Ash Analysis Apparatus and Method

Abstract

The present invention provides an ash analysis device and method. A method for ash analysis includes: using a database function to perform: obtaining a fluorescence feature vector of a sample to be tested; searching in a database for a set of standard sample feature vectors that are similar in acquisition time and have the same channel address as the fluorescence feature vector of the sample to be tested, where the set of standard sample feature vectors includes one or more standard sample feature vectors, and the standard sample feature vectors indicate the fluorescence reaction characteristics and ash values of standard samples; calculating the similarity between the fluorescence feature vector of the sample to be tested and each standard sample feature vector in the set of standard sample feature vectors; and based on the similarity between the fluorescence feature vector of the sample to be tested and each standard sample feature vector in the set of standard sample feature vectors, outputting a data set to be analyzed, where the data set to be analyzed is output to a data analysis device through the database for the data analysis device to calculate the ash value of the sample to be tested.

Description

Technical Field

[0001] The present invention generally relates to an on-line fluorescence analyzer, and more particularly to an apparatus and method for ash analysis of pulp using an on-line fluorescence analyzer. Background Art

[0002] In mining operations, it is often necessary to detect and analyze the ash content of ore materials (e.g., ores, pulp, etc.) to determine the content of target substances and the types and contents of impurities therein, so as to perform cleaning and impurity removal on the ores, pulp, etc. subsequently.

[0003] Generally, a fluorescence analyzer can be used to determine the content of target substances and the types and contents of impurities in ore materials. In the prior art, fluorescence analyzers have been widely used for ash analysis of ore samples. However, since ash analysis involves the processing of a large amount of fluorescence data, and the determination of various substances contained in the ore sample and the calculation of their contents based on various ash analysis models (e.g., various neural network-based ash analysis models, etc.), it is usually performed locally and requires a large amount of storage and computing resources in the terminal device. Thus, as the data accumulates, it will cause serious occupation of storage and computing resources, and further lead to a decline in the efficiency and accuracy of ash analysis, as well as a degradation in the operation of the terminal device. Summary of the Invention

[0004] In order to solve at least one or more of the above-mentioned technical problems, the present invention proposes an apparatus and method for ash analysis in multiple aspects, making full use of the built-in functions provided by the database to complete simple screening and calculation steps in the database.

[0005] The inventors have found that the current database provides rich built-in functions, which match well with many screening and calculation operations involved in ash analysis, and the execution efficiency of these built-in functions in the database is higher than that of these screening and calculation operations involved in the ash analysis method executed on the terminal device. Thus, some operations in ash analysis can be migrated to the database, and the terminal device only needs to obtain the intermediate calculation results from the database and calculate the ash value of the ore material based on these intermediate calculation results, which can significantly save the storage and computing resources of the terminal device and improve the efficiency and accuracy of ash analysis. In addition, in the embodiments of the present invention, the terminal device obtains the intermediate calculation results processed by the database from the database, rather than the originally stored sampling data. Correspondingly, the terminal device only needs to temporarily store these intermediate calculation results and calculate the ash value based on these intermediate calculation results, so that a large number of memory access operations can be avoided, thereby avoiding problems such as memory crash and data leakage.

[0006] In a first aspect of the present invention, there is provided an apparatus for ash analysis, comprising: a data acquisition module configured to obtain a fluorescence feature vector of a sample to be tested; a database module coupled to the data acquisition module and configured to perform, using a database function: obtaining the fluorescence feature vector of the sample to be tested from the data acquisition module; searching in a database for a set of standard sample feature vectors having a collection time similar to that of the fluorescence feature vector of the sample to be tested and the same channel address, wherein the set of standard sample feature vectors includes one or more standard sample feature vectors, and the standard sample feature vectors indicate fluorescence reaction characteristics and ash values of standard samples; calculating a similarity between the fluorescence feature vector of the sample to be tested and each standard sample feature vector in the set of standard sample feature vectors; and outputting a data set to be analyzed based on the similarity between the fluorescence feature vector of the sample to be tested and each standard sample feature vector in the set of standard sample feature vectors; and a data analysis module coupled to the database module and configured to calculate an ash value of the sample to be tested based on the data set to be analyzed.

[0007] The apparatus as described above, wherein the database module is further configured to perform, using a database function: determining whether a similarity between the fluorescence feature vector of the sample to be tested and each standard sample feature vector in the set of standard sample feature vectors meets a similarity criterion; and in response to determining that the similarity between the fluorescence feature vector of the sample to be tested and at least one standard sample feature vector in the set of standard sample feature vectors meets the similarity criterion, outputting the data set to be analyzed, wherein the data set to be analyzed indicates a subset of the set of standard sample feature vectors and a similarity between the fluorescence feature vector of the sample to be tested and the corresponding standard sample feature vector in the subset, and the subset of the set of standard sample feature vectors includes standard sample feature vectors whose similarity to the fluorescence feature vector of the sample to be tested meets the similarity criterion, and wherein the data analysis module is configured to: calculate an ash value of the sample to be tested based on the subset of the set of standard sample feature vectors and a subset similarity between the fluorescence feature vector of the sample to be tested and the corresponding standard sample feature vector in the subset.

[0008] The device according to any one of the above, wherein the database module is further configured to use database functions to: sort the similarities between the fluorescence feature vector of the sample to be tested and each standard sample feature vector in the set of standard sample feature vectors; and output the data set to be analyzed based on the sorting result, wherein a subset of the set of standard sample feature vectors includes the top N standard sample feature vectors whose similarities with the fluorescence feature vector of the sample to be tested meet the similarity standard, and N is an integer not less than 1.

[0009] The device according to any one of the above, wherein the calculated similarity between the fluorescence feature vector of the sample to be tested and each standard sample feature vector in the set of standard sample feature vectors is a first similarity calculated based on a first similarity algorithm, and wherein the database module is further configured to use database functions to: calculate a second similarity between the fluorescence feature vector of the sample to be tested and each standard sample feature vector in the set of standard sample feature vectors based on a second similarity algorithm, the second similarity algorithm being different from the first similarity algorithm; sort the second similarities between the fluorescence feature vector of the sample to be tested and each standard sample feature vector in the set of standard sample feature vectors; and output the data set to be analyzed based on the sorting result, wherein a subset of the set of standard sample feature vectors includes the top N standard sample feature vectors whose first similarities with the fluorescence feature vector of the sample to be tested meet the similarity standard and whose second similarities are the highest, and N is an integer not less than 1.

[0010] The device according to any one of the above, wherein the database module is further configured to use database functions to: in response to determining that the similarities between the fluorescence feature vector of the sample to be tested and each standard sample feature vector in the set of standard sample feature vectors do not meet the similarity standard, output the data set to be analyzed indicating the fluorescence feature vector of the sample to be tested, and wherein the data analysis module is configured to: calculate the ash value of the sample to be tested according to the sample ash analysis model based on the fluorescence feature vector of the sample to be tested.

[0011] The device according to any one of the above, wherein the device further includes a database update module, the database update module being coupled to the data analysis module and configured to: update the database based on the fluorescence feature vector of the sample to be tested whose similarities with each standard sample feature vector in the set of standard sample feature vectors are all less than the similarity threshold and the calculated ash value of the sample to be tested.

[0012] The device according to any one of the above, wherein the data acquisition module is configured to generate a fluorescence feature vector of the sample to be measured based on the fluorescence reaction data of a plurality of sampling points on the sample to be measured.

[0013] In a second aspect of the present invention, there is provided a method for ash analysis, comprising: using a database function to: obtain a fluorescence feature vector of a sample to be measured; search in a database for a set of standard sample feature vectors that are similar in acquisition time and have the same channel address as the fluorescence feature vector of the sample to be measured, wherein the set of standard sample feature vectors includes one or more standard sample feature vectors, and the standard sample feature vectors indicate the fluorescence reaction characteristics and ash values of standard samples; calculate the similarity between the fluorescence feature vector of the sample to be measured and each standard sample feature vector in the set of standard sample feature vectors; and based on the similarity between the fluorescence feature vector of the sample to be measured and each standard sample feature vector in the set of standard sample feature vectors, output a data set to be analyzed, wherein the data set to be analyzed is output to a data analysis device through the database for the data analysis device to calculate the ash value of the sample to be measured based on the data set to be analyzed.

[0014] The method as described above, wherein using a database function to output a data set to be analyzed includes: determining whether the similarity between the fluorescence feature vector of the sample to be measured and each standard sample feature vector in the set of standard sample feature vectors meets a similarity criterion; and in response to determining that the similarity between the fluorescence feature vector of the sample to be measured and at least one standard sample feature vector in the set of standard sample feature vectors meets the similarity criterion, output the data set to be analyzed, wherein the data set to be analyzed indicates a subset of the set of standard sample feature vectors and the similarity between the fluorescence feature vector of the sample to be measured and the corresponding standard sample feature vector in the subset, and the subset of the set of standard sample feature vectors includes standard sample feature vectors whose similarity to the fluorescence feature vector of the sample to be measured meets the similarity criterion, and wherein the data analysis device calculates the ash value of the sample to be measured based on the subset of the set of standard sample feature vectors and the subset similarity between the fluorescence feature vector of the sample to be measured and the corresponding standard sample feature vector in the subset.

[0015] The method according to any one of the above, wherein outputting the data set to be analyzed by using a database function includes: sorting the similarities between the fluorescence feature vector of the sample to be tested and each standard sample feature vector in the set of standard sample feature vectors; and outputting the data set to be analyzed based on the sorting result, wherein a subset of the set of standard sample feature vectors includes the top N standard sample feature vectors whose similarities with the fluorescence feature vector of the sample to be tested meet the similarity criterion, and N is an integer not less than 1.

[0016] The method according to any one of the above, wherein the calculated similarity between the fluorescence feature vector of the sample to be tested and each standard sample feature vector in the set of standard sample feature vectors is a first similarity calculated based on a first algorithm, and wherein outputting the data set to be analyzed by using a database function includes: calculating a second similarity between the fluorescence feature vector of the sample to be tested and each standard sample feature vector in the set of standard sample feature vectors based on a second algorithm, the second algorithm being different from the first algorithm; sorting the second similarities between the fluorescence feature vector of the sample to be tested and each standard sample feature vector in the set of standard sample feature vectors; and outputting the data set to be analyzed based on the sorting result, wherein a subset of the set of standard sample feature vectors includes the top N standard sample feature vectors whose first similarities with the fluorescence feature vector of the sample to be tested meet the similarity criterion and whose second similarities are the highest, and N is an integer not less than 1.

[0017] The method according to any one of the above, wherein outputting the data set to be analyzed by using a database function includes: if the similarities between the fluorescence feature vector of the sample to be tested and each standard sample feature vector in the set of standard sample feature vectors do not meet the similarity criterion, outputting the data set to be analyzed indicating the fluorescence feature vector of the sample to be tested, and wherein the data analysis device calculates the ash value of the sample to be tested according to a sample ash analysis model based on the fluorescence feature vector of the sample to be tested.

[0018] The method according to any one of the above, further comprising: updating the database based on the fluorescence feature vectors of the samples to be tested whose similarities with each standard sample feature vector in the set of standard sample feature vectors are all less than the similarity threshold, and the calculated ash values of the samples to be tested.

[0019] In a third aspect of the present invention, an on-line fluorescence analyzer is provided, comprising: a fluorescence device configured to emit excitation light to a sample to be tested and receive a fluorescence signal from the sample to be tested; and the device according to any one of the above.

[0020] In a fourth aspect of the present invention, there is provided a machine-readable medium including machine-readable instructions which, when executed, cause a machine to perform the method according to any one of claims 8-13.

[0021] In a fourth aspect of the present invention, there is provided a computer program product including computer instructions which, when executed, cause a machine to perform the method according to any one of claims 8-13. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] The above and other objects, features and advantages of the exemplary embodiments of the present invention will become readily apparent by reading the following detailed description with reference to the accompanying drawings. In the drawings, several embodiments of the present invention are shown by way of example and not limitation, and like or corresponding reference numerals indicate like or corresponding parts, wherein:

[0023] Figure 1 A schematic block diagram showing an ash analysis device according to at least some embodiments of the present invention;

[0024] Figure 2 A schematic flow chart showing an ash analysis method according to at least some embodiments of the present invention;

[0025] Figure 3 A schematic flow chart showing an ash analysis method according to some other embodiments of the present invention;

[0026] Figure 4 A schematic flow chart showing an ash analysis method according to some other embodiments of the present invention;

[0027] Figure 5 A schematic flow chart showing an ash analysis method according to some other embodiments of the present invention; and

[0028] Figure 6 A schematic block diagram showing a fluorescence analyzer according to an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0029] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0030] It should be understood that the terms "comprising" and "including" as used in the specification and claims of the present invention indicate the presence of the described features, wholes, steps, operations, elements and / or components, but do not preclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or their combinations.

[0031] It should also be understood that the terms used in the specification of the present invention are for the purpose of describing particular embodiments only and are not intended to limit the present invention. As used in the specification and claims of the present invention, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include the plural forms. It should be further understood that the term "and / or" as used in the specification and claims of the present invention refers to any combination and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0032] As used in this specification and the claims, the term "if" can be interpreted as "when" or "once" or "in response to determining" or "in response to detecting" depending on the context. Similarly, the phrase "if determined" or "if [the described condition or event] is detected" can be interpreted as meaning "once determined" or "in response to determining" or "once [the described condition or event] is detected" or "in response to detecting [the described condition or event]" depending on the context.

[0033] Figure 1 An ash analysis device 10 according to at least some embodiments of the present invention is shown.

[0034] As Figure 1 shown, the ash analysis device 10 may include a data acquisition module 102. According to at least some embodiments of the present invention, the data acquisition module 102 is configured to obtain a fluorescence feature vector of a sample to be tested. In at least some embodiments of the present invention, ore can be obtained by a mining device, and an ore sample with an appropriate physical form (e.g., size, shape, etc.) can be prepared to facilitate the emission of excitation light thereto by a light source. The ore sample is irradiated with the excitation light and produces a fluorescence reaction, thereby providing a fluorescence signal. According to an embodiment of the present invention, the data acquisition module 102 can obtain the fluorescence signal from the ore sample, extract the fluorescence features of the ore sample from the fluorescence signal, and generate a fluorescence feature vector of the ore sample. As an exemplary implementation, the signal peak values of the fluorescence signal in one or more sampling periods can be used as the corresponding fluorescence feature values, and the fluorescence feature vector of the ore sample can be generated based on the fluorescence feature values.

[0035] According to at least some embodiments of the present invention, the data acquisition module 102 may acquire fluorescence signals for a plurality of sampling points on the ore sample to be measured, and calculate fluorescence characteristic values based on the fluorescence signals of the plurality of sampling points (for example, averaging the fluorescence characteristic values of each sampling point, etc.). As an exemplary implementation, the ore sample to be measured may be placed on a conveying mechanism, and the ore sample to be measured moves through the area below the excitation light source as the conveying mechanism moves, so that the excitation light source irradiates different positions on the ore sample in different emission rounds, thereby enabling fluorescence signals to be obtained from a plurality of different sampling points. As another exemplary implementation, an end effector may be used to grasp the ore sample to be measured and rotate it near the excitation light source, so that the excitation light source irradiates different positions on the ore sample in different emission rounds, and fluorescence signals are obtained from a plurality of different sampling points. According to the embodiments of the present invention, the number of sampling points may be any integer greater than 0. For example, the number of sampling points may be set to 1, 2, 3, 4, 10, 15, 20, and so on. According to at least some embodiments of the present invention, considering the sampling efficiency and calculation accuracy, the data acquisition module 102 may acquire fluorescence signals for 4 sampling points on the ore sample to be measured, calculate fluorescence characteristic values based on the fluorescence signals of these sampling points, and finally generate a fluorescence characteristic vector of the ore sample.

[0036] As Figure 1 shown, the ash analysis device 10 may include a database module 104, and the database module 104 may be coupled to the data acquisition module 102. According to at least some embodiments of the present invention, the database module 104 may be configured to obtain the fluorescence characteristic vector of the ore sample to be measured from the data acquisition module 102 by using database functions. As an exemplary implementation, the inset() function may be used to insert the fluorescence characteristic vector obtained from the data acquisition module 102 into the database. As another exemplary implementation, a database connection tool may be used to automatically read the fluorescence characteristic vector of the ore sample to be measured from the data acquisition module 102 into the database.

[0037] According to the embodiments of the present invention, the databases used herein may come from various suppliers and pre-store characteristic vectors for various standard samples. The characteristic vectors of these standard samples indicate the fluorescence reaction characteristics and ash values of the standard samples. According to the embodiments of the present invention, the standard sample is an ore sample with a known ash value, and the fluorescence reaction characteristic of the standard sample is based on the fluorescence signal obtained by irradiating the standard sample with a known ash with excitation light.

[0038] According to an embodiment of the present invention, the database module 104 may be configured to search, in a database, for a set of standard sample feature vectors that are similar in fluorescence feature vector acquisition time to the test sample and have the same channel address by using database functions. According to an embodiment of the present invention, the set of standard sample feature vectors includes one or more standard sample feature vectors, and each standard sample feature vector indicates the fluorescence reaction characteristics and ash values of the corresponding standard sample. As an example, assume that the fluorescence feature vector obtained by the database module 104 from the data acquisition module 102 is based on the fluorescence signal collected at the sampling time T and having the channel addresses C1-C N Then, the database module 104 may use the select() function and set the keywords to the sampling time T and the channel addresses C1-C N to retrieve the standard sample feature vectors in the database.

[0039] According to an embodiment of the present invention, the database module 104 may be configured to calculate the similarity between the fluorescence feature vector of the test sample and each standard sample feature vector in the set of standard sample feature vectors. As an exemplary implementation, the Euclidean distance between the fluorescence feature vector of the test sample and each standard sample feature vector in the set of standard sample feature vectors may be calculated. For example, functions such as the sum() function and the pow() function may be used to calculate the Euclidean distance between the fluorescence feature vector of the test sample and each standard sample feature vector in the set of standard sample feature vectors.

[0040] According to an embodiment of the present invention, the database module 104 may also be configured to output a dataset to be analyzed based on the similarity between the fluorescence feature vector of the test sample and each standard sample feature vector in the set of standard sample feature vectors.

[0041] As Figure 1 shown, the ash analysis device 10 may further include a data analysis module 106, and the data analysis module 106 may be coupled to the database module 104. According to an embodiment of the present invention, the data analysis module 106 may be configured to calculate the ash value of the test sample based on the dataset to be analyzed. As described above, the standard sample is an aggregate sample with a known ash value. Therefore, according to an embodiment of the present invention, the ash value of the test sample may be calculated based on the ash value of the standard sample indicated by the dataset to be analyzed and similar to the test sample, according to the similarity of the fluorescence reaction.

[0042] According to at least some embodiments of the present invention, the database module 104 may screen the set of standard sample feature vectors obtained from the database search based on similarity, and eliminate the standard sample feature vectors that are not similar to the fluorescence feature vector of the sample to be measured, so as to output the data to be analyzed based on the standard sample feature vectors that are similar to the fluorescence feature vector of the sample to be measured. On this basis, the data analysis module 106 may calculate the ash value of the sample to be measured based on the ash values of the standard samples that are sufficiently similar to the sample to be measured, thereby reducing the computational complexity and improving the accuracy of ash analysis.

[0043] According to an embodiment of the present invention, the database module 104 may be configured to determine whether the similarity between the fluorescence feature vector of the sample to be measured and each standard sample feature vector in the set of standard sample feature vectors meets the similarity criterion.

[0044] As an exemplary implementation, the database module 104 may calculate the Euclidean distance between the fluorescence feature vector of the sample to be measured and each standard sample feature vector in the set of standard sample feature vectors, and use the select() function to determine whether there is a standard sample feature vector in the set of standard sample feature vectors whose Euclidean distance from the fluorescence feature vector of the sample to be measured is less than the Euclidean distance threshold. For example, the select() function may be used to compare the Euclidean distance between the fluorescence feature vector of the sample to be measured and at least one standard sample feature vector in the set of standard sample feature vectors with the Euclidean distance threshold. When the Euclidean distance between the fluorescence feature vector of the sample to be measured and the standard sample feature vector is less than the Euclidean distance threshold, the select() function returns "1", and when the Euclidean distance between the fluorescence feature vector of the sample to be measured and the standard sample feature vector is not less than the Euclidean distance threshold, the select() function returns "0".

[0045] According to an embodiment of the present invention, if the database module 104 determines that there is a standard sample feature vector in the standard sample feature vector set whose Euclidean distance from the fluorescence feature vector of the sample to be measured is less than the Euclidean distance threshold (that is, its similarity meets the similarity threshold), then the database module 104 can output the data to be analyzed based on these standard sample feature vectors whose Euclidean distance from the fluorescence feature vector of the sample to be measured is less than the Euclidean distance threshold and the corresponding Euclidean distances. As another exemplary implementation, the database module 104 can directly use the select() function to return the sample feature vectors whose similarity meets the similarity standard to obtain the data set to be analyzed. According to an embodiment of the present invention, the data set to be analyzed indicates a subset of the standard sample feature vector set and the similarity between the fluorescence feature vector of the sample to be measured and the corresponding standard sample feature vectors in the subset. According to an embodiment of the present invention, the data analysis module 106 can calculate the ash value of the sample to be measured based on the fluorescence feature values indicated by the standard sample feature vectors in the subset of the standard sample feature vector set, the similarity between the corresponding standard sample feature vectors and the fluorescence feature vector of the sample to be measured, and the ash values of the corresponding standard samples.

[0046] According to an embodiment of the present invention, if the database module 104 determines that there is no standard sample feature vector in the standard sample feature vector set whose Euclidean distance from the fluorescence feature vector of the sample to be measured is less than the Euclidean distance threshold (that is, the similarity of each standard sample feature vector does not meet the similarity threshold), then the database module 104 can output the data set to be analyzed based on the fluorescence feature vector of the sample to be measured. For example, the database module 104 can directly use the select() function to return the sample feature vectors whose similarity meets the similarity standard. If the select() function can return "null", it indicates that the database module 104 determines that there is no standard sample feature vector in the standard sample feature vector set whose Euclidean distance from the fluorescence feature vector of the sample to be measured is less than the Euclidean distance threshold. According to an embodiment of the present invention, the fact that the similarity between each standard sample feature vector in the standard sample feature vector set and the fluorescence feature vector of the sample to be measured does not meet the similarity standard may indicate that there is no relevant data of a standard sample in the current database that is similar enough to the sample to be measured and can be used to calculate the ash value of the sample to be measured. According to an embodiment of the present invention, the data analysis module 106 can calculate the ash value of the sample to be measured according to any known fluorescence ash analysis model and based on various fluorescence features indicated by the fluorescence feature vector of the sample to be measured.

[0047] To further improve the accuracy of ash analysis, in at least one embodiment of the present invention, the database module 104 may further screen a subset of the standard sample feature vector set. As an exemplary implementation, the database module 104 may use the sort() function to sort the similarity between the fluorescence feature vector of the sample to be tested and the corresponding standard sample feature vector in the standard sample feature vector set, and select the N (N is an integer not less than 1) standard sample feature vectors with the highest similarity to the fluorescence feature vector of the sample to be tested (for example, the smallest Euclidean distance from the fluorescence feature vector of the sample to be tested), and output the data set to be analyzed based on these N standard sample feature vectors. According to an embodiment of the present invention, the output data set to be analyzed indicates a subset of the standard sample feature vector set and the similarity between the fluorescence feature vector of the sample to be tested and the corresponding standard sample feature vector in the subset, and the subset of the standard sample feature vector set includes these N standard sample feature vectors with respect to the fluorescence feature vector of the sample to be tested.

[0048] The similarity between the fluorescence feature vector of the sample to be tested and the standard sample feature vector is described above with reference to the Euclidean distance. However, those skilled in the art should understand that in other embodiments of the present invention, the cosine similarity, dot product similarity, etc. between the fluorescence feature vector of the sample to be tested and the standard sample feature vector can also be calculated.

[0049] According to at least some embodiments of the present invention, the database module 104 may be configured to calculate the Euclidean distance between the fluorescence feature vector of the sample to be tested and each standard sample feature vector in the standard sample feature vector set, and perform a first screening on the standard sample feature vector set based on whether the Euclidean distance is less than the Euclidean distance threshold to obtain a subset of the standard sample feature vector set. In addition, the database module 104 may be configured to calculate the cosine similarity between the fluorescence feature vector of the sample to be tested and each standard sample feature vector in the subset of the standard sample feature vector set, and sort the cosine similarity, and output the data set to be analyzed based on the N (N is an integer not less than 1) standard sample feature vectors with the highest cosine similarity to the fluorescence feature vector of the sample to be tested. According to an embodiment of the present invention, the output data set to be analyzed indicates a subset of the standard sample feature vector set and the cosine similarity between the fluorescence feature vector of the sample to be tested and the corresponding standard sample feature vector in the subset, and the subset of the standard sample feature vector set includes N (N is an integer not less than 1) standard sample feature vectors with an Euclidean distance less than the Euclidean distance threshold and the highest cosine similarity to the fluorescence feature vector of the sample to be tested.

[0050] As Figure 1As shown, the ash analysis device 10 may optionally further include a database update module 108. According to an embodiment of the present invention, the database update module 108 may be coupled to the data analysis module 106 to receive the relevant fluorescence characteristics and ash values of the sample to be tested from the data analysis module 106. According to an embodiment of the present invention, in the case where there is no relevant data of a standard sample similar to the fluorescence feature vector of the sample to be tested in the database (for example, the database module 104 determines that the similarity between each standard sample feature vector in the standard sample feature vector set and the fluorescence feature vector of the sample to be tested does not meet the similarity threshold), the data analysis module 106 calculates the ash value of the sample to be tested based on the fluorescence ash analysis model, and outputs the fluorescence feature vector and the calculated ash value of the sample to be tested to the database update module 108. According to an embodiment of the present invention, the database update module 108 performs data fusion on the fluorescence feature vector and ash value of the sample to be tested, and periodically inserts this data into the database to update the database, thereby facilitating the ash analysis of subsequent samples to be tested.

[0051] As described above, in the ash analysis device proposed by the present invention, the database module uses the built-in functions of the database to screen the original data and perform simple calculations, so that at least some operations in the ash analysis are migrated to the database. The data analysis module only needs to obtain the data to be analyzed from the database module (specifically, the ash value of the screened standard sample and the similarity between the sample to be tested and it) to calculate the ash value of the mineral aggregate, which can significantly save the storage and computing resources of the terminal device and improve the efficiency and accuracy of the ash analysis. In addition, the ash analysis device proposed by the present invention can avoid a large number of memory access operations, thereby avoiding problems such as memory crashes and data leaks.

[0052] The present invention also provides a method for performing ash analysis. These methods provided by the present invention are executed by the database based on the built-in functions of the database.

[0053] Figure 2 FIG. shows a schematic block diagram of an ash analysis method 20 according to an embodiment of the present invention.

[0054] As Figure 2 shown, the method 20 may include: at 202, obtaining a fluorescence feature vector of the mineral aggregate sample to be tested by using a database function. As an exemplary implementation, the inset() function may be used to insert the fluorescence feature vector of the mineral aggregate sample to be tested into the database. As another exemplary implementation, a database connection tool may be used to automatically read the fluorescence feature vector of the mineral aggregate sample to be tested into the database.

[0055] As Figure 2As shown, method 20 may further include: at 204, searching in a database for a set of standard sample feature vectors that are similar in fluorescence feature vector acquisition time to the sample to be tested and have the same channel address. According to an embodiment of the present invention, the set of standard sample feature vectors includes one or more standard sample feature vectors, and each standard sample feature vector indicates the fluorescence reaction feature and the ash value of the standard sample. As an example, the fluorescence feature vector of the sample to be tested is based on the fluorescence signal collected at sampling time T and with channel addresses C1 - C N then the select() function can be used to set the keyword as sampling time T and channel addresses C1 - C N to retrieve the standard sample feature vectors in the database.

[0056] As Figure 2 shown, method 20 may further include: at 206, calculating the similarity between the fluorescence feature vector of the sample to be tested and each standard sample feature vector in the set of standard sample feature vectors. As an exemplary implementation, the Euclidean distance between the fluorescence feature vector of the sample to be tested and each standard sample feature vector in the set of standard sample feature vectors can be calculated. For example, functions such as sum() and pow() can be used to calculate the Euclidean distance between the fluorescence feature vector of the sample to be tested and each standard sample feature vector in the set of standard sample feature vectors.

[0057] As Figure 2 shown, method 20 may further include: at 208, outputting a data set to be analyzed based on the similarity between the fluorescence feature vector of the sample to be tested and each standard sample feature vector in the set of standard sample feature vectors.

[0058] According to an embodiment of the present invention, the data set to be analyzed is output to the data analysis device of the terminal device (for example, the data analysis module 106 described above), and the data analysis device calculates the ash value of the sample to be tested based on the data set to be analyzed. Figure 1 As described above, the ash analysis method 20 according to an embodiment of the present invention is executed in the database based on the built-in functions of the database, thereby being able to significantly relieve the storage and calculation pressure of the terminal device and being able to avoid a large number of memory access operations, thus being able to avoid problems such as memory crashes and data leaks.

[0059]

[0060] Figure 3

[0061] Figure 3 Figure 3 As Figure 3As shown, method 30 may include: at 302, obtaining a fluorescence feature vector of a mineral sample to be tested by using a database function; at 304, searching in the database for a set of standard sample feature vectors that are similar in acquisition time and have the same channel address as the fluorescence feature vector of the sample to be tested by using a database function; at 306, calculating the similarity between the fluorescence feature vector of the sample to be tested and each standard sample feature vector in the set of standard sample feature vectors by using a database function.

[0062] As Figure 3 As further shown, method 30 may further include: at 308, determining whether the similarity between the fluorescence feature vector of the sample to be tested and each standard sample feature vector in the set of standard sample feature vectors meets a similarity criterion by using a database function. According to at least some embodiments of the present invention, method 30 may screen the set of standard sample feature vectors obtained from the database search based on the similarity, and eliminate the standard sample feature vectors that are not similar to the fluorescence feature vector of the sample to be tested, so as to output data to be analyzed based on the standard sample feature vectors that are similar to the fluorescence feature vector of the sample to be tested. As an exemplary implementation, the Euclidean distance between the fluorescence feature vector of the sample to be tested and each standard sample feature vector in the set of standard sample feature vectors may be calculated, and the select() function may be used to determine whether there is a standard sample feature vector in the set of standard sample feature vectors whose Euclidean distance from the fluorescence feature vector of the sample to be tested is less than the Euclidean distance threshold. For example, the select() function may be used to compare the Euclidean distance between the fluorescence feature vector of the sample to be tested and at least one standard sample feature vector in the set of standard sample feature vectors with the Euclidean distance threshold. When the Euclidean distance between the fluorescence feature vector of the sample to be tested and the standard sample feature vector is less than the Euclidean distance threshold, the select() function returns "1", and when the Euclidean distance between the fluorescence feature vector of the sample to be tested and the standard sample feature vector is not less than the Euclidean distance threshold, the select() function returns "0".

[0063] As Figure 3As shown, method 30 may further include: in response to determining that the similarity between the fluorescence feature vector of the sample to be tested and at least one standard sample feature vector in the set of standard sample feature vectors meets the similarity criterion at 308 (for example, the Euclidean distance between the fluorescence feature vector of the sample to be tested and at least one standard sample feature vector in the set of standard sample feature vectors is less than the Euclidean distance threshold), at 310, use a database function to output a data set to be analyzed based on the at least one standard sample feature vector whose similarity with the fluorescence feature vector of the sample to be tested meets the similarity criterion. As an exemplary implementation, the standard sample feature vector corresponding to when the select() function returns "1" can be used to output the data set to be analyzed. According to an embodiment of the present invention, the data set to be analyzed output at 310 may indicate a subset of the set of standard sample feature vectors, and the similarity between the fluorescence feature vector of the sample to be tested and the corresponding standard sample feature vector in the subset, and the subset of the set of standard sample feature vectors includes the standard sample feature vectors whose similarity with the fluorescence feature vector of the sample to be tested meets the similarity criterion.

[0064] As Figure 3 As shown, method 30 may further include: in response to determining that the similarity between the fluorescence feature vector of the sample to be tested and each standard sample feature vector in the set of standard sample feature vectors does not meet the similarity criterion at 308 (for example, the Euclidean distance between the fluorescence feature vector of the sample to be tested and each standard sample feature vector in the set of standard sample feature vectors is not less than the Euclidean distance threshold), at 312, a data set to be analyzed may be output based on the fluorescence feature vector of the sample to be tested. According to an embodiment of the present invention, the fact that the similarity between each standard sample feature vector in the set of standard sample feature vectors and the fluorescence feature vector of the sample to be tested does not meet the similarity criterion may indicate that there is no relevant data of a standard sample in the current database that is similar enough to the sample to be tested to be used for calculating the gray value of the sample to be tested. Thus, a data set to be analyzed is directly output based on the fluorescence feature vector of the sample to be tested.

[0065] According to an embodiment of the present invention, the data set to be analyzed is output to the data analysis device of the terminal device (for example, as referred to above Figure 1The described data analysis module 106), and the data analysis device calculates the ash value of the sample to be measured based on the dataset to be analyzed. Since the data analysis device can calculate the ash value of the sample to be measured based on the ash value of a standard sample that is sufficiently similar to the sample to be measured, the calculation complexity can be reduced and the accuracy of ash analysis can be improved. According to an embodiment of the present invention, in the case where there is no standard sample in the database that is sufficiently similar to the sample to be measured, the dataset to be analyzed based on the fluorescence feature vector of the sample to be measured is output to the data analysis device of the terminal device, and the data analysis device of the terminal device can calculate the ash value of the sample to be measured according to any known fluorescence ash analysis model and based on various fluorescence features indicated by the fluorescence feature vector of the sample to be measured, thereby further ensuring the calculation of the ash value of the sample to be measured.

[0066] The ash analysis method 30 described according to an embodiment of the present invention describes screening data in the database based on similarity and outputting the data to be analyzed to the data analysis device of the terminal device based on the screened data, thereby further alleviating the storage and calculation pressure of the terminal device.

[0067] Figure 4 An ash analysis method 40 according to some other embodiments of the present invention is shown.

[0068] As Figure 4 shown, the method 40 may include: at 402, obtaining the fluorescence feature vector of the ore sample to be measured using a database function; at 404, searching in the database using a database function for a set of standard sample feature vectors that are similar in acquisition time and have the same channel address as the fluorescence feature vector of the sample to be measured; at 406, calculating the similarity between the fluorescence feature vector of the sample to be measured and each standard sample feature vector in the set of standard sample feature vectors using a database function; at 408, determining using a database function whether the similarity between the fluorescence feature vector of the sample to be measured and each standard sample feature vector in the set of standard sample feature vectors satisfies the similarity criterion; in response to determining at 408 that the similarity between the fluorescence feature vector of the sample to be measured and each standard sample feature vector in the set of standard sample feature vectors does not satisfy the similarity criterion, at 412, outputting the dataset to be analyzed based on the fluorescence feature vector of the sample to be measured using a database function.

[0069] In addition, as Figure 4As shown, method 40 may further include: in response to determining that the similarity between the fluorescence feature vector of the sample to be tested and at least one standard sample feature vector in the standard sample feature vector set satisfies the similarity criterion at 408 (for example, the Euclidean distance between the fluorescence feature vector of the sample to be tested and at least one standard sample feature vector in the standard sample feature vector set is less than the Euclidean distance threshold), at 410, use a database function to sort the similarity between the fluorescence feature vector of the sample to be tested and each standard sample feature vector in the standard sample feature vector set. As an exemplary implementation, the sort() function may be used to sort the similarity between the fluorescence feature vector of the sample to be tested and the corresponding standard sample feature vector in the standard sample feature vector set.

[0070] As Figure 4 As shown, method 40 may further include: at 414, based on the result of the similarity sorting performed at 410, use a database function to output a data set to be analyzed. According to an embodiment of the present invention, the data set to be analyzed output at 410 may indicate a subset of the standard sample feature vector set and the similarity between the fluorescence feature vector of the sample to be tested and the corresponding standard sample feature vector in the subset, and the subset of the standard sample feature vector set includes the N (N is an integer not less than 1) standard sample feature vectors with the highest similarity that satisfy the similarity criterion with the fluorescence feature vector of the sample to be tested. As an exemplary implementation, based on the sorting result of the sort() function, select the N (N is an integer not less than 1) standard sample feature vectors with the highest similarity (for example, the smallest Euclidean distance) to the fluorescence feature vector of the sample to be tested, and output the data set to be analyzed based on these N standard sample feature vectors. According to an embodiment of the present invention, the output data set to be analyzed indicates a subset of the standard sample feature vector set and the similarity between the fluorescence feature vector of the sample to be tested and the corresponding standard sample feature vector in the subset, and the subset of the standard sample feature vector set includes these N standard sample feature vectors with the fluorescence feature vector of the sample to be tested.

[0071] According to an embodiment of the present invention, the data set to be analyzed is output to the data analysis device of the terminal device (for example, the data analysis module 106 described above with reference to Figure 1 ), and the data analysis device calculates the ash value of the sample to be tested based on the data set to be analyzed.

[0072] The method 40 according to an embodiment of the present invention further screens the data in the database based on similarity, and outputs the data to be analyzed to the data analysis device of the terminal device based on the data after two screenings, so that the data analysis device can calculate the ash value of the sample to be measured based on the ash value of the standard sample that is sufficiently similar to the sample to be measured, thereby further reducing the calculation complexity and improving the accuracy of ash analysis.

[0073] Figure 5 An ash analysis method 50 according to some other embodiments of the present invention is shown.

[0074] As Figure 5 shown, the method 50 may include: at 502, obtaining a fluorescence feature vector of the sample of ore to be measured by using a database function; at 504, searching in the database by using the database function for a set of standard sample feature vectors whose collection time of the fluorescence feature vector is similar to that of the sample to be measured and whose channel addresses are the same.

[0075] As Figure 5 shown, the method 50 may further include: at 506, calculating a first similarity between the fluorescence feature vector of the sample to be measured and each standard sample feature vector in the set of standard sample feature vectors based on a first similarity algorithm by using the database function. As an exemplary implementation, the Euclidean distance between the fluorescence feature vector of the sample to be measured and each standard sample feature vector in the set of standard sample feature vectors may be calculated by using the database function to indicate the first similarity between the fluorescence feature vector of the sample to be measured and each standard sample feature vector in the set of standard sample feature vectors.

[0076] As Figure 5 shown, the method 50 may further include: at 508, determining whether the first similarity between the fluorescence feature vector of the sample to be measured and each standard sample feature vector in the set of standard sample feature vectors meets the similarity standard by using the database function. As an exemplary implementation, it may be determined whether the Euclidean distance between the fluorescence feature vector of the sample to be measured and each standard sample feature vector in the set of standard sample feature vectors is less than the Euclidean distance threshold for the set of standard sample feature vectors, where a Euclidean distance less than the Euclidean distance threshold indicates that the first similarity meets the similarity standard, and a Euclidean distance not less than (i.e., greater than or equal to) the Euclidean distance threshold indicates that the first similarity does not meet the similarity standard.

[0077] As Figure 5As shown, method 50 may further include: in response to determining that the similarity between the fluorescence feature vector of the sample to be tested and at least one standard sample feature vector in the standard sample feature vector set satisfies the similarity criterion (e.g., the Euclidean distance between the fluorescence feature vector of the sample to be tested and at least one standard sample feature vector in the standard sample feature vector set is less than the Euclidean distance threshold) at 508, at 510, use a database function to calculate the second similarity between the fluorescence feature vector of the sample to be tested and the at least one standard sample feature vector based on a second similarity algorithm. According to an embodiment of the present invention, the second similarity algorithm is different from the first similarity algorithm used at 506. As an exemplary implementation, a database function can be used to calculate the cosine similarity between the fluorescence feature vector of the sample to be tested and the corresponding standard sample feature vector in the standard sample feature vector set to indicate the second similarity between the fluorescence feature vector of the sample to be tested and the corresponding standard sample feature vector in the standard sample feature vector set.

[0078] As Figure 5 shown, method 50 may further include: at 514, use a database function to sort the second similarities between the fluorescence feature vector of the sample to be tested and the corresponding standard sample feature vectors in the standard sample feature vector set. As an exemplary implementation, the sort() function can be used to sort the cosine similarities between the fluorescence feature vector of the sample to be tested and the corresponding standard sample feature vectors in the standard sample feature vector set.

[0079] As Figure 5 shown, method 50 may further include: at 516, use a database function to output a dataset to be analyzed based on the result of the similarity sorting performed at 514. According to an embodiment of the present invention, the dataset to be analyzed output at 516 may indicate a subset of the standard sample feature vector set and the second similarity between the fluorescence feature vector of the sample to be tested and the corresponding standard sample feature vectors in the subset, and the subset of the standard sample feature vector set includes N (N is an integer not less than 1) standard sample feature vectors whose first similarity with the fluorescence feature vector of the sample to be tested satisfies the similarity criterion and whose second similarity is the highest. As an exemplary implementation, based on the sorting result of the sort() function, select N (N is an integer not less than 1) standard sample feature vectors with the highest second similarity (e.g., the highest cosine similarity) to the fluorescence feature vector of the sample to be tested, and output a dataset to be analyzed based on these N standard sample feature vectors.

[0080] As Figure 5As shown, method 50 may further include: in response to determining that the similarity between the fluorescence feature vector of the sample to be tested and each standard sample feature vector in the set of standard sample feature vectors does not meet the similarity criterion at 508, at 512, outputting a data set to be analyzed based on the fluorescence feature vector of the sample to be tested using a database function.

[0081] The first similarity and the second similarity between the fluorescence feature vector of the sample to be tested and the standard sample feature vectors are described above with reference to the Euclidean distance and the cosine similarity. However, those skilled in the art should understand that the cosine similarity can also be used as a measure of the first similarity and the Euclidean distance can be used as a measure of the second similarity. Moreover, in other embodiments of the present invention, other similarity measures between the fluorescence feature vector of the sample to be tested and the standard sample feature vectors can also be calculated, such as dot product similarity, etc.

[0082] In the ash analysis method 50 according to an embodiment of the present invention, first, the first similarity between the fluorescence feature vector of the sample to be tested and each standard sample feature vector in the set of standard sample feature vectors is calculated based on the first similarity algorithm, and the first similarity is used to screen the data to obtain a subset of the set of standard sample feature vectors; subsequently, the second similarity between the fluorescence feature vector of the sample to be tested and each standard sample feature vector in the subset of the set of standard sample feature vectors is calculated based on the second similarity algorithm, and the second similarity is used to screen the data again, so as to ensure that the similarity between the standard sample indicated by the output data to be analyzed and the sample to be tested is high enough, which can significantly relieve the storage and calculation pressure of the terminal device and ensure the accuracy of the ash analysis.

[0083] Figure 6 A schematic block diagram of a fluorescence analyzer 60 according to an embodiment of the present invention is shown.

[0084] As Figure 6 shown, the fluorescence analyzer 60 may include a fluorescence device 602 and an ash analysis device 10 (for example, the ash analysis device 10 described above with reference to Figure 1 . According to an embodiment of the present invention, the fluorescence device 602 may be coupled to the ash analysis device 10 for providing fluorescence feature data of the sample to be tested to the ash analysis device 10 (specifically, the data acquisition module 102 of the ash analysis device 10). According to an embodiment of the present invention, the fluorescence device 602 may include an excitation light source (not shown) for irradiating the sample to be tested with excitation light; in addition, the fluorescence device 602 may further include a fluorescence acquisition module for receiving the fluorescence generated by the sample to be tested under the action of the excitation light. According to an embodiment of the present invention, the fluorescence device 602 may directly provide a fluorescence image of the sample to be tested to the ash analysis device 10.

[0085] In the fluorescence analyzer 60 according to an embodiment of the present invention, the fluorescence data received by the fluorescence device 602 is provided to the ash analysis device 10, and data collection, screening, and calculation are performed by each module in the ash analysis module 10, and finally the ash value of the sample to be measured is calculated. According to an embodiment of the present invention, since some operations in ash analysis are migrated to the database and database built-in functions are used to execute, the terminal device only needs to obtain the intermediate calculation result from the database and calculate the ash value of the mineral aggregate based on the intermediate calculation result, which can significantly save the storage and calculation resources of the terminal device and improve the efficiency and accuracy of ash analysis, and there is less data transmission between the database and the terminal device. Thus, the fluorescence analyzer provided by the present invention is particularly suitable for online use to provide an efficient, accurate, and large-capacity ash analysis solution.

[0086] The devices, mechanisms, modules, etc. described in the embodiments of the present invention can be implemented in the form of hardware, software, or a combination thereof.

[0087] The present invention can be implemented by a computer program. By applying the program code to input instructions, the methods described herein are executed. For the purposes of this application, a control system / module can be used to execute the methods described herein, and the control system / module can include any system / module having a processor, such as, for example, a digital signal processor (DSP), a microcontroller, an application specific integrated circuit (ASIC), or a microprocessor. The program code can be implemented in a high-level procedural programming language or an object-oriented programming language to communicate with the control system / module.

[0088] One or more aspects of at least one embodiment of the present invention can be implemented by representative instructions stored on a machine-readable medium that represent various logics in a processor, which, when read by the machine, cause the machine to fabricate the logics for performing the technologies described herein.

[0089] The preferred embodiments of the present invention have been described in detail above. However, it should be understood that the present invention can adopt various embodiments and variations without departing from its broad spirit and scope. Those of ordinary skill in the art can make many modifications and changes based on the concept of the present invention without creative labor. Therefore, all technical solutions that can be obtained by those skilled in the art based on the concept of the present invention through logical analysis, reasoning, or limited experiments on the basis of the prior art shall fall within the protection scope determined by the claims of the present invention.

Claims

1. An apparatus for ash analysis, comprising: A data acquisition module configured to obtain a fluorescence feature vector of a sample to be measured; A database module coupled to the data acquisition module and configured to use database functions to: Obtain the fluorescence feature vector of the sample to be measured from the data acquisition module; Search in the database for a set of standard sample feature vectors with a similar acquisition time and the same channel address as the fluorescence feature vector of the sample to be measured, wherein the set of standard sample feature vectors includes one or more standard sample feature vectors, and the standard sample feature vectors indicate the fluorescence reaction characteristics and ash values of the standard samples; Calculate the similarity between the fluorescence feature vector of the sample to be measured and each standard sample feature vector in the set of standard sample feature vectors; and Output a data set to be analyzed based on the similarity between the fluorescence feature vector of the sample to be measured and each standard sample feature vector in the set of standard sample feature vectors; And A data analysis module coupled to the database module and configured to calculate the ash value of the sample to be measured based on the data set to be analyzed.

2. The apparatus according to claim 1, Among them, The database module is further configured to use database functions to: Determine whether the similarity between the fluorescence feature vector of the sample to be measured and each standard sample feature vector in the set of standard sample feature vectors meets the similarity criterion; and In response to determining that the similarity between the fluorescence feature vector of the sample to be measured and at least one standard sample feature vector in the set of standard sample feature vectors meets the similarity criterion, output the data set to be analyzed, wherein the data set to be analyzed indicates a subset of the set of standard sample feature vectors and the similarity between the fluorescence feature vector of the sample to be measured and the corresponding standard sample feature vector in the subset, and the subset of the set of standard sample feature vectors includes standard sample feature vectors whose similarity to the fluorescence feature vector of the sample to be measured meets the similarity criterion, And wherein the data analysis module is configured to: Calculate the ash value of the sample to be measured based on the subset of the set of standard sample feature vectors and the subset similarity between the fluorescence feature vector of the sample to be measured and the corresponding standard sample feature vector in the subset.

3. The device according to claim 2, wherein, The database module is further configured to use database functions to: Sort the similarities between the fluorescence feature vector of the sample to be measured and each standard sample feature vector in the set of standard sample feature vectors; And Output the data set to be analyzed based on the sorting result, wherein the subset of the set of standard sample feature vectors includes the top N standard sample feature vectors whose similarity to the fluorescence feature vector of the sample to be measured meets the similarity criterion, where N is an integer not less than 1.

4. The device according to claim 2, wherein, The calculated similarity between the fluorescence feature vector of the sample to be measured and each standard sample feature vector in the set of standard sample feature vectors is the first similarity calculated based on a first similarity algorithm, and wherein the database module is further configured to perform, using a database function: Calculating a second similarity between the fluorescence feature vector of the sample to be measured and each standard sample feature vector in the set of standard sample feature vectors based on a second similarity algorithm, the second similarity algorithm being different from the first similarity algorithm; Sorting the second similarities between the fluorescence feature vector of the sample to be measured and each standard sample feature vector in the set of standard sample feature vectors; and Outputting the data set to be analyzed based on the sorting result, wherein a subset of the set of standard sample feature vectors includes N standard sample feature vectors whose first similarity with the fluorescence feature vector of the sample to be measured meets the similarity criterion and whose second similarity is the highest, where N is an integer not less than 1.

5. The apparatus according to claim 2, Among them, The database module is further configured to perform, using a database function: In response to determining that the similarity between the fluorescence feature vector of the sample to be measured and each standard sample feature vector in the set of standard sample feature vectors does not meet the similarity criterion, outputting the data set to be analyzed indicating the fluorescence feature vector of the sample to be measured, And wherein the data analysis module is configured to: Calculating the ash content value of the sample to be measured according to a sample ash content analysis model based on the fluorescence feature vector of the sample to be measured.

6. The device according to claim 5, wherein, The apparatus further includes a database update module, the database update module being coupled to the data analysis module and configured to: Updating the database based on the fluorescence feature vector of the sample to be measured whose similarity with each standard sample feature vector in the set of standard sample feature vectors is less than a similarity threshold and the calculated ash content value of the sample to be measured.

7. The apparatus according to claim 1, wherein The data acquisition module is configured to generate a fluorescence feature vector of the sample to be measured based on fluorescence response data of a plurality of sampling points on the sample to be measured.

8. A method for ash content analysis, comprising: Performing, using a database function: Obtaining a fluorescence feature vector of a sample to be measured; Searching in a database for a set of standard sample feature vectors whose acquisition time is similar to that of the fluorescence feature vector of the sample to be measured and whose channel address is the same, wherein the set of standard sample feature vectors includes one or more standard sample feature vectors, and the standard sample feature vectors indicate the fluorescence response characteristics and ash content values of standard samples; Calculating the similarity between the fluorescence feature vector of the sample to be measured and each standard sample feature vector in the set of standard sample feature vectors; and Outputting a data set to be analyzed based on the similarity between the fluorescence feature vector of the sample to be measured and each standard sample feature vector in the set of standard sample feature vectors Wherein, the dataset to be analyzed is output to the data analysis device through the database, so that the data analysis device calculates the ash value of the sample to be measured based on the dataset to be analyzed.

9. The method according to claim 8, Among them, Outputting the dataset to be analyzed by using a database function includes: Determining whether the similarity between the fluorescence feature vector of the sample to be measured and each standard sample feature vector in the set of standard sample feature vectors meets the similarity criterion; and In response to determining that the similarity between the fluorescence feature vector of the sample to be measured and at least one standard sample feature vector in the set of standard sample feature vectors meets the similarity criterion, outputting the dataset to be analyzed, wherein the dataset to be analyzed indicates a subset of the set of standard sample feature vectors and the similarity between the fluorescence feature vector of the sample to be measured and the corresponding standard sample feature vector in the subset, and the subset of the set of standard sample feature vectors includes the standard sample feature vectors whose similarity to the fluorescence feature vector of the sample to be measured meets the similarity criterion. And wherein, the data analysis device calculates the ash value of the sample to be measured based on the subset of the set of standard sample feature vectors and the subset similarity between the fluorescence feature vector of the sample to be measured and the corresponding standard sample feature vector in the subset.

10. The method according to claim 9, wherein, Outputting the dataset to be analyzed by using a database function includes: Sorting the similarities between the fluorescence feature vector of the sample to be measured and each standard sample feature vector in the set of standard sample feature vectors; and Outputting the dataset to be analyzed based on the sorting result, wherein the subset of the set of standard sample feature vectors includes the top N standard sample feature vectors whose similarity to the fluorescence feature vector of the sample to be measured meets the similarity criterion, where N is an integer not less than 1.

11. The method according to claim 9, wherein, The calculated similarity between the fluorescence feature vector of the sample to be measured and each standard sample feature vector in the set of standard sample feature vectors is the first similarity calculated based on the first algorithm, and wherein outputting the dataset to be analyzed by using a database function includes: Calculating a second similarity between the fluorescence feature vector of the sample to be measured and each standard sample feature vector in the set of standard sample feature vectors based on a second algorithm, the second algorithm being different from the first algorithm; Sorting the second similarities between the fluorescence feature vector of the sample to be measured and each standard sample feature vector in the set of standard sample feature vectors; and Outputting the dataset to be analyzed based on the sorting result, wherein the subset of the set of standard sample feature vectors includes the top N standard sample feature vectors whose first similarity to the fluorescence feature vector of the sample to be measured meets the similarity criterion and whose second similarity is the highest, where N is an integer not less than 1.

12. The method according to claim 9, Among them, Outputting the dataset to be analyzed by using a database function includes: If the similarity between the fluorescence feature vector of the sample to be tested and each standard sample feature vector in the standard sample feature vector set does not meet the similarity standard, output the data set to be analyzed indicating the fluorescence feature vector of the sample to be tested. And wherein, the data analysis device calculates the ash content value of the sample to be tested according to the sample ash analysis model based on the fluorescence feature vector of the sample to be tested.

13. The method according to claim 12, wherein, The method further includes: Updating the database based on the fluorescence feature vector of the sample to be tested whose similarity with each standard sample feature vector in the standard sample feature vector set is less than the similarity threshold, and the calculated ash content value of the sample to be tested.

14. An on-line fluorescence analyzer, comprising: A fluorescence device configured to emit excitation light to a sample to be tested and receive a fluorescence signal from the sample to be tested; And The device according to any one of claims 1-7.

15. A machine-readable medium comprising machine-readable instructions that, when executed, cause a machine to perform the method according to any one of claims 8-13.

16. A computer program product comprising computer instructions that, when executed, cause a machine to perform the method according to any one of claims 8-13.

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