A method and system for tracing origin of products based on big data

Abstract

The present invention relates to the technical field of crude oil detection, and in particular to a method and system for tracing the origin of crude oil based on big data. The method provided by the present invention comprises obtaining spectral variation characteristics, calculating spectral variation characterization parameters for the crude oil to be tested, determining the spectral variation category of the crude oil to be tested, determining a beam diameter and a sample scanning time when performing spectral analysis on the crude oil to be tested based on the spectral variation characterization parameters, determining spectral data acquired within a predetermined time, dividing the spectral data in a time domain dimension, comparing the obtained spectral data segments, determining whether the spectral analysis meets standards based on similarity between the spectral data segments, adjusting the scanning speed of a spectrometer, acquiring spectral data again and storing it, or acquiring spectral data while maintaining initial operating parameters of the spectrometer and storing it, tracing the origin of the crude oil to be tested based on a similarity comparison result between the stored spectral data and spectral data of corresponding samples from each origin, thereby improving the accuracy of tracing.

Description

Technical Field

[0001] The present invention relates to the technical field of crude oil detection, and in particular to a method and system for tracing the origin of crude oil based on big data. Background Art

[0002] Spectrometers are important tools in the process of tracing the origin of crude oil. This is because crude oil from different origins has different chemical compositions, and the various chemical components in crude oil have unique absorption and emission characteristics for specific wavelengths of light. By analyzing these spectral characteristics, the presence and concentration of specific components in crude oil can be identified, thereby inferring the origin of the crude oil, which is of great significance to the authenticity and security of crude oil trade.

[0003] Chinese patent publication number: CN114280000A, discloses a method and system for origin tracing based on big data. The method provided includes collecting crude oil samples from different countries and obtaining sample terahertz spectra of the crude oil samples; using a continuous wavelet algorithm to transform the sample terahertz spectrum into a wavelet space, extracting features from the wavelet space through cluster analysis and deep learning methods, and constructing a standard spectrum library of crude oil samples from different countries based on the weighted least squares method. The crude oil samples are traced and identified based on the standard spectrum library; the system includes a data acquisition module, a first data processing module, a second data processing module, a data storage module, and a display module; the device includes a terahertz time-domain spectrometer, a data processing and analysis device, and a display device. The advantages of the present invention are that the method is simple, has strong structure and logic, and has high traceability accuracy, providing a new technical idea for crude oil traceability.

[0004] However, the prior art still has the following problems:

[0005] When tracing the origin of crude oil, high accuracy of spectral data is required. However, in reality, the particle size dispersion in crude oil affects the transmittance and reflectivity of light, and viscosity leads to enhanced interactions between particles. The combination of these factors can easily lead to spectral anomalies and inconsistency in spectrometer measurement results, reducing the accuracy of crude oil tracing. Summary of the Invention

[0006] To this end, the present invention provides a method and system for tracing the origin of crude oil based on big data, which is used to solve the problem that in actual situations, the particle size dispersion in crude oil affects the transmittance and reflectivity of light, and the viscosity leads to enhanced interaction between particles. The combination of the above factors can easily lead to spectral anomalies and inconsistency in spectrometer measurement results, thereby reducing the accuracy of crude oil origin tracing.

[0007] To achieve the above objectives, the present invention provides a method for tracing the origin of products based on big data, which comprises:

[0008] Step S1, extracting an analysis sample from the crude oil to be tested, analyzing it, and obtaining spectral variation characteristics. The analysis includes placing the analysis sample in a detection plane, obtaining particle size information of different regions of the analysis sample to obtain particle size dispersion, and measuring the viscosity of the analysis sample.

[0009] Step S2, calculating a spectral variation characterization parameter for the current crude oil to be tested based on the spectral variation characteristics to determine the spectral variation category of the current crude oil to be tested;

[0010] Step S3, performing spectral analysis based on the spectral variation category of the crude oil to be tested, and obtaining spectral data, including:

[0011] Determining a beam diameter and a sample scanning time for spectral analysis of the crude oil to be tested based on the spectral variation characterization parameter, determining spectral data acquired within a predetermined time, segmenting the spectral data in the time domain, and comparing the resulting spectral data segments. Determining whether the spectral analysis meets the standard based on similarity between the spectral data segments, adjusting the scanning speed of the spectrometer, and acquiring and storing spectral data again.

[0012] Alternatively, maintaining the initial operating parameters of the spectrometer to obtain and store spectral data;

[0013] Step S4: tracing the origin of the crude oil to be tested based on the similarity comparison result between the stored spectral data and the spectral data of the samples corresponding to each origin.

[0014] Furthermore, in step S1, the process of determining the particle size dispersion includes:

[0015] Determine the particle size information of different areas in the analysis sample to calculate the variance of the particle size information;

[0016] The square root of the variance was determined as the particle size dispersion.

[0017] Furthermore, in step S2, the process of calculating the spectral variation characterization parameter for the currently tested crude oil includes:

[0018] Determine the ratio of the reference particle size dispersion to the crude oil particle size dispersion as the first influencing parameter;

[0019] determining the ratio of the crude oil viscosity to the benchmark crude oil viscosity as the second influencing parameter;

[0020] The weighted sum of the first influencing parameter and the second influencing parameter is determined as a spectral variation characterization parameter.

[0021] Furthermore, in step S2, the process of determining the spectral variation category of the crude oil to be tested includes:

[0022] If the spectral variation characterization parameter is greater than the spectral variation characterization parameter threshold, the spectral variation category is determined to be a strong variation category;

[0023] If the spectral variation characterization parameter is less than or equal to the spectral variation characterization parameter threshold, the spectral variation category is determined to be a weak variation category.

[0024] Furthermore, in step S3, spectral analysis is performed according to the spectral variation category of the crude oil to be tested, wherein:

[0025] If the spectral variation category of the crude oil to be tested is a strong variation category, the beam diameter and sample scanning time for spectral analysis of the crude oil to be tested are determined based on the spectral variation characterization parameter, the spectral data acquired within a predetermined time is determined, the spectral data is segmented in the time domain dimension, and the resulting spectral data segments are compared. Whether the spectral analysis meets the standard is determined based on the similarity between the spectral data segments, and the scanning speed of the spectrometer is adjusted to acquire and store the spectral data again.

[0026] Alternatively, the spectrometer is maintained at its initial operating parameters to acquire and store spectral data.

[0027] Furthermore, in step S3, the beam diameter and sample scanning time for spectral analysis of the crude oil to be tested are determined, wherein:

[0028] The beam diameter is negatively correlated with the spectral variation characterization parameter;

[0029] The sample scanning time is correlated with the spectral variation characterization parameter.

[0030] Furthermore, in step S3, the process of determining whether the spectral analysis meets the standard includes:

[0031] If the similarity between the spectral data segments is greater than or equal to the benchmark similarity, the spectral analysis is determined to meet the standard;

[0032] If the similarity between the spectral data segments is less than the reference similarity, it is determined that the spectral analysis does not meet the standard.

[0033] Furthermore, in step S3, the scanning speed of the spectrometer is adjusted, wherein:

[0034] If the mean similarity between the spectral data segments is less than the benchmark similarity, reduce the scanning speed of the spectrometer;

[0035] If the mean similarity between the spectral data segments is greater than or equal to the benchmark similarity, the scanning speed of the spectrometer is maintained.

[0036] Furthermore, in step S4, the process of tracing the origin of the crude oil to be tested includes:

[0037] Call the stored spectrum data of the crude oil to be tested;

[0038] Determine the similarity between the stored spectral data and the spectral data of the corresponding samples from each origin;

[0039] The origin of the sample spectral data corresponding to the maximum similarity is determined to be the origin of the current crude oil to be tested.

[0040] On the other hand, a crude oil origin tracing system is provided, which includes:

[0041] A feature extraction module is used to extract an analytical sample from the crude oil to be tested, perform analysis, and obtain spectral variation characteristics. The analysis includes placing the analytical sample in a detection plane, obtaining particle size information of different regions of the analytical sample to obtain particle size dispersion, and measuring the viscosity of the analytical sample;

[0042] an abnormality response module connected to the sample analysis module and configured to calculate a spectral abnormality characterization parameter for the crude oil to be tested based on the spectral abnormality characteristics, so as to determine the spectral abnormality category of the crude oil to be tested;

[0043] The data analysis module is connected to the abnormality response module and is used to perform spectral analysis based on the spectral abnormality category of the crude oil to be tested and obtain spectral data, including:

[0044] Determining a beam diameter and a sample scanning time for spectral analysis of the crude oil to be tested based on the spectral variation characterization parameter, determining spectral data acquired within a predetermined time, segmenting the spectral data in the time domain, and comparing the resulting spectral data segments. Determining whether the spectral analysis meets the standard based on similarity between the spectral data segments, adjusting the scanning speed of the spectrometer, and acquiring and storing spectral data again.

[0045] Alternatively, maintaining the initial operating parameters of the spectrometer to obtain and store spectral data;

[0046] The origin tracing module is connected to the data analysis module and is used to trace the origin of the current crude oil to be tested based on the similarity comparison results between the stored spectral data and the spectral data of the corresponding samples of each origin.

[0047] Compared with the prior art, the method provided by the present invention includes obtaining spectral variation characteristics, calculating spectral variation characterization parameters for the current crude oil to be tested, determining the spectral variation category of the current crude oil to be tested, determining the beam diameter and sample scanning time when performing spectral analysis on the crude oil to be tested based on the spectral variation characterization parameters, determining the spectral data obtained within a predetermined time, dividing the spectral data in the time domain dimension and comparing the obtained spectral data segments, determining whether the spectral analysis meets the standard based on the similarity between the spectral data segments, adjusting the scanning speed of the spectrometer, obtaining and storing the spectral data again, or maintaining the initial operating parameters of the spectrometer to obtain and store the spectral data, tracing the origin of the current crude oil to be tested based on the similarity comparison results between the stored spectral data and the spectral data of corresponding samples from each origin, thereby improving the accuracy of tracing.

[0048] In particular, the present invention calculates the spectral variation characterization parameters for the current crude oil to be tested. In the process of tracing the origin of crude oil, when the crude oil is subjected to spectral analysis, the particle size dispersion in the crude oil will affect the transmittance and reflectivity of light, and the viscosity will lead to enhanced interactions between particles, causing the spectral data to mutate. In this case, it will cause differences in the analysis results of the spectrometer, resulting in inconsistent spectral data when analyzing the same crude oil. Based on this, the present invention calculates the spectral variation characterization parameters to determine the spectral variation category of the crude oil to be tested, and determines the spectral analysis method suitable for each category of crude oil, thereby improving the targeted crude oil analysis and the accuracy of crude oil origin tracing.

[0049] In particular, the present invention determines the beam diameter and sample scanning time for spectral analysis of the crude oil to be tested based on the spectral variation characterization parameters. In actual analysis, for crude oil to be tested of a strong variation category, if the preset beam diameter and scanning time are still maintained, the spectrometer's analysis results for the crude oil to be tested will be different, resulting in inconsistent spectral data. Based on this, the present invention determines the beam diameter and sample scanning time. Preferably, reducing the beam diameter of the spectrometer can reduce the number of times the crude oil is scattered, and increasing the scanning time can increase the signal-to-noise ratio of the spectral data, thereby improving the targeted crude oil analysis and the accuracy of crude oil origin tracing.

[0050] In particular, the present invention adjusts the scanning speed of the spectrometer so that the spectral analysis meets the standards. In actual situations, during the spectral analysis of crude oil, if the scanning speed of the spectrometer is too fast, it may lead to incomplete signal acquisition. If the scanning speed of the spectrometer is too slow, it may cause the collected data points to change slowly. Both situations will cause some spectral data segments to be abnormal. The abnormal segments may affect the accuracy of crude oil origin tracing. Based on this, the present invention considers adjusting the scanning speed of the spectrometer, re-analyzing and storing the spectral data with abnormal segments, thereby improving the targeted crude oil analysis and the accuracy of crude oil origin tracing. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 This is a schematic diagram of the steps of a method for tracing origin based on big data according to an embodiment of the invention;

[0052] Figure 2 A logic block diagram for determining the spectral variation category of the crude oil to be tested according to an embodiment of the present invention;

[0053] Figure 3 A logic block diagram for determining whether the spectrum analysis of an embodiment of the invention complies with the standard;

[0054] Figure 4 Schematic diagram of the structure of the crude oil origin tracing system according to an embodiment of the invention. DETAILED DESCRIPTION

[0055] In order to make the objects and advantages of the present invention more clearly understood, the present invention is further described below in conjunction with embodiments; it should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0056] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are only used to explain the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0057] It should be noted that, in the description of the present invention, unless otherwise expressly specified or limited, the terms "mounted," "connected," and "connected" should be understood in a broad sense. For example, they may refer to fixed connections, detachable connections, or integral connections; mechanical connections or electrical connections; direct connections or indirect connections through an intermediate medium; and internal connections between two components. Those skilled in the art will understand the specific meanings of the above terms in the present invention based on specific circumstances.

[0058] See also Figures 1 to 4 As shown, Figure 1 This is a schematic diagram of the steps of the method for tracing the origin of an invention based on big data, Figure 2The logic block diagram for determining the spectral variation category of the crude oil to be tested according to the embodiment of the present invention is as follows: Figure 3 To determine whether the spectrum analysis of the embodiment of the invention meets the standard logic block diagram, Figure 4 This is a schematic diagram of the structure of the crude oil origin tracing system according to an embodiment of the invention. The origin tracing method based on big data of the present invention includes:

[0059] In step S1, an analytical sample is extracted from the crude oil to be tested, analyzed, and spectral variation characteristics are obtained. The analysis includes placing the analytical sample in a detection plane, obtaining particle size information of different regions of the analytical sample to obtain particle size dispersion, and measuring the viscosity of the analytical sample. It is understood that there are many types of spectrometers for analyzing the analytical sample. Those skilled in the art can select a spectrometer based on actual conditions, and this will not be described in detail here.

[0060] Step S2, calculating a spectral variation characterization parameter for the current crude oil to be tested based on the spectral variation characteristics to determine the spectral variation category of the current crude oil to be tested;

[0061] Step S3, performing spectral analysis based on the spectral variation category of the crude oil to be tested, and obtaining spectral data, including:

[0062] Determining a beam diameter and a sample scanning time for spectral analysis of the crude oil to be tested based on the spectral variation characterization parameter, determining spectral data acquired within a predetermined time, segmenting the spectral data in the time domain, and comparing the resulting spectral data segments. Determining whether the spectral analysis meets the standard based on similarity between the spectral data segments, adjusting the scanning speed of the spectrometer, and acquiring and storing spectral data again.

[0063] Alternatively, maintaining the initial operating parameters of the spectrometer to obtain and store spectral data;

[0064] Step S4: tracing the origin of the crude oil to be tested based on the similarity comparison result between the stored spectral data and the spectral data of the samples corresponding to each origin.

[0065] Specifically, before analyzing the sample, a series of preparatory work needs to be done, including shaking, etc., to ensure the uniformity of the sample. This is existing technology and will not be described in detail.

[0066] Specifically, there is no limitation on the specific method for determining the similarity of spectral data. For example, the Spectral Angle Mapper (SAM) algorithm can be used to solve the similarity between spectral data. Of course, other methods can also be used, and those skilled in the art can freely choose, which will not be repeated here.

[0067] It can be understood that the scan time refers to the total time required for the spectrometer to complete a full frequency range scan and record all data, and the scan speed is the number of complete spectra that the spectrometer can collect per unit time.

[0068] Specifically, in step S1, the process of determining the particle size dispersion includes:

[0069] Determine the particle size information of different areas in the analysis sample to calculate the variance of the particle size information;

[0070] The square root of the variance was determined as the particle size dispersion.

[0071] Specifically, the particle size information includes the average particle size of the analyzed samples in the region. The particle size information can be measured by a laser particle size analyzer, which will not be described in detail here.

[0072] Specifically, in step S2, the process of calculating the spectral variation characterization parameters for the currently tested crude oil includes:

[0073] Determine the ratio of the reference particle size dispersion to the crude oil particle size dispersion as the first influencing parameter;

[0074] determining the ratio of the crude oil viscosity to the benchmark crude oil viscosity as the second influencing parameter;

[0075] The weighted sum of the first influencing parameter and the second influencing parameter is determined as a spectral variation characterization parameter.

[0076] Specifically, the reference particle size dispersion is pre-set, wherein a number of crude oil samples are obtained in advance for analysis, and the average particle size dispersion of the number of crude oil samples is obtained. The reference particle size dispersion is set within 1.15 times to 1.25 times the average particle size dispersion.

[0077] Specifically, the benchmark crude oil viscosity is pre-set, wherein a number of crude oil samples are obtained in advance for analysis to obtain an average crude oil viscosity of the number of crude oil samples, and the benchmark crude oil viscosity is set within a range of 1.1 times to 1.23 times the average crude oil viscosity.

[0078] Specifically, the weight coefficient of the first influencing parameter is 0.43, and the weight coefficient of the second influencing parameter is 0.57.

[0079] Specifically, the present invention calculates the spectral variation characterization parameters for the current crude oil to be tested. In the process of tracing the origin of crude oil, when performing spectral analysis on the crude oil, the particle size dispersion in the crude oil will affect the transmittance and reflectivity of light, and the viscosity will cause the interaction between particles to increase, causing the spectral data to mutate. In this case, it will cause differences in the analysis results of the spectrometer, resulting in inconsistent spectral data when analyzing the same crude oil. Based on this, the present invention calculates the spectral variation characterization parameters to determine the spectral variation category of the crude oil to be tested, and determines the spectral analysis method suitable for each category of crude oil, thereby improving the targeted crude oil analysis and the accuracy of crude oil origin tracing.

[0080] Specifically, in step S2, the process of determining the spectral variation category of the crude oil to be tested includes:

[0081] If the spectral variation characterization parameter is greater than the spectral variation characterization parameter threshold, the spectral variation category is determined to be a strong variation category;

[0082] If the spectral variation characterization parameter is less than or equal to the spectral variation characterization parameter threshold, the spectral variation category is determined to be a weak variation category.

[0083] Specifically, the threshold of the spectral variation characterization parameter is determined within the interval [1.12, 1.38].

[0084] Specifically, in step S3, spectral analysis is performed according to the spectral variation category of the crude oil to be tested, wherein:

[0085] If the spectral variation category of the crude oil to be tested is a strong variation category, the beam diameter and sample scanning time for spectral analysis of the crude oil to be tested are determined based on the spectral variation characterization parameter, the spectral data acquired within a predetermined time is determined, the spectral data is segmented in the time domain dimension, and the resulting spectral data segments are compared. Whether the spectral analysis meets the standard is determined based on the similarity between the spectral data segments, and the scanning speed of the spectrometer is adjusted to acquire and store the spectral data again.

[0086] Alternatively, the spectrometer is maintained at its initial operating parameters to acquire and store spectral data.

[0087] Specifically, the beam diameter may cause differences in spectral results. A beam diameter that is too large will lead to increased light reflection, which will aggravate the spectral variation effect for crude oil with strong variation. Therefore, the beam diameter is adaptively determined based on the spectral variation characterization parameters.

[0088] Specifically, when the crude oil to be tested is of the highly variable type, the particle size dispersion in the crude oil is high, which can easily affect the transmittance and reflectance of light. The high viscosity will lead to enhanced interactions between particles, affecting the variability of spectral data. Under the influence of the above factors, the scanning time during sample scanning is more critical and can easily affect the accuracy of the measurement results. For example, too little sample scanning time will lead to a decrease in the number of sampling points of the spectral data, affecting the resolution and detail of the spectrum. Therefore, the sample scanning time is determined based on the adaptability of the spectral variation characterization parameters.

[0089] Specifically, the predetermined time is 20% of the sample scanning time.

[0090] Specifically, in step S3, the beam diameter and sample scanning time for spectral analysis of the crude oil to be tested are determined, wherein:

[0091] The beam diameter is negatively correlated with the spectral variation characterization parameter;

[0092] The sample scanning time is positively correlated with the spectral variation characterization parameter.

[0093] Specifically, when the spectral variation characterization parameter is greater than or equal to 1.5 times the spectral variation characterization parameter threshold, the beam diameter is 0.85 times the initial beam diameter, and the sample scanning time is 1.25 times the initial sample scanning time;

[0094] When the spectral variation characterization parameter is greater than 1.2 times the spectral variation characterization parameter threshold and less than 1.5 times the spectral variation characterization parameter threshold, the beam diameter is the initial beam diameter, and the sample scanning time is the initial sample scanning time;

[0095] When the spectral variation characterization parameter is less than or equal to 1.2 times the spectral variation characterization parameter threshold, the beam diameter is 1.25 times the reference beam diameter, and the sample scanning time is 0.85 times the initial sample scanning time.

[0096] Specifically, the present invention determines the beam diameter and sample scanning time when performing spectral analysis on the crude oil to be tested based on the spectral variation characterization parameters. In actual analysis, for the crude oil to be tested of the strong variation category, if the preset beam diameter and scanning time are still maintained, the analysis results of the spectrometer for the crude oil to be tested will be different, resulting in inconsistent spectral data. Based on this, the present invention determines the beam diameter and sample scanning time. Preferably, reducing the beam diameter of the spectrometer can reduce the number of times the crude oil is scattered, and increasing the scanning time can increase the signal-to-noise ratio of the spectral data, thereby improving the targeted crude oil analysis and the accuracy of crude oil origin tracing.

[0097] Specifically, in step S3, the process of determining whether the spectrum analysis meets the standard includes:

[0098] If the similarity between the spectral data segments is greater than or equal to the benchmark similarity, the spectral analysis is determined to meet the standard;

[0099] If the similarity between the spectral data segments is less than the reference similarity, it is determined that the spectral analysis does not meet the standard.

[0100] Specifically, the benchmark similarity is pre-set, wherein a number of spectral data segments are obtained in advance for analysis, and the average similarity between the number of spectral data segments is obtained. The benchmark similarity is set within a range of 0.9 times to 1.2 times the average similarity.

[0101] Specifically, in step S3, the scanning speed of the spectrometer is adjusted, wherein:

[0102] If the mean similarity between spectral data segments is less than the benchmark similarity, reducing the scanning speed of the spectrometer can improve the signal-to-noise ratio, reduce the error of rapid scanning, and reduce spectral variation.

[0103] If the mean similarity between the spectral data segments is greater than or equal to the benchmark similarity, the scanning speed of the spectrometer is maintained.

[0104] Specifically, the present invention adjusts the scanning speed of the spectrometer so that the spectral analysis meets the standards. In actual situations, during the spectral analysis of crude oil, if the scanning speed of the spectrometer is too fast, it may lead to incomplete signal acquisition. If the scanning speed of the spectrometer is too slow, it may cause the collected data points to change slowly. Both situations will cause some spectral data segments to be abnormal. The abnormal segments may affect the accuracy of crude oil origin tracing. Based on this, the present invention considers adjusting the scanning speed of the spectrometer, re-analyzing and storing the spectral data with abnormal segments, thereby improving the targeted crude oil analysis and the accuracy of crude oil origin tracing.

[0105] Specifically, in step S4, the process of tracing the origin of the current crude oil to be tested includes:

[0106] Call the stored spectrum data of the crude oil to be tested;

[0107] Determine the similarity between the stored spectral data and the spectral data of the corresponding samples from each origin;

[0108] The origin of the sample spectral data corresponding to the maximum similarity is determined to be the origin of the current crude oil to be tested.

[0109] The sample spectral data can be obtained by those skilled in the art by pre-collecting spectral data corresponding to crude oil from various origins, which will not be described in detail here.

[0110] It is understandable that, in order to facilitate data acquisition, the collected sample spectral data may be stored in a crude oil fingerprint database, and the sample spectral data corresponds one-to-one to the crude oil production area.

[0111] Specifically, it also includes the crude oil origin traceability system, which includes,

[0112] A feature extraction module is used to extract an analytical sample from the crude oil to be tested, perform analysis, and obtain spectral variation characteristics. The analysis includes placing the analytical sample in a detection plane, obtaining particle size information of different regions of the analytical sample to obtain particle size dispersion, and measuring the viscosity of the analytical sample;

[0113] an abnormality response module connected to the sample analysis module and configured to calculate a spectral abnormality characterization parameter for the crude oil to be tested based on the spectral abnormality characteristics, so as to determine the spectral abnormality category of the crude oil to be tested;

[0114] The data analysis module is connected to the abnormality response module and is used to perform spectral analysis based on the spectral abnormality category of the crude oil to be tested and obtain spectral data, including:

[0115] Determining a beam diameter and a sample scanning time for spectral analysis of the crude oil to be tested based on the spectral variation characterization parameter, determining spectral data acquired within a predetermined time, segmenting the spectral data in the time domain, and comparing the resulting spectral data segments. Determining whether the spectral analysis meets the standard based on similarity between the spectral data segments, adjusting the scanning speed of the spectrometer, and acquiring and storing spectral data again.

[0116] Alternatively, maintaining the initial operating parameters of the spectrometer to obtain and store spectral data;

[0117] The origin tracing module is connected to the data analysis module and is used to trace the origin of the current crude oil to be tested based on the similarity comparison results between the stored spectral data and the spectral data of the corresponding samples of each origin.

[0118] Thus far, the technical solutions of the present invention have been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art may make equivalent changes or substitutions to the relevant technical features, and the technical solutions after such changes or substitutions will fall within the scope of protection of the present invention.

Claims

1. A method for tracing origin based on big data, characterized in that: include: Step S1, extracting an analysis sample from the crude oil to be tested, analyzing it, and obtaining spectral variation characteristics. The analysis includes placing the analysis sample in a detection plane, obtaining particle size information of different regions of the analysis sample to obtain particle size dispersion, and measuring the viscosity of the analysis sample. Step S2, calculating a spectral variation characterization parameter for the current crude oil to be tested based on the spectral variation characteristics to determine the spectral variation category of the current crude oil to be tested; Step S3, performing spectral analysis based on the spectral variation category of the crude oil to be tested, and obtaining spectral data, including: Determining a beam diameter and a sample scanning time for spectral analysis of the crude oil to be tested based on the spectral variation characterization parameter, determining spectral data acquired within a predetermined time, segmenting the spectral data in the time domain, and comparing the resulting spectral data segments. Determining whether the spectral analysis meets the standard based on similarity between the spectral data segments, adjusting the scanning speed of the spectrometer, and acquiring and storing spectral data again. Alternatively, maintaining the initial operating parameters of the spectrometer to obtain and store spectral data; Step S4, tracing the origin of the current crude oil to be tested based on the similarity comparison result between the stored spectral data and the spectral data of the samples corresponding to each origin; In step S2, the process of calculating the spectral variation characterization parameter for the currently tested crude oil includes: Determine the ratio of the reference particle size dispersion to the crude oil particle size dispersion as the first influencing parameter; determining the ratio of the crude oil viscosity to the benchmark crude oil viscosity as the second influencing parameter; The weighted sum of the first influencing parameter and the second influencing parameter is determined as a spectral variation characterization parameter.

2. The origin tracing method based on big data according to claim 1 is characterized in that: In step S1, the process of determining the particle size dispersion includes: Determine the particle size information of different areas in the analysis sample to calculate the variance of the particle size information; The square root of the variance was determined as the particle size dispersion.

3. The origin tracing method based on big data according to claim 1, characterized in that: In step S2, the process of determining the spectral variation category of the crude oil to be tested includes: If the spectral variation characterization parameter is greater than the spectral variation characterization parameter threshold, the spectral variation category is determined to be a strong variation category; If the spectral variation characterization parameter is less than or equal to the spectral variation characterization parameter threshold, the spectral variation category is determined to be a weak variation category.

4. The origin tracing method based on big data according to claim 1, characterized in that: In step S3, spectral analysis is performed according to the spectral variation category of the crude oil to be tested, wherein: If the spectral variation category of the crude oil to be tested is a strong variation category, the beam diameter and sample scanning time for spectral analysis of the crude oil to be tested are determined based on the spectral variation characterization parameter, the spectral data acquired within a predetermined time is determined, the spectral data is segmented in the time domain dimension, and the resulting spectral data segments are compared. Whether the spectral analysis meets the standard is determined based on the similarity between the spectral data segments, and the scanning speed of the spectrometer is adjusted to acquire and store the spectral data again. Alternatively, the spectrometer is maintained at its initial operating parameters to acquire and store spectral data.

5. The origin tracing method based on big data according to claim 1 is characterized in that: In step S3, the beam diameter and sample scanning time for spectral analysis of the crude oil to be tested are determined, wherein: The beam diameter is negatively correlated with the spectral variation characterization parameter; The sample scanning time is positively correlated with the spectral variation characterization parameter.

6. The origin tracing method based on big data according to claim 1, characterized in that: In step S3, the process of determining whether the spectrum analysis meets the standard includes: If the similarity between the spectral data segments is greater than or equal to the benchmark similarity, the spectral analysis is determined to meet the standard; If the similarity between the spectral data segments is less than the reference similarity, it is determined that the spectral analysis does not meet the standard.

7. The origin tracing method based on big data according to claim 1, characterized in that: In step S3, the scanning speed of the spectrometer is adjusted, wherein: If the mean similarity between the spectral data segments is less than the benchmark similarity, reduce the scanning speed of the spectrometer; If the mean similarity between the spectral data segments is greater than or equal to the benchmark similarity, the scanning speed of the spectrometer is maintained.

8. The origin tracing method based on big data according to claim 1, characterized in that: In step S4, the process of tracing the origin of the crude oil to be tested includes: Call the stored spectrum data of the crude oil to be tested; Determine the similarity between the stored spectral data and the spectral data of the corresponding samples from each origin; The origin of the sample spectral data corresponding to the maximum similarity is determined to be the origin of the current crude oil to be tested.

9. A system using the big data-based origin tracing method according to any one of claims 1 to 8, characterized in that: include, A feature extraction module is used to extract an analytical sample from the crude oil to be tested, perform analysis, and obtain spectral variation characteristics. The analysis includes placing the analytical sample in a detection plane, obtaining particle size information of different regions of the analytical sample to obtain particle size dispersion, and measuring the viscosity of the analytical sample; an abnormality response module connected to the sample analysis module and configured to calculate a spectral abnormality characterization parameter for the crude oil to be tested based on the spectral abnormality characteristics, so as to determine the spectral abnormality category of the crude oil to be tested; The data analysis module is connected to the abnormality response module and is used to perform spectral analysis based on the spectral abnormality category of the crude oil to be tested and obtain spectral data, including: Determining a beam diameter and a sample scanning time for spectral analysis of the crude oil to be tested based on the spectral variation characterization parameter, determining spectral data acquired within a predetermined time, segmenting the spectral data in the time domain, and comparing the resulting spectral data segments. Determining whether the spectral analysis meets the standard based on similarity between the spectral data segments, adjusting the scanning speed of the spectrometer, and acquiring and storing spectral data again. Alternatively, maintaining the initial operating parameters of the spectrometer to obtain and store spectral data; The origin tracing module is connected to the data analysis module and is used to trace the origin of the current crude oil to be tested based on the similarity comparison results between the stored spectral data and the spectral data of the corresponding samples of each origin.

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