CN116126831A - Training method of stability prediction model and database stability detection method - Google Patents

Abstract

The disclosure provides a training method of a database stability prediction model, relates to the technical field of artificial intelligence, and can be applied to the technical field of finance. The method comprises the following steps: inputting sample data into an initial prediction model to obtain a database stability prediction value, wherein the initial prediction model comprises a highest layer, a middle layer and a lowest layer, the highest layer is related to the database stability, the middle layer is related to a plurality of evaluation factors of the database stability, and the lowest layer is related to a plurality of index factors corresponding to the evaluation factors; obtaining sample loss according to the difference between the database stability predicted value and the database stability actual value; and adjusting parameters of the initial prediction model according to the sample loss to obtain a trained database stability prediction model. The present disclosure also provides a database stability detection method, apparatus, device, storage medium, and program product.

Description

Training method of stability prediction model and database stability detection method

Technical Field

The present disclosure relates to the field of computer technology, and may be applied to the field of financial technology, and more particularly, to a training method of a database stability prediction model, a database stability detection method, apparatus, device, medium, and program product.

Background

Any system or program requires a database to store data, and for most enterprises, the stability of the database is critical, and once the database is down or has reduced performance, the influence is immeasurable. The existing method is usually to analyze the problem when the service cannot be used normally in the using process. The traditional database monitoring means is to alarm when a database has a problem. The existing method lacks predictive and advanced predictive evaluation, and cannot timely, effectively and comprehensively manage and control complex conditions. Moreover, the judgment of various abnormal indexes is easy to be interfered by human, and the large amount of input human resources also greatly increases the economic cost of database monitoring.

In addition, most of the current database monitoring relies on single-point data acquisition, but the monitoring items are too many, but various indexes cannot be combined to form multi-source data, and a prediction model is not constructed by utilizing the multi-element data, so that the database stability is effectively predicted.

Disclosure of Invention

In view of the foregoing, the present disclosure provides a training method for a database stability prediction model, a database stability detection method, apparatus, device, medium, and program product. The initial prediction model comprises a highest layer, a middle layer and a lowest layer, wherein the highest layer is related to the stability of the database through selection of monitoring data, the middle layer is related to a plurality of evaluation factors of the stability of the database, and the lowest layer is related to a plurality of index factors corresponding to the evaluation factors. The initial prediction model can be utilized to obtain a database stability prediction value, then a sample loss is obtained according to the difference between the database stability prediction value and the database stability actual value, and parameters of the initial prediction model are adjusted according to the sample loss to obtain a trained database stability prediction model. The stability of the database to be detected is predicted and evaluated in a targeted manner through the database stability prediction model, so that the accuracy of database stability prediction is improved.

According to a first aspect of the present disclosure, there is provided a training method of a database stability prediction model, including: inputting sample data into an initial prediction model to obtain a database stability prediction value, wherein the initial prediction model comprises a highest layer, a middle layer and a bottommost layer, the highest layer is related to the database stability, the middle layer is related to a plurality of evaluation factors of the database stability, and the bottommost layer is related to a plurality of index factors corresponding to the evaluation factors; obtaining sample loss according to the difference between the database stability predicted value and the database stability actual value; and adjusting parameters of the initial prediction model according to the sample loss to obtain a trained database stability prediction model.

According to an embodiment of the present disclosure, the training method of the database stability prediction model further includes: acquiring an evaluation result of a first target object aiming at each index factor to obtain a first score set; acquiring an evaluation result of a second target object aiming at each index factor to obtain a second score set; and determining a plurality of index factors of the bottommost layer according to the first score set and the second score set.

According to an embodiment of the disclosure, the adjusting parameters of the initial prediction model according to the sample loss to obtain a trained database stability prediction model includes: and adjusting a first initial weight corresponding to each evaluation factor and a second initial weight corresponding to each index factor according to the sample loss until the difference between the database stability predicted value and the database stability actual value converges, so as to obtain a database stability predicted model.

A second aspect of the present disclosure provides a database stability detection method, including: acquiring data information corresponding to each index factor in a target database; inputting the data information into a database stability prediction model to obtain a database stability prediction value of the target database; wherein the database stability prediction model is derived from the training provided by the present disclosure.

According to an embodiment of the present disclosure, the database stability detection method further includes: and generating early warning information under the condition that the stability predicted value of the database is smaller than or equal to a threshold value.

A third aspect of the present disclosure provides a training apparatus for a database stability prediction model, including: the prediction module is used for inputting sample data into an initial prediction model to obtain a database stability predicted value, the initial prediction model comprises a highest layer, a middle layer and a bottommost layer, the highest layer is related to the database stability, the middle layer is related to a plurality of evaluation factors of the database stability, and the bottommost layer is related to a plurality of index factors corresponding to the evaluation factors; the determining module is used for obtaining sample loss according to the difference between the database stability predicted value and the database stability actual value; and the training module is used for adjusting parameters of the initial prediction model according to the sample loss to obtain a trained database stability prediction model.

A fourth aspect of the present disclosure provides a database stability detection apparatus, including: the acquisition module is used for acquiring data information corresponding to each index factor in the target database; the detection module is used for inputting the data information into a database stability prediction model to obtain a database stability prediction value of the target database; the database stability prediction model is obtained through training according to the device provided by the disclosure.

A fifth aspect of the present disclosure provides an electronic device, comprising: one or more processors; and a memory for storing one or more programs, wherein the one or more programs, when executed by the one or more processors, cause the one or more processors to perform the methods disclosed above.

A sixth aspect of the present disclosure also provides a computer readable storage medium having stored thereon executable instructions which, when executed by a processor, cause the processor to perform the method disclosed above.

A seventh aspect of the present disclosure also provides a computer program product comprising a computer program which, when executed by a processor, implements the method disclosed above.

According to the training method of the database stability prediction model, the initial prediction model comprises a highest layer, a middle layer and a lowest layer, the highest layer is related to the database stability through selection of monitoring data, the middle layer is related to a plurality of evaluation factors of the database stability, and the lowest layer is related to a plurality of index factors corresponding to all the evaluation factors. The initial prediction model can be utilized to obtain a database stability prediction value, then a sample loss is obtained according to the difference between the database stability prediction value and the database stability actual value, and parameters of the initial prediction model are adjusted according to the sample loss to obtain a trained database stability prediction model. The stability of the database to be detected is predicted and evaluated in a targeted manner through the database stability prediction model, so that the accuracy of database stability prediction is improved.

Drawings

The foregoing and other objects, features and advantages of the disclosure will be more apparent from the following description of embodiments of the disclosure with reference to the accompanying drawings, in which:

FIG. 1 schematically illustrates an application scenario diagram of a database stability prediction model training method, database stability detection method, apparatus, device, medium, and program product according to an embodiment of the present disclosure;

FIG. 2 schematically illustrates a flow chart of a training method of a database stability prediction model according to an embodiment of the present disclosure;

FIG. 3 schematically illustrates a flow chart of a database stability detection method according to an embodiment of the present disclosure;

FIG. 4 schematically illustrates a block diagram of a training apparatus for a database stability prediction model according to an embodiment of the present disclosure;

FIG. 5 schematically illustrates a block diagram of a database stability detection apparatus according to an embodiment of the present disclosure; and

fig. 6 schematically illustrates a block diagram of an electronic device adapted to implement a training method of a database stability prediction model and/or a database stability detection method according to an embodiment of the present disclosure.

Detailed Description

Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. It should be understood that the description is only exemplary and is not intended to limit the scope of the present disclosure. In the following detailed description, for purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the embodiments of the present disclosure. It may be evident, however, that one or more embodiments may be practiced without these specific details. In addition, in the following description, descriptions of well-known structures and techniques are omitted so as not to unnecessarily obscure the concepts of the present disclosure.

The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure. The terms "comprises," "comprising," and/or the like, as used herein, specify the presence of stated features, steps, operations, and/or components, but do not preclude the presence or addition of one or more other features, steps, operations, or components.

All terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art unless otherwise defined. It should be noted that the terms used herein should be construed to have meanings consistent with the context of the present specification and should not be construed in an idealized or overly formal manner.

Where expressions like at least one of "A, B and C, etc. are used, the expressions should generally be interpreted in accordance with the meaning as commonly understood by those skilled in the art (e.g.," a system having at least one of A, B and C "shall include, but not be limited to, a system having a alone, B alone, C alone, a and B together, a and C together, B and C together, and/or A, B, C together, etc.).

The embodiment of the disclosure provides a training method and a training device for a database stability prediction model, wherein sample data are input into an initial prediction model to obtain a database stability prediction value, the initial prediction model comprises a highest layer, a middle layer and a lowest layer, the highest layer is related to the database stability, the middle layer is related to a plurality of evaluation factors of the database stability, and the lowest layer is related to a plurality of index factors corresponding to the evaluation factors; obtaining sample loss according to the difference between the database stability predicted value and the database stability actual value; and adjusting parameters of the initial prediction model according to the sample loss to obtain a trained database stability prediction model.

Fig. 1 schematically illustrates an application scenario diagram of a database stability prediction model training method, a database stability detection method, an apparatus, a device, a medium, and a program product according to an embodiment of the present disclosure.

As shown in fig. 1, an application scenario 100 according to this embodiment may include terminal devices 101, 102, 103, a network 104, and a server 105. The network 104 is used as a medium to provide communication links between the terminal devices 101, 102, 103 and the server 105. The network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, among others.

The user may interact with the server 105 via the network 104 using the terminal devices 101, 102, 103 to receive or send messages or the like. Various communication client applications, such as shopping class applications, web browser applications, search class applications, instant messaging tools, mailbox clients, social platform software, etc. (by way of example only) may be installed on the terminal devices 101, 102, 103.

The terminal devices 101, 102, 103 may be a variety of electronic devices having a display screen and supporting web browsing, including but not limited to smartphones, tablets, laptop and desktop computers, and the like.

The server 105 may be a server providing various services, such as a background management server (by way of example only) providing support for websites browsed by users using the terminal devices 101, 102, 103. The background management server may analyze and process the received data such as the user request, and feed back the processing result (e.g., the web page, information, or data obtained or generated according to the user request) to the terminal device.

It should be noted that, the training method of the database stability prediction model and/or the database stability detection method provided by the embodiments of the present disclosure may be generally executed by the server 105. Accordingly, the training device for the database stability prediction model and/or the database stability detection device provided by the embodiments of the present disclosure may be generally disposed in the server 105. The training method of the database stability prediction model and/or the database stability detection method provided by the embodiments of the present disclosure may also be performed by a server or a server cluster that is different from the server 105 and is capable of communicating with the terminal devices 101, 102, 103 and/or the server 105. Accordingly, the training device of the database stability prediction model and/or the database stability detection device provided by the embodiments of the present disclosure may also be provided in a server or a server cluster that is different from the server 105 and is capable of communicating with the terminal devices 101, 102, 103 and/or the server 105.

It should be understood that the number of terminal devices, networks and servers in fig. 1 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation.

The training method of the database stability prediction model of the disclosed embodiment will be described in detail below with reference to fig. 2 based on the scenario described in fig. 1.

Fig. 2 schematically illustrates a flowchart of a method of training a database stability prediction model according to an embodiment of the present disclosure.

As shown in fig. 2, this embodiment includes operations S210 to S230, and the training method of the database stability prediction model may be performed by a server.

In the technical scheme of the disclosure, the processes of acquiring, collecting, storing, using, processing, transmitting, providing, disclosing, applying and the like of the data all conform to the regulations of related laws and regulations, necessary security measures are adopted, and the public order harmony is not violated.

In operation S210, sample data is input into an initial prediction model to obtain a database stability prediction value, where the initial prediction model includes a highest layer, a middle layer, and a lowest layer, the highest layer is related to database stability, the middle layer is related to multiple evaluation factors of database stability, and the lowest layer is related to multiple index factors corresponding to the evaluation factors.

In operation S220, a sample loss is obtained according to a difference between the database stability prediction value and the database stability actual value.

In operation S230, parameters of the initial predictive model are adjusted according to the sample loss, resulting in a trained database stability predictive model.

The most important role of the database is to provide data support for the business system, and in the internet industry, the stability of the database is critical for enterprises. Database stability is not only a problem of failure to provide service after downtime, but is only poor in query performance or slow in access, which can cause unacceptable to users. In general, an enterprise monitors a database and the server in real time by using open source monitoring software through a monitoring server, a monitoring process generally takes key values as monitoring indexes according to the existing general templates, requirements, experiences and the like, displays the key values in a text and graphic mode, and notifies a manager in a WeChat, a short message, a mail and the like mode when an abnormality occurs in a certain index according to a set alarm threshold.

It can be understood that only a single point of detection of a certain index of the database, the database is checked through the abnormality of the certain index or the monitoring item, and the overall control of the state of the database is lacked. The main reasons are that the monitoring indexes are single and the relevance of each monitoring index is weak.

There are many influencing factors that influence the stability of the database, such as server hardware: CPU, disk, memory, load, etc.; such as a network: bandwidth, traffic, network plug-ins, data packets, etc.; such as a database: data volume, connection number, large transaction, etc.; meanwhile, many other factors are involved due to different services and functions.

It is understood that selection of the monitoring data corresponding to the influence factors and determination of the data weights are performed in advance, and the monitoring data corresponds to the evaluation factors and the index factors. An initial prediction model is established, wherein the initial prediction model comprises a highest layer, a middle layer and a lowest layer, the highest layer is related to the stability of the database, the middle layer is related to a plurality of evaluation factors of the stability of the database, and the lowest layer is related to a plurality of index factors corresponding to the evaluation factors.

The hierarchical analysis method can be used for correlating with influencing factors of the database, and an initial prediction model is established, so that the initial prediction model comprises a highest layer, a middle layer and a lowest layer.

For example, the target layer of the analytic hierarchy process is a database stability evaluation index system; the criterion layer is an evaluation factor affecting the stability of the data; the scheme layer/index layer is an index layer of each evaluation factor. The index factors of the index layer influencing the database may include cpu, memory, load, disk io, database data size, connection number, traffic access volume, etc. at the server system level. And (3) distributing scripts or calling monitoring information on the server to obtain sample data, inputting the sample data into an initial prediction model, and judging a state value of a database corresponding to the sample data to obtain a database stability prediction value.

The selection of the evaluation factors and the index factors is important to the prediction accuracy of the prediction model. Suitable evaluation indexes and index factors can be selected according to requirements. For example, bandwidth is large, no problem is caused by the network, and bandwidth traffic may not be selected. For example, because a particular index factor induces an unstable condition, the index factor may be weighted more heavily. For example, the database is slow to react due to the fact that the writing amount is large after a certain period of time, and the weight of TPS can be increased.

It will be appreciated that the model is modified by adjusting parameters of the initial predictive model based on sample loss. The accuracy of the model can be verified by methods such as pressure measurement, simulation, expert evaluation and the like, and the model is continuously adjusted so as to ensure the accuracy of database stability prediction. For example, a hierarchical analysis method is used for making certain correction, firstly, in the process of selecting an index layer, enterprise database management personnel and an expert are combined to perform scoring, and in the process of selecting, the actual situation is combined; and secondly, an average division method is adopted in the process of comparing the index layers by two factors, so that objectivity is achieved, and the method is beneficial to being matched with reality.

For example, the weights of the respective evaluation indexes and index factors may be determined using a hierarchical analysis method. Specifically, step one constructs a matrix: after the correlation with the evaluation factors of the database stability by the analytic hierarchy process, for the index factors of a certain layer, when comparing the importance of the ith element and the jth element relative to the certain evaluation factors of the previous layer, the relative importance a of quantitative analysis is used _ij To express, assuming that there are n elements in total to participate in the comparison, the matrix is equation one.

The matrix is a judgment matrix. When a is _ij When=1, it means that two elements have the same importance; when a is _ij When=3, this element is slightly more important than another element; when a is _ij When=5, this element is significantly more important than another element; when a is _ij When=7, this means that the element is more important than another element; when a is _ij When=9, this element is absolutely important than another element; i.e. of increasing importance. Wherein, 2,4,6 and 8 are respectively between the importance degrees corresponding to 1,3,5,7 and 9. Step two, consistency test: 1) Calculating the maximum eigenvalue lambda of the judgment matrix A _max . 2) Using the formula two ci= (λ _max -n)/(n-1), and obtaining a consistency index; where ci=0 indicates perfect coincidence, the larger the CI, the more inconsistent. 3) The corresponding average random consistency index RI is obtained by using a random simulation averaging method, or the RI table obtained by directly simulating 1000 times by using Satty is obtained. 4) The consistency ratio is calculated, cr=ci/RI. 5) Weights are determined. And after the weight is set, predicting the sample data by using an initial prediction model.

According to the training method for the database stability prediction model, the initial prediction model comprises a highest layer, a middle layer and a lowest layer, the highest layer is related to the database stability through selection of monitoring data, the middle layer is related to a plurality of evaluation factors of the database stability, and the lowest layer is related to a plurality of index factors corresponding to the evaluation factors. The initial prediction model can be utilized to obtain a database stability prediction value, then a sample loss is obtained according to the difference between the database stability prediction value and the database stability actual value, and parameters of the initial prediction model are adjusted according to the sample loss to obtain a trained database stability prediction model. The stability of the database to be detected is predicted and evaluated in a targeted manner through the database stability prediction model, so that the accuracy of database stability prediction is improved.

The training method of the database stability prediction model further comprises the following steps: acquiring an evaluation result of a first target object aiming at each index factor to obtain a first score set; acquiring an evaluation result of a second target object aiming at each index factor to obtain a second score set; and determining a plurality of index factors at the bottommost layer according to the first score set and the second score set.

It will be appreciated that in order to make the prediction result of the prediction model more accurate, it is more adapted. The evaluation result of the index factor may be scored by an administrator or expert.

For example, the first target object is an administrator and the second target object is an expert.

For example, by laying out scripts on MySQL database servers, real-time data specifying index factors are retrieved in real time, values are output to specified texts, and uploaded to a management end, which may be a specified server and device, as sample data. The first target object and the second target object score the index factors, the scoring results of the two target objects are combined, the combined value of each index factor is determined, and a plurality of index factors at the bottom layer are determined according to the combined values.

According to the training method of the database stability prediction model, a first score set is obtained by obtaining an evaluation result of a first target object aiming at each index factor; acquiring an evaluation result of a second target object aiming at each index factor to obtain a second score set; and determining a plurality of index factors at the bottommost layer according to the first score set and the second score set, so that the index factors can be more adaptive, the selection of the index factors is particularly important, and the prediction result of the prediction model is more accurate.

Adjusting parameters of the initial predictive model according to the sample loss to obtain a trained database stability predictive model, comprising: and adjusting a first initial weight corresponding to each evaluation factor and a second initial weight corresponding to each index factor according to the sample loss until the difference between the database stability predicted value and the database stability actual value converges, so as to obtain a database stability predicted model.

It can be appreciated that the model is modified by tuning parameters to obtain a database stability prediction model that enables convergence of the difference between the database stability prediction value and the database stability actual value.

For example, the first and second initial values are adjusted by verifying the accuracy of the prediction by comparing the predicted value with the actual value. For example, a partial index detection is performed on the database by adopting a pressure measurement mode, so that the effectiveness and accuracy of the index factors are evaluated, and the index factors are further adjusted or processed.

According to the training method for the database stability prediction model, which is provided by the embodiment, the first initial weight corresponding to each evaluation factor and the second initial weight corresponding to each index factor can be adjusted according to the sample loss, so that the difference between the database stability prediction value and the database stability actual value can be converged, and the database stability prediction model is obtained.

Fig. 3 schematically illustrates a flowchart of a database stability detection method according to an embodiment of the present disclosure.

As shown in fig. 3, this embodiment includes operations S310 to S320, and the database stability detection method may be performed by a server.

In the technical scheme of the disclosure, the processes of acquiring, collecting, storing, using, processing, transmitting, providing, disclosing, applying and the like of the data all conform to the regulations of related laws and regulations, necessary security measures are adopted, and the public order harmony is not violated.

In operation S310, data information corresponding to each index factor in the target database is acquired.

In operation S320, inputting the data information into the database stability prediction model to obtain a database stability prediction value of the target database; the database stability prediction model is obtained by training according to a training method of the database stability prediction model.

By collecting key factors of the database, such as data information corresponding to each index factor, in real time. Inputting the data information into a database stability prediction model, comprehensively evaluating a plurality of index factors represented by the data information by the prediction model to obtain a database stability prediction value of a target database, and predicting the database stability. Further, according to the stability predicted value of the database, under the condition that the judgment result is unstable, corresponding measures can be taken to ensure the stable operation of the database.

According to the database stability detection method, the collected data information corresponding to each index factor can be comprehensively evaluated through the database stability prediction model, so that the database stability prediction value of the target database is obtained, and the database stability is predicted. Meanwhile, the database stability prediction model correlates each index factor, so that the prediction result of the database stability is more objective and comprehensive.

The database stability detection method further comprises the following steps: and generating early warning information under the condition that the stability predicted value of the database is smaller than or equal to a threshold value.

It can be appreciated that by setting a corresponding threshold for the database stability prediction value, the early warning information can be generated in the case where it is determined that the database stability prediction value is equal to or less than the threshold. The early warning information can be notified to the associated personnel in a mail or short message mode and the like, so that the associated personnel can maintain in advance.

According to the database stability detection method, early warning information can be generated under the condition that the stability predicted value of the database is smaller than or equal to the threshold value, and related personnel can be convenient to maintain in advance, so that faults of the database are reduced, and property loss of enterprises is reduced.

Based on the training method of the database stability prediction model, the invention also provides a training device of the database stability prediction model. The device will be described in detail below in connection with fig. 4.

Fig. 4 schematically shows a block diagram of a training apparatus of a database stability prediction model according to an embodiment of the present disclosure.

As shown in fig. 4, the training apparatus 400 of the database stability prediction model of this embodiment includes a prediction module 410, a determination module 420, and a training module 430.

The prediction module 410 is configured to input sample data into an initial prediction model to obtain a database stability prediction value, where the initial prediction model includes a highest layer, a middle layer, and a lowest layer, the highest layer is related to database stability, the middle layer is related to multiple evaluation factors of database stability, and the lowest layer is related to multiple index factors corresponding to the evaluation factors; a determining module 420, configured to obtain a sample loss according to a difference between the database stability predicted value and the database stability actual value; and a training module 430 for adjusting parameters of the initial predictive model according to the sample loss to obtain a trained database stability predictive model.

In some embodiments, the training device of the database stability prediction model further comprises: the first sub-acquisition module is used for acquiring an evaluation result of the first target object aiming at each index factor to obtain a first score set; the second acquisition sub-module is used for acquiring an evaluation result of the second target object aiming at each index factor to obtain a second score set; and a determining submodule, configured to determine a plurality of index factors of a bottommost layer according to the first score set and the second score set.

In some embodiments, the training module comprises: and the training sub-module is used for adjusting a first initial weight corresponding to each evaluation factor and a second initial weight corresponding to each index factor according to the sample loss until the difference between the database stability predicted value and the database stability actual value converges, so as to obtain a database stability predicted model.

Any of the plurality of modules of the prediction module 410, the determination module 420, and the training module 430 may be combined in one module to be implemented, or any of the plurality of modules may be split into a plurality of modules, according to embodiments of the present disclosure. Alternatively, at least some of the functionality of one or more of the modules may be combined with at least some of the functionality of other modules and implemented in one module. According to embodiments of the present disclosure, at least one of the prediction module 410, the determination module 420, and the training module 430 may be implemented at least in part as hardware circuitry, such as a Field Programmable Gate Array (FPGA), a Programmable Logic Array (PLA), a system-on-chip, a system-on-substrate, a system-on-package, an Application Specific Integrated Circuit (ASIC), or may be implemented in hardware or firmware in any other reasonable manner of integrating or packaging circuitry, or in any one of or a suitable combination of any of three implementations of software, hardware, and firmware. Alternatively, at least one of the prediction module 410, the determination module 420, and the training module 430 may be at least partially implemented as a computer program module that, when executed, performs the corresponding functions.

Based on the database stability detection method, the disclosure also provides a database stability detection device. The device will be described in detail below in connection with fig. 5.

Fig. 5 schematically shows a block diagram of a database stability detection apparatus according to an embodiment of the present disclosure.

As shown in fig. 5, the database stability detection apparatus 500 of this embodiment includes an acquisition module 510 and a detection module 520.

An obtaining module 510, configured to obtain data information corresponding to each index factor in the target database; and a detection module 520, configured to input data information into the database stability prediction model to obtain a database stability prediction value of the target database;

the database stability prediction model is obtained through training according to a training device of the database stability prediction model.

Any of the plurality of modules in the acquisition module 510 and the detection module 520 may be combined in one module to be implemented, or any of the plurality of modules may be split into a plurality of modules, according to embodiments of the present disclosure. Alternatively, at least some of the functionality of one or more of the modules may be combined with at least some of the functionality of other modules and implemented in one module. According to embodiments of the present disclosure, at least one of acquisition module 510 and detection module 520 may be implemented at least in part as a hardware circuit, such as a Field Programmable Gate Array (FPGA), a Programmable Logic Array (PLA), a system on a chip, a system on a substrate, a system on a package, an Application Specific Integrated Circuit (ASIC), or in hardware or firmware, such as any other reasonable manner of integrating or packaging the circuits, or in any one of or a suitable combination of three of software, hardware, and firmware. Alternatively, at least one of the acquisition module 510 and the detection module 520 may be at least partially implemented as a computer program module, which when executed may perform the respective functions.

Fig. 6 schematically illustrates a block diagram of an electronic device adapted to implement a training method and/or a database stability detection method of a database stability prediction model, according to an embodiment of the present disclosure.

As shown in fig. 6, an electronic device 600 according to an embodiment of the present disclosure includes a processor 601 that can perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM) 602 or a program loaded from a storage section 608 into a Random Access Memory (RAM) 603. The processor 601 may include, for example, a general purpose microprocessor (e.g., a CPU), an instruction set processor and/or an associated chipset and/or a special purpose microprocessor (e.g., an Application Specific Integrated Circuit (ASIC)), or the like. Processor 601 may also include on-board memory for caching purposes. The processor 601 may comprise a single processing unit or a plurality of processing units for performing different actions of the method flows according to embodiments of the disclosure.

In the RAM 603, various programs and data necessary for the operation of the electronic apparatus 600 are stored. The processor 601, the ROM602, and the RAM 603 are connected to each other through a bus 604. The processor 601 performs various operations of the method flow according to the embodiments of the present disclosure by executing programs in the ROM602 and/or the RAM 603. Note that the program may be stored in one or more memories other than the ROM602 and the RAM 603. The processor 601 may also perform various operations of the method flow according to embodiments of the present disclosure by executing programs stored in the one or more memories.

According to an embodiment of the present disclosure, the electronic device 600 may also include an input/output (I/O) interface 605, the input/output (I/O) interface 605 also being connected to the bus 604. The electronic device 600 may also include one or more of the following components connected to the I/O interface 605: an input portion 606 including a keyboard, mouse, etc.; an output portion 607 including a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, a speaker, and the like; a storage section 608 including a hard disk and the like; and a communication section 609 including a network interface card such as a LAN card, a modem, or the like. The communication section 609 performs communication processing via a network such as the internet. The drive 610 is also connected to the I/O interface 605 as needed. Removable media 611 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is installed as needed on drive 610 so that a computer program read therefrom is installed as needed into storage section 608.

The present disclosure also provides a computer-readable storage medium that may be embodied in the apparatus/device/system described in the above embodiments; or may exist alone without being assembled into the apparatus/device/system. The computer-readable storage medium carries one or more programs which, when executed, implement methods in accordance with embodiments of the present disclosure.

According to embodiments of the present disclosure, the computer-readable storage medium may be a non-volatile computer-readable storage medium, which may include, for example, but is not limited to: a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this disclosure, a computer-readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. For example, according to embodiments of the present disclosure, the computer-readable storage medium may include ROM602 and/or RAM 603 and/or one or more memories other than ROM602 and RAM 603 described above.

Embodiments of the present disclosure also include a computer program product comprising a computer program containing program code for performing the methods shown in the flowcharts. When the computer program product runs in a computer system, the program code is used for enabling the computer system to realize a training method and/or a database stability detection method of a database stability prediction model provided by the embodiment of the disclosure.

The above-described functions defined in the system/apparatus of the embodiments of the present disclosure are performed when the computer program is executed by the processor 601. The systems, apparatus, modules, units, etc. described above may be implemented by computer program modules according to embodiments of the disclosure.

In one embodiment, the computer program may be based on a tangible storage medium such as an optical storage device, a magnetic storage device, or the like. In another embodiment, the computer program may also be transmitted, distributed in the form of signals over a network medium, and downloaded and installed via the communication section 609, and/or installed from the removable medium 611. The computer program may include program code that may be transmitted using any appropriate network medium, including but not limited to: wireless, wired, etc., or any suitable combination of the foregoing.

In such an embodiment, the computer program may be downloaded and installed from a network through the communication portion 609, and/or installed from the removable medium 611. The above-described functions defined in the system of the embodiments of the present disclosure are performed when the computer program is executed by the processor 601. The systems, devices, apparatus, modules, units, etc. described above may be implemented by computer program modules according to embodiments of the disclosure.

According to embodiments of the present disclosure, program code for performing computer programs provided by embodiments of the present disclosure may be written in any combination of one or more programming languages, and in particular, such computer programs may be implemented in high-level procedural and/or object-oriented programming languages, and/or assembly/machine languages. Programming languages include, but are not limited to, such as Java, c++, python, "C" or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's device, partly on a remote computing device, or entirely on the remote computing device or server. In the case of remote computing devices, the remote computing device may be connected to the user computing device through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computing device (e.g., connected via the Internet using an Internet service provider).

The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams or flowchart illustration, and combinations of blocks in the block diagrams or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.

Those skilled in the art will appreciate that the features recited in the various embodiments of the disclosure and/or in the claims may be combined in various combinations and/or combinations, even if such combinations or combinations are not explicitly recited in the disclosure. In particular, the features recited in the various embodiments of the present disclosure and/or the claims may be variously combined and/or combined without departing from the spirit and teachings of the present disclosure. All such combinations and/or combinations fall within the scope of the present disclosure.

The embodiments of the present disclosure are described above. However, these examples are for illustrative purposes only and are not intended to limit the scope of the present disclosure. Although the embodiments are described above separately, this does not mean that the measures in the embodiments cannot be used advantageously in combination. The scope of the disclosure is defined by the appended claims and equivalents thereof. Various alternatives and modifications can be made by those skilled in the art without departing from the scope of the disclosure, and such alternatives and modifications are intended to fall within the scope of the disclosure.

Claims (10)

1. A method of training a database stability prediction model, comprising:

inputting sample data into an initial prediction model to obtain a database stability prediction value, wherein the initial prediction model comprises a highest layer, a middle layer and a bottommost layer, the highest layer is related to the database stability, the middle layer is related to a plurality of evaluation factors of the database stability, and the bottommost layer is related to a plurality of index factors corresponding to the evaluation factors;

obtaining sample loss according to the difference between the database stability predicted value and the database stability actual value; and

and adjusting parameters of the initial prediction model according to the sample loss to obtain a trained database stability prediction model.

2. The method of claim 1, further comprising:

acquiring an evaluation result of a first target object aiming at each index factor to obtain a first score set;

acquiring an evaluation result of a second target object aiming at each index factor to obtain a second score set; and

and determining a plurality of index factors of the bottommost layer according to the first score set and the second score set.

3. The method of claim 1, wherein said adjusting parameters of an initial predictive model based on said sample loss results in a trained database stability predictive model comprising:

and adjusting a first initial weight corresponding to each evaluation factor and a second initial weight corresponding to each index factor according to the sample loss until the difference between the database stability predicted value and the database stability actual value converges, so as to obtain a database stability predicted model.

4. A database stability detection method, comprising:

acquiring data information corresponding to each index factor in a target database; and

inputting the data information into a database stability prediction model to obtain a database stability prediction value of the target database;

wherein the database stability prediction model is trained according to the method of any one of claims 1 to 3.

5. The method of claim 4, further comprising:

and generating early warning information under the condition that the stability predicted value of the database is smaller than or equal to a threshold value.

6. A training device for a database stability prediction model, comprising:

the prediction module is used for inputting sample data into an initial prediction model to obtain a database stability predicted value, the initial prediction model comprises a highest layer, a middle layer and a bottommost layer, the highest layer is related to the database stability, the middle layer is related to a plurality of evaluation factors of the database stability, and the bottommost layer is related to a plurality of index factors corresponding to the evaluation factors;

the determining module is used for obtaining sample loss according to the difference between the database stability predicted value and the database stability actual value; and

and the training module is used for adjusting parameters of the initial prediction model according to the sample loss to obtain a trained database stability prediction model.

7. A database stability detection apparatus comprising:

the acquisition module is used for acquiring data information corresponding to each index factor in the target database; and

the detection module is used for inputting the data information into a database stability prediction model to obtain a database stability prediction value of the target database;

wherein the database stability prediction model is trained from the apparatus of claim 6.

8. An electronic device, comprising:

one or more processors;

storage means for storing one or more programs,

wherein the one or more programs, when executed by the one or more processors, cause the one or more processors to perform the method of any of claims 1-5.

9. A computer readable storage medium having stored thereon executable instructions which, when executed by a processor, cause the processor to perform the method according to any of claims 1-5.

10. A computer program product comprising a computer program which, when executed by a processor, implements the method according to any one of claims 1 to 5.

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