CN116559210B - Mineral product phase detection method and system - Google Patents
Mineral product phase detection method and system Download PDFInfo
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- 229910052500 inorganic mineral Inorganic materials 0.000 title claims abstract description 80
- 239000011707 mineral Substances 0.000 title claims abstract description 80
- 238000001514 detection method Methods 0.000 title claims abstract description 48
- 238000012937 correction Methods 0.000 claims abstract description 88
- 238000000034 method Methods 0.000 claims abstract description 37
- 238000010494 dissociation reaction Methods 0.000 claims abstract description 29
- 230000005593 dissociations Effects 0.000 claims abstract description 29
- 238000011156 evaluation Methods 0.000 claims abstract description 19
- 230000008569 process Effects 0.000 claims abstract description 17
- 238000004458 analytical method Methods 0.000 claims abstract description 12
- 238000002050 diffraction method Methods 0.000 claims abstract description 11
- 150000001875 compounds Chemical class 0.000 claims description 24
- 238000010521 absorption reaction Methods 0.000 claims description 16
- 238000012544 monitoring process Methods 0.000 claims description 9
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- 238000010586 diagram Methods 0.000 claims description 6
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- 238000004891 communication Methods 0.000 claims 1
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- 230000004048 modification Effects 0.000 description 4
- 238000001228 spectrum Methods 0.000 description 4
- 239000010949 copper Substances 0.000 description 3
- 238000010276 construction Methods 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- RYGMFSIKBFXOCR-UHFFFAOYSA-N Copper Chemical compound [Cu] RYGMFSIKBFXOCR-UHFFFAOYSA-N 0.000 description 1
- 230000004075 alteration Effects 0.000 description 1
- 235000021167 banquet Nutrition 0.000 description 1
- 229910052947 chalcocite Inorganic materials 0.000 description 1
- 238000012512 characterization method Methods 0.000 description 1
- 229910052802 copper Inorganic materials 0.000 description 1
- BERDEBHAJNAUOM-UHFFFAOYSA-N copper(I) oxide Inorganic materials [Cu]O[Cu] BERDEBHAJNAUOM-UHFFFAOYSA-N 0.000 description 1
- LBJNMUFDOHXDFG-UHFFFAOYSA-N copper;hydrate Chemical compound O.[Cu].[Cu] LBJNMUFDOHXDFG-UHFFFAOYSA-N 0.000 description 1
- 239000013078 crystal Substances 0.000 description 1
- 230000001678 irradiating effect Effects 0.000 description 1
- 238000005259 measurement Methods 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 230000000704 physical effect Effects 0.000 description 1
- 238000000053 physical method Methods 0.000 description 1
- 238000013441 quality evaluation Methods 0.000 description 1
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Abstract
The invention discloses a mineral product phase detection method and a mineral product phase detection system, which relate to the technical field of data processing, wherein the method comprises the following steps: based on the attribution type and the treatment process of the target mineral product, obtaining storability phase data and constructing a phase database, carrying out diffraction analysis on the target mineral product, obtaining a target diffraction pattern, carrying out hierarchical analysis stripping on the target diffraction pattern, determining a dissociation pattern, carrying out performance parameter evaluation on an X-ray diffractometer, generating a diffraction influence coefficient, inputting the dissociation pattern and the diffraction influence coefficient into a diffraction correction model, outputting a target correction pattern, traversing the phase database, carrying out matching correction on the target correction pattern, and generating a phase attribution system as a phase detection result of the target mineral product. The invention solves the technical problem of low mineral product phase detection precision caused by the existence of mineral product phase detection influence factors in the prior art, and achieves the technical effects of weakening and correcting the mineral product phase detection influence factors and improving the mineral product phase detection precision.
Description
Technical Field
The invention relates to the technical field of data processing, in particular to a mineral product phase detection method and system.
Background
The phase is a phase having a specific physicochemical structure in the substance, and the same element may exist in different compound states in the substance. The method has important effects on quality evaluation and application guidance of the mineral products by accurately and quantitatively analyzing the phase content of the mineral products.
The methods of phase analysis currently in use on the market fall into two categories. One is a method of researching the composition and content of a phase by means of chemical analysis based on differences in chemical properties of compounds, which is called a chemical method of phase analysis. The other is a method of researching the composition and content of a phase by using an instrument and equipment based on the difference of physical properties such as optical properties and electrical properties of a compound, and is called a physical method of phase analysis.
However, the currently used detection method has influencing factors in the detection process, so that the technical problem of low detection precision of the mineral product phase in the current mineral product phase detection method also exists.
Disclosure of Invention
The application provides a mineral product phase detection method and system, which are used for solving the technical problem of low mineral product phase detection precision caused by the existence of mineral product phase detection influence factors in the prior art.
In a first aspect of the application there is provided a method of phase detection with a mineral product, the method comprising: acquiring storability phase data based on the attribution type and the treatment process of the target mineral product, and building a phase database; carrying out diffraction analysis on the target mineral product based on the X-ray diffractometer to obtain a target diffraction pattern; performing level analysis stripping on the target diffraction pattern to determine a dissociation pattern; performing performance parameter evaluation on the X-ray diffractometer to generate a diffraction influence coefficient; inputting the dissociation map and the diffraction influence coefficient into a diffraction correction model, and outputting a target correction map; traversing the phase database, carrying out matching correction on the target correction map, generating a phase attribution system, and taking the phase attribution system as a phase detection result of the target mineral product.
One or more technical schemes provided by the application have at least the following technical effects or advantages:
The application provides a mineral product phase detection method, which relates to the technical field of data processing, and is characterized in that the method comprises the steps of obtaining storability phase data through the attribution type and the processing technology of a target mineral product, building a phase database, obtaining a target diffraction pattern through diffraction analysis on the target mineral product, carrying out level analysis and stripping on the target diffraction pattern to determine a dissociation pattern, carrying out performance parameter evaluation on an X-ray diffractometer to generate a diffraction influence coefficient, inputting the dissociation pattern and the diffraction influence coefficient into a diffraction correction model, outputting the target correction pattern, traversing the phase database, carrying out matching correction on the target correction pattern, and generating a phase attribution system as a phase detection result of the target mineral product. The technical problem that the mineral product phase detection precision is low due to the existence of mineral product phase detection influence factors in the prior art is solved, the technical effects of weakening and correcting the mineral product phase detection influence factors and improving the mineral product phase detection precision are achieved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic flow chart of a mineral product phase detection method according to an embodiment of the present application;
FIG. 2 is a schematic flow chart of generating diffraction influence coefficients in a mineral product phase detection method according to an embodiment of the present application;
Fig. 3 is a schematic structural diagram of an embodiment of a mineral product phase detection system.
Reference numerals illustrate: the system comprises a data acquisition module 11, a diffraction analysis module 12, a hierarchy analysis stripping module 13, a performance parameter evaluation module 14, a diffraction correction module 15 and a matching correction module 16.
Detailed Description
The application provides a mineral product phase detection method, which is used for solving the technical problem that the mineral product phase detection precision is low due to the existence of mineral product phase detection influence factors in the prior art.
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application. It will be apparent that the described embodiments are only some, but not all, embodiments of the application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
It is noted that the terms "comprises" and "comprising," and any variations thereof, in the description and claims of the present application and in the foregoing figures, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or server comprising a list of steps or elements is not necessarily limited to those steps or elements expressly listed or inherent to such process, method, article, or apparatus, but may include other steps or modules not expressly listed or inherent to such process, method, article, or apparatus.
Example 1
As shown in fig. 1, the present application provides a mineral product phase detection method comprising:
S100: acquiring storability phase data based on the attribution type and the treatment process of the target mineral product, and building a phase database;
specifically, the types of the substances contained in the related ore data can be obtained by referring to the related ore data according to the types of the target ore products, the types of the substances and the element data possibly existing in the target ore products can be determined by screening according to the phase composition data of the ore products under different treatment processes, and the data are classified and counted according to the specific physicochemical properties of the substances and the element data to form a phase database.
Further, step S100 of the embodiment of the present application further includes:
s110: determining a survivable elemental compound based on the attribution type and the treatment process;
s120: taking the storable element compounds as index targets, and obtaining standard phase states based on big data statistics, wherein each storable element compound at least corresponds to one standard phase state;
S130: and carrying out characteristic identification on the standard phase state to generate the phase database.
Specifically, which substances are contained in the target mineral product, such as chalcocite (Cu 2S) and cuprite (Cu 2O) contained in the copper ore, can be determined firstly by the type of the target mineral product, and secondly, the phase composition of the mineral product is different due to the different treatment processes, so that the type of the element compound possibly existing in the mineral product can be roughly determined according to the type of the target mineral product and the treatment processes. Searching one or more isomers corresponding to each compound by using big data by taking all possible compounds as an index target, wherein each isomer represents one standard phase state, and each standard phase state corresponds to one standard diffraction pattern, and the pulse peak value or single pulse phase on the standard diffraction pattern can be used as an identification characteristic for marking, wherein the standard diffraction pattern corresponds to the standard phase state and the identification characteristic map, and the phase database is generated and used as a reference set for matching the subsequent target mineral products.
S200: carrying out diffraction analysis on the target mineral product based on the X-ray diffractometer to obtain a target diffraction pattern;
specifically, the X-ray diffractometer is an instrument for analyzing a substance structure by utilizing the diffraction effect of rays in a crystalline substance. The method comprises the steps of irradiating a target mineral product with X-rays with enough energy, diffracting the X-rays at different angles by crystals in the target mineral product due to different incident angles, and recording the diffraction at different angles to obtain a target diffraction pattern.
S300: performing level analysis stripping on the target diffraction pattern to determine a dissociation pattern;
Specifically, since the distribution of the substances in the target mineral product is uneven, there are positions where there are coincident phase states, resulting in overlapping relationship of diffraction patterns generated at these positions, and for convenience of subsequent use, it is necessary to strip the overlapped target diffraction pattern into a plurality of patterns including only one phase state, that is, a dissociation pattern.
S400: performing performance parameter evaluation on the X-ray diffractometer to generate a diffraction influence coefficient;
Specifically, in order to ensure the accuracy of the measurement data of the X-ray diffractometer, the operation parameters of the current state of the X-ray diffractometer and the standard parameters in the optimal state need to be acquired and compared to determine the current parameter deviation value of the instrument, and the diffraction influence coefficient can be calculated from the parameter deviation value and can be used for correcting the performance parameters of the X-ray diffractometer.
Further, as shown in fig. 2, step S400 of the embodiment of the present application further includes:
S410: determining a multi-dimensional performance evaluation index based on the X-ray diffractometer, wherein the multi-dimensional performance evaluation index comprises ray source stability, diffraction angle accuracy and resolution;
s420: determining equipment standard index parameters according to the ray source stability, the diffraction angle precision and the resolution, wherein the equipment standard index parameters are index parameters in an optimal running state;
s430: performing real-time operation monitoring on the X-ray diffractometer, and determining real-time index parameters of equipment;
S440: calibrating the standard index parameters of the equipment and the real-time index parameters of the equipment to determine an index deviation value;
S450: the diffraction influence coefficient is generated based on the index deviation value.
Specifically, the main technical parameters of the X-ray diffractometer, including the source stability related to the reliability of the measured diffraction intensity and the accuracy and stability of all components, as well as the diffraction angle accuracy and resolution of the instrument, can be used as a multidimensional performance evaluation index for measuring the detection performance of the X-ray diffractometer. Obtaining the stability, diffraction angle accuracy and resolution of a ray source of the X-ray diffractometer in the optimal running state by reading a specification or consulting a manufacturer, and taking the stability, diffraction angle accuracy and resolution as standard index parameters of equipment; the X-ray diffractometer is monitored in real time, and the stability, diffraction angle accuracy and resolution of a ray source in the current running state of the equipment are recorded and used as real-time index parameters of the equipment; calibrating the standard index parameters of the equipment and the real-time index parameters of the equipment one by one, and calculating the difference between the real-time parameters and the standard parameters to be used as an index deviation value; and calculating the diffraction influence coefficient according to the index deviation value of each parameter and the influence degree of each parameter, and using the diffraction influence coefficient as basic data for carrying out parameter correction on equipment.
S500: inputting the dissociation map and the diffraction influence coefficient into a diffraction correction model, and outputting a target correction map;
specifically, a diffraction correction model capable of expressing the relation among parameters is trained by utilizing a dissociation spectrum and a diffraction influence coefficient in the past period (for example, the past month, half year and the like, and the specific time can be adaptively adjusted according to actual requirements), then the dissociation spectrum and the diffraction influence coefficient of the current target mineral product are input into the diffraction correction model, and the target correction spectrum of the current target mineral product can be output after the dissociation spectrum of the current target mineral product is corrected by the diffraction correction model.
Further, step S500 of the embodiment of the present application further includes:
s510: the connector phase monitoring system is used for calling historical diffraction record data;
S520: identifying and evaluating the historical diffraction record data to generate a constructed sample, wherein the constructed sample comprises a sample map, a sample diffraction influence coefficient and a sample correction parameter;
S530: and training a neural network based on the sample map, the sample diffraction influence coefficient and the correction parameter to generate the diffraction correction model, wherein the diffraction correction model comprises a data identification layer, a deviation correction layer and an adjustment output layer.
Specifically, the phase monitoring system is a master control system for performing phase detection full period monitoring, diffraction record data in a past period of time (for example, the past month, half year and the like, and the specific time can be adaptively adjusted according to actual requirements) can be acquired by connecting the phase monitoring system, and the historical diffraction record data are subjected to type identification and classified and sorted into a sample data set, wherein the sample data set type comprises a sample map, a sample diffraction influence coefficient and a sample correction parameter.
Mapping the sample map and the sample diffraction influence coefficient with the correction parameters as training data, determining a plurality of sample sequences, determining a hierarchy identification node based on the sample map and the sample diffraction influence coefficient, determining a hierarchy decision node based on the correction parameters, and generating the diffraction correction model by training a neural network, wherein the diffraction correction model comprises a data identification layer for identifying the map and the diffraction influence data, a deviation correction layer for generating correction parameters, and an adjustment output layer for correcting the dissociation map.
Further, step S500 of the embodiment of the present application further includes:
s540: inputting the dissociation diagram and the diffraction influence coefficient into the diffraction correction model;
S550: based on the data identification layer and the deviation correction layer, carrying out input data identification matching to determine correction parameters, wherein the correction parameters comprise an adjustment direction and an adjustment amplitude;
S560: and inputting the adjustment direction and the adjustment amplitude into the adjustment output layer, adjusting and correcting the dissociation map, and generating the target input map for model output.
Specifically, the obtained dissociation atlas and the diffraction influence coefficient are input into the data identification layer of the diffraction correction model for input data identification matching, then the identification result is input into the deviation correction layer for calculating the adjustment direction and the adjustment amplitude, finally the obtained adjustment direction and the adjustment amplitude are input into the adjustment output layer of the model, and the target input atlas can be output by the model after the adjustment output layer adjusts and corrects the dissociation atlas.
S600: traversing the phase database, carrying out matching correction on the target correction map, generating a phase attribution system, and taking the phase attribution system as a phase detection result of the target mineral product.
Specifically, the obtained target correction patterns are matched with all patterns in the phase database one by one, the types of the compounds contained in the target mineral products and the types of the isomers contained in each compound can be determined, the number of the isomers of each type is calculated according to the data provided by the target correction patterns, the types of all the compounds contained in the target mineral products, the types of the isomers contained in each compound and the number of the isomers of each type are arranged into a data system, and the data system can be used as the phase detection result of the target mineral products.
Further, step S600 of the embodiment of the present application further includes:
s610: traversing the phase database, carrying out matching check on the target correction map, and determining a target matching result;
s620: determining the content of a plurality of phases according to the intensity of the diffraction lines;
S630: and generating the phase attribution system based on the target matching result and the plurality of phase contents, wherein the phase attribution system is characterized as a category level-an isomer level-a quantity level.
Specifically, the obtained target corrected pattern is matched with all patterns in the phase database one by one, and the types of the compounds contained in the target mineral product and the types of the isomers contained in each compound can be determined as target matching results. The number of each isomer is calculated from the total diffraction intensity of one isomer and the diffraction intensity of a single isomer, and is used as the content of a plurality of phases. And (3) arranging the target matching result and the content of the plurality of phases in a one-to-one correspondence manner, and expressing the phases of the target mineral product according to a hierarchical inclusion relationship, so that a phase attribution system comprising the types of all the compounds, the types of the isomers contained in each compound and the number of each isomer can be obtained, and accurate phase data can be obtained.
Further, step S620 of the embodiment of the present application further includes:
s621: determining a plurality of line absorption coefficients based on the target matching result, wherein the target matching result corresponds to the plurality of line absorption coefficients one by one;
s622: based on the plurality of line absorption coefficients, mapping and matching the diffraction intensities to determine phase identification diffraction intensity;
S623: determining the plurality of phase contents based on the phase-identifying diffraction line intensities, wherein the plurality of phase contents is proportional to the phase-identifying diffraction intensities.
Specifically, the absorption coefficient of each isomer to X-rays is determined according to the target matching result, namely the isomer type contained in the target mineral product, the plurality of line absorption coefficients are characterization data of the absorption capacity of each isomer to X-rays, and the larger the plurality of line absorption coefficients are, the stronger the absorption capacity of the isomer to X-rays is, the more the absorbed X-ray energy is, the less the energy is diffracted, and the weaker the diffraction intensity is. And carrying out identification classification on the diffraction intensities with the same absorption coefficient, obtaining the sum of diffraction intensities of the same identified isomers, dividing the sum by the diffraction intensity of a single isomer to obtain the number of the isomers, and obtaining the number of all the isomers in the same way, wherein the number of the isomers is taken as the content of the multiple phases.
In summary, the embodiment of the application has at least the following technical effects:
The application obtains the storability phase data and builds a phase database through the attribution type and the treatment process of the target mineral products, obtains the target diffraction pattern through diffraction analysis on the target mineral products, analyzes and peels the level of the target diffraction pattern to determine the dissociation pattern, evaluates the performance parameters of the X-ray diffractometer to generate the diffraction influence coefficient, inputs the dissociation pattern and the diffraction influence coefficient into a diffraction correction model, outputs the target correction pattern, traverses the phase database finally, and performs matching correction on the target correction pattern to generate a phase attribution system as a phase detection result of the target mineral products. Solves the technical problem of low mineral product phase detection precision caused by the existence of mineral product phase detection influence factors in the prior art.
The technical effects of weakening and correcting the influence factors of mineral product phase detection and improving the mineral product phase detection accuracy are achieved.
Example two
Based on the same inventive concept as the method of detecting a mineral product phase in the previous embodiments, as shown in fig. 3, the present application provides a mineral product phase detection system, and the system and method embodiments in the embodiments of the present application are based on the same inventive concept. Wherein the system comprises:
The data acquisition module 11 is used for acquiring the storability phase data based on the attribution type and the processing technology of the target mineral product, and building a phase database;
The diffraction analysis module 12 is used for carrying out diffraction analysis on the target mineral product based on the X-ray diffractometer to obtain a target diffraction pattern;
A level profile stripping module 13, wherein the level profile stripping module 13 is used for performing level profile stripping on the target diffraction pattern to determine a dissociation pattern;
The performance parameter evaluation module 14 is used for evaluating the performance parameters of the X-ray diffractometer and generating a diffraction influence coefficient;
a diffraction correction module 15, wherein the diffraction correction module 15 is configured to input the dissociation diagram and the diffraction influence coefficient into a diffraction correction model, and output a target correction diagram;
And the matching and checking module 16 is used for traversing the phase database, performing matching and checking on the target correction map, generating a phase attribution system, and taking the phase attribution system as a phase detection result of the target mineral product.
Further, the system further comprises:
a survivability element compound acquisition module for determining survivability element compounds based on the attribution type and the treatment process;
The standard phase state acquisition module is used for taking the storable element compounds as index targets and acquiring standard phase states based on big data statistics, wherein each storable element compound at least corresponds to one standard phase state;
the characteristic identification module is used for carrying out characteristic identification on the standard phase state to generate the phase database;
further, the system further comprises:
a multi-dimensional performance evaluation module for determining a multi-dimensional performance evaluation index based on the X-ray diffractometer, wherein the multi-dimensional performance evaluation index comprises a ray source stability, a diffraction angle accuracy, and a resolution;
The device standard index parameter acquisition module is used for determining device standard index parameters according to the ray source stability, the diffraction angle accuracy and the resolution, wherein the device standard index parameters are index parameters in an optimal running state;
The real-time operation monitoring module is used for carrying out real-time operation monitoring on the X-ray diffractometer and determining real-time index parameters of equipment;
The parameter correction module is used for correcting the standard index parameters of the equipment and the real-time index parameters of the equipment and determining an index deviation value;
A diffraction influence coefficient generation module for generating the diffraction influence coefficient based on the index deviation value;
further, the system further comprises:
the historical data calling module is used for connecting the phase supervision system and calling historical diffraction record data;
The identification evaluation module is used for carrying out identification evaluation on the historical diffraction record data to generate a construction sample, wherein the construction sample comprises a sample map, a sample diffraction influence coefficient and a sample correction parameter;
The diffraction correction model training module is used for performing neural network training to generate the diffraction correction model based on the sample map, the sample diffraction influence coefficient and the correction parameter, wherein the diffraction correction model comprises a data identification layer, a deviation correction layer and an adjustment output layer;
further, the system further comprises:
the data input module is used for inputting the dissociation diagram and the diffraction influence coefficient into the diffraction correction model;
The identification matching module is used for carrying out input data identification matching based on the data identification layer and the deviation correction layer and determining correction parameters, wherein the correction parameters comprise an adjustment direction and an adjustment amplitude;
The adjustment and correction model training module is used for inputting the adjustment direction and the adjustment amplitude into the adjustment output layer, adjusting and correcting the dissociation atlas, generating the target input atlas and outputting the model;
further, the system further comprises:
The matching and checking module is used for traversing the phase database, checking and checking the target correction map and determining a target matching result;
The phase content obtaining module is used for determining a plurality of phase contents according to the diffraction line intensity;
The object attribution system generation module is used for generating an object attribution system based on the object matching result and the object contents, wherein the object attribution system is characterized by a class level-an isomer level-a quantity level;
further, the system further comprises:
The line absorption coefficient acquisition modules are used for determining a plurality of line absorption coefficients based on the target matching result, wherein the target matching result corresponds to the line absorption coefficients one by one;
The diffraction intensity mapping and matching module is used for carrying out mapping and matching on the diffraction intensities based on the plurality of line absorption coefficients and determining phase identification banquet strength;
And a phase content generation module for determining the plurality of phase contents based on the phase-identified diffraction line intensities, wherein the plurality of phase contents are proportional to the phase-identified diffraction intensities.
It should be noted that the sequence of the embodiments of the present application is only for description, and does not represent the advantages and disadvantages of the embodiments. And the foregoing description has been directed to specific embodiments of this specification. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims can be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.
The foregoing description of the preferred embodiments of the application is not intended to limit the application to the precise form disclosed, and any such modifications, equivalents, and alternatives falling within the spirit and scope of the application are intended to be included within the scope of the application.
The specification and figures are merely exemplary illustrations of the present application and are considered to cover any and all modifications, variations, combinations, or equivalents that fall within the scope of the application. It will be apparent to those skilled in the art that various modifications and variations can be made to the present application without departing from the scope of the application. Thus, the present application is intended to include such modifications and alterations insofar as they come within the scope of the application or the equivalents thereof.
Claims (2)
1. A mineral product phase detection method, characterized in that the method is applied to a mineral product phase detection system, which is in communication with an X-ray diffractometer, the method comprising:
acquiring storability phase data based on the attribution type and the treatment process of the target mineral product, and building a phase database;
carrying out diffraction analysis on the target mineral product based on the X-ray diffractometer to obtain a target diffraction pattern;
performing level analysis stripping on the target diffraction pattern to determine a dissociation pattern;
Performing performance parameter evaluation on the X-ray diffractometer to generate a diffraction influence coefficient;
inputting the dissociation map and the diffraction influence coefficient into a diffraction correction model, and outputting a target correction map;
traversing the phase database, carrying out matching correction on the target correction map, generating a phase attribution system, and taking the phase attribution system as a phase detection result of the target mineral product;
the building phase database comprises:
determining a survivable elemental compound based on the attribution type and the treatment process;
taking the storable element compounds as index targets, and obtaining standard phase states based on big data statistics, wherein each storable element compound at least corresponds to one standard phase state;
performing characteristic identification on the standard phase state to generate the phase database;
the performance parameter evaluation is performed on the X-ray diffractometer, and a diffraction influence coefficient is generated, and the method comprises the following steps:
determining a multi-dimensional performance evaluation index based on the X-ray diffractometer, wherein the multi-dimensional performance evaluation index comprises ray source stability, diffraction angle accuracy and resolution;
Determining equipment standard index parameters according to the ray source stability, the diffraction angle precision and the resolution, wherein the equipment standard index parameters are index parameters in an optimal running state;
Performing real-time operation monitoring on the X-ray diffractometer, and determining real-time index parameters of equipment;
Calibrating the standard index parameters of the equipment and the real-time index parameters of the equipment to determine an index deviation value;
generating the diffraction influence coefficient based on the index deviation value;
The inputting the dissociation-map and the diffraction-influencing coefficient into a diffraction correction model, previously comprising:
the connector phase monitoring system is used for calling historical diffraction record data;
Identifying and evaluating the historical diffraction record data to generate a constructed sample, wherein the constructed sample comprises a sample map, a sample diffraction influence coefficient and a sample correction parameter;
based on the sample map, the sample diffraction influence coefficient and the correction parameter, performing neural network training to generate the diffraction correction model, wherein the diffraction correction model comprises a data identification layer, a deviation correction layer and an adjustment output layer;
The output target correction map includes:
Inputting the dissociation diagram and the diffraction influence coefficient into the diffraction correction model;
Based on the data identification layer and the deviation correction layer, carrying out input data identification matching to determine correction parameters, wherein the correction parameters comprise an adjustment direction and an adjustment amplitude;
inputting the adjustment direction and the adjustment amplitude into the adjustment output layer, adjusting and correcting the dissociation atlas, generating the target input atlas, and outputting a model;
and traversing the phase database, carrying out matching correction on the target correction map, and generating a phase attribution system, wherein the method comprises the following steps of:
Traversing the phase database, carrying out matching check on the target correction map, and determining a target matching result;
Determining the content of a plurality of phases according to the intensity of the diffraction lines;
generating the phase attribution system based on the target matching result and the plurality of phase contents, wherein the phase attribution system is characterized as a category hierarchy-an isomer hierarchy-a quantity hierarchy;
the determining the content of the plurality of phases based on the diffraction line intensities comprises:
Determining a plurality of line absorption coefficients based on the target matching result, wherein the target matching result corresponds to the plurality of line absorption coefficients one by one;
Based on the plurality of line absorption coefficients, mapping and matching the diffraction intensities to determine phase identification diffraction intensity;
determining the plurality of phase contents based on the phase-identifying diffraction line intensities, wherein the plurality of phase contents is proportional to the phase-identifying diffraction intensities.
2. A mineral product phase detection system, characterized in that the system is applied to the method of claim 1, the system comprising:
the data acquisition module is used for acquiring the storable phase data based on the attribution type and the treatment process of the target mineral product and building a phase database;
The diffraction analysis module is used for carrying out diffraction analysis on the target mineral product based on an X-ray diffractometer to obtain a target diffraction pattern;
the hierarchy analysis stripping module is used for performing hierarchy analysis stripping on the target diffraction pattern to determine a dissociation pattern;
The performance parameter evaluation module is used for evaluating the performance parameters of the X-ray diffractometer and generating a diffraction influence coefficient;
The diffraction correction module is used for inputting the dissociation map and the diffraction influence coefficient into a diffraction correction model and outputting a target correction map;
And the matching and checking module is used for traversing the phase database, performing matching and checking on the target correction map, generating a phase attribution system, and taking the phase attribution system as a phase detection result of the target mineral product.
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