory device; it is judged whether the predicted remaining life is greater than the set value, and if so, a prompt is issued.
- An embodiment of the present application provides a computer-readable storage medium, where a computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, the following steps can be implemented:
- Pre-set the remaining life prediction strategy corresponding to the business model according to the different business models in the non-volatile memory device collect the operating parameters of the non-volatile memory device, and determine the current business mode of the non-volatile memory device according to the operating parameters;
- the remaining life prediction strategy corresponding to the current business model of the non-volatile memory device predicts the remaining life of the non-volatile memory device; it is judged whether the predicted remaining life is greater than the set value, and if so, a prompt is issued.
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Abstract
一种非易失内存器件剩余寿命预测方法、装置、设备及计算机可读存储介质，方法包括：预先根据非易失内存器件中不同的业务模式设定与业务模式对应的剩余寿命预测策略；采集非易失内存器件的运行参数，根据运行参数确定非易失内存器件当前的业务模式；利用与非易失内存器件当前的业务模式对应的剩余寿命预测策略对非易失内存器件进行剩余寿命预测；判断预测出的剩余寿命是否大于设定值，若是，发出提示。本申请公开的上述技术方案，利用与非易失内存器件当前的业务模式对应的剩余寿命预测策略对非易失内存器件进行剩余寿命预测，并在剩余寿命大于设定值时发出提示，以便于用户可以及时对非易失内存器件进行更换，从而降低数据的丢失率。
Description
本申请要求于2020年01月19日提交中国专利局、申请号为202010062739.0、发明名称为“非易失内存器件剩余寿命预测方法、装置、设备及介质”的中国专利申请的优先权，其全部内容通过引用结合在本申请中。
本申请涉及存储器件技术领域，更具体地说，涉及一种非易失内存器件剩余寿命预测方法、装置、设备及计算机可读存储介质。
非易失内存器件因具有可靠性高、不易发生数据丢失而被广泛应用在存储器件中。其中，Intel的optane DCPMM器件(其是使用DIMM(双列直插式存储模块)内存条物理规模的持久性存储器件)具有宽度大、读写速度快、大容量、长寿命、可字节方位等优点。
目前，在使用非易失内存器件的过程中，当非易失内存器件的使用时间过长时则会出现大量的坏块，而坏块的产生会导致数据丢失的可能性急剧增大，从而会降低非易失性内存器件的可靠性。
综上所述，如何降低非易失内存器件中数据的丢失率，以提高非易失性内存器件的可靠性，是目前本领域技术人员亟待解决的技术问题。
发明内容
有鉴于此，本申请的目的是提供一种非易失内存器件剩余寿命预测方法、装置、设备及计算机可读存储介质，用于降低非易失内存器件中数据的丢失率，以提高非易失性内存器件的可靠性。
为了实现上述目的，本申请提供如下技术方案：
一种非易失内存器件剩余寿命预测方法，包括：
预先根据非易失内存器件中不同的业务模式设定与所述业务模式对应的剩余寿命预测策略；
采集所述非易失内存器件的运行参数，根据所述运行参数确定所述非易失内存器件当前的业务模式；
利用与所述非易失内存器件当前的业务模式对应的剩余寿命预测策略对所述非易失内存器件进行剩余寿命预测；
判断预测出的剩余寿命是否大于设定值，若是，则发出提示。
优选的，当确定所述非易失内存器件当前的业务模式为固定业务模式时，则利用与所述非易失内存器件当前的业务模式对应的剩余寿命预测策略对所述非易失内存器件进行剩余寿命预测，包括：
获取所述非易失内存器件在进入所述固定业务模式时的剩余百分比寿命初始值，并获取所述非易失内存器件在第一时间点处的第一剩余百分比寿命，且每隔第一预设时间间隔获取一次所述非易失内存器件的第二剩余百分比寿命；
判断所述第二剩余百分比寿命与所述第一剩余百分比寿命是否相同，若否，则记录与所述第一剩余百分比寿命不同的所述第二剩余百分比寿命，并记录与所述第一剩余百分比寿命不同的所述第二剩余百分比寿命对应的第二时间点；
利用所述剩余百分比寿命初始值、所述第一时间点、所述第一剩余百分比寿命、与所述第一剩余百分比寿命不同的所述第二剩余百分比寿命、所述第二时间点计算所述非易失内存器件在所述固定业务模式下的剩余寿命。
优选的，利用所述剩余百分比寿命初始值、所述第一时间点、所述第一剩余百分比寿命、与所述第一剩余百分比寿命不同的所述第二剩余百分比寿命、所述第二时间点计算所述非易失内存器件在所述固定业务模式下的剩余寿命，包括：
利用
计算所述非易失内存器件在所述固定业务模式下的剩余寿命
其中，p0为所述剩余百分比寿命初始值，t2为所述第二时间点，t1为所述第一时间点，p2为与所述第一剩余百分比寿命不同的所述第二剩余百 分比寿命，p1为所述第一剩余百分比寿命。
优选的，当确定所述非易失内存器件当前的业务模式为非固定业务模式时，则利用与所述非易失内存器件当前的业务模式对应的剩余寿命预测策略对所述非易失内存器件进行剩余寿命预测，包括：
获取所述非易失内存在第三时间点处的第三剩余百分比寿命，并开始记录所述非易失内存器件的数据写入量；
每隔第二预设时间间隔获取一次所述非易失内存器件的第四剩余百分比寿命；
判断所述第四剩余百分比寿命与所述第三剩余百分比寿命是否相同，若否，则记录与所述第三剩余百分比寿命不同的所述第四剩余百分比寿命，并结束所述记录所述非易失内存器件的数据写入量的步骤；
获取所述非易失性内存器件从开始记录到结束记录的数据写入总量，并获取所述非易失内存器件的月均数据写入量；
根据所述数据写入总量、所述月均数据写入量、所述第三剩余百分比寿命、与所述第三剩余百分比寿命不同的所述第四剩余百分比寿命计算所述非易失内存器件在所述非固定业务模式下的剩余寿命。
优选的，根据所述数据写入总量、所述月均数据写入、所述第三剩余百分比寿命、与所述第三剩余百分比寿命不同的所述第四剩余百分比寿命计算所述非易失内存器件在所述非固定业务模式下的剩余寿命，包括：
根据所述数据写入总量、所述第三剩余百分比寿命、与所述第三剩余百分比寿命不同的所述第四剩余百分比寿命计算所述非易失内存器件的写入耐受性；
根据所述非易失内存器件的写入耐受性、所述月均数据写入量、所述第三剩余百分比寿命计算所述非易失内存器件在所述非固定业务模式下的剩余寿命。
优选的，根据所述数据写入总量、所述第三剩余百分比寿命、与所述第三剩余百分比寿命不同的所述第四剩余百分比寿命计算所述非易失内存器件的写入耐受性，包括：
利用
计算所述非易失内存器件的写入耐受性endurance
write；
相应地，根据所述非易失内存器件的写入耐受性、所述月均数据写入量、所述第三剩余百分比寿命计算所述非易失内存器件在所述非固定业务模式下的剩余寿命，包括：
利用
计算所述非易失内存器件在所述非固定业务模式下的剩余寿命
其中，Data
write为所述数据写入总量，p4为与所述第三剩余百分比寿命不同的所述第四剩余百分比寿命，p3为所述第三剩余百分比寿命，month_avarage
write为所述月均数据写入量。
优选的，在利用与所述非易失内存器件当前的业务模式对应的剩余寿命预测策略对所述非易失内存器件进行剩余寿命预测时，还包括：
判断所述非易失内存器件的健康等级是否发生降级，若是，则重新执行所述利用与所述非易失内存器件当前的业务模式对应的剩余寿命预测策略对所述非易失内存器件进行剩余寿命预测的步骤；
其中，所述非易失内存器件的健康等级为预先划分得到的，包括依次降低的正常、非严重、重要和致命四个健康等级。
一种非易失内存器件剩余寿命预测装置，包括：
预先设定模块，用于预先根据非易失内存器件中不同的业务模式设定与所述业务模式对应的剩余寿命预测策略；
确定模式模块，用于采集所述非易失内存器件的运行参数，根据所述运行参数确定所述非易失内存器件当前的业务模式；
寿命预测模块，用于利用与所述非易失内存器件当前的业务模式对应的剩余寿命预测策略对所述非易失内存器件进行剩余寿命预测；
提示模块，用于判断预测出的剩余寿命是否大于设定值，若是，则发出提示。
一种非易失内存器件剩余寿命预测设备，包括：
存储器，用于存储计算机程序；
处理器，用于执行所述计算机程序时实现如上述任一项所述的非易失内存器件剩余寿命预测方法的步骤。
一种计算机可读存储介质，所述计算机可读存储介质上存储有计算机程序，所述计算机程序被处理器执行时实现如上述任一项所述的非易失内存器件剩余寿命预测方法的步骤。
本申请提供了一种非易失内存器件剩余寿命预测方法、装置、设备及计算机可读存储介质，其中，该方法包括：预先根据非易失内存器件中不同的业务模式设定与业务模式对应的剩余寿命预测策略；采集非易失内存器件的运行参数，根据运行参数确定非易失内存器件当前的业务模式；利用与非易失内存器件当前的业务模式对应的剩余寿命预测策略对非易失内存器件进行剩余寿命预测；判断预测出的剩余寿命是否大于设定值，若是，则发出提示。
本申请公开的上述技术方案，预先设定与非易失内存器件的业务模式对应的剩余寿命预测策略，并利用与非易失内存器件当前的业务模式对应的剩余寿命预测策略来对非易失内存器件进行剩余寿命预测，判断预测出的剩余寿命是否大于设定值，若是，则发出提示，以便于用户可以及时对非易失内存器件进行更换，从而尽量防止出现坏块，以降低数据的丢失率，进而提高非易失内存器件的可靠性。
为了更清楚地说明本发明实施例或现有技术中的技术方案，下面将对实施例或现有技术描述中所需要使用的附图作简单地介绍，显而易见地，下面描述中的附图仅仅是本发明的实施例，对于本领域普通技术人员来讲，在不付出创造性劳动的前提下，还可以根据提供的附图获得其他的附图。
图1为本申请实施例提供的一种非易失内存器件剩余寿命预测方法的流程图；
图2为本申请实施例提供的在固定业务模式下进行剩余寿命预测的流程图；
图3为本申请实施例提供的在非固定业务模式下进行剩余寿命预测的流程图；
图4为本申请实施例提供的一种非易失内存器件剩余寿命预测装置的结构示意图；
图5为本申请实施例提供的一种非易失内存器件剩余寿命预测设备的结构示意图。
下面将结合本发明实施例中的附图，对本发明实施例中的技术方案进行清楚、完整地描述，显然，所描述的实施例仅仅是本发明一部分实施例，而不是全部的实施例。基于本发明中的实施例，本领域普通技术人员在没有做出创造性劳动前提下所获得的所有其他实施例，都属于本发明保护的范围。
参见图1，其示出了本申请实施例提供的一种非易失内存器件剩余寿命预测方法的流程图，本申请实施例提供的一种非易失内存器件剩余寿命预测方法，可以包括：
S11：预先根据非易失内存器件中不同的业务模式设定与业务模式对应的剩余寿命预测策略。
考虑到非易失内存器件中不同的业务模式会对非易失内存器件中的剩余寿命造成不同的影响，因此，为了便于并为了提高对非易失内存器件进行剩余寿命预测的准确性，则可以预先根据非易失内存器件中不同的业务模式设定分别与每种业务模式对应的剩余寿命预测策略。
其中，这里提及的业务模式具体可以指固定业务模式(即客户对非易失内存器件的访问、读写比例、每日吞吐量等均比较稳定)、非固定业务模式(即客户对非易失内存器件的访问、读写比例、每日吞吐量都存在较大的变动)。另外，本申请中所提及的非易失内存器件可以为DCPMM器件，也可以为其他类型的非易失内存器件，本申请对此不做任何限定。
S12：采集非易失内存器件的运行参数，根据运行参数确定非易失内存器件当前的业务模式。
在非易失内存器件运行过程中，采集非易失内存器件的运行参数(包含但不限于读写比例、每日吞吐量)，根据运行参数确定非易失内存器件当前的业务模式。
S13：利用与非易失内存器件当前的业务模式对应的剩余寿命预测策略对非易失内存器件进行剩余寿命预测。
在确定出非易失内存器件当前的业务模式之后，可以从预先设定的剩余预测策略中获取与非易失内存器件当前的业务模式对应的剩余寿命预测策略，并利用该剩余寿命预测策略对非易失内存器件进行剩余寿命预测，以得到非易失内存器件的剩余寿命。
S14：判断预测出的剩余寿命是否大于设定值；若是，则执行步骤S15，若否，则返回执行步骤S12。
S15：发出提示。
判断预测出的剩余寿命是否大于设定值，若否，则返回执行步骤S12，若是，则发出提示，以便于工作人员可以及时获知非易失内存器件的寿命即将殆尽，并便于工作人员可以及时对非易失内存器件进行更换等处理，从而防止当前所使用的非易失内存器件产生大量的坏块，以降低数据的丢失率。除此之外，对非易失内存器件进行剩余寿命预测还便于工作人员及时了解非易失内存器件的健康状态，从而便于工作人员提前进行替换器件的购买和准备，以便于在剩余寿命大于设定值时可以及时替换到当前正在使用的非易失内存器件。其中，这里提及的设定值具体可以根据非易失内存器件的性能进行设置。
本申请公开的上述技术方案，预先设定与非易失内存器件的业务模式对应的剩余寿命预测策略，并利用与非易失内存器件当前的业务模式对应的剩余寿命预测策略来对非易失内存器件进行剩余寿命预测，判断预测出的剩余寿命是否大于设定值，若是，则发出提示，以便于用户可以及时对非易失内存器件进行更换，从而尽量防止出现坏块，以降低数据的丢失率，进而提高非易失内存器件的可靠性。
参见图2，其示出了本申请实施例提供的在固定业务模式下进行剩余寿命预测的流程图，本申请实施例提供的一种非易失内存器件剩余寿命预测方法，当确定非易失内存器件当前的业务模式为固定业务模式时，利用与非易失内存器件当前的业务模式对应的剩余寿命预测策略对非易失内存器件进行剩余寿命预测，可以包括：
S1211：获取非易失内存器件在进入固定业务模式时的剩余百分比寿命初始值，并获取非易失内存器件在第一时间点处的第一剩余百分比寿命，且每隔第一预设时间间隔获取一次非易失内存器件的第二剩余百分比寿命；
S1212：判断第二剩余百分比寿命与第一剩余百分比寿命是否相同，若否，则记录与第一剩余百分比寿命不同的第二剩余百分比寿命，并记录与第一剩余百分比寿命不同的第二剩余百分比寿命对应的第二时间点；
S1213：利用剩余百分比寿命初始值、第一时间点、第一剩余百分比寿命、与第一剩余百分比寿命不同的第二剩余百分比寿命、第二时间点计算非易失内存器件在固定业务模式下的剩余寿命。
考虑到非易失内存器件内部没有类似于机械硬盘和SSD硬盘(Solid State Disk，固态硬盘)中的S.M.A.R.T(Self-Monitoring Analysis And Reporting Technology，自我检测分析与报告技术)数据可以进行剩余寿命预测，因此，本申请从非易失内存器件固件提供的接口中的剩余百分比寿命(PercentageRemaining)对非易失内存器件进行剩余寿命预测。
具体地，当非易失内存器件处于固定业务模式时，利用与非易失内存器件当前的业务模式对应的剩余寿命预测策略对非易失内存器件进行剩余寿命预测的具体过程为：
获取非易失内存器件在进入固定业务模式时的剩余百分比寿命初始值(记为p0)，在进入固定业务模式之后，假设从第一时间点(记为t1)处开始进行剩余寿命预测，则记录非易失内存器件在第一时间点处的第一剩余百分比寿命(记为p1)，且每个第一预设时间间隔获取一次非易失内存器件的第二剩余百分比寿命(记为
)；其中，第一时间点处具体可以以 小时为时间单元进行记录，且第一预设时间间隔具体可以为5天，当然，也可以为其他时间间隔；
判断所记录的第二剩余百分比寿命与第一剩余百分比寿命是否相同，即判断
是否等于p1，若是，则继续每隔第一预设时间间隔获取一次非易失内存器件的第二剩余百分比寿命的步骤，若否，则记录与第一剩余百分比寿命不同的第二剩余百分比寿命(记为p2)，并同时记录p2对应的第二时间点(记为t2)；
利用p0、t1、p1、p2、t2计算非易失内存器件在固定业务模式下的剩余寿命。
通过上述方式实现对非易失内存器件在固定业务模式下的剩余寿命预测，以便于及时了解非易失内存器件的健康状态。
本申请实施例提供的一种非易失内存器件剩余寿命预测方法，利用剩余百分比寿命初始值、第一时间点、第一剩余百分比寿命、与第一剩余百分比寿命不同的第二剩余百分比寿命、第二时间点计算非易失内存器件在固定业务模式下的剩余寿命，可以包括：
利用
计算非易失内存器件在固定业务模式下的剩余寿命
其中，p0为剩余百分比寿命初始值，t2为第二时间点，t1为第一时间点，p2为与第一剩余百分比寿命不同的第二剩余百分比寿命，p1为第一剩余百分比寿命。
利用p0、t1、p1、p2、t2计算非易失内存器件在固定业务模式下的剩余寿命的具体过程可以为：
利用
计算非易失内存器件的寿命lifetime
predict，并利用
计算非易失内存器件在固定业务模式下的剩余寿命
其中，lifetime
predict和
的 单位均为年。
参见图3，其示出了本申请实施例提供的在非固定业务模式下进行剩余寿命预测的流程图，本申请实施例提供的一种非易失内存器件剩余寿命预测方法，当确定非易失内存器件当前的业务模式为非固定业务模式时，则利用与非易失内存器件当前的业务模式对应的剩余寿命预测策略对非易失内存器件进行剩余寿命预测，可以包括：
S1221：获取非易失内存在第三时间点处的第三剩余百分比寿命，并开始记录非易失内存器件的数据写入量；
S1222：每隔第二预设时间间隔获取一次非易失内存器件的第四剩余百分比寿命；
S1223：判断第四剩余百分比寿命与第三剩余百分比寿命是否相同，若否，则记录与第三剩余百分比寿命不同的第四剩余百分比寿命，并结束记录非易失内存器件的数据写入量的步骤；
S1224：获取非易失性内存器件从开始记录到结束记录的数据写入总量，并获取非易失内存器件的月均数据写入量；
S1225：根据数据写入总量、月均数据写入量、第三剩余百分比寿命、与第三剩余百分比寿命不同的第四剩余百分比寿命计算非易失内存器件在非固定业务模式下的剩余寿命。
当非易失内存器件处于非固定业务模式时，利用与非易失内存器件当前的业务模式对应的剩余寿命预测策略对非易失内存器件进行剩余寿命预测的具体过程为：
获取非易失内存器件在第三时间点处(记为t3)的第三剩余百分比寿命(记为p3)，并开始记录非易失内存器件的数据写入量，具体为主机端向非易失内存器件写入的数据量；
每隔第二预设时间间隔获取一次非易失内存器件的第四剩余百分比寿命(记为
)；其中，第二预设时间间隔具体可以为5天，当然，也可以为其他时间间隔；
判断
是否等于p3，若是，则继续执行每隔第二预设时间间隔获取一次非易失内存器件的第四剩余百分比寿命的步骤，若否，则记录与第三 剩余百分比寿命不同的第四剩余百分比寿命(记为p4)，同时，结束对非易失内存器件数据写入量的记录；
统计非易失内存器件在数据写入量记录期间的数据写入总量(记为Data
write)，并获取非易失内存器件在非固定业务模式下的月均数据写入量(记为month_avarage
write)；
利用Data
write、month_avarage
write、p3、p4计算非易失内存器件在非固定业务模式下的剩余寿命。
本申请实施例提供的一种非易失内存器件剩余寿命预测方法，根据数据写入总量、月均数据写入、第三剩余百分比寿命、与第三剩余百分比寿命不同的第四剩余百分比寿命计算非易失内存器件在非固定业务模式下的剩余寿命，可以包括：
根据数据写入总量、第三剩余百分比寿命、与第三剩余百分比寿命不同的第四剩余百分比寿命计算非易失内存器件的写入耐受性；
根据非易失内存器件的写入耐受性、月均数据写入量、第三剩余百分比寿命计算非易失内存器件在非固定业务模式下的剩余寿命。
设定非易失内存器件的数据保存能力下降主要是由数据写入引起的，则在利用Data
write、month_avarage
write、p3、p4计算非易失内存器件在非固定业务模式下的剩余寿命时，可以先根据Data
write、p3、p4计算出非易失内存器件的写入耐受性(记为endurance
write，单位可以为GB)，即非易失内存器件寿命可以用该器件能承受的总写入数量来表示。然后，可以利用endurance
write、month_avarage
write、p3计算非易失内存器件在非固定业务模式下的剩余寿命。
本申请实施例提供的一种非易失内存器件剩余寿命预测方法，根据数据写入总量、第三剩余百分比寿命、与第三剩余百分比寿命不同的第四剩余百分比寿命计算非易失内存器件的写入耐受性，可以包括：
利用
计算非易失内存器件的写入耐受性endurance
write；
相应地，根据非易失内存器件的写入耐受性、月均数据写入量、第三 剩余百分比寿命计算非易失内存器件在非固定业务模式下的剩余寿命，可以包括：
利用
计算非易失内存器件在非固定业务模式下的剩余寿命
其中，Data
write为数据写入总量，p4为与第三剩余百分比寿命不同的第四剩余百分比寿命，p3为第三剩余百分比寿命，month_avarage
write为月均数据写入量。
需要说明的是，上述所计算出的life
1
remain的单位为年。
本申请实施例提供的一种非易失内存器件剩余寿命预测方法，在利用与非易失内存器件当前的业务模式对应的剩余寿命预测策略对非易失内存器件进行剩余寿命预测时，还可以包括：
判断非易失内存器件的健康等级是否发生降级，若是，则重新执行利用与非易失内存器件当前的业务模式对应的剩余寿命预测策略对非易失内存器件进行剩余寿命预测的步骤；
其中，非易失内存器件的健康等级为预先划分得到的，可以包括健康等级依次降低的正常、非严重、重要和致命。
在利用与非易失内存器件当前的业务模式对应的剩余寿命预测策略对非易失内存器件进行剩余寿命预测时，还可以判断非易失内存器件的健康等级是否发生降级，若否，则继续利用与非易失内存器件当前的业务模式对应的剩余寿命预测策略对非易失内存器件进行剩余寿命预测的步骤，若是，则重新进行剩余寿命的预测，即重新执行利用与非易失内存器件当前的业务模式对应的剩余寿命预测策略对非易失内存器件进行剩余寿命预测的步骤，以防止因非易失内存器件健康等级发生降级而对非易失内存器件的剩余寿命预测造成影响，以提高剩余寿命预测的准确性。
其中，非易失内存器件的健康等级为预先划分得到的，可以包括正常、非严重、重要和致命这四个健康等级，且这四个健康等级依次降低。另外，需要说明的是，为了提高剩余寿命预测的准确性，并为了提高非易失内存器件的可靠性，则可以在健康等级为正常和非严重的情况下利用与非易失 内存器件当前的业务模式对应的剩余寿命预测策略对非易失内存器件进行剩余寿命预测。
本申请实施例还提供了一种非易失内存器件剩余寿命预测装置，参见图4，其示出了本申请实施例提供的一种非易失内存器件剩余寿命预测装置的结构示意图，可以包括：
预先设定模块41，用于预先根据非易失内存器件中不同的业务模式设定与业务模式对应的剩余寿命预测策略；
确定模式模块42，用于采集非易失内存器件的运行参数，根据运行参数确定非易失内存器件当前的业务模式；
寿命预测模块43，用于利用与非易失内存器件当前的业务模式对应的剩余寿命预测策略对非易失内存器件进行剩余寿命预测；
提示模块44，用于判断预测出的剩余寿命是否大于设定值，若是，则发出提示。
本申请实施例提供的一种非易失内存器件剩余寿命预测装置，当确定非易失内存器件当前的业务模式为固定业务模式时，确定模式模块42可以包括：
第一获取单元，用于获取非易失内存器件在进入固定业务模式时的剩余百分比寿命初始值，并获取非易失内存器件在第一时间点处的第一剩余百分比寿命，且每隔第一预设时间间隔获取一次非易失内存器件的第二剩余百分比寿命；
第一判断单元，用于判断第二剩余百分比寿命与第一剩余百分比寿命是否相同，若否，则记录与第一剩余百分比寿命不同的第二剩余百分比寿命，并记录与第一剩余百分比寿命不同的第二剩余百分比寿命对应的第二时间点；
第一计算单元，用于利用剩余百分比寿命初始值、第一时间点、第一剩余百分比寿命、与第一剩余百分比寿命不同的第二剩余百分比寿命、第二时间点计算非易失内存器件在固定业务模式下的剩余寿命。
本申请实施例提供的一种非易失内存器件剩余寿命预测装置，第一计 算单元可以包括：
第一计算子单元，用于利用
计算非易失内存器件在固定业务模式下的剩余寿命
其中，p0为剩余百分比寿命初始值，t2为第二时间点，t1为第一时间点，p2为与第一剩余百分比寿命不同的第二剩余百分比寿命，p1为第一剩余百分比寿命。
本申请实施例提供的一种非易失内存器件剩余寿命预测装置，当确定非易失内存器件当前的业务模式为非固定业务模式时，确定模式模块42可以包括：
第二获取单元，用于获取非易失内存在第三时间点处的第三剩余百分比寿命，并开始记录非易失内存器件的数据写入量；
第三获取单元，用于每隔第二预设时间间隔获取一次非易失内存器件的第四剩余百分比寿命；
第二判断单元，用于判断第四剩余百分比寿命与第三剩余百分比寿命是否相同，若否，则记录与第三剩余百分比寿命不同的第四剩余百分比寿命，并结束记录非易失内存器件的数据写入量的步骤；
第四获取单元，用于获取非易失性内存器件从开始记录到结束记录的数据写入总量，并获取非易失内存器件的月均数据写入量；
第二计算单元，用于根据数据写入总量、月均数据写入量、第三剩余百分比寿命、与第三剩余百分比寿命不同的第四剩余百分比寿命计算非易失内存器件在非固定业务模式下的剩余寿命。
本申请实施例提供的一种非易失内存器件剩余寿命预测装置，第二计算单元可以包括：
第二计算子单元，用于根据数据写入总量、第三剩余百分比寿命、与第三剩余百分比寿命不同的第四剩余百分比寿命计算非易失内存器件的写入耐受性；
第三计算子单元，用于根据非易失内存器件的写入耐受性、月均数据写入量、第三剩余百分比寿命计算非易失内存器件在非固定业务模式下的 剩余寿命。
本申请实施例提供的一种非易失内存器件剩余寿命预测装置，第二计算子单元可以包括：
第四计算子单元，用于利用
计算非易失内存器件的写入耐受性endurance
write；
第五计算子单元，用于利用
计算非易失内存器件在非固定业务模式下的剩余寿命
其中，Data
write为数据写入总量，p4为与第三剩余百分比寿命不同的第四剩余百分比寿命，p3为第三剩余百分比寿命，month_avarage
write为月均数据写入量。
本申请实施例提供的一种非易失内存器件剩余寿命预测装置，还可以包括：
判断模块，用于在利用与非易失内存器件当前的业务模式对应的剩余寿命预测策略对非易失内存器件进行剩余寿命预测时，判断非易失内存器件的健康等级是否发生降级，若是，则重新执行利用与非易失内存器件当前的业务模式对应的剩余寿命预测策略对非易失内存器件进行剩余寿命预测的步骤；其中，非易失内存器件的健康等级为预先划分得到的，可以包括健康等级依次降低的正常、非严重、重要和致命。
本申请实施例提供的一种非易失内存器件剩余寿命预测设备，参见图5，其示出了本申请实施例提供的一种非易失内存器件剩余寿命预测设备的结构示意图，可以包括：
存储器51，用于存储计算机程序；
处理器52，用于执行存储器51存储的计算机程序时可实现如下步骤：
预先根据非易失内存器件中不同的业务模式设定与业务模式对应的剩余寿命预测策略；采集非易失内存器件的运行参数，根据运行参数确定非 易失内存器件当前的业务模式；利用与非易失内存器件当前的业务模式对应的剩余寿命预测策略对非易失内存器件进行剩余寿命预测；判断预测出的剩余寿命是否大于设定值，若是，则发出提示。
本申请实施例提供的一种计算机可读存储介质，计算机可读存储介质上存储有计算机程序，计算机程序被处理器执行时可实现如下步骤：
预先根据非易失内存器件中不同的业务模式设定与业务模式对应的剩余寿命预测策略；采集非易失内存器件的运行参数，根据运行参数确定非易失内存器件当前的业务模式；利用与非易失内存器件当前的业务模式对应的剩余寿命预测策略对非易失内存器件进行剩余寿命预测；判断预测出的剩余寿命是否大于设定值，若是，则发出提示。
本申请实施例提供的一种非易失内存器件剩余寿命预测装置、设备及计算机可读存储介质中相关部分的说明可以参见本申请实施例提供的一种非易失内存器件剩余寿命预测方法中对应部分的详细说明，在此不再赘述。
需要说明的是，在本文中，诸如第一和第二等之类的关系术语仅仅用来将一个实体或者操作与另一个实体或操作区分开来，而不一定要求或者暗示这些实体或操作之间存在任何这种实际的关系或者顺序。而且，术语“包括”、“包含”或者其任何其他变体意在涵盖非排他性的包含，从而使得包括一系列要素的过程、方法、物品或者设备所固有的要素。在没有更多限制的情况下，由语句“包括一个……”限定的要素，并不排除在包括所述要素的过程、方法、物品或者设备中还存在另外的相同要素。另外，本申请实施例提供的上述技术方案中与现有技术中对应技术方案实现原理一致的部分并未详细说明，以免过多赘述。对所公开的实施例的上述说明，使本领域技术人员能够实现或使用本申请。对这些实施例的多种修改对本领域技术人员来说将是显而易见的，本文中所定义的一般原理可以在不脱离本申请的精神或范围的情况下，在其它实施例中实现。因此，本申请将不会被限制于本文所示的这些实施例，而是要符合与本文所公开的原理和新颖特点相一致的最宽的范围。
Claims (10)
- 一种非易失内存器件剩余寿命预测方法，其特征在于，包括：预先根据非易失内存器件中不同的业务模式设定与所述业务模式对应的剩余寿命预测策略；采集所述非易失内存器件的运行参数，根据所述运行参数确定所述非易失内存器件当前的业务模式；利用与所述非易失内存器件当前的业务模式对应的剩余寿命预测策略对所述非易失内存器件进行剩余寿命预测；判断预测出的剩余寿命是否大于设定值，若是，则发出提示。
- 根据权利要求1所述的非易失内存器件剩余寿命预测方法，其特征在于，当确定所述非易失内存器件当前的业务模式为固定业务模式时，则利用与所述非易失内存器件当前的业务模式对应的剩余寿命预测策略对所述非易失内存器件进行剩余寿命预测，包括：获取所述非易失内存器件在进入所述固定业务模式时的剩余百分比寿命初始值，并获取所述非易失内存器件在第一时间点处的第一剩余百分比寿命，且每隔第一预设时间间隔获取一次所述非易失内存器件的第二剩余百分比寿命；判断所述第二剩余百分比寿命与所述第一剩余百分比寿命是否相同，若否，则记录与所述第一剩余百分比寿命不同的所述第二剩余百分比寿命，并记录与所述第一剩余百分比寿命不同的所述第二剩余百分比寿命对应的第二时间点；利用所述剩余百分比寿命初始值、所述第一时间点、所述第一剩余百分比寿命、与所述第一剩余百分比寿命不同的所述第二剩余百分比寿命、所述第二时间点计算所述非易失内存器件在所述固定业务模式下的剩余寿命。
- 根据权利要求2所述的非易失内存器件剩余寿命预测方法，其特征在于，利用所述剩余百分比寿命初始值、所述第一时间点、所述第一剩余百分比寿命、与所述第一剩余百分比寿命不同的所述第二剩余百分比寿命、所述第二时间点计算所述非易失内存器件在所述固定业务模式下的剩余寿 命，包括：利用计算所述非易失内存器件在所述固定业务模式下的剩余寿命
其中，p0为所述剩余百分比寿命初始值，t2为所述第二时间点，t1为所述第一时间点，p2为与所述第一剩余百分比寿命不同的所述第二剩余百分比寿命，p1为所述第一剩余百分比寿命。 - 根据权利要求1所述的非易失内存器件剩余寿命预测方法，其特征在于，当确定所述非易失内存器件当前的业务模式为非固定业务模式时，则利用与所述非易失内存器件当前的业务模式对应的剩余寿命预测策略对所述非易失内存器件进行剩余寿命预测，包括：获取所述非易失内存在第三时间点处的第三剩余百分比寿命，并开始记录所述非易失内存器件的数据写入量；每隔第二预设时间间隔获取一次所述非易失内存器件的第四剩余百分比寿命；判断所述第四剩余百分比寿命与所述第三剩余百分比寿命是否相同，若否，则记录与所述第三剩余百分比寿命不同的所述第四剩余百分比寿命，并结束所述记录所述非易失内存器件的数据写入量的步骤；获取所述非易失性内存器件从开始记录到结束记录的数据写入总量，并获取所述非易失内存器件的月均数据写入量；根据所述数据写入总量、所述月均数据写入量、所述第三剩余百分比寿命、与所述第三剩余百分比寿命不同的所述第四剩余百分比寿命计算所述非易失内存器件在所述非固定业务模式下的剩余寿命。
- 根据权利要求4所述的非易失内存器件剩余寿命预测方法，其特征在于，根据所述数据写入总量、所述月均数据写入、所述第三剩余百分比寿命、与所述第三剩余百分比寿命不同的所述第四剩余百分比寿命计算所述非易失内存器件在所述非固定业务模式下的剩余寿命，包括：根据所述数据写入总量、所述第三剩余百分比寿命、与所述第三剩余百分比寿命不同的所述第四剩余百分比寿命计算所述非易失内存器件的写 入耐受性；根据所述非易失内存器件的写入耐受性、所述月均数据写入量、所述第三剩余百分比寿命计算所述非易失内存器件在所述非固定业务模式下的剩余寿命。
- 根据权利要求5所述的非易失内存器件剩余寿命预测方法，其特征在于，根据所述数据写入总量、所述第三剩余百分比寿命、与所述第三剩余百分比寿命不同的所述第四剩余百分比寿命计算所述非易失内存器件的写入耐受性，包括：利用计算所述非易失内存器件的写入耐受性endurance write；
相应地，根据所述非易失内存器件的写入耐受性、所述月均数据写入量、所述第三剩余百分比寿命计算所述非易失内存器件在所述非固定业务模式下的剩余寿命，包括：利用计算所述非易失内存器件在所述非固定业务模式下的剩余寿命
其中，Data write为所述数据写入总量，p4为与所述第三剩余百分比寿命不同的所述第四剩余百分比寿命，p3为所述第三剩余百分比寿命，month_avarage write为所述月均数据写入量。 - 根据权利要求1至6任一项所述的非易失内存器件剩余寿命预测方法，其特征在于，在利用与所述非易失内存器件当前的业务模式对应的剩余寿命预测策略对所述非易失内存器件进行剩余寿命预测时，还包括：判断所述非易失内存器件的健康等级是否发生降级，若是，则重新执行所述利用与所述非易失内存器件当前的业务模式对应的剩余寿命预测策略对所述非易失内存器件进行剩余寿命预测的步骤；其中，所述非易失内存器件的健康等级为预先划分得到的，包括依次降低的正常、非严重、重要和致命四个健康等级。
- 一种非易失内存器件剩余寿命预测装置，其特征在于，包括：预先设定模块，用于预先根据非易失内存器件中不同的业务模式设定与所述业务模式对应的剩余寿命预测策略；确定模式模块，用于采集所述非易失内存器件的运行参数，根据所述运行参数确定所述非易失内存器件当前的业务模式；寿命预测模块，用于利用与所述非易失内存器件当前的业务模式对应的剩余寿命预测策略对所述非易失内存器件进行剩余寿命预测；提示模块，用于判断预测出的剩余寿命是否大于设定值，若是，则发出提示。
- 一种非易失内存器件剩余寿命预测设备，其特征在于，包括：存储器，用于存储计算机程序；处理器，用于执行所述计算机程序时实现如权利要求1至7任一项所述的非易失内存器件剩余寿命预测方法的步骤。
- 一种计算机可读存储介质，其特征在于，所述计算机可读存储介质上存储有计算机程序，所述计算机程序被处理器执行时实现如权利要求1至7任一项所述的非易失内存器件剩余寿命预测方法的步骤。
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| US20170160338A1 (en) * | 2015-12-07 | 2017-06-08 | Intel Corporation | Integrated circuit reliability assessment apparatus and method |
| CN109524048A (zh) * | 2018-10-22 | 2019-03-26 | 郑州云海信息技术有限公司 | 一种ssd盘的寿命预警方法及相关装置 |
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| US20170160338A1 (en) * | 2015-12-07 | 2017-06-08 | Intel Corporation | Integrated circuit reliability assessment apparatus and method |
| CN109582527A (zh) * | 2017-09-29 | 2019-04-05 | 群晖科技股份有限公司 | 存储服务器及其固态硬盘寿命监控方法 |
| CN109524048A (zh) * | 2018-10-22 | 2019-03-26 | 郑州云海信息技术有限公司 | 一种ssd盘的寿命预警方法及相关装置 |
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