CN113406018B - Marine fuel oil sulfur content detector and detection method - Google Patents
Marine fuel oil sulfur content detector and detection method Download PDFInfo
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- NINIDFKCEFEMDL-UHFFFAOYSA-N Sulfur Chemical compound [S] NINIDFKCEFEMDL-UHFFFAOYSA-N 0.000 title claims abstract description 61
- 229910052717 sulfur Inorganic materials 0.000 title claims abstract description 60
- 239000011593 sulfur Substances 0.000 title claims abstract description 60
- 238000001514 detection method Methods 0.000 title claims abstract description 20
- 239000010762 marine fuel oil Substances 0.000 title description 6
- 230000002572 peristaltic effect Effects 0.000 claims abstract description 29
- 238000007405 data analysis Methods 0.000 claims abstract description 24
- 239000000295 fuel oil Substances 0.000 claims abstract description 17
- XOLBLPGZBRYERU-UHFFFAOYSA-N tin dioxide Chemical class O=[Sn]=O XOLBLPGZBRYERU-UHFFFAOYSA-N 0.000 claims abstract description 14
- 238000000034 method Methods 0.000 claims abstract description 7
- 239000000243 solution Substances 0.000 claims description 30
- 239000000446 fuel Substances 0.000 claims description 23
- 238000013527 convolutional neural network Methods 0.000 claims description 19
- 238000012360 testing method Methods 0.000 claims description 10
- 239000007788 liquid Substances 0.000 claims description 9
- 238000007781 pre-processing Methods 0.000 claims description 9
- 239000011259 mixed solution Substances 0.000 claims description 8
- 235000013619 trace mineral Nutrition 0.000 claims description 8
- 239000011573 trace mineral Substances 0.000 claims description 8
- LFQSCWFLJHTTHZ-UHFFFAOYSA-N Ethanol Chemical compound CCO LFQSCWFLJHTTHZ-UHFFFAOYSA-N 0.000 claims description 6
- 230000005284 excitation Effects 0.000 claims description 6
- 238000004458 analytical method Methods 0.000 claims description 5
- HTIRHQRTDBPHNZ-UHFFFAOYSA-N Dibutyl sulfide Chemical compound CCCCSCCCC HTIRHQRTDBPHNZ-UHFFFAOYSA-N 0.000 claims description 4
- 238000012545 processing Methods 0.000 claims description 4
- 230000009467 reduction Effects 0.000 claims description 3
- 239000000126 substance Substances 0.000 abstract description 4
- 230000008901 benefit Effects 0.000 abstract description 3
- 230000008569 process Effects 0.000 abstract description 3
- 231100000956 nontoxicity Toxicity 0.000 abstract description 2
- 239000003921 oil Substances 0.000 description 7
- 230000006870 function Effects 0.000 description 5
- 238000005259 measurement Methods 0.000 description 5
- 238000010586 diagram Methods 0.000 description 4
- 238000010521 absorption reaction Methods 0.000 description 3
- 239000000284 extract Substances 0.000 description 3
- 239000007789 gas Substances 0.000 description 3
- 238000011176 pooling Methods 0.000 description 3
- RAHZWNYVWXNFOC-UHFFFAOYSA-N Sulphur dioxide Chemical compound O=S=O RAHZWNYVWXNFOC-UHFFFAOYSA-N 0.000 description 2
- 230000004913 activation Effects 0.000 description 2
- 238000006243 chemical reaction Methods 0.000 description 2
- 230000003993 interaction Effects 0.000 description 2
- 230000007246 mechanism Effects 0.000 description 2
- 238000010606 normalization Methods 0.000 description 2
- 230000003287 optical effect Effects 0.000 description 2
- 238000011946 reduction process Methods 0.000 description 2
- 238000005070 sampling Methods 0.000 description 2
- 150000003568 thioethers Chemical class 0.000 description 2
- RWSOTUBLDIXVET-UHFFFAOYSA-N Dihydrogen sulfide Chemical class S RWSOTUBLDIXVET-UHFFFAOYSA-N 0.000 description 1
- 239000005864 Sulphur Substances 0.000 description 1
- 238000000862 absorption spectrum Methods 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 239000003153 chemical reaction reagent Substances 0.000 description 1
- 238000004140 cleaning Methods 0.000 description 1
- 238000002485 combustion reaction Methods 0.000 description 1
- 239000012895 dilution Substances 0.000 description 1
- 238000010790 dilution Methods 0.000 description 1
- -1 disulphides Chemical class 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
- 239000003344 environmental pollutant Substances 0.000 description 1
- 238000002189 fluorescence spectrum Methods 0.000 description 1
- 238000007689 inspection Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000012544 monitoring process Methods 0.000 description 1
- 230000008520 organization Effects 0.000 description 1
- 238000005424 photoluminescence Methods 0.000 description 1
- 231100000719 pollutant Toxicity 0.000 description 1
- 239000002096 quantum dot Substances 0.000 description 1
- 230000001105 regulatory effect Effects 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 230000003595 spectral effect Effects 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 150000003464 sulfur compounds Chemical class 0.000 description 1
- 235000010269 sulphur dioxide Nutrition 0.000 description 1
- 239000004291 sulphur dioxide Substances 0.000 description 1
- 229930192474 thiophene Natural products 0.000 description 1
- 150000003577 thiophenes Chemical class 0.000 description 1
- 238000012549 training Methods 0.000 description 1
- 238000012795 verification Methods 0.000 description 1
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
- G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
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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
- G06N3/045—Combinations of networks
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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
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Abstract
The invention discloses a marine fuel sulfur content detector and a detection method, wherein the marine fuel sulfur content detector comprises a detector body and a shell, a containing cavity is arranged in the detector body, a light source, a first sample pool, a second sample pool, a spectrometer, an LED display screen, a data analysis module, a peristaltic pump and a power supply are arranged in the containing cavity, the power supply is arranged on a bottom plate of the containing cavity, and the peristaltic pump is arranged on the power supply; one side of the peristaltic pump is provided with a data analysis module, and the other side of the peristaltic pump is provided with a second sample cell; the data analysis module is provided with a light source, a sample cell is arranged on the light source, and one side of the first sample cell is provided with the spectrometer; the LED display screen is arranged on the shell. The detector and the detection method designed by the invention do not need to ignite samples in the use process, cannot generate pollution to the environment and harmful components to human bodies, and the tin dioxide quantum dots have the advantages of good chemical stability, no toxicity and low cost, and are high in detection speed, high in accuracy, safe, rapid and environment-friendly.
Description
Technical Field
The invention relates to the technical field of fuel oil sulfur content detection, in particular to a marine fuel oil sulfur content detector and a marine fuel oil sulfur content detection method.
Background
Shipping is critical to the role of international trade, with a large number of international cargo being carried out by sea every year. However, the atmospheric pollution is aggravated when the ship sails, and a large number of research results show that SO in the exhaust gas discharged by the ship 2 Accounting for about 4 to 9 percent. This is due to the fact that fuel oils for marine use contain a large number of sulphur-containing components, such as mercaptans, sulphides, disulphides, thiophenes and derivatives thereof, among which mainly organic sulphides, which are very harmful to the human body and the atmosphere, and most of the sulphur dioxide formed during combustion is discharged with the exhaust gases. In order to effectively control the emission of pollutants in ship tail gas, international maritime organization prescribes that ships with sulfur content higher than 0.5% are forbidden to navigate in 2020, and maritime supervision departments gradually strengthen the supervision of the emission-exceeding ships.
At present, the traditional fuel sulfur content detection method commonly used by maritime supervision departments comprises third party detection and portable sulfur content detector detection. The conventional third party verification steps include: and (5) manually judging suspicious ships and monitoring personnel boarding to collect oil samples, and sending the oil samples to a third-party inspection mechanism for detection to obtain detection results. The whole detection period generally needs more than 3 days, and from the perspective of real-time performance, the method is difficult to meet the actual supervision requirement. Therefore, maritime supervision departments often adopt a portable sulfur content detector to measure the sulfur content in oil samples, and the main principle is that primary rays emitted by a ray tube are utilized to excite the samples, the sulfur content in the samples can emit characteristic rays, a ray detector is utilized to measure the characteristic rays and record the ray intensity, the ray intensity is in direct proportion to the sulfur content, and the sulfur content in various oil products can be measured by utilizing a curve calibrated in advance. However, when a fluorescent sulfur meter is used for measuring a sample, particularly a sample containing trace sulfur and sulfur compounds, the proportion of sulfur rays absorbed by air is large, and certain errors are generated when the sulfur rays are received by a detector, so that the measurement accuracy is low, the stability is low, the measurement effect is influenced, and the price is high.
Disclosure of Invention
The invention provides a marine fuel sulfur content detector and a detection method, which are used for solving the problems of low measurement accuracy, low stability and high price of the existing detector.
In order to achieve the above object, the technical scheme of the present invention is as follows:
the marine fuel oil sulfur content detector comprises a detector body and a shell, wherein a containing cavity is formed in the detector body, a light source, a first sample pool, a second sample pool, a spectrometer, an LED display screen, a data analysis module, a peristaltic pump and a power supply are arranged in the containing cavity, the power supply is arranged on a bottom plate of the containing cavity, and the peristaltic pump is arranged on the power supply;
one side of the peristaltic pump is provided with the data analysis module, and the other side of the peristaltic pump is provided with the second sample pool;
the data analysis module is provided with the light source, the light source is provided with the first sample cell, and one side of the first sample cell is provided with the spectrometer;
the LED display screen is arranged on the shell.
Further, the liquid inlet of the peristaltic pump is connected with the second sample cell, and the liquid outlet of the peristaltic pump is connected with the first sample cell.
Further, the first sample cell is connected with the light source and the spectrometer respectively.
Further, the second sample cell is of a detachable structure.
Furthermore, an algorithm for trace element analysis of the marine fuel is carried in the data analysis module.
The detection method of the marine fuel oil sulfur content detector comprises the following steps:
s1: starting a light source to irradiate the solution in the first sample cell, starting a peristaltic pump to dropwise add a sulfur-containing calibration object to the solution in the first sample cell, and starting a spectrometer to collect data in the first sample cell in real time;
s2: the data in the first sample pool acquired in the spectrometer in the S1 is stored in real time through a data analysis module;
s3: carrying out data preprocessing on the data stored by the data analysis module in the S2 to obtain preprocessed data;
s4: classifying and testing the preprocessed data in the step S3 by using a convolutional neural network to obtain classified tested data;
s5: outputting the data of the classification test in the S4 by using a convolutional neural network to obtain a classification probability value;
s6: and (5) carrying out weighted sum on the probability value classified in the S5 and the true concentration value, and then outputting class serial numbers and element contents to obtain the sulfur content in the fuel to be detected.
Further, the data preprocessing in S3 uses a dimension reduction process.
Further, the solution in the first sample cell in S1 is a mixed solution of tin dioxide quantum dots and diluted fuel solution.
Further, the diluted fuel solution is diluted with an alcohol solution.
The marine sulfur content detector and the detection method have the characteristics of high detection speed, high accuracy, safety and environmental protection, do not need to ignite samples in the use process, can reduce pollution to the environment and generate unfavorable components to human bodies, and the tin dioxide quantum dots used in detection have the advantages of good chemical stability, no toxicity and low cost.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions of the prior art, the drawings that are needed in the embodiments or the description of the prior art will be briefly described below, it will be obvious that the drawings in the following description are some embodiments of the present invention, and that other drawings can be obtained according to these drawings without inventive effort to a person skilled in the art.
FIG. 1 is a schematic structural view of a marine fuel sulfur content detector of the present invention;
FIG. 2 is a schematic block diagram of sulfur determination;
FIG. 3 is a flow chart for sulfur determination;
FIG. 4 is a fluorescence spectrum of a mixed tin dioxide quantum dot and fuel solution;
FIG. 5 is a graph showing the change in fluorescence intensity measured at 310nm for different sulfur contents;
FIG. 6 is an overall block diagram of an algorithm;
fig. 7 is a diagram showing a structure of the core multi-path convolutional neural network MCNN.
In the figure, 1, a power supply, 2, a peristaltic pump, 3, a data analysis module, 4, a first sample cell, 5, a spectrometer, 6, an LED display screen, 7, a detector body, 8, a light source, 9 and a second sample cell.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments of the present invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The marine fuel sulfur content detector comprises a detector body 7 and a shell, wherein a containing cavity is formed in the detector body 7, a light source 8, a first sample cell 4, a second sample cell 9, a spectrometer 5, an LED display screen 6, a data analysis module 3, a peristaltic pump 2 and a power supply 4 are arranged in the containing cavity, the power supply 1 is arranged on a bottom plate of the containing cavity, and the peristaltic pump 2 is arranged on the power supply 1; in this embodiment, preferably, the light source 8 is an LED light source, and the data analysis module 3 is a sulfur content data analyzer for establishing a characteristic curve between the sulfur content and the fluorescence value, and the spectrometer 5 is a display panel for measuring the fluorescence intensity of the solution in the sample cell and displaying the sulfur content data.
One side of the peristaltic pump 2 is provided with the data analysis module 3, and the other side is provided with the second sample cell 9; further, the second sample cell 9 is a detachable structure. In this embodiment, the second sample cell 9 is detachable and can be detached from the detector body, so that cleaning is facilitated.
The data analysis module 3 is provided with the light source 8, the light source 8 is provided with the first sample cell 4, and one side of the first sample cell 4 is provided with the spectrometer 5; the LED display screen 6 is arranged on the shell. In this embodiment, preferably, the first sample cell 4 is a four-way cuvette, the light source 8 is an LED excitation light source, the LED excitation light source irradiates the reagent from one side through the cuvette to generate fluorescence, and the generated fluorescence is collected through the cuvette by a spectrometer which is distributed at 90 degrees with the LED light source.
Further, the liquid inlet of the peristaltic pump 2 is connected with the second sample cell 9, and the liquid outlet of the peristaltic pump 2 is connected with the first sample cell 4.
Further, the first sample cell 4 is connected to the light source 8 and the spectrometer 5, respectively.
Further, an algorithm for trace element analysis of the marine fuel is carried in the data analysis module 3.
The detection method of the marine fuel sulfur content detector shown in fig. 2-7 comprises the following steps:
s1: starting a light source to irradiate the solution in the first sample cell, starting a peristaltic pump to dropwise add a sulfur-containing calibration object to the solution in the first sample cell, and starting a spectrometer to collect data in the first sample cell in real time; in this example, it is preferable to add 10 sulfur-containing calibration substances, and each drop of liquid is calculated to increase the sulfur content in the sample cell by o.1%, and 10 times of the sulfur-containing calibration substances are added to measure the fluorescence intensity respectively, as shown in fig. 5, and curves of multiple drops of n-butyl sulfide are drawn in one graph, and the characteristics are identified by the convolutional neural network. 0.1% is the mass fraction of sulfur in the fuel, we increase the overall sulfur content by increasing the mass fraction of sulfur in the test oil sample. The mass of the oil added to the tin dioxide quantum dots after dilution of the test oil sample was fixed, and the sulfur content in the fuel oil was increased instead of increasing the sulfur according to the mass of the sulfur. For example, the mass fraction of sulfur in 1g of fuel is 3%, and the increase in sulfur by 0.1% is 0.1% of that in 1g of fuel.
S2: the data in the first sample pool acquired in the spectrometer in the S1 is stored in real time through a data analysis module; in this embodiment, the data acquisition module is configured to automatically save the fluorescence value data acquired by the spectrometer at intervals of 40 s. When in actual measurement, a start key on the control panel is pressed, and the singlechip sends out an external control signal and simultaneously triggers the peristaltic pump to automatically drip the sample and the data acquisition work of the spectrometer.
S3: carrying out data preprocessing on the data stored by the data analysis module 3 in the S2 to obtain preprocessed data; in this embodiment, the preprocessed data is led into the trained convolutional neural network, the data is processed, and the data preprocessing adopts a max-min normalization, PCA and discrete sampling method to process the 11×350 input data into the 1×150 preprocessed data.
S4: classifying and testing the preprocessed data in the step S3 by using a convolutional neural network to obtain classified tested data; in this embodiment, the convolutional neural network part adopts the above network structure and adopts the Softmax function as the activation function at the output layer to perform the task of multi-classification.
S5: outputting the data of the classification test in the S4 by using a convolutional neural network to obtain a classification probability value;
s6: and (5) carrying out weighted sum on the probability value classified in the S5 and the true concentration value, and then outputting class serial numbers and element contents to obtain the sulfur content in the fuel to be detected. In this embodiment, the output part performs weighted sum on the class probability value and the true concentration value output by the convolutional neural network, and finally outputs the class serial number and the element content, and the convolutional neural network extracts the characteristics of data sets with different sulfur contents through training of a large number of samples in an initial stage, so as to realize measurement of the sulfur content in the fuel.
Further, the data preprocessing in S3 uses a dimension reduction process. In this embodiment, the algorithm first performs dimension reduction processing and GAMMA conversion on the acquired data to better extract the data features.
Further, the solution in the first sample cell 4 in S1 is a mixed solution of tin dioxide quantum dots and a diluted fuel solution.
Further, the diluted fuel solution is diluted with an alcohol solution. In this embodiment, preferably, the fuel is diluted with an alcohol solution, and the amount of the alcohol solution to be used is 100 times the amount of the fuel.
In this embodiment, an excitation light source at an absorption wavelength of 300nm of the LED light source 8 is used to irradiate the mixed solution of the tin dioxide quantum dots and the diluted fuel oil in the first sample cell 4, so that the mixed solution generates fluorescence, and meanwhile, a fluorescence signal is collected by the spectrometer 5, and the optical signal is converted into an electrical signal. The four-way cuvette is connected with the LED light source and the spectrometer, and the LED light source and the spectrometer are distributed at 90 degrees. The liquid inlet of the peristaltic pump is connected with the sample cell of the front shell, and the liquid outlet is communicated with the four-way cuvette cell. And the second sample pool 9 is filled with n-butyl thioether solution, the rotating speed of a peristaltic pump is automatically controlled by a singlechip, and the flow rate of the n-butyl thioether solution is regulated. Since the maximum absorption wavelength of the ultraviolet absorption spectrum of tin dioxide is about 310nm, the maximum absorption wavelength of the excitation light source of about 300nm is selected as the excitation wavelength of tin dioxide photoluminescence.
As shown in fig. 6 and 7, the singlechip is used as a man-machine interaction plate, a written program is mobilized, and the functions of starting and closing the peristaltic pump 2, adjusting the rotating speed and collecting data of the spectrometer 5 are realized through the external control module. The spectrometer 5 converts the collected optical signals into electric signals and transmits the electric signals to the sulfur content data analyzer 3 for storage and processing, the sulfur content data analyzer is used as a data analysis module 3, an algorithm special for trace element analysis tasks of marine fuel is carried, the algorithm firstly performs dimension reduction processing and GAMMA conversion on the collected data to better extract data characteristics, then an innovative thought of a multipath convolutional neural network and an asymmetric convolutional kernel is adopted to classify and test the received data, the sulfur content data analyzer feeds test results back to the singlechip, and a man-machine interaction interface of the singlechip displays sulfur content and whether the sulfur content is qualified or not.
The algorithm takes the convolutional neural network as a core to simultaneously carry out tasks of fuel classification and trace element content estimation. As shown in fig. 7, the overall structure of the algorithm includes: firstly, inputting spectral line data; preprocessing the data; then, carrying out fuel classification and trace element content estimation by a convolutional neural network; and finally, outputting an analysis result. The algorithm structure of the prior MCNN multipath convolutional neural network is referred, and three convolutional kernels with the sizes of 1 multiplied by 3, 1 multiplied by 7 and 1 multiplied by 9 are respectively selected on the basis of the algorithm structure. And a pooling layer with a pooling kernel size of 1 x 2 is added after each convolution layer. Firstly, the data preprocessing cost is that input data of 11 multiplied by 350 is processed into preprocessed data of 1 multiplied by 150 by adopting a max-min normalization, PCA and discrete sampling method. Secondly, the convolutional neural network part adopts the network structure and adopts a Softmax function as an activation function at an output layer to carry out multi-classification tasks. And finally, the output part carries out weighted sum on the class probability value and the true concentration value output by the convolutional neural network, and finally outputs class serial numbers and element contents.
Fig. 7 shows a structure diagram of a core multipath convolutional neural network MCNN, which is the core of an algorithm for completing tasks of fuel classification and trace element content estimation. Mainly comprises the following parts: firstly, an input layer, wherein the scale is the dimension of matrixing data; secondly, extracting features by a multipath convolution layer; important characteristic information is reserved by the pooling layer, so that the calculation complexity is reduced; then recombining the features through the planarization layer; then entering a full link layer through a Dropout mechanism; and finally, fuel classification and trace element content estimation are carried out.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention, and not for limiting the same; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some or all of the technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit of the invention.
Claims (4)
1. The marine fuel sulfur content detector is characterized by comprising a detector body (7) and a shell, wherein a containing cavity is formed in the detector body (7), a light source (8), a first sample pool (4), a second sample pool (9), a spectrometer (5), an LED display screen (6), a data analysis module (3), a peristaltic pump (2) and a power supply (1) are arranged in the containing cavity, the power supply (1) is arranged on a bottom plate of the containing cavity, and the peristaltic pump (2) is arranged on the power supply (1);
one side of the peristaltic pump (2) is provided with the data analysis module (3), the other side of the peristaltic pump is provided with the second sample cell (9), and the second sample cell (9) is internally provided with n-butyl thioether solution;
the data analysis module (3) is provided with the light source (8), the light source (8) is provided with the first sample cell (4), the first sample cell (4) is internally provided with tin dioxide quantum dots and fuel mixed solution diluted by alcohol solution, the light source (8) generates excitation light to irradiate the mixed solution in the first sample cell (4) so as to generate fluorescence, and one side of the first sample cell (4) is provided with the spectrometer (5) for collecting fluorescence signals;
the liquid inlet of the peristaltic pump (2) is connected with the second sample cell (9), and the liquid outlet of the peristaltic pump (2) is connected with the first sample cell (4);
an algorithm for trace element analysis of the marine fuel is carried in the data analysis module (3) and is used for obtaining the sulfur content in the fuel mixed solution;
the LED display screen (6) is arranged on the shell.
2. The marine fuel sulfur content detector as claimed in claim 1, wherein the second sample cell (9) is of a detachable structure.
3. A detection method based on the marine fuel sulfur content detector as claimed in any one of claims 1 or 2, comprising the steps of:
s1: starting a light source (8) to irradiate the solution in the first sample cell (4), starting a peristaltic pump to dropwise add a sulfur-containing calibration object to the solution in the first sample cell (4), and simultaneously starting a spectrometer (5) to collect data in the first sample cell in real time, wherein the solution in the first sample cell (4) is a mixed solution of tin dioxide quantum dots and a diluted fuel solution, and the diluted fuel solution is diluted by using an alcohol solution;
s2: the data in the first sample pool acquired in the spectrometer (5) in the step S1 are stored in real time through a data analysis module (3);
s3: carrying out data preprocessing on the data stored by the data analysis module (3) in the S2 to obtain preprocessed data;
s4: classifying and testing the preprocessed data in the step S3 by using a convolutional neural network to obtain classified tested data;
s5: outputting the data of the classification test in the S4 by using a convolutional neural network to obtain a classification probability value;
s6: and (5) carrying out weighted sum on the probability value classified in the S5 and the true concentration value, and then outputting class serial numbers and element contents to obtain the sulfur content in the fuel to be detected.
4. A method according to claim 3, wherein the data preprocessing in S3 uses dimension reduction processing.
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