Mass data storage optimization method and device

Abstract

The invention discloses a mass data storage optimization method and device. The method comprises the following steps: acquiring a first data label and a second data label; putting the first data content corresponding to the first data label into a front storage pool, and putting the second data content corresponding to the second data label into a rear storage pool; performing storage pool gap calculation on the first storage parameter of the first storage pool and the second storage parameter of the second storage pool to obtain storage position gap data; and placing the first storage pool and the second storage pool in a main storage pool according to the storage position gap data. According to the method and the device, the technical problems of low efficiency and low precision of storage pool optimization caused by the fact that a mass data optimization method in the prior art only performs direct optimization and deterioration removal on the whole section of mass data and cannot quickly optimize the storage pool when the data volume is larger are solved.

Description

Technical Field

[0001] The present invention relates to the field of data optimized storage, and more particularly, to a method and device for optimizing the storage of massive data. Background Art

[0002] With the continuous development of intelligent technology, intelligent devices are increasingly used in people's lives, work, and study. Using intelligent technology means has improved the quality of people's lives and increased the efficiency of people's study and work.

[0003] Currently, in the process of optimizing massive data, usually technicians will optimize the placement of the massive data storage pool according to the operation and maintenance complexity and timeliness, such as ratio storage, poor storage, etc. However, the existing massive data optimization methods only directly optimize and eliminate the inferior of the entire massive data segment, and cannot quickly optimize the storage pool when the data volume is larger, resulting in low efficiency and low accuracy of the storage pool optimization.

[0004] For the above problems, no effective solution has been proposed yet. Summary of the Invention

[0005] Embodiments of the present invention provide a method and device for optimizing the storage of massive data, so as to at least solve the technical problem that the existing massive data optimization methods only directly optimize and eliminate the inferior of the entire massive data segment, and cannot quickly optimize the storage pool when the data volume is larger, resulting in low efficiency and low accuracy of the storage pool optimization.

[0006] According to one aspect of the embodiments of the present invention, a method for optimizing the storage of massive data is provided, including: obtaining a first data tag and a second data tag; putting the first data content corresponding to the first data tag into a pre-storage pool, and putting the second data content corresponding to the second data tag into a post-storage pool; calculating the storage position gap data by calculating the first storage parameter of the first storage pool and the second storage parameter of the second storage pool; and placing the first storage pool and the second storage pool in a main storage pool according to the storage position gap data.

[0007] Optionally, the first data tag is obtained from the front end of the data segment, and the second data tag is obtained from the last segment of the data segment.

[0008] Optionally, putting the first data content corresponding to the first data tag into the pre-storage pool and putting the second data content corresponding to the second data tag into the post-storage pool includes: obtaining a data truncation threshold, where the data truncation threshold includes a front-segment threshold and a rear-segment threshold; generating the first data content according to the front-segment threshold and the first data tag; generating the second data content according to the rear-segment threshold and the second data tag; storing the first data content in the pre-storage pool and storing the second data content in the post-storage pool.

[0009] Optionally, calculating the storage position gap data by performing a storage pool gap calculation on the first storage parameter of the first storage pool and the second storage parameter of the second storage pool includes: extracting the first storage parameter of the first storage pool; extracting the second storage parameter of the second storage pool; through the formula

[0010]

[0011] calculating the storage position gap data, where J is the storage position gap data, T ij is the two-dimensional first storage parameter, i and j are respectively the capacity variable and the position variable of the first storage parameter, T d is the one-dimensional second storage parameter, and d is the connection variable of the second storage parameter.

[0012] According to another aspect of the embodiments of the present invention, there is also provided a massive data storage optimization device, including: an acquisition module, configured to acquire a first data tag and a second data tag; a first storage module, configured to put the first data content corresponding to the first data tag into a pre-storage pool and put the second data content corresponding to the second data tag into a post-storage pool; a calculation module, configured to perform a storage pool gap calculation on the first storage parameter of the first storage pool and the second storage parameter of the second storage pool to obtain storage position gap data; a second storage module, configured to place the first storage pool and the second storage pool in a main storage pool according to the storage position gap data.

[0013] Optionally, the first data tag is obtained from the very front end of the data segment, and the second data tag is obtained from the very last end of the data segment.

[0014] Optionally, the first storage module includes: a threshold unit for obtaining a data truncation threshold, where the data truncation threshold includes a front-segment threshold and a rear-segment threshold; a generation unit for generating the first data content according to the front-segment threshold and the first data tag; the generation unit is further configured to generate the second data content according to the rear-segment threshold and the second data tag; a storage unit for storing the first data content in the pre-storage pool and storing the second data content in the post-storage pool.

[0015] Optionally, the calculation module includes: extracting the first storage parameter of the first storage pool; extracting the second storage parameter of the second storage pool; through the formula

[0016]

[0017] calculating the storage position gap data, where J is the storage position gap data, T ij is a two-dimensional first storage parameter, i and j are respectively the capacity variable and the position variable of the first storage parameter, T d is a one-dimensional second storage parameter, and d is the connection variable of the second storage parameter.

[0018] According to another aspect of the embodiments of the present invention, a non-volatile storage medium is further provided. The non-volatile storage medium includes a stored program, where when the program runs, it controls a device where the non-volatile storage medium is located to execute a method for optimizing mass data storage.

[0019] According to another aspect of the embodiments of the present invention, an electronic device is further provided, including a processor and a memory; a computer-readable instruction is stored in the memory, and the processor is configured to run the computer-readable instruction, where when the computer-readable instruction runs, it executes a method for optimizing mass data storage.

[0020] In the embodiments of the present invention, by obtaining a first data tag and a second data tag; putting the first data content corresponding to the first data tag into the pre-storage pool, and putting the second data content corresponding to the second data tag into the post-storage pool; calculating the storage pool gap between the first storage parameter of the first storage pool and the second storage parameter of the second storage pool to obtain storage position gap data; and placing the first storage pool and the second storage pool in the main storage pool according to the storage position gap data, the technical problem in the prior art that the mass data optimization method only directly optimizes and eliminates inferiority for the entire segment of mass data, and cannot quickly optimize the storage pool when the data volume is larger, resulting in low efficiency and low accuracy of storage pool optimization is solved. Description of the Drawings

[0021] The accompanying drawings described herein are used to provide a further understanding of the present invention, and form a part of this application. The illustrative embodiments of the present invention and their descriptions are used to explain the present invention, and do not constitute an improper limitation of the present invention. In the drawings:

[0022] Figure 1 is a flowchart of a method for optimizing mass data storage according to an embodiment of the present invention;

[0023] Figure 2 is a structural block diagram of a device for optimizing mass data storage according to an embodiment of the present invention;

[0024] Figure 3 is a block diagram of a terminal device for executing the method according to the present invention according to an embodiment of the present invention;

[0025] Figure 4 is a storage unit for holding or carrying program code for implementing the method according to the present invention according to an embodiment of the present invention. Detailed implementation manners

[0026] In order to enable those skilled in the art to better understand the solution of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0027] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and do not necessarily need to be used to describe a specific order or sequence. It should be understood that such data can be interchanged under appropriate circumstances so that the embodiments of the present invention described herein can be implemented in an order different from those illustrated or described herein. In addition, the terms "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device comprising a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0028] According to an embodiment of the present invention, a method embodiment of a method for optimizing mass data storage is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that here.

[0029] Example 1

[0030] Figure 1 is a flowchart of a method for optimizing the storage of massive data according to an embodiment of the present invention. As Figure 1 shown, the method includes the following steps:

[0031] Step S102, obtain a first data tag and a second data tag.

[0032] Step S104, put the first data content corresponding to the first data tag into the pre - storage pool, and put the second data content corresponding to the second data tag into the post - storage pool.

[0033] Step S106, perform a storage pool gap calculation on the first storage parameter of the first storage pool and the second storage parameter of the second storage pool to obtain storage location gap data.

[0034] Step S108, place the first storage pool and the second storage pool into the main storage pool according to the storage location gap data.

[0035] Optionally, the first data tag is obtained from the very front end of the data segment, and the second data tag is obtained from the very last end of the data segment.

[0036] Optionally, the step of putting the first data content corresponding to the first data tag into the pre - storage pool and putting the second data content corresponding to the second data tag into the post - storage pool includes: obtaining a data truncation threshold, where the data truncation threshold includes a front - segment threshold and a rear - segment threshold; generating the first data content according to the front - segment threshold and the first data tag; generating the second data content according to the rear - segment threshold and the second data tag; storing the first data content in the pre - storage pool and storing the second data content in the post - storage pool.

[0037] Optionally, the step of performing a storage pool gap calculation on the first storage parameter of the first storage pool and the second storage parameter of the second storage pool to obtain storage location gap data includes: extracting the first storage parameter of the first storage pool; extracting the second storage parameter of the second storage pool; through the formula

[0038]

[0039] calculate the storage location gap data, where J is the storage location gap data, T ij is the two - dimensional first storage parameter, i and j are respectively the capacity variable and the position variable of the first storage parameter, T d is the one - dimensional second storage parameter, and d is the connection variable of the second storage parameter.

[0040] Through the above embodiments, the technical problem in the prior art that the massive data optimization method only directly optimizes and eliminates inferiority from the entire segment of massive data, and cannot quickly optimize the storage pool when the data volume is larger, resulting in low efficiency and low accuracy in optimizing the storage pool, is solved.

[0041] Embodiment 2

[0042] Figure 2 is a structural block diagram of a massive data storage optimization device according to an embodiment of the present invention. As Figure 2 shown, the device includes:

[0043] An acquisition module 20, configured to acquire a first data tag and a second data tag.

[0044] A first storage module 22, configured to put the first data content corresponding to the first data tag into a pre-storage pool, and put the second data content corresponding to the second data tag into a post-storage pool.

[0045] A calculation module 24, configured to perform a storage pool gap calculation on the first storage parameter of the first storage pool and the second storage parameter of the second storage pool to obtain storage position gap data.

[0046] A second storage module 26, configured to place the first storage pool and the second storage pool in a main storage pool according to the storage position gap data.

[0047] Optionally, the first data tag is acquired from the front end of the data segment, and the second data tag is acquired from the last segment of the data segment.

[0048] Optionally, the first storage module includes: a threshold unit, configured to acquire a data truncation threshold, where the data truncation threshold includes a front segment threshold and a rear segment threshold; a generation unit, configured to generate the first data content according to the front segment threshold and the first data tag; the generation unit is further configured to generate the second data content according to the rear segment threshold and the second data tag; a storage unit, configured to store the first data content in the pre-storage pool and store the second data content in the post-storage pool.

[0049] Optionally, the calculation module includes: extracting the first storage parameter of the first storage pool; extracting the second storage parameter of the second storage pool; through the formula

[0050]

[0051] calculate the storage position gap data, where J is the storage position gap data, and T ijis the first storage parameter in two dimensions, where i and j are the capacity variable and the position variable of the first storage parameter respectively, and T d is the second storage parameter in one dimension, and d is the connection variable of the second storage parameter.

[0052] Through the above embodiments, the technical problem in the prior art that the massive data optimization method only directly optimizes and eliminates inferiority for the entire segment of massive data, and cannot quickly optimize the storage pool when the data volume is larger, resulting in low efficiency and low accuracy of the storage pool optimization, is solved.

[0053] According to another aspect of the embodiments of the present invention, a non-volatile storage medium is further provided. The non-volatile storage medium includes a stored program, wherein when the program runs, it controls a device where the non-volatile storage medium is located to execute a massive data storage optimization method.

[0054] Specifically, the above method includes: an acquisition module for acquiring a first data tag and a second data tag; a first storage module for putting first data content corresponding to the first data tag into a pre-storage pool and putting second data content corresponding to the second data tag into a post-storage pool; a calculation module for performing storage pool gap calculation on the first storage parameter of the first storage pool and the second storage parameter of the second storage pool to obtain storage position gap data; a second storage module for placing the first storage pool and the second storage pool in a main storage pool according to the storage position gap data. Optionally, the first data tag is acquired from the front end of the data segment, and the second data tag is acquired from the last segment of the data segment. Optionally, the first storage module includes: a threshold unit for acquiring a data truncation threshold, where the data truncation threshold includes a front segment threshold and a rear segment threshold; a generation unit for generating the first data content according to the front segment threshold and the first data tag; the generation unit is further for generating the second data content according to the rear segment threshold and the second data tag; a storage unit for storing the first data content in the pre-storage pool and storing the second data content in the post-storage pool. Optionally, the calculation module includes: extracting the first storage parameter of the first storage pool; extracting the second storage parameter of the second storage pool; through the formula

[0055]

[0056] calculate the storage position gap data, where J is the storage position gap data, T ij is the first storage parameter in two dimensions, where i and j are the capacity variable and the position variable of the first storage parameter respectively, and T d is the second storage parameter in one dimension, and d is the connection variable of the second storage parameter.

[0057] According to another aspect of the embodiments of the present invention, an electronic device is further provided, which includes a processor and a memory; computer-readable instructions are stored in the memory, and the processor is configured to run the computer-readable instructions. When the computer-readable instructions run, a method for optimizing the storage of massive data is executed.

[0058] Specifically, the above method includes: an acquisition module for acquiring a first data tag and a second data tag; a first storage module for putting the first data content corresponding to the first data tag into a pre-storage pool and putting the second data content corresponding to the second data tag into a post-storage pool; a calculation module for calculating a storage pool gap between the first storage parameter of the first storage pool and the second storage parameter of the second storage pool to obtain storage position gap data; a second storage module for placing the first storage pool and the second storage pool in a main storage pool according to the storage position gap data. Optionally, the first data tag is acquired from the very front end of the data segment, and the second data tag is acquired from the very last end of the data segment. Optionally, the first storage module includes: a threshold unit for acquiring a data truncation threshold, where the data truncation threshold includes a front segment threshold and a rear segment threshold; a generation unit for generating the first data content according to the front segment threshold and the first data tag; the generation unit is further configured to generate the second data content according to the rear segment threshold and the second data tag; a storage unit for storing the first data content in the pre-storage pool and storing the second data content in the post-storage pool. Optionally, the calculation module includes: extracting the first storage parameter of the first storage pool; extracting the second storage parameter of the second storage pool; calculating the storage position gap data through the formula

[0059]

[0060] where J is the storage position gap data, T ij is a two-dimensional first storage parameter, i and j are respectively the capacity variable and the position variable of the first storage parameter, T d is a one-dimensional second storage parameter, and d is the connection variable of the second storage parameter.

[0061] The serial numbers of the above embodiments of the present invention are only for description and do not represent the advantages or disadvantages of the embodiments.

[0062] In the above embodiments of the present invention, the descriptions of the respective embodiments have their own emphases. For the parts not detailed in a certain embodiment, reference may be made to the relevant descriptions of other embodiments.

[0063] In several embodiments provided by this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are merely illustrative. For example, the division of the units can be a logical function division. In actual implementation, there can be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections to each other can be through some interfaces. The indirect couplings or communication connections of units or modules can be in electrical or other forms.

[0064] The units described as separate components may or may not be physically separated. The components displayed as units may or may not be physical units, that is, they can be located in one place or distributed to multiple units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0065] In addition, Figure 3 is a schematic diagram of the hardware structure of a terminal device provided in an embodiment of this application. As Figure 3 shown, the terminal device may include an input device 30, a processor 31, an output device 32, a memory 33, and at least one communication bus 34. The communication bus 34 is used to implement communication connections between components. The memory 33 may contain high-speed RAM memory and may also include non-volatile storage NVM, such as at least one disk memory. Various programs can be stored in the memory 33 to complete various processing functions and implement the method steps of this embodiment.

[0066] Optionally, the above-mentioned processor 31 can be implemented, for example, as a central processing unit (CPU), an application-specific integrated circuit (ASIC), a digital signal processor (DSP), a digital signal processing device (DSPD), a programmable logic device (PLD), a field programmable gate array (FPGA), a controller, a microcontroller, a microprocessor, or other electronic components. The processor 31 is coupled to the above-mentioned input device 30 and output device 32 through a wired or wireless connection.

[0067] Optionally, the above input device 30 may include various input devices, for example, it may include at least one of a user interface facing the user, a device interface facing the device, a programmable interface of software, a camera, and a sensor. Optionally, the device interface facing the device may be a wired interface for data transmission between devices, or may also be a hardware insertion interface for data transmission between devices (such as a USB interface, a serial port, etc.); Optionally, the user interface facing the user may be, for example, a control button facing the user, a voice input device for receiving voice input, and a touch sensing device for receiving user touch input (such as a touch screen with touch sensing function, a touchpad, etc.); Optionally, the above programmable interface of software may be, for example, an entry for the user to edit or modify the program, such as an input pin interface or an input interface of a chip, etc.; Optionally, the above transceiver may be a radio frequency transceiver chip with communication function, a baseband processing chip, and a transceiver antenna, etc. An audio input device such as a microphone can receive voice data. The output device 32 may include output devices such as a display and a speaker.

[0068] In this embodiment, the processor of the terminal device includes functions for executing each module of the data processing device in each device. For the specific functions and technical effects, refer to the above embodiments, and details will not be described here.

[0069] Figure 4 FIG. is a schematic hardware structure diagram of a terminal device provided in another embodiment of the present application. Figure 4 is for Figure 3 a specific embodiment in the implementation process. As Figure 4 shown, the terminal device of this embodiment includes a processor 41 and a memory 42.

[0070] The processor 41 executes the computer program code stored in the memory 42 to implement the method in the above embodiment.

[0071] The memory 42 is configured to store various types of data to support the operation of the terminal device. Examples of these data include instructions for any application or method operating on the terminal device, such as messages, pictures, videos, etc. The memory 42 may include a random access memory (RAM), and may also include non-volatile memory, such as at least one disk memory.

[0072] Optionally, the processor 41 is disposed in the processing component 40. The terminal device may further include: a communication component 43, a power component 44, a multimedia component 45, an audio component 46, an input / output interface 47, and / or a sensor component 48. The specific components included in the terminal device are set according to actual requirements, and this embodiment does not limit this.

[0073] The processing component 40 generally controls the overall operation of the terminal device. The processing component 40 may include one or more processors 41 to execute instructions to complete all or part of the steps of the above method. In addition, the processing component 40 may include one or more modules to facilitate the interaction between the processing component 40 and other components. For example, the processing component 40 may include a multimedia module to facilitate the interaction between the multimedia component 45 and the processing component 40.

[0074] The power component 44 provides power for various components of the terminal device. The power component 44 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power for the terminal device.

[0075] The multimedia component 45 includes a display screen that provides an output interface between the terminal device and the user. In some embodiments, the display screen may include a liquid crystal display (LCD) and a touch panel (TP). If the display screen includes a touch panel, the display screen may be implemented as a touch screen to receive input signals from the user. The touch panel includes one or more touch sensors to sense touches, swipes, and gestures on the touch panel. The touch sensors can not only sense the boundaries of touch or swipe actions, but also detect the duration and pressure associated with the touch or swipe operation.

[0076] The audio component 46 is configured to output and / or input audio signals. For example, the audio component 46 includes a microphone (MIC). When the terminal device is in an operating mode, such as a voice recognition mode, the microphone is configured to receive external audio signals. The received audio signals may be further stored in the memory 42 or sent via the communication component 43. In some embodiments, the audio component 46 further includes a speaker for outputting audio signals.

[0077] The input / output interface 47 provides an interface between the processing component 40 and a peripheral interface module, and the above peripheral interface module may be a click wheel, a button, etc. These buttons may include, but are not limited to: volume buttons, start buttons, and lock buttons.

[0078] The sensor assembly 48 includes one or more sensors for providing a status assessment of various aspects of the terminal device. For example, the sensor assembly 48 can detect the on / off state of the terminal device, the relative positioning of components, and the presence or absence of user contact with the terminal device. The sensor assembly 48 can include a proximity sensor configured to detect the presence of nearby objects without any physical contact, including detecting the distance between the user and the terminal device. In some embodiments, the sensor assembly 48 can further include a camera, etc.

[0079] The communication component 43 is configured to facilitate communication between the terminal device and other devices in a wired or wireless manner. The terminal device can access a wireless network based on a communication standard, such as WiFi, 2G, or 3G, or a combination thereof. In one embodiment, the terminal device can include a SIM card slot for inserting a SIM card, enabling the terminal device to log in to the GPRS network and establish communication with the server through the Internet.

[0080] As can be seen from the above, in Figure 4 the embodiments, the communication component 43, the audio component 46, and the input / output interface 47, and the sensor assembly 48 can all be used as Figure 3 implementation manners of the input device in the embodiments.

[0081] In several embodiments provided in the present application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are merely illustrative. For example, the division of the units can be a logical function division, and in actual implementation, there can be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections to each other can be through some interfaces, and the indirect couplings or communication connections of the units or modules can be in an electrical or other form.

[0082] The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they can be located in one place, or they can be distributed to multiple units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0083] In addition, in each embodiment of the present invention, the functional units can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit. The above-mentioned integrated units can be implemented in the form of hardware or in the form of software functional units.

[0084] When the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes: various media such as USB flash drives, read-only memories (ROMs), random access memories (RAMs), mobile hard disks, magnetic disks, or optical discs that can store program codes.

[0085] The above are only the preferred embodiments of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the principle of the present invention, several improvements and refinements can be made, and these improvements and refinements should also be regarded as the protection scope of the present invention.

Claims

1. A method for optimizing the storage of massive data, characterized in that, Including: Obtain a first data tag and a second data tag; Put the first data content corresponding to the first data tag into a pre - storage pool, and put the second data content corresponding to the second data tag into a post - storage pool; Perform a storage pool gap calculation on the first storage parameter of the first storage pool and the second storage parameter of the second storage pool to obtain storage location gap data; Place the first storage pool and the second storage pool in a main storage pool according to the storage location gap data.

2. The method according to claim 1, wherein The first data tag is obtained from the very front end of the data segment, and the second data tag is obtained from the very last segment of the data segment.

3. The method according to claim 1, wherein The step of putting the first data content corresponding to the first data tag into a pre - storage pool and putting the second data content corresponding to the second data tag into a post - storage pool includes: Obtain a data truncation threshold, where the data truncation threshold includes a front - segment threshold and a back - segment threshold; Generate the first data content according to the front - segment threshold and the first data tag; Generate the second data content according to the back - segment threshold and the second data tag; Store the first data content in the pre - storage pool and store the second data content in the post - storage pool.

4. The method according to claim 1, wherein The step of performing a storage pool gap calculation on the first storage parameter of the first storage pool and the second storage parameter of the second storage pool to obtain storage location gap data includes: Extract the first storage parameter of the first storage pool; Extract the second storage parameter of the second storage pool; Through the formula Calculate the storage location gap data, where J is the storage location gap data, and T ij is the first two-dimensional storage parameter, and i and j are the capacity variable and location variable of the first storage parameter respectively, and T d is the second one-dimensional storage parameter, and d is the connection variable of the second storage parameter.

5. A massive data storage optimization device, characterized in that, Including: An acquisition module, configured to obtain a first data tag and a second data tag; A first storage module, configured to put the first data content corresponding to the first data tag into a pre - storage pool and put the second data content corresponding to the second data tag into a post - storage pool; A calculation module, configured to perform a storage pool gap calculation on the first storage parameter of the first storage pool and the second storage parameter of the second storage pool to obtain storage location gap data; A second storage module, configured to place the first storage pool and the second storage pool in a main storage pool according to the storage location gap data.

6. The device according to claim 5, characterized in that, The first data tag is obtained from the very front end of the data segment, and the second data tag is obtained from the very last segment of the data segment.

7. The device according to claim 5, characterized in that, The first storage module includes: A threshold unit, configured to obtain a data truncation threshold, where the data truncation threshold includes a front - segment threshold and a back - segment threshold; A generation unit, configured to generate the first data content according to the front - segment threshold and the first data tag; The generation unit is further configured to generate the second data content according to the back - segment threshold and the second data tag; A storage unit, configured to store the first data content in the pre - storage pool and store the second data content in the post - storage pool.

8. The device according to claim 5, characterized in that The calculation module includes: Extract the first storage parameter of the first storage pool; Extract the second storage parameter of the second storage pool; Through the formula Calculate the storage location gap data, where J is the storage location gap data, and T ij is the first two-dimensional storage parameter, and i and j are the capacity variable and location variable of the first storage parameter respectively, and T d is the one-dimensional second storage parameter, and d is the connection variable of the second storage parameter.

9. A non-volatile storage medium, characterized in that, The non - volatile storage medium includes a stored program, where when the program runs, it controls the device where the non - volatile storage medium is located to execute the method according to any one of claims 1 to 4.

10. An electronic device, characterized in that, It includes a processor and a memory; computer-readable instructions are stored in the memory, and the processor is configured to run the computer-readable instructions, wherein when the computer-readable instructions are running, the method according to any one of claims 1 to 4 is executed.

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