A data layer optimization method and device based on a power data architecture

Abstract

The application discloses a data layer optimization method and device based on a power data architecture. The method comprises the following steps: collecting first power data and second power data; performing fitting operation on the first power data and the second power data to obtain a data layer to-be-optimized data set; inputting the data layer to-be-optimized data set into a spiral screening matrix to obtain a simplified data optimization result; and generating a data layer cyclic optimization scheme according to the simplified data optimization result. The application solves the technical problem that the existing power data architecture data layer optimization process only cleans or classifies data, and cannot flexibly plan and optimize and simplify different data according to application scenarios, thereby reducing the actual efficiency of optimization.

Description

Technical Field

[0001] This invention relates to the field of data optimization, and more specifically, to a data layer optimization method and apparatus based on a power data architecture. Background Technology

[0002] With the continuous development of intelligent technology, people are using more and more intelligent devices in their lives, work and study. The use of intelligent technology has improved people's quality of life and increased their learning and work efficiency.

[0003] Currently, the data layer optimization process for power data architecture typically utilizes different data types to build a unified execution standard and constructs different data pools for data processing and transmission based on different application scenarios. However, existing technologies for power data architecture data layer optimization merely clean or classify data, failing to flexibly plan and optimize different data holistically according to application scenarios, thus reducing the actual efficiency of optimization.

[0004] There is currently no effective solution to the above problems. Summary of the Invention

[0005] This invention provides a data layer optimization method and apparatus based on a power data architecture, which at least solves the technical problem that the existing power data architecture data layer optimization process only cleans or classifies the data, and cannot flexibly plan and optimize different data as a whole according to the application scenario, thus reducing the actual efficiency of optimization.

[0006] According to one aspect of the present invention, a data layer optimization method based on a power data architecture is provided, comprising: collecting first power data and second power data; performing a fitting operation on the first power data and the second power data to obtain a data layer dataset to be optimized; inputting the data layer dataset to be optimized into a spiral sieving matrix to obtain a simplified data optimization result; and generating a data layer cyclic optimization scheme based on the simplified data optimization result.

[0007] Optionally, before collecting the first power data and the second power data, the method further includes: collecting raw power data; inputting the raw power data into a power data architecture decomposition model to obtain the first power data and the second power data.

[0008] Optionally, the step of fitting the first power data and the second power data to obtain the dataset to be optimized at the data layer includes: obtaining a preset scenario identifier; extracting common factors from the first power data and the second power data based on the preset scenario identifier to obtain first data to be fitted and second data to be fitted; and inputting the first data to be fitted and the second data to be fitted into the fitting formula.

[0009]

[0010] In the process, the dataset to be optimized in the data layer is obtained, where l represents the dataset to be optimized in the data layer, a and b represent the first data to be fitted and the second data to be fitted, respectively, and fl represents the shell function of the data layer.

[0011] Optionally, generating a data layer loop optimization scheme based on the simplified data optimization result includes: performing a verification operation on the simplified data optimization result to obtain a verification result; and outputting the data layer loop optimization scheme when the verification result is true.

[0012] According to another aspect of the present invention, a method is also provided, comprising: a data acquisition module for acquiring first power data and second power data; a fitting module for performing a fitting operation on the first power data and the second power data to obtain a data layer dataset to be optimized; a filtering module for inputting the data layer dataset to be optimized into a spiral filtering matrix to obtain a simplified data optimization result; and a generation module for generating a data layer cyclic optimization scheme based on the simplified data optimization result.

[0013] Optionally, the device further includes: a collection module for collecting raw power data; and a decomposition module for inputting the raw power data into a power data architecture decomposition model to obtain the first power data and the second power data.

[0014] Optionally, the fitting module includes: an acquisition unit for acquiring a preset scene identifier; an extraction unit for extracting common factors of the first power data and the second power data based on the preset scene identifier to obtain first data to be fitted and second data to be fitted; and a fitting unit for inputting the first data to be fitted and the second data to be fitted into a fitting formula.

[0015]

[0016] In the process, the dataset to be optimized in the data layer is obtained, where l represents the dataset to be optimized in the data layer, a and b represent the first data to be fitted and the second data to be fitted, respectively, and fl represents the shell function of the data layer.

[0017] Optionally, the generation module includes: performing a verification operation on the simplified data optimization result to obtain a verification result; and outputting the data layer loop optimization scheme when the verification result is true.

[0018] According to another aspect of the present invention, a non-volatile storage medium is also provided, the non-volatile storage medium including a stored program, wherein the program, when running, controls the device where the non-volatile storage medium is located to execute a data layer optimization method based on a power data architecture.

[0019] According to another aspect of the present invention, an electronic device is also provided, comprising a processor and a memory; the memory stores computer-readable instructions, and the processor is configured to execute the computer-readable instructions, wherein the computer-readable instructions, when executed, perform a data layer optimization method based on a power data architecture.

[0020] In this embodiment of the invention, a method is adopted to collect first power data and second power data; perform a fitting operation on the first power data and the second power data to obtain a data layer dataset to be optimized; input the data layer dataset to be optimized into a spiral filtering matrix to obtain a simplified data optimization result; and generate a data layer cyclic optimization scheme based on the simplified data optimization result. This method solves the technical problem in the prior art that the power data architecture data layer optimization process only cleans or classifies the data, and cannot flexibly plan and optimize different data in an overall manner according to the application scenario, thus reducing the actual efficiency of optimization. Attached Figure Description

[0021] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this application, illustrate exemplary embodiments of the invention and, together with their description, serve to explain the invention and do not constitute an undue limitation thereof. In the drawings:

[0022] Figure 1 This is a flowchart of a data layer optimization method based on a power data architecture according to an embodiment of the present invention;

[0023] Figure 2 This is a structural block diagram according to an embodiment of the present invention;

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

[0025] Figure 4 It is a storage unit according to an embodiment of the present invention for holding or carrying program code that implements the method according to the present invention. Detailed Implementation

[0026] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0027] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0028] According to an embodiment of the present invention, a method embodiment of a data layer optimization method based on a power data architecture is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0029] Example 1

[0030] Figure 1 This is a flowchart of a data layer optimization method based on a power data architecture according to an embodiment of the present invention, such as... Figure 1 As shown, the method includes the following steps:

[0031] Step S102: Collect the first power data and the second power data.

[0032] Step S104: Perform a fitting operation on the first power data and the second power data to obtain the dataset to be optimized in the data layer.

[0033] Step S106: Input the dataset to be optimized in the data layer into the spiral sieving matrix to obtain the simplified data optimization result.

[0034] Step S108: Based on the simplified data optimization results, generate a data layer loop optimization scheme.

[0035] Optionally, before collecting the first power data and the second power data, the method further includes: collecting raw power data; inputting the raw power data into a power data architecture decomposition model to obtain the first power data and the second power data.

[0036] Optionally, the step of fitting the first power data and the second power data to obtain the dataset to be optimized at the data layer includes: obtaining a preset scenario identifier; extracting common factors from the first power data and the second power data based on the preset scenario identifier to obtain first data to be fitted and second data to be fitted; and inputting the first data to be fitted and the second data to be fitted into the fitting formula.

[0037]

[0038] In the process, the dataset to be optimized in the data layer is obtained, where l represents the dataset to be optimized in the data layer, a and b represent the first data to be fitted and the second data to be fitted, respectively, and fl represents the shell function of the data layer.

[0039] Optionally, generating a data layer loop optimization scheme based on the simplified data optimization result includes: performing a verification operation on the simplified data optimization result to obtain a verification result; and outputting the data layer loop optimization scheme when the verification result is true.

[0040] The above embodiments solve the technical problem that the existing technology for optimizing the power data architecture data layer only cleans or classifies the data, and cannot flexibly plan and optimize different data in an overall manner according to the application scenario, thus reducing the actual efficiency of optimization.

[0041] Example 2

[0042] Figure 2 This is a structural block diagram according to an embodiment of the present invention, such as... Figure 2 As shown, the device includes:

[0043] The acquisition module 20 is used to acquire the first power data and the second power data.

[0044] The fitting module 22 is used to perform a fitting operation on the first power data and the second power data to obtain the dataset to be optimized in the data layer.

[0045] The filtering module 24 is used to input the dataset to be optimized in the data layer into the spiral filtering matrix to obtain the simplified data optimization result.

[0046] The generation module 26 is used to generate a data layer loop optimization scheme based on the simplified data optimization results.

[0047] Optionally, the device further includes: a collection module for collecting raw power data; and a decomposition module for inputting the raw power data into a power data architecture decomposition model to obtain the first power data and the second power data.

[0048] Optionally, the fitting module includes: an acquisition unit for acquiring a preset scene identifier; an extraction unit for extracting common factors of the first power data and the second power data based on the preset scene identifier to obtain first data to be fitted and second data to be fitted; and a fitting unit for inputting the first data to be fitted and the second data to be fitted into a fitting formula.

[0049]

[0050] In the process, the dataset to be optimized in the data layer is obtained, where l represents the dataset to be optimized in the data layer, a and b represent the first data to be fitted and the second data to be fitted, respectively, and fl represents the shell function of the data layer.

[0051] Optionally, the generation module includes: performing a verification operation on the simplified data optimization result to obtain a verification result; and outputting the data layer loop optimization scheme when the verification result is true.

[0052] The above embodiments solve the technical problem that the existing technology for optimizing the power data architecture data layer only cleans or classifies the data, and cannot flexibly plan and optimize different data in an overall manner according to the application scenario, thus reducing the actual efficiency of optimization.

[0053] According to another aspect of the present invention, a non-volatile storage medium is also provided, the non-volatile storage medium including a stored program, wherein the program, when running, controls the device where the non-volatile storage medium is located to execute a data layer optimization method based on a power data architecture.

[0054] Specifically, the above method includes: collecting first power data and second power data; performing a fitting operation on the first power data and the second power data to obtain a data layer dataset to be optimized; inputting the data layer dataset to be optimized into a spiral sieving matrix to obtain a simplified data optimization result; and generating a data layer cyclic optimization scheme based on the simplified data optimization result. Optionally, before collecting the first power data and the second power data, the method further includes: collecting raw power data; inputting the raw power data into a power data architecture decomposition model to obtain the first power data and the second power data. Optionally, performing a fitting operation on the first power data and the second power data to obtain a data layer dataset to be optimized includes: obtaining a preset scenario identifier; extracting common factors from the first power data and the second power data based on the preset scenario identifier to obtain first data to be fitted and second data to be fitted; and inputting the first data to be fitted and the second data to be fitted into a fitting formula.

[0055]

[0056] In the process, the dataset to be optimized in the data layer is obtained, where l represents the dataset to be optimized in the data layer, a and b represent the first data to be fitted and the second data to be fitted, respectively, and fl represents the data layer shell function. Optionally, generating a data layer iterative optimization scheme based on the simplified data optimization result includes: performing a verification operation on the simplified data optimization result to obtain a verification result; when the verification result is true, outputting the data layer iterative optimization scheme.

[0057] According to another aspect of the present invention, an electronic device is also provided, comprising a processor and a memory; the memory stores computer-readable instructions, and the processor is configured to execute the computer-readable instructions, wherein the computer-readable instructions, when executed, perform a data layer optimization method based on a power data architecture.

[0058] Specifically, the above method includes: collecting first power data and second power data; performing a fitting operation on the first power data and the second power data to obtain a data layer dataset to be optimized; inputting the data layer dataset to be optimized into a spiral sieving matrix to obtain a simplified data optimization result; and generating a data layer cyclic optimization scheme based on the simplified data optimization result. Optionally, before collecting the first power data and the second power data, the method further includes: collecting raw power data; inputting the raw power data into a power data architecture decomposition model to obtain the first power data and the second power data. Optionally, performing a fitting operation on the first power data and the second power data to obtain a data layer dataset to be optimized includes: obtaining a preset scenario identifier; extracting common factors from the first power data and the second power data based on the preset scenario identifier to obtain first data to be fitted and second data to be fitted; and inputting the first data to be fitted and the second data to be fitted into a fitting formula.

[0059]

[0060] In the process, the dataset to be optimized in the data layer is obtained, where l represents the dataset to be optimized in the data layer, a and b represent the first data to be fitted and the second data to be fitted, respectively, and fl represents the data layer shell function. Optionally, generating a data layer iterative optimization scheme based on the simplified data optimization result includes: performing a verification operation on the simplified data optimization result to obtain a verification result; when the verification result is true, outputting the data layer iterative optimization scheme.

[0061] The sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0062] In the above embodiments of the present invention, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0063] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units can be a logical functional division, and in actual implementation, there may be other division methods. For instance, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling, direct coupling, or communication connection may be through some interfaces; the indirect coupling or communication connection between units or modules may be electrical or other forms.

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

[0065] in addition, Figure 3 This is a schematic diagram of the hardware structure of a terminal device provided in an embodiment of this application. Figure 3 As 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 realize communication connections between components. The memory 33 may include high-speed RAM memory, and may also include non-volatile memory (NVM), such as at least one disk storage device. The memory 33 may store various programs for performing various processing functions and implementing the method steps of this embodiment.

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

[0067] Optionally, the input device 30 may include various input devices, such as a user interface, a device interface, a programmable software interface, a camera, and a sensor. Optionally, the device interface may be a wired interface for data transmission between devices, or a hardware interface (e.g., USB interface, serial port) for data transmission between devices. Optionally, the user interface may be a user-facing control button, a voice input device for receiving voice input, or a touch-sensing device for receiving user touch input (e.g., a touchscreen, touchpad). Optionally, the programmable software interface may be an entry point for users to edit or modify programs, such as a chip's input pin interface or input interface. Optionally, the transceiver may be a radio frequency transceiver chip with communication functions, a baseband processing chip, and a transceiver antenna. Audio input devices such as microphones can receive voice data. Output device 32 may include displays, speakers, and other output devices.

[0068] In this embodiment, the processor of the terminal device includes functions for executing the modules of the data processing device in each device. The specific functions and technical effects can be referred to in the above embodiments, and will not be repeated here.

[0069] Figure 4 This is a schematic diagram of the hardware structure of a terminal device provided in another embodiment of this application. Figure 4 Yes Figure 3 A specific implementation example in the implementation process. For example... Figure 4 As shown, the terminal device in 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 embodiments.

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

[0072] Optionally, the processor 41 is located in the processing component 40. The terminal device may also include: a communication component 43, a power supply 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 determined according to actual needs, and this embodiment does not limit this.

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

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

[0075] Multimedia component 45 includes a display screen that provides an output interface between a terminal device and a 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 touchscreen 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 may sense not only the boundaries of the touch or swipe action but also the duration and pressure associated with the touch or swipe operation.

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

[0077] Input / output interface 47 provides an interface between processing component 40 and peripheral interface modules, such as click wheels, buttons, etc. These buttons may include, but are not limited to, volume buttons, start buttons, and lock buttons.

[0078] Sensor assembly 48 includes one or more sensors for providing status assessments of various aspects of the terminal device. For example, 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. Sensor assembly 48 may 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, sensor assembly 48 may also include a camera, etc.

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

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

[0081] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units can be a logical functional division, and in actual implementation, there may be other division methods. For instance, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling, direct coupling, or communication connection may be through some interfaces; the indirect coupling or communication connection between units or modules may be electrical or other forms.

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

[0083] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0084] If the integrated unit is implemented as 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. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or 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 capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0085] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A data layer optimization method based on a power data architecture, characterized in that, include: Collect first and second power data; The first power data and the second power data are fitted to obtain the dataset to be optimized in the data layer. The dataset to be optimized in the data layer is input into the spiral sieving matrix to obtain the simplified data optimization result; Based on the results of the data simplification optimization, a data layer loop optimization scheme is generated.

2. The method according to claim 1, characterized in that, Before collecting the first power data and the second power data, the method further includes: Collect raw power data; The raw power data is input into the power data architecture decomposition model to obtain the first power data and the second power data.

3. The method according to claim 1, characterized in that, The step of fitting the first power data and the second power data to obtain the data layer dataset to be optimized includes: Obtain the preset scene identifier; Based on the preset scenario identifier, extract the common factor of the first power data and the second power data to obtain the first data to be fitted and the second data to be fitted. Input the first data to be fitted and the second data to be fitted into the fitting formula. In the process, the dataset to be optimized in the data layer is obtained, where l represents the dataset to be optimized in the data layer, a and b represent the first data to be fitted and the second data to be fitted, respectively, and fl represents the shell function of the data layer.

4. The method according to claim 1, characterized in that, The step of generating a data layer loop optimization scheme based on the simplified data optimization results includes: The results of the data optimization were verified to obtain the verification results. When the verification result is true, the data layer loop optimization scheme is output.

5. Characterized by, include: The data acquisition module is used to acquire the first power data and the second power data. The fitting module is used to perform a fitting operation on the first power data and the second power data to obtain the dataset to be optimized in the data layer. The filtering module is used to input the dataset to be optimized in the data layer into the spiral filtering matrix to obtain the simplified data optimization result; The generation module is used to generate a data layer loop optimization scheme based on the results of the simplified data optimization.

6. The apparatus according to claim 5, characterized in that, The device further includes: The collection module is used to collect raw power data; The decomposition module is used to input the original power data into the power data architecture decomposition model to obtain the first power data and the second power data.

7. The apparatus according to claim 5, characterized in that, The fitting module includes: The acquisition unit is used to acquire the preset scene identifier; The extraction unit is used to extract the common factor of the first power data and the second power data according to the preset scene identifier to obtain the first data to be fitted and the second data to be fitted. The fitting unit is used to input the first data to be fitted and the second data to be fitted into the fitting formula. In the process, the dataset to be optimized in the data layer is obtained, where l represents the dataset to be optimized in the data layer, a and b represent the first data to be fitted and the second data to be fitted, respectively, and fl represents the shell function of the data layer.

8. The apparatus according to claim 5, characterized in that, The generation module includes: The results of the data optimization were verified to obtain the verification results. When the verification result is true, the data layer loop optimization scheme is output.

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

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

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