CN116451338A - MCU-based integrated architecture design method - Google Patents
MCU-based integrated architecture design method Download PDFInfo
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- CN116451338A CN116451338A CN202310103299.2A CN202310103299A CN116451338A CN 116451338 A CN116451338 A CN 116451338A CN 202310103299 A CN202310103299 A CN 202310103299A CN 116451338 A CN116451338 A CN 116451338A
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- 238000013461 design Methods 0.000 title claims abstract description 28
- 238000000034 method Methods 0.000 title claims abstract description 19
- 230000000007 visual effect Effects 0.000 claims abstract description 14
- 238000012549 training Methods 0.000 claims description 30
- 238000000605 extraction Methods 0.000 claims description 22
- 238000003062 neural network model Methods 0.000 claims description 12
- 238000005286 illumination Methods 0.000 claims description 9
- 238000012795 verification Methods 0.000 claims description 9
- 238000012544 monitoring process Methods 0.000 claims description 4
- 208000035139 partial with pericentral spikes epilepsy Diseases 0.000 claims description 4
- 238000004891 communication Methods 0.000 claims description 3
- 238000003745 diagnosis Methods 0.000 claims description 3
- 238000001914 filtration Methods 0.000 claims description 3
- 238000002372 labelling Methods 0.000 claims description 3
- 238000005457 optimization Methods 0.000 claims description 3
- 238000012827 research and development Methods 0.000 abstract description 3
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/10—Geometric CAD
- G06F30/15—Vehicle, aircraft or watercraft design
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/90—Details of database functions independent of the retrieved data types
- G06F16/904—Browsing; Visualisation therefor
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/40—Extraction of image or video features
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
- G06V10/77—Processing image or video features in feature spaces; using data integration or data reduction, e.g. principal component analysis [PCA] or independent component analysis [ICA] or self-organising maps [SOM]; Blind source separation
- G06V10/774—Generating sets of training patterns; Bootstrap methods, e.g. bagging or boosting
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
- G06V10/82—Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
- G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
Abstract
The invention relates to the technical field of motorcycles, in particular to an integrated architecture design method based on MCU, which comprises the steps of utilizing an MCU embedded system to carry out integrated unified management on a plurality of modules of a motorcycle to obtain management data; establishing a visual model based on the management data; adjusting display brightness and display height of the display screen; and displaying the visual model through the adjusted display screen. According to the invention, the MCU embedded system is used for carrying out integrated and unified management on a plurality of modules of the motorcycle, so that the problem that each module is matched with an independent MCU module in the traditional module architecture design is solved, the number of the MCU modules is reduced, and the purpose of reducing the BOM cost of products is realized. The MCU integrated architecture design shortens the research and development period and solves the problem of higher product cost of the existing automobile electronic and electric appliance architecture design.
Description
Technical Field
The invention relates to the technical field of motorcycles, in particular to an integrated architecture design method based on MCU.
Background
Automotive electronics architecture designs typically design meters, IVI, BCM, PEPS, tire pressure monitoring, voice control, TBOX, etc. according to the appliance function as separate modules, such architecture designs resulting in high product costs.
Disclosure of Invention
The invention aims to provide an integrated architecture design method based on MCU, which aims to solve the problem of higher product cost of the existing automobile electronic and electric appliance architecture design.
In order to achieve the above object, the present invention provides an integrated architecture design method based on an MCU, comprising the following steps:
the MCU embedded system is utilized to carry out integrated and unified management on a plurality of modules of the motorcycle, so as to obtain management data;
establishing a visual model based on the management data;
adjusting display brightness and display height of the display screen;
and displaying the visual model through the adjusted display screen.
The multiple modules of the motorcycle comprise an instrument module, an IVI module, a BCM module, a PEPS module, a tire pressure monitoring module, a 4G module, a voice module, a power management module, a CAN communication module, a UDS diagnosis module, an I2S module, an I2C module and an SPI module.
Wherein, the adjusting the display brightness of the display screen includes:
acquiring the current illumination intensity of a scene where the display screen is positioned and the current display brightness of the display screen;
acquiring a brightness difference value between the current illumination intensity and the current display brightness;
and adjusting the display brightness of the display screen based on the brightness difference value.
Wherein, the adjusting the display height of the display screen comprises:
acquiring the head characteristics of the display range of the display screen;
capturing eye position points of the head features;
calculating the height difference between the eye position point and the display screen;
and adjusting the display height of the display screen based on the height difference value.
The obtaining the head feature of the display range of the display screen comprises the following steps:
shooting a head picture in the display range of the display screen;
and constructing a feature extraction model to extract features of the head picture to obtain head features.
The constructing the feature extraction model to perform feature extraction on the head picture to obtain head features includes:
constructing a neural network model;
acquiring a training data set to train the neural network model to obtain a feature extraction model;
and inputting the head picture into the feature extraction model for training to obtain head features.
The acquiring the training data set to train the neural network model to obtain a feature extraction model comprises the following steps:
acquiring a training data set;
sequentially filtering, labeling and dividing the training data set to obtain a training set and a verification set;
training the neural network model by using the training set to obtain a pre-training model;
and performing verification and optimization on the pre-training model by using the verification set to obtain a feature extraction model.
The invention relates to an integrated architecture design method based on MCU, which is characterized in that a plurality of modules of a motorcycle are integrated and uniformly managed by utilizing an MCU embedded system to obtain management data; establishing a visual model based on the management data; adjusting display brightness and display height of the display screen; and displaying the visual model through the adjusted display screen. According to the invention, the MCU embedded system is used for carrying out integrated and unified management on a plurality of modules of the motorcycle, so that the problem that each module is matched with an independent MCU module in the traditional module architecture design is solved, the number of the MCU modules is reduced, and the purpose of reducing the BOM cost of products is realized. The MCU integrated architecture design shortens the research and development period and solves the problem of higher product cost of the existing automobile electronic and electric appliance architecture design.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flowchart of an integrated architecture design method based on MCU provided by the present invention.
Fig. 2 is a flowchart of adjustment of display brightness for a display screen.
Fig. 3 is a flow chart of an adjustment of display height to a display screen.
Detailed Description
Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to like or similar elements or elements having like or similar functions throughout. The embodiments described below by referring to the drawings are illustrative and intended to explain the present invention and should not be construed as limiting the invention.
Referring to fig. 1 to 3, the present invention provides an integrated architecture design method based on MCU, comprising the following steps:
s1, integrating and uniformly managing a plurality of modules of a motorcycle by utilizing an MCU embedded system to obtain management data;
specifically, the multiple modules of the motorcycle comprise a meter module, an IVI module, a BCM module, a PEPS module, a tire pressure monitoring module, a 4G module, a voice module, a power management module, a CAN communication module, a UDS diagnosis module, an I2S module, an I2C module and an SPI module.
S2, establishing a visual model based on the management data;
s3, adjusting display brightness and display height of the display screen;
the adjusting the display brightness of the display screen comprises the following steps:
s301, acquiring the current illumination intensity of a scene where the display screen is located and the current display brightness of the display screen;
s302, obtaining a brightness difference value between the current illumination intensity and the current display brightness;
specifically, the current illumination intensity is subtracted from the current display brightness to obtain a difference value, if the current display brightness is lower than the current illumination intensity, the difference value is a negative value, and if the current display brightness is higher than the current illumination intensity, the difference value is a positive value.
And S303, adjusting the display brightness of the display screen based on the brightness difference value.
Specifically, if the difference is a negative value, it is proved that the display screen is in a strong light environment, and the display brightness of the display screen is lightened based on the difference, so that the display definition of the visual model is improved. And if the difference value is positive, proving that the display screen is in a weak light environment, dimming the display brightness of the display screen based on the difference value, and saving electric energy while improving the vision protection of a user.
The adjusting the display height of the display screen comprises the following steps:
s311, acquiring the head characteristic of the display range of the display screen;
specifically, shooting a head picture in the display range of the display screen; and constructing a feature extraction model to extract features of the head picture to obtain head features.
The constructing a feature extraction model to perform feature extraction on the head picture to obtain head features, including: constructing a neural network model; acquiring a training data set to train the neural network model to obtain a feature extraction model; and carrying out format adjustment on the head picture based on the input format of the feature extraction model, and then inputting the head picture into the feature extraction model for training to obtain head features.
The obtaining a training data set to train the neural network model to obtain a feature extraction model comprises the following steps: acquiring a training data set; sequentially filtering, labeling and dividing the training data set to obtain a training set and a verification set; training the neural network model by using the training set to obtain a pre-training model; and performing verification and optimization on the pre-training model by using the verification set to obtain a feature extraction model.
S312 capturing eye position points of the head features;
s313, calculating a height difference value between the eye position point and the display screen;
s314, adjusting the display height of the display screen based on the height difference value.
Specifically, the display height of the display screen is adjusted based on the height difference value until the eye position point and the display screen are at a uniform level, so that the problem that the user looks at the display screen for a long time with head up or head down to cause neck fatigue is avoided.
And S4, displaying the visual model through the adjusted display screen.
After the step of displaying the visual model through the adjusted display screen, the method further comprises the following steps:
s5, establishing foreground data of the display range of the display screen;
s6, acquiring current scene data of the display range of the display screen;
s7, capturing a dynamic target on the current scene data based on the foreground data;
and S8, if the dynamic target is not captured in the preset time, adjusting the display screen to a screen-off state until the dynamic target is captured again, and recovering the display screen to a normal state.
Specifically, avoid when nobody, the continuous work of display screen leads to the fact the electric energy extravagant.
The invention relates to an integrated architecture design method based on MCU, which is characterized in that a plurality of modules of a motorcycle are integrated and uniformly managed by utilizing an MCU embedded system to obtain management data; establishing a visual model based on the management data; adjusting display brightness and display height of the display screen; and displaying the visual model through the adjusted display screen. According to the invention, the MCU embedded system is used for carrying out integrated and unified management on a plurality of modules of the motorcycle, so that the problem that each module is matched with an independent MCU module in the traditional module architecture design is solved, the number of the MCU modules is reduced, and the purpose of reducing the BOM cost of products is realized. The MCU integrated architecture design shortens the research and development period and solves the problem of higher product cost of the existing automobile electronic and electric appliance architecture design.
The above disclosure is merely illustrative of a preferred embodiment of an MCU-based integrated architecture design method of the present invention, and it is not intended to limit the scope of the present invention.
Claims (7)
1. The integrated architecture design method based on the MCU is characterized by comprising the following steps of:
the MCU embedded system is utilized to carry out integrated and unified management on a plurality of modules of the motorcycle, so as to obtain management data;
establishing a visual model based on the management data;
adjusting display brightness and display height of the display screen;
and displaying the visual model through the adjusted display screen.
2. The method for designing an MCU-based integrated architecture of claim 1,
the multiple modules of the motorcycle comprise a meter module, an IVI module, a BCM module, a PEPS module, a tire pressure monitoring module, a 4G module, a voice module, a power management module, a CAN communication module, a UDS diagnosis module, an I2S module, an I2C module and an SPI module.
3. The method for designing an MCU-based integrated architecture of claim 2,
the adjusting the display brightness of the display screen comprises the following steps:
acquiring the current illumination intensity of a scene where the display screen is positioned and the current display brightness of the display screen;
acquiring a brightness difference value between the current illumination intensity and the current display brightness;
and adjusting the display brightness of the display screen based on the brightness difference value.
4. The method for designing an MCU-based integrated architecture of claim 3,
the adjusting the display height of the display screen comprises the following steps:
acquiring the head characteristics of the display range of the display screen;
capturing eye position points of the head features;
calculating the height difference between the eye position point and the display screen;
and adjusting the display height of the display screen based on the height difference value.
5. The method for MCU-based integrated architecture design of claim 4,
the obtaining the head characteristic of the display range of the display screen comprises the following steps:
shooting a head picture in the display range of the display screen;
and constructing a feature extraction model to extract features of the head picture to obtain head features.
6. The method for MCU-based integrated architecture design of claim 5,
the constructing a feature extraction model to perform feature extraction on the head picture to obtain head features, including:
constructing a neural network model;
acquiring a training data set to train the neural network model to obtain a feature extraction model;
and inputting the head picture into the feature extraction model for training to obtain head features.
7. The method for MCU-based integrated architecture design of claim 6,
the obtaining a training data set to train the neural network model to obtain a feature extraction model comprises the following steps:
acquiring a training data set;
sequentially filtering, labeling and dividing the training data set to obtain a training set and a verification set;
training the neural network model by using the training set to obtain a pre-training model;
and performing verification and optimization on the pre-training model by using the verification set to obtain a feature extraction model.
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