Data adaptive adjustment method and device

Abstract

The present invention provides a data adaptive adjustment method and device, which relates to the field of artificial intelligence technology. The adjustment method includes: obtaining historical resource utilization data and current resource utilization data of a business system; establishing a resource utilization prediction model based on the historical resource utilization data and an artificial intelligence algorithm; determining a set of predicted values ​​of resource utilization corresponding to the current resource utilization data according to the resource utilization prediction model; determining an adjustment time set of data of the business system based on each predicted value in the predicted value set and a preset clearing threshold; and adjusting the data of the business system in each time period within the adjustment time set. The present invention can reduce the cost of human monitoring, improve the processing efficiency of the business system, and save system resources.

Description

Technical Field

[0001] The present invention relates to the field of artificial intelligence technology, and in particular to a data adaptive adjustment method and device. Background Art

[0002] Banking business processing requires registering various types of data and storing them in data tables to ensure the integrity, validity, and non-repudiation of business information. With the explosive growth of online business, database storage is becoming increasingly large, and the current routine processing of databases is to clean up batches at the end of the day, and a cleaning program must be written, with a fixed life cycle set in advance. The cleaning period is relatively concentrated, and a large amount of business system resources are required for centralized processing, which is prone to efficiency problems and occupies a large amount of hardware resources, affecting the normal resource allocation of the business system, thereby wasting business system resources.

[0003] In recent years, machine learning has been widely used in various fields. By modeling and training a large amount of historical data, corresponding rules and budget results are obtained. In order to solve the problem of resource waste and improve the resource efficiency of business systems, how to predict the resource utilization rate of business systems through artificial learning algorithms and automatically adjust the cleanup time and data of business systems according to the predicted values ​​is a technical problem that needs to be solved urgently. Summary of the invention

[0004] In view of the problems in the prior art, the present invention provides a data adaptive adjustment method and device, which can effectively improve the performance and resource utilization of a business system.

[0005] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0006] In a first aspect, the present invention provides a data adaptive adjustment method, comprising:

[0007] Obtain historical and current resource utilization data of business systems;

[0008] Establishing a resource utilization prediction model based on the historical resource utilization data and the artificial intelligence algorithm;

[0009] Determining a set of predicted values ​​of resource utilization corresponding to the current resource utilization data according to the prediction model of resource utilization;

[0010] Determining an adjustment time set for data of the business system based on each prediction value in the prediction value set and a preset clearing threshold;

[0011] The data of the business system is adjusted in each time period within the adjustment time set.

[0012] Furthermore, it also includes:

[0013] Get the data life cycle corresponding to the data in the business system;

[0014] Determine the data set that needs to be processed in the business system according to the data life cycle;

[0015] Correspondingly, the data of the business system is adjusted in each time period within the adjustment time set, including:

[0016] The data set to be processed is adjusted in each time period within the adjustment time set.

[0017] Furthermore, after the data life cycle corresponding to the data of the business system is obtained, the method further includes:

[0018] A configuration instruction is received, and a data life cycle corresponding to the data of the business system is adjusted according to the configuration instruction.

[0019] Furthermore, determining the data set to be processed in the business system according to the data life cycle includes:

[0020] Determine that data whose data life cycle is greater than a preset period threshold and whose data access frequency is less than a preset access threshold is data that needs to be processed;

[0021] Among them, all data that need to be processed constitute the data set that needs to be processed.

[0022] Wherein, the resource utilization prediction model is established based on the historical resource utilization data and the artificial intelligence algorithm, including:

[0023] Determining a resource utilization index of the task system according to the resource utilization in the historical resource utilization data;

[0024] Based on the resource utilization index and the time period of data collection in the historical resource utilization data, an artificial intelligence algorithm is used to establish a resource utilization prediction model;

[0025] The historical resource utilization data includes: the time period of data collection and the resource utilization of the business system corresponding to the time period; the resource utilization prediction model is the baseline between the idle time period and the resource utilization index of the task system.

[0026] In a second aspect, the present invention provides a data adaptive adjustment device, comprising:

[0027] The collection module is used to obtain the historical resource utilization data and current resource utilization data of the business system;

[0028] A modeling module, used to establish a resource utilization prediction model based on the historical resource utilization data and an artificial intelligence algorithm;

[0029] A prediction module, used to determine a set of predicted values ​​of resource utilization corresponding to the current resource utilization data according to the prediction model of resource utilization;

[0030] An adjustment module, used to determine an adjustment time set for data of the business system based on each prediction value in the prediction value set and a preset clearing threshold;

[0031] The cleaning module is used to adjust the data of the business system in each time period within the adjustment time set.

[0032] Furthermore, it also includes:

[0033] The cycle module is used to obtain the data life cycle corresponding to the data of the business system;

[0034] A data module, used to determine a data set that needs to be processed in the business system according to the data life cycle;

[0035] Correspondingly, the data of the business system is adjusted in each time period within the adjustment time set, including:

[0036] The data set to be processed is adjusted in each time period within the adjustment time set.

[0037] Furthermore, after the data life cycle corresponding to the data of the business system is obtained, the method further includes:

[0038] The instruction module is used to receive configuration instructions and adjust the data life cycle corresponding to the data of the business system according to the configuration instructions.

[0039] Furthermore, the data module includes:

[0040] A judgment unit, used to determine that data whose data life cycle is greater than a preset period threshold and whose data access frequency is less than a preset access threshold is data that needs to be processed;

[0041] Among them, all data that need to be processed constitute the data set that needs to be processed.

[0042] Wherein, the modeling module includes:

[0043] A utilization index unit, used to determine a resource utilization index of a task system according to resource utilization in the historical resource utilization data;

[0044] A modeling unit, configured to establish a resource utilization prediction model using an artificial intelligence algorithm based on the resource utilization index and the time period of data collection in the historical resource utilization data;

[0045] The historical resource utilization data includes: the time period of data collection and the resource utilization of the business system corresponding to the time period; the resource utilization prediction model is the baseline between the idle time period and the resource utilization index of the task system.

[0046] In a third aspect, the present invention provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the data adaptive adjustment method when executing the program.

[0047] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, which implements the steps of the data adaptive adjustment method when executed by a processor.

[0048] It can be seen from the above technical scheme that the present invention provides a data adaptive adjustment method and device, which obtains historical resource utilization data and current resource utilization data of a business system; establishes a resource utilization prediction model based on the historical resource utilization data and an artificial intelligence algorithm; determines a set of predicted value of resource utilization corresponding to the current resource utilization data according to the resource utilization prediction model; determines an adjustment time set of data of the business system based on each predicted value in the predicted value set and a preset clearing threshold; and adjusts the data of the business system in each time period within the adjustment time set, which can reduce human monitoring costs, improve the processing efficiency of the business system and save system resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0050] Figure 1 Schematic diagram of a first flow chart of a data adaptive adjustment method in an embodiment of the present invention.

[0051] Figure 2 Schematic diagram of a second flow chart of the data adaptive adjustment method in an embodiment of the present invention.

[0052] Figure 3 Schematic diagram of a third flow chart of the data adaptive adjustment method in an embodiment of the present invention.

[0053] Figure 4 Schematic diagram of the structure of the data adaptive adjustment device in an embodiment of the present invention.

[0054] Figure 5 Schematic diagram of the structure of an electronic device in an embodiment of the present invention. DETAILED DESCRIPTION

[0055] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0056] The present invention provides an embodiment of a data adaptive adjustment method, see Figure 1 , the data adaptive adjustment method specifically includes the following contents:

[0057] S101: Acquire historical resource utilization data and current resource utilization data of the business system;

[0058] In this step, resource utilization data of the business system is obtained, wherein the resource utilization data refers to the data on the utilization efficiency of business system resources (including CPU, disk, memory, etc.).

[0059] The resource utilization data is divided into historical resource utilization data and current resource utilization data according to a preset timeline, and a corresponding resource utilization set T is established based on the historical resource utilization data.

[0060] T=(x1,y1),(x2,y2),...(x n ,y n );

[0061] Among them, x is the collection time period, which is an input parameter; y is the resource utilization corresponding to the time period x.

[0062] It is understandable that the historical resource utilization data includes: the time period for data collection and the resource utilization of the business system corresponding to the time period.

[0063] It should be noted that the timeline is reset according to the continuous operation of the business system, so that the resource utilization set T is continuously updated with data, making the resource utilization set more representative.

[0064] S102: Establishing a resource utilization prediction model based on the historical resource utilization data and the artificial intelligence algorithm;

[0065] In this step, the resource utilization index of the task system is determined according to the resource utilization in the historical resource utilization data; based on the resource utilization index and the time period of data collection in the historical resource utilization data, an artificial intelligence algorithm is used to establish a resource utilization prediction model;

[0066] The resource utilization prediction model is a baseline between the idle time period and the resource utilization index of the task system.

[0067] S103: Determine a set of predicted values ​​of resource utilization corresponding to the current resource utilization data according to the prediction model of resource utilization;

[0068] In this step, the current resource utilization data is input into the prediction model of the resource utilization, and the prediction model of the resource utilization determines or outputs a set of predicted resource utilization values ​​corresponding to the input current resource utilization data.

[0069] S104: determining an adjustment time set for data of the business system based on each prediction value in the prediction value set and a preset clearing threshold;

[0070] In this step, a set of predicted values ​​of the business system resource utilization is obtained based on the resource utilization prediction model. When each predicted value in the predicted value set is less than the set cleanup threshold, it is used as a set of time intervals for data cleansing. The set of time intervals for data cleansing is the adjustment time set of the business system data.

[0071] S105: Adjusting the data of the business system in each time period within the adjustment time set.

[0072] In this step, the data of the business system is cleaned up in different time periods according to each time period in the adjustment time set, so as to improve the processing efficiency of the business system and save business system resources.

[0073] From the above description, it can be seen that the data adaptive adjustment method provided by the embodiment of the present invention obtains historical resource utilization data and current resource utilization data of the business system; establishes a resource utilization prediction model based on the historical resource utilization data and an artificial intelligence algorithm; determines a set of predicted values ​​of resource utilization corresponding to the current resource utilization data according to the resource utilization prediction model; determines an adjustment time set of data of the business system based on each predicted value in the predicted value set and a preset cleanup threshold; and adjusts the data of the business system in each time period within the adjustment time set, which can reduce human monitoring costs, improve the processing efficiency of the business system and save system resources.

[0074] In one embodiment of the present invention, see Figure 2, the data adaptive adjustment method specifically includes the following contents:

[0075] S201: Acquire the data life cycle corresponding to the data of the business system;

[0076] S202: Determine a data set that needs to be processed in a business system according to the data life cycle;

[0077] Correspondingly, the data of the business system is adjusted in each time period within the adjustment time set, including:

[0078] The data set to be processed is adjusted in each time period within the adjustment time set.

[0079] In this embodiment, the data of the business system corresponds to the data life cycle, and the data exceeding the data life cycle is determined as the data to be processed, that is, the data is cleared. Among them, all the data to be processed constitute the data set to be processed.

[0080] Furthermore, when the data of the business system is adjusted in each time period within the adjustment time set, the data sets that need to be processed in the business system are cleared.

[0081] In this embodiment, the data set to be cleaned up in the business system can be determined according to the data life cycle, and the data set can be cleaned up, thereby improving the processing efficiency of the business system and saving system resources.

[0082] Based on the above embodiments, see Figure 3 , after step S201, further comprising:

[0083] S203: Receive a configuration instruction, and adjust the data life cycle corresponding to the data of the business system according to the configuration instruction.

[0084] In this embodiment, the flexibility of the life cycle is increased by adjusting the data life cycle corresponding to the data of the business system.

[0085] The embodiment of the present invention provides a specific implementation of step S202, including:

[0086] Determine that data whose data life cycle is greater than a preset period threshold and whose data access frequency is less than a preset access threshold is data that needs to be processed;

[0087] Among them, all data that need to be processed constitute the data set that needs to be processed.

[0088] In this embodiment, the determination of the data set to be processed is not only related to the data life cycle, but also to the data access frequency of the data. Only when the data life cycle is greater than a preset cycle threshold and the data access frequency of the data is less than a preset access threshold, the data is determined to be the data to be processed. The data to be processed can be divided in more detail, thereby improving the accuracy of data cleaning.

[0089] In one embodiment of the present invention, a specific implementation of step S102 in the data adaptive adjustment method specifically includes the following contents:

[0090] After determining the resource utilization set T, the baseline of the business system resource utilization and the idle time period is found according to the artificial intelligence algorithm, and then the system resource usage in a certain time period in the future is predicted.

[0091] First, the average value M in each cycle (the cycle value is set to 15 minutes) of each day is calculated based on the obtained training set sample space sequence t , the formula is as follows:

[0092]

[0093] Where t=K,K+1, T is the period, R t It is the resource utilization rate every 15 minutes of each day (T).

[0094] The mean value M is calculated by simple translation. t Perform preprocessing to eliminate the influence of abnormal factors or remove the periodic variation components in the data.

[0095] Among them, if K is an even number, the corresponding corrections are K / 2, K / 2+1, TK / 2, that is:

[0096] R' K / 2 =M K ,R' K / 2+1 =M K+1 ,.....R' T-K / 2+1 =M T ;

[0097] If the data is odd, correct them respectively by (K+1) / 2, (K+1) / 2+1, T-(K-1) / 2, that is:

[0098] R' K / 2 =M K ,R' K / 2+1 =M K+1 ,.....R' T-K / 2+1 =M T ;

[0099] Based on the corrected data, calculate the central moving average R″ t , and then calculate the seasonal index S for each cycle t ', the formula is

[0100] Where t starts from the first centered moving average.

[0101] Calculate the system resource utilization index:

[0102]

[0103] Calculate the system resource utilization index:

[0104] Normalize the resource utilization index so that its sum equals K. The formula is: Among them, F j (j=1,2,....K) is the final resource utilization index.

[0105] According to the resource utilization index, a resource prediction model is established. The prediction model is: R t+l =(a t +b t L)F j , R t+l is the predicted value, t is the period of the prediction model, L is the interval period between the prediction period and the test model (the present invention predicts every day, then it is 1), F j is the resource utilization index, a t is the last centralized moving average of the observation period, b t It is the change trend value based on the last two centralized moving averages during the observation period.

[0106] The embodiment of the present invention provides a specific implementation of a data adaptive adjustment device that can implement all the contents of the data adaptive adjustment method, see Figure 4 , the data adaptive adjustment device specifically includes the following contents:

[0107] The acquisition module 10 is used to obtain the historical resource utilization data and the current resource utilization data of the business system;

[0108] A modeling module 20, for establishing a resource utilization prediction model based on the historical resource utilization data and an artificial intelligence algorithm;

[0109] A prediction module 30, configured to determine a set of predicted resource utilization values ​​corresponding to the current resource utilization data according to the prediction model of the resource utilization;

[0110] An adjustment module 40, configured to determine an adjustment time set for data of a business system based on each prediction value in the prediction value set and a preset clearing threshold;

[0111] The cleaning module 50 is used to adjust the data of the business system in each time period within the adjustment time set.

[0112] Furthermore, it also includes:

[0113] The cycle module is used to obtain the data life cycle corresponding to the data of the business system;

[0114] A data module, used to determine a data set that needs to be processed in the business system according to the data life cycle;

[0115] Correspondingly, the data of the business system is adjusted in each time period within the adjustment time set, including:

[0116] The data set to be processed is adjusted in each time period within the adjustment time set.

[0117] Furthermore, after the data life cycle corresponding to the data of the business system is obtained, the method further includes:

[0118] The instruction module is used to receive configuration instructions and adjust the data life cycle corresponding to the data of the business system according to the configuration instructions.

[0119] Furthermore, the data module includes:

[0120] A judgment unit, used to determine that data whose data life cycle is greater than a preset period threshold and whose data access frequency is less than a preset access threshold is data that needs to be processed;

[0121] Among them, all data that need to be processed constitute the data set that needs to be processed.

[0122] Wherein, the modeling module includes:

[0123] A utilization index unit, used to determine a resource utilization index of a task system according to resource utilization in the historical resource utilization data;

[0124] A modeling unit, configured to establish a resource utilization prediction model using an artificial intelligence algorithm based on the resource utilization index and the time period of data collection in the historical resource utilization data;

[0125] The historical resource utilization data includes: the time period of data collection and the resource utilization of the business system corresponding to the time period; the resource utilization prediction model is the baseline between the idle time period and the resource utilization index of the task system.

[0126] The embodiment of the data adaptive adjustment device provided by the present invention can be specifically used to execute the processing flow of the embodiment of the data adaptive adjustment method in the above embodiment. Its functions are not repeated here, and reference can be made to the detailed description of the above method embodiment.

[0127] From the above description, it can be seen that the data adaptive adjustment device provided by the embodiment of the present invention obtains historical resource utilization data and current resource utilization data of the business system; establishes a resource utilization prediction model based on the historical resource utilization data and an artificial intelligence algorithm; determines a set of predicted values ​​of resource utilization corresponding to the current resource utilization data according to the resource utilization prediction model; determines an adjustment time set of data of the business system based on each predicted value in the predicted value set and a preset cleanup threshold; and adjusts the data of the business system in each time period within the adjustment time set, which can reduce human monitoring costs, improve the processing efficiency of the business system and save system resources.

[0128] The present application provides an embodiment of an electronic device for implementing all or part of the content of the data adaptive adjustment method. The electronic device specifically includes the following content:

[0129] Processor, memory, communication interface and bus; wherein the processor, memory and communication interface communicate with each other through the bus; the communication interface is used to realize information transmission between related devices; the electronic device can be a desktop computer, a tablet computer and a mobile terminal, etc., but the present embodiment is not limited thereto. In the present embodiment, the electronic device can be implemented with reference to the embodiment for implementing the data adaptive adjustment method and the embodiment for implementing the data adaptive adjustment device, and the contents thereof are incorporated herein and the repeated parts are not repeated.

[0130] Figure 5 FIG. 9 is a schematic block diagram of the system structure of the electronic device 9600 according to an embodiment of the present application. Figure 5 As shown, the electronic device 9600 may include a central processor 9100 and a memory 9140; the memory 9140 is coupled to the central processor 9100. It is worth noting that Figure 5 is exemplary; other types of structures may also be used to supplement or replace this structure to implement telecommunication functions or other functions.

[0131] In one embodiment, the data adaptive adjustment function may be integrated into the central processing unit 9100. The central processing unit 9100 may be configured to perform the following control:

[0132] Acquire historical resource utilization data and current resource utilization data of the business system; establish a resource utilization prediction model based on the historical resource utilization data and an artificial intelligence algorithm; determine a set of predicted resource utilization values ​​corresponding to the current resource utilization data according to the resource utilization prediction model; determine an adjustment time set for the business system data based on each predicted value in the prediction value set and a preset cleanup threshold; and adjust the business system data in each time period within the adjustment time set.

[0133] From the above description, it can be seen that the electronic device provided by the embodiment of the present application obtains historical resource utilization data and current resource utilization data of the business system; establishes a resource utilization prediction model based on the historical resource utilization data and an artificial intelligence algorithm; determines a set of predicted resource utilization values ​​corresponding to the current resource utilization data according to the resource utilization prediction model; determines an adjustment time set for the data of the business system based on each predicted value in the prediction value set and a preset clearing threshold; and adjusts the data of the business system in each time period within the adjustment time set, which can reduce human monitoring costs, improve the processing efficiency of the business system, and save system resources.

[0134] In another embodiment, the data adaptive adjustment device can be configured separately from the central processing unit 9100. For example, the data adaptive adjustment device can be configured as a chip connected to the central processing unit 9100, and the data adaptive adjustment function can be implemented under the control of the central processing unit.

[0135] like Figure 5 As shown, the electronic device 9600 may also include: a communication module 9110, an input unit 9120, an audio processor 9130, a display 9160, and a power supply 9170. It is worth noting that the electronic device 9600 does not necessarily have to include Figure 5 In addition, the electronic device 9600 may also include Figure 5 For components not shown, reference may be made to the prior art.

[0136] like Figure 5 As shown, the central processing unit 9100 is sometimes also referred to as a controller or an operation control, and may include a microprocessor or other processor device and / or logic device. The central processing unit 9100 receives input and controls the operation of various components of the electronic device 9600.

[0137] The memory 9140 may be, for example, one or more of a cache, a flash memory, a hard drive, a removable medium, a volatile memory, a non-volatile memory or other suitable devices. The above-mentioned information related to the failure may be stored, and a program for executing the relevant information may also be stored. The CPU 9100 may execute the program stored in the memory 9140 to implement information storage or processing, etc.

[0138] The input unit 9120 provides input to the central processing unit 9100. The input unit 9120 is, for example, a key or a touch input device. The power supply 9170 is used to provide power to the electronic device 9600. The display 9160 is used to display display objects such as images and texts. The display may be, for example, an LCD display, but is not limited thereto.

[0139] The memory 9140 may be a solid-state memory, such as a read-only memory (ROM), a random access memory (RAM), a SIM card, etc. It may also be a memory that saves information even when the power is off, can be selectively erased, and is provided with more data, examples of which are sometimes referred to as EPROMs, etc. The memory 9140 may also be some other type of device. The memory 9140 includes a buffer memory 9141 (sometimes referred to as a buffer). The memory 9140 may include an application / function storage unit 9142, which is used to store application programs and function programs or processes for executing the operation of the electronic device 9600 through the central processor 9100.

[0140] The memory 9140 may also include a data storage unit 9143 for storing data, such as contacts, digital data, pictures, sounds, and / or any other data used by the electronic device. The driver storage unit 9144 of the memory 9140 may include various drivers for communication functions of the electronic device and / or for executing other functions of the electronic device (such as messaging applications, address book applications, etc.).

[0141] The communication module 9110 is a transmitter / receiver 9110 that sends and receives signals via an antenna 9111. The communication module (transmitter / receiver) 9110 is coupled to the central processor 9100 to provide input signals and receive output signals, which may be the same as the case of a conventional mobile communication terminal.

[0142] Based on different communication technologies, multiple communication modules 9110 may be provided in the same electronic device, such as a cellular network module, a Bluetooth module and / or a wireless LAN module, etc. The communication module (transmitter / receiver) 9110 is also coupled to a speaker 9131 and a microphone 9132 via an audio processor 9130 to provide an audio output via the speaker 9131 and receive an audio input from the microphone 9132, thereby realizing a common telecommunication function. The audio processor 9130 may include any suitable buffer, decoder, amplifier, etc. In addition, the audio processor 9130 is also coupled to the central processor 9100, so that recording can be performed on the local machine through the microphone 9132, and the sound stored on the local machine can be played through the speaker 9131.

[0143] An embodiment of the present invention also provides a computer-readable storage medium capable of implementing all the steps in the data adaptive adjustment method in the above embodiment. The computer-readable storage medium stores a computer program. When the computer program is executed by a processor, all the steps of the data adaptive adjustment method in the above embodiment are implemented. For example, when the processor executes the computer program, the following steps are implemented:

[0144] Acquire historical resource utilization data and current resource utilization data of the business system; establish a resource utilization prediction model based on the historical resource utilization data and an artificial intelligence algorithm; determine a set of predicted resource utilization values ​​corresponding to the current resource utilization data according to the resource utilization prediction model; determine an adjustment time set for the business system data based on each predicted value in the prediction value set and a preset cleanup threshold; and adjust the business system data in each time period within the adjustment time set.

[0145] From the above description, it can be seen that the computer-readable storage medium provided by the embodiment of the present invention obtains historical resource utilization data and current resource utilization data of the business system; establishes a resource utilization prediction model based on the historical resource utilization data and an artificial intelligence algorithm; determines a set of predicted values ​​of resource utilization corresponding to the current resource utilization data according to the resource utilization prediction model; determines an adjustment time set of data of the business system based on each predicted value in the predicted value set and a preset cleanup threshold; and adjusts the data of the business system in each time period within the adjustment time set, which can reduce human monitoring costs, improve the processing efficiency of the business system, and save system resources.

[0146] Although the present invention provides method operation steps as described in the embodiments or flowcharts, more or fewer operation steps may be included based on conventional or non-creative labor. The order of steps listed in the embodiments is only one way of executing the order of many steps and does not represent the only execution order. When the actual device or client product is executed, it can be executed in sequence or in parallel according to the method shown in the embodiments or the drawings (for example, in a parallel processor or multi-threaded processing environment).

[0147] It should be understood by those skilled in the art that the embodiments of this specification may be provided as methods, devices (systems) or computer program products. Therefore, the embodiments of this specification may take the form of complete hardware embodiments, complete software embodiments or embodiments combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0148] The present invention is described with reference to flowcharts and / or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as a combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the process in the flowchart. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0149] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0150] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0151] Each embodiment in this specification is described in a progressive manner, and the same and similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment. It should be noted that the embodiments and features in the embodiments of the present invention can be combined with each other in the absence of conflict. The present invention is not limited to any single aspect, nor is it limited to any single embodiment, nor is it limited to any combination and / or permutation of these aspects and / or embodiments. Moreover, each aspect and / or embodiment of the present invention can be used alone or in combination with one or more other aspects and / or embodiments thereof.

[0152] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein by equivalents. These modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be included in the scope of the claims and specification of the present invention.

Claims

1. A data adaptive adjustment method, characterized in that: include: Obtain historical and current resource utilization data of business systems; Establishing a resource utilization prediction model based on the historical resource utilization data and the artificial intelligence algorithm; Determining a predicted value set of resource utilization corresponding to the current resource utilization data according to the prediction model of resource utilization; the predicted value set is a set composed of various predicted values ​​of resource utilization; Determine an adjustment time set of data of the business system based on each prediction value in the prediction value set and a preset cleaning threshold; the adjustment time set is a set of time intervals for data cleaning when each prediction value in the prediction value set is less than the preset cleaning threshold; Adjusting the data of the business system in each time period within the adjustment time set; Obtain historical and current resource utilization data for business systems, including: Divide the resource utilization data into historical resource utilization data and current resource utilization data according to a preset timeline; establish a corresponding resource utilization set T based on the historical resource utilization data; T=(x1,y1),(x2,y2),...(x n ,y n ); Among them, x1…x n is the time period, which is the input parameter; y1…y n is the time period x1…x n The corresponding resource utilization rate; Historical resource utilization data, including: time period and resource utilization of the business system corresponding to the time period; A resource utilization prediction model is established based on the historical resource utilization data and the artificial intelligence algorithm, including: Determining a resource utilization index of the task system according to the resource utilization in the historical resource utilization data; establishing a resource utilization prediction model using an artificial intelligence algorithm based on the resource utilization index and the time period in the historical resource utilization data; The resource utilization prediction model is a baseline between the idle time period and the resource utilization index of the task system; After determining the resource utilization set T, the artificial intelligence algorithm is used to find the baseline of the business system resource utilization and the idle time period, and then the system resource usage in a certain time period in the future is predicted, including: First, the average value M in each cycle of each day is calculated based on the obtained training set sample space sequence. t , the formula is as follows: Where t is the period, R t is the resource utilization rate of each cycle every day in history, and K is the sum of the resource utilization index after normalization; The mean value M is calculated by simple translation. t Perform preprocessing to eliminate the influence of primary abnormal factors or remove periodic variation components in the data; If K is an even number, the resource utilization corresponding to K / 2, K / 2+1, and tK / 2 is corrected respectively, that is: R' K / 2 =M K ,R' K / 2+1 =M K+1 ,.....R' T-K / 2+1 =M t ; Among them, R' K / 2 , R' K / 2+1 ...R' T-K / 2+1 are the corresponding corrected resource utilization rates respectively; If K is an odd number, the resource utilization corresponding to (K+1) / 2, (K+1) / 2+1, and t-(K-1) / 2 are corrected respectively, that is: R’ (K+1) / 2 =M K ,R’ (K+1) / 2+1 =M K+1 ,.....R' T-(K+1) / 2+1 =M t ; Among them, R' (K+1) / 2 , R' (K+1) / 2+1 ...R' T-(K+1) / 2+1 are the corresponding corrected resource utilization rates respectively; Based on the corrected data, calculate the central moving average R" t , then calculate the modified seasonal index S' for each period t , the formula is Where t starts from the first centralized moving average; Compute resource utilization index: Where j = 1, 2...K; N is the number of cycles t per day, S j ,S j+k ...S j(N-1)K is the seasonal index; Normalize the resource utilization index so that its sum is equal to K. The formula is: Among them, F j (j=1,2,....K) is the final resource utilization index; A resource prediction model is established based on the resource utilization index. The prediction model is: R t+l =(a t +b t L)F j , R t+l is the predicted value, L is the interval period between the prediction period and the test model, a t is the last centralized moving average of the observation period, b t It is the change trend value based on the last two centralized moving averages during the observation period.

2. The data adaptive adjustment method according to claim 1, characterized in that: Also includes: Get the data life cycle corresponding to the data in the business system; Determine the data set that needs to be processed in the business system according to the data life cycle; Correspondingly, the data of the business system is adjusted in each time period within the adjustment time set, including: The data set to be processed is adjusted in each time period within the adjustment time set.

3. The data adaptive adjustment method according to claim 2, characterized in that: After the data life cycle corresponding to the data of the business system is obtained, the following is further included: A configuration instruction is received, and a data life cycle corresponding to the data of the business system is adjusted according to the configuration instruction.

4. The data adaptive adjustment method according to claim 3, characterized in that: Determining the data set to be processed in the business system according to the data life cycle includes: Determine that data whose data life cycle is greater than a preset period threshold and whose data access frequency is less than a preset access threshold is data that needs to be processed; Among them, all data that need to be processed constitute the data set that needs to be processed.

5. The data adaptive adjustment method according to claim 1, characterized in that: The resource utilization prediction model is established based on the historical resource utilization data and the artificial intelligence algorithm, including: Determining a resource utilization index of the task system according to the resource utilization in the historical resource utilization data; Based on the resource utilization index and the time period of data collection in the historical resource utilization data, an artificial intelligence algorithm is used to establish a resource utilization prediction model; The historical resource utilization data includes: the time period of data collection and the resource utilization of the business system corresponding to the time period; the resource utilization prediction model is the baseline between the idle time period and the resource utilization index of the task system.

6. A data adaptive adjustment device, characterized in that: include: The collection module is used to obtain the historical resource utilization data and current resource utilization data of the business system; A modeling module, used to establish a resource utilization prediction model based on the historical resource utilization data and an artificial intelligence algorithm; A prediction module, used to determine a set of predicted values ​​of resource utilization corresponding to the current resource utilization data according to the prediction model of resource utilization; the predicted value set is a set composed of various predicted values ​​of resource utilization; An adjustment module, used to determine an adjustment time set for data of the business system based on each prediction value in the prediction value set and a preset cleaning threshold; the adjustment time set is a set of time intervals for data cleaning when each prediction value in the prediction value set is less than the preset cleaning threshold; A cleaning module, used to adjust the data of the business system in each time period within the adjustment time set; Obtain historical and current resource utilization data for business systems, including: Divide the resource utilization data into historical resource utilization data and current resource utilization data according to a preset timeline; establish a corresponding resource utilization set T based on the historical resource utilization data; T=(x1,y1),(x2,y2),...(x n ,y n ); Among them, x1…x n is the time period, which is the input parameter; y1…y n is the time period x1…x n The corresponding resource utilization rate; Historical resource utilization data, including: time period and resource utilization of the business system corresponding to the time period; A resource utilization prediction model is established based on the historical resource utilization data and the artificial intelligence algorithm, including: Determining a resource utilization index of the task system according to the resource utilization in the historical resource utilization data; establishing a resource utilization prediction model using an artificial intelligence algorithm based on the resource utilization index and the time period in the historical resource utilization data; The resource utilization prediction model is a baseline between the idle time period and the resource utilization index of the task system; After determining the resource utilization set T, the artificial intelligence algorithm is used to find the baseline of the business system resource utilization and the idle time period, and then the system resource usage in a certain time period in the future is predicted, including: First, the average value M in each cycle of each day is calculated based on the obtained training set sample space sequence. t , the formula is as follows: Where t is the period, R t is the resource utilization rate of each cycle every day in history, and K is the sum of the resource utilization index after normalization; The mean value M is calculated by simple translation. t Perform preprocessing to eliminate the influence of primary abnormal factors or remove periodic variation components in the data; If K is an even number, the resource utilization corresponding to K / 2, K / 2+1, and tK / 2 is corrected respectively, that is: R' K / 2 =M K ,R' K / 2+1 =M K+1 ,.....R' T-K / 2+1 =M t ; Among them, R' K / 2 , R' K / 2+1 ...R' T-K / 2+1 are the corresponding corrected resource utilization rates respectively; If K is an odd number, the resource utilization corresponding to (K+1) / 2, (K+1) / 2+1, and t-(K-1) / 2 are corrected respectively, that is: R’ (K+1) / 2 =M K ,R’ (K+1) / 2+1 =M K+1 ,.....R' T-(K+1) / 2+1 =M t ; Among them, R' (K+1) / 2 , R' (K+1) / 2+1 ...R' T-(K+1) / 2+1 are the corresponding corrected resource utilization rates respectively; Based on the corrected data, calculate the central moving average R t ", and then calculate the modified seasonal index S for each period t ', the formula is Where t starts from the first centralized moving average; Compute resource utilization index: Where j = 1, 2...K; N is the number of cycles t per day, S j ,S j+k ...S j(N-1)K is the seasonal index; Normalize the resource utilization index so that its sum is equal to K. The formula is: Among them, F j (j=1,2,....K) is the final resource utilization index; A resource prediction model is established based on the resource utilization index. The prediction model is: R t+l =(a t +b t L)F j , R t+l is the predicted value, L is the interval period between the prediction period and the test model, a t is the last centralized moving average of the observation period, b t It is the change trend value based on the last two centralized moving averages during the observation period.

7. The data adaptive adjustment device according to claim 6, characterized in that: Also includes: The cycle module is used to obtain the data life cycle corresponding to the data of the business system; A data module, used to determine a data set that needs to be processed in the business system according to the data life cycle; Correspondingly, the data of the business system is adjusted in each time period within the adjustment time set, including: The data set to be processed is adjusted in each time period within the adjustment time set.

8. The data adaptive adjustment device according to claim 6, characterized in that: After the data life cycle corresponding to the data of the business system is obtained, the following is further included: The instruction module is used to receive configuration instructions and adjust the data life cycle corresponding to the data of the business system according to the configuration instructions.

9. The data adaptive adjustment device according to claim 8, characterized in that: The data module includes: A judgment unit, used to determine that data whose data life cycle is greater than a preset period threshold and whose data access frequency is less than a preset access threshold is data that needs to be processed; Among them, all data that need to be processed constitute the data set that needs to be processed.

10. The data adaptive adjustment device according to claim 6, characterized in that: The modeling module includes: A utilization index unit, used to determine a resource utilization index of a task system according to resource utilization in the historical resource utilization data; A modeling unit, configured to establish a resource utilization prediction model using an artificial intelligence algorithm based on the resource utilization index and the time period of data collection in the historical resource utilization data; The historical resource utilization data includes: the time period of data collection and the resource utilization of the business system corresponding to the time period; the resource utilization prediction model is the baseline between the idle time period and the resource utilization index of the task system.

11. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the steps of the data adaptive adjustment method according to any one of claims 1 to 5 are implemented.

12. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the data adaptive adjustment method according to any one of claims 1 to 5 are implemented.

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