CN114565820A - Mineral sample identification system based on space-time big data analysis - Google Patents
Mineral sample identification system based on space-time big data analysis Download PDFInfo
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Abstract
The invention provides a mineral sample identification system based on space-time big data analysis, which comprises a front end acquisition layer, a middle end processing layer and a rear end data layer, wherein the output end of the front end acquisition layer is connected with the input end of the middle end processing layer, the front end acquisition layer comprises sample acquisition equipment and an image acquisition module, the output end of the middle end processing layer is connected with the input end of the rear end data layer, the middle end processing layer comprises an image processing module, a feature extraction module, an identification analysis module, an information fusion module and a space-time data analysis module, and the rear end data layer comprises a data wireless receiving module and an identification interpretation database. The mineral sample is identified, the accuracy of identifying the mineral sample is effectively improved, and meanwhile, the operation is more convenient and quicker.
Description
Technical Field
The invention relates to the technical field of mineral sample identification, in particular to a mineral sample identification system based on space-time big data analysis.
Background
Mineral resources, also called mineral resources, are formed through geological mineralization, naturally occurring in the earth crust or buried underground or exposed on the surface, in solid, liquid or gaseous state, and have the development and utilization value of minerals or useful element aggregate, mineral resources belong to non-renewable resources, the reserves are limited, there are more than 160 kinds of known minerals in the world, of which more than 80 kinds are widely used, and generally divided into four types according to their characteristics and uses: 11 kinds of energy minerals; 59 kinds of metal mineral products; 92 kinds of non-metal minerals; the water gas mineral products are 6. There are 168 kinds of ore;
the mineral resource is rich, people cannot know various mineral resources, and when people want to know a mineral resource sample, the mineral resource can be identified only by looking up data and utilizing the form of minerals, so that the efficiency is low, the identification degree is poor, if the minerals need to be identified accurately, special equipment is needed, the cost is increased, and the special equipment has a plurality of difficulties in carrying, so that the interest of people in understanding the mineral resource is easily eliminated, and therefore, the invention provides a mineral sample identification system based on space-time large data analysis to solve the problems in the prior art.
Disclosure of Invention
In view of the above problems, the present invention provides a mineral sample identification system based on space-time big data analysis, which has the advantages of high efficiency and convenient use, and solves the problems of complex identification and high cost in the prior art.
In order to realize the purpose of the invention, the invention is realized by the following technical scheme: a mineral sample identification system based on spatio-temporal big data analysis comprises a front-end acquisition layer, a middle-end processing layer and a back-end data layer, wherein the output end of the front-end acquisition layer is connected with the input end of the middle-end processing layer, the front-end acquisition layer comprises a sample acquisition device for acquisition and an image acquisition module for acquiring sample images, the output end of the middle-end processing layer is connected with the input end of the back-end data layer, the middle-end processing layer comprises an image processing module for processing sample image data, a feature extraction module for extracting sample features, an identification and analysis module for identifying and analyzing sample features, a spatio-temporal data analysis module for adopting an image feature level information fusion algorithm based on a fuzzy neural network and analyzing and fusing feature information, the back-end data layer comprises a data wireless receiving module for data receiving and an identification and interpretation database for data storage and calling, and a mineral sample identification model is preset in the identification and interpretation database, and a data wireless uploading module is arranged in the sample acquisition equipment.
The further improvement lies in that: still be equipped with the location collection module in the sample collection equipment, the output of location collection module is connected with the output of data upload module, and the location collection module is used for collecting the positional information of sample collection equipment.
The further improvement lies in that: the identification interpretation database is also internally provided with a data acquisition module and a wireless network module, the input end of the data acquisition module is connected with an external network through the wireless network module, the data acquisition module is used for acquiring the mineral sample information data disclosed on the external network, and the output end of the data acquisition module is connected with the input end of the identification interpretation database.
The further improvement lies in that: the middle-end processing layer further comprises a result output module and a display module, the input end of the result output module is connected with the output end of the space-time data analysis module, the output end of the result output module is connected with the input end of the display module, and the display module is used for displaying the output result of the result output module.
The further improvement lies in that: the system comprises an identification interpretation database, a data query module and a user login module, wherein the identification interpretation database is also internally provided with the data query module and the user login module, the output end of the data query module is connected with the input end of the identification interpretation database, the data query module is used for querying mineral sample information data in the identification interpretation database, and the user login module is used for user verification login.
The further improvement is that: the mineral sample recognition model is trained through a deep convolutional neural network before the mineral sample is recognized.
The further improvement lies in that: the output result is to output a plurality of analysis results, and the plurality of groups of analysis results are sorted according to the similarity, and the analysis results comprise mineral composition, mineral elements and mineral action of the mineral samples.
The further improvement lies in that: the mineral sample information data includes color, crystal, hardness, element, and distribution information of the confirmed mineral sample.
The beneficial effects of the invention are as follows: this kind of mineral products sample identification system based on big data analysis of time and space is through sample acquisition equipment and the image acquisition module that sets up, comparatively convenient is gathered and is collected the information to the sample, the identification interpretation database that empty data analysis module and utilize integrated big data technology to establish when the rethread, discern the mineral products sample, the effectual precision of having promoted the mineral products sample discernment, then when the user discerns the mineral products sample, more convenient, it is swift, need not the user and inquire the data by oneself, can be under the condition of utilizing big data analysis of time and space, swift carries out the analysis to the mineral products sample.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
Fig. 1 is a schematic structural view of the present invention.
Detailed Description
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, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Example one
According to fig. 1, the embodiment proposes a mineral sample identification system based on spatio-temporal big data analysis, which includes a front end acquisition layer, a middle end processing layer and a back end data layer, wherein an output end of the front end acquisition layer is connected with an input end of the middle end processing layer, the front end acquisition layer includes a sample acquisition device for acquisition and an image acquisition module for acquiring sample images, an output end of the middle end processing layer is connected with an input end of the back end data layer, the middle end processing layer includes an image processing module for processing sample image data, a feature extraction module for extracting sample features, an identification analysis module for identifying and analyzing sample features, a spatio-temporal data analysis module for adopting an image feature level information fusion algorithm based on a fuzzy neural network and for analyzing and fusing feature information, the back end data layer includes a data wireless receiving module for data reception and an identification interpretation database for data storage and data retrieval, in the embodiment, the mineral sample can be collected through the sample collecting device, the image information of the mineral sample is collected through the image collecting module, then the image information is subjected to noise reduction through the image processing module, a wavelet transformation algorithm is preset in the image processing module, after the noise reduction processing is carried out on the image through the wavelet transformation algorithm, the characteristic data of the mineral sample is extracted through the characteristic extracting module, as the characteristic data of the mineral sample is only one, namely, a plurality of characteristic data are fused through the information fusion module, then the space-time data analyzing module is used for analyzing, and the space-time data analyzing module calls the data of the identification and interpretation database and the identification model of the mineral field sample, and performing identification analysis according to the fused information data, and finally outputting, wherein the mine field sample identification model is constructed based on the information data and the vector data of the confirmed mineral samples.
Still be equipped with the location collection module in the sample collection equipment, the output of location collection module is connected with the output of data upload module, and the location collection module is used for collecting the positional information of sample collection equipment, according to the positional information who collects collection equipment, confirm the position that current mineral products sample is in, can search out the relevant mineral products distribution information in corresponding position on the network according to the position, come the priority according to this mineral products distribution information again and match with these mineral products, be favorable to promoting the identification efficiency of mineral products sample.
The identification interpretation database is also internally provided with a data acquisition module and a wireless network module, the input end of the data acquisition module is connected with an external network through the wireless network module, the data acquisition module is used for acquiring mineral sample information data disclosed on the external network, and the output end of the data acquisition module is connected with the input end of the identification interpretation database.
The middle-end processing layer further comprises a result output module and a display module, the input end of the result output module is connected with the output end of the space-time data analysis module, the output end of the result output module is connected with the input end of the display module, and the display module is used for displaying the output result of the result output module.
The identification and interpretation database is also internally provided with a data query module and a user login module, the output end of the data query module is connected with the input end of the identification and interpretation database, the data query module is used for querying mineral sample information data in the identification and interpretation database, and the user login module is used for user verification login.
Before the mineral sample identification model identifies the mineral sample, the deep convolutional neural network is used for training.
The output result is to output a plurality of analysis results, and the plurality of groups of analysis results are sorted according to the similarity, and the analysis results comprise mineral composition, mineral elements and mineral action of the mineral samples.
The mineral sample information data includes color, crystal, hardness, element, and distribution information of the confirmed mineral sample.
Example two
The embodiment provides a mineral sample identification system based on spatio-temporal big data analysis, which comprises a front-end acquisition layer, a middle-end processing layer and a rear-end data layer, wherein the output end of the front-end acquisition layer is connected with the input end of the middle-end processing layer, the front-end acquisition layer comprises an image acquisition module for acquiring a sample image, the output end of the middle-end processing layer is connected with the input end of the rear-end data layer, the middle-end processing layer comprises an image processing module for processing sample image data, a feature extraction module for extracting sample features, an identification analysis module for identifying and analyzing the sample features, a spatio-temporal data analysis module for adopting an image feature level information fusion algorithm based on a fuzzy neural network and analyzing and fusing feature information, the rear-end data layer comprises a data wireless receiving module for receiving data and an identification interpretation database for storing and calling data, and a mineral sample identification model is preset in the identification and interpretation database, and a data wireless uploading module is arranged in the image acquisition module.
The identification interpretation database is also internally provided with a data acquisition module and a wireless network module, the input end of the data acquisition module is connected with an external network through the wireless network module, the data acquisition module is used for acquiring mineral sample information data disclosed on the external network, and the output end of the data acquisition module is connected with the input end of the identification interpretation database.
The middle-end processing layer further comprises a result output module and a display module, the input end of the result output module is connected with the output end of the space-time data analysis module, the output end of the result output module is connected with the input end of the display module, and the display module is used for displaying the output result of the result output module.
The identification and interpretation database is also internally provided with a data query module and a user login module, the output end of the data query module is connected with the input end of the identification and interpretation database, the data query module is used for querying mineral sample information data in the identification and interpretation database, and the user login module is used for user verification login.
The mineral sample recognition model is trained through a deep convolutional neural network before the mineral sample is recognized.
The output result is to output a plurality of analysis results, and the plurality of groups of analysis results are sorted according to the similarity, and the analysis results include, but are not limited to, mineral composition, mineral elements and mineral action of the mineral samples.
The mineral sample information data includes, but is not limited to, color, crystal, hardness, element, and distribution information of the identified mineral sample.
The embodiment is mainly different from the first embodiment in that a sample acquisition device is omitted, an image uploading module is additionally arranged, the output end of the image uploading module is connected with the input end of the image processing module, the image uploading module is used for users to upload shot mineral images, namely, the users can conveniently upload the mineral images to identify themselves, meanwhile, a standby database is also arranged in the identification and interpretation database, when the mineral images uploaded by the users fail to identify results, the standby database is marked and recorded and stored, so that the intervention of subsequent managers is facilitated, the manual review is carried out on the information on the images, and the method aims to prepare for the subsequent mining of more unconfirmed mineral samples.
The foregoing illustrates and describes the principles, general features, and advantages of the present invention. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, which are described in the specification and illustrated only to illustrate the principle of the present invention, but that various changes and modifications may be made therein without departing from the spirit and scope of the present invention, which fall within the scope of the invention as claimed. The scope of the invention is defined by the appended claims and equivalents thereof.
Claims (8)
1. A mineral sample identification system based on space-time big data analysis is characterized in that: the system comprises a front-end acquisition layer, a middle-end processing layer and a rear-end data layer, wherein the output end of the front-end acquisition layer is connected with the input end of the middle-end processing layer, the front-end acquisition layer comprises sample acquisition equipment for acquisition and an image acquisition module for acquiring sample images, the output end of the middle-end processing layer is connected with the input end of the rear-end data layer, the middle-end processing layer comprises an image processing module for processing sample image data, a feature extraction module for extracting sample features, an identification analysis module for identifying and analyzing the sample features, a spatiotemporal data analysis module for analyzing fused feature information by adopting an image feature level information fusion algorithm based on a fuzzy neural network, the rear-end data layer comprises a data wireless receiving module for data receiving and an identification and interpretation database for data storage and calling, and a mineral sample identification model is preset in the identification and interpretation database, and a data wireless uploading module is arranged in the sample acquisition equipment.
2. The system for identifying mineral samples based on spatiotemporal big data analysis according to claim 1, wherein: still be equipped with the location collection module in the sample collection equipment, the output of location collection module is connected with the output of data upload module, and the location collection module is used for collecting the positional information of sample collection equipment.
3. The system for identifying mineral samples based on spatiotemporal big data analysis according to claim 1, wherein: the identification interpretation database is also internally provided with a data acquisition module and a wireless network module, the input end of the data acquisition module is connected with an external network through the wireless network module, the data acquisition module is used for acquiring the mineral sample information data disclosed on the external network, and the output end of the data acquisition module is connected with the input end of the identification interpretation database.
4. The system for identifying mineral samples based on spatiotemporal big data analysis according to claim 1, wherein: the middle-end processing layer further comprises a result output module and a display module, the input end of the result output module is connected with the output end of the space-time data analysis module, the output end of the result output module is connected with the input end of the display module, and the display module is used for displaying the output result of the result output module.
5. The system for identifying mineral samples based on spatiotemporal big data analysis according to claim 1, wherein: the system comprises an identification interpretation database, a data query module and a user login module, wherein the identification interpretation database is also internally provided with the data query module and the user login module, the output end of the data query module is connected with the input end of the identification interpretation database, the data query module is used for querying mineral sample information data in the identification interpretation database, and the user login module is used for user verification login.
6. The system for identifying mineral samples based on spatiotemporal big data analysis according to claim 1, wherein: the mineral sample recognition model is trained through a deep convolutional neural network before the mineral sample is recognized.
7. The system for identifying mineral samples based on spatiotemporal big data analysis according to claim 4, wherein: the output result is to output a plurality of analysis results, and the plurality of groups of analysis results are sorted according to the similarity, and the analysis results comprise mineral composition, mineral elements and mineral action of the mineral samples.
8. The system for identifying mineral samples based on spatiotemporal big data analysis according to claim 3, wherein: the mineral sample information data includes color, crystal, hardness, element, and distribution information of the confirmed mineral sample.
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