Compression state operation method and system based on electric power big data

Abstract

The invention discloses a compressed state operation method based on electric power big data, and the method comprises the steps: detecting a storage environment of target electric power big data, obtaining a storage environment detection result, and presetting a storage space for the target electric power big data according to the storage environment detection result; based on the query frequency of the target electric power big data, setting a response speed required for querying the target electric power big data; according to a preset storage space for the target electric power big data and a response speed required for querying the target electric power big data, a compression state operation mode of the target electric power big data is selected, and the compression state operation mode of the target electric power big data comprises a low compression state operation mode, a medium compression state operation mode and a high compression state operation mode; and performing compression operation on the target power big data according to the selected compression state operation mode. The method has the advantages that under the condition that the storage space of the electric power big data is fixed, the response speed when the target electric power big data is queried is increased, and the like.

Description

Technical Field

[0001] This application relates to the technical field of big data squeezed state operations, and particularly relates to a method and system for squeezed state operations based on power big data. Background Art

[0002] Due to the very wide coverage and long power supply time of the power system, in order to enable the power system to operate efficiently and stably, power big data is usually used to manage the power system. Since the amount of power big data is huge, a very large storage space is required to store the power big data. In the case where the storage space of some storage devices for storing data is limited, the power big data is often compressed first and then stored. If the same squeezed state operation method is used to process the power big data, when the required response speed for querying the power big data is fast, the storage space of the storage device will increase, and when the storage space of the storage device is limited, the response speed required for querying the power big data will slow down. Therefore, it is inevitable that there will be a conflict between the limited storage space of the storage device and the fast response speed required for querying the power big data. Therefore, there is an urgent need for a solution that can alleviate the conflict between the limited storage space of the storage device and the fast response speed required for querying the power big data to solve the above problems. Summary of the Invention

[0003] This application provides a method and system for squeezed state operations based on power big data, aiming to optimize related technical solutions.

[0004] In a first aspect, a method for squeezed state operations based on power big data provided by an embodiment of this application may include the following steps:

[0005] Detect the storage environment of the target power big data, obtain the storage environment detection result, and preset a storage space for the target power big data according to the storage environment detection result;

[0006] Set the response speed required for querying the target power big data based on the query frequency of the target power big data;

[0007] Select a squeezed state operation method for the target power big data according to the preset storage space for the target power big data and the response speed required for querying the target power big data. The squeezed state operation methods for the target power big data include: a low squeezed state operation method, a medium squeezed state operation method, and a high squeezed state operation method. The compression rate of the low squeezed state operation method for the original target power big data is below 20%, the compression rate of the medium squeezed state operation method for the original target power big data is greater than the compression rate of the low squeezed state operation method and less than the compression rate of the high squeezed state operation method, and the compression rate of the high squeezed state operation method for the original target power big data is 90% or more;

[0008] Perform compression operations on the target power big data according to the selected compression state operation method.

[0009] In the above technical solution, the compression state operation methods of the target power big data are divided into three types: low compression state operation method, medium compression state operation method, and high compression state operation method. According to the storage and application environment of the target power big data, a matching compression state operation method is selected, which improves the compression efficiency of the target power big data. In the case of limited storage space, the response speed when querying the target power big data is effectively accelerated, thus alleviating the conflict between the limited storage space of the storage device and the fast response speed required when querying the power big data.

[0010] The solution of the first aspect of this application can be further configured in a preferred example as follows:

[0011] The described compression state operation method based on power big data may further include the following steps:

[0012] Automatically configure the compression parameters of the selected compression state operation method according to the operation speed of the selected compression state operation method and the compression ratio of the selected compression state operation method for the target power data.

[0013] Through the above technical solution, the compression parameters of the selected compression state operation method can be intelligently configured.

[0014] The solution of the first aspect of this application can be further configured in a preferred example as follows:

[0015] In the step of automatically configuring the compression parameters of the selected compression state operation method according to the operation speed of the selected compression state operation method and the compression ratio of the selected compression state operation method for the target power data, the automatic configuration method of the compression parameters includes the following expression:

[0016]

[0017] In the formula, θ represents the compression parameter, N represents the number of all power data in the compressed target power data, 1 ≤ n ≤ N, n is a positive integer, D n represents the compression ratio for the nth compressed power data, and S represents the operation speed of the selected compression state operation method.

[0018] Through the above technical solution, the compression parameters can be configured very efficiently and conveniently.

[0019] The solution of the first aspect of this application can be further configured in a preferred example as follows:

[0020] The described method for squeezed state operation based on power big data may further include the following steps:

[0021] Collect the data on the response speed required when querying the target power big data to form a response speed data set, and based on the response speed data set, construct an evaluation model for selecting the squeezed state operation method;

[0022] According to the evaluation model for selecting the squeezed state operation method, evaluate the matching degree between the target power big data and the selected squeezed state operation method.

[0023] In the above technical solution, by the response speed required when querying the target power big data, the matching degree between the target power big data and the selected squeezed state operation method is evaluated, so as to verify whether the matching degree of the selected squeezed state operation method for the target power big data meets the standard.

[0024] The solution of the first aspect of the present application can be further configured in a preferred example as follows:

[0025] The described method for squeezed state operation based on power big data may further include the following steps:

[0026] When the matching degree is lower than the matching degree threshold, suspend the compression operation for the target power big data, preset the storage space again and set the response speed required when querying the target power big data again, until the matching degree is greater than or equal to the matching degree threshold, and then start the compression operation for the target power big data.

[0027] In the above technical solution, when the matching degree of the selected squeezed state operation method for the target power big data does not meet the standard, the matching degree can be adjusted to meet the standard in time.

[0028] The solution of the first aspect of the present application can be further configured in a preferred example as follows:

[0029] The described method for squeezed state operation based on power big data may further include the following steps:

[0030] Compress the data of the same type in the target power data into the same compressed file, and configure a unique type index for the same compressed file, and query the compressed target power data through the type index.

[0031] In the above technical solution, querying the compressed target power data through the type index reduces the execution cost of the query load.

[0032] Second aspect, a squeezed state operation system based on power big data provided by an embodiment of the present application may include:

[0033] A storage space preset module for detecting the storage environment of target power big data, obtaining a storage environment detection result, and presetting a storage space for the target power big data according to the storage environment detection result;

[0034] A response speed setting module for setting the required response speed when querying the target power big data based on the query frequency of the target power big data;

[0035] A compressed state operation mode selection module for selecting a compressed state operation mode of the target power big data according to the preset storage space for the target power big data and the required response speed when querying the target power big data. The compressed state operation modes of the target power big data include: a low compression state operation mode, a medium compression state operation mode, and a high compression state operation mode. The compression rate of the low compression state operation mode for the original target power big data is below 20%. The compression rate of the medium compression state operation mode for the original target power big data is greater than the compression rate of the low compression state operation mode and less than the compression rate of the high compression state operation mode. The compression rate of the high compression state operation mode for the original target power big data is above 90%;

[0036] A compression module for performing a compression operation on the target power big data according to the selected compressed state operation mode.

[0037] The solution of the second aspect of the present application can be further configured in a preferred example as follows:

[0038] A compression parameter automatic configuration module for automatically configuring the compression parameters of the selected compressed state operation mode according to the operation speed of the selected compressed state operation mode and the compression rate of the selected compressed state operation mode for the target power data.

[0039] The solution of the second aspect of the present application can be further configured in a preferred example as follows:

[0040] The described compression state operation system based on power big data may further include:

[0041] A compressed state operation mode selection evaluation model construction module for collecting data on the required response speed when querying the target power big data, forming a response speed data set, and constructing a compressed state operation mode selection evaluation model based on the response speed data set;

[0042] A matching degree evaluation module for evaluating the matching degree between the target power big data and the selected compressed state operation mode according to the compressed state operation mode selection evaluation model.

[0043] The solution of the second aspect of the present application can be further configured in a preferred example as follows:

[0044] The described compressed state operation system based on power big data may further include:

[0045] A type index configuration module, configured to compress data of the same type in the target power data into the same compressed file, and configure a unique type index for the same compressed file, and query the compressed target power data through the type index.

[0046] Based on the above method item embodiment, the present application correspondingly provides a terminal item embodiment;

[0047] The present application provides a terminal, including a processor, a memory, and a computer program stored in the above memory and configured to be executed by the above processor. When the above processor executes the above computer program, it implements a compressed state operation method based on power big data according to any embodiment of the present application.

[0048] Based on the above method item embodiment, the present application correspondingly provides a storage medium item embodiment;

[0049] The present application provides a storage medium, including a processor, a memory, and a computer program stored in the above memory and configured to be executed by the above processor. When the above processor executes the above computer program, it implements a compressed state operation method based on power big data according to any embodiment of the present application.

[0050] The present application has at least the following beneficial effects:

[0051] A compressed state operation method based on power big data provided by the present application effectively speeds up the response speed when querying target power big data under the condition that the storage space of power big data is certain, and can avoid the conflict between the limited storage space of the storage device and the fast response speed required for querying power big data to the greatest extent. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 is a flowchart of a compressed state operation method based on power big data according to an embodiment of the present application.

[0053] Figure 2 is a block diagram of a compressed state operation system based on power big data according to an embodiment of the present application. DETAILED DESCRIPTION

[0054] The following will clearly and completely describe the technical solutions in the present application with reference to the drawings in the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments in the present application without creative efforts shall fall within the protection scope of the present application.

[0055] As Figure 1 shown, a compression state operation method based on power big data provided by an embodiment of the present application may specifically include the following steps:

[0056] Step S1: Detect the storage environment of the target power big data, obtain the storage environment detection result, and preset a storage space for the target power big data according to the storage environment detection result;

[0057] Step S2: Set the response speed required for querying the target power big data based on the query frequency of the target power big data;

[0058] Step S3: Select a compression state operation mode for the target power big data according to the preset storage space for the target power big data and the response speed required for querying the target power big data. The compression state operation mode of the target power big data includes: a low compression state operation mode, a medium compression state operation mode, and a high compression state operation mode. The compression rate of the low compression state operation mode for the original target power big data is below 20%. The compression rate of the medium compression state operation mode for the original target power big data is greater than the compression rate of the low compression state operation mode and less than the compression rate of the high compression state operation mode. The compression rate of the high compression state operation mode for the original target power big data is 90% or more;

[0059] Step S4: Perform compression operation on the target power big data according to the selected compression state operation mode.

[0060] Exemplarily, the target power big data selected for compression operation using the low compression state operation method is basically the same as directly querying the original target power big data. Its decompression speed is fast, so it is very convenient to query the target power big data. Among all the compression state operation methods, with a certain storage space, the response speed when querying the target power big data is the fastest, but the storage space occupied is also the largest; the medium compression state operation method has a certain reasonable compression rate for the original target power big data. The compression rate in this medium compression state operation method is less than the compression rate in the high compression state operation method. Among all the compression state operation methods, the response speed when querying the target power big data is moderate, and the storage space of the target power big data is also moderate; the high compression state operation method has a compression rate of more than 90% for the original target power big data. Among all the compression state operation methods, with a certain storage space, the response speed when querying the target power big data is the slowest, and the storage space of the target power big data is the smallest. In the compression state operation method based on power big data in this embodiment, according to the storage and application environment of the target power big data, a matching compression state operation method is selected, which improves the compression efficiency of the target power big data. When the storage space is limited, the response speed when querying the target power big data is effectively accelerated, thereby alleviating the conflict between the limited storage space of the storage device and the fast response speed required when querying power big data.

[0061] In order to intelligently configure the compression parameters of the selected compression state operation method, the compression state operation method based on power big data may specifically further include the following steps:

[0062] Automatically configure the compression parameters of the selected compression state operation method according to the operation speed of the selected compression state operation method and the compression rate of the selected compression state operation method for the target power data.

[0063] In order to configure the compression parameters very efficiently and conveniently, in the step of automatically configuring the compression parameters of the selected compression state operation method according to the operation speed of the selected compression state operation method and the compression rate of the selected compression state operation method for the target power data, the automatic configuration method of the compression parameters includes the following expression:

[0064]

[0065] In the formula, θ represents the compression parameter, N represents the number of all power data in the compressed target power data, 1 ≤ n ≤ N, n is a positive integer, D n represents the compression rate for the nth compressed power data, and S represents the operation speed of the selected compression state operation method.

[0066] In order to evaluate the matching degree between the target power big data and the selected squeezed state operation method by the response speed required when querying the target power big data, so as to verify whether the matching degree of the selected squeezed state operation method for the target power big data meets the standard, a squeezed state operation method based on power big data may specifically further include the following steps:

[0067] Collect data on the response speed required when querying the target power big data to form a response speed data set, and based on the response speed data set, construct an evaluation model for selecting the squeezed state operation method;

[0068] According to the evaluation model for selecting the squeezed state operation method, evaluate the matching degree between the target power big data and the selected squeezed state operation method.

[0069] In order to be able to adjust the matching degree to the standard in time when the matching degree of the selected squeezed state operation method for the target power big data does not meet the standard, a squeezed state operation method based on power big data may specifically further include the following steps:

[0070] When the matching degree is lower than the matching degree threshold, suspend the compression operation on the target power big data, preset the storage space again and set the response speed required when querying the target power big data again, until the matching degree is greater than or equal to the matching degree threshold, and then start the compression operation on the target power big data.

[0071] In order to query the compressed target power data through type indexing and reduce the execution cost of the query load, a squeezed state operation method based on power big data may specifically further include the following steps:

[0072] Compress the data of the same type in the target power data into the same compressed file, and configure a unique type index for the same compressed file, and query the compressed target power data through the type index.

[0073] As Figure 2 shown, a squeezed state operation system based on power big data may specifically include:

[0074] A storage space preset module, which is used to detect the storage environment of the target power big data, obtain a storage environment detection result, and preset a storage space for the target power big data according to the storage environment detection result;

[0075] A response speed setting module, which is used to set the response speed required when querying the target power big data based on the query frequency of the target power big data;

[0076] A compressed state operation mode selection module is used to select a compressed state operation mode for target power big data according to the preset storage space for the target power big data and the response speed required when querying the target power big data. The compressed state operation modes of the target power big data include: a low compression state operation mode, a medium compression state operation mode, and a high compression state operation mode. The compression rate of the low compression state operation mode for the original target power big data is below 20%. The compression rate of the medium compression state operation mode for the original target power big data is greater than the compression rate of the low compression state operation mode and less than the compression rate of the high compression state operation mode. The compression rate of the high compression state operation mode for the original target power big data is above 90%.

[0077] A compression module is used to perform compression operations on the target power big data according to the selected compressed state operation mode.

[0078] The described compressed state operation system based on power big data may specifically further include:

[0079] A compression parameter automatic configuration module is used to automatically configure the compression parameters of the selected compressed state operation mode according to the operation speed of the selected compressed state operation mode and the compression rate of the selected compressed state operation mode for the target power data.

[0080] The described compressed state operation system based on power big data may specifically further include:

[0081] A compressed state operation mode selection evaluation model construction module is used to collect data on the response speed required when querying the target power big data to form a response speed data set, and based on the response speed data set, construct a compressed state operation mode selection evaluation model;

[0082] A matching degree evaluation module is used to evaluate the matching degree between the target power big data and the selected compressed state operation mode according to the compressed state operation mode selection evaluation model.

[0083] The described compressed state operation system based on power big data may specifically further include:

[0084] A type index configuration module is used to compress data of the same type in the target power data into the same compressed file, and configure a unique type index for the same compressed file, and query the compressed target power data through the type index.

[0085] It should be noted that the system embodiments described above are merely illustrative. Among them, the modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical modules, that is, they may be located in one place or distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment. In addition, in the attached drawings of the system embodiments provided in this application, the connection relationship between modules indicates that they have a communication connection, which can be specifically implemented as one or more communication buses or signal lines. Those of ordinary skill in the art can understand and implement it without creative efforts. The above schematic diagram is merely an example of a compressed state operation system based on power big data, and does not constitute a limitation on a compressed state operation system based on power big data. It may include more or fewer components than shown in the figure, or combine some components, or different components.

[0086] Based on the above method item embodiments, this application correspondingly provides terminal item embodiments.

[0087] Another embodiment of this application provides a terminal, including a processor, a memory, and a computer program stored in the above memory and configured to be executed by the above processor. When the above processor executes the above computer program, it implements a compressed state operation method based on power big data described in any one of the embodiments of this application.

[0088] Exemplarily, in this embodiment, the above computer program can be divided into one or more modules. The above one or more modules are stored in the above memory and executed by the above processor to complete this application. The above one or more module elements can be a series of computer program instruction segments capable of completing specific functions, and these instruction segments are used to describe the execution process of the above computer program in the above device;

[0089] The above terminal can be a computing device such as a desktop computer, a notebook, a palm computer, and a cloud server. The above device may include, but is not limited to, a processor and a memory.

[0090] The so-called processor may be a central processing module (Central Processing Unit, CPU), or may also be other general-purpose processors, digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), field-programmable gate arrays (Field-Programmable Gate Array, FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc. The above-mentioned processor is the control center of the above-mentioned device, and uses various interfaces and circuits to connect all parts of the entire device.

[0091] The above-mentioned memory can be used to store the above-mentioned computer programs and / or modules. The above-mentioned processor realizes various functions of the above-mentioned device by running or executing the computer programs and / or modules stored in the above-mentioned memory, and by calling the data stored in the memory. The above-mentioned memory mainly includes a program storage area and a data storage area. Among them, the program storage area can store an operating system, application programs required for at least one function, etc.; in addition, the memory can include high-speed random access memory, and can also include non-volatile memory, such as a hard disk, memory, plug-in hard disk, smart media card (Smart Media Card, SMC), secure digital (Secure Digital, SD) card, flash card (Flash Card), at least one magnetic disk storage device, flash device, or other volatile solid-state storage devices.

[0092] Based on the above method item embodiments, the present application correspondingly provides storage medium item embodiments.

[0093] Another embodiment of the present application provides a storage medium. The above storage medium includes a stored computer program. Among them, when the above computer program runs, it controls the device where the above storage medium is located to execute a method for compressive state operation based on power big data described in any one of the embodiments of the present application.

[0094] In this embodiment, the above storage medium is a computer-readable storage medium, the above computer program includes computer program code, and the above computer program code may be in the form of source code, object code, executable file or some intermediate form, etc. The above computer-readable medium may include: any entity or device capable of carrying the above computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disc, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electrical carrier signal, telecommunication signal, and software distribution medium, etc.

[0095] In the above embodiment of the present application, the internal and external networks of the enterprise are integrated, so that the internal staff of the enterprise participating in the enterprise internal network project can obtain the external network information related to the enterprise internal network project only by logging in to the enterprise internal network, and can very conveniently obtain the relevant information of the enterprise internal network project; by setting up an external network information access account for the internal staff of the enterprise participating in the enterprise internal network project to access the external network information related to the project, and setting different external network information access permissions for different external network information access accounts, the internal staff of the enterprise participating in the enterprise internal network project can conveniently and accurately obtain the external network information related to the enterprise internal network project they participate in.

[0096] The above is the preferred implementation manner of the present application. It should be noted that for those of ordinary skill in the art in this technical field, without departing from the principle of the present application, several improvements and retouches can be made, and these improvements and retouches are also regarded as the protection scope of the present application.

Claims

1. A squeezed state operation method based on power big data, characterized in that, Including the following steps: Detect the storage environment of the target power big data, obtain the storage environment detection result, and preset a storage space for the target power big data according to the storage environment detection result; Based on the query frequency of the target power big data, set the response speed required when querying the target power big data; According to the preset storage space for the target power big data and the response speed required when querying the target power big data, select a compression state operation method for the target power big data. The compression state operation methods for the target power big data include: low compression state operation method, medium compression state operation method, and high compression state operation method. The compression rate of the low compression state operation method for the original target power big data is below 20%, the compression rate of the medium compression state operation method for the original target power big data is greater than the compression rate of the low compression state operation method and less than the compression rate of the high compression state operation method, and the compression rate of the high compression state operation method for the original target power big data is above 90%; Perform compression operation on the target power big data according to the selected compression state operation method.

2. The compressed state operation method based on power big data according to claim 1, wherein Also including the following steps: Automatically configure the compression parameters of the selected compression state operation method according to the operation speed of the selected compression state operation method and the compression rate of the selected compression state operation method for the target power data.

3. The compressed state operation method based on power big data according to claim 2, characterized in that, In the step of automatically configuring the compression parameters of the selected compression state operation method according to the operation speed of the selected compression state operation method and the compression rate of the selected compression state operation method for the target power data, the automatic configuration method of the compression parameters includes the following expressions: Where, θ represents the compression parameter, N represents the number of all power data in the target power data to be compressed, 1 ≤ n ≤ N, n is a positive integer, and D n represents the compression rate for the nth compressed power data, and S represents the operation speed of the selected compression state operation method.

4. A squeezed state operation method based on power big data according to claim 1, characterized in that Also including the following steps: Collect the data of the response speed required when querying the target power big data to form a response speed data set, and based on the response speed data set, construct an evaluation model for selecting the compression state operation method; Evaluate the matching degree between the target power big data and the selected compression state operation method according to the evaluation model for selecting the compression state operation method.

5. A squeezed state operation method based on power big data according to claim 4, characterized in that, Also including the following steps: When the matching degree is lower than the matching degree threshold, pause the compression operation on the target power big data, preset the storage space again and set the response speed required when querying the target power big data again until the matching degree is greater than or equal to the matching degree threshold, and then start the compression operation on the target power big data.

6. The compressed state operation method based on power big data according to claim 1, characterized in that Also including the following steps: Compress the data of the same type in the target power data into the same compression file, and configure a unique type index for the same compression file, and query the compressed target power data through the type index.

7. A squeezed state operation system based on power big data, characterized in that, Including: A storage space preset module for detecting the storage environment of the target power big data, obtaining the storage environment detection result, and presetting a storage space for the target power big data according to the storage environment detection result; A response speed setting module for setting the response speed required when querying the target power big data based on the query frequency of the target power big data; The compressed state operation mode selection module is used to select the compressed state operation mode of the target power big data according to the preset storage space for the target power big data and the response speed required when querying the target power big data. The compressed state operation modes of the target power big data include: low compressed state operation mode, medium compressed state operation mode, and high compressed state operation mode. The compression rate of the low compressed state operation mode for the original target power big data is below 20%. The compression rate of the medium compressed state operation mode for the original target power big data is greater than the compression rate of the low compressed state operation mode and less than the compression rate of the high compressed state operation mode. The compression rate of the high compressed state operation mode for the original target power big data is above 90%. The compression module is used to perform compression operation on the target power big data according to the selected compressed state operation mode.

8. A squeezed state operation system based on power big data according to claim 7, characterized in that, It also includes: The compression parameter automatic configuration module is used to automatically configure the compression parameters of the selected compressed state operation mode according to the operation speed of the selected compressed state operation mode and the compression rate of the selected compressed state operation mode for the target power data.

9. A squeezed state operation system based on power big data according to claim 7, characterized in that It also includes: The compressed state operation mode selection evaluation model construction module is used to collect the data of the response speed required when querying the target power big data, form a response speed data set, and construct a compressed state operation mode selection evaluation model based on the response speed data set. The matching degree evaluation module is used to evaluate the matching degree between the target power big data and the selected compressed state operation mode according to the compressed state operation mode selection evaluation model.

10. A squeezed state operation system based on power big data according to claim 7, characterized in that, It also includes: The type index configuration module is used to compress the data with the same type in the target power data into the same compressed file, and configure a unique type index for the same compressed file, and query the compressed target power data through the type index.

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