A method, device, electronic device and storage medium for detecting quality indicators of synthetic hydrocarbon components produced by HEFA process

Abstract

The present invention provides a detection method, device, electronic device and storage medium for quality index of synthetic hydrocarbon components produced by HEFA process, the detection method comprising: performing infrared spectrum detection on non-petroleum-based synthetic hydrocarbon component samples to obtain a spectrum data matrix; measuring the quality index of non-petroleum-based synthetic hydrocarbon component samples to obtain a quality index matrix; decomposing the spectrum data matrix to obtain high-frequency components and low-frequency components, and calculating change characteristics; calculating a weighted feature matrix according to the high-frequency components, low-frequency components and change characteristics; constructing a prediction model according to the weighted feature matrix and the quality index matrix; testing the product of synthetic hydrocarbon components produced by HEFA process by infrared spectrum, bringing into the prediction model, and obtaining quality indexes. The detection method has high detection efficiency, is easy to operate, and is suitable for various production environments.

Description

Technical Field

[0001] The invention belongs to the field of fuel manufacturing, and relates to a method, a device, an electronic device and a storage medium for detecting quality indicators of synthetic hydrocarbon components produced by a HEFA process. Background Art

[0002] As the aviation industry continues to increase its requirements for carbon emission reduction, sustainable aviation fuel (SAF) has gradually become an important alternative to aviation fuel. Among them, HEFA-SPK (hydrogenated esters and fatty acids synthetic paraffin jet fuel) has become one of the most widely used SAF types due to its mature process, wide source of raw materials and good performance. HEFA-SPK is prepared by hydrogenating esters and fatty acids in bio-based raw materials (such as vegetable oils, animal fats, waste oils, etc.). Its quality characteristics including density, freezing point, flash point, distillation range, etc. directly affect its blending performance with traditional jet fuel and its safety of use. EW

[0003] In the production and application of aviation fuel, rapid detection of HEFA-SPK quality characteristics is crucial. However, most existing detection technologies rely on national standard methods. Although these methods are highly accurate, they have problems such as low efficiency, complex operation, and poor field adaptability, making it difficult to meet the needs of rapid quality detection of HEFA-SPK. Summary of the invention

[0004] In order to solve the above technical problems, the present application provides a method, device, electronic equipment and storage medium for detecting quality indicators of synthetic hydrocarbon components produced by HEFA process. The detection method has high detection efficiency, simple operation and is suitable for various production environments.

[0005] In order to achieve the above technical effects, the present invention adopts the following technical solutions:

[0006] One of the purposes of the present invention is to provide a method for detecting the quality index of a synthetic hydrocarbon component produced by a HEFA process, the method comprising:

[0007] Conduct infrared spectroscopy detection on non-petroleum-based synthetic hydrocarbon component samples to obtain a spectral data matrix;

[0008] The quality indexes of non-petroleum-based synthetic hydrocarbon component samples were measured to obtain a quality index matrix;

[0009] Decomposing the spectral data matrix to obtain high-frequency components and low-frequency components, and calculating change characteristics;

[0010] Calculating a weighted feature matrix according to the high frequency component, the low frequency component and the variation feature;

[0011] Constructing a prediction model based on the weighted feature matrix and the quality indicator matrix;

[0012] The products of synthetic hydrocarbon components produced according to the HEFA process are tested by infrared spectroscopy and brought into the prediction model to obtain quality indicators.

[0013] As a preferred technical solution of the present invention, the quality index includes at least one of density, freezing point, flash point and distillation range.

[0014] And / or, the density, freezing point, flash point and distillation range are measured using national standards.

[0015] As a preferred technical solution of the present invention, before decomposing the spectral data matrix, a smoothing algorithm and a second-order derivative method are used to perform noise removal on the spectral data matrix.

[0016] As a preferred technical solution of the present invention, wavelet decomposition is used to obtain the high-frequency component and the low-frequency component.

[0017] As a preferred technical solution of the present invention, a differential method is used to calculate the change characteristics.

[0018] As a preferred technical solution of the present invention, the calculation method of the weighted feature matrix is ​​shown in the following formula:

[0019] X weighted =αX low +βX high +γΔX

[0020] Among them, X weighted is the weighted feature matrix, X low is the low frequency component, X high is the high frequency component, △X is the change feature, and α, β and γ are weight coefficients.

[0021] As a preferred technical solution of the present invention, the prediction model is shown as follows:

[0022]

[0023] Among them, K(X,X weighted,i )=exp(-‖XX weighted,i ‖ 2 ), θ i is the maximum value among α, β and γ, X is the infrared spectrum test result of the sample to be tested, n is the total number of samples, Y k is the target value of the k-th support vector, K(X k ,X i ) is the kernel value between the kth support vector and the ith support vector, Y pred Predict results for quality indicators.

[0024] The second object of the present invention is to provide a detection device for the quality index of the synthetic hydrocarbon component produced by the HEFA process, the detection device comprising:

[0025] A detection module for performing infrared spectroscopy detection and quality index determination on non-petroleum-based synthetic hydrocarbon component samples;

[0026] A feature extraction module, used for decomposing the spectral data matrix to obtain high-frequency components and low-frequency components, calculating change characteristics, and calculating a weighted feature matrix according to the high-frequency components, low-frequency components and change characteristics;

[0027] A model training module, used for building a prediction model according to the weighted feature matrix and the quality indicator matrix;

[0028] The output module is used to test the infrared spectrum of the product of synthetic hydrocarbon components produced according to the HEFA process, bring it into the prediction model, and obtain quality indicators.

[0029] A third object of the present invention is to provide an electronic device, the electronic device comprising:

[0030] at least one processor; and a memory communicatively coupled to the at least one processor;

[0031] Wherein, the memory stores a computer program that can be executed by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can perform the method for detecting quality indicators of synthetic hydrocarbon components produced by the HEFA process provided in one of the purposes.

[0032] The fourth object of the present invention is to provide a computer storage medium, in which computer executable instructions are stored. When the computer executable instructions are executed by a processor, the method for detecting quality indicators of synthetic hydrocarbon components produced by the HEFA process provided in the first object is implemented.

[0033] Compared with the prior art, the present invention has at least the following beneficial effects:

[0034] (1) The present invention provides a method for detecting quality indicators of synthetic hydrocarbon components produced by the HEFA process. The method can complete the simultaneous prediction of multiple quality indicators in a short time, significantly shorten the detection time, support on-site real-time monitoring and dynamic adjustment, and meet the production and on-site detection needs;

[0035] (2) The present invention provides a method for detecting the quality index of synthetic hydrocarbon components produced by the HEFA process. The method does not require complicated sample processing or professional skills, and the spectrometer is easy to operate and is suitable for daily detection in large-scale production;

[0036] (3) The present invention provides a method for detecting the quality index of the synthetic hydrocarbon components produced by the HEFA process. The method can use a portable near-infrared spectrometer, which has good portability and can achieve efficient detection in on-site environments such as oil depots and blending stations;

[0037] (4) The present invention provides a method for detecting the quality indicators of synthetic hydrocarbon components produced by the HEFA process. The method can correct the detection deviation caused by equipment or environmental changes by introducing a dynamic calibration mechanism to ensure the consistency of results in long-term use. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 Schematic diagram of an electronic device provided in Example 3.

[0039] In the figure, 10 - electronic device, 11 - processor, 12 - ROM, 13 - RAM, 14 - bus, 15 - I / O interface, 16 - input unit, 17 - output unit, 18 - storage unit, 19 - communication unit.

[0040] The present invention is further described in detail below. However, the following examples are only simplified examples of the present invention and do not represent or limit the scope of protection of the present invention. The scope of protection of the present invention shall be subject to the claims. DETAILED DESCRIPTION

[0041] The technical solution of the present application is further described below through specific implementation methods.

[0042] The specific embodiment of the present invention provides a method for detecting the quality index of the synthetic hydrocarbon component produced by the HEFA process, and the detection method comprises:

[0043] Conduct infrared spectroscopy detection on non-petroleum-based synthetic hydrocarbon component samples to obtain a spectral data matrix;

[0044] The quality indexes of non-petroleum-based synthetic hydrocarbon component samples were measured to obtain a quality index matrix;

[0045] Decomposing the spectral data matrix to obtain high-frequency components and low-frequency components, and calculating change characteristics;

[0046] Calculating a weighted feature matrix according to the high frequency component, the low frequency component and the variation feature;

[0047] Constructing a prediction model based on the weighted feature matrix and the quality indicator matrix;

[0048] The products of synthetic hydrocarbon components produced according to the HEFA process are tested by infrared spectroscopy and brought into the prediction model to obtain quality indicators.

[0049] In one specific embodiment of the present invention, representative HEFA-SPK samples are selected to ensure diverse sample sources.

[0050] In a specific embodiment of the present invention, the optical path difference of the sample cell can be selected according to the properties of the HEFA-SPK sample, and is not specifically limited here. Preferably, the sample is injected into a sample cell with an optical path difference of 1 mm.

[0051] In one embodiment of the present invention, the wave number range of infrared spectrum detection can be 11000~4000cm -1 .

[0052] In a specific embodiment of the present invention, the generated spectral data matrix is ​​as follows:

[0053]

[0054] Where n is the number of samples and p is the number of wave variables.

[0055] In a specific implementation manner of the present invention, the light source of the infrared spectrometer is preheated by testing the intensity, and the preheating time may be about 20 minutes.

[0056] In a specific embodiment of the present invention, each sample should be repeatedly collected to eliminate random noise as much as possible, and the number of collections may be 64 times, etc.

[0057] In a specific embodiment of the present invention, the number of collected data points for each sample should be as large as possible, such as more than 1200.

[0058] In one specific embodiment of the present invention, the number of HEFA-SPK samples is more than 100.

[0059] In a specific embodiment of the present invention, the quality index includes at least one of density, freezing point, flash point and distillation range.

[0060] In a specific implementation manner of the present invention, the density, freezing point, flash point and distillation range are measured using national standards.

[0061] In a specific embodiment of the present invention, the density is detected by GB / T 1884-2000, the freezing point is detected by GB / T 2430-2008, the flash point is detected by GB / T 261-2008, and the distillation range is detected by GB / T 6536-2010.

[0062] In a specific embodiment of the present invention, before the spectral data matrix is ​​decomposed, a smoothing algorithm and a second-order derivative method are used to perform noise removal on the spectral data matrix.

[0063] In a specific embodiment of the present invention, the original spectral data is preprocessed using the Savitzky-Golay smoothing algorithm and the second-order derivative method.

[0064] In a specific implementation of the present invention, in the Savitzky-Golay smoothing algorithm, Xsmooth=SGFilter(X,w,d), where w is the window size and d is the polynomial order. For example, w can be 5 and d can be 3.

[0065] In a specific embodiment of the present invention, the pre-processed data should eliminate system noise, environmental interference and background differences.

[0066] In a specific implementation manner of the present invention, wavelet decomposition is used to obtain the high-frequency component and the low-frequency component.

[0067] In a specific implementation manner of the present invention, the wavelet decomposition formula is X low ,X high =W(X,ψ), where X low is the low frequency component, X high is the high frequency component, W is the weight matrix, X is the decomposition variable (spectral data matrix), and ψ is the scaling function.

[0068] In a specific implementation of the present invention, the low-frequency component and the high-frequency component can be calculated by wavelet basis function Daubechies and convolution function conv.

[0069] In a specific implementation of the present invention, a differential method is used to calculate the change characteristics.

[0070] In one specific embodiment of the present invention, the formula for calculating the change characteristics by the difference method is ΔX=X i+1 -X i , where ΔX is the change characteristic, X i+1 and X i are the i+1th and ith data in the spectral data matrix, where i is an integer greater than 0.

[0071] In a specific implementation of the present invention, the calculation method of the weighted feature matrix is ​​shown in the following formula:

[0072] X weighted =αX low +βX high +γΔX

[0073] Among them, X weighted is the weighted feature matrix, X low is the low frequency component, X high is the high frequency component, △X is the change feature, and α, β and γ are weight coefficients.

[0074] In a specific embodiment of the present invention, the calculation formulas of α, β and γ can be:

[0075]

[0076]

[0077]

[0078] Among them, var is the variance function.

[0079] In a specific embodiment of the present invention, the prediction model is shown as follows:

[0080]

[0081] Among them, K(X,X weighted,i )=exp(-‖XX weighted,i ‖ 2 ), θ i is the maximum value among α, β and γ, X is the infrared spectrum test result of the sample to be tested, n is the total number of samples, Y k is the target value of the k-th support vector, K(X k ,X i ) is the kernel value between the kth support vector and the ith support vector, Y pred Predict results for quality indicators.

[0082] In a specific embodiment of the present invention, a dynamic calibration mechanism can be introduced to correct detection deviations caused by changes in equipment or environment, thereby ensuring consistency of results during long-term use.

[0083] In one embodiment of the present invention, a sample calibration model with known physical and chemical properties can be used, and the formula is: Where W old is the current weight matrix, W new is the updated weight matrix, η is the learning rate (adjustment step size), and L is the loss function (reflecting the difference between the predicted value and the true value) is the gradient of the loss function with respect to the weight.

[0084] For the convenience of understanding the present invention, the present invention lists the following embodiments. It should be understood by those skilled in the art that the embodiments are only to help understand the present invention and should not be regarded as specific limitations of the present invention.

[0085] Example 1

[0086] This embodiment provides a method for detecting quality indicators of synthetic hydrocarbon components produced by HEFA process, the detection method comprising:

[0087] Conduct infrared spectrum detection on a non-petroleum-based synthetic hydrocarbon component sample to obtain a spectrum data matrix;

[0088] The sample was injected into a 1 mm optical path difference sample cell and a near infrared spectrometer was used to analyze the sample at 11000-4000 cm -1 Transmission spectrum data were collected within the wavenumber range. The light source should be preheated for 20 minutes. The data were collected 64 times for each sample and the average spectrum was taken to reduce random noise. 1200 data points were collected for each sample and 100 samples were collected for HEFA-SPK.

[0089] The Savitzky-Golay smoothing algorithm and the second-order derivative method are used for preprocessing. In the Savitzky-Golay smoothing algorithm, Xsmooth = SGFilter (X, w, d), where w is the window size and d is the polynomial order. w is 5 and d is 3, and X is obtained. smooth .

[0090] The density of each sample is tested and the density matrix is ​​obtained as follows:

[0091] Y=[788.2,790.1,794.1,792.9,…,792.1,790.2,793.7]

[0092] The difference method calculates the change characteristics, ΔX = X i+1 -X i , we get (taking the first 10 values ​​as an example):

[0093] ΔX=[-0.0249,-0.0122,-0.0021,0.0056,0.0109,0.0079,0.0159,0.0139,0.0108,0.0105]

[0094] Low frequency component: X low =Conv(Xsmooth,h[n]);

[0095] High frequency component: X high =Conv(Xsmooth,g[n]);

[0096] The filters h[n] and g[n] are automatically generated based on the selected wavelet basis function Daubechies;

[0097] X low =[0.5058,0.4980,0.4941,0.4934,0.4952,0.4986,0.5011,0.5062,0.5106,0.5140];

[0098] X high=[1.3869,1.3620,1.3498,1.3477,1.3533,1.3643,1.3722,1.3880,1.4019,1.4127];

[0099] Calculate the weighted feature matrix, X weighted =αX low +βX high +γΔX

[0100]

[0101] α=0.0966, β=0.9012, γ=0.0022, we get:

[0102] X weighted =[1.2987,1.2755,1.2642,1.2622,1.2675,1.2776,1.2850,1.2998,1.3128,1.3228]

[0104] The prediction model is as follows:

[0105]

[0106] Among them, K(X,X weighted,i )=exp(-‖XX weighted,i ‖ 2 ),

[0107] but

[0108] The infrared spectrum test results of some samples obtained by actual detection are brought into the above prediction model and compared with the actual test results. The results are shown in Table 1.

[0109] Table 1

[0110] Sample No. Measured value Predicted value Difference 1 792.0 791.5 0.5 2 790.3 790.6 0.3 3 789.9 790.2 0.3 4 792.3 791.9 0.4 5 788.9 789.9 1.0 6 792.3 792.0 0.3 7 792.1 792.0 0.1

[0111] It can be seen from the test results in Table 1 that the prediction model given in Example 1 is used to predict the density of the sample with high prediction accuracy, and the difference with the measured value is within 1.0, and the minimum difference can be as low as 0.1.

[0112] Example 2

[0113] This embodiment provides a device for detecting the quality index of a component of synthetic hydrocarbon produced by a HEFA process. The device is used in the method for detecting the quality index of a component of synthetic hydrocarbon produced by a HEFA process in Embodiment 1. The detection device comprises:

[0114] A detection module for performing infrared spectroscopy detection and quality index determination on non-petroleum-based synthetic hydrocarbon component samples;

[0115] A feature extraction module, used for decomposing the spectral data matrix to obtain high-frequency components and low-frequency components, calculating change characteristics, and calculating a weighted feature matrix according to the high-frequency components, low-frequency components and change characteristics;

[0116] A model training module, used for building a prediction model based on the weighted feature matrix and the quality indicator matrix;

[0117] The output module is used to test the infrared spectrum of the product of the synthetic hydrocarbon component produced according to the HEFA process, bring it into the prediction model, and obtain the quality index.

[0118] Example 3

[0119] This embodiment provides an electronic device, such as Figure 1 , which is a schematic diagram of the structure of an electronic device 10 for implementing an embodiment of the present invention. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0120] like Figure 1 As shown, the electronic device 10 includes at least one processor 11, and a memory connected to the at least one processor 11, such as a read-only memory ROM12, a random access memory RAM13, etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory ROM12 or the computer program loaded from the storage unit 18 to the random access memory RAM13. In RAM13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, ROM12 and RAM13 are connected to each other through a bus 14; the I / O interface 15 is also connected to the bus 14.

[0121] A number of components in the electronic device 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0122] The processor 11 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The processor 11 executes the detection method for the quality index of the synthetic hydrocarbon component produced by the HEFA process provided in Example 1.

[0123] In some embodiments, the method for detecting the quality index of the synthetic hydrocarbon component produced by the HEFA process can be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed on the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the method for detecting the quality index of the synthetic hydrocarbon component produced by the HEFA process can be performed. Alternatively, in other embodiments, the processor 11 can be configured as a method for detecting the quality index of the synthetic hydrocarbon component produced by the HEFA process by any other appropriate means (for example, by means of firmware).

[0124] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0125] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that when the computer program is executed by the processor, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer program may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0126] In the context of the present invention, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in combination with an instruction execution system, device or equipment. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0127] To provide interaction with a user, the systems and techniques described herein may be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0128] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0129] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The client and server relationship is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services.

[0130] The applicant declares that the present invention illustrates the detailed structural features of the present invention through the above-mentioned embodiments, but the present invention is not limited to the above-mentioned detailed structural features, that is, it does not mean that the present invention must rely on the above-mentioned detailed structural features to be implemented. Those skilled in the art should understand that any improvement of the present invention, equivalent replacement of the components selected by the present invention, addition of auxiliary components, selection of specific methods, etc., all fall within the protection scope and disclosure scope of the present invention.

[0131] The preferred embodiments of the present invention are described in detail above. However, the present invention is not limited to the specific details in the above embodiments. Within the technical concept of the present invention, a variety of simple modifications can be made to the technical solution of the present invention, and these simple modifications all belong to the protection scope of the present invention.

[0132] It should also be noted that the various specific technical features described in the above specific embodiments can be combined in any suitable manner without contradiction. In order to avoid unnecessary repetition, the present invention will not further describe various possible combinations.

[0133] In addition, various embodiments of the present invention may be arbitrarily combined, and as long as they do not violate the concept of the present invention, they should also be regarded as the contents disclosed by the present invention.

Claims

1. A method for detecting quality indicators of synthetic hydrocarbon components produced by HEFA process, characterized in that: The detection method comprises: Conduct infrared spectroscopy detection on non-petroleum-based synthetic hydrocarbon component samples to obtain a spectral data matrix; The quality indexes of non-petroleum-based synthetic hydrocarbon component samples were measured to obtain a quality index matrix; Decomposing the spectral data matrix to obtain high-frequency components and low-frequency components, and calculating change characteristics; Calculating a weighted feature matrix according to the high frequency component, the low frequency component and the variation feature; Constructing a prediction model based on the weighted feature matrix and the quality indicator matrix; The products of synthetic hydrocarbon components produced according to the HEFA process are tested by infrared spectroscopy and brought into the prediction model to obtain quality indicators.

2. The detection method according to claim 1, characterized in that: The quality index includes at least one of density, freezing point, flash point and distillation range; And / or, the density, freezing point, flash point and distillation range are measured using national standards.

3. The detection method according to claim 1, characterized in that: Before decomposing the spectral data matrix, a smoothing algorithm and a second-order derivative method are used to perform noise removal on the spectral data matrix.

4. The detection method according to claim 1, characterized in that: The high frequency component and the low frequency component are obtained by wavelet decomposition.

5. The detection method according to claim 1, characterized in that: The variation characteristics are calculated using a difference method.

6. The detection method according to claim 1, characterized in that: The calculation method of the weighted feature matrix is ​​shown in the following formula: X weighted =αX low +βX high +γΔX Among them, X weighted is the weighted feature matrix, X low is the low frequency component, X high is the high frequency component, △X is the change feature, and α, β and γ are weight coefficients.

7. The detection method according to claim 6, characterized in that: The prediction model is shown as follows: Among them, K(X,X weighted,i )=exp(-||XX weighted,i || 2 ), θ i is the maximum value among α, β and γ, X is the infrared spectrum test result of the sample to be tested, n is the total number of samples, Y k is the target value of the k-th support vector, K(X k ,X i ) is the kernel value between the kth support vector and the ith support vector, Y pred Predict results for quality indicators.

8. A device for detecting quality indicators of synthetic hydrocarbon components produced by HEFA process, characterized in that: The detection device comprises: A detection module for performing infrared spectroscopy detection and quality index determination on non-petroleum-based synthetic hydrocarbon component samples; A feature extraction module, used for decomposing the spectral data matrix to obtain high-frequency components and low-frequency components, calculating change characteristics, and calculating a weighted feature matrix according to the high-frequency components, low-frequency components and change characteristics; A model training module, used for building a prediction model based on the weighted feature matrix and the quality indicator matrix; The output module is used to test the infrared spectrum of the product of the synthetic hydrocarbon component produced according to the HEFA process, bring it into the prediction model, and obtain the quality index.

9. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively coupled to the at least one processor; Wherein, the memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the method for detecting quality indicators of synthetic hydrocarbon components produced by the HEFA process as described in any one of claims 1-7.

10. A computer storage medium, characterized in that: The computer storage medium stores computer executable instructions, which, when executed by a processor, implement the method for detecting quality indicators of synthetic hydrocarbon components produced by the HEFA process according to any one of claims 1 to 7.

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