WO2021212753A1

WO2021212753A1 - Computer performance data determining method and apparatus, computer device, and storage medium

Info

Publication number: WO2021212753A1
Application number: PCT/CN2020/118939
Authority: WO
Inventors: 徐锐杰
Original assignee: 平安科技（深圳）有限公司
Priority date: 2020-04-23
Filing date: 2020-09-29
Publication date: 2021-10-28
Also published as: CN111679959A

Abstract

A computer performance data determining method and a related apparatus. The method comprises: obtaining a plurality of historical performance data sequences of a computer (101); pre-training a long short term memory network by using the plurality of historical performance data sequences (102); classifying the plurality of historical performance data sequences to obtain N sequence types and N historical performance data sequence subsets having one-to-one correspondence to the N sequence types (103); training a performance pre-determination model consisting of a pre-trained long short term memory network and a full connection layer located behind the pre-trained long short term memory network by using each historical performance data sequence subset, so as to obtain N trained performance pre-determination models having one-to-one correspondence to the N sequence types; and pre-determining the performance data of the computer by means of the trained performance pre-determination model corresponding to the sequence type of a performance data sequence to be pre-determined of the computer.

Description

Method and device for determining computer performance data, computer equipment and storage medium

This application claims the priority of the Chinese patent application filed with the Chinese Patent Office on April 23, 2020, the application number is CN202010328656.1, and the invention title is "Computer performance data determination method, device, computer equipment and storage medium", all of which The content is incorporated in this application by reference.

Technical field

This application relates to blockchain technology, in particular to a method, device, computer equipment, and computer-readable storage medium for determining computer performance data.

Background technique

In cloud computing platform service providers, performance (such as capacity) prediction is an important application scenario of AIOPS (AI-based IT operations). The existing performance prediction methods do not have high accuracy for cloud computing platform service vendors to perform performance predictions, and are not effective in long-term performance predictions.

technical problem

The inventor realized that when there are large-scale performance indicators that need to be predicted, if one model is used for each performance indicator, and the parameters of the model need to be adjusted for different performance indicators, the required labor input costs are huge. On the other hand, when the training data of a certain performance index is small, the prediction of the performance index may be over-fitting.

Technical solutions

A method for determining computer performance data, the method comprising:

Obtain multiple historical performance data sequences of the computer;

Pre-training the long and short-term memory network using the multiple historical performance data sequences to obtain a pre-trained long and short-term memory network;

Classify the multiple historical performance data sequences to obtain N sequence types and N historical performance data sequence subsets corresponding to the N sequence types one-to-one;

Use each subset of historical performance data sequence to train a performance prediction model composed of the pre-trained long-term short-term memory network and the fully connected layer behind the pre-trained long-term short-term memory network to obtain N training performance prediction models corresponding to N sequence types one to one;

The performance data of the computer is predicted through the trained performance prediction model corresponding to the sequence type of the performance data sequence to be predicted of the computer.

A device for determining computer performance data, the device comprising:

The acquisition module is used to acquire multiple historical performance data sequences of the computer;

The first training module is configured to use the multiple historical performance data sequences to pre-train the long and short-term memory network to obtain the pre-trained long and short-term memory network;

The classification module is configured to classify the multiple historical performance data sequences to obtain N sequence types and N historical performance data sequence subsets corresponding to the N sequence types one-to-one;

The second training module is used to use each historical performance data sequence subset to train a performance prediction composed of the pre-trained long short-term memory network and the fully connected layer behind the pre-trained long short-term memory network. Judgment model to obtain N trained performance prediction models corresponding to the N sequence types one-to-one;

The prediction module is used to predict the performance data of the computer through the trained performance prediction model corresponding to the sequence type of the performance data sequence to be predicted of the computer.

A computer device includes a processor, and the processor implements the following steps when the processor is used to execute a computer program stored in a memory:

Obtain multiple historical performance data sequences of the computer;

The fourth aspect of the present application provides a computer-readable storage medium on which a computer program is stored, and when the computer program is executed by a processor, the following steps are implemented:

Obtain multiple historical performance data sequences of the computer;

Use each subset of historical performance data sequence to train a performance prediction model consisting of the pre-trained long short-term memory network and the fully connected layer behind the pre-trained long short-term memory network to obtain N training performance prediction models corresponding to N sequence types one to one;

Beneficial effect

This application realizes the prediction of computer performance based on the historical performance data of the computer.

Description of the drawings

Fig. 1 is a flowchart of a method for determining computer performance data provided by an embodiment of the present application.

Fig. 2 is a structural diagram of a computer performance data determining apparatus provided by an embodiment of the present application.

Fig. 3 is a schematic diagram of a computer device provided by an embodiment of the present application.

Embodiments of the present invention

In order to be able to understand the above objectives, features and advantages of the application more clearly, the application will be described in detail below with reference to the accompanying drawings and specific embodiments. It should be noted that the embodiments of the application and the features in the embodiments can be combined with each other if there is no conflict.

In the following description, many specific details are set forth in order to fully understand the present application. The described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in this application, all other embodiments obtained by those of ordinary skill in the art without creative work shall fall within the protection scope of this application.

Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field of this application. The terms used in the specification of the application herein are only for the purpose of describing specific embodiments, and are not intended to limit the application.

Preferably, the method for determining computer performance data of the present application is applied to one or more computer devices. The computer device is a device that can automatically perform numerical calculation and/or information processing in accordance with pre-set or stored instructions. Its hardware includes, but is not limited to, a microprocessor, an application specific integrated circuit (Application Specific Integrated Circuit). Specific Integrated Circuit, ASIC), Programmable Gate Array (Field-Programmable Gate Array, FPGA), Digital Signal Processor (DSP), embedded devices, etc.

The computer device may be a computing device such as a desktop computer, a notebook, a palmtop computer, and a cloud server. The computer device can interact with the user through a keyboard, a mouse, a remote control, a touch panel, or a voice control device.

Example one

FIG. 1 is a flowchart of a method for determining computer performance data provided in Embodiment 1 of the present application. The computer performance data determination method is applied to computer equipment to predict computer performance data based on historical computer performance data. The computer performance data may include computer CPU data, computer GPU data, computer memory data, or computer storage data.

As shown in Figure 1, the method for determining computer performance data includes:

101. Acquire multiple historical performance data sequences of the computer.

In a specific embodiment, the acquiring multiple historical performance data sequences of the computer includes:

Obtain the main sequence of historical performance data of multiple computers, and each element in the main sequence of historical performance data is a state vector of computer performance;

Taking the preset length h as the sliding window length and 1 as the step length, multiple historical performance data sequences are intercepted from each main sequence of historical performance data.

The next element of the historical performance data sequence in the main historical performance data sequence where each historical performance data sequence is located may be marked as the label of the historical performance data sequence. For example, the main sequence of historical performance data of a computer is {X1,X2,...,Xi,...,Xn}, where Xi is the state vector of the CPU at time i; the preset length h is the sliding window length, and the Intercept multiple historical performance data sequences from each main sequence of historical performance data for the step size as {X1,X2,…Xh}, {X2,X3,… Xh+1}, {X3,X4,… Xh+2} Etc.; historical performance data sequence {X1,X2,...Xh}, {X2,X3,... The tags of Xh+1}, {X3, X4,... Xh+2} are Xh+1, Xh+2, Xh+3, respectively.

Each historical performance data series is a time series.

102. Use the multiple historical performance data sequences to pre-train the long and short-term memory network to obtain a pre-trained long and short-term memory network.

Specifically, the parameters in the long and short-term memory network can be initialized, each historical performance data sequence is input into the long- and short-term memory network, and the output value calculated by the long and short-term memory network according to the input value and the historical performance data can be calculated The distance between the tags of the sequence is optimized according to the distance in the parameters in the long- and short-term memory network.

In another embodiment, before using the multiple historical performance data sequences to pre-train the long- and short-term memory network, the method further includes:

Preprocessing the multiple historical performance data sequences.

Missing value preprocessing, abnormal data preprocessing, etc. may be performed on the multiple historical performance data sequences. Performing missing value preprocessing on the historical performance data sequence set can prevent missing values in the multiple historical performance sequences and reduce the impact on training the long and short-term memory network. Performing abnormal data preprocessing on the multiple historical performance data sequences increases the training convergence speed of the long- and short-term memory network, and reduces the time for training the long- and short-term memory network. When there are missing values in a historical performance data sequence (due to data source errors or software design errors, etc.), the historical performance data sequence can be preprocessed with missing values using interpolation or replacement methods. When there is abnormal data in a historical performance data sequence (that is, the historical performance data sequence includes values that are not within the preset range), if the abnormal data is greater than the maximum value of the preset range, the abnormal data is set to the maximum value of the preset range Value; if the abnormal data is less than the minimum value of the preset range, the abnormal data is set to the minimum value of the preset range.

103. Classify the multiple historical performance data sequences to obtain N sequence types and N historical performance data sequence subsets corresponding to the N sequence types in a one-to-one manner.

In a specific embodiment, the classifying the multiple historical performance data sequences includes:

Clustering the multiple historical performance data sequences, and modifying the clustering result according to the received modification instruction; or

Classify the multiple historical performance data sequences by using the trained preset convolutional neural network model.

The multiple historical performance data sequences in the historical performance data sequence set may be clustered according to the DBScan clustering algorithm, where the distance function in the DBScan clustering algorithm uses one-dimensional NCC (normalized cross correlation) algorithm; when the clustering result is abnormal, delete the historical performance data sequence in the clustering result that is beyond the preset range from the cluster center according to the received delete instruction.

Specifically, the clustering of the multiple historical performance data sequences includes:

Selecting N central point sequences from the multiple historical performance data sequences;

Calculate the distance between each historical performance data sequence and the N central point sequences, and obtain the central point sequence closest to the historical performance data sequence;

The historical performance data sequence is divided into the cluster to which the center point sequence closest to the historical performance data sequence belongs.

Determine a cluster with each historical performance data sequence as the center, and obtain W clusters;

If W is greater than N, calculate the distance between every two clusters and merge the two clusters with the smallest distance to obtain W-1 clusters;

If W-1 is greater than N, calculate the distance between every two clusters, merge the two clusters with the smallest distance, and get W-2 clusters; and so on, when W-R is equal to N, get N clusters.

Before classifying the multiple historical performance data sequences with the trained preset convolutional neural network model, training the preset convolutional neural network model:

Input a historical performance data sequence into the preset convolutional neural network model, and each historical performance data sequence corresponds to a type label;

Based on the output of the preset convolutional neural network model and the type label of the historical performance data sequence, the preset convolutional neural network model is backpropagated, and the parameters of the preset convolutional neural network model are optimized.

104. Use each subset of historical performance data sequence to train a performance prediction model consisting of the pre-trained long-term and short-term memory network and the fully connected layer behind the pre-trained long- and short-term memory network, to obtain and The N training performance prediction models corresponding to the N sequence types one-to-one.

In a specific embodiment, the training of each historical performance data sequence subset is composed of the pre-trained long and short-term memory network and the fully connected layer behind the pre-trained long and short-term memory network. Performance prediction models include:

Determine whether the number of historical performance data sequences is greater than a preset threshold;

If the number of historical performance data sequences is greater than the preset threshold, use the historical performance data sequence subset to optimize the parameters of the performance prediction model corresponding to the historical performance data sequence subset according to the loss function;

If the number of historical performance data sequences is not greater than the preset threshold, the historical performance data sequence subset is used according to the loss function to optimize the parameters of the fully connected layer of the performance prediction model corresponding to the historical performance data sequence subset.

When the number of historical performance data sequences is not greater than the preset threshold, only optimizing the parameters of the fully connected layer of the performance prediction model corresponding to the subset of historical performance data sequences can reduce the occurrence of overfitting, and due to the performance The prediction model is pre-trained and will not lead to low prediction accuracy.

In another embodiment, the training of each historical performance data sequence subset is composed of the pre-trained long and short-term memory network and the fully connected layer behind the pre-trained long- and short-term memory network Performance prediction models include:

Use the historical performance data sequence subset to optimize the parameters of the performance prediction model corresponding to the historical performance data sequence subset according to the loss function.

105. Prejudge the performance data of the computer through the trained performance prediction model corresponding to the sequence type of the performance data sequence to be predicted of the computer.

Before predicting the performance data of the computer through the trained performance prediction model corresponding to the sequence type of the performance data sequence to be predicted of the computer, the method further includes:

(1) Obtain the to-be-judged performance data sequence of the computer.

For example, the computer’s CPU performance sequence is {X1, X2,... X15}, where X1=(2019, 06, 12, 13, 15, 43), the first 5 dimensions are the acquisition time, and the 6th dimension is the computer’s acquisition time CPU usage by time; X2=(2019,06,12,13,16,80); X15=(2019,06,12,13,30,90). Similarly, the to-be-predicted performance data sequence of the computer also includes computer GPU performance, computer memory performance, or computer storage performance.

(2) Determine the sequence type of the performance data sequence to be predicted according to the subset of N historical performance data sequences.

In a specific embodiment, the determining the sequence type of the performance data sequence to be predicted according to the subset of N historical performance data sequences includes:

Calculate N central sequences of N historical performance data sequence subsets, and determine the sequence type corresponding to the central sequence closest to the performance data sequence to be predicted as the target sequence type; or

Use N historical performance data sequence subsets to train the preset neural network, where the label of each historical performance data sequence in each historical performance data sequence subset is the sequence type of the historical performance data sequence, and the performance to be predicted The data sequence is input to the preset neural network after training, and the sequence type of the performance data sequence to be predicted is determined according to the output of the preset neural network after training.

When training the preset neural network, the historical performance data sequence may be used as the input of the preset neural network, and the sequence type of the historical performance data sequence may be used as the label of the historical performance data sequence; The labels of the output and historical performance data sequences are optimized through the back-propagation algorithm to optimize the parameters of the preset neural network.

The performance data of the computer is predicted through the trained performance prediction model corresponding to the sequence type of the performance data sequence to be predicted of the computer. For example, the computer CPU performance sequence is {X1, X2,... X15}, Among them, X1=(2019,06,12,13,15,43), the first 5 dimensions are the acquisition time, and the 6th dimension is the CPU usage rate of the computer at the acquisition time; X2=(2019,06,12,13 , 16, 80); X15 = (2019, 06, 12, 13, 30, 90). The computer CPU performance sequence corresponds to the performance prediction model A, and the performance prediction model A can more accurately extract the characteristics of the computer CPU performance sequence (because the performance prediction model A is also trained according to the sequence type during the training process). The computer CPU performance sequence is input into the performance prediction model A, and the CPU performance of the computer is predicted through the performance prediction model A to obtain X16=(2019, 06, 12, 13, 31, 95).

The computer performance data determination method of the first embodiment obtains a computer's historical performance data sequence set, the historical performance data sequence set includes a plurality of historical performance data sequences and a label of each historical performance data sequence; the historical performance data sequence set is used Perform pre-training on the long and short-term memory network to obtain the pre-trained long and short-term memory network; classify the multiple historical performance data sequences to obtain N sequence types and N sequence types corresponding to the N sequence types one-to-one Historical performance data sequence subsets; each historical performance data sequence subset is used to train a performance prediction composed of the pre-trained long-short-term memory network and the fully connected layer behind the pre-trained long- and short-term memory network To obtain the N performance prediction models corresponding to the N sequence types one-to-one; obtain the performance data sequence to be predicted of the computer; determine the sequence type of the performance data sequence to be predicted; The performance prediction model corresponding to the sequence type of the performance data sequence to be predicted predicts the performance data of the computer. The first embodiment predicts the computer performance based on the historical performance data of the computer.

It should be emphasized that, in order to further ensure the privacy and security of the above-mentioned performance data, the above-mentioned performance data may also be stored in a node of a blockchain.

In another embodiment, the method further includes:

If the prediction result of the computer's performance data is not within the preset normal state range, a notification of abnormal computer performance is returned. When an abnormality occurs, stop running the new task in the computer.

For example, the performance data prediction result of the computer is X16=(2019, 06, 12, 13, 31, 95), 95 is the predicted CPU usage rate of the computer, the preset normal state range is 0-90, return The computer CPU is abnormally reminded to the user; and the new task in the computer is stopped running.

In another embodiment, the method further includes:

(1) Obtain multiple historical CPU usage sequences of the computer.

For example, the main sequence of historical CPU usage of a computer is {X1,X2,...,Xi,...,Xn}, where Xi is the state vector of the CPU at time i; the preset length h is the sliding window length and the 1 is the step size to intercept multiple historical CPU usage sequence from each main sequence of historical CPU usage as {X1,X2,...Xh}, {X2,X3,... Xh+1}, {X3,X4,... Xh +2} etc.; historical CPU usage rate sequence {X1,X2,...Xh}, {X2,X3,... The tags of Xh+1}, {X3, X4,... Xh+2} are Xh+1, Xh+2, Xh+3, respectively.

Each historical CPU usage sequence is a time sequence.

(2) Pre-training the long and short-term memory network using the multiple historical CPU usage rate sequences to obtain a pre-trained long and short-term memory network.

Specifically, the parameters in the long and short-term memory network may be initialized, each historical CPU usage rate sequence is input into the long-term short-term memory network, and the output value calculated by the long-term short-term memory network according to the input value and the historical CPU Use the distance of the tags of the rate sequence to optimize the parameters in the long and short-term memory network according to the distance.

(3) Classify the multiple historical CPU usage rate sequences to obtain N sequence types and N historical CPU usage rate sequence subsets corresponding to the N sequence types one-to-one.

In a specific embodiment, the classifying the multiple historical CPU usage rate sequences includes:

Clustering the multiple historical CPU usage rate sequences, and modifying the clustering result according to the received modification instruction; or

Use the trained preset convolutional neural network model to classify the multiple historical CPU usage rate sequences.

(4) Use each historical CPU usage sequence subset to train a CPU usage prediction composed of the pre-trained long and short-term memory network and the fully connected layer after the pre-trained long and short-term memory network Model to obtain N trained CPU usage prediction models corresponding to the N sequence types one-to-one.

(5) Prejudge the CPU performance of the computer through the trained CPU usage prediction model corresponding to the sequence type of the CPU usage sequence to be predicted for the computer.

Obtain the CPU usage rate sequence to be predicted of the computer.

For example, the computer’s CPU performance sequence is {X1, X2,... X15}, where X1=(2019, 06, 12, 13, 15, 43), the first 5 dimensions are the acquisition time, and the 6th dimension is the computer’s acquisition time CPU usage by time; X2=(2019,06,12,13,16,80); X15=(2019,06,12,13,30,90).

The sequence type of the CPU usage rate sequence to be predicted is determined according to the N historical CPU usage rate sequence subsets.

The computer CPU performance sequence corresponds to the prediction model A of the CPU usage rate. The computer CPU performance sequence is input into the CPU usage prediction model A, and the CPU performance of the computer is predicted through the CPU usage prediction model A to obtain X16=(2019, 06, 12, 13, 31, 95).

Example two

Fig. 2 is a structural diagram of a computer performance data determining device provided in the second embodiment of the present application. The computer performance data determining device 20 is applied to computer equipment. The computer performance data determining device 20 is used to predict the computer performance data based on the historical performance data of the computer. The computer performance data may include computer CPU data, computer GPU data, computer memory data, or computer storage data.

As shown in FIG. 2, the device 20 for determining computer performance data may include an acquisition module 201, a first training module 202, a classification module 203, a second training module 204, and a pre-judgment module 205.

The obtaining module 201 is used to obtain multiple historical performance data sequences of the computer.

The next element of the historical performance data sequence in the main historical performance data sequence where each historical performance data sequence is located may be marked as the label of the historical performance data sequence. For example, the main sequence of historical performance data of a computer is {X1,X2,...,Xi,...,Xn}, where Xi is the state vector of the CPU at time i; the preset length h is the sliding window length, and 1 Intercept multiple historical performance data sequences from each main sequence of historical performance data for the step size as {X1,X2,…Xh}, {X2,X3,… Xh+1}, {X3,X4,… Xh+2} Etc.; historical performance data sequence {X1,X2,...Xh}, {X2,X3,... The tags of Xh+1}, {X3, X4,... Xh+2} are Xh+1, Xh+2, Xh+3, respectively.

Each historical performance data series is a time series.

The first training module 202 is configured to use the multiple historical performance data sequences to pre-train the long and short-term memory network to obtain a pre-trained long- and short-term memory network.

Preprocessing the multiple historical performance data sequences.

Missing value preprocessing, abnormal data preprocessing, etc. may be performed on the multiple historical performance data sequences. Performing missing value preprocessing on the historical performance data sequence set can prevent missing values in the multiple historical performance sequences and reduce the impact on training the long and short-term memory network. Performing abnormal data preprocessing on the multiple historical performance data sequences increases the training convergence speed of the long- and short-term memory network, and reduces the time for training the long- and short-term memory network. When there are missing values in a historical performance data sequence (due to data source errors or software design errors, etc.), interpolation or replacement methods can be used to preprocess the missing values of the historical performance data sequence. When there is abnormal data in a historical performance data sequence (that is, the historical performance data sequence includes values that are not within the preset range), if the abnormal data is greater than the maximum value of the preset range, the abnormal data is set to the maximum value of the preset range Value; if the abnormal data is less than the minimum value of the preset range, the abnormal data is set to the minimum value of the preset range.

The classification module 203 is configured to classify the multiple historical performance data sequences to obtain N sequence types and N historical performance data sequence subsets corresponding to the N sequence types one-to-one.

The second training module 204 is configured to use each subset of historical performance data sequences to train a performance consisting of the pre-trained long-short-term memory network and the fully connected layer behind the pre-trained long- and short-term memory network The predictive model obtains N trained performance predictive models corresponding to the N sequence types in a one-to-one manner.

The predicting module 205 is configured to predict the performance data of the computer through the trained performance predicting model corresponding to the sequence type of the to-be-predicted performance data sequence of the computer.

(1) Obtain the to-be-judged performance data sequence of the computer.

The computer performance data determining device 20 of the second embodiment predicts the computer performance based on the historical performance data of the computer.

In another embodiment, the computer performance data determining device 20 further includes a returning module for returning a computer performance abnormality reminder if the computer performance data prediction result is not within the preset normal state range.

When an abnormality occurs, stop running the new task in the computer.

Example three

This embodiment provides a computer-readable storage medium. The computer-readable storage medium may be volatile or non-volatile. A computer program is stored on the computer-readable storage medium, and the computer program is processed. The following steps are implemented when the device is executed:

Obtain multiple historical performance data sequences of the computer;

Or, when the computer program is executed by the processor, the function of each module in the above-mentioned device embodiment is realized, for example, the modules 201-205 in FIG. 2.

Example four

FIG. 3 is a schematic diagram of a computer device provided in Embodiment 3 of this application. The computer device 30 includes a memory 301, a processor 302, and a computer program 303 that is stored in the memory 301 and can run on the processor 302, such as a computer performance data determination program. When the processor 302 executes the computer program 303, the following steps are implemented:

Obtain multiple historical performance data sequences of the computer;

Exemplarily, the computer program 303 may be divided into one or more modules, and the one or more modules are stored in the memory 301 and executed by the processor 302 to complete the method. The one or more modules may be a series of computer program instruction segments capable of completing specific functions, and the instruction segments are used to describe the execution process of the computer program 303 in the computer device 30. For example, the computer program 303 may be divided into an acquisition module 201, a first training module 202, a classification module 203, a second training module 204, and a pre-judgment module 205 in FIG. 2. For specific functions of each module, refer to the second embodiment.

Those skilled in the art can understand that the schematic diagram 3 is only an example of the computer device 30, and does not constitute a limitation on the computer device 30. It may include more or less components than those shown in the figure, or combine certain components, or different components. For example, the computer device 30 may also include input and output devices, network access devices, buses, and so on.

The so-called processor 302 may be a central processing unit (Central Processing Unit, CPU), other general-purpose processors, digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), Field-Programmable Gate Array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or the processor 302 can also be any conventional processor, etc. The processor 302 is the control center of the computer device 30, which uses various interfaces and lines to connect the entire computer device 30. Various parts.

The memory 301 may be used to store the computer program 303, and the processor 302 implements the computer device by running or executing the computer program or module stored in the memory 301 and calling data stored in the memory 301 30 various functions. The memory 301 may mainly include a storage program area and a storage data area. The storage program area may store an operating system, an application program required by at least one function (such as a sound playback function, an image playback function, etc.), etc.; the storage data area may Data and the like created in accordance with the use of the computer device 30 are stored. In addition, the memory 301 may include non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a Secure Digital (SD) card, a flash memory card (Flash Card), At least one magnetic disk storage device, flash memory device, or other non-volatile solid state storage device.

If the integrated module of the computer device 30 is implemented in the form of a software function module and sold or used as an independent product, it can be stored in a computer readable storage medium. Based on this understanding, the present application implements all or part of the processes in the above-mentioned embodiments and methods, and can also be completed by instructing relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the program is executed by the processor, it can implement the steps of the foregoing method embodiments. Wherein, the computer program includes computer program code, and the computer program code may be in the form of source code, object code, executable file, or some intermediate forms. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory) .

Further, the computer usable storage medium may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required by at least one function, etc.; the storage data area may store a block chain node Use the created data, etc.

The blockchain referred to in this application is a new application mode of computer technology such as distributed data storage, point-to-point transmission, consensus mechanism, and encryption algorithm. Blockchain, essentially a decentralized database, is a series of data blocks associated with cryptographic methods. Each data block contains a batch of network transaction information for verification. The validity of the information (anti-counterfeiting) and the generation of the next block. The blockchain can include the underlying platform of the blockchain, the platform product service layer, and the application service layer.

In the several embodiments provided in this application, it should be understood that the disclosed system, device, and method can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the modules is only a logical function division, and there may be other division methods in actual implementation.

The modules described as separate components may or may not be physically separated, and the components displayed as modules may or may not be physical modules, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules can be selected according to actual needs to achieve the objectives of the solutions of the embodiments.

In addition, the functional modules in the various embodiments of the present application may be integrated into one processing module, or each module may exist alone physically, or two or more modules may be integrated into one module. The above-mentioned integrated modules can be implemented in the form of hardware, or in the form of hardware plus software functional modules.

The above-mentioned integrated modules implemented in the form of software functional modules may be stored in a computer readable storage medium. The above-mentioned software function module is stored in a storage medium and includes several instructions to make a computer device (which may be a personal computer, a server, or a network device, etc.) or a processor execute the method described in the various embodiments of this application. Part of the steps.

For those skilled in the art, it is obvious that the present application is not limited to the details of the foregoing exemplary embodiments, and the present application can be implemented in other specific forms without departing from the spirit or basic characteristics of the application. Therefore, no matter from which point of view, the embodiments should be regarded as exemplary and non-limiting. The scope of this application is defined by the appended claims rather than the above description, and therefore it is intended to fall into the claims. All changes in the meaning and scope of the equivalent elements of are included in this application. Any associated diagram marks in the claims should not be regarded as limiting the claims involved. In addition, it is obvious that the word "including" does not exclude other modules or steps, and the singular does not exclude the plural. Multiple modules or devices stated in the system claims can also be implemented by one module or device through software or hardware. Words such as first and second are used to denote names, but do not denote any specific order.

Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the application and not to limit them. Although the application has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the application can be Make modifications or equivalent replacements without departing from the spirit and scope of the technical solution of the present application.

Claims

A method for determining computer performance data, wherein the method includes:

Obtain multiple historical performance data sequences of the computer;

Pre-training the long and short-term memory network using the multiple historical performance data sequences to obtain a pre-trained long and short-term memory network;

Classify the multiple historical performance data sequences to obtain N sequence types and N historical performance data sequence subsets corresponding to the N sequence types one-to-one;

Use each subset of historical performance data sequence to train a performance prediction model composed of the pre-trained long-term short-term memory network and the fully connected layer behind the pre-trained long-term short-term memory network to obtain N training performance prediction models corresponding to N sequence types one to one;

The performance data of the computer is predicted through the trained performance prediction model corresponding to the sequence type of the performance data sequence to be predicted of the computer.
The method according to claim 1, wherein said acquiring a plurality of historical performance data sequences of the computer comprises:

Obtain the main sequence of historical performance data of multiple computers, and each element in the main sequence of historical performance data is a state vector of computer performance;

Taking the preset length h as the sliding window length and 1 as the step length, multiple historical performance data sequences are intercepted from each main sequence of historical performance data.
The method of claim 1, wherein the classifying the plurality of historical performance data sequences comprises:

Clustering the multiple historical performance data sequences, and modifying the clustering result according to the received modification instruction; or

Classify the multiple historical performance data sequences by using the trained preset convolutional neural network model.
The method according to claim 1, wherein the training of each historical performance data sequence subset is composed of the pre-trained long short-term memory network and the pre-trained long short-term memory network. The performance prediction model composed of the connection layer includes:

Determine whether the number of historical performance data sequences is greater than a preset threshold;

If the number of historical performance data sequences is greater than the preset threshold, use the historical performance data sequence subset to optimize the parameters of the performance prediction model corresponding to the historical performance data sequence subset according to the loss function;

If the number of historical performance data sequences is not greater than the preset threshold, the historical performance data sequence subset is used according to the loss function to optimize the parameters of the fully connected layer of the performance prediction model corresponding to the historical performance data sequence subset.
The method according to claim 1, wherein, before the performance data of the computer is predicted by the trained performance prediction model corresponding to the sequence type of the performance data sequence to be predicted of the computer, the The method also includes:

The sequence type of the performance data sequence to be predicted is determined according to the subset of N historical performance data sequences.
The method of claim 5, wherein the determining the sequence type of the performance data sequence to be predicted according to the subset of N historical performance data sequences comprises:

Calculate N central sequences of N historical performance data sequence subsets, and determine the sequence type corresponding to the central sequence closest to the performance data sequence to be predicted as the target sequence type; or

Use N historical performance data sequence subsets to train the preset neural network, where the label of each historical performance data sequence in each historical performance data sequence subset is the sequence type of the historical performance data sequence, and the performance to be predicted The data sequence is input to the preset neural network after training, and the sequence type of the performance data sequence to be predicted is determined according to the output of the preset neural network after training.
The method according to any one of claims 1-5, wherein the method further comprises:

If the prediction result of the computer's performance data is not within the preset normal state range, a notification of abnormal computer performance is returned.
A device for determining computer performance data, wherein the device includes:

The acquisition module is used to acquire multiple historical performance data sequences of the computer;

The first training module is configured to use the multiple historical performance data sequences to pre-train the long and short-term memory network to obtain the pre-trained long and short-term memory network;

The classification module is configured to classify the multiple historical performance data sequences to obtain N sequence types and N historical performance data sequence subsets corresponding to the N sequence types one-to-one;

The second training module is used to use each historical performance data sequence subset to train a performance prediction composed of the pre-trained long short-term memory network and the fully connected layer behind the pre-trained long short-term memory network. Judgment model to obtain N trained performance prediction models corresponding to the N sequence types one-to-one;

The prediction module is used to predict the performance data of the computer through the trained performance prediction model corresponding to the sequence type of the performance data sequence to be predicted of the computer.
A computer device, wherein the computer device includes a processor, and the processor is configured to execute a computer program stored in a memory to implement the following steps:

Obtain multiple historical performance data sequences of the computer;

Pre-training the long and short-term memory network using the multiple historical performance data sequences to obtain a pre-trained long and short-term memory network;

Classify the multiple historical performance data sequences to obtain N sequence types and N historical performance data sequence subsets corresponding to the N sequence types one-to-one;

Use each subset of historical performance data sequence to train a performance prediction model composed of the pre-trained long-term short-term memory network and the fully connected layer behind the pre-trained long-term short-term memory network to obtain N training performance prediction models corresponding to N sequence types one to one;

The performance data of the computer is predicted through the trained performance prediction model corresponding to the sequence type of the performance data sequence to be predicted of the computer.
9. The computer device according to claim 9, wherein said acquiring a plurality of historical performance data sequences of the computer comprises:

Obtain the main sequence of historical performance data of multiple computers, and each element in the main sequence of historical performance data is a state vector of computer performance;

Taking the preset length h as the sliding window length and 1 as the step length, multiple historical performance data sequences are intercepted from each main sequence of historical performance data.
9. The computer device of claim 9, wherein said classifying said plurality of historical performance data sequences comprises:

Clustering the multiple historical performance data sequences, and modifying the clustering result according to the received modification instruction; or

Classify the multiple historical performance data sequences by using the trained preset convolutional neural network model.
The computer device according to claim 9, wherein said training a long short-term memory network after the pre-training and a long-short-term memory network after the pre-training is performed with each subset of historical performance data sequences. The performance prediction model composed of the fully connected layer includes:

Determine whether the number of historical performance data sequences is greater than a preset threshold;

If the number of historical performance data sequences is greater than the preset threshold, use the historical performance data sequence subset to optimize the parameters of the performance prediction model corresponding to the historical performance data sequence subset according to the loss function;

If the number of historical performance data sequences is not greater than the preset threshold, the historical performance data sequence subset is used according to the loss function to optimize the parameters of the fully connected layer of the performance prediction model corresponding to the historical performance data sequence subset.
The computer device according to claim 9, wherein, before the performance data of the computer is predicted by the trained performance prediction model corresponding to the sequence type of the performance data sequence to be predicted of the computer, The processor is used to execute the computer program stored in the memory and further implements the following steps:

The sequence type of the performance data sequence to be predicted is determined according to the subset of N historical performance data sequences.
The computer device according to claim 13, wherein the determining the sequence type of the performance data sequence to be predicted according to the subset of N historical performance data sequences comprises:

Calculate N central sequences of N historical performance data sequence subsets, and determine the sequence type corresponding to the central sequence closest to the performance data sequence to be predicted as the target sequence type; or

Use N historical performance data sequence subsets to train the preset neural network, where the label of each historical performance data sequence in each historical performance data sequence subset is the sequence type of the historical performance data sequence, and the performance to be predicted The data sequence is input to the preset neural network after training, and the sequence type of the performance data sequence to be predicted is determined according to the output of the preset neural network after training.
The computer device according to any one of claims 9-13, wherein the processor is configured to execute the computer program stored in the memory and further implements the following steps:

If the prediction result of the computer's performance data is not within the preset normal state range, a notification of abnormal computer performance is returned.
A computer-readable storage medium having a computer program stored on the computer-readable storage medium, wherein, when the computer program is executed by a processor, the following steps are implemented:

Obtain multiple historical performance data sequences of the computer;

Pre-training the long and short-term memory network using the multiple historical performance data sequences to obtain a pre-trained long and short-term memory network;

Classify the multiple historical performance data sequences to obtain N sequence types and N historical performance data sequence subsets corresponding to the N sequence types one-to-one;

Use each subset of historical performance data sequence to train a performance prediction model composed of the pre-trained long-term short-term memory network and the fully connected layer behind the pre-trained long-term short-term memory network to obtain N training performance prediction models corresponding to N sequence types one to one;

The performance data of the computer is predicted through the trained performance prediction model corresponding to the sequence type of the performance data sequence to be predicted of the computer.
15. The computer-readable storage medium of claim 16, wherein said acquiring a plurality of historical performance data sequences of the computer comprises:

Obtain the main sequence of historical performance data of multiple computers, and each element in the main sequence of historical performance data is a state vector of computer performance;

Taking the preset length h as the sliding window length and 1 as the step length, multiple historical performance data sequences are intercepted from each main sequence of historical performance data.
15. The computer-readable storage medium of claim 16, wherein the classifying the plurality of historical performance data sequences comprises:

Clustering the multiple historical performance data sequences, and modifying the clustering result according to the received modification instruction; or

Classify the multiple historical performance data sequences by using the trained preset convolutional neural network model.
The computer-readable storage medium according to claim 16, wherein said training a long and short-term memory network after said pre-training and said long- and short-term memory after said pre-training is performed with each subset of historical performance data sequences. The performance prediction model composed of the fully connected layer after the network includes:

Determine whether the number of historical performance data sequences is greater than a preset threshold;

If the number of historical performance data sequences is greater than the preset threshold, use the historical performance data sequence subset to optimize the parameters of the performance prediction model corresponding to the historical performance data sequence subset according to the loss function;

If the number of historical performance data sequences is not greater than the preset threshold, the historical performance data sequence subset is used according to the loss function to optimize the parameters of the fully connected layer of the performance prediction model corresponding to the historical performance data sequence subset.
The computer-readable storage medium of claim 16, wherein the performance of the computer is predicted by the trained performance prediction model corresponding to the sequence type of the performance data sequence to be predicted of the computer Before data, when the computer program is executed by the processor, the following steps are also implemented:

The sequence type of the performance data sequence to be predicted is determined according to the subset of N historical performance data sequences.