CN114429256B - Data monitoring method, device, electronic device and storage medium - Google Patents

The application discloses a data monitoring method, a data monitoring device, electronic equipment and a storage medium. The monitoring method comprises the steps of obtaining original data of a detection procedure, preprocessing the original data of the detection procedure to obtain a plurality of groups of bad defect data, establishing a data monitoring model, monitoring the plurality of groups of bad defect data by using the data monitoring model, and determining bad fluctuation data according to a monitoring result of the data monitoring model. In the data monitoring method, the plurality of groups of bad defect data are obtained by processing the original data of the detection procedure, and the plurality of groups of bad defect data are monitored by the data monitoring model, so that bad fluctuation data in the production process can be monitored timely and comprehensively, the bad monitoring timeliness of a factory is improved, and therefore, the quality and the productivity of the whole product can be effectively improved, and the competitiveness of enterprises is improved.

Data monitoring method and device, electronic equipment and storage medium

Technical Field

The present application relates to the field of big data, and in particular, to a data monitoring method, a data monitoring device, an electronic device, and a storage medium.

Background

The production and manufacturing industry needs to monitor the product defects of the detection procedure so as to realize the management and control of the product production process, discover problems in time and correct the problems, thereby improving the overall quality of the product. However, the conventional system management method is limited to the problem of poor system computing capability, and only monitoring of a known small amount of faults can be completed, so that root cause analysis is difficult to be performed according to the known small amount of faults, and related personnel are difficult to quickly determine and correct the fault occurrence cause. Therefore, how to comprehensively monitor the bad fluctuation of the product so as to improve the overall quality of the product is a urgent problem to be solved.

Disclosure of Invention

In view of the above, the present application provides a data monitoring method, a monitoring device, an electronic apparatus, and a storage medium.

The data monitoring method of the application comprises the following steps:

Acquiring detection procedure original data and preprocessing the detection procedure original data to obtain a plurality of groups of bad defect data;

establishing a data monitoring model and monitoring a plurality of groups of bad defect data by using the data monitoring model, and

And determining bad fluctuation data according to the monitoring result of the data monitoring model.

In some embodiments, the acquiring inspection process raw data and preprocessing the inspection process raw data to obtain a plurality of sets of bad defect data includes:

Preprocessing the detection procedure original data according to preset logic by the ETL tool to obtain a plurality of groups of bad defect data, wherein the detection procedure original data comprises a production batch number, an identification mark of a production raw material, detection time, a bad type and a bad position, and the bad defect data comprises a factory, a procedure, a production batch number, an identification mark of the production raw material, time, a bad type and a bad number and a bad proportion.

In some embodiments, the establishing a data monitoring model and monitoring the plurality of sets of the bad defect data using the data monitoring model comprises:

establishing the data monitoring model by utilizing a single-factor analysis of variance technology and/or a multi-factor analysis of variance technology;

optimizing the bad defect data according to the detected site information;

The bad defect data is monitored chronologically by a calculation engine.

In certain embodiments, the data monitoring method further comprises:

And sending the bad fluctuation notification according to the bad fluctuation data. .

In certain embodiments, the data monitoring method further comprises:

Performing production record analysis, process parameter analysis and equipment time sequence state analysis according to the bad defect data and the bad fluctuation data;

and pushing the analysis results to users respectively.

In some embodiments, the performing production history analysis, process parameter analysis, and equipment timing state analysis based on the defect data and the defect fluctuation data comprises:

Determining that the bad fluctuation data is bad sample data, and the bad defect data except the bad fluctuation data is good sample data;

Carrying out the production record analysis on the bad sample data according to the good sample data to determine problem factors, wherein the problem factors comprise production equipment problems, working procedure processing time problems and waiting time problems;

process parameter analysis is performed based on the problem factors to determine problem parameters including process parameters, measurement parameters, and/or electrical parameters, and/or equipment timing state analysis is performed based on the problem factors to determine a problem state.

In certain embodiments, the production historian analysis includes processing time, waiting time, equipment variance, unit variance, chamber/layer variance, equipment continuity, process routes, transport paths, equipment parameter variance.

The monitoring device of the embodiment of the application comprises:

The processing module can be used for acquiring the original data of the detection procedure and preprocessing the original data of the detection procedure to obtain a plurality of groups of bad defect data;

A monitoring module, which can be used for establishing a data monitoring model and monitoring a plurality of groups of bad defect data by using the data monitoring model, and

And the determining module can be used for determining the bad fluctuation data according to the monitoring result of the data monitoring model.

The data monitoring device of the present application comprises:

The processing module can be used for acquiring vehicle use data related to vehicle charging and preprocessing the vehicle use data to obtain data to be detected;

the monitoring module can be used for detecting the data to be detected according to a trained detection model and

And the determining module can be used for determining the bad fluctuation data according to the monitoring result of the data monitoring model.

The electronic device of the present application includes:

One or more processors, memory, and

One or more programs, wherein the one or more programs are stored in the memory and executed by the one or more processors, the programs comprising instructions for performing the data monitoring method of any of the above.

The application also provides a non-transitory computer readable storage medium of a computer program which, when executed by one or more processors, causes the processors to perform the data monitoring method of any one of the above.

In the data monitoring method, the monitoring device, the electronic equipment and the computer storage medium, the plurality of groups of bad defect data are obtained by processing the original data of the detection procedure, and the plurality of groups of bad defect data are monitored through the data monitoring model, so that bad fluctuation data in the production process can be monitored timely and comprehensively, and the factory bad monitoring timeliness is improved, so that the quality and the productivity of the whole product can be effectively improved, and the enterprise competitiveness is improved.

Additional aspects and advantages of the application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the application.

Drawings

The foregoing and/or additional aspects and advantages of the application will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings, in which:

FIG. 1 is a flow chart of a data monitoring method according to some embodiments of the present application;

FIG. 2 is a block diagram of a data monitoring device according to some embodiments of the present application;

FIG. 3 is a block diagram of an electronic device in accordance with certain embodiments of the present application;

FIG. 4 is a block diagram of a storage medium coupled to a processor in accordance with certain embodiments of the present application;

FIG. 5 is a schematic illustration of preprocessing raw data for a detection process in accordance with certain embodiments of the present application;

FIG. 6 is a flow chart of a data monitoring method according to some embodiments of the present application;

FIG. 7 is a flow chart of a data monitoring method according to some embodiments of the present application;

FIG. 8 is a flow chart of a data monitoring method according to some embodiments of the application.

Description of main reference numerals:

The monitoring apparatus 10, the processing module 12, the monitoring module 14, the determining module 16, the analyzing module 18, the pushing module 19, the electronic device 100, the processor 20, the memory 30, the program 32, the storage medium 40, the computer program 42, the communication module 50.

Detailed Description

Embodiments of the present application are described in detail below, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to like or similar elements or elements having like or similar functions throughout. The embodiments described below by referring to the drawings are illustrative and intended to explain the present application and should not be construed as limiting the application.

Referring to fig. 1, the present application provides a data monitoring method, the monitoring method includes the steps of:

S12, acquiring original data of a detection procedure and preprocessing the original data of the detection procedure to obtain a plurality of groups of bad defect data;

S14, establishing a data monitoring model, monitoring a plurality of groups of bad defect data by using the data monitoring model, and

S16, determining bad fluctuation data according to a monitoring result of the data monitoring model.

Referring to fig. 2, a data monitoring apparatus 10 is provided according to an embodiment of the present application. The monitoring device 10 includes a processing module 12, a monitoring module 14, and a determining module 16.

S12 may be implemented by the processing module 12, S14 may be implemented by the monitoring module 14, and S16 may be implemented by the determining module 16.

Alternatively, the processing module 12 may be configured to obtain the inspection process raw data and pre-process the inspection process raw data to obtain multiple sets of defect data.

The monitoring module 14 may be configured to build a data monitoring model and monitor multiple sets of bad defect data using the data monitoring model.

The determination module 16 may be configured to determine bad fluctuation data based on the monitoring results of the data monitoring model.

Referring to fig. 3, the electronic device 100 of the present application further includes one or more processors 20, a memory 30, and one or more programs 32, wherein the one or more programs 32 are stored in the memory 30 and executed by the one or more processors 20, and the program 32 is executed by the processor 20 with instructions of the data monitoring method described above.

Referring to fig. 4, the embodiment of the present application further provides a non-volatile computer readable storage medium 40, where the computer readable storage medium 40 stores a computer program 42, and when the computer program 42 is executed by one or more processors 20, the processor 20 is caused to perform the data monitoring method described above.

In the data monitoring method, the monitoring device 10, the electronic equipment 100 and the storage medium 40 according to the embodiment of the application, the plurality of groups of bad defect data are obtained by processing the original data of the detection procedure, and the plurality of groups of bad defect data are monitored by the data monitoring model, so that bad fluctuation data in the production process can be monitored timely and comprehensively, and the bad monitoring timeliness of a factory is improved, so that problems can be found and corrected according to the bad fluctuation data, the quality and the productivity of the whole product are improved, and the competitiveness of enterprises is improved.

In some embodiments, the electronic device 100 may be a server, where the server may include a large data platform, and the large data platform is a platform integrating data access, data processing, data storage, query searching, analysis mining, and the like, and an application interface, so that the electronic device 100 can implement the data monitoring method according to the embodiments of the present application.

In some embodiments, the monitoring apparatus 10 may be part of the electronic device 100. Alternatively, the electronic device 100 comprises the monitoring apparatus 10.

In some embodiments, the monitoring device 10 may be a discrete component assembled in a manner to have the aforementioned functionality, or a chip in the form of an integrated circuit having the aforementioned functionality, or a computer software code segment that when run on a computer causes the computer to have the aforementioned functionality.

In some embodiments, the monitoring device 10 may be attached to a computer or computer system as hardware, either separately or as an additional peripheral component. The monitoring apparatus 10 may also be integrated into a computer or computer system, for example, the monitoring apparatus 10 may be integrated onto the processor 20 when the monitoring apparatus 10 is part of the electronic device 100.

In some embodiments in which the monitoring apparatus 10 is part of the electronic device 100, the code segments corresponding to the monitoring apparatus 10 may be stored as software on the memory 30 for execution by the processor 20 to perform the aforementioned functions. Or the monitoring device 10 includes one or more of the programs 32 described above, or the one or more programs 32 described above includes the monitoring device 10.

In some embodiments, the computer readable storage medium 40 may be a storage medium built in the electronic device 100, for example, may be the memory 30, or may be a storage medium that is removably plugged into the electronic device 100, for example, an SD card, or the like.

Referring further to fig. 3, in some embodiments, the electronic device 10 may further include a communication module 50, where the electronic device 10 outputs data that is processed by the detection process through the communication module 50, and/or obtains data to be processed by the electronic device 10 from an external device, for example, the communication module 50 is connected to a relational database of a manufacturing factory, so as to obtain raw data of the detection process in the relational database.

A relational database is understood by those skilled in the relevant art to refer to a database that employs a relational model to organize data, which stores data in rows and columns for ease of user understanding, the series of rows and columns of the relational database being referred to as tables, a set of tables comprising the database.

The raw data of the inspection process refers to the related data generated in the production process of the product, and it is understood that the related data is generated in each production process.

The raw data of the detection process comprises a growth batch number, an identity mark of the production raw material, detection time, a bad type, a bad position and the like, and the bad defect data can comprise a factory, a process, a growth batch number, an identity mark of the production raw material, monitoring time, a bad type, a bad number, a bad proportion and the like.

For example, referring to fig. 5, in the production process of the display panel, in the production process a, the production lot number, the glass identity, the processing time, the defect type, and the occurrence position are recorded, and in the production process B, the production lot number, the glass identity, the display panel identity, the glass identity, the processing time, the defect type, and the occurrence position are recorded. In the production step C, the production lot number, the glass identity, the processing time, the generation position, the thin film transistor (Thin Film Transistor, TFT) production lot number, and the glass identity for forming the thin film transistor are recorded. The defective defect data obtained according to the raw data of the inspection process may include an identity of defective glass, a manufacturer, a production lot number, a production time, a defective type, a defective defect number, a defective defect ratio, and the like.

Of course, the inspection process raw data may include all the processes in a plurality of factories that produce the same product, and it is understood that the more the inspection process raw data, the more the defective defect data is generated from the inspection process raw data, and further, the more accurate the monitoring result of the defective defect data by the data monitoring model.

In addition, as is clear from the above examples, in the data monitoring method of the present application, the data acquired during the production and manufacturing process of the display panel is described as an example. It should be understood that the foregoing is merely an example of a data monitoring method, and is not intended to limit the application of the data monitoring method in the embodiments of the present application strictly.

It should be further noted that the monitoring model refers to a mathematical model for monitoring the bad defect data, and the detection model may be established according to a preset logic and a mathematical algorithm. The preset logic is service logic, which refers to rules and procedures that one entity unit should possess in order to provide services to another entity unit.

The processor 20 may preprocess the inspection process raw data according to a predetermined logic by an Extract-Transform-Load (ETL) tool to obtain a plurality of sets of defective defect data. Data warehouse technology refers to a data processing technology for extracting (extracting), converting (transforming), and loading (load) acquired data to obtain target service data. Thus, it will be understood that the ETL tool refers to a tool that performs processes such as extraction, conversion, and loading on data to obtain target service data using ETL technology. That is, in the embodiment of the present application, the ETL tool may be disposed in the processor 20, or the processor 20 includes the ETL tool, so that the processor 20 may perform preprocessing such as extraction, conversion, loading, etc. on the raw data of the monitoring process according to the preset logic, so as to obtain multiple sets of defective data.

Specifically, the processor 20 firstly extracts the data to be processed from the raw data of the detection process, then washes the extracted data to be processed, converts the extracted data to a format acceptable by the target database according to a preset specification, and loads the washed data to a designated position of the data storage device, so as to obtain a plurality of groups of bad defect data which are consistent with the business logic and the format.

After the multiple groups of bad defect data are obtained, the multiple groups of bad defect data are imported into a data center, and then the multiple groups of bad defect data of the data center are extracted and stored into a data warehouse through ETL according to business logic, so that a monitoring model can monitor the multiple groups of bad defect data, and bad fluctuation data are obtained. Therefore, the monitoring of the bad fluctuation of the product is realized comprehensively, so that related personnel can quickly locate the bad occurrence reason, the problem can be found out and improved at the highest speed, and the quality and productivity influence caused by the bad occurrence is reduced.

Referring to fig. 6, in some embodiments, step S14 includes the further steps of:

S142, establishing a data monitoring model by utilizing a single-factor analysis of variance technology and/or a multi-factor analysis of variance technology;

s144, optimizing the bad defect data according to the detected site information;

s146, monitoring the bad defect data according to time sequence through a computing engine.

Referring further to fig. 2, in some embodiments, steps S142 and S144 may be implemented by the monitoring module 14. That is, the monitoring module 14 may be configured to build a data monitoring model using a single-factor analysis-of-variance technique and/or a multi-factor analysis-of-variance technique. The monitoring module 14 may also be configured to optimize the defective defect data based on the inspection site information and monitor the defective defect data in a time sequence by the calculation engine.

Referring further to fig. 3, in some embodiments, processor 20 may be configured to build a data monitoring model using a one-factor analysis-of-variance technique and/or a multi-factor analysis-of-variance technique. The processor 20 may also be configured to optimize the processing of the bad defect data based on the inspection site information and to monitor the bad defect data by the calculation engine in a time sequence.

Analysis of variance (Analysis of Variance, ANOVA), also known as "analysis of variance", was invented by r.a. fisher for the significance of differences in mean of two or more samples. The data obtained from the study appears to be fluctuating due to the influence of various factors, and the causes of the fluctuations can be divided into two categories, namely, uncontrollable random factors and controllable factors which exert influence on the results in the study. That is, the analysis of variance is performed by analyzing the contribution of the variation of different sources to the total variation, so as to determine the influence of the controllable factors on the research result.

One-way analysis of variance refers to the comparison of the means of multiple samples designed in groups, and analysis of variance should be used with a completely random design. The multi-single-factor analysis of variance refers to that when two or more factors affect the dependent variable, the analysis can be performed by a multi-factor analysis of variance method, that is, by a variance comparison method, whether the multiple factors have significant effect on the dependent variable is determined by a hypothesis testing process.

The monitoring model is built according to analysis of variance and business logic, the number of the monitoring models can be one or a plurality of the monitoring models, and the number of the monitoring models can be built according to specific requirements of businesses. One or more data monitoring models can be established according to business logic by a single-factor analysis of variance technology, or one or more data monitoring models can be established according to business logic by a plurality of factor analysis of variance technologies.

For example, for monitoring all production lot numbers to obtain bad fluctuation trend, a monitoring model can be established by a single factor analysis of variance technology, so that analysis of variance of all production lot numbers can be realized to obtain bad fluctuation trend of production lot numbers. For obtaining the trend of bad fluctuation by monitoring the production time period and the production lot number, a monitoring model can be established by a multi-factor analysis of variance technology, so that the analysis of variance of the production lot number in a certain time period and the time period can be realized, and the trend of the production lot number with continuous fixed time to the bad fluctuation can be obtained.

And after the monitoring model is established, carrying out optimization processing on the bad defect data according to the detection site information. It will be appreciated that the defects detected by the raw materials at the respective detection sites may be different according to actual service conditions, and thus the data needs to be optimized, for example, the defects detected by each glass at the respective detection sites may be different according to service requirements during the process of producing the display panel by processing raw material glass. Therefore, the data needs to be optimized. Specifically, when each failure is analyzed, if the production raw material is detected at the detection site to be analyzed, the production raw material is included in the analysis sample, and the failure that has not occurred is subjected to the 0-compensating treatment. Thus, the normal distribution premise of using the significance theory is ensured to be established.

Further, the processor 20 may monitor the adverse defect data over time by a computing engine that includes big data distributed cloud computing. It can be appreciated that the big data distributed cloud computing can fully support the execution of hundreds of thousands of services, so that the computing engine employing the big data distributed cloud computing can implement all bad variance monitoring for all detection procedures. For example, the defective data may be monitored by Spark, which is a fast and versatile computing engine designed for large-scale data processing, with fast, efficient, easy and versatile characteristics.

Specifically, the processing is programmed by Spark program, each detection procedure is taken as a task, the newly extracted production raw material data of the ETL is monitored according to a monitoring model by performing the grouping of the number of bad defects or the defect ratio on the production raw material in time sequence (to meet the first-in first-out requirement of monitoring), and thus the monitoring result is obtained. In this manner, the processor 20 may derive bad fluctuation data from the monitoring results.

Further, in some embodiments, the processor 20 may also generate and send bad fluctuation notifications from the bad fluctuation data to notify relevant personnel, thus facilitating quick review and analysis by relevant personnel. The notification mode is not limited to short message notification, telephone notification, mail notification and the like. For example, after monitoring the bad fluctuation, the processor 20 alerts different process managers by mail and in real-time alarm mode, so that the process managers can quickly look up analysis details according to the mail, so as to find problems in time and improve.

Referring to fig. 7, in some embodiments, the monitoring method further comprises the steps of:

S18, carrying out production record analysis, process parameter analysis and equipment time sequence state analysis according to the bad defect data and the bad fluctuation data;

And S20, pushing analysis results to users respectively.

In certain embodiments, the monitoring device 10 further comprises an analysis module 18 and a pushing module 19, wherein step S18 may be performed by the analysis module 18 and step S20 may be performed by the pushing module 19. Alternatively, the analysis module 18 may be configured to perform production history analysis, process parameter analysis, and equipment time sequence status analysis according to the defect data and the fluctuation data, and the pushing module 19 may be configured to push the analysis results to the user, respectively.

In some embodiments, the processor 20 may be configured to perform production historic analysis, process parameter analysis, and equipment timing state analysis based on the bad defect data and the bad fluctuation data. The processor 20 may also be used to push the analysis results to the user separately.

The production histories may include, but are not limited to, processing time, waiting time, equipment variance, unit variance, room variance, layer variance, equipment continuity, process routes, transport paths, equipment parameter variance, and the like.

Process parameters may include, but are not limited to, process parameters, measurement parameters, and electrical characteristics.

The device timing state may include, but is not limited to, temperature of the device, pressure within the device chamber, humidity, etc.

It is understood that the analysis of the defective fluctuation data and the production history of the defective fluctuation data makes it possible to determine whether or not the defective fluctuation data is a problem of a certain production facility, and the analysis of the process parameters and the facility time sequence state of the defective fluctuation data and the production history of the defective fluctuation data makes it possible to locate a specific cause of occurrence of defective fluctuation.

Specifically, the processor 20 may further include an intelligent analysis system for a defect root, which is capable of regenerating a sample from the input defect data and fluctuation data, and performing production history, process parameter analysis, and equipment time-series state analysis, respectively, and obtaining each analysis result.

Further, after the intelligent analysis system obtains the analysis result of each step, the bad root causes generate notification of the analysis result and push the notification to related personnel, so that the related personnel can acquire the analysis result in time.

The bad root can be obtained by acquiring bad defect data and bad fluctuation data by the intelligent analysis system, and can be manually input by related personnel or actively monitored by the intelligent analysis system.

It is understood that the system for intelligent analysis of the root cause of the failure can be a system for programming and developing based on the production history, process parameter analysis and equipment time sequence state analysis theory. Of course, the bad root cause intelligent analysis system can also be generated based on other theories, for example, the bad root cause intelligent analysis system can be developed based on the traditional statistical theory, so that the bad root cause intelligent analysis system can sort bad defect data and bad fluctuation data to obtain analysis results. Or developed based on machine learning algorithm theory such as decision tree, random forest, gradient descent tree algorithm (Gradient Boost Decision Tree, GBDT), and (eXtreme Gradient Boosting, GXBoost), so that the machine learning algorithm can process the bad defect data and the bad fluctuation data to obtain analysis results.

Referring to fig. 8, in some embodiments, step S18 includes the steps of:

s182, determining bad fluctuation data as bad sample data, and bad defect data except the bad fluctuation data as good sample data;

s184, carrying out production record analysis on bad sample data according to the good sample data to determine problem factors;

s186, performing process parameter analysis according to the problem factors to determine problem parameters, and/or performing equipment time sequence state analysis according to the problem factors to determine problem states.

Referring further to fig. 2, in some embodiments, steps S182, S184, and S186 may be implemented by the analysis module 18. Alternatively, the analysis module 18 may be configured to determine bad fluctuation data as bad sample data and bad defect data other than bad fluctuation data as good sample data. The analysis module 18 may be used to perform production historic analysis on bad sample data from good sample data to determine problem factors, and the analysis module 18 may be used to perform process parameter analysis based on problem factors to determine problem parameters, and/or equipment timing state analysis based on problem factors to determine problem states.

In some embodiments, the processor 20 may be configured to determine that bad fluctuation data is bad sample data and bad defect data other than bad fluctuation data is good sample data. The processor 20 may also be used to perform production biographic analysis on bad sample data from good sample data to determine problem factors. The processor 20 may also be used to perform process parameter analysis based on the problem factors to determine problem parameters and/or equipment timing state analysis based on the problem factors to determine problem states.

It should be noted that the problem factors include, but are not limited to, production equipment problems, process duration problems, and waiting duration problems. The problem parameters may be process parameters, measurement parameters, and/or electrical parameters, etc.

It can be understood that, because the bad sample data is bad fluctuation data, the bad sample data and the good sample data can be compared to obtain specific problem factors which cause bad fluctuation data, and further, the process parameter analysis is performed on the problem factors, so that the bad fluctuation caused by the specific problem parameters can be obtained. Or analyzing the time sequence state of the equipment in the generating procedure, and obtaining the specific states which cause bad fluctuation.

In the above embodiments, it may be implemented in whole or in part by software, hardware, firmware, or any other combination. When implemented in software, may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When loaded and executed on a computer, produces a flow or function in accordance with embodiments of the present application, in whole or in part. The computer may be a general purpose computer, a special purpose computer, a computer network, or other programmable apparatus. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions may be transmitted from one website, computer, server, or data center to another website, computer, server, or data center by a wired (e.g., coaxial cable, fiber optic, digital subscriber line (digital subscriber line, DSL)) or wireless (e.g., infrared, wireless, microwave, etc.). The computer readable storage medium may be any available medium that can be accessed by a computer or a data storage device such as a server, data center, etc. that contains an integration of one or more available media. The usable medium may be a magnetic medium (e.g., a floppy disk, a hard disk, a magnetic tape), an optical medium (e.g., a digital video disc (digital video disc, DVD)), or a semiconductor medium (e.g., a Solid State Drive (SSD)), or the like.

Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.

In the several embodiments provided by the present application, it should be understood that the disclosed systems, devices, and methods may be implemented in other manners. For example, the apparatus embodiments described above are merely illustrative, e.g., the division of the units is merely a logical function division, and there may be additional divisions when actually implemented, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, which may be in electrical, mechanical or other form.

In addition, each functional unit in the embodiments of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit.

The foregoing is merely illustrative of the present application, and the present application is not limited thereto, and any person skilled in the art will readily recognize that variations or substitutions are within the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.