Method, device, electronic device and computer readable medium for spare disk scheduling

Abstract

Embodiments of the present disclosure disclose spare part disk scheduling method and device, electronic equipment and computer readable medium. A specific embodiment of the method comprises: obtaining a list of to-be-monitored data center identifiers; for each data center corresponding to each data center identifier in the list of to-be-monitored data center identifiers, performing the following steps: collecting disk health status information of each disk included in the data center; based on the collected disk health status information, generating each remaining useful life prediction information corresponding to each disk; generating replacement disk information corresponding to a preset storage center within a preset time period; sending the replacement disk information to a replacement terminal corresponding to the data center as a replacement warning; generating disk spare part inventory information; and scheduling at least one spare part disk corresponding to the disk spare part inventory information to the data center. This embodiment reduces the situation of spare part scheduling lag, excess or deficiency.

Description

Technical Field

[0001] Embodiments of this disclosure relate to the field of computer technology, and more specifically to spare parts disk scheduling methods, apparatus, electronic devices, and computer-readable media. Background Technology

[0002] Modern society relies on services provided by data centers in every aspect (such as cloud computing, mobile payments, social media, and online office work). Any server outage can cause significant economic losses and negative social impacts. As the core carrier of data storage, the reliability of hard drives directly determines the continuity of the entire data service. Spare disk scheduling is the technology of dispatching spare disks to data centers. Currently, the common method for dispatching spare disks to data centers is to prepare inventory based on a fixed equipment ratio or lagging historical failure data, and then periodically dispatch spare disks to the data center.

[0003] However, when scheduling spare disks to the data center using the above method, the following technical problems often arise: Preparing inventory based on a fixed equipment ratio or outdated historical failure data, and periodically scheduling spare disks to the data center, can easily overlook the actual operating conditions and current health status of the disks. This leads to inaccurate inventory forecasts and often fails to meet the actual replacement needs caused by natural wear and tear of disks in a specific future period, thereby increasing the likelihood of delayed, excessive, or insufficient spare parts scheduling.

[0004] The information disclosed in this background section is only intended to enhance the understanding of the background of the inventive concept, and therefore may contain information that does not form prior art known to those skilled in the art. Summary of the Invention

[0005] The summary portion of this disclosure is intended to provide a brief overview of the concepts, which will be described in detail in the detailed description portion. This summary portion is not intended to identify key or essential features of the claimed technical solutions, nor is it intended to limit the scope of the claimed technical solutions.

[0006] Some embodiments of this disclosure provide spare parts disk scheduling methods, apparatuses, electronic devices, and computer-readable media to address one or more of the technical problems mentioned in the background section above.

[0007] In a first aspect, some embodiments of this disclosure provide a spare disk scheduling method, the method comprising: obtaining a list of data center identifiers to be monitored, wherein each data center identifier in the list of data center identifiers to be monitored corresponds to a data center including various disks; for each data center identifier in the list of data center identifiers to be monitored, performing the following steps: for each disk included in the data center, collecting disk health status information of the disk within a preset time period, wherein the disk corresponds to a disk identifier; generating remaining lifespan prediction information corresponding to each disk based on the collected disk health status information; generating replacement disk information corresponding to the preset storage center within the preset time period based on the remaining lifespan prediction information and a dynamic lifespan threshold; sending the replacement disk information as replacement warning information to a replacement terminal corresponding to the data center; generating disk spare parts inventory information based on the replacement disk information; and scheduling at least one spare disk corresponding to the disk spare parts inventory information to the data center.

[0008] Secondly, some embodiments of this disclosure provide a spare disk scheduling device, the device comprising: an acquisition unit configured to acquire a list of data center identifiers to be monitored, wherein each data center identifier in the list of data center identifiers to be monitored corresponds to a data center including various disks; and a scheduling unit configured to perform the following steps for each data center identifier in the list of data center identifiers to be monitored: for each disk included in the data center, collecting disk health status information of the disk within a preset time period, wherein the disk corresponds to a disk identifier; generating remaining lifespan prediction information corresponding to each disk based on the collected disk health status information; generating replacement disk information corresponding to the preset storage center within the preset time period based on the remaining lifespan prediction information and a dynamic lifespan threshold; sending the replacement disk information as replacement warning information to a replacement terminal corresponding to the data center; generating disk spare parts inventory information based on the replacement disk information; and scheduling at least one spare disk corresponding to the disk spare parts inventory information to the data center.

[0009] Thirdly, some embodiments of this disclosure provide an electronic device, including: one or more processors; and a storage device having one or more programs stored thereon, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the method described in any implementation of the first aspect above.

[0010] Fourthly, some embodiments of this disclosure provide a computer-readable medium having a computer program stored thereon, wherein the program, when executed by a processor, implements the method described in any of the implementations of the first aspect above.

[0011] The above embodiments of this disclosure have the following beneficial effects: the spare parts disk scheduling method of some embodiments of this disclosure reduces the situation of delayed, excessive, or insufficient spare parts scheduling. Specifically, the reason for the increase in delayed, excessive, or insufficient spare parts scheduling is that: inventory preparation based on a fixed equipment ratio or lagging historical fault data, and periodically scheduling spare parts disks to the data center, easily overlooks the actual operating conditions and current operating status of the disks, resulting in inaccurate inventory predictions. This often fails to meet the actual replacement needs caused by natural disk wear and tear in a specific future period, thus increasing the situation of delayed, excessive, or insufficient spare parts scheduling. Based on this, the spare parts disk scheduling method of some embodiments of this disclosure first obtains a list of data center identifiers to be monitored, wherein each data center identifier in the list of data center identifiers to be monitored corresponds to a data center including various disks. Thus, the list of data center identifiers to be monitored corresponding to each data center can be clearly defined. Then, for each data center identifier in the list of data center identifiers to be monitored, the following steps are performed: for each disk included in the data center, disk health status information of the disk is collected within a preset time period, wherein each disk corresponds to a disk identifier. Therefore, within a preset time period, health status information reflecting the actual operating status and current operating condition of each disk in each data center can be collected. Next, based on the collected disk health status information, predicted remaining lifespan information corresponding to each disk is generated. This allows for the prediction of the remaining lifespan of each disk based on its current operating health status information, thus assessing future disk replacement needs. Then, based on the aforementioned predicted remaining lifespan information and dynamic lifespan thresholds, replacement disk information corresponding to the aforementioned data center within the preset time period is generated. This allows for the generation of replacement disk information to determine replacement needs. Then, this replacement disk information is sent as replacement warning information to the replacement terminal corresponding to the aforementioned data center. Next, based on the aforementioned replacement disk information, disk spare parts inventory information is generated. This allows for the generation of disk spare parts inventory information that better meets future spare parts needs. Finally, at least one spare disk corresponding to the aforementioned disk spare parts inventory information is scheduled to the aforementioned data center. This allows for the scheduling of spare disks matching the data center's needs based on the generated disk spare parts inventory information. Because for each disk in the data center corresponding to each data center identifier, the remaining lifespan is predicted by real-time monitoring of disk health status and prediction of remaining lifespan. Based on the actual disk health status information, remaining lifespan prediction information is generated, and disk spare parts inventory information that is more in line with future spare parts needs is generated.Based on disk spare parts inventory information that better meets future spare parts needs, spare disks that match the data center’s needs are dispatched, reducing situations of delayed, excessive, or insufficient spare parts dispatch. Attached Figure Description

[0012] The above and other features, advantages, and aspects of the embodiments of this disclosure will become more apparent from the accompanying drawings and the following detailed description. Throughout the drawings, the same or similar reference numerals denote the same or similar elements. It should be understood that the drawings are schematic, and elements are not necessarily drawn to scale.

[0013] Figure 1 This is a flowchart of some embodiments of the spare parts disk scheduling method according to the present disclosure; Figure 2 This is a schematic diagram of the structure of some embodiments of the spare parts disk scheduling device according to the present disclosure; Figure 3 This is a schematic diagram of the structure of an electronic device suitable for implementing some embodiments of the present disclosure. Detailed Implementation

[0014] Embodiments of this disclosure will now be described in more detail with reference to the accompanying drawings. While some embodiments of this disclosure are shown in the drawings, it should be understood that this disclosure can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of this disclosure. It should be understood that the accompanying drawings and embodiments of this disclosure are for illustrative purposes only and are not intended to limit the scope of protection of this disclosure.

[0015] It should also be noted that, for ease of description, only the parts relevant to the invention are shown in the accompanying drawings. Unless otherwise specified, the embodiments and features described in this disclosure can be combined with each other.

[0016] It should be noted that the concepts of "first" and "second" mentioned in this disclosure are used only to distinguish different devices, modules or units, and are not used to limit the order of functions performed by these devices, modules or units or their interdependencies.

[0017] It should be noted that the terms "a" and "a plurality of" used in this disclosure are illustrative rather than restrictive, and those skilled in the art should understand that, unless otherwise expressly indicated in the context, they should be understood as "one or more".

[0018] The names of messages or information exchanged between multiple devices in the embodiments of this disclosure are for illustrative purposes only and are not intended to limit the scope of such messages or information.

[0019] This disclosure will now be described in detail with reference to the accompanying drawings and embodiments.

[0020] Figure 1 A flow 100 of some embodiments of a spare parts disk scheduling method according to the present disclosure is shown. The spare parts disk scheduling method includes the following steps: Step 101: Obtain the list of data center identifiers to be monitored.

[0021] In some embodiments, the execution entity (e.g., a computing device) of the spare disk scheduling method can obtain a list of data center identifiers to be monitored. Each data center identifier in the list corresponds to a data center including various disks. In practice, the execution entity can obtain the list of data center identifiers to be monitored from a preset configuration management database. The data center can be a physical facility that centrally stores and manages computer servers, storage devices, and network devices. The list of data center identifiers to be monitored can be a list storing the data center identifiers corresponding to various preset data centers. The data center identifier can be a symbol, number, or code (e.g., DC01) used to uniquely identify the data center.

[0022] Step 102: For each data center corresponding to each data center identifier in the list of data center identifiers to be monitored, perform the following steps: Step 1021: Collect disk health status information for each disk in the data center within a preset time period.

[0023] In some embodiments, the executing entity collects disk health status information for each disk among the disks included in the data center within a preset time period, wherein each disk has a corresponding disk identifier. The disk identifier can be a symbol, number, or code used to uniquely identify the disk.

[0024] In some optional implementations of certain embodiments, the aforementioned execution entity may collect disk health status information of the aforementioned disk within a preset time period through the following steps: The first step involves invoking the disk's preset monitoring interface to collect disk health parameter information within a preset time period at a preset sampling frequency, resulting in a health parameter information sequence. Each health parameter in this sequence includes at least three sub-items: read error rate, reallocated sector count, unmapped sector count, uncorrectable sector count, head flight altitude, temperature, start / stop count, and power-on time. Each health parameter corresponds to a sampling time. The preset monitoring interface can be a command-line tool interface (e.g., the smartctl command-line tool interface) used for collecting health parameter information. The health parameter data can be SMART data from the disk. The health parameter information sequence can be an ordered set of multiple health parameter information pieces acquired within the preset time period and at the preset sampling frequency, arranged chronologically. The read error rate can be the frequency of hardware-level errors occurring when reading data from the disk within a certain period (e.g., the sampling interval). The above-mentioned reallocation sector count represents the total number of times the disk moved data from "unstable" or "corrupted" original sectors to spare sectors within a certain period. The above-mentioned unmapped sector count represents the number of sectors marked as "unstable" by the disk firmware (hard drive firmware) within a certain period. The above-mentioned uncorrectable sector count represents the number of sectors encountered within a certain period that could not be repaired by error correction mechanisms. The above-mentioned head flight altitude represents the average distance between the read / write head and the disk surface within a certain period. The above-mentioned temperature represents the disk's operating temperature within a certain period. The above-mentioned start / stop count represents the number of times the hard drive was turned on or off. The above-mentioned power-on time represents the cumulative power-on time of the disk since its last boot.

[0025] The second step is to determine the above sequence of health parameter information as disk health status information.

[0026] Step 1022: Based on the collected health status information of each disk, generate prediction information of the remaining lifespan of each disk.

[0027] In some embodiments, the executing entity can generate remaining lifespan prediction information corresponding to each disk based on the collected disk health status information. Each remaining lifespan prediction information corresponds to a disk health status information among the disk health status information. The remaining lifespan prediction information includes a remaining lifespan value (e.g., 30 days). The remaining lifespan prediction information can represent the remaining lifespan of the disk. Each remaining lifespan prediction information corresponds to one disk among the disks included in the data center, and the remaining lifespan prediction information includes a remaining lifespan value.

[0028] In some optional implementations of certain embodiments, the aforementioned execution entity can generate remaining lifespan prediction information corresponding to each disk based on the collected health status information of each disk through the following steps: The first step involves inputting each disk health status information from the aforementioned disk health status information into a pre-trained disk remaining lifespan prediction model to obtain remaining lifespan prediction information. This disk remaining lifespan prediction model can be a regression model (e.g., a Support Vector Regression model) trained using a machine learning algorithm, capable of learning the degradation patterns from disk health status information and predicting its remaining lifespan. As an example, at least one sample disk health status information and the corresponding sample remaining lifespan prediction information for each sample disk health status information can be obtained first. Then, using each sample disk health status information as input and the corresponding sample remaining lifespan prediction information as the desired output, the disk remaining lifespan prediction model is trained.

[0029] In some optional implementations of certain embodiments, the aforementioned execution entity can generate remaining lifespan prediction information corresponding to each disk based on the collected health status information of each disk through the following steps: The first step is to perform the following steps for each disk health status information in the above-mentioned disk health status information: The first sub-step involves performing the following health determination steps for each health parameter included in the aforementioned disk health status information: Sub-step one involves obtaining at least three warning thresholds and at least three dynamic weights corresponding to at least three health parameter sub-information items included in the aforementioned health parameter information. Each of the at least three warning thresholds corresponds to one of the at least three health parameter sub-information items and one of the at least three dynamic weights. The warning thresholds can be preset thresholds corresponding to the health parameter sub-information items. In practice, the executing entity can determine the collection time corresponding to the health parameter information as the target collection time. Then, the executing entity can determine at least three target health parameter sub-information items as at least three target health parameter sub-information items. Then, for each target health parameter sub-information item, the executing entity can obtain the disk health status information and the corresponding remaining lifetime of each disk for a preset time period before the target collection time. Then, a model such as Random Forest or XGBoost is used to evaluate the SHAP value (i.e., dynamic weight) of the target health parameter sub-information on the remaining lifetime of the disk. For example, at least three target health parameter sub-information can be the reallocated sector count, the unmapped sector count, and the read error rate. The dynamic weight corresponding to the reallocated sector count can be 0.5, the dynamic weight corresponding to the unmapped sector count can be 0.3, and the dynamic weight corresponding to the read error rate can be 0.2. The preset time period before the target acquisition time can be a time period with a minimum interval of 1 / 2000 before the target acquisition time and a preset time length of 1 / 2000. As an example, the target acquisition time is 10:00 on November 15, 2025, the preset interval is 1 month (counting backwards from the target acquisition time), and the preset time period length is 30 minutes. The preset time period can be from 9:30 to 10:00 on October 15, 2025. Optionally, each of the above at least three dynamic weights can be manually set as a weight value corresponding to the health parameter sub-information. Optionally, the process of assigning dynamic weights to each health parameter sub-information is as follows: First, construct a training dataset: Obtain a historical dataset containing a sequence of health status information of multiple disks during their lifecycle. For each health status information in the sequence, its multiple health parameter sub-information (e.g., read error rate, reallocation sector count, power-on time) are treated as a feature (X) of a sample. Simultaneously, a true remaining lifetime value (Y) is assigned to this health status information, which is the actual time difference between the time the health status information was collected and the time of disk failure. The second step is to train a prediction model and evaluate its contribution: using the training dataset described above, a regression model (e.g., XGBoost or Random Forest regression model) is trained. After the model training is complete, SHAP value analysis is used to evaluate the contribution of each health parameter sub-information to the model's prediction results.The third step is to determine the dynamic weights: the average absolute SHAP value of a health parameter sub-information over all training samples is normalized and used as the dynamic weight of that health parameter sub-information.

[0030] Sub-step two involves generating at least three single-dimensional health scores based on the aforementioned at least three warning thresholds and at least three health parameter sub-information items. Each of the at least three single-dimensional health scores corresponds to one of the aforementioned at least three health parameter sub-information items. The single-dimensional health score can be the health score corresponding to its corresponding health parameter sub-information (e.g., 0.5).

[0031] Sub-step three involves generating a health score corresponding to the aforementioned health parameter information based on the at least three single-dimensional health score information and the at least three dynamic weights. Each of the at least three dynamic weights corresponds to a single-dimensional health score information among the at least three single-dimensional health score information. In practice, firstly, for each single-dimensional health score information among the at least three single-dimensional health score information, the executing entity can determine the dynamic weight corresponding to the single-dimensional health score information among the at least three dynamic weights as the target dynamic weight. Then, the executing entity can determine the target single-dimensional health score by multiplying the health score represented by the single-dimensional health score information by the target dynamic weight. Finally, the executing entity can determine the mean of the determined target single-dimensional health scores as the health score corresponding to the aforementioned health parameter information.

[0032] The second sub-step involves generating a target health score based on the generated health scores. In practice, the aforementioned implementing entity can determine the target health score as the average of the various health scores.

[0033] The third sub-step involves identifying the disk identifier corresponding to the aforementioned disk health status information as the disk identifier to be queried.

[0034] The fourth sub-step involves obtaining the lifetime decay curve information corresponding to the disk identifier to be queried. This lifetime decay curve information can be a structured data model defining the mapping relationship between disk health range information and remaining lifespan information. The lifetime decay curve information includes information on each mapping point, and each mapping point includes health range information and remaining lifespan information. In practice, the lifetime decay curve information corresponding to the disk identifier to be queried can be obtained from a preset configuration management database. The health range information can represent the range of health (e.g., [0.9-1]). The remaining lifespan information can be the remaining lifespan value of the disk, i.e., the remaining usable time, when the health falls within the range represented by the health range information.

[0035] The fifth sub-step involves generating remaining useful life prediction information based on the aforementioned target health level and the aforementioned lifespan decay curve information. In practice, the executing entity can query the health level range information corresponding to the range where the target health level is located in the lifespan decay curve information, and determine the health level range information corresponding to the range where the target health level is located as the target health level range information. Next, the mapping point information where the target health level range information is located can be determined as the target mapping point information. Afterward, the remaining useful life information included in the target mapping point information can be determined as the remaining useful life prediction information.

[0036] In some optional implementations of certain embodiments, the aforementioned executing entity may generate at least three single-dimensional health score information based on the aforementioned at least three warning thresholds and the aforementioned at least three health parameter sub-information through the following steps: First, for each of the at least three health parameter sub-information items mentioned above, perform the following steps: The first sub-step involves determining the target warning threshold as the warning threshold that corresponds to the health parameter sub-information among the above at least three warning thresholds.

[0037] The second sub-step involves generating a single-dimensional health score based on the aforementioned target warning threshold and the aforementioned health parameter sub-information. In practice, the executing entity can determine the parameter value represented by the health parameter sub-information as the target parameter value. Then, the ratio of the target parameter value to the target warning threshold can be determined as the first value. Next, the difference between a preset value (e.g., 1) and the first value is determined as the target difference. In response to determining that the target difference is greater than or equal to zero, the executing entity can determine the target difference as the single-dimensional health score. In response to determining that the target difference is less than zero, zero can be determined as the single-dimensional health score. For example, the parameter value represented by the aforementioned health parameter sub-information can be a reallocation sector count of 200, and the target warning threshold corresponding to the reallocation sector count can be 400. The first value can be the ratio of 200 to 400, i.e., 200 / 400. The target difference can be the difference between 1 and 200 / 400, which can be 0.5.

[0038] Step 1023: Based on the remaining useful life prediction information and dynamic useful life threshold, generate disk replacement information corresponding to the preset storage center within the preset time period.

[0039] In some embodiments, the execution entity may generate disk replacement information corresponding to the preset storage center within a preset time period based on the remaining useful life prediction information and dynamic useful life threshold.

[0040] In addressing the technical challenges mentioned above, the application scenario of disk operation and maintenance management in data centers (or cloud platforms), particularly those requiring extremely high business continuity and data reliability (e.g., online real-time services like payments, social media, and games), often presents the following technical issues: Because different data centers have varying business requirements and storage loads, using static lifespan thresholds for disk replacement decisions and generating replacement information for all data centers fails to consider the workload and usage patterns of different data centers. Disks in some high-load data centers may age faster than the static thresholds predict, resulting in poor accuracy of replacement information and inadequate timeliness of disk replacement warnings. Therefore, this application scenario requires the following characteristics: suitable for data center scenarios requiring extremely high business continuity and data reliability.

[0041] In some optional implementations of certain embodiments, the aforementioned execution entity can generate disk replacement information corresponding to the aforementioned preset storage center within a preset time period based on the aforementioned remaining lifetime prediction information and dynamic lifetime thresholds through the following steps: The first step is to obtain the service identifier and storage load information corresponding to the aforementioned data center. The storage load information includes the average disk utilization rate. This average disk utilization rate can be the average utilization rate of each disk within the data center. The aforementioned service refers to the identifier of the service carried by the data center (e.g., database service, big data processing).

[0042] The second step is to obtain the preset service lifetime mapping table and the preset load lifetime threshold mapping table. The preset service lifetime threshold mapping table includes lifetime threshold mapping information for each service, and the preset load lifetime threshold mapping table includes lifetime threshold mapping information for each load lifetime threshold. Each load lifetime threshold mapping information includes a load level identifier and a lifetime threshold. The preset service lifetime mapping table can be a table recording the correspondence between service identifiers and lifetime thresholds. The preset load lifetime threshold mapping table can be a table recording the correspondence between load level identifiers and lifetime thresholds.

[0043] The third step is to determine the service lifetime threshold mapping information containing the aforementioned service identifier in the preset service lifetime mapping table as the target service lifetime threshold mapping information. This target service lifetime threshold mapping information includes both the service identifier and the lifetime threshold.

[0044] The fourth step is to determine the lifespan threshold included in the above target service lifespan threshold mapping information as the first lifespan threshold.

[0045] The fifth step is to generate a load level identifier to be mapped based on the storage load information. In practice, the execution entity determines the average disk utilization included in the storage load information as the target average disk utilization. Then, the execution entity can determine a preset utilization range that includes the target average disk utilization as the target preset utilization range. Each preset utilization range corresponds to a load level identifier. This load level identifier can be a load grade identifier (e.g., Level 1). Finally, the execution entity can determine the load grade identifier corresponding to the target preset utilization range as the load level identifier to be mapped. For example, each preset utilization range could be: Preset utilization range 1: 0%-40%, corresponding to a Level 1 load level identifier; Preset utilization range 2: 40%-70%, corresponding to a Level 2 load level identifier; Preset utilization range 3: 70%-90%, corresponding to a Level 3 load level identifier; Preset utilization range 4: 90%-100%, corresponding to a Level 4 load level identifier.

[0046] The sixth step is to determine the load lifetime threshold mapping information whose load level identifier is the same as the load level identifier to be mapped mentioned above as the target load lifetime threshold mapping information.

[0047] Step 7: Determine the lifetime threshold included in the above target load lifetime threshold mapping information as the second lifetime threshold.

[0048] Step 8: Determine the dynamic lifetime threshold as the lifetime threshold that satisfies the preset condition between the first lifetime threshold and the second lifetime threshold. The preset condition can be the smaller of the two.

[0049] Step 9: Based on the above dynamic lifetime threshold and the above remaining lifetime prediction information, generate disk replacement information corresponding to the above preset storage center within the preset time period.

[0050] The above technical solution and its related content, combined with step 1024, serve as an inventive point of this disclosure, solving the technical problem of "poor timeliness of disk replacement warnings." Factors leading to poor timeliness of disk replacement warnings are often as follows: Since the service requirements and storage loads of different data centers vary, using static lifespan thresholds for disk replacement decisions across all different data centers to generate replacement information does not consider the workload and usage of different data centers. Disks in some high-load data centers may age faster than the static thresholds predict, resulting in poor accuracy of the generated replacement information and consequently, poor timeliness of disk replacement warnings. Solving these factors can improve the timeliness of disk replacement warnings. To achieve this, firstly, the service identifier and storage load information corresponding to the aforementioned data centers are obtained. Then, a preset service lifespan mapping table and a preset load lifespan threshold mapping table are obtained. The preset service lifespan threshold mapping table includes various service lifespan threshold mapping information, and the load lifespan threshold mapping table includes various load lifespan threshold mapping information. Each load lifespan threshold mapping information includes a load level identifier and a lifespan threshold. Next, the service lifetime threshold mapping information containing the aforementioned service identifier in the preset service lifetime mapping table is determined as the target service lifetime threshold mapping information, wherein the target service lifetime threshold mapping information includes the service identifier and the lifetime threshold. Then, the lifetime threshold included in the target service lifetime threshold mapping information is determined as the first lifetime threshold. Thus, a baseline lifetime expectation value, i.e., the first lifetime threshold, is matched for the specific service type currently carried by the data center. Next, a load level identifier to be mapped is generated based on storage load information. Then, the load lifetime threshold mapping information containing the same load level identifier as the load level identifier to be mapped is determined as the target load lifetime threshold mapping information. Then, the lifetime threshold included in the target load lifetime threshold mapping information is determined as the second lifetime threshold. Thus, another lifetime expectation value based on the current workload, i.e., the second lifetime threshold, can be matched according to the real-time operating load of the data center. The lifetime threshold that meets the preset conditions among the first lifetime threshold and the second lifetime threshold is determined as the dynamic lifetime threshold. Therefore, a lifetime threshold that meets preset conditions (e.g., the smaller of the two) can be determined as the dynamic lifetime threshold. This integrates information from both business characteristics and real-time load, generating a dynamic lifetime threshold that addresses the dynamic changes of the data center. Subsequently, based on the dynamic lifetime threshold and the remaining lifetime prediction information, disk replacement information corresponding to the preset storage center within a preset time period is generated. Thus, by combining the dynamic lifetime threshold generated from both business type and real-time load dimensions, disk replacement information can be generated, overcoming the limitation of a single static threshold being unable to adapt to different workloads and usage scenarios, and improving the accuracy of disk replacement information.Because it uses a combination of business type and real-time load to generate dynamic lifetime thresholds, and then generates disk replacement information based on these thresholds, it overcomes the limitations of a single static threshold that cannot adapt to different workloads and usage scenarios. Considering the workload and usage of different data centers, the accuracy of the generated disk replacement information is improved. Combined with step 1024, the aforementioned disk replacement information is sent as replacement warning information to the replacement terminal corresponding to the data center. Therefore, more accurate disk replacement information can be used as replacement warning information for the replacement terminal, improving the timeliness of disk replacement warnings.

[0051] In addressing the technical problems mentioned above, the application scenario of disk operation and maintenance management in large-scale distributed data centers (or cloud platforms) often presents the following challenges: The generated disk replacement information is typically a list of identifiers corresponding to a pre-defined storage center, containing identifiers of the disks to be replaced. Data center personnel cannot determine which disk should be replaced first from the list alone, nor are they provided with suggestions or a planned replacement time, resulting in a poor user experience. This application scenario requires the following characteristics: It must be suitable for disk replacement in large-scale distributed data centers. Faced with these technical problems, we have decided to adopt the following solution: In some optional implementations of certain embodiments, the aforementioned execution entity can generate disk replacement information corresponding to the aforementioned preset storage center within a preset time period based on the aforementioned dynamic lifetime threshold and the aforementioned remaining lifetime prediction information through the following steps: The first step is to determine, in response to the determination that there is a remaining useful life value less than or equal to the dynamic useful life threshold among the remaining useful life values ​​included in the above remaining useful life prediction information, at least one remaining useful life value less than or equal to the dynamic useful life threshold among the above remaining useful life values.

[0052] The second step is to sort at least one of the remaining useful life values ​​from smallest to largest to obtain a sequence of remaining useful life values.

[0053] The third step is to determine at least one disk identifier corresponding to at least one disk with the at least one remaining useful life value among the disks included in the data center as a disk identifier set.

[0054] The fourth step is to obtain the work time information of the replacement personnel from the aforementioned replacement terminal. This work time information can be a list of work hours or time periods, such as weekdays 09:00-18:00 or [(09:00-17:00"), (18:00-22:00)].

[0055] Fifth step, obtain the current time. The current time can be the real-time system time.

[0056] Step 6: Input the current time, the disk identifier set, the remaining lifetime value sequence, and the replacement personnel's working time information into the replacement time planning model to obtain the planned replacement time set. Each planned replacement time in the planned replacement time set corresponds to a disk identifier in the disk identifier set. The replacement time planning model can be a mathematical model (e.g., a mixed integer programming (MIP) model) that takes the current time, disk identifier set, remaining lifetime value sequence, and replacement personnel's working time information as input and uses an optimization algorithm or scheduling algorithm to plan an optimal replacement time for each disk. The objective function of the mathematical model can be to prioritize replacing disks with shorter remaining lifetimes, and the replacement time must fall within the time range represented by the replacement personnel's working time information. The constraint condition of the mathematical model can be a lifetime constraint: replacement must be completed within the remaining lifetime, and only one disk can be replaced at a time. The executing entity can input the disk identifier set, remaining lifetime value sequence, and replacement personnel's working time information into the replacement time planning model and solve for the optimal solution using a solver (e.g., CPLEX, Gurobi) to obtain the planned replacement time set. As an example, the current time could be 16:10 on November 15, 2025. The disk identifier set could be {Disk D1, Disk D2}. The remaining lifetime value sequence could be {Disk D1: 0.5 days, Disk D2: 1 day}, then the replacement time set could be {Replacement time of Disk D1: 16:30 on November 15, 2025; Replacement time of Disk D2: 09:00 on November 16, 2025}.

[0057] Step 6: For each disk identifier in the aforementioned disk identifier set, query the disk specification information corresponding to the disk identifier from the preset database, and determine the planned replacement time, the disk specification information, and the disk identifier as the disk replacement sub-information. The disk specification information represents the disk's technical parameters and characteristics, describing key factors such as performance, capacity, and interface type.

[0058] Step 7: Confirm the identified sub-information of each disk to be replaced as the disk replacement information.

[0059] The above technical solution and its related content, combined with step 1024, serve as an inventive point of this disclosure, solving the technical problem of "poor user experience when replacing terminals." Factors leading to a poor user experience when replacing terminals often include: the generated disk replacement information is typically an identifier list containing disk identifiers to be replaced, corresponding to a preset storage center. Data center personnel cannot determine from the list which disk should be replaced first, nor do they provide suggestions or a planned replacement time, resulting in a poor user experience when replacing terminals. Solving these factors can improve the user experience when replacing terminals. To achieve this, firstly, in response to determining that among the remaining lifespan values ​​included in the aforementioned remaining lifespan prediction information, there exists a value less than or equal to the aforementioned dynamic lifespan threshold, at least one remaining lifespan value less than or equal to the aforementioned dynamic lifespan threshold is determined. Thus, at least one remaining lifespan value that reaches or falls below the dynamic lifespan threshold can be filtered out. Next, the at least one remaining lifespan value is sorted from smallest to largest to obtain a remaining lifespan value sequence. Then, at least one disk identifier corresponding to at least one disk among the disks included in the aforementioned data center that corresponds to the at least one remaining lifespan value is determined as a disk identifier set. Therefore, a set of disk identifiers for each disk that needs to be replaced can be obtained. Then, the working time information of the replacement personnel is obtained from the replacement terminal. This allows for the acquisition of the time range within which the replacement personnel can perform the replacement work, ensuring that the replacement operation can be carried out within the available personnel time when planning the disk replacement time. Next, the current time is obtained. Then, the current time, the disk identifier set, the remaining lifespan value sequence, and the replacement personnel's working time information are input into the replacement time planning model to obtain a planned replacement time set, where each planned replacement time in the planned replacement time set corresponds to a disk identifier in the disk identifier set. Thus, a specific planned replacement time can be generated for each disk that needs to be replaced using the replacement time planning model. Afterwards, for each disk identifier in the disk identifier set, the disk specification information corresponding to the disk identifier is queried from a preset database, and the planned replacement time, the disk specification information, and the disk identifier corresponding to the disk identifier are determined as the replacement disk sub-information. This allows for the acquisition of detailed specification information for each disk that needs to be replaced, which is then integrated with the planned replacement time and the disk identifier into replacement disk sub-information. Finally, each determined replacement disk sub-information is used as the replacement disk information. Because a specific replacement time is planned for each disk, replacement disk sub-information is generated, which includes the planned replacement time, the disk specification information, and the disk identifier. Combined with step 1024, this replacement disk information is sent as replacement warning information to the replacement terminal corresponding to the data center.Therefore, disk replacement information, including the planned replacement time for each disk to be replaced, the disk specifications, and the disk identifier, can be sent to the replacement terminal. Data center replacement personnel can then determine which disk to replace first based on this information and know the planned replacement time, thus improving the user experience at the replacement terminal.

[0060] In some optional implementations of certain embodiments, after generating the remaining lifespan prediction information corresponding to each disk based on the collected disk health status information, the execution entity may further perform the following steps: In response to determining that there is a remaining life value less than or equal to the preset life threshold among the remaining life values ​​included in the above remaining life prediction information, at least one remaining life prediction information containing at least one remaining life value less than or equal to the preset life threshold is determined as at least one target remaining life prediction information.

[0061] The second step involves identifying the disk identifier of at least one disk corresponding to the aforementioned at least one target remaining useful life prediction information as at least one data disk identifier to be transferred. Each of the at least one data disk identifiers to be transferred corresponds to one of the at least one remaining useful life values. The third step is to sort the at least one data disk identifier to be transferred based on the above-mentioned at least one remaining useful life value to obtain the data disk identifier sequence.

[0062] The fourth step is to determine the disk identifiers corresponding to each disk in the aforementioned data center as a disk identifier set; The fifth step is to remove at least one disk identifier from the disk identifier set to update the disk identifier set, thus obtaining an updated disk identifier set.

[0063] Step 6: For each data disk identifier in the data disk identifier sequence, select an updated disk identifier from the updated disk identifier set as the transfer disk identifier, and transfer the data stored on the disk corresponding to the data disk identifier to the disk corresponding to the aforementioned transfer disk identifier.

[0064] The above-described technical solution and its related content, as an inventive point of this disclosure, solve the technical problem of "reducing data loss in data centers". Factors contributing to increased data loss in data centers often include: passive data recovery methods are typically used, triggering data recovery only after the disk actually malfunctions or fails completely (e.g., using RAID redundancy). This approach is extremely risky because the disk may fail completely without warning, resulting in unrecoverable data and increasing the likelihood of data loss in data centers. Solving these factors allows for a shift in data protection strategies from "passive recovery" to "active migration," enabling secure data transfer before physical disk failure occurs, thereby reducing data loss in data centers. To achieve this, firstly, in response to determining that at least one of the remaining lifetime values ​​included in the aforementioned remaining lifetime prediction information is less than or equal to the aforementioned preset lifetime threshold, at least one remaining lifetime prediction information containing at least one remaining lifetime value less than or equal to the aforementioned preset lifetime threshold is determined as at least one target remaining lifetime prediction information. Then, the disk identifiers of at least one disk corresponding to the predicted remaining useful life of the at least one target are determined as at least one data disk identifier to be transferred, wherein each of the at least one data disk identifier to be transferred corresponds to a remaining useful life value among at least one remaining useful life value. Thus, disks with remaining useful life below a threshold can be marked as "disks to be transferred," resulting in at least one data disk identifier to be transferred. Next, based on the at least one remaining useful life value, the at least one data disk identifier to be transferred is sorted to obtain a data disk identifier sequence. Then, the disk identifiers corresponding to each disk included in the data center are determined as a disk identifier set. Next, at least one data disk identifier to be transferred is deleted from the disk identifier set to update the disk identifier set, resulting in an updated disk identifier set. Thus, an updated disk identifier set corresponding to each disk receiving the transferred data can be obtained. Next, for each data disk identifier to be transferred in the data disk identifier sequence, an updated disk identifier is selected from the updated disk identifier set as a transfer disk identifier, and the data stored on the disk corresponding to the data disk identifier to be transferred is transferred to the disk corresponding to the transfer disk identifier. Therefore, a healthy disk can be selected from the updated disk identifier set as the target disk to begin data migration, ensuring that the data on the original disk can be successfully migrated to the new disk before a failure occurs. This reduces the possibility of data loss when disks within the data center fail.

[0065] Step 1024: Send the disk replacement information as a replacement warning to the replacement terminal corresponding to the data center.

[0066] In some embodiments, the executing entity may send the disk replacement information as a replacement warning to the replacement terminal corresponding to the data center. The replacement terminal may be a terminal device (e.g., a mobile phone or computer) used by the replacement personnel in the data center.

[0067] Step 1025: Generate disk spare parts inventory information based on the disk replacement information.

[0068] In some embodiments, the aforementioned executing entity may generate disk spare parts inventory information based on the aforementioned disk replacement information.

[0069] In some optional implementations of certain embodiments, the aforementioned execution entity may generate disk spare parts inventory information based on the aforementioned disk replacement information through the following steps: The first step is to deduplicate each of the disk replacement sub-information included in the disk replacement sub-information to obtain each deduplicated disk replacement sub-information.

[0070] The second step is to determine the number of deduplication replacement disk sub-information in each of the above deduplication replacement disk sub-information as the number of spare disks corresponding to the above deduplication replacement disk sub-information, and to determine the deduplication replacement disk sub-information and the number of spare disks as disk spare inventory sub-information.

[0071] The third step is to determine the disk spare parts inventory sub-information as the disk spare parts inventory information.

[0072] Step 1026: Schedule at least one spare disk corresponding to the disk spare parts inventory information to the data center.

[0073] In some embodiments, the executing entity may schedule at least one spare disk corresponding to the disk spare parts inventory information to the data center. In practice, the executing entity may send the disk spare parts inventory information and the data center identifier corresponding to the data center to the terminal of the disk warehouse management personnel to notify the disk warehouse management personnel to schedule at least one spare disk corresponding to the disk spare parts inventory information to the data center.

[0074] The above embodiments of this disclosure have the following beneficial effects: the spare parts disk scheduling method of some embodiments of this disclosure reduces the situation of delayed, excessive, or insufficient spare parts scheduling. Specifically, the reason for the increase in delayed, excessive, or insufficient spare parts scheduling is that: inventory preparation based on a fixed equipment ratio or lagging historical fault data, and periodically scheduling spare parts disks to the data center, easily overlooks the actual operating conditions and current operating status of the disks, resulting in inaccurate inventory predictions. This often fails to meet the actual replacement needs caused by natural disk wear and tear in a specific future period, thus increasing the situation of delayed, excessive, or insufficient spare parts scheduling. Based on this, the spare parts disk scheduling method of some embodiments of this disclosure first obtains a list of data center identifiers to be monitored, wherein each data center identifier in the list of data center identifiers to be monitored corresponds to a data center including various disks. Thus, the list of data center identifiers to be monitored corresponding to each data center can be clearly defined. Then, for each data center identifier in the list of data center identifiers to be monitored, the following steps are performed: for each disk included in the data center, disk health status information of the disk is collected within a preset time period, wherein each disk corresponds to a disk identifier. Therefore, within a preset time period, health status information reflecting the actual operating status and current operating condition of each disk in each data center can be collected. Next, based on the collected health status information of each disk, prediction information of the remaining lifespan corresponding to each disk is generated. Thus, by using the health status information of the disk's current operating status, the remaining lifespan of each disk can be predicted to assess future disk replacement needs. Then, based on the aforementioned remaining lifespan prediction information and dynamic lifespan thresholds, replacement disk information corresponding to the aforementioned data center within the preset time period is generated. This generates replacement disk information used to determine replacement needs. Then, this replacement disk information is sent as replacement warning information to the replacement terminal corresponding to the aforementioned data center. Next, based on the aforementioned replacement disk information, disk spare parts inventory information is generated. Thus, based on the replacement disk information, disk spare parts inventory information that better meets future spare parts needs can be generated. Finally, at least one spare disk corresponding to the aforementioned disk spare parts inventory information is scheduled to the aforementioned data center. Thus, based on the generated disk spare parts inventory information, spare disks matching the data center's needs can be scheduled to the data center. Because for each disk in the data center corresponding to each data center identifier, the remaining lifespan is predicted by real-time monitoring of disk health status and prediction of remaining lifespan. Based on the actual disk health status information, remaining lifespan prediction information is generated, and disk spare parts inventory information that is more in line with future spare parts needs is generated.Based on disk spare parts inventory information that better meets future spare parts needs, spare disks that match the data center’s needs are dispatched, reducing situations of delayed, excessive, or insufficient spare parts dispatch.

[0075] Further reference Figure 2 As an implementation of the methods shown in the figures, this disclosure provides some embodiments of a spare parts disk scheduling device, which are similar to... Figure 1 Corresponding to the method embodiments shown, the device can be specifically applied to various electronic devices.

[0076] like Figure 2 As shown, a spare disk scheduling device 200 in some embodiments includes an acquisition unit 201 and a scheduling unit 204. The acquisition unit 201 is configured to acquire a list of data center identifiers to be monitored, wherein each data center identifier in the list corresponds to a data center including various disks. The scheduling unit 202 is configured to perform the following steps for each data center identifier in the list: for each disk included in the data center, collect disk health status information of the disk within a preset time period, wherein each disk corresponds to a disk identifier; based on the collected disk health status information, generate remaining lifespan prediction information corresponding to each disk; based on the remaining lifespan prediction information and a dynamic lifespan threshold, generate replacement disk information corresponding to the preset storage center within the preset time period; send the replacement disk information as replacement warning information to the replacement terminal corresponding to the data center; generate disk spare parts inventory information based on the replacement disk information; and schedule at least one spare disk corresponding to the disk spare parts inventory information to the data center.

[0077] It is understandable that the units described in the device 200 are related to the reference. Figure 1 The steps in the method described above correspond to each other. Therefore, the operations, features, and beneficial effects described above for the method also apply to the device 200 and the units contained therein, and will not be repeated here.

[0078] The following is for reference. Figure 3 It shows a schematic diagram of the structure of an electronic device 300 suitable for implementing some embodiments of the present disclosure. Figure 3 The electronic device shown is merely an example and should not be construed as limiting the functionality and scope of the embodiments of this disclosure.

[0079] like Figure 3As shown, the electronic device 300 may include a processing unit (e.g., a central processing unit, a graphics processing unit, etc.) 301, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 302 or a program loaded from a storage device 308 into a random access memory (RAM) 303. The RAM 303 also stores various programs and data required for the operation of the electronic device 300. The processing unit 301, ROM 302, and RAM 303 are interconnected via a bus 304. An input / output (I / O) interface 305 is also connected to the bus 304.

[0080] Typically, the following devices can be connected to I / O interface 305: input devices 306 including, for example, touchscreens, touchpads, keyboards, mice, cameras, microphones, accelerometers, gyroscopes, etc.; output devices 307 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; storage devices 308 including, for example, magnetic tapes, hard disks, etc.; and communication devices 309. Communication device 309 allows electronic device 300 to communicate wirelessly or wiredly with other devices to exchange data. Although Figure 3 An electronic device 300 with various devices is shown; however, it should be understood that it is not required to implement or possess all of the devices shown. More or fewer devices may be implemented or possessed alternatively. Figure 3 Each box shown can represent a device or multiple devices as needed.

[0081] In particular, according to some embodiments of this disclosure, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, some embodiments of this disclosure include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication device 309, or installed from storage device 308, or installed from ROM 302. When the computer program is executed by processing device 301, it performs the functions defined in the methods of some embodiments of this disclosure.

[0082] It should be noted that, in some embodiments of this disclosure, the computer-readable medium may be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium may be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In some embodiments of this disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In some embodiments of this disclosure, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium can be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wires, optical fibers, RF (radio frequency), etc., or any suitable combination thereof.

[0083] In some implementations, clients and servers can communicate using any currently known or future-developed network protocol such as HTTP (Hypertext Transfer Protocol) and can interconnect with digital data communication (e.g., communication networks) of any form or medium. Examples of communication networks include local area networks (“LANs”), wide area networks (“WANs”), the Internet (e.g., the Internet of Things), and end-to-end networks (e.g., ad hoc end-to-end networks), as well as any currently known or future-developed networks.

[0084] Computer-readable media may be contained within an electronic device or may exist independently, not assembled into the electronic device. The computer-readable media carries one or more programs that, when executed by the electronic device, cause the electronic device to: acquire a list of data center identifiers to be monitored, wherein each data center identifier in the list corresponds to a data center including various disks; for each data center identifier in the list, perform the following steps: for each disk included in the data center, collect disk health status information of the disk within a preset time period, wherein each disk corresponds to a disk identifier; based on the collected disk health status information, generate remaining lifespan prediction information corresponding to each disk; based on the remaining lifespan prediction information and a dynamic lifespan threshold, generate disk replacement information corresponding to the preset storage center within the preset time period; send the disk replacement information as replacement warning information to the replacement terminal corresponding to the data center; based on the disk replacement information, generate disk spare parts inventory information; and schedule at least one spare disk corresponding to the disk spare parts inventory information to the data center.

[0085] Computer program code for performing operations of some embodiments of this disclosure can be written in one or more programming languages ​​or a combination thereof. Programming languages ​​include object-oriented programming languages—such as Java, Smalltalk, and C++—and conventional procedural programming languages—such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0086] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0087] The units described in some embodiments of this disclosure can be implemented in software or hardware. The described units can also be housed in a processor; for example, a processor may be described as including an acquisition unit and a scheduling unit. The names of these units do not necessarily limit the specific unit; for example, an acquisition unit may also be described as "a unit that acquires a list of data center identifiers to be monitored."

[0088] The functions described above in this document can be performed at least in part by one or more hardware logic components. For example, exemplary types of hardware logic components that can be used, without limitation, include: field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), system-on-a-chip (SoCs), complex programmable logic devices (CPLDs), and so on.

[0089] The above description is merely a selection of preferred embodiments of this disclosure and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of the invention involved in the embodiments of this disclosure is not limited to technical solutions formed by specific combinations of technical features, but should also cover other technical solutions formed by arbitrary combinations of technical features or their equivalents without departing from the inventive concept. For example, technical solutions formed by substituting features with (but not limited to) technical features with similar functions disclosed in the embodiments of this disclosure.

Claims

1. A spare parts disk scheduling method, comprising: Obtain a list of data center identifiers to be monitored, wherein each data center identifier in the list of data center identifiers to be monitored corresponds to a data center including each disk; For each data center corresponding to each data center identifier in the list of data center identifiers to be monitored, perform the following steps: For each disk in the data center, disk health status information is collected within a preset time period, wherein each disk has a corresponding disk identifier; Based on the collected health status information of each disk, predictive information of the remaining lifespan of each disk is generated. Based on the remaining lifetime prediction information and dynamic lifetime threshold, disk replacement information corresponding to a preset storage center within a preset time period is generated, including: Obtain the service identifier and storage load information corresponding to the data center; Obtain the preset service lifetime mapping table and the preset load lifetime threshold mapping table; The business lifetime threshold mapping information containing the business identifier in the preset business lifetime mapping table is determined as the target business lifetime threshold mapping information; The lifetime threshold included in the target service lifetime threshold mapping information is determined as the first lifetime threshold; Based on storage load information, generate a load level identifier to be mapped; The load lifetime threshold mapping information containing the same load level identifier as the load level identifier to be mapped is determined as the target load lifetime threshold mapping information; The lifetime threshold included in the target load lifetime threshold mapping information is determined as the second lifetime threshold; The lifespan threshold that meets the preset conditions between the first lifespan threshold and the second lifespan threshold is determined as the dynamic lifespan threshold; Based on the dynamic lifetime threshold and the remaining lifetime prediction information, disk replacement information corresponding to the preset storage center is generated within a preset time period. The disk replacement information is sent as a replacement warning to the replacement terminal corresponding to the data center. Based on the disk replacement information, generate disk spare parts inventory information; At least one spare disk corresponding to the disk spare parts inventory information is dispatched to the data center.

2. The method according to claim 1, wherein, The step of collecting disk health status information within a preset time period includes: The disk's preset monitoring interface is invoked, and health parameter information of the disk is collected within a preset time period according to a preset collection frequency to obtain a health parameter information sequence. Each health parameter information in the health parameter information sequence includes at least three health parameter sub-information items, and the at least three health parameter sub-information items are at least three of the following parameter values: read error rate, reallocated sector count, unmapped sector count, uncorrectable sector count, head flight altitude, temperature, start / stop count, and power-on time. The health parameter information corresponds to the collection time. The sequence of health parameter information is determined as disk health status information.

3. The method according to claim 1, wherein, The process of generating remaining lifespan prediction information for each disk based on the collected health status information of each disk includes: For each disk health status information, the disk health status information is input into a pre-trained disk remaining lifespan prediction model to obtain remaining lifespan prediction information.

4. The method according to claim 2, wherein, The process of generating remaining lifespan prediction information for each disk based on the collected health status information of each disk includes: For each disk health status information in the aforementioned disk health status information, perform the following steps: For each health parameter included in the disk health status information, perform the following health determination steps: Obtain at least three warning thresholds and at least three dynamic weights corresponding to at least three health parameter sub-informations included in the health parameter information, wherein each of the at least three warning thresholds corresponds to one of the health parameter sub-informations and one of the dynamic weights. Based on the at least three warning thresholds and the at least three health parameter sub-information, at least three single-dimensional health score information are generated; Based on the at least three single-dimensional health score information and the at least three dynamic weights, a health score corresponding to the health parameter information is generated; Based on the generated health scores, a target health score is generated. The identifier of the disk corresponding to the disk health status information is determined as the disk identifier to be queried; Obtain the lifetime decay curve information corresponding to the disk identifier to be queried, wherein the lifetime decay curve information includes information of each mapping point, and each mapping point information includes health range information and remaining lifespan information; Based on the target health status and the lifespan decay curve information, remaining lifespan prediction information is generated.

5. The method according to claim 4, wherein, Based on the at least three warning thresholds and the at least three health parameter sub-information, at least three single-dimensional health score information are generated, including: For each of the at least three health parameter sub-information, perform the following steps: The warning threshold that corresponds to the health parameter sub-information among the at least three warning thresholds is determined as the target warning threshold; Based on the target early warning threshold and the health parameter sub-information, a single-dimensional health score is generated.

6. The method according to claim 1, wherein, The replacement disk information includes various replacement disk sub-information, each replacement disk sub-information including disk specification information and disk identifier, and the generation of disk spare parts inventory information based on the replacement disk information, including: The replacement disk sub-information is deduplicated to obtain each deduplicated replacement disk sub-information. For each deduplication replacement disk sub-information in the various deduplication replacement disk sub-information, the quantity of the deduplication replacement disk sub-information in the various replacement disk sub-information is determined as the number of spare disks corresponding to the deduplication replacement disk sub-information, and the deduplication replacement disk sub-information and the number of spare disks are determined as disk spare parts inventory sub-information. The determined disk spare parts inventory sub-information is designated as disk spare parts inventory information.

7. A spare parts disk scheduling device, comprising: The acquisition unit is configured to acquire a list of data center identifiers to be monitored, wherein each data center identifier in the list of data center identifiers to be monitored corresponds to a data center including various disks; The scheduling unit is configured to perform the following steps for each data center corresponding to each data center identifier in the list of data center identifiers to be monitored: For each disk included in the data center, collect disk health status information of the disk within a preset time period, wherein each disk corresponds to a disk identifier; based on the collected disk health status information, generate remaining lifespan prediction information corresponding to each disk; based on the remaining lifespan prediction information and dynamic lifespan thresholds, generate disk replacement information corresponding to a preset storage center within the preset time period, including: obtaining the service identifier and storage load information corresponding to the data center; obtaining a preset service lifespan mapping table and a preset load lifespan threshold mapping table; determining the service lifespan threshold mapping information containing the service identifier in the preset service lifespan mapping table as the target service lifespan threshold mapping information; and including the target service lifespan threshold mapping information... The lifespan threshold is determined as a first lifespan threshold; based on storage load information, a load level identifier to be mapped is generated; the load lifespan threshold mapping information containing the same load level identifier as the load level identifier to be mapped is determined as the target load lifespan threshold mapping information; the lifespan thresholds included in the target load lifespan threshold mapping information are determined as a second lifespan threshold; the lifespan thresholds that meet preset conditions among the first lifespan threshold and the second lifespan threshold are determined as dynamic lifespan thresholds; based on the dynamic lifespan thresholds and the remaining lifespan prediction information, replacement disk information corresponding to the preset storage center within a preset time period is generated; the replacement disk information is sent as replacement warning information to the replacement terminal corresponding to the data center; based on the replacement disk information, disk spare parts inventory information is generated; at least one spare disk corresponding to the disk spare parts inventory information is scheduled to the data center.

8. An electronic device, comprising: One or more processors; A storage device on which one or more programs are stored; When the one or more programs are executed by the one or more processors, the one or more processors implement the method as described in any one of claims 1 to 6.

9. A computer-readable medium having a computer program stored thereon, wherein, When the program is executed by the processor, it implements the method as described in any one of claims 1 to 6.

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