CN111814524A - Self-adaptive big data analysis platform - Google Patents
Self-adaptive big data analysis platform Download PDFInfo
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- CN111814524A CN111814524A CN201910907799.5A CN201910907799A CN111814524A CN 111814524 A CN111814524 A CN 111814524A CN 201910907799 A CN201910907799 A CN 201910907799A CN 111814524 A CN111814524 A CN 111814524A
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- 230000003044 adaptive effect Effects 0.000 claims description 29
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Abstract
The invention relates to a self-adaptive big data analysis platform, which comprises: the target identification device is used for executing target identification on the received single-shot image so as to obtain each target image block and each non-target image block; a Sobel sharpening device for performing Sobel operator-based image sharpening on each target image block to obtain a corresponding sharpened block, and not performing Sobel operator-based image sharpening on each non-target image block; and the band-elimination filtering equipment is used for executing band-elimination filtering processing on each sharpened block to obtain a corresponding filtering block, and not executing the band-elimination filtering processing on each non-target image block. The self-adaptive data analysis platform is simple and convenient to operate, and manual links are reduced. The shot scene of the single-reflex equipment is detected with high precision, so that WB values in different ranges are selected based on the detection result, and the self-adaptive parameter setting of the single-reflex equipment is completed.
Description
Technical Field
The invention relates to the field of data analysis, in particular to a self-adaptive big data analysis platform.
Background
In the field of statistics, some divide data analysis into descriptive statistical analysis, exploratory data analysis, and confirmatory data analysis; where exploratory data analysis focuses on finding new features among the data, while confirmatory data analysis focuses on validation or authentication of existing assumptions.
Exploratory data analysis refers to a method of analyzing data to form hypothesis-worthy tests, which is complementary to conventional statistical hypothesis testing approaches. This method is named by the american famous statistician john diagram base.
Qualitative data analysis, also known as "qualitative data analysis," "qualitative research," or "qualitative research data analysis," refers to the analysis of non-numerical data (or data) such as words, photographs, observations, and the like.
Disclosure of Invention
The invention needs to have the following two important points:
(1) high-precision detection is carried out on a shooting scene where the single-reflex equipment is located, and WB values in different ranges are selected based on a detection result, so that self-adaptive parameter setting of the single-reflex equipment is completed;
(2) image sharpening processing and band elimination filtering processing based on a Sobel operator are sequentially executed on image blocks with targets, and corresponding processing is not carried out on image blocks without targets, so that the image processing effect is guaranteed, and meanwhile, the operation amount of image processing is reduced.
According to an aspect of the present invention, there is provided an adaptive data analysis platform, the platform comprising:
the target identification device is used for executing target identification on the single-reflex snapshot image received from the single-reflex device so as to obtain each target image block and each non-target image block;
the Sobel sharpening device is connected with the target recognition device and is used for carrying out image sharpening processing based on a Sobel operator on each target image block to obtain a corresponding sharpened block, and simultaneously carrying out image sharpening processing based on the Sobel operator on each non-target image block;
the band-elimination filtering device is connected with the Sobel sharpening device and is used for executing band-elimination filtering processing on each sharpened block to obtain corresponding filtering blocks and simultaneously not executing band-elimination filtering processing on each non-target image block;
the data merging equipment is connected with the band-stop filtering equipment and used for receiving each filtering block and each non-target image block and merging each filtering block and each non-target image block to obtain a data merging image corresponding to the single-shot image;
the signal enhancement device is connected with the data merging device and is used for executing image enhancement processing based on exponential transformation on the received data merging image so as to obtain a corresponding exponential transformation enhanced image;
the scene detection device is connected with the signal enhancement device and used for executing scene recognition on the exponentially transformed enhanced image based on the sunlight scene imaging characteristics, and when the sunlight scene imaging characteristics exist in the exponentially transformed enhanced image, a sunlight scene recognition command is sent out, otherwise, other scene recognition commands are sent out;
and the MCU control chip is respectively connected with the single-reflex equipment and the scene detection equipment and is used for setting a white balance value (WB value) of an image captured by the single-reflex equipment to be between 5000K and 5500K when receiving a sunlight scene identification command.
According to another aspect of the invention, an adaptive data analysis method is further provided, and the method comprises the step of using an adaptive data analysis platform as described above for high-precision detection of a shooting scene where a single-reaction device is located, so as to select WB values in different ranges based on a detection result.
The self-adaptive data analysis platform is simple and convenient to operate, and manual links are reduced. The shot scene of the single-reflex equipment is detected with high precision, so that WB values in different ranges are selected based on the detection result, and the self-adaptive parameter setting of the single-reflex equipment is completed.
Drawings
Embodiments of the invention will now be described with reference to the accompanying drawings, in which:
fig. 1 is a block diagram illustrating an external appearance of a single-reaction device used in an adaptive data analysis platform according to an embodiment of the present invention.
Detailed Description
Embodiments of the adaptive data analysis platform of the present invention will be described in detail below with reference to the accompanying drawings.
The research object of the adaptive control is a system with a certain degree of uncertainty, and the term "uncertainty" means that a mathematical model describing the controlled object and the environment thereof is not completely determined, and comprises some unknown factors and random factors.
Any one actual system has varying degrees of uncertainty, sometimes highlighted inside the system and sometimes highlighted outside the system. From the inside of the system, the structure and parameters of the mathematical model describing the controlled object are not necessarily known accurately by the designer in advance. As the influence of the external environment on the system can be equivalently represented by a number of disturbances. These disturbances are often unpredictable. In addition, some uncertainty factors generated during measurement enter the system. In the face of these various kinds of uncertainty, how to design a proper control action to make a certain specified performance index reach and keep the optimal or approximately optimal is a problem to be researched and solved by adaptive control.
The self-adaptive control is the same as the conventional feedback control and the optimal control, and is a control method based on a mathematical model, and the difference is that the prior knowledge about the model and the disturbance, which is the basis of the self-adaptive control, is less, and the information about the model needs to be continuously extracted in the running process of the system, so that the model is gradually improved. Specifically, model parameters can be continuously identified based on input and output data of the object, and this process is called online identification of the system. With the continuous production process, the model can become more accurate and closer to reality through online identification. Since models are constantly being developed, it is clear that the control actions integrated on the basis of such models will also be constantly being developed. In this sense, the control system has a certain adaptability. For example, when the system is in the design stage, the system may not perform well when being put into operation at the beginning due to the lack of initial information of the object characteristics, but as long as a period of operation elapses, the control system gradually adapts to adjust itself to a satisfactory working state through online identification and control. For example, certain control objects may have characteristics that vary significantly during operation, but the system can also adapt gradually by identifying and changing the controller parameters online.
At present, the single-reflex equipment has a high requirement on the stability of the equipment, so that human operation links need to be reduced as much as possible to ensure the stability of the equipment, however, the requirement on the image quality also exists in the design of the single-reflex equipment, and how to balance the stability of the equipment and the image quality is a technical problem which needs to be solved at present.
In order to overcome the defects, the invention builds a self-adaptive data analysis platform, and can effectively solve the corresponding technical problem.
Fig. 1 is a block diagram illustrating an external appearance of a single-reaction device used in an adaptive data analysis platform according to an embodiment of the present invention.
An adaptive data analysis platform shown according to an embodiment of the present invention includes:
the target identification device is used for executing target identification on the single-reflex snapshot image received from the single-reflex device so as to obtain each target image block and each non-target image block;
the Sobel sharpening device is connected with the target recognition device and is used for carrying out image sharpening processing based on a Sobel operator on each target image block to obtain a corresponding sharpened block, and simultaneously carrying out image sharpening processing based on the Sobel operator on each non-target image block;
the band-elimination filtering device is connected with the Sobel sharpening device and is used for executing band-elimination filtering processing on each sharpened block to obtain corresponding filtering blocks and simultaneously not executing band-elimination filtering processing on each non-target image block;
the data merging equipment is connected with the band-stop filtering equipment and used for receiving each filtering block and each non-target image block and merging each filtering block and each non-target image block to obtain a data merging image corresponding to the single-shot image;
the signal enhancement device is connected with the data merging device and is used for executing image enhancement processing based on exponential transformation on the received data merging image so as to obtain a corresponding exponential transformation enhanced image;
the scene detection device is connected with the signal enhancement device and used for executing scene recognition on the exponentially transformed enhanced image based on the sunlight scene imaging characteristics, and when the sunlight scene imaging characteristics exist in the exponentially transformed enhanced image, a sunlight scene recognition command is sent out, otherwise, other scene recognition commands are sent out;
the MCU control chip is respectively connected with the single-reflex equipment and the scene detection equipment and is used for setting a white balance value (WB value) of an image captured by the single-reflex equipment to be between 5000K and 5500K when a sunlight scene identification command is received;
the self-adaptive control equipment is further used for setting the WB value of the single-reaction equipment to be beyond 5000K-5500K when other scene identification commands are received;
the sunlight scene imaging characteristic is the whole brightness value distribution range of the image or a solar target in the image.
Next, the detailed structure of the adaptive data analysis platform of the present invention will be further described.
In the adaptive data analysis platform:
in the target identification device, the target image blocks and the non-target image blocks are combined to form the single-shot image.
The adaptive data analysis platform can further comprise:
and the geometric mean filtering device is used for executing geometric mean filtering processing on the single-reaction snapshot image received from the single-reaction device so as to obtain and output a corresponding geometric mean filtering image.
The adaptive data analysis platform can further comprise:
the contrast analysis equipment is connected with the geometric mean filtering equipment and used for executing contrast analysis on the received geometric mean filtering image and sending a contrast reliable instruction when the analyzed contrast is greater than or equal to a preset contrast threshold;
wherein the contrast analyzing device is further configured to issue a contrast unreliability instruction when the analyzed contrast is smaller than the preset contrast threshold.
The adaptive data analysis platform can further comprise:
and the inverse harmonic mean filtering equipment is respectively connected with the geometric mean filtering equipment and the contrast analysis equipment and is used for executing inverse harmonic mean filtering processing on the received geometric mean filtering image when the contrast unreliable instruction is received so as to obtain and output a corresponding secondary filtering image.
In the adaptive data analysis platform:
the inverse harmonic mean filtering device is further configured to output the received geometric mean filtered image as a secondary filtered image upon receiving the contrast reliability instruction,
the MCU control chip is also respectively connected with the geometric mean value filtering equipment, the contrast analyzing equipment and the inverse harmonic mean value filtering equipment and is used for receiving the contrast unreliable instruction or the contrast reliable instruction.
The adaptive data analysis platform can further comprise:
and the CF storage card is connected with the contrast analysis equipment and is used for pre-storing the preset contrast threshold.
The adaptive data analysis platform can further comprise:
and the geometric correction equipment is respectively connected with the target identification equipment and the inverse harmonic mean filtering equipment and is used for receiving the secondary filtering image, executing geometric correction processing on the secondary filtering image to obtain a corresponding geometric correction image, and replacing the single-reverse snapshot image with the geometric correction image and sending the geometric correction image to the target identification equipment.
In the adaptive data analysis platform:
the geometric correction device, the geometric mean filtering device and the inverse harmonic mean filtering device share the same field timing device.
Meanwhile, in order to overcome the defects, the invention also builds a self-adaptive data analysis method, and the method comprises the step of using the self-adaptive data analysis platform for carrying out high-precision detection on the shooting scene where the single-lens reflex equipment is located so as to select WB values in different ranges based on the detection result.
In addition, a Micro Control Unit (MCU), also called a single chip Microcomputer (single chip Microcomputer) or a single chip Microcomputer (MCU), properly reduces the frequency and specification of a Central Processing Unit (CPU), and integrates peripheral interfaces such as a memory (memory), a counter (Timer), a USB, an a/D converter, a UART, a PLC, a DMA, and the like, and even an LCD driving circuit on a single chip to form a chip-level computer, which performs different combination control for different applications. Such as mobile phones, PC peripherals, remote controls, to automotive electronics, industrial stepper motors, robotic arm controls, etc., see the silhouette of the MCU.
The 32-bit MCU can be said to be the mainstream of the MCU market, the price of a single MCU is between 1.5 and 4 dollars, the working frequency is mostly between 100 and 350MHz, the execution efficiency is better, and the application types are also multiple. However, the length of the program code with the same function of the 32-bit MCU is increased by 30-40% compared with that of the 8/16-bit MCU due to the increase of the operand and the length of the memory, which causes that the capacity of the embedded OTP/FlashROM memory cannot be too small, and the number of external pins of the chip is greatly increased, thereby further limiting the cost reduction capability of the 32-bit MCU.
In the description herein, references to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention.
Furthermore, the terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature.
Although the present invention has been described with reference to the above embodiments, it should be understood by those skilled in the art that various changes may be made and equivalents may be substituted without departing from the spirit and scope of the invention. Therefore, the protection scope of the present invention should be subject to the scope defined by the claims of the present application.
Claims (10)
1. An adaptive data analysis platform, the platform comprising:
the target identification device is used for executing target identification on the single-reflex snapshot image received from the single-reflex device so as to obtain each target image block and each non-target image block;
the Sobel sharpening device is connected with the target recognition device and is used for carrying out image sharpening processing based on a Sobel operator on each target image block to obtain a corresponding sharpened block, and simultaneously carrying out image sharpening processing based on the Sobel operator on each non-target image block;
the band-elimination filtering device is connected with the Sobel sharpening device and is used for executing band-elimination filtering processing on each sharpened block to obtain corresponding filtering blocks and simultaneously not executing band-elimination filtering processing on each non-target image block;
the data merging equipment is connected with the band-stop filtering equipment and used for receiving each filtering block and each non-target image block and merging each filtering block and each non-target image block to obtain a data merging image corresponding to the single-shot image;
the signal enhancement device is connected with the data merging device and is used for executing image enhancement processing based on exponential transformation on the received data merging image so as to obtain a corresponding exponential transformation enhanced image;
the scene detection device is connected with the signal enhancement device and used for executing scene recognition on the exponentially transformed enhanced image based on the sunlight scene imaging characteristics, and when the sunlight scene imaging characteristics exist in the exponentially transformed enhanced image, a sunlight scene recognition command is sent out, otherwise, other scene recognition commands are sent out;
the MCU control chip is respectively connected with the single-reflex equipment and the scene detection equipment and is used for setting a white balance value (WB value) of an image captured by the single-reflex equipment to be between 5000K and 5500K when a sunlight scene identification command is received;
the self-adaptive control equipment is further used for setting the WB value of the single-reaction equipment to be beyond 5000K-5500K when other scene identification commands are received;
the sunlight scene imaging characteristic is the whole brightness value distribution range of the image or a solar target in the image.
2. The adaptive data analysis platform of claim 1, wherein:
in the target identification device, the target image blocks and the non-target image blocks are combined to form the single-shot image.
3. The adaptive data analysis platform of claim 2, wherein the platform further comprises:
and the geometric mean filtering device is used for executing geometric mean filtering processing on the single-reaction snapshot image received from the single-reaction device so as to obtain and output a corresponding geometric mean filtering image.
4. The adaptive data analysis platform of claim 3, wherein the platform further comprises:
the contrast analysis equipment is connected with the geometric mean filtering equipment and used for executing contrast analysis on the received geometric mean filtering image and sending a contrast reliable instruction when the analyzed contrast is greater than or equal to a preset contrast threshold;
wherein the contrast analyzing device is further configured to issue a contrast unreliability instruction when the analyzed contrast is smaller than the preset contrast threshold.
5. The adaptive data analysis platform of claim 4, wherein the platform further comprises:
and the inverse harmonic mean filtering equipment is respectively connected with the geometric mean filtering equipment and the contrast analysis equipment and is used for executing inverse harmonic mean filtering processing on the received geometric mean filtering image when the contrast unreliable instruction is received so as to obtain and output a corresponding secondary filtering image.
6. The adaptive data analysis platform of claim 5, wherein:
the inverse harmonic mean filtering device is further configured to output the received geometric mean filtered image as a secondary filtered image upon receiving the contrast reliability instruction,
the MCU control chip is also respectively connected with the geometric mean value filtering equipment, the contrast analyzing equipment and the inverse harmonic mean value filtering equipment and is used for receiving the contrast unreliable instruction or the contrast reliable instruction.
7. The adaptive data analysis platform of claim 6, wherein the platform further comprises:
and the CF storage card is connected with the contrast analysis equipment and is used for pre-storing the preset contrast threshold.
8. The adaptive data analysis platform of claim 7, wherein the platform further comprises:
and the geometric correction equipment is respectively connected with the target identification equipment and the inverse harmonic mean filtering equipment and is used for receiving the secondary filtering image, executing geometric correction processing on the secondary filtering image to obtain a corresponding geometric correction image, and replacing the single-reverse snapshot image with the geometric correction image and sending the geometric correction image to the target identification equipment.
9. The adaptive data analysis platform of claim 8, wherein:
the geometric correction device, the geometric mean filtering device and the inverse harmonic mean filtering device share the same field timing device.
10. An adaptive data analysis method, the method comprising providing an adaptive data analysis platform according to any one of claims 1 to 9 for performing high-precision detection on a shot scene in which a single-lens reflex device is located, so as to select different ranges of WB values based on the detection result.
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