CN116625448B - Oil consumption detection method and device based on artificial intelligence and storage medium - Google Patents
Oil consumption detection method and device based on artificial intelligence and storage medium Download PDFInfo
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- 238000001514 detection method Methods 0.000 title claims abstract description 51
- 238000013473 artificial intelligence Methods 0.000 title claims abstract description 37
- 239000000446 fuel Substances 0.000 claims abstract description 180
- 238000000034 method Methods 0.000 claims abstract description 14
- 238000012937 correction Methods 0.000 claims description 40
- 238000005286 illumination Methods 0.000 claims description 28
- 238000012360 testing method Methods 0.000 claims description 12
- 230000011218 segmentation Effects 0.000 claims description 11
- 238000012549 training Methods 0.000 claims description 10
- 238000011156 evaluation Methods 0.000 claims description 9
- 238000004364 calculation method Methods 0.000 claims description 8
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- 238000004590 computer program Methods 0.000 claims description 6
- 238000010276 construction Methods 0.000 claims description 6
- 238000010586 diagram Methods 0.000 claims description 5
- 238000012545 processing Methods 0.000 claims description 5
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- 230000006870 function Effects 0.000 description 27
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- 238000004458 analytical method Methods 0.000 description 4
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- OKTJSMMVPCPJKN-UHFFFAOYSA-N Carbon Chemical compound [C] OKTJSMMVPCPJKN-UHFFFAOYSA-N 0.000 description 1
- 238000013528 artificial neural network Methods 0.000 description 1
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Classifications
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01F—MEASURING VOLUME, VOLUME FLOW, MASS FLOW OR LIQUID LEVEL; METERING BY VOLUME
- G01F9/00—Measuring volume flow relative to another variable, e.g. of liquid fuel for an engine
- G01F9/001—Measuring volume flow relative to another variable, e.g. of liquid fuel for an engine with electric, electro-mechanic or electronic means
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C22/00—Measuring distance traversed on the ground by vehicles, persons, animals or other moving solid bodies, e.g. using odometers, using pedometers
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V30/00—Character recognition; Recognising digital ink; Document-oriented image-based pattern recognition
- G06V30/10—Character recognition
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V30/00—Character recognition; Recognising digital ink; Document-oriented image-based pattern recognition
- G06V30/10—Character recognition
- G06V30/14—Image acquisition
- G06V30/146—Aligning or centring of the image pick-up or image-field
- G06V30/1475—Inclination or skew detection or correction of characters or of image to be recognised
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V30/00—Character recognition; Recognising digital ink; Document-oriented image-based pattern recognition
- G06V30/10—Character recognition
- G06V30/14—Image acquisition
- G06V30/148—Segmentation of character regions
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V30/00—Character recognition; Recognising digital ink; Document-oriented image-based pattern recognition
- G06V30/10—Character recognition
- G06V30/18—Extraction of features or characteristics of the image
- G06V30/1801—Detecting partial patterns, e.g. edges or contours, or configurations, e.g. loops, corners, strokes or intersections
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V2201/00—Indexing scheme relating to image or video recognition or understanding
- G06V2201/02—Recognising information on displays, dials, clocks
Abstract
The invention relates to the field of intelligent detection, and discloses an artificial intelligence-based oil consumption detection method, an artificial intelligence-based oil consumption detection device and a storage medium, wherein the method comprises the following steps: acquiring a fuel consumption to-be-detected vehicle and a fuel filling device, and correspondingly acquiring a device fuel gauge image of the fuel filling device and a vehicle fuel gauge image of the fuel consumption to-be-detected vehicle; identifying an oil meter pointer scale of a vehicle oil meter image and device oil meter data of a device oil meter image, and constructing an association relationship between the oil meter pointer scale and the device oil meter data; positioning a mileage starting point and a mileage ending point of a fuel consumption vehicle to be detected, acquiring corresponding starting point mileage and corresponding ending point mileage, identifying a driving path between the mileage starting point and the mileage ending point, and calculating driving path mileage corresponding to the driving path according to the starting point mileage and the ending point mileage; and identifying the oil consumption influence factor of the running path corresponding to the oil consumption to-be-detected vehicle, and calculating the oil consumption level of the oil consumption to-be-detected vehicle based on the oil consumption influence factor, the running path mileage, the oil meter pointer scale and the association relation. The invention can improve the accuracy of oil consumption detection.
Description
Technical Field
The invention relates to the field of intelligent detection, in particular to an artificial intelligence-based oil consumption detection method, an artificial intelligence-based oil consumption detection device and a storage medium.
Background
Along with the consumption of a large amount of fossil energy and continuous exploitation, the conservation and utilization of energy are gradually brought up to the national agenda, the fuel economy is also focused on, and the fuel consumption is an important index for measuring the fuel economy of an automobile, so that the method has important practical significance on how to improve the detection precision of the fuel consumption.
At present, the measurement methods of fuel consumption are mainly divided into two main categories: direct measurement and indirect measurement. The direct measurement method is to directly measure the fuel consumption by disassembling an engine oil way and connecting the engine oil way with a flow measuring instrument, mainly comprises a volumetric method, a mass method and the like, but the method has the advantages of higher measurement accuracy, complex and time-consuming operation, and damage to the original oil way structure of the vehicle, so that a manufacturer cannot repair the vehicle; indirect measurement methods, i.e., non-disassembly measurement methods, include carbon balance methods, ultrasonic methods, electrospray measurement methods, air-fuel ratio measurement methods, radioactive tracking test methods, and the like, but such methods do not require disassembly of the oil path structure, but have relatively low measurement accuracy.
Disclosure of Invention
The invention provides an artificial intelligence-based oil consumption detection method, an artificial intelligence-based oil consumption detection device and a storage medium, and mainly aims to improve the accuracy of oil consumption detection.
In order to achieve the above object, the invention provides an artificial intelligence-based oil consumption detection method, which comprises the following steps:
acquiring a fuel consumption to-be-detected vehicle and a fuel filling device, and filling fuel into the fuel consumption to-be-detected vehicle by using the fuel filling device so as to correspondingly acquire a device fuel gauge image of the fuel filling device and a vehicle fuel gauge image of the fuel consumption to-be-detected vehicle;
identifying an oil meter pointer scale of the vehicle oil meter image, identifying device oil meter data of the device oil meter image, and constructing an association relationship between the oil meter pointer scale and the device oil meter data;
positioning a mileage starting point and a mileage ending point of the fuel consumption to-be-detected vehicle, identifying a driving path between the mileage starting point and the mileage ending point, respectively obtaining a starting point mileage and an ending point mileage of the mileage starting point and the mileage ending point, and calculating a driving path mileage corresponding to the driving path according to the starting point mileage and the ending point mileage;
and identifying the oil consumption influence factor of the oil consumption to-be-detected vehicle corresponding to the driving path, and calculating the oil consumption level of the oil consumption to-be-detected vehicle based on the oil consumption influence factor, the driving path mileage, the oil meter pointer scale and the association relation.
Optionally, the identifying the oil gauge pointer scale of the vehicle oil gauge image includes:
and carrying out graying treatment on the vehicle oil meter image by using the following formula to obtain a vehicle oil meter gray scale map:
wherein, the liquid crystal display device comprises a liquid crystal display device,representing the gray scale of the oil meter of the vehicle,/->Red component image representing a vehicle oil gauge image, < +.>Green component image representing vehicle oil gauge image,/->Blue component image representing a vehicle oil gauge image, < ->，/>Respectively representing the abscissa and the ordinate of the gray level diagram of the vehicle oil meter;
extracting an oil meter dial image in the vehicle oil meter gray scale image, and identifying an oil meter pointer and dial scales in the oil meter dial image;
and identifying the pointer position of the oil meter pointer corresponding to the dial scale, and determining the oil meter pointer scale of the oil meter pointer according to the pointer position.
Optionally, the identifying the device oil level data of the device oil level image includes:
carrying out gray processing on the device oil meter image to obtain a device oil meter gray image, and carrying out illumination non-uniformity correction on the device oil meter gray image to obtain an illumination correction image;
performing edge detection on the illumination correction image to obtain an edge image, positioning a character area in the edge image, and intercepting a character area image in the edge image based on the character area;
And carrying out position correction on the character area image to obtain a character correction image, and carrying out digital character recognition on the character correction image to obtain device oil meter data of the device oil meter image.
Optionally, the performing illumination non-uniformity correction on the device oil table gray scale map to obtain an illumination corrected image includes:
according to the preset scale parameters, calculating the reflection component information entropy of the device oil meter gray level chart by using the following formula:
wherein, the liquid crystal display device comprises a liquid crystal display device,reflection component information entropy, < >>Representing the gray scale of the device oil meter,/->Representing a Gaussian function>Representing the magnitude of the Gaussian scale, +.>Representing scale parameters->Representing logarithmic sign>Representing a convolution symbol;
normalizing the reflection component information entropy by using the following formula to obtain normalized reflection information entropy:
wherein, the liquid crystal display device comprises a liquid crystal display device,representing normalized reflection information entropy, < >>Representing reflected component information entropy, < >>Scale parameter series representing histogram, ++>Representing the minimisation function sign +_>Representing a maximization function symbol;
constructing a scale information entropy histogram according to the scale parameters and the normalized reflection information entropy, determining an optimal scale parameter according to the scale information entropy histogram, and determining an adaptive scale range based on the optimal scale parameter;
And calculating an illumination correction image of the device oil meter gray scale according to the self-adaptive scale range.
Optionally, the calculating the illumination correction image of the device oil meter gray scale according to the adaptive scale range includes:
calculating an illumination correction image of the device oil level gray map using the following formula:
wherein, the liquid crystal display device comprises a liquid crystal display device,representing an illumination corrected image,/->Indicate->Weight coefficient, +.>Representing the gray scale of the device oil meter,/->Indicate->A Gaussian function->Indicate->Personal scale parameter->Sequence number representing a gaussian function, +.>The sign of the logarithm is represented,representing the convolved symbols.
Optionally, the performing position correction on the character area image to obtain a character correction image includes:
performing left-right average segmentation on the character area image to obtain a left character area, a right character area and a segmentation straight line, obtaining an edge image corresponding to the character area image, and marking an upper edge intersection point of the segmentation straight line and the edge image;
calculating the intersection point distance between the intersection point of the upper edge and the upper region edge of the character region image, calculating the region width of the character region image, and calculating the inclination angle of the character region image according to the intersection point distance and the region width;
And calculating an image center of the character area image, and rotating the character area image according to the image center and the inclination angle to obtain the character correction image.
Optionally, the constructing the association relationship between the oil gauge pointer scale and the device oil gauge data includes:
constructing a related data pair of the oil meter pointer scale and the device oil meter data, and dividing the related data pair into a training data pair and a test data pair;
constructing a candidate interpolation function according to the training data pair, and calculating an interpolation error of the candidate interpolation function according to the test data;
and selecting a candidate interpolation function corresponding to the minimum interpolation error as a target interpolation function, and determining the association relationship between the oil gauge pointer scale and the device oil gauge data based on the target interpolation function.
Optionally, the calculating the fuel consumption level of the fuel consumption to-be-detected vehicle based on the fuel consumption influencing factor, the driving path mileage, the fuel gauge pointer scale and the association relation includes:
and calculating the fuel consumption level of the fuel consumption to-be-detected vehicle by using the following formula:
wherein, the liquid crystal display device comprises a liquid crystal display device, Indicating the fuel consumption level of the vehicle to be tested per hundred kilometers,/->Indicating the scale of the pointer of the oil meter,/->Indicating the association between the oil meter pointer scale and the device oil meter data, +.>Indicating the distance travelled,>fuel consumption influencing factor value representing the current state of a fuel consumption test vehicle, < >>Fuel consumption influencing factors representing other states of the fuel consumption test vehicle, +.>Number indicating fuel consumption influencing factor, < >>Indicating the number of fuel consumption influencing factors.
In order to solve the above problems, the present invention further provides an artificial intelligence-based fuel consumption detection device, which includes:
the oil meter image acquisition module is used for acquiring an oil consumption to-be-detected vehicle and an oil filling device, and filling oil to the oil consumption to-be-detected vehicle by utilizing the oil filling device so as to correspondingly acquire a device oil meter image of the oil filling device and a vehicle oil meter image of the oil consumption to-be-detected vehicle;
the association relation construction module is used for identifying the oil meter pointer scale of the vehicle oil meter image, identifying the device oil meter data of the device oil meter image and constructing the association relation between the oil meter pointer scale and the device oil meter data;
the driving path mileage calculation module is used for positioning the mileage starting point and mileage ending point of the fuel consumption to-be-detected vehicle, identifying the driving path between the mileage starting point and mileage ending point, respectively obtaining the starting point mileage and the ending point mileage of the mileage starting point and the mileage ending point, and calculating the driving path mileage corresponding to the driving path according to the starting point mileage and the ending point mileage;
The fuel consumption level calculation module is used for identifying the fuel consumption influence factor of the fuel consumption to-be-detected vehicle corresponding to the driving path and calculating the fuel consumption level of the fuel consumption to-be-detected vehicle based on the fuel consumption influence factor, the driving path mileage, the fuel gauge pointer scale and the association relation.
In order to solve the above problems, the present invention further provides a computer readable storage medium having at least one computer program stored therein, the at least one computer program being executed by a processor in an electronic device to implement the artificial intelligence-based fuel consumption detection method described above.
It can be seen that, in the embodiment of the invention, by acquiring the oil consumption to-be-detected vehicle and the oil consumption device, an implementation object and auxiliary facilities of oil consumption detection can be obtained, the oil consumption to-be-detected vehicle is fueled by the oil consumption device, the oil meter change condition of the oil consumption to-be-detected vehicle can be obtained in real time, so as to provide data support for the association relation between the pointer scale of the oil meter to be subsequently constructed and the data of the oil meter of the oil consumption to-be-detected vehicle, two different expression forms of the oil consumption to-be-detected vehicle can be obtained correspondingly by acquiring the device oil meter image of the oil consumption device and the vehicle oil meter image of the oil consumption to-be-detected vehicle, so as to establish the corresponding relation between fuzzy expression and accurate expression to improve the accuracy of data, and basic fuzzy data can be provided for the subsequent association relation between the pointer scale of the oil meter to be subsequently constructed and the data of the oil meter to be detected by identifying the device oil meter data of the device oil meter image; secondly, according to the embodiment of the invention, the fuzzy oil meter pointer scale can be converted into accurate oil quantity data by constructing the association relation between the oil meter pointer scale and the device oil meter data, so that the accuracy of oil consumption detection is improved, the mileage starting point and mileage end point of the oil consumption to-be-detected vehicle can be positioned, the mileage object corresponding to the oil consumption detection can be determined, and as the premise of subsequent analysis, the driving path between the mileage starting point and mileage end point is identified, and the starting point mileage and the end point mileage of the mileage starting point and the mileage end point are respectively acquired, so that the specific detection path of the oil consumption to-be-detected vehicle and the mileage data of the oil meter at the starting point and the end point can be determined; further, according to the embodiment of the invention, the driving path mileage corresponding to the driving path is calculated according to the starting point mileage and the ending point mileage, the driving mileage of the vehicle to be detected with oil consumption can be obtained, the oil consumption influence factor of the vehicle to be detected with oil consumption corresponding to the driving path is identified, the factors influencing oil consumption can be determined, and the oil consumption level of the vehicle to be detected with oil consumption can be calculated more comprehensively and accurately based on the oil consumption influence factor, the driving path mileage, the oil meter pointer scale and the association relation, so that the intention of the problem can be realized more accurately. Therefore, the oil consumption detection method, the oil consumption detection device and the storage medium based on the artificial intelligence provided by the embodiment of the invention can improve the accuracy of oil consumption detection.
Drawings
FIG. 1 is a schematic flow chart of an artificial intelligence-based fuel consumption detection method according to an embodiment of the present invention;
FIG. 2 is a functional block diagram of an artificial intelligence-based fuel consumption detection device according to an embodiment of the present invention;
the achievement of the objects, functional features and advantages of the present invention will be further described with reference to the accompanying drawings, in conjunction with the embodiments.
Detailed Description
It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
The embodiment of the invention provides an artificial intelligence-based oil consumption detection method. The execution subject of the artificial intelligence-based fuel consumption detection method includes, but is not limited to, at least one of a server, a terminal and the like capable of being configured to execute the method provided by the embodiment of the invention. In other words, the artificial intelligence-based fuel consumption detection method may be performed by software or hardware installed in a terminal device or a server device, and the software may be a blockchain platform. The service end includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, and the like. The server may be an independent server, or may be a cloud server that provides cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content delivery networks (Content Delivery Network, CDN), and basic cloud computing services such as big data and artificial intelligence platforms.
Referring to fig. 1, a flow chart of an artificial intelligence-based fuel consumption detection method according to an embodiment of the present invention is shown. In an embodiment of the present invention, the oil consumption detection method based on artificial intelligence includes:
s1, acquiring a fuel consumption to-be-detected vehicle and a fuel filling device, and filling the fuel consumption to-be-detected vehicle by using the fuel filling device so as to correspondingly acquire a device fuel gauge image of the fuel filling device and a vehicle fuel gauge image of the fuel consumption to-be-detected vehicle.
According to the embodiment of the invention, the implementation object and auxiliary facilities of oil consumption detection can be obtained by acquiring the oil consumption to-be-detected vehicle and the oil filling device, wherein the oil consumption to-be-detected vehicle and the oil filling device can be acquired through the data script, and the data script can be compiled through the JS script language. The vehicle to be detected for oil consumption is a wheeled vehicle which is driven or pulled by a power device to be detected, is used for passengers traveling on roads or is used for transporting articles and carrying out engineering special work, and can be divided into operating vehicles and non-operating vehicles. The oiling device is metering equipment which mainly comprises a pipeline, a meter, an oil pump and the like and is used for directly conveying oil to the motor vehicle, and the metering equipment, the oil storage tank, the pipeline and a valve form a complete oil supply system.
Furthermore, according to the embodiment of the invention, the oil consumption to-be-detected vehicle is fueled by using the oiling device, so that the oiling device and the oil meter change condition of the oil consumption to-be-detected vehicle can be obtained in real time, and data support is provided for the subsequent establishment of the association relationship between the oil meter pointer scale and the device oil meter data. The fuel consumption to-be-detected vehicle is refueled by the fuel filling device through connecting a fuel tank port of the fuel consumption to-be-detected vehicle with a fuel gun of the fuel filling device and starting a fuel filling program.
Further, according to the embodiment of the invention, two different expression forms of the oil filling amount of the oil consumption to-be-detected vehicle can be obtained by correspondingly acquiring the device oil meter image of the oil filling device and the vehicle oil meter image of the oil consumption to-be-detected vehicle, so that the corresponding relation between fuzzy expression and accurate expression is established for the follow-up, and the accuracy of data is improved. The device oil meter image of the oiling device and the vehicle oil meter image of the vehicle to be detected in oil consumption can be acquired correspondingly through photographing of a high-definition mobile phone or photographing of a digital CCD camera.
S2, identifying an oil meter pointer scale of the vehicle oil meter image, identifying device oil meter data of the device oil meter image, and constructing an association relationship between the oil meter pointer scale and the device oil meter data.
According to the embodiment of the invention, the oil meter pointer scale of the vehicle oil meter image is identified, so that basic fuzzy data can be provided for subsequent construction and analysis of the association relationship between the oil meter pointer scale and device oil meter data.
Further, as an alternative embodiment of the present invention, the identifying the oil gauge pointer scale of the vehicle oil gauge image includes:
gray processing is carried out on the vehicle oil meter image by using the following formula to obtain the gray level of the vehicle oil meterThe figure:
wherein, the liquid crystal display device comprises a liquid crystal display device,representing the gray scale of the oil meter of the vehicle,/->Red component image representing a vehicle oil gauge image, < +.>Green component image representing vehicle oil gauge image,/->Blue component image representing a vehicle oil gauge image, < ->，/>Respectively representing the abscissa and the ordinate of the gray level diagram of the vehicle oil meter;
extracting an oil meter dial image in the vehicle oil meter gray scale image, and identifying an oil meter pointer and dial scales in the oil meter dial image;
and identifying the pointer position of the oil meter pointer corresponding to the dial scale, and determining the oil meter pointer scale of the oil meter pointer according to the pointer position.
Alternatively, the extracting the oil table disc image in the vehicle oil table gray scale map may be extracted by a threshold segmentation algorithm. The oil meter pointer and the dial scale in the oil meter dial image can be identified by constructing a large number of samples of the oil meter pointer image and the dial scale image, and performing deep learning on the samples to construct a deep learning model.
Further, according to the embodiment of the invention, accurate data of the oil filling amount of the oil consumption to-be-detected vehicle can be obtained by identifying the device oil meter data of the device oil meter image.
Further, as an alternative embodiment of the present invention, the device oil level data identifying the device oil level image includes: carrying out gray processing on the device oil meter image to obtain a device oil meter gray image, and carrying out illumination non-uniformity correction on the device oil meter gray image to obtain an illumination correction image; performing edge detection on the illumination correction image to obtain an edge image, positioning a character area in the edge image, and intercepting a character area image in the edge image based on the character area; and carrying out position correction on the character area image to obtain a character correction image, and carrying out digital character recognition on the character correction image to obtain device oil meter data of the device oil meter image.
Further, as an optional embodiment of the present invention, the performing illumination non-uniformity correction on the device oil level gray scale map to obtain an illumination corrected image includes:
according to the preset scale parameters, calculating the reflection component information entropy of the device oil meter gray level chart by using the following formula:
Wherein, the liquid crystal display device comprises a liquid crystal display device,reflection component information entropy, < >>Representing the gray scale of the device oil meter,/->Representing a Gaussian function>Representing the magnitude of the Gaussian scale, +.>Representing scale parameters->Representing logarithmic sign>Representing a convolution symbol;
normalizing the reflection component information entropy by using the following formula to obtain normalized reflection information entropy:
wherein, the liquid crystal display device comprises a liquid crystal display device,representing normalized reflection information entropy, < >>Representing reflected component information entropy, < >>Scale parameter series representing histogram, ++>Representing the minimisation function sign +_>Representing a maximization function symbol;
constructing a scale information entropy histogram according to the scale parameters and the normalized reflection information entropy, determining an optimal scale parameter according to the scale information entropy histogram, and determining an adaptive scale range based on the optimal scale parameter;
and calculating an illumination correction image of the device oil meter gray scale according to the self-adaptive scale range.
Optionally, the calculating the reflection component information entropy of the device oil table gray scale map according to the preset scale parameter includes:
optionally, the calculating the illumination correction image of the device oil meter gray scale according to the adaptive scale range includes:
calculating an illumination correction image of the device oil level gray map using the following formula:
Wherein, the liquid crystal display device comprises a liquid crystal display device,representing an illumination corrected image,/->Indicate->Weight coefficient, +.>Representing the gray scale of the device oil meter,/->Indicate->A Gaussian function->Indicate->Personal scale parameter->Sequence number representing a gaussian function, +.>The sign of the logarithm is represented,representing the convolved symbols.
Optionally, the edge detection is performed on the illumination correction image, and the obtaining of the edge image can be achieved through a Canny algorithm. The locating of the character areas in the edge image may be by determining the largest bounding rectangle of the edge image by a row and column scan of the edge image.
Optionally, the performing position correction on the character area image to obtain a character correction image includes: performing left-right average segmentation on the character area image to obtain a left character area, a right character area and a segmentation straight line, obtaining an edge image corresponding to the character area image, and marking an upper edge intersection point of the segmentation straight line and the edge image; calculating the intersection point distance between the intersection point of the upper edge and the upper region edge of the character region image, calculating the region width of the character region image, and calculating the inclination angle of the character region image according to the intersection point distance and the region width; and calculating an image center of the character area image, and rotating the character area image according to the image center and the inclination angle to obtain the character correction image.
Optionally, the performing digital character recognition on the character correction image to obtain device oil table data of the device oil table image includes: performing character segmentation on the character correction image to obtain segmented characters, and extracting character features of the segmented characters; based on the character characteristics, a pre-constructed digital recognition neural network is utilized to determine the digital type corresponding to the segmented character, and device oil meter data of the device oil meter image are determined according to the digital type.
Further, according to the embodiment of the invention, the fuzzy oil meter pointer scale can be converted into accurate oil quantity data by constructing the association relation between the oil meter pointer scale and the device oil meter data, so that the accuracy of oil consumption detection is improved.
Further, as an optional embodiment of the present invention, the constructing an association relationship between the oil gauge pointer scale and the device oil gauge data includes: constructing a related data pair of the oil meter pointer scale and the device oil meter data, and dividing the related data pair into a training data pair and a test data pair; constructing a candidate interpolation function according to the training data pair, and calculating an interpolation error of the candidate interpolation function according to the test data; and selecting a candidate interpolation function corresponding to the minimum interpolation error as a target interpolation function, and determining the association relationship between the oil gauge pointer scale and the device oil gauge data based on the target interpolation function.
Alternatively, the construction of the candidate interpolation function according to the training data pair may be constructed by pre-constructing a functional form, substituting the training data pair into the functional form, generating a system of equations, calculating a solution of the system of equations, and substituting the solution back into the functional form. The function forms comprise polynomials, trigonometric functions, rational partial formulas, exponential functions and the like.
S3, positioning a mileage starting point and a mileage ending point of the fuel consumption to-be-detected vehicle, identifying a driving path between the mileage starting point and the mileage ending point, respectively obtaining a starting mileage and an ending mileage of the mileage starting point and the mileage ending point, and calculating a driving path mileage corresponding to the driving path according to the starting mileage and the ending mileage.
According to the embodiment of the invention, the mileage object corresponding to the oil consumption detection can be determined by positioning the mileage starting point and the mileage ending point of the oil consumption to-be-detected vehicle, and the mileage object can be used as a premise of subsequent analysis. And the positioning of the mileage starting point and the mileage ending point of the fuel consumption to-be-detected vehicle can be realized through inquiring of map software, a GPS and a geographic information system.
Further, the embodiment of the invention can determine the specific detection path of the fuel consumption to-be-detected vehicle and the mileage data of the oil meter at the starting point and the ending point by identifying the driving path between the mileage starting point and the mileage ending point and respectively acquiring the starting point mileage and the ending point mileage of the mileage starting point and the mileage ending point. And identifying the driving path between the mileage starting point and the mileage ending point, and respectively acquiring the starting mileage and the ending mileage of the mileage starting point and the mileage ending point.
Further, according to the embodiment of the invention, the driving path mileage corresponding to the driving path is calculated according to the starting mileage and the ending mileage, so that the driving mileage of the vehicle with the fuel consumption to be detected can be obtained, and the driving path mileage can be obtained by carrying out differential operation on the starting mileage and the ending mileage.
And S4, identifying the oil consumption influence factor of the oil consumption to-be-detected vehicle corresponding to the driving path, and calculating the oil consumption level of the oil consumption to-be-detected vehicle based on the oil consumption influence factor, the driving path mileage, the oil meter pointer scale and the association relation.
According to the embodiment of the invention, the factors influencing the oil consumption can be determined by identifying the oil consumption influence factors of the to-be-detected oil consumption vehicle corresponding to the running path. The fuel consumption influence factor refers to a factor that affects the fuel consumption of a vehicle when the vehicle runs on a road, such as road conditions of a running path, vehicle displacement, driving habits, driving speed and the like.
Further, as an optional embodiment of the present invention, the identifying the fuel consumption influencing factor of the fuel consumption to-be-detected vehicle corresponding to the driving path includes: identifying oil consumption evaluation indexes and initial oil consumption factors of the oil consumption to-be-detected vehicle, and calculating correlation coefficients between the oil consumption evaluation indexes and the initial oil consumption influence factors; screening target oil consumption factors in the initial oil consumption influence factors according to the correlation coefficients, and configuring factor influence references of the target oil consumption factors; and calculating a factor influence coefficient of the target oil consumption factor according to the factor influence standard, and determining the oil consumption influence factor according to the target oil consumption factor and the factor influence coefficient.
Alternatively, the calculating the correlation coefficient between the fuel consumption evaluation index and the initial fuel consumption influence factor may be obtained by calculating a pearson correlation coefficient. And screening the target oil consumption factor in the initial oil consumption influence factors according to the correlation coefficient, wherein the target oil consumption factor in the initial oil consumption influence factors can be obtained by presetting a coefficient threshold and screening the initial oil consumption influence factor corresponding to the correlation coefficient larger than the coefficient threshold. The factor influence benchmark for configuring the target fuel consumption factor may be configured according to an average level of the target fuel consumption factor for a high probability road segment. The factor influence coefficient of the target fuel consumption factor is calculated according to the factor influence standard, and the fuel consumption evaluation index value of the current target fuel consumption factor and the fuel consumption evaluation index value of the factor influence standard can be calculated to determine.
Further, according to the embodiment of the invention, the oil consumption level of the oil consumption to-be-detected vehicle is calculated based on the oil consumption influence factor, the driving path mileage, the oil meter pointer scale and the association relation, so that the oil consumption level of the oil consumption to-be-detected vehicle can be calculated more comprehensively and accurately, and the problem intention can be realized more accurately.
Further, as an optional embodiment of the present invention, the calculating the fuel consumption level of the fuel consumption to-be-detected vehicle based on the fuel consumption influencing factor, the driving path mileage, the fuel gauge pointer scale, and the association relation includes:
and calculating the fuel consumption level of the fuel consumption to-be-detected vehicle by using the following formula:
wherein, the liquid crystal display device comprises a liquid crystal display device,indicating the fuel consumption level of the vehicle to be tested per hundred kilometers,/->Indicating the scale of the pointer of the oil meter,/->Indicating the association between the oil meter pointer scale and the device oil meter data, +.>Indicating the distance travelled,>fuel consumption influencing factor value representing the current state of a fuel consumption test vehicle, < >>Fuel consumption influencing factors representing other states of the fuel consumption test vehicle, +.>Number indicating fuel consumption influencing factor, < >>Indicating the number of fuel consumption influencing factors.
It can be seen that, in the embodiment of the invention, by acquiring the oil consumption to-be-detected vehicle and the oil consumption device, an implementation object and auxiliary facilities of oil consumption detection can be obtained, the oil consumption to-be-detected vehicle is fueled by the oil consumption device, the oil meter change condition of the oil consumption to-be-detected vehicle can be obtained in real time, so as to provide data support for the association relation between the pointer scale of the oil meter to be subsequently constructed and the data of the oil meter of the oil consumption to-be-detected vehicle, two different expression forms of the oil consumption to-be-detected vehicle can be obtained correspondingly by acquiring the device oil meter image of the oil consumption device and the vehicle oil meter image of the oil consumption to-be-detected vehicle, so as to establish the corresponding relation between fuzzy expression and accurate expression to improve the accuracy of data, and basic fuzzy data can be provided for the subsequent association relation between the pointer scale of the oil meter to be subsequently constructed and the data of the oil meter to be detected by identifying the device oil meter data of the device oil meter image; secondly, according to the embodiment of the invention, the fuzzy oil meter pointer scale can be converted into accurate oil quantity data by constructing the association relation between the oil meter pointer scale and the device oil meter data, so that the accuracy of oil consumption detection is improved, the mileage starting point and mileage end point of the oil consumption to-be-detected vehicle can be positioned, the mileage object corresponding to the oil consumption detection can be determined, and as the premise of subsequent analysis, the driving path between the mileage starting point and mileage end point is identified, and the starting point mileage and the end point mileage of the mileage starting point and the mileage end point are respectively acquired, so that the specific detection path of the oil consumption to-be-detected vehicle and the mileage data of the oil meter at the starting point and the end point can be determined; further, according to the embodiment of the invention, the driving path mileage corresponding to the driving path is calculated according to the starting point mileage and the ending point mileage, the driving mileage of the vehicle to be detected with oil consumption can be obtained, the oil consumption influence factor of the vehicle to be detected with oil consumption corresponding to the driving path is identified, the factors influencing oil consumption can be determined, and the oil consumption level of the vehicle to be detected with oil consumption can be calculated more comprehensively and accurately based on the oil consumption influence factor, the driving path mileage, the oil meter pointer scale and the association relation, so that the intention of the problem can be realized more accurately. Therefore, the oil consumption detection method, the oil consumption detection device and the storage medium based on the artificial intelligence provided by the embodiment of the invention can improve the accuracy of oil consumption detection.
As shown in fig. 2, the present invention is a functional block diagram of an artificial intelligence-based fuel consumption detection device.
The artificial intelligence-based fuel consumption detection device 100 of the present invention may be installed in an electronic apparatus. According to the implemented functions, the artificial intelligence-based fuel consumption detection device may include a fuel gauge image acquisition module 101, an association relationship construction module 102, a driving path mileage calculation module 103, and a fuel consumption level calculation module 104. The module according to the invention, which may also be referred to as a unit, refers to a series of computer program segments, which are stored in the memory of the electronic device, capable of being executed by the processor of the electronic device and of performing a fixed function.
In the present embodiment, the functions concerning the respective modules/units are as follows:
the oil meter image acquisition module 101 is configured to acquire an oil consumption to-be-detected vehicle and an oil filling device, and perform oil filling on the oil consumption to-be-detected vehicle by using the oil filling device so as to correspondingly acquire a device oil meter image of the oil filling device and a vehicle oil meter image of the oil consumption to-be-detected vehicle;
the association relation construction module 102 is configured to identify an oil meter pointer scale of the vehicle oil meter image, identify device oil meter data of the device oil meter image, and construct an association relation between the oil meter pointer scale and the device oil meter data;
The driving path mileage calculation module 103 is configured to locate a mileage start point and a mileage end point of the fuel consumption vehicle to be detected, identify a driving path between the mileage start point and the mileage end point, respectively obtain a start mileage and an end mileage of the mileage start point and the mileage end point, and calculate a driving path mileage corresponding to the driving path according to the start mileage and the end mileage;
the fuel consumption level calculation module 104 is configured to identify a fuel consumption influence factor of the fuel consumption to-be-detected vehicle corresponding to the driving path, and calculate a fuel consumption level of the fuel consumption to-be-detected vehicle based on the fuel consumption influence factor, the driving path mileage, the fuel gauge pointer scale and the association relation.
In detail, the modules in the oil consumption detection device 100 based on artificial intelligence in the embodiment of the present invention use the same technical means as the oil consumption detection method based on artificial intelligence described in fig. 1, and can produce the same technical effects, which are not described herein.
The present invention also provides a computer readable storage medium storing a computer program which, when executed by a processor of an electronic device 1, may implement:
Acquiring a fuel consumption to-be-detected vehicle and a fuel filling device, and filling fuel into the fuel consumption to-be-detected vehicle by using the fuel filling device so as to correspondingly acquire a device fuel gauge image of the fuel filling device and a vehicle fuel gauge image of the fuel consumption to-be-detected vehicle;
identifying an oil meter pointer scale of the vehicle oil meter image, identifying device oil meter data of the device oil meter image, and constructing an association relationship between the oil meter pointer scale and the device oil meter data;
positioning a mileage starting point and a mileage ending point of the fuel consumption to-be-detected vehicle, identifying a driving path between the mileage starting point and the mileage ending point, respectively obtaining a starting point mileage and an ending point mileage of the mileage starting point and the mileage ending point, and calculating a driving path mileage corresponding to the driving path according to the starting point mileage and the ending point mileage;
and identifying the oil consumption influence factor of the oil consumption to-be-detected vehicle corresponding to the driving path, and calculating the oil consumption level of the oil consumption to-be-detected vehicle based on the oil consumption influence factor, the driving path mileage, the oil meter pointer scale and the association relation.
In the several embodiments provided by the present invention, it should be understood that the disclosed apparatus, device and method may be implemented in other manners. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the modules is merely a logical function division, and there may be other manners of division when actually implemented.
The modules described as separate components may or may not be physically separate, and components shown as modules may or may not be physical units, may be located in one place, or may be distributed over multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional module in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units can be realized in a form of hardware or a form of hardware and a form of software functional modules.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential characteristics thereof.
The present embodiments are, therefore, to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference signs in the claims shall not be construed as limiting the claim concerned.
The embodiment of the invention can acquire and process the related data based on the artificial intelligence technology. Among these, artificial intelligence (Artificial Intelligence, AI) is the theory, method, technique and application system that uses a digital computer or a digital computer-controlled machine to simulate, extend and extend human intelligence, sense the environment, acquire knowledge and use knowledge to obtain optimal results.
Furthermore, it is evident that the word "comprising" does not exclude other elements or steps, and that the singular does not exclude a plurality. A plurality of units or means recited in the system claims can also be implemented by means of software or hardware by means of one unit or means. The terms second, etc. are used to denote a name, but not any particular order.
Finally, it should be noted that the above-mentioned embodiments are merely for illustrating the technical solution of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications and equivalents may be made to the technical solution of the present invention without departing from the spirit and scope of the technical solution of the present invention.
Claims (8)
1. An artificial intelligence-based oil consumption detection method is characterized by comprising the following steps:
Acquiring a fuel consumption to-be-detected vehicle and a fuel filling device, and filling fuel into the fuel consumption to-be-detected vehicle by using the fuel filling device so as to correspondingly acquire a device fuel gauge image of the fuel filling device and a vehicle fuel gauge image of the fuel consumption to-be-detected vehicle;
identifying an oil meter pointer scale of the vehicle oil meter image, identifying device oil meter data of the device oil meter image, and constructing an association relationship between the oil meter pointer scale and the device oil meter data;
the constructing the association relation between the oil meter pointer scale and the device oil meter data comprises the following steps: constructing a related data pair of the oil meter pointer scale and the device oil meter data, and dividing the related data pair into a training data pair and a test data pair; constructing a candidate interpolation function according to the training data pair, and calculating an interpolation error of the candidate interpolation function according to the test data; selecting a candidate interpolation function corresponding to the minimum interpolation error as a target interpolation function, and determining an association relationship between the oil gauge pointer scale and the device oil gauge data based on the target interpolation function;
positioning a mileage starting point and a mileage ending point of the fuel consumption to-be-detected vehicle, identifying a driving path between the mileage starting point and the mileage ending point, respectively obtaining a starting point mileage and an ending point mileage of the mileage starting point and the mileage ending point, and calculating a driving path mileage corresponding to the driving path according to the starting point mileage and the ending point mileage;
Identifying a fuel consumption influence factor of the fuel consumption to-be-detected vehicle corresponding to the driving path, and calculating the fuel consumption level of the fuel consumption to-be-detected vehicle based on the fuel consumption influence factor, the driving path mileage, the oil meter pointer scale and the association relation;
the identifying the fuel consumption influence factor of the fuel consumption to-be-detected vehicle corresponding to the driving path comprises the following steps: identifying oil consumption evaluation indexes and initial oil consumption factors of the oil consumption to-be-detected vehicle, and calculating correlation coefficients between the oil consumption evaluation indexes and the initial oil consumption influence factors; screening target oil consumption factors in the initial oil consumption influence factors according to the correlation coefficients, and configuring factor influence references of the target oil consumption factors; calculating a factor influence coefficient of the target oil consumption factor according to the factor influence standard, and determining the oil consumption influence factor according to the target oil consumption factor and the factor influence coefficient; the fuel consumption influence factor is a factor which has influence on the fuel consumption of the vehicle driving on the road, and comprises the following components: road conditions of a driving path, vehicle displacement, driving habits and driving speeds;
the calculating the fuel consumption level of the fuel consumption to-be-detected vehicle based on the fuel consumption influence factor, the driving path mileage, the fuel gauge pointer scale and the association relation comprises the following steps:
And calculating the fuel consumption level of the fuel consumption to-be-detected vehicle by using the following formula:
wherein Z is con The method comprises the steps of representing the fuel consumption level of a vehicle to be detected per hundred kilometers, q representing the scale of an oil meter pointer, f representing the association relation between the scale of the oil meter pointer and data of an oil meter, S representing the mileage of a driving path, alpha representing the fuel consumption influence factor value of the current state of the vehicle to be detected, beta representing the fuel consumption influence factors of other states of the vehicle to be detected, j representing the serial numbers of the fuel consumption influence factors, and m representing the number of the fuel consumption influence factors.
2. The artificial intelligence based fuel consumption detection method of claim 1 wherein the identifying the oil gauge pointer scale of the vehicle oil gauge image comprises:
and carrying out graying treatment on the vehicle oil meter image by using the following formula to obtain a vehicle oil meter gray scale map:
g(x,y)＝0.299·R(x,y)+0.578·G(x,y)+0.114·B(x,y)
wherein G represents a vehicle oil meter gray scale, R represents a red component image of a vehicle oil meter image, G represents a green component image of a vehicle oil meter image, B represents a blue component image of a vehicle oil meter image, and x, y respectively represent the abscissa and the ordinate of the vehicle oil meter gray scale;
extracting an oil meter dial image in the vehicle oil meter gray scale image, and identifying an oil meter pointer and dial scales in the oil meter dial image;
And identifying the pointer position of the oil meter pointer corresponding to the dial scale, and determining the oil meter pointer scale of the oil meter pointer according to the pointer position.
3. The artificial intelligence based fuel consumption detection method of claim 1 wherein the device oil level data identifying the device oil level image includes:
carrying out gray processing on the device oil meter image to obtain a device oil meter gray image, and carrying out illumination non-uniformity correction on the device oil meter gray image to obtain an illumination correction image;
performing edge detection on the illumination correction image to obtain an edge image, positioning a character area in the edge image, and intercepting a character area image in the edge image based on the character area;
and carrying out position correction on the character area image to obtain a character correction image, and carrying out digital character recognition on the character correction image to obtain device oil meter data of the device oil meter image.
4. The artificial intelligence-based fuel consumption detection method according to claim 3, wherein the performing illumination non-uniformity correction on the device oil level gray scale map to obtain an illumination corrected image comprises:
According to the preset scale parameters, calculating the reflection component information entropy of the device oil meter gray level chart by using the following formula:
wherein R represents the information entropy of the reflection component, I represents the gray level diagram of the oil meter of the device, G represents the Gaussian function, lambda represents the Gaussian scale amplitude, sigma represents the scale parameter, log represents the logarithmic sign,representing a convolution symbol;
normalizing the reflection component information entropy by using the following formula to obtain normalized reflection information entropy:
wherein R is nor Representing normalized reflection information entropy, R representing reflection component information entropy, L representing scale parameter series of a histogram, min representing a minimized function symbol, and max representing a maximized function symbol;
constructing a scale information entropy histogram according to the scale parameters and the normalized reflection information entropy, determining an optimal scale parameter according to the scale information entropy histogram, and determining an adaptive scale range based on the optimal scale parameter;
and calculating an illumination correction image of the device oil meter gray scale according to the self-adaptive scale range.
5. The artificial intelligence based fuel consumption detection method of claim 4 wherein the calculating an illumination corrected image of the device oil level gray scale map from the adaptive scale range comprises:
Calculating an illumination correction image of the device oil level gray map using the following formula:
wherein R is adj Representing an illumination corrected image, ω k Represents the kth weight coefficient, I represents the device oil level gray scale, G k Represents the kth Gaussian function, σ k Represents the kth scale parameter, k represents the number of the gaussian function, log represents the log symbol,representing the convolved symbols.
6. The artificial intelligence based fuel consumption detection method according to claim 3, wherein the performing the position correction on the character area image to obtain a character corrected image includes:
performing left-right average segmentation on the character area image to obtain a left character area, a right character area and a segmentation straight line, obtaining an edge image corresponding to the character area image, and marking an upper edge intersection point of the segmentation straight line and the edge image;
calculating the intersection point distance between the intersection point of the upper edge and the upper region edge of the character region image, calculating the region width of the character region image, and calculating the inclination angle of the character region image according to the intersection point distance and the region width;
and calculating an image center of the character area image, and rotating the character area image according to the image center and the inclination angle to obtain the character correction image.
7. An artificial intelligence-based fuel consumption detection device, characterized in that the device comprises:
the oil meter image acquisition module is used for acquiring an oil consumption to-be-detected vehicle and an oil filling device, and filling oil to the oil consumption to-be-detected vehicle by utilizing the oil filling device so as to correspondingly acquire a device oil meter image of the oil filling device and a vehicle oil meter image of the oil consumption to-be-detected vehicle;
the association relation construction module is used for identifying the oil meter pointer scale of the vehicle oil meter image, identifying the device oil meter data of the device oil meter image and constructing the association relation between the oil meter pointer scale and the device oil meter data;
the constructing the association relation between the oil meter pointer scale and the device oil meter data comprises the following steps: constructing a related data pair of the oil meter pointer scale and the device oil meter data, and dividing the related data pair into a training data pair and a test data pair; constructing a candidate interpolation function according to the training data pair, and calculating an interpolation error of the candidate interpolation function according to the test data; selecting a candidate interpolation function corresponding to the minimum interpolation error as a target interpolation function, and determining an association relationship between the oil gauge pointer scale and the device oil gauge data based on the target interpolation function;
The driving path mileage calculation module is used for positioning the mileage starting point and mileage ending point of the fuel consumption to-be-detected vehicle, identifying the driving path between the mileage starting point and mileage ending point, respectively obtaining the starting point mileage and the ending point mileage of the mileage starting point and the mileage ending point, and calculating the driving path mileage corresponding to the driving path according to the starting point mileage and the ending point mileage;
the fuel consumption level calculation module is used for identifying a fuel consumption influence factor of the fuel consumption to-be-detected vehicle corresponding to the driving path and calculating the fuel consumption level of the fuel consumption to-be-detected vehicle based on the fuel consumption influence factor, the driving path mileage, the fuel gauge pointer scale and the association relation;
the identifying the fuel consumption influence factor of the fuel consumption to-be-detected vehicle corresponding to the driving path comprises the following steps: identifying oil consumption evaluation indexes and initial oil consumption factors of the oil consumption to-be-detected vehicle, and calculating correlation coefficients between the oil consumption evaluation indexes and the initial oil consumption influence factors; screening target oil consumption factors in the initial oil consumption influence factors according to the correlation coefficients, and configuring factor influence references of the target oil consumption factors; calculating a factor influence coefficient of the target oil consumption factor according to the factor influence standard, and determining the oil consumption influence factor according to the target oil consumption factor and the factor influence coefficient; the fuel consumption influence factor is a factor which has influence on the fuel consumption of the vehicle driving on the road, and comprises the following components: road conditions of a driving path, vehicle displacement, driving habits and driving speeds;
The calculating the fuel consumption level of the fuel consumption to-be-detected vehicle based on the fuel consumption influence factor, the driving path mileage, the fuel gauge pointer scale and the association relation comprises the following steps:
and calculating the fuel consumption level of the fuel consumption to-be-detected vehicle by using the following formula:
wherein Z is con The fuel consumption level of the vehicle to be detected per hundred kilometers is represented by q, the scale of the pointer of the oil meter is represented by q, and f, the scale of the pointer of the oil meter and the scale of the pointer of the oil meter are represented by fAnd the device oil meter data are related, S represents a driving path mileage, alpha represents a fuel consumption influence factor value of the current state of the fuel consumption to-be-detected vehicle, beta represents fuel consumption influence factors of other states of the fuel consumption to-be-detected vehicle, j represents serial numbers of the fuel consumption influence factors, and m represents the number of the fuel consumption influence factors.
8. A computer readable storage medium storing a computer program, wherein the computer program when executed by a processor implements the artificial intelligence based fuel consumption detection method according to any one of claims 1 to 6.
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