Component analysis method, device and equipment and readable storage medium

Abstract

The invention discloses a component analysis method, device and equipment and a readable storage medium. The method comprises the following steps: acquiring first spectral data of a to-be-analyzed substance and second spectral data of each pure substance; synthesizing a penalty coefficient used for representing a peak position distance between the first spectral data and each second spectral data, a weight coefficient used for representing peak value information of each characteristic peak in the first spectral data, and a distribution coefficient used for representing position uniqueness of each characteristic peak in the first spectral data; evaluating the matching degree of the first spectral data and the corresponding second spectral data; and performing similar identification on each piece of second spectral data and the first spectral data of which the corresponding matching degree meets a matching condition, and taking a pure substance of the second spectral data similar to the first spectral data as each component of the to-be-analyzed substance. Therefore, according to the method, the peak position distance, the peak value size and the peak position uniqueness can be synthesized, the pure substance is subjected to primary screening, then the pure substance is subjected to secondary screening through similarity recognition, and the accuracy of component recognition is improved.

Description

Technical Field

[0001] The present application relates to the technical field of component analysis, and more specifically, to a component analysis method, device, equipment and readable storage medium. Background Art

[0002] Identification of material components is of indispensable importance in many fields. For example, in the food processing industry, by continuously monitoring product ingredients, abnormal ingredients can be discovered in a timely manner, quality problems and food safety hazards can be prevented, and consumer health can be protected. In environmental protection, by analyzing the composition and content of heavy metals and organic matter in contaminated soil, the source of pollution can be traced, and targeted control measures can be taken to prevent further spread of pollution.

[0003] In the prior art, attempts are usually made to divide the gears according to the peak intensity, and then evaluate whether the mixture contains the corresponding pure substance based on the intersection between the strong peak gear of the mixture and the strong peak gear of the pure substance. However, this evaluation method has obvious limitations. It only focuses on the strong peak gear and completely ignores the important factor of the weak peak gear. Since the weak peak gear is not taken into consideration, it is very likely that the components will be misscreened or missed in the actual operation process. Once these situations occur, it will further lead to errors in the identification of material components, affecting the accuracy of the final identification results, and bringing adverse effects to subsequent related work.

[0004] In view of this, how to provide an effective component analysis method has naturally become a key issue that technicians in this field focus on and urgently need to overcome. Summary of the invention

[0005] In view of this, the present application provides a component analysis method, device, equipment and readable storage medium to solve the shortcomings of component identification in existing material component identification technology that is prone to errors.

[0006] In order to achieve the above objectives, the proposed solution is as follows:

[0007] A component analysis method, comprising:

[0008] Acquire first spectrum data of the substance to be analyzed, and second spectrum data of each pure substance;

[0009] Determining a penalty coefficient for characterizing a peak position distance between the first spectrum data and each second spectrum data;

[0010] Determining a weight coefficient for characterizing peak value information of each characteristic peak in the first spectral data;

[0011] Determining a distribution coefficient for characterizing the uniqueness of each characteristic peak position in the first spectral data;

[0012] Comprehensively evaluating the matching degree between the first spectral data and the corresponding second spectral data by combining the weight coefficient of the first spectral data, the distribution coefficient, and the penalty coefficient of the first spectral data and each second spectral data;

[0013] Each second spectral data whose corresponding matching degree meets the preset matching condition is similarly identified with the first spectral data, one or more second spectral data similar to the first spectral data are determined, and the pure substances corresponding to the determined second spectral data are used as the components of the substance to be analyzed.

[0014] Optionally, the determining of a penalty coefficient for characterizing a peak distance between the first spectrum data and each second spectrum data includes:

[0015] For each second spectral data, the number of characteristic peaks of the second spectral data is determined, and with reference to the number of characteristic peaks, based on the peak distance between each characteristic peak in the first spectral data and each characteristic peak of the second spectral data, a penalty coefficient corresponding to the first spectral data and the second spectral data is generated.

[0016] Optionally, referring to the number of characteristic peaks and based on the peak distance between each characteristic peak in the first spectral data and each characteristic peak in the second spectral data, generating a penalty coefficient corresponding to the first spectral data and the second spectral data includes:

[0017] When the number of the characteristic peaks is 1, a preset first penalty formula is obtained, and the penalty coefficients corresponding to the first spectral data and the second spectral data are calculated by combining the first penalty formula and the peak distance between each characteristic peak in the first spectral data and each characteristic peak in the second spectral data;

[0018] When the number of the characteristic peaks is more than two, a preset second penalty formula is obtained, and the penalty coefficients corresponding to the first spectral data and the second spectral data are calculated by combining the second penalty formula and the peak distance between each characteristic peak in the first spectral data and each characteristic peak in the second spectral data;

[0019] The first penalty formula is as follows:

[0020]

[0021] The second penalty formula is as follows:

[0022]

[0023] Among them, clim, and is a preset constant; delta i is the peak distance between the i-th characteristic peak of the first spectrum data and each corresponding characteristic peak of the second spectrum data; is the penalty coefficient corresponding to the first spectral data and the corresponding second spectral data.

[0024] Optionally, determining a weight coefficient for characterizing peak value information of each characteristic peak in the first spectral data includes:

[0025] Determine the peak value of each characteristic peak in the first spectral data;

[0026] Obtaining a preset comparison weight expression, and combining the peak value of each characteristic peak and the comparison weight expression, calculating a weight coefficient reflecting the peak value information of each characteristic peak in the first spectral data;

[0027] The comparison weight expression is as follows:

[0028]

[0029] in, is the weight coefficient of the i-th characteristic peak of the first spectrum data; and c is a preset constant; is the peak value of the i-th characteristic peak of the first spectral data.

[0030] Optionally, the determining of a distribution coefficient for characterizing the uniqueness of the position of each characteristic peak in the first spectral data includes:

[0031] Determine the wavelength band interval where each characteristic peak of each second spectrum data is located;

[0032] Combine the band intervals corresponding to each characteristic peak and count the number of peaks corresponding to each band interval;

[0033] Based on the number of peaks corresponding to each band interval, the probability of the characteristic peak appearing in each band interval is determined, and based on the probability corresponding to each band interval, the uniqueness of the characteristic peak corresponding to each band interval is determined;

[0034] Determine the wavelength range corresponding to each characteristic peak in the first spectral data;

[0035] The uniqueness of the characteristic peak of each band range matching band interval is used as the distribution coefficient of the corresponding characteristic peak in the first spectrum data.

[0036] Optionally, the step of comprehensively evaluating the matching degree between the first spectral data and the corresponding second spectral data by combining the weight coefficient of the first spectral data, the distribution coefficient, and the penalty coefficient of the first spectral data and each second spectral data includes:

[0037] In combination with a preset spectrum matching expression, based on the weight coefficient of the first spectrum data, the distribution coefficient, and the penalty coefficient of the first spectrum data and each second spectrum data, a matching degree between the first spectrum data and the corresponding second spectrum data is calculated;

[0038] The spectrum matching expression is as follows:

[0039]

[0040] in, is the matching degree between the first spectral data and the corresponding second spectral data; is a penalty coefficient corresponding to the first spectral data and the corresponding second spectral data; is the weight coefficient of the i-th characteristic peak of the first spectrum data; is the distribution coefficient of the i-th characteristic peak of the first spectral data; n is the number of characteristic peaks contained in the first spectral data.

[0041] A component analysis method, comprising:

[0042] Acquire first spectrum data of each pure substance and second spectrum data of the substance to be analyzed;

[0043] Determining a penalty coefficient for characterizing a peak position distance between the second spectrum data and each first spectrum data;

[0044] Determine a weight coefficient for characterizing peak value information of each characteristic peak in each first spectrum data;

[0045] Determine a distribution coefficient for characterizing the uniqueness of each characteristic peak position in each first spectrum data;

[0046] Comprehensively evaluating the matching degree between the second spectral data and the corresponding first spectral data by combining the weight coefficient of each first spectral data, the distribution coefficient, and the penalty coefficient of the second spectral data and each first spectral data;

[0047] Each first spectral data whose corresponding matching degree meets the preset matching condition is similarly identified with the second spectral data, one or more first spectral data similar to the second spectral data are determined, and the pure substances corresponding to the determined first spectral data are used as the components of the substance to be analyzed.

[0048] A component analysis device, comprising:

[0049] A spectrum data acquisition module, used to acquire first spectrum data of the substance to be analyzed, and second spectrum data of each pure substance;

[0050] A penalty coefficient determination module, used to determine a penalty coefficient for characterizing a peak position distance between the first spectrum data and each second spectrum data;

[0051] A weight coefficient determination module, used to determine a weight coefficient for characterizing the peak value information of each characteristic peak in the first spectral data;

[0052] A distribution coefficient determination module, used to determine a distribution coefficient for characterizing the uniqueness of each characteristic peak position in the first spectral data;

[0053] a matching evaluation module, configured to evaluate the matching degree between the first spectral data and the corresponding second spectral data by comprehensively considering the weight coefficient of the first spectral data, the distribution coefficient, and the penalty coefficient of the first spectral data and each second spectral data;

[0054] The component determination module is used to perform similarity identification between each second spectral data whose corresponding matching degree meets the preset matching condition and the first spectral data, determine one or more second spectral data similar to the first spectral data, and use the pure substances corresponding to the determined second spectral data as the components of the substance to be analyzed.

[0055] A component analysis device, comprising a memory and a processor;

[0056] The memory is used to store programs;

[0057] The processor is used to execute the program to implement each step of the above-mentioned component analysis method.

[0058] A readable storage medium stores a computer program, and when the computer program is executed by a processor, each step of the above-mentioned component analysis method is implemented.

[0059] It can be seen from the above technical solutions that the component analysis method provided by the present application takes into account that spectral data can reflect the internal structure and composition information of a substance, and therefore, the method obtains the first spectral data of the substance to be analyzed, and the second spectral data of each pure substance; taking into account that the characteristic peaks of the same substance appear at the same position, therefore, a penalty coefficient for characterizing the peak position distance between the first spectral data and each second spectral data can be determined; based on this, the degree of difference in the characteristic peaks of the first spectral data and the second spectral data can be quantified by the penalty coefficient; taking into account that the peak information can reflect the overall influence of the corresponding component on the substance to be analyzed, therefore, a weight coefficient for characterizing the peak information of each characteristic peak in the first spectral data can be determined; based on this, the peak condition of each characteristic peak in the first spectral data can be quantified by the weight coefficient; then, taking into account that there are some characteristic peaks with special positions in the spectral data of some substances, therefore, a distribution coefficient for characterizing the uniqueness of the position of each characteristic peak in the first spectral data can be determined; based on this, the present application can quantify the characteristic peaks with relatively special positions in the first spectral data by the distribution coefficient, and use the characteristic peaks at special positions in the first spectral data to assist the component The analysis process improves the accuracy of component identification; subsequently, the weight coefficient of the first spectral data, the distribution coefficient and the penalty coefficient of the first spectral data and each second spectral data can be comprehensively evaluated to evaluate the matching degree between the first spectral data and the corresponding second spectral data; based on this, the present application can comprehensively and systematically evaluate the matching degree between the first spectral data and the second spectral data from multiple angles such as peak distance, characteristic peak value and characteristic peak position particularity, make full use of the spectral information of the substance to be analyzed, avoid the one-sidedness caused by single factor judgment, fully consider the multiple dimensional information between the spectral data, accurately measure the matching situation between the substance to be analyzed and the pure substance, and avoid matching errors; finally, each second spectral data whose corresponding matching degree meets the preset matching condition can be similarly identified with the first spectral data, determine more than one second spectral data similar to the first spectral data, and use the pure substances corresponding to the determined second spectral data as the components of the substance to be analyzed; based on this, the present application screens the components of the substance to be analyzed from each pure substance through the dual screening mechanism of matching degree calculation and similarity identification, and further improves the reliability and accuracy of component determination. It can be seen that the present application can comprehensively consider the peak distance between the first spectral data and the second spectral data, as well as the peak size of the first spectral data and the uniqueness of the characteristic peak position, to perform a round of screening on the pure substance, and then perform a second round of screening on the pure substance through similarity recognition, thereby improving the reliability and accuracy of component identification. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0061] Figure 1 A flow chart of a component analysis method disclosed in an embodiment of the present application;

[0062] Figure 2 A schematic diagram of a penalty coefficient varying with peak distance as an example of an embodiment of the present application;

[0063] Figure 3 A schematic diagram of another penalty coefficient varying with peak distance according to an example of an embodiment of the present application;

[0064] Figure 4 A schematic diagram of a weight coefficient varying with peak value as exemplified in an embodiment of the present application;

[0065] Figure 5 A schematic diagram of a distribution coefficient varying with wavelength bands as exemplified in an embodiment of the present application;

[0066] Figure 6 This is a structural block diagram of a component analysis device disclosed in an embodiment of the present application;

[0067] Figure 7 This is a hardware structure block diagram of a component analysis device disclosed in an embodiment of the present application. DETAILED DESCRIPTION

[0068] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0069] The present application embodiment provides a component analysis method, which can be applied to various component analysis systems or trace detection systems, and can also be applied to various computer terminals or smart terminals. The execution subject can be a processor or server of the computer terminal or smart terminal. The method flow chart of the component analysis method is as follows Figure 1 As shown, specifically including:

[0070] Next, combine Figure 1 The component analysis method of this application is introduced in detail, including the following steps:

[0071] Step S1, obtaining first spectrum data of a substance to be analyzed and second spectrum data of each pure substance.

[0072] Specifically, the first spectrum data of the substance to be analyzed and the second spectrum data of each pure substance can be obtained through a spectrum acquisition instrument or a spectrum parameter calculation formula.

[0073] The spectrum acquisition instrument may include a Fourier transform infrared spectrometer, a Raman spectrometer, an atomic absorption spectrometer, and the like.

[0074] The first spectral data and each second spectral data may correspond to the same spectral interval.

[0075] Step S2: determining a penalty coefficient for characterizing the peak distance between the first spectrum data and each second spectrum data.

[0076] Specifically, the penalty coefficient for characterizing the peak position distance between the first spectrum data and each second spectrum data may be determined in a variety of ways.

[0077] For example, for each second spectrum data, the sum of the distances between each characteristic peak in the first spectrum data and all characteristic peaks in the second spectrum data can be calculated, and based on the sum of the distances corresponding to the characteristic peaks of the first spectrum data, a penalty coefficient for characterizing the peak position distance between the first spectrum data and the second spectrum data is determined;

[0078] For another example, for each second spectral data, discrete distance data of each characteristic peak in the first spectral data can be generated, and any distance data in each discrete distance data indicates the distance between the corresponding characteristic peak in the first spectral data and the corresponding characteristic peak in the second spectral data; based on each discrete distance data of the first spectral data, a penalty coefficient for characterizing the peak position distance between the first spectral data and the second spectral data is determined.

[0079] Step S3: determine a weight coefficient for characterizing the peak value information of each characteristic peak in the first spectral data.

[0080] Specifically, the peak value of each characteristic peak in the first spectrum data may be determined, and according to the peak value of each characteristic peak, the weight coefficient corresponding to each characteristic peak in the first spectrum data may be calculated.

[0081] Step S4: determining a distribution coefficient for characterizing the uniqueness of the position of each characteristic peak in the first spectral data.

[0082] Specifically, a distribution coefficient for characterizing the uniqueness of the position of each characteristic peak in the first spectral data may be generated according to the probability of the characteristic peak appearing in the wavelength band corresponding to each characteristic peak in the first spectral data.

[0083] Step S5: Comprehensively consider the weight coefficient of the first spectral data, the distribution coefficient, and the penalty coefficient of the first spectral data and each second spectral data to evaluate the matching degree between the first spectral data and the corresponding second spectral data.

[0084] Specifically, the matching degree between the first spectral data and the corresponding second spectral data is evaluated by taking the penalty coefficients of the first spectral data and each second spectral data as the main factors and taking the weight coefficient and the distribution coefficient of the first spectral data as the auxiliary factors.

[0085] Among them, it can be achieved in various ways to use the penalty coefficient of the first spectral data and each second spectral data as the main factor, and the weight coefficient and the distribution coefficient of the first spectral data as auxiliary factors to evaluate the matching degree between the first spectral data and the corresponding second spectral data.

[0086] For example, a first weight corresponding to the penalty coefficient may be set, a second weight corresponding to the weight coefficient may be set, and a third weight corresponding to the distribution coefficient may be set, wherein the first weight is greater than the second weight and greater than the third weight; based on the penalty coefficient and the first weight of the first spectral data and each second spectral data, the weight coefficient and the second weight of the first spectral data, and the distribution coefficient and the third weight of the first spectral data, the matching degree between the first spectral data and the corresponding second spectral data is calculated;

[0087] The ratio of the product of the penalty coefficient, distribution coefficient and weight coefficient of the first spectral data and each second spectral data to the product of the distribution coefficient and weight coefficient of the first spectral data may also be used as the matching degree between the first spectral data and the corresponding second spectral data.

[0088] Step S6: perform similarity identification between each second spectral data whose corresponding matching degree meets the preset matching condition and the first spectral data, determine one or more second spectral data similar to the first spectral data, and use the pure substances corresponding to the determined second spectral data as the components of the substance to be analyzed.

[0089] Specifically, each second spectral data whose corresponding matching degree exceeds a preset matching threshold can be similarly identified with the first spectral data; or the second spectral data can be sorted from large to small according to the matching degree, and the first N second spectral data can be similarly identified with the first spectral data.

[0090] The second spectral data and the first spectral data may be similarly identified in a variety of ways to determine one or more second spectral data similar to the first spectral data.

[0091] For example, the least square method may be run to fit each second spectral data whose corresponding matching degree exceeds a preset matching threshold with the first spectral data, and each second spectral data exceeding the fitting goodness threshold is regarded as each second spectral data similar to the first spectral data;

[0092] It is also possible to combine similarity algorithms such as cosine distance calculation algorithm and Manhattan distance calculation algorithm to calculate the spectral similarity between each second spectral data whose corresponding matching degree exceeds a preset matching threshold and the first spectral data, and regard each second spectral data whose spectral similarity exceeds the similarity threshold as each second spectral data similar to the first spectral data.

[0093] It can be seen from the above technical solutions that the component analysis method provided by the present application takes into account that spectral data can reflect the internal structure and composition information of a substance, and therefore, the method obtains the first spectral data of the substance to be analyzed, and the second spectral data of each pure substance; taking into account that the characteristic peaks of the same substance appear at the same position, therefore, a penalty coefficient for characterizing the peak position distance between the first spectral data and each second spectral data can be determined; based on this, the degree of difference in the characteristic peaks of the first spectral data and the second spectral data can be quantified by the penalty coefficient; taking into account that the peak information can reflect the overall influence of the corresponding component on the substance to be analyzed, therefore, a weight coefficient for characterizing the peak information of each characteristic peak in the first spectral data can be determined; based on this, the peak condition of each characteristic peak in the first spectral data can be quantified by the weight coefficient; then, taking into account that there are some characteristic peaks with special positions in the spectral data of some substances, therefore, a distribution coefficient for characterizing the uniqueness of the position of each characteristic peak in the first spectral data can be determined; based on this, the present application can quantify the characteristic peaks with relatively special positions in the first spectral data by the distribution coefficient, and use the characteristic peaks at special positions in the first spectral data to assist the component The analysis process improves the accuracy of component identification; subsequently, the weight coefficient of the first spectral data, the distribution coefficient and the penalty coefficient of the first spectral data and each second spectral data can be comprehensively evaluated to evaluate the matching degree between the first spectral data and the corresponding second spectral data; based on this, the present application can comprehensively and systematically evaluate the matching degree between the first spectral data and the second spectral data from multiple angles such as peak distance, characteristic peak value and characteristic peak position particularity, make full use of the spectral information of the substance to be analyzed, avoid the one-sidedness caused by single factor judgment, fully consider the multiple dimensional information between the spectral data, accurately measure the matching situation between the substance to be analyzed and the pure substance, and avoid matching errors; finally, each second spectral data whose corresponding matching degree meets the preset matching condition can be similarly identified with the first spectral data, determine more than one second spectral data similar to the first spectral data, and use the pure substances corresponding to the determined second spectral data as the components of the substance to be analyzed; based on this, the present application screens the components of the substance to be analyzed from each pure substance through the dual screening mechanism of matching degree calculation and similarity identification, and further improves the reliability and accuracy of component determination. It can be seen that the present application can comprehensively consider the peak distance between the first spectral data and the second spectral data, as well as the peak size of the first spectral data and the uniqueness of the characteristic peak position, to perform a round of screening on the pure substance, and then perform a second round of screening on the pure substance through similarity recognition, thereby improving the reliability and accuracy of component identification.

[0094] In some embodiments of the present application, the process of step S2, determining the penalty coefficient for characterizing the peak position distance between the first spectrum data and each second spectrum data, is described in detail, and the steps are as follows:

[0095] S20. For each second spectral data, determine the number of characteristic peaks of the second spectral data, and generate penalty coefficients corresponding to the first spectral data and the second spectral data based on the peak distance between each characteristic peak in the first spectral data and each characteristic peak of the second spectral data with reference to the number of characteristic peaks.

[0096] Specifically, the number of characteristic peaks contained in each second spectrum data may be counted;

[0097] The peak distance between each characteristic peak in the first spectral data and each characteristic peak in the second spectral data is calculated, and with reference to the number of characteristic peaks corresponding to the second spectral data, a penalty coefficient corresponding to the first spectral data and the second spectral data is generated based on the peak distance corresponding to each characteristic peak in the first spectral data and corresponding to the second spectral data.

[0098] It can be seen from the above technical solution that this embodiment provides an optional method for determining the penalty coefficient used to characterize the peak distance between the first spectral data and each second spectral data. Through the above method, the penalty coefficient can be further determined in combination with the number of characteristic peaks and the peak distance, thereby further improving the reliability of the penalty coefficient.

[0099] In some embodiments of the present application, the process of generating the penalty coefficients corresponding to the first spectral data and the second spectral data based on the peak distance between each characteristic peak in the first spectral data and each characteristic peak in the second spectral data with reference to the number of characteristic peaks in step S20 is described in detail, and the steps are as follows:

[0100] S200. When the number of the characteristic peaks is 1, obtain a preset first penalty formula, and calculate the penalty coefficients corresponding to the first spectral data and the second spectral data by combining the first penalty formula and the peak distance between each characteristic peak in the first spectral data and each characteristic peak in the second spectral data.

[0101] Specifically, the first penalty formula is as follows:

[0102]

[0103] In the formula, clim is a preset constant; delta i is the peak distance between the i-th characteristic peak of the first spectrum data and each corresponding characteristic peak of the second spectrum data; is the penalty coefficient corresponding to the first spectral data and the corresponding second spectral data.

[0104] The size of the penalty coefficient varies with the peak distance, such as Figure 2 shown.

[0105] or,

[0106] S200. When the number of the characteristic peaks is more than two, obtain a preset second penalty formula, and calculate the penalty coefficients corresponding to the first spectral data and the second spectral data by combining the second penalty formula and the peak distance between each characteristic peak in the first spectral data and each characteristic peak in the second spectral data.

[0107] Specifically, the second penalty formula is as follows:

[0108]

[0109] In the formula, and are preset constants.

[0110] When the peak distance is too large or too small, the penalty coefficient may approach 0, such as Figure 3 shown.

[0111] It can be seen from the above technical solution that this embodiment provides an optional method to generate a penalty coefficient by comprehensively considering the number of characteristic peaks and the peak distance. Through the above method, different penalty coefficient calculation steps can be provided for the second spectral data with different numbers of characteristic peaks, further improving the wide application of the present application.

[0112] In some embodiments of the present application, the process of step S3, determining the weight coefficient for characterizing the peak value information of each characteristic peak in the first spectral data, is described in detail, and the steps are as follows:

[0113] S30, determining the peak value of each characteristic peak in the first spectral data.

[0114] Specifically, the first spectral data may be proportionally compressed to a target peak interval, and the peak value of each characteristic peak in the first spectral data within the corresponding target peak interval may be collected.

[0115] Among them, the target peak interval can be [0,4].

[0116] S31, obtaining a preset comparison weight expression, and combining the peak value of each characteristic peak and the comparison weight expression to calculate a weight coefficient reflecting the peak value information of each characteristic peak in the first spectrum data.

[0117] Specifically, the comparison weight expression can be as follows:

[0118]

[0119] in, is the weight coefficient of the i-th characteristic peak of the first spectrum data; and c is a preset constant; is the peak value of the i-th characteristic peak of the first spectral data.

[0120] and The change relationship between satisfies the sigmoid distribution, such as Figure 4 shown.

[0121] It can be seen from the above technical solution that this embodiment provides an optional method for determining the weight coefficient used to characterize the peak value information of each characteristic peak in the first spectral data. Through the above method, the peak value of each characteristic peak in the first spectral data can be further quantified in combination with the e-function, thereby further improving the reliability of the present application.

[0122] In some embodiments of the present application, the process of step S4, determining the distribution coefficient for characterizing the uniqueness of the position of each characteristic peak in the first spectral data, is described in detail, and the steps are as follows:

[0123] S40, determining the wavelength band interval where each characteristic peak of each second spectrum data is located.

[0124] Specifically, the wavelength band interval where each characteristic peak in the second spectrum data of each pure substance is located can be determined.

[0125] S41. Combining the band intervals corresponding to the characteristic peaks, count the number of peaks corresponding to each band interval.

[0126] Specifically, the number of characteristic peaks contained in each band interval may be counted.

[0127] S42. Based on the number of peaks corresponding to each band interval, determine the probability of a characteristic peak appearing in each band interval, and based on the probability corresponding to each band interval, determine the uniqueness of the characteristic peak corresponding to each band interval.

[0128] Specifically, the ratio of the number of peaks in each band interval to the sum of the number of peaks in all band intervals can be used as the probability of the occurrence of a characteristic peak in the corresponding band interval, and the difference between 1 and the probability of the occurrence of a characteristic peak in each band interval can be used as the uniqueness of the characteristic peak corresponding to the corresponding band interval.

[0129] The band interval with a greater probability of characteristic peak occurrence has a lower uniqueness of characteristic peak; the band interval with a smaller probability of characteristic peak occurrence has a greater uniqueness of characteristic peak.

[0130] S43: Determine the wavelength range corresponding to each characteristic peak in the first spectrum data.

[0131] Specifically, the wavelength range of each characteristic peak in the first spectrum data of the substance to be analyzed may be determined.

[0132] S44, taking the uniqueness of the characteristic peak of each band range matching band interval as the distribution coefficient of the corresponding characteristic peak in the first spectrum data.

[0133] Specifically, the uniqueness of the characteristic peak of the band interval matched by each band range of the first spectral data may be used as the distribution coefficient of the corresponding characteristic peak in the first spectral data.

[0134] The correspondence between the distribution coefficient and the wave number can be shown as Figure 5 shown.

[0135] It can be seen from the above technical solution that this embodiment provides an optional method for determining a distribution coefficient used to characterize the uniqueness of the position of each characteristic peak in the first spectral data. Through the above method, the position uniqueness of different characteristic peaks in the first spectral data can be statistically analyzed, and the uniqueness of different characteristic peaks in the first spectral data in this application can be further quantified.

[0136] In some embodiments of the present application, step S5, a process of evaluating the matching degree between the first spectral data and the corresponding second spectral data by integrating the weight coefficient of the first spectral data, the distribution coefficient, and the penalty coefficient of the first spectral data and each second spectral data, is described in detail, and the steps are as follows:

[0137] S50, in combination with a preset spectrum matching expression, based on the weight coefficient of the first spectrum data, the distribution coefficient, and the penalty coefficient of the first spectrum data and each second spectrum data, calculate the matching degree between the first spectrum data and the corresponding second spectrum data.

[0138] Specifically, the weight coefficient of the first spectral data, the distribution coefficient, and the penalty coefficient of the first spectral data and each second spectral data may be substituted into a preset spectral matching expression to calculate the matching degree between the first spectral data and each second spectral data.

[0139] The spectrum matching expression can be as follows:

[0140]

[0141] in, is the matching degree between the first spectral data and the corresponding second spectral data; is a penalty coefficient corresponding to the first spectral data and the corresponding second spectral data; is the weight coefficient of the i-th characteristic peak of the first spectrum data; is the distribution coefficient of the i-th characteristic peak of the first spectral data; n is the number of characteristic peaks contained in the first spectral data.

[0142] It can be seen from the above technical solution that the present application provides an optional method for calculating the matching degree between the first spectrum data and each second spectrum data. Through the above method, the peak distance between the first spectrum data and each second spectrum data can be highlighted, and the matching degree calculation is performed mainly based on the peak distance, supplemented by the peak information and the peak position, so as to further improve the reliability of the matching degree.

[0143] Next, we will combine Figure 6 The component analysis device provided in the present application is introduced in detail. The component analysis device provided below can be compared with the component analysis method provided above.

[0144] See also Figure 6 It can be found that the component analysis device may include:

[0145] The spectrum data acquisition module 10 is used to acquire the first spectrum data of the substance to be analyzed and the second spectrum data of each pure substance;

[0146] A penalty coefficient determination module 20, used to determine a penalty coefficient for characterizing a peak position distance between the first spectrum data and each second spectrum data;

[0147] A weight coefficient determination module 30, used to determine a weight coefficient for characterizing peak value information of each characteristic peak in the first spectral data;

[0148] A distribution coefficient determination module 40, used to determine a distribution coefficient for characterizing the uniqueness of each characteristic peak position in the first spectral data;

[0149] A matching evaluation module 50, configured to evaluate the matching degree between the first spectral data and the corresponding second spectral data by comprehensively considering the weight coefficient of the first spectral data, the distribution coefficient, and the penalty coefficient of the first spectral data and each second spectral data;

[0150] The component determination module 60 is used to perform similarity identification between each second spectral data whose corresponding matching degree meets the preset matching condition and the first spectral data, determine one or more second spectral data similar to the first spectral data, and use the pure substances corresponding to the determined second spectral data as the components of the substance to be analyzed.

[0151] Further, the penalty coefficient determination module 20 may include:

[0152] A characteristic peak number determination unit is used to determine the number of characteristic peaks of each second spectral data, and refer to the number of characteristic peaks to generate a penalty coefficient corresponding to the first spectral data and the second spectral data based on the peak distance between each characteristic peak in the first spectral data and each characteristic peak of the second spectral data.

[0153] Further, the characteristic peak quantity determination unit may include:

[0154] The component for utilizing the number of characteristic peaks is used to obtain a preset first penalty formula when the number of characteristic peaks is one, and to calculate the penalty coefficients corresponding to the first spectral data and the second spectral data by combining the first penalty formula and the peak distances between each characteristic peak in the first spectral data and each characteristic peak in the second spectral data; and to obtain a preset second penalty formula when the number of characteristic peaks is two or more, and to calculate the penalty coefficients corresponding to the first spectral data and the second spectral data by combining the second penalty formula and the peak distances between each characteristic peak in the first spectral data and each characteristic peak in the second spectral data;

[0155] The first penalty formula is as follows:

[0156]

[0157] The second penalty formula is as follows:

[0158]

[0159] Among them, clim, and is a preset constant; delta i is the peak distance between the i-th characteristic peak of the first spectrum data and each corresponding characteristic peak of the second spectrum data; is the penalty coefficient corresponding to the first spectral data and the corresponding second spectral data.

[0160] Further, the weight coefficient determination module 30 may include:

[0161] A peak value determination unit, used to determine the peak value of each characteristic peak in the first spectrum data;

[0162] A comparison weight expression utilizing unit, used to obtain a preset comparison weight expression, and calculate a weight coefficient reflecting the peak value information of each characteristic peak in the first spectral data by combining the peak value of each characteristic peak and the comparison weight expression;

[0163] The comparison weight expression is as follows:

[0164]

[0165] in, is the weight coefficient of the i-th characteristic peak of the first spectrum data; and c is a preset constant; is the peak value of the i-th characteristic peak of the first spectral data.

[0166] Further, the distribution coefficient determination module 40 may include:

[0167] A first distribution coefficient determination unit is used to determine the wavelength interval where each characteristic peak of each second spectrum data is located;

[0168] The second distribution coefficient determination unit is used to combine the band intervals corresponding to each characteristic peak and count the number of peaks corresponding to each band interval;

[0169] A third distribution coefficient determination unit is used to determine the probability of the characteristic peak appearing in each band interval based on the number of peaks corresponding to each band interval, and determine the uniqueness of the characteristic peak corresponding to each band interval based on the probability corresponding to each band interval;

[0170] A fourth distribution coefficient determination unit, used to determine a wavelength range corresponding to each characteristic peak in the first spectrum data;

[0171] The fifth distribution coefficient determination unit is used to use the uniqueness of the characteristic peak of each band range matching band interval as the distribution coefficient of the corresponding characteristic peak in the first spectrum data.

[0172] Furthermore, the matching evaluation module 50 may include:

[0173] a spectral matching expression utilizing unit, configured to calculate the matching degree between the first spectral data and the corresponding second spectral data based on the weight coefficient of the first spectral data, the distribution coefficient, and the penalty coefficient of the first spectral data and each second spectral data in combination with a preset spectral matching expression;

[0174] The spectrum matching expression is as follows:

[0175]

[0176] in, is the matching degree between the first spectral data and the corresponding second spectral data; is a penalty coefficient corresponding to the first spectral data and the corresponding second spectral data; is the weight coefficient of the i-th characteristic peak of the first spectrum data; is the distribution coefficient of the i-th characteristic peak of the first spectral data; n is the number of characteristic peaks contained in the first spectral data.

[0177] In some embodiments of the present application, considering that in the process of component analysis, the distribution coefficient and weight coefficient of each pure substance spectral data can also be calculated, and the distribution coefficient, weight coefficient of each pure substance spectral data and the penalty coefficient between the first spectral data are comprehensively analyzed for matching, thus, the above component analysis process can be supplemented. Next, another execution process of the component analysis method will be provided, and the steps are as follows:

[0178] S110, obtaining first spectrum data of each pure substance and second spectrum data of the substance to be analyzed.

[0179] The steps here are the same as the aforementioned step S1. For details, please refer to the introduction of the aforementioned step S1, and will not be repeated here.

[0180] S120: Determine a penalty coefficient for characterizing a peak distance between the second spectrum data and each first spectrum data.

[0181] Specifically, the number of characteristic peaks of the second spectral data can be determined, and with reference to the number of characteristic peaks, based on the peak distance between each characteristic peak in each first spectral data and each characteristic peak of the second spectral data, a penalty coefficient corresponding to the first spectral data and the corresponding second spectral data can be generated.

[0182] Further, when the number of characteristic peaks is 1, the penalty coefficient corresponding to each first spectral data and the second spectral data is calculated by combining the first penalty formula and the peak distance between each characteristic peak in each first spectral data and each characteristic peak in the second spectral data;

[0183] When the number of the characteristic peaks is more than two, the penalty coefficient corresponding to each first spectral data and the second spectral data is calculated by combining the second penalty formula and the peak distance between each characteristic peak in each first spectral data and each characteristic peak in the second spectral data;

[0184] S130, determining a weight coefficient for characterizing peak value information of each characteristic peak in each first spectrum data.

[0185] Specifically, for each first spectral data, the peak value of each characteristic peak in the first spectral data can be determined; combined with the peak value of each characteristic peak in the first spectral data and the comparison weight expression, a weight coefficient reflecting the peak value information of each characteristic peak in the first spectral data is calculated.

[0186] S140, determining a distribution coefficient for characterizing the uniqueness of the position of each characteristic peak in each first spectrum data.

[0187] Specifically, the band interval in which each characteristic peak of each first spectral data is located can be determined; in combination with the band intervals corresponding to each characteristic peak, the number of peaks corresponding to each band interval can be counted; based on the number of peaks corresponding to each band interval, the probability of the characteristic peak appearing in each band interval can be determined; and based on the probability corresponding to each band interval, the distribution coefficient corresponding to each band interval can be determined.

[0188] S150 , comprehensively evaluating the weight coefficient of each first spectral data, the distribution coefficient, and the penalty coefficient of the second spectral data and each first spectral data to evaluate the matching degree between the second spectral data and the corresponding first spectral data.

[0189] Specifically, the matching degree between each first spectral data and the corresponding second spectral data can be calculated based on the weight coefficient of each first spectral data, the distribution coefficient and the penalty coefficient of each first spectral data and the second spectral data in combination with the spectral matching expression.

[0190] S160, performing similarity identification between each first spectral data whose corresponding matching degree meets the preset matching condition and the second spectral data, determining one or more first spectral data similar to the second spectral data, and using the pure substances corresponding to the determined first spectral data as the components of the substance to be analyzed.

[0191] It can be seen from the above technical solution that this embodiment provides another component analysis method. Through the above method, another optional implementation method is provided, which combines the peak distance between each first spectral data and the second spectral data, and the peak size of each first spectral data and the uniqueness of the characteristic peak position, performs a round of screening on the pure substance, and then performs a second round of screening on the pure substance through similar recognition, thereby further improving the reliability and accuracy of component identification.

[0192] The component analysis apparatus described below can be compared with the component analysis method described above.

[0193] An acquisition module, used for acquiring first spectrum data of each pure substance and second spectrum data of the substance to be analyzed;

[0194] A determination module, used to determine a penalty coefficient for characterizing the peak distance between the second spectral data and each first spectral data; determine a weight coefficient for characterizing the peak value information of each characteristic peak in each first spectral data; and determine a distribution coefficient for characterizing the uniqueness of the position of each characteristic peak in each first spectral data;

[0195] An evaluation module, configured to evaluate a matching degree between the second spectral data and the corresponding first spectral data by combining the weight coefficient of each first spectral data, the distribution coefficient, and the penalty coefficient of the second spectral data and each first spectral data;

[0196] The identification module is used to perform similarity identification between each first spectral data whose corresponding matching degree meets the preset matching condition and the second spectral data, determine one or more first spectral data similar to the second spectral data, and use the pure substances corresponding to the determined first spectral data as the components of the substance to be analyzed.

[0197] The component analysis device provided in the embodiment of the present application can be applied to component analysis equipment, such as PC terminals, cloud platforms, servers and server clusters. Figure 7 The hardware structure diagram of the component analysis equipment is shown in FIG. Figure 7 ,The hardware structure of the component analysis device may include: at least one processor 1, at least one communication interface 2, at least one memory 3 and at least one communication bus 4;

[0198] In the embodiment of the present application, the number of the processor 1, the communication interface 2, the memory 3, and the communication bus 4 is at least one, and the processor 1, the communication interface 2, and the memory 3 communicate with each other through the communication bus 4;

[0199] The processor 1 may be a central processing unit CPU, or an application-specific integrated circuit ASIC (Application Specific Integrated Circuit), or one or more integrated circuits configured to implement the embodiments of the present invention, etc.;

[0200] The memory 3 may include a high-speed RAM memory, and may also include a non-volatile memory (non-volatile memory), etc., such as at least one disk memory;

[0201] The memory stores a program, and the processor can call the program stored in the memory, wherein the program is used to:

[0202] Acquire first spectrum data of the substance to be analyzed, and second spectrum data of each pure substance;

[0203] Determining a penalty coefficient for characterizing a peak position distance between the first spectrum data and each second spectrum data;

[0204] Determine a weight coefficient for characterizing peak value information of each characteristic peak in each first spectrum data;

[0205] Determine a distribution coefficient for characterizing the uniqueness of each characteristic peak position in each first spectrum data;

[0206] Comprehensively evaluating the matching degree between the first spectral data and the corresponding second spectral data by combining the weight coefficient of the first spectral data, the distribution coefficient, and the penalty coefficient of the first spectral data and each second spectral data;

[0207] Each second spectral data whose corresponding matching degree meets the preset matching condition is similarly identified with the first spectral data, one or more second spectral data similar to the first spectral data are determined, and the pure substances corresponding to the determined second spectral data are used as the components of the substance to be analyzed.

[0208] or,

[0209] Acquire first spectrum data of each pure substance and second spectrum data of the substance to be analyzed;

[0210] Determining a penalty coefficient for characterizing a peak position distance between the second spectrum data and each first spectrum data;

[0211] Determining a weight coefficient for characterizing peak value information of each characteristic peak in the first spectral data;

[0212] Determining a distribution coefficient for characterizing the uniqueness of each characteristic peak position in the first spectral data;

[0213] Comprehensively evaluating the matching degree between the second spectral data and the corresponding first spectral data by combining the weight coefficient of each first spectral data, the distribution coefficient, and the penalty coefficient of the second spectral data and each first spectral data;

[0214] Each first spectral data whose corresponding matching degree meets the preset matching condition is similarly identified with the second spectral data, one or more first spectral data similar to the second spectral data are determined, and the pure substances corresponding to the determined first spectral data are used as the components of the substance to be analyzed.

[0215] Optionally, the detailed functions and extended functions of the program may refer to the above description.

[0216] The embodiment of the present application further provides a readable storage medium, which may store a program suitable for execution by a processor, wherein the program is used to:

[0217] Acquire first spectrum data of the substance to be analyzed, and second spectrum data of each pure substance;

[0218] Determining a penalty coefficient for characterizing a peak position distance between the first spectrum data and each second spectrum data;

[0219] Determine a weight coefficient for characterizing peak value information of each characteristic peak in each first spectrum data;

[0220] Determine a distribution coefficient for characterizing the uniqueness of each characteristic peak position in each first spectrum data;

[0221] Comprehensively evaluating the matching degree between the first spectral data and the corresponding second spectral data by combining the weight coefficient of the first spectral data, the distribution coefficient, and the penalty coefficient of the first spectral data and each second spectral data;

[0222] Each second spectral data whose corresponding matching degree meets the preset matching condition is similarly identified with the first spectral data, one or more second spectral data similar to the first spectral data are determined, and the pure substances corresponding to the determined second spectral data are used as the components of the substance to be analyzed.

[0223] or,

[0224] Acquire first spectrum data of each pure substance and second spectrum data of the substance to be analyzed;

[0225] Determining a penalty coefficient for characterizing a peak position distance between the second spectrum data and each first spectrum data;

[0226] Determining a weight coefficient for characterizing peak value information of each characteristic peak in the first spectral data;

[0227] Determining a distribution coefficient for characterizing the uniqueness of each characteristic peak position in the first spectral data;

[0228] Comprehensively evaluating the matching degree between the second spectral data and the corresponding first spectral data by combining the weight coefficient of each first spectral data, the distribution coefficient, and the penalty coefficient of the second spectral data and each first spectral data;

[0229] Each first spectral data whose corresponding matching degree meets the preset matching condition is similarly identified with the second spectral data, one or more first spectral data similar to the second spectral data are determined, and the pure substances corresponding to the determined first spectral data are used as the components of the substance to be analyzed.

[0230] Optionally, the detailed functions and extended functions of the program may refer to the above description.

[0231] Finally, it should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the presence of other identical elements in the process, method, article or device including the elements.

[0232] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referenced to each other.

[0233] The above description of the disclosed embodiments enables professionals and technicians in the field to implement or use the present application. Various modifications to these embodiments will be apparent to professionals and technicians in the field, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present application. The various embodiments of the present application can be combined with each other. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A component analysis method, characterized in that: include: Acquire first spectrum data of the substance to be analyzed, and second spectrum data of each pure substance; Determining a penalty coefficient for characterizing a peak position distance between the first spectrum data and each second spectrum data; Determining a weight coefficient for characterizing peak value information of each characteristic peak in the first spectral data; Determining a distribution coefficient for characterizing the uniqueness of each characteristic peak position in the first spectral data; Comprehensively evaluating the matching degree between the first spectral data and the corresponding second spectral data by combining the weight coefficient of the first spectral data, the distribution coefficient, and the penalty coefficient of the first spectral data and each second spectral data; Each second spectral data whose corresponding matching degree meets the preset matching condition is similarly identified with the first spectral data, one or more second spectral data similar to the first spectral data are determined, and the pure substances corresponding to the determined second spectral data are used as the components of the substance to be analyzed.

2. The component analysis method according to claim 1, characterized in that: The step of determining a penalty coefficient for characterizing a peak position distance between the first spectrum data and each second spectrum data comprises: For each second spectral data, the number of characteristic peaks of the second spectral data is determined, and with reference to the number of characteristic peaks, based on the peak distance between each characteristic peak in the first spectral data and each characteristic peak of the second spectral data, a penalty coefficient corresponding to the first spectral data and the second spectral data is generated.

3. The component analysis method according to claim 2, characterized in that: Referring to the number of characteristic peaks, based on the peak distance between each characteristic peak in the first spectral data and each characteristic peak in the second spectral data, generating penalty coefficients corresponding to the first spectral data and the second spectral data, including: When the number of the characteristic peaks is 1, a preset first penalty formula is obtained, and the penalty coefficients corresponding to the first spectral data and the second spectral data are calculated by combining the first penalty formula and the peak distance between each characteristic peak in the first spectral data and each characteristic peak in the second spectral data; When the number of the characteristic peaks is more than two, a preset second penalty formula is obtained, and the penalty coefficients corresponding to the first spectral data and the second spectral data are calculated by combining the second penalty formula and the peak distance between each characteristic peak in the first spectral data and each characteristic peak in the second spectral data; The first penalty formula is as follows: The second penalty formula is as follows: Among them, clim, and is a preset constant; delta i is the peak distance between the i-th characteristic peak of the first spectrum data and each corresponding characteristic peak of the second spectrum data; is the penalty coefficient corresponding to the first spectral data and the corresponding second spectral data.

4. The component analysis method according to claim 1, characterized in that: The determining of a weight coefficient for characterizing peak value information of each characteristic peak in the first spectral data comprises: Determine the peak value of each characteristic peak in the first spectral data; Obtaining a preset comparison weight expression, and combining the peak value of each characteristic peak and the comparison weight expression, calculating a weight coefficient reflecting the peak value information of each characteristic peak in the first spectral data; The comparison weight expression is as follows: in, is the weight coefficient of the i-th characteristic peak of the first spectrum data; and c is a preset constant; is the peak value of the i-th characteristic peak of the first spectral data.

5. The component analysis method according to claim 1, characterized in that: The determining of a distribution coefficient for characterizing the uniqueness of the position of each characteristic peak in the first spectral data comprises: Determine the wavelength band interval where each characteristic peak of each second spectrum data is located; Combine the band intervals corresponding to each characteristic peak and count the number of peaks corresponding to each band interval; Based on the number of peaks corresponding to each band interval, the probability of the characteristic peak appearing in each band interval is determined, and based on the probability corresponding to each band interval, the uniqueness of the characteristic peak corresponding to each band interval is determined; Determine the wavelength range corresponding to each characteristic peak in the first spectral data; The uniqueness of the characteristic peak of each band range matching band interval is used as the distribution coefficient of the corresponding characteristic peak in the first spectrum data.

6. The component analysis method according to claim 1, characterized in that: The step of comprehensively evaluating the matching degree between the first spectral data and the corresponding second spectral data by combining the weight coefficient of the first spectral data, the distribution coefficient, and the penalty coefficient of the first spectral data and each second spectral data includes: In combination with a preset spectrum matching expression, based on the weight coefficient of the first spectrum data, the distribution coefficient, and the penalty coefficient of the first spectrum data and each second spectrum data, a matching degree between the first spectrum data and the corresponding second spectrum data is calculated; The spectrum matching expression is as follows: in, is the matching degree between the first spectral data and the corresponding second spectral data; is a penalty coefficient corresponding to the first spectral data and the corresponding second spectral data; is the weight coefficient of the i-th characteristic peak of the first spectrum data; is the distribution coefficient of the i-th characteristic peak of the first spectral data; n is the number of characteristic peaks contained in the first spectral data.

7. A component analysis method, characterized in that: include: Acquire first spectrum data of each pure substance and second spectrum data of the substance to be analyzed; Determining a penalty coefficient for characterizing a peak position distance between the second spectrum data and each first spectrum data; Determine a weight coefficient for characterizing peak value information of each characteristic peak in each first spectrum data; Determine a distribution coefficient for characterizing the uniqueness of each characteristic peak position in each first spectrum data; Comprehensively evaluating the matching degree between the second spectral data and the corresponding first spectral data by combining the weight coefficient of each first spectral data, the distribution coefficient, and the penalty coefficient of the second spectral data and each first spectral data; Each first spectral data whose corresponding matching degree meets the preset matching condition is similarly identified with the second spectral data, one or more first spectral data similar to the second spectral data are determined, and the pure substances corresponding to the determined first spectral data are used as the components of the substance to be analyzed.

8. A component analysis device, characterized in that: include: A spectrum data acquisition module, used to acquire first spectrum data of the substance to be analyzed, and second spectrum data of each pure substance; A penalty coefficient determination module, used to determine a penalty coefficient for characterizing a peak position distance between the first spectrum data and each second spectrum data; A weight coefficient determination module, used to determine a weight coefficient for characterizing the peak value information of each characteristic peak in the first spectral data; A distribution coefficient determination module, used to determine a distribution coefficient for characterizing the uniqueness of each characteristic peak position in the first spectral data; a matching evaluation module, configured to evaluate the matching degree between the first spectral data and the corresponding second spectral data by comprehensively considering the weight coefficient of the first spectral data, the distribution coefficient, and the penalty coefficient of the first spectral data and each second spectral data; The component determination module is used to perform similarity identification between each second spectral data whose corresponding matching degree meets the preset matching condition and the first spectral data, determine one or more second spectral data similar to the first spectral data, and use the pure substances corresponding to the determined second spectral data as the components of the substance to be analyzed.

9. A component analysis device, characterized in that: including memory and processor; The memory is used to store programs; The processor is used to execute the program to implement each step of the component analysis method as described in any one of claims 1 to 6 and claim 7.

10. A readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, each step of the component analysis method according to any one of claims 1 to 6 and claim 7 is implemented.

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