CN211122559U

CN211122559U - Quick decision maker of automobile-used fuel cleanliness

Info

Publication number: CN211122559U
Application number: CN201922051547.5U
Authority: CN
Inventors: 朱仁成; 陈新; 刘凯; 苗嘉璐; 林安晴; 冯晓龙; 鲍晓峰; 王运静
Original assignee: Zhengzhou University
Current assignee: Zhengzhou University
Priority date: 2019-11-25
Filing date: 2019-11-25
Publication date: 2020-07-28
Anticipated expiration: 2029-11-25

Abstract

The utility model relates to a quick decision maker of clean nature of automobile-used fuel, including the casing, have the detection zone in the casing, be provided with in the detection zone and examine test table, be provided with camera and light source in the top of detection zone on the casing, be provided with in the casing and store the categorised treater of fuel spraying image grade, the treater is connected with the camera, is provided with the display of being connected with the treater on the casing. The method comprises the steps of collecting samples sprayed with fuel, classifying the samples into grades, storing the samples in a processor, placing a fuel spraying plate on a detection table in a detection area during determination, shooting and collecting images of the fuel spraying plate by a camera, and comparing the images with the images in the processor to obtain the cleanliness grade of the fuel to be detected. The method is based on image recognition for comparison, and is simple to operate and high in accuracy.

Description

Quick decision maker of automobile-used fuel cleanliness

Technical Field

The utility model relates to a technical field is judged to automobile-used fuel cleanliness factor, concretely relates to quick decision maker of automobile-used fuel cleanliness factor.

Background

In recent years, with the development of science and technology and the improvement of living standard of people, the quantity of motor vehicles is increased sharply, and the pollution caused by the tail gas of the motor vehicles is more and more serious. In order to reduce the pollution caused by the fuel oil of the motor vehicle, relevant authorities set the emission standards of the tail gas of the motor vehicle, and in order to meet the standards, the modern engine technology is continuously improved, and the requirement on the cleanliness of the fuel for the motor vehicle is higher and higher. Compared with foreign fuel oil, the catalytic reforming gasoline in China has a large proportion and high unsaturated hydrocarbon content, carbon deposit is easily formed in an engine, and the impurities easily block a filter and a nozzle, so that fuel oil injection and engine running performance are affected, fuel oil is incompletely combusted, fuel consumption is increased, and emission is deteriorated.

The detergent is added into the fuel, so that the detergency of the fuel can be improved to a certain extent, and a plurality of enterprises for producing the detergent are promoted. The current standard method for evaluating the detergency of vehicle fuel oil is a nozzle coking experiment method (XUD-9 method), which is based on an engine bench test, installs a clean nozzle with qualified flow rate inspection on the engine, and operates for 10 hours according to a specified working condition. The detergency of diesel oil was evaluated by measuring the air loss of the nozzle needle before and after the diesel oil to be tested for combustion on the XUD-9 bench. The method can truly reflect the detergency of the vehicle fuel, but the test has long time consumption, large oil consumption, large equipment volume and high manufacturing cost. In the standard of 'vehicle fuel detergent' (GB19592-2004) in China, a fuel detergency simulation device meeting the requirement is adopted to mix a certain amount of test fuel with air through a nozzle under a specified test condition, inject the mixture onto a sediment collector which is weighed and heated to a test temperature condition, simulate the generation of sediments of an air inlet valve, weigh the generated sediments and take pictures for storage; at present, the legislation judges the cleanliness of fuel oil only according to the amount of deposits generated by heating the fuel oil on a deposit collector. The method has the advantages of visual result, convenient operation and short time consumption, but compared with the sediment, the collector has larger mass and higher requirement on mass weighing, and the mass needs to be accurate to 0.1 mg.

However, as the standards of the vehicle fuel are gradually tightened, the requirement on the fuel detergency is relatively improved, and the higher the fuel detergency is, the less the deposits on the deposition plate are, and the accuracy of the detection on the detergency becomes lower, that is, when the method for detecting the fuel detergency in the prior art is used for detecting the detergency of the fuel with high cleanliness, the detection accuracy is poorer.

SUMMERY OF THE UTILITY MODEL

The utility model aims at providing a quick decision-making device of automobile-used fuel detergency to solve among the prior art the relatively poor problem of fuel detergency testing result accuracy.

In order to achieve the above object, the utility model discloses a quick determination device of clean nature of automobile-used fuel adopts following technical scheme: the utility model provides a quick decision maker of automobile-used fuel detergency, includes the casing, has the detection zone in the casing, is provided with in the detection zone and examines the platform, is provided with camera and light source in the top of detection zone on the casing, is provided with in the casing and stores the treater that fuel spraying image grade is categorised, and the treater is connected with the camera, is provided with the display of being connected with the treater on the casing.

An L-type partition plate is arranged inside the shell, the L-type partition plate divides the inner cavity of the shell into a detection area and an installation area, and the processor is arranged in the installation area.

And the shell wall of the shell and the detection area is provided with a cabin door capable of being opened and closed.

The cabin door is made of transparent materials.

The display is a touch display screen capable of inputting information.

The utility model has the advantages that: the method comprises the steps of collecting samples sprayed by fuel oil, classifying the samples, storing the samples in a processor, placing a fuel oil spraying plate to be detected on a detection table in a detection area during determination, shooting and collecting images of the fuel oil spraying plate by a camera, processing the images by the processor, and comparing the images with the images in the processor to obtain the cleanliness grade of the fuel oil to be detected. The method is based on image recognition for comparison, and is simple to operate and high in accuracy.

Drawings

Fig. 1 is a schematic structural view of an embodiment of a device for quickly determining the detergency of a vehicle fuel according to the present invention;

fig. 2 is a schematic view of the internal structure of the vehicle fuel cleanliness rapid determination device in fig. 1.

Detailed Description

The utility model discloses an embodiment of a quick decision-making device of automobile-used fuel detergency, as shown in fig. 1-fig. 2, including casing 1, casing 1 has detection zone 7, be provided with in the detection zone 7 and examine test table 9, be provided with camera 2 and light source 3 in the top of detection zone on the casing 1, camera 2 is used for gathering the image information of fuel shower plate, light source 3 can provide good illumination environment, improve camera image acquisition's accuracy, be provided with in the casing 1 and store the categorised treater 10 of fuel spraying image grade, treater 2 is connected with camera 10, light source 3 also is connected with treater 2, be provided with the display 6 of being connected with treater 10 on the casing 1, the inside L type baffle 4 that is provided with of casing 1, L type baffle 4 cuts apart into detection zone 7 and installing zone 8 with the casing inner chamber, treater 10 sets up in installing zone 8 casing 1 and be provided with the 5 that can the switching hatch door on the casing 1 the casing wall of detection zone 5 adopt transparent material display 6 is the touch display screen that can input information.

The image information contrast in this embodiment adopts colour characteristic information and texture characteristic information and the fuel nozzle plate that awaits the survey to contrast, can accurately obtain the cleanliness factor of the sample that awaits measuring, and the device convenient to use is swift, and labour saving and time saving accords with market demand.

The measuring method of the device for quickly judging the cleanliness of the vehicle fuel oil in the embodiment comprises the following steps:

the method comprises the following steps: and establishing a fuel injection plate database.

The fuel nozzle plate database stores fuel cleanliness grades and color features and texture feature images corresponding to the fuel cleanliness grades.

The method comprises the steps of firstly grading the cleanliness of fuel oil, then carrying out a bench test on the fuel oil with each grade of cleanliness to obtain a fuel oil spray plate image with each grade of cleanliness, finally extracting color characteristic and texture characteristic images of the fuel oil spray plate image with each grade of cleanliness, and storing the color characteristic and texture characteristic images into a fuel oil spray plate database, thereby establishing the fuel oil spray plate database.

Step two: and acquiring a fuel nozzle plate image sample to be detected, placing the fuel nozzle plate image sample in the detection area of the judging device, taking a picture by the camera, extracting the color characteristic of the fuel nozzle plate image to be detected by the processor, comparing the color characteristic with the color characteristic stored in the nozzle plate database, and acquiring a characteristic sample set from the nozzle plate database.

The characteristic sample set is a set formed by the cleanliness grades of which the similarity between the color characteristics of the spray plate image samples in the spray plate database and the similarity between the to-be-detected fuel spray plate images is higher than 99%.

Step three: and extracting the texture characteristic image of the spray plate image sample of the fuel oil to be detected, and comparing the texture characteristic image with the texture characteristic images of the characteristic sample set in each cleanliness grade to obtain the cleanliness grade with the similarity higher than 99%, wherein the cleanliness grade is the cleanliness grade of the fuel oil to be detected.

In this embodiment, the color feature of the fuel nozzle plate image is a ratio of color blocks of the image, and the method for extracting the color feature of the fuel nozzle plate image includes the following steps:

(1) dividing the fuel injection plate image into unit color blocks, wherein each unit color block in the embodiment is a unit block with the size of 10 × 10 pixels, namely dividing the fuel injection plate image into a plurality of unit blocks with the size of 10 × 10 pixels;

(2) and counting the colors of the unit color blocks to obtain the number of the unit color blocks of each color, then calculating the proportion of the unit color blocks of each color, and outputting a corresponding statistical chart to obtain the proportion of the color blocks of the fuel nozzle plate image.

The method for judging the color feature similarity of the spray plate image comprises the following steps:

firstly, calculating the relative error of the proportion of each color unit color block in two kinds of spray plate images;

assuming that the ratio of color patches of one color in one of the nozzle plate images is A1 and the ratio of color patches of one color in the other nozzle plate image is A2, the relative error μ is

μ＝(A1-A2)/A1×100％

And then judging whether the errors of the color unit color blocks in the two spray plate images are within (-1%, 1%), and if so, judging that the color feature similarity of the two spray plate images is greater than a first set value.

The method for extracting the texture feature image of the fuel nozzle plate image in the embodiment comprises the following steps:

(1) carrying out gray level processing on the fuel injection plate image to obtain a gray level image of the fuel injection plate image;

the fuel oil spray plate image is an RGB image, single wave bands representing RGB are respectively solved, and one wave band is selected for texture feature calculation;

(2) and carrying out gray level quantization on the gray level image of the fuel nozzle plate image.

In practical applications, the gray scale of one gray scale image is generally 256 levels, but since the number of levels is too many, the calculation amount is huge, and the calculation time is long, in this embodiment, the gray scale is divided into 8 levels again, that is, the actual gray scale of the image is divided by 32 to obtain an integer, and the 0-255 gray scale in practical applications is converted into 0-8 gray scale.

(3) And calculating a texture characteristic matrix of the fuel nozzle plate image, and converting the texture characteristic matrix into a texture characteristic image.

The method comprises the steps of obtaining a texture characteristic matrix of a fuel nozzle plate image by adopting a statistical method based on a gray level co-occurrence matrix, wherein the gray level co-occurrence matrix is obtained by counting the condition that two pixels which keep a certain distance on the image respectively have set gray levels, taking any point (x, y) and another point (x + a, y + b) which deviates from the point in the image (N × N), setting the gray level value of the point as (g1, g2), moving the point (x, y) on the whole picture, obtaining various (g1, g2) values, setting the level number of the gray level as k, sharing the square of k in the combination of (g1, g2), counting the occurrence frequency of each (g1, g2) value for the whole picture, then arranging the values into a square matrix, and integrating the occurrence frequency of (g1, g2) into probability P (g1, g 2).

Based on this, when calculating the texture feature matrix of the fuel nozzle plate image, firstly, the sliding window, the step length and the moving direction need to be determined.

In the embodiment, the sliding windows are the sliding windows with the sizes of 5 × 5 and 7 × 7, the step length is 1, namely, the sliding window moves by one unit length every time the sliding window moves, the moving direction is in four directions of 0 degrees, 45 degrees, 90 degrees and 135 degrees, if the four directions are not comprehensively moved, various features can be obtained in each direction, and therefore the obtained texture features are too numerous and not beneficial to use, and therefore the feature values in the four directions are averaged and used as a final feature value co-occurrence matrix.

And calculating the gray level co-occurrence matrix and the texture characteristic value of the image of the sliding window by using the sub-image formed by each sliding window through a texture characteristic calculation program, and then assigning the value representing the texture characteristic of the window to the central point of the window, thereby finishing the texture characteristic calculation of the first sliding window. Then the window is moved by a pixel to form another small window image, and new co-occurrence matrix and texture characteristic value are repeatedly calculated. And by analogy, the whole fuel injection plate image forms a texture characteristic value matrix made of texture characteristic values, and the texture characteristic value matrix is converted into a texture characteristic image.

The method for calculating the similarity of the texture characteristic images of the fuel nozzle plate image comprises the following steps:

since the gray level co-occurrence matrix cannot directly describe the texture information of the image, the texture information of the image is described by calculating some corresponding characteristic values through the co-occurrence matrix. Four characteristic values are used for the analytical calculation, namely contrast Con, entropy Ent, correlation Cor and energy Asm, and are defined as follows

The contrast reflects the definition of the image and the depth of the grooves of the texture, the greater the contrast, the deeper the grooves, the clearer the image; conversely, the smaller the contrast and the shallower the groove, the more blurred the image.

Entropy is a measure of randomness of image texture, reflecting the complexity of the image texture. When the entropy takes the maximum value, all values in the co-occurrence matrix are almost equal, and then fine textures are distributed in the image; on the contrary, when the entropy takes the minimum value, all values in the co-occurrence matrix are extremely uneven, and few fine textures are distributed in the image.

In the formula: μ x, μ y mean, σ x, σ y standard deviation

The correlation reflects the consistency of the image texture. When the element values in the co-occurrence matrix are uniform and equal, the correlation value is larger; on the contrary, when there is a large difference in the values of the elements in the co-occurrence matrix, the correlation value is small.

The energy is the measure of the image uniformity and reflects the uniformity of the image gray distribution and the thickness degree of the texture, and if the image is more uniform, the value of the texture is larger; conversely, the more uneven an image, i.e. the smaller its value for a fine texture.

In order to reduce the amount of calculation, the gray level of the image is compressed before the gray level co-occurrence matrix is calculated so as to reduce the calculation time, and meanwhile, the gray level co-occurrence matrix is normalized before the characteristic value is extracted, and the following formula is used in the process:

the mark gray levels are respectively a, b and the directions are

Two pixels with the interval d, lambda is a power coefficient, and R is a normalization constant set according to requirements.

The similarity of the texture characteristic images of the fuel nozzle plate images is greater than a second set value, which means that the similarities of the contrast Con, the entropy Ent, the correlation Cor and the energy Asm of the texture characteristic images of the two fuel nozzle plate images are greater than 99%, taking the contrast as an example, the contrast distribution of the texture characteristic images of the two fuel nozzle plate images is Con1 and Con2, and the similarity of the two images is as follows:

θ＝|Con1-Con2|/Con1。

if the characteristic sample with the similarity meeting the requirement is not matched in the input identification stage of the sample to be tested, manual bench test needs to be carried out on the sample to be tested, the result of the manual test is input in a data mode, the system automatically establishes a new independent unit with the color characteristic diagram and the texture characteristic image of the sample to be tested, the new independent unit is used as a new characteristic sample to be stored in a fuel accompanying database, and the detergency grade of the new independent unit is judged according to the industry established standard, so that the expansion of the data quantity of the fuel spray plate is completed, and the determination of more types of fuel spray plates and the judgment of the detergency registration can be completed.

The utility model discloses a quick decision maker of automobile-used fuel detergency is when using, will await measuring the fuel spout the board and place on the detection bench in the detection zone, is shot the collection by the image of camera to the fuel spout the board, carries out data processing and draws colour characteristic and textural feature by the treater to compare with the database of storing in the treater, feed back out in the display screen with the display result.

In other embodiments of the present invention, the door can be made of opaque material, such as metal door; the detection area and electronic elements such as a processor, a display screen and the like are not mutually influenced, and a partition plate is not required to be arranged inside the shell when electric shock danger caused by the electronic elements during manual operation can be prevented; an external keyboard can also be arranged, and the keyboard is connected with the processor at the moment.

Claims

1. A vehicle fuel cleanliness rapid determination device is characterized in that: including the casing, have the detection zone in the casing, be provided with in the detection zone and examine test table, be provided with camera and light source in the top of detection zone on the casing, be provided with in the casing and store the treater that has fuel spraying image classification of grade, the treater is connected with the camera, is provided with the display of being connected with the treater on the casing.

2. The device for rapidly determining the detergency of a vehicle fuel as claimed in claim 1, wherein an L-type partition is provided in the housing, a L-type partition divides an inner cavity of the housing into a detection area and an installation area, and the processor is provided in the installation area.

3. The device for quickly determining the detergency of a vehicle fuel according to claim 1, wherein: and the shell wall of the shell and the detection area is provided with a cabin door capable of being opened and closed.

4. The device for quickly determining the detergency of a vehicle fuel according to claim 3, wherein: the cabin door is made of transparent materials.

5. The device for quickly determining the detergency of a fuel for a vehicle according to any one of claims 1 to 4, wherein: the display is a touch display screen capable of inputting information.