SSD remote data management method and system

Abstract

The invention provides an SSD remote data management method and system, each SSD is integrated with a BLE chip, and the method comprises the steps that the BLE chip sends the running state, running data and performance data of the SSD to a central receiving system; the central receiving system analyzes the operation state, the operation data and the performance data through a big data analysis algorithm to obtain an analysis result; inputting an analysis result into a deep learning network model to obtain key information; generating a visual comprehensive report according to the key information; wherein the visual comprehensive report comprises performance trend information, anomaly detection information and optimization suggestion information; predicting potential faults and residual service life of the SSD through a deep learning network model; generating early warning information according to the potential fault and the remaining service life; and the early warning information and the visual comprehensive report are displayed through the display terminal.

Description

Technical Field

[0001] The present invention relates to the technical field of data management, and in particular to a method and system for managing SSD remote data. Background Art

[0002] As the demand for data storage increases rapidly, SSDs have been widely used in data centers and personal computers due to their excellent read and write speeds and high reliability. However, existing SSD management technologies have the following drawbacks:

[0003] 1) In terms of centralized management, traditional SSD management is highly dependent on the built-in monitoring system, making remote and batch monitoring difficult to achieve;

[0004] 2) In the data collection process, the current data collection method relies too much on the host system, making it difficult to obtain the actual operating status of the SSD in real time;

[0005] 3) In terms of maintenance costs, due to the lack of effective fault warning and life prediction mechanisms, maintenance costs continue to rise. Summary of the invention

[0006] In view of this, the purpose of the present invention is to provide a management method and system for SSD remote data, wherein a BLE chip is integrated on each SSD to realize wireless communication with a central receiving system; the central receiving system centrally collects the BLE transmission data of all SSDs, and uniformly processes and stores them; the potential failures and remaining service life of the SSD are predicted through a deep learning network model, thereby improving the accuracy and reliability of the prediction and effectively reducing the risk of data loss due to failures.

[0007] In a first aspect, an embodiment of the present invention provides a method for managing SSD remote data, wherein each SSD is integrated with a BLE chip, and the method comprises:

[0008] The BLE chip sends the operating status, operating data and performance data of the SSD to a central receiving system;

[0009] The central receiving system analyzes the operating status, the operating data and the performance data through a big data analysis algorithm to obtain an analysis result;

[0010] Inputting the analysis results into a deep learning network model to obtain key information;

[0011] Generate a visual comprehensive report based on the key information; wherein the visual comprehensive report includes performance trend information, anomaly detection information and optimization suggestion information;

[0012] Predicting potential failures and remaining service life of the SSD by using the deep learning network model;

[0013] generating early warning information according to the potential failure and the remaining service life;

[0014] The warning information and the visual comprehensive report are displayed via a display terminal.

[0015] Furthermore, predicting the potential failure and remaining service life of the SSD by the deep learning network model includes:

[0016] Cleaning and preprocessing the operation data to obtain preprocessed data;

[0017] Inputting the preprocessed data into the deep learning network model for training to obtain feature data and patterns;

[0018] The potential fault is predicted based on the characteristic data and the pattern.

[0019] Furthermore, predicting the potential failure and remaining service life of the SSD by the deep learning network model includes:

[0020] Obtain long-term trends and cyclical changes in historical data;

[0021] The current operating state, the long-term trend and the periodic change of the SSD are input into the deep learning network model to obtain the remaining service life.

[0022] Furthermore, the operating status includes a normal state and an abnormal state, the operating data includes the number of read and write times, error rate, temperature and usage time, and the performance data includes IOPS, throughput and latency.

[0023] Furthermore, the method further comprises:

[0024] The central receiving system sends update information to the BLE, wherein the update information includes firmware;

[0025] The SSD updates the firmware.

[0026] Furthermore, the method further comprises:

[0027] The central receiving system dynamically adjusts the power consumption of the SSD according to the actual workload of the SSD and the operating status.

[0028] In a second aspect, an embodiment of the present invention provides a management system for SSD remote data, each of the SSDs is integrated with a BLE chip, and the system includes the SSD, a central receiving system, an intelligent data processing center, and a display terminal;

[0029] The SSD is used to send the operating status, operating data and performance data of the SSD to the central receiving system through the BLE chip;

[0030] The central receiving system is used to analyze the operating status, the operating data and the performance data through a big data analysis algorithm to obtain analysis results; input the analysis results into a deep learning network model to obtain key information; generate a visual comprehensive report based on the key information; wherein the visual comprehensive report includes performance trend information, anomaly detection information and optimization suggestion information;

[0031] The intelligent data processing center is used to predict the potential failure and remaining service life of the SSD through the deep learning network model; and generate warning information according to the potential failure and the remaining service life;

[0032] The display terminal is used to display the warning information and the visual comprehensive report.

[0033] Furthermore, the central receiving system is specifically used for:

[0034] Cleaning and preprocessing the operation data to obtain preprocessed data;

[0035] Inputting the preprocessed data into the deep learning network model for training to obtain feature data and patterns;

[0036] The potential fault is predicted based on the characteristic data and the pattern.

[0037] In a third aspect, an embodiment of the present invention provides an electronic device, including a memory and a processor, wherein the memory stores a computer program executable on the processor, and the processor implements the method described above when executing the computer program.

[0038] In a fourth aspect, an embodiment of the present invention provides a computer-readable medium having a non-volatile program code executable by a processor, wherein the program code enables the processor to execute the method as described above.

[0039] An embodiment of the present invention provides a management method and system for SSD remote data, wherein each SSD is integrated with a BLE chip, including: the BLE chip sends the operating status, operating data and performance data of the SSD to a central receiving system; the central receiving system analyzes the operating status, operating data and performance data through a big data analysis algorithm to obtain analysis results; the analysis results are input into a deep learning network model to obtain key information; a visual comprehensive report is generated based on the key information; wherein the visual comprehensive report includes performance trend information, anomaly detection information and optimization suggestion information; the potential failures and remaining service life of the SSD are predicted through a deep learning network model; early warning information is generated based on the potential failures and remaining service life; the early warning information and the visual comprehensive report are displayed through a display terminal; a BLE chip is integrated on each SSD to realize wireless communication with the central receiving system; the central receiving system centrally collects the BLE transmission data of all SSDs, and uniformly processes and stores them; the potential failures and remaining service life of the SSD are predicted through a deep learning network model to improve the accuracy and reliability of the prediction, and effectively reduce the risk of data loss caused by failures.

[0040] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention are realized and obtained by the structures particularly pointed out in the description, claims and drawings.

[0041] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0043] Figure 1 A flow chart of a method for managing SSD remote data provided in Embodiment 1 of the present invention;

[0044] Figure 2 A flow chart of step S105 in the SSD remote data management method provided in the first embodiment of the present invention;

[0045] Figure 3 A flowchart of step S105 in another SSD remote data management method provided in the first embodiment of the present invention;

[0046] Figure 4 This is a schematic diagram of a management system for SSD remote data provided by Embodiment 2 of the present invention. DETAILED DESCRIPTION

[0047] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0048] To facilitate understanding of this embodiment, the embodiment of the present invention is described in detail below.

[0049] Embodiment 1:

[0050] Figure 1 This is a flow chart of the SSD remote data management method provided in the first embodiment of the present invention.

[0051] Reference Figure 1 , each SSD (Solid State Drive) is integrated with a BLE (Bluetooth Low Energy) chip, and the method includes the following steps:

[0052] Step S101, the BLE chip sends the operating status, operating data and performance data of the SSD to the central receiving system;

[0053] Specifically, each SSD is integrated with a BLE chip, which can break the limitations of traditional wired communications, realize wireless communication with the central receiving system, and greatly improve the flexibility and convenience of communication. Compared with the existing technology that relies on complex wired connections, it reduces wiring costs and maintenance difficulties. Through the BLE protocol, the SSD can establish a stable, low-power communication connection with the central receiving system and upload operating status and performance indicators in real time.

[0054] The SSD is provided with a capacitor, which is used to supply power. When the SSD is in an offline state, the capacitor is used to supply power, and offline data transmission is achieved through the BLE chip.

[0055] Step S102, the central receiving system analyzes the operating status, operating data and performance data through a big data analysis algorithm to obtain analysis results;

[0056] Specifically, as the core hub for data collection, the central receiving system can efficiently receive the operating status and performance data of all SSDs transmitted via BLE. Compared with the traditional decentralized data collection method, the central receiving system realizes centralized management and improves the efficiency and accuracy of data collection.

[0057] The central receiving system can centrally collect the BLE transmission data of all SSDs, and uniformly process and store them, ensuring real-time synchronization of data from multiple SSDs and reducing the risk of data loss. Using high-performance servers as the central receiving system has strong data throughput and processing speed to ensure the real-time and accuracy of data.

[0058] Step S103, inputting the analysis results into the deep learning network model to obtain key information;

[0059] Step S104, generating a visual comprehensive report based on the key information; wherein the visual comprehensive report includes performance trend information, anomaly detection information and optimization suggestion information;

[0060] Specifically, the central receiving system generates a detailed visual comprehensive report by analyzing the collected data in real time. Compared with the simple data presentation of the prior art, this application provides users with in-depth insights and decision-making basis.

[0061] The central receiving system analyzes the operating status, operating data and performance data through big data analysis algorithms to obtain analysis results; then combines the deep learning network model to extract key information to generate a visual comprehensive report.

[0062] Step S105, predicting potential failures and remaining service life of the SSD through a deep learning network model;

[0063] Specifically, the deep learning network model is used to accurately predict the potential failure and remaining service life of the SSD. Compared with the traditional experience-based prediction method, it can improve the accuracy and reliability of the prediction and effectively reduce the risk of data loss caused by failure.

[0064] Step S106, generating warning information according to potential failures and remaining service life;

[0065] Step S107, displaying the warning information and the visual comprehensive report via the display terminal.

[0066] Specifically, the display terminal can provide an intuitive and easy-to-operate user interface, allowing users to easily monitor the SSD status, receive early warning information in a timely manner, and view visual comprehensive reports. Compared with traditional complex user interfaces, this application can improve user experience and operating efficiency.

[0067] The display terminal can display the SSD status in real time, and users can manage and configure it through an intuitive user interface. Users can customize the view according to their needs and quickly access the data and analysis results that they care about most.

[0068] The display terminal is a responsive web interface developed to support multi-platform access, combined with modern front-end technologies such as SVG charts and CSS animations to enhance user experience.

[0069] Further, see Figure 2 , step S105 comprises the following steps:

[0070] Step S201, cleaning and preprocessing the operation data to obtain preprocessed data;

[0071] Step S202, inputting the preprocessed data into a deep learning network model for training to obtain feature data and patterns;

[0072] Step S203: predicting potential faults based on the characteristic data and patterns.

[0073] Specifically, a deep learning network model is constructed; the deep learning network model can automatically extract deep feature data and patterns from a large amount of SSD operation data. Feature data includes but is not limited to multiple key parameters such as read and write times, error rate, temperature, and usage time.

[0074] In the data processing stage, the collected SSD operation data is cleaned and preprocessed to remove noise and outliers to ensure the quality and reliability of the data. Then, the preprocessed data is input into the deep learning network model for training.

[0075] During the training process, the deep learning network model continuously learns the hidden relationships and patterns in the data, gradually optimizing its own parameters to improve the accuracy of failure and life prediction. Through multiple iterations and optimizations, the deep learning network model can capture subtle changes and trends, thereby discovering potential signs of failure in advance.

[0076] Further, see Figure 3 , step S105 comprises the following steps:

[0077] Step S301, obtaining long-term trends and periodic changes of historical data;

[0078] Step S302, input the current operating status, long-term trend and periodic changes of the SSD into the deep learning network model to obtain the remaining service life.

[0079] Specifically, for the prediction of the remaining service life of SSDs, the deep learning network model not only considers the current operating status, but also makes full use of the long-term trends and periodic changes in historical data. Through in-depth analysis and mining of historical data, the deep learning network model can accurately predict the remaining service life of SSDs and provide strong support for the formulation of reasonable maintenance plans.

[0080] In order to further improve the accuracy of predictions, a strategy of real-time updating and adjusting the deep learning network model is also adopted. As new data is continuously generated, the deep learning network model can be retrained and optimized in a timely manner to adapt to changes in SSD performance and new operating modes.

[0081] Furthermore, the operating status includes a normal status and an abnormal status. The operating data includes but is not limited to the number of read and write operations, error rate, temperature and usage time. The performance data includes but is not limited to IOPS (Input / Output Operations PerSecond, the number of read and write (I / O) operations per second), throughput and latency.

[0082] Furthermore, the method further comprises the following steps:

[0083] Step S401, the central receiving system sends update information to BLE, the update information includes firmware;

[0084] Step S402: SSD updates firmware.

[0085] Specifically, the present application realizes remote updating of SSD firmware through the BLE channel without physical contact, avoiding the inconvenience and potential risks caused by on-site operations; automatically manages firmware versions to ensure that all SSDs run on the optimal firmware version; and utilizes secure wireless communication protocols to realize encrypted transmission and reliable updates of firmware packages, reducing maintenance time and costs.

[0086] Furthermore, the method further comprises the following steps:

[0087] Step S501: The central receiving system dynamically adjusts the power consumption of the SSD according to the actual workload and operating status of the SSD.

[0088] Specifically, it monitors the energy consumption of SSD in real time and performs optimized power management, effectively extending the service life of SSD. Compared with traditional energy management methods, it is more intelligent and efficient, achieving the dual goals of energy saving and emission reduction and cost reduction. Combined with energy consumption analysis and prediction algorithms, it intelligently adjusts the power supply to achieve energy saving and emission reduction.

[0089] This application realizes efficient, intelligent and environmentally friendly management of SSDs by integrating BLE chips and intelligent management components; it not only improves the reliability and performance of SSDs, but also provides strong technical support for the modern management of data centers, and has important commercial value and broad market prospects.

[0090] Embodiment 2:

[0091] Figure 4 A schematic diagram of a management system for SSD remote data provided by an embodiment of the present invention.

[0092] Reference Figure 4 ,Each SSD is integrated with a BLE chip, and the system includes SSD, central receiving system, intelligent data processing center and display terminal;

[0093] SSD, used to send the operating status, operating data and performance data of the SSD to the central receiving system through the BLE chip;

[0094] The central receiving system is used to analyze the operating status, operating data and performance data through the big data analysis algorithm to obtain the analysis results; input the analysis results into the deep learning network model to obtain key information; generate a visual comprehensive report based on the key information; wherein the visual comprehensive report includes performance trend information, anomaly detection information and optimization suggestion information;

[0095] Intelligent data processing center, used to predict potential failures and remaining service life of SSDs through deep learning network models; generate early warning information based on potential failures and remaining service life;

[0096] Display terminal, used to display early warning information and visual comprehensive reports.

[0097] Furthermore, the central receiving system is specifically used for:

[0098] Cleaning and preprocessing the operating data to obtain preprocessed data;

[0099] Input the preprocessed data into the deep learning network model for training to obtain feature data and patterns;

[0100] Predict potential failures based on signature data and patterns.

[0101] Compared with the prior art, this application has the following advantages:

[0102] 1) Improve management efficiency: Wireless communication reduces wiring requirements and reduces management costs.

[0103] 2) Enhanced data security: Real-time monitoring and fault warning reduce the risk of data loss.

[0104] 3) Improve maintenance convenience: Remote firmware updates and energy efficiency management reduce the need for on-site maintenance.

[0105] 4) Optimize energy usage: Intelligent energy management helps reduce the overall energy consumption of the data center.

[0106] Through the joint action of SSD, central receiving system, intelligent data processing center and display terminal, efficient, intelligent and environmentally friendly management of SSD equipment is achieved. By applying BLE technology to SSD monitoring, this application not only improves the convenience and efficiency of management, but also brings users a more reliable and economical storage solution.

[0107] An embodiment of the present invention further provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the SSD remote data management method provided in the above embodiment are implemented.

[0108] An embodiment of the present invention further provides a computer-readable medium having a non-volatile program code executable by a processor, wherein a computer program is stored on the computer-readable medium, and when the computer program is executed by the processor, the steps of the SSD remote data management method of the above embodiment are executed.

[0109] The computer program product provided in the embodiment of the present invention includes a computer-readable storage medium storing program code. The instructions included in the program code can be used to execute the method described in the previous method embodiment. The specific implementation can be found in the method embodiment and will not be repeated here.

[0110] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the system and device described above can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0111] In addition, in the description of the embodiments of the present invention, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0112] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc., which can store program codes.

[0113] In the description of the present invention, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc., indicating the orientation or positional relationship, are based on the orientation or positional relationship shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, the terms "first", "second", and "third" are used for descriptive purposes only, and cannot be understood as indicating or implying relative importance.

[0114] Finally, it should be noted that the above-described embodiments are only specific implementations of the present invention, which are used to illustrate the technical solutions of the present invention, rather than to limit them. The protection scope of the present invention is not limited thereto. Although the present invention is described in detail with reference to the above-described embodiments, ordinary technicians in the field should understand that any technician familiar with the technical field can still modify the technical solutions recorded in the above-described embodiments within the technical scope disclosed by the present invention, or can easily think of changes, or make equivalent replacements for some of the technical features therein; and these modifications, changes or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be based on the protection scope of the claims.

Claims

1. A method for managing SSD remote data, characterized in that: Each of the SSDs is integrated with a BLE chip, and the method comprises: The BLE chip sends the operating status, operating data and performance data of the SSD to a central receiving system; The central receiving system analyzes the operating status, the operating data and the performance data through a big data analysis algorithm to obtain an analysis result; Inputting the analysis results into a deep learning network model to obtain key information; Generate a visual comprehensive report based on the key information; wherein the visual comprehensive report includes performance trend information, anomaly detection information and optimization suggestion information; Predicting potential failures and remaining service life of the SSD by using the deep learning network model; generating early warning information according to the potential failure and the remaining service life; The warning information and the visual comprehensive report are displayed via a display terminal.

2. The SSD remote data management method according to claim 1, characterized in that: Predicting potential failures and remaining service life of the SSD by using the deep learning network model includes: Cleaning and preprocessing the operating data to obtain preprocessed data; Inputting the preprocessed data into the deep learning network model for training to obtain feature data and patterns; The potential fault is predicted based on the characteristic data and the pattern.

3. The SSD remote data management method according to claim 1, characterized in that: Predicting potential failures and remaining service life of the SSD by using the deep learning network model includes: Obtain long-term trends and cyclical changes in historical data; The current operating state of the SSD, the long-term trend, and the periodic change are input into the deep learning network model to obtain the remaining service life.

4. The SSD remote data management method according to claim 1, characterized in that: The operating status includes a normal status and an abnormal status, the operating data includes the number of read and write times, error rate, temperature and usage time, and the performance data includes IOPS, throughput and latency.

5. The SSD remote data management method according to claim 1, characterized in that: The method further comprises: The central receiving system sends update information to the BLE, wherein the update information includes firmware; The SSD updates the firmware.

6. The SSD remote data management method according to claim 1, characterized in that: The method further comprises: The central receiving system dynamically adjusts the power consumption of the SSD according to the actual workload of the SSD and the operating status.

7. A management system for SSD remote data, characterized in that: Each of the SSDs is integrated with a BLE chip, and the system includes the SSD, a central receiving system, an intelligent data processing center, and a display terminal; The SSD is used to send the operating status, operating data and performance data of the SSD to the central receiving system through the BLE chip; The central receiving system is used to analyze the operating status, the operating data and the performance data through a big data analysis algorithm to obtain an analysis result; Inputting the analysis results into a deep learning network model to obtain key information; Generate a visual comprehensive report based on the key information; wherein the visual comprehensive report includes performance trend information, anomaly detection information and optimization suggestion information; The intelligent data processing center is used to predict the potential failure and remaining service life of the SSD through the deep learning network model; and generate warning information according to the potential failure and the remaining service life; The display terminal is used to display the warning information and the visual comprehensive report.

8. The SSD remote data management system according to claim 7, characterized in that: The central receiving system is specifically used for: Cleaning and preprocessing the operating data to obtain preprocessed data; Inputting the preprocessed data into the deep learning network model for training to obtain feature data and patterns; The potential fault is predicted based on the characteristic data and the pattern.

9. An electronic device comprising a memory and a processor, wherein the memory stores a computer program that can be run on the processor, wherein: When the processor executes the computer program, the method according to any one of claims 1 to 6 is implemented.

10. A computer readable medium having a non-volatile program code executable by a processor, characterized in that: The program code enables the processor to execute the method according to any one of claims 1 to 6.

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