Carbonate clay type lithium ore element spectrum data acquisition device

Abstract

The application provides a kind of carbonate clay type lithium mineral element spectral data acquisition device, including display module, for displaying the first interface including preset map. The preset map includes at least one sub-region. Data acquisition module is used to obtain the hyperspectral data of target element corresponding to each sampling point included in any sub-region. The target element is associated with carbonate clay type lithium mine. The display module is also used to display the second interface including the label selection area and the data display area. The label selection area is used to display the label selection control. The data analysis module is used to determine the sampling point to be processed in response to the input selection operation of any label selection control. The data analysis module is also used to determine the spectral reflectance peak contrast curve corresponding to the sampling point to be processed based on the hyperspectral data of the target element corresponding to the sampling point to be processed. The display module is also used to display the spectral reflectance peak contrast curve to visually display the exploration result and improve the exploration efficiency of mineral resources.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of geological exploration and data analysis, and particularly relates to a carbonate clay type lithium mineral element spectrum data acquisition device. BACKGROUND

[0002] As an important strategic resource, carbonate clay type lithium mineral resources are widely used in important fields such as batteries, aerospace and building materials. With the rapid development of the new energy industry, the demand for carbonate clay type lithium mineral resources has increased dramatically, so efficient and accurate detection and positioning of the required carbonate clay type lithium mineral resources have become one of the important tasks of current mineral resource exploration.

[0003] The exploration of carbonate clay type lithium mineral resources usually includes field collection of high-spectral data corresponding to carbonate clay type lithium minerals. Specifically, the collection personnel need to carry a large volume of antiform high-spectral imaging equipment to the target area to collect high-spectral data on the ground or near the ground. After the collection is completed, the collection personnel need to bring the data back to the laboratory, process and analyze the data using professional software to determine whether the target area has the required mineral resource distribution. If the analysis result shows that the target area does not have carbonate clay type lithium mineral resources, the collection personnel need to re-collect in the field and bring the data back to the laboratory for processing again. This process often needs to go back and forth between the collection site and the laboratory many times, resulting in low exploration efficiency. SUMMARY

[0004] The present application provides a carbonate clay type lithium mineral element spectrum data acquisition device, which integrates data acquisition function, data processing function and data display function, can display the on-site data collection result intuitively while exploring, shorten the exploration period of carbonate clay type lithium mineral resources, and improve the exploration efficiency of carbonate clay type lithium mineral resources.

[0005] The technical solution of the present application to solve the above technical problems is as follows:

[0006] The present application provides a carbonate clay type lithium mineral element spectrum data acquisition device, comprising:

[0007] The display module is used for displaying a first interface. The first interface includes a preset map, and the preset map is a map corresponding to a preset area. The preset map includes at least one sub-area.

[0008] The data acquisition module is used for acquiring high-spectral data of a target element corresponding to a plurality of sampling points included in any sub-area in response to an input sampling instruction for any sub-area, and acquiring environmental information of any sub-area. The target element is associated with carbonate clay type lithium mineral. The environmental information of any sub-area includes temperature, humidity and light intensity of any sub-area.

[0009] The display module is further configured to display a second interface. The second interface comprises a label selection region and a data display region. The label selection region is configured to display at least one label selection control of a plurality of label selection controls, and one label selection control of the plurality of label selection controls corresponds to one sampling point of a plurality of sampling points.

[0010] The data analysis module is configured to determine a to-be-processed sampling point in response to an input selection operation on any label selection control of the at least one label selection control. The to-be-processed sampling point is the sampling point corresponding to the any label selection control.

[0011] The data analysis module is further configured to filter target hyperspectral data from the hyperspectral data of the target element corresponding to the to-be-processed sampling point based on environmental information of the region where the to-be-processed sampling point is located, and determine a spectral reflectance peak contrast curve corresponding to the to-be-processed sampling point based on the hyperspectral data of the target element. The spectral reflectance peak contrast curve is used to represent the correlation between the hyperspectral data of the target element corresponding to the to-be-processed sampling point and the preset hyperspectral data corresponding to the target element.

[0012] The display module is further configured to display the spectral reflectance peak contrast curve corresponding to the to-be-processed sampling point in the data display region.

[0013] Based on the above technical solutions, the application can be further improved as follows.

[0014] Further, the application further comprises a map import module. The display module is further configured to display a third interface, and the third interface comprises a map import control. The map import module is configured to instruct the display module to display a map receiving page in response to an input selection operation on the map import control, and the map receiving page is configured to receive an imported map image. The display module is further configured to display the map receiving page. The map import module is further configured to perform a division operation on a preset map to determine the spatial distribution of at least one sub-region on the preset map in response to an input import operation on the preset map. The map import module is further configured to generate a region selection control corresponding to any sub-region based on the spatial distribution of the any sub-region on the preset map. The display module is further configured to display the first interface comprising the region selection control corresponding to the any sub-region in response to an input operation for instructing to display the first interface. The display module is further configured to display a fourth interface in response to an input selection operation on the region selection control corresponding to the any sub-region in the first interface. The fourth interface comprises the any sub-region and a region sampling control corresponding to the any sub-region. The area of the any sub-region in the fourth interface is greater than the area of the any sub-region in the first interface. The display module is further configured to send a sampling instruction for the any sub-region to the data collection module in response to an input selection operation on the region sampling control corresponding to the any sub-region in the fourth interface.

[0015] Further, the data acquisition module is specifically configured to determine a plurality of sampling points in any sub-region in response to an input sampling instruction for the sub-region. For any sampling point in the plurality of sampling points, visible-near infrared spectrum data and short-wave infrared spectrum data of a target element at a corresponding position of the any sampling point in the preset area are acquired; the wavelength range of the visible-near infrared spectrum data is 350-1000 nanometers; the wavelength range of the short-wave infrared spectrum data is 1000-2500 nanometers. Based on the visible-near infrared spectrum data and the short-wave infrared spectrum data of the target element at the corresponding position of the any sampling point in the preset area, hyperspectral data of the target element at the corresponding position of the any sampling point in the preset area are determined. The hyperspectral data of the corresponding position of the any sampling point in the preset area are determined as the hyperspectral data of the target element corresponding to the any sampling point.

[0016] Further, the data acquisition module is further configured to receive an input sampling point replacement instruction for indicating that a first sampling point is replaced by a second sampling point, and replace the first sampling point by the second sampling point. The plurality of sampling points include the first sampling point and do not include the second sampling point, and the second sampling point is located in any sub-region. And / or, receive an input sampling point deletion instruction for indicating that a third sampling point in the plurality of sampling points is deleted, and delete the third sampling point from the plurality of sampling points.

[0017] Further, the data acquisition module is specifically configured to determine the number of the plurality of sampling points based on the area of a corresponding region of any sub-region in the preset area in response to an input sampling instruction for the sub-region. The plurality of sampling points are determined in any sub-region based on the number of the plurality of sampling points, the position of the region boundary of any sub-region, and the position of the region center point of any sub-region.

[0018] Further, the data acquisition module is specifically configured to display a sampling point input interface in response to an input sampling instruction for any sub-region. The plurality of sampling points are determined in any sub-region in response to position information corresponding to the plurality of sampling points input in the sampling point input interface. The position information corresponding to any sampling point includes position information of a corresponding position of any sampling point in the preset area or position information of a corresponding position of any sampling point in any sub-region.

[0019] Further, the display module is further configured to:

[0020] In response to an input data viewing instruction, a second interface is displayed. The second interface further includes a map display area.

[0021] A preset map is displayed in the map display area, and a sampling state label is added to a sampling region in the preset map displayed in the map display area, and the sampling region is a sub-region including a sampling point in at least one sub-region.

[0022] In response to the selection operation on the selected sampling state label corresponding to the target sampling region, the content displayed in the map display area is updated. The target sampling region is displayed in the updated map display area. The area of the target sampling region displayed in the updated map display area is larger than the area of the target sampling region displayed in the previous map display area. The position of the sampling point included in the target sampling region displayed in the updated map display area is added with a sampling point label.

[0023] The label selection area displays the label selection control corresponding to the sampling point included in the target sampling region.

[0024] The selection operation on any label selection control displayed in the label selection area is received.

[0025] In response to the determination of the spectral reflectance coefficient peak contrast curve corresponding to the to-be-processed sampling point by the data analysis module, the spectral reflectance coefficient peak contrast curve corresponding to the to-be-processed sampling point is displayed in the data display area.

[0026] Further, the second interface further includes an element content display area. The data analysis module is further configured to determine the element content corresponding to the to-be-processed sampling point based on the target hyperspectral data. The element corresponding to the to-be-processed sampling point is an element included in the position corresponding to the to-be-processed sampling point in the preset region. The display module is further configured to display the element content corresponding to the to-be-processed sampling point in the element content display area.

[0027] Further, the monitoring module is further configured to generate a first prompt information in a case where it is determined that the element corresponding to the to-be-processed sampling point does not include the preset element. The first prompt information is used to prompt that the element corresponding to the to-be-processed sampling point does not include the preset element. And / or, the monitoring module is further configured to generate a second prompt information in a case where it is determined that the element corresponding to the to-be-processed sampling point includes the preset element, and the element content of the preset element corresponding to the to-be-processed sampling point is less than the element content threshold corresponding to the preset element. The second prompt information is used to prompt that the element content of the preset element corresponding to the to-be-processed sampling point is less than the element content threshold corresponding to the preset element.

[0028] Further, the data analysis module is specifically configured to perform a data preprocessing operation on the hyperspectral data of the target element corresponding to the to-be-processed sampling point. The data preprocessing operation includes a data cleaning operation, a data denoising operation, and a data correction operation. Based on the hyperspectral data of the target element corresponding to the to-be-processed sampling point after the preprocessing operation, the spectral reflectance coefficient peak contrast curve corresponding to the to-be-processed sampling point is determined. Based on the hyperspectral data of the target element corresponding to the to-be-processed sampling point after the preprocessing operation, the content of the target element corresponding to the to-be-processed sampling point is determined.

[0029] The present application has the advantages that the data acquisition function, the data processing function and the data display function are integrated, the user can quickly identify and analyze the geological conditions of the collection area, the exploration strategy of the collection personnel can be quickly adjusted, the exploration period is shortened, and the exploration efficiency is improved. BRIEF DESCRIPTION OF DRAWINGS

[0030] Figure 1 A software structure schematic diagram of a carbonate clay type lithium ore element spectrum data acquisition device is provided for the present application;

[0031] Figure 2 A first interface schematic diagram is provided for the present application;

[0032] Figure 3 A second interface schematic diagram is provided for the present application;

[0033] Figure 4 A third interface schematic diagram is provided for the present application;

[0034] Figure 5 A fourth interface schematic diagram is provided for the present application;

[0035] Figure 6 An updated second interface schematic diagram is provided for the present application;

[0036] Figure 7 Another second interface schematic diagram is provided for the present application;

[0037] Figure 8 An element monitoring setting page schematic diagram is provided for the present application;

[0038] Figure 9 An appearance schematic diagram of a data display device is provided for the present application. DETAILED DESCRIPTION

[0039] The technical solutions in the embodiments of the present application will be described below with reference to the drawings in the embodiments of the present application. In the description of the present application, unless otherwise specified, “ / ” represents a “or” relationship of the objects before and after the “ / ”, for example, A / B can represent A or B; “and / or” in the present application is only a description of the relationship between the associated objects, which means that there can be three relationships, for example, A and / or B, which can represent: A alone, A and B together, and B alone, where A and B can be singular or plural. In the description of the present application, unless otherwise specified, “multiple” means two or more than two. “At least one of the following” or the like means any combination of the items, including any combination of single item or multiple items. For example, at least one of a, b, or c can represent: a, b, c, a-b, a-c, b-c, or a-b-c, where a, b, and c can be single or multiple. In addition, in order to clearly describe the technical solutions of the embodiments of the present application, in the embodiments of the present application, “first”, “second”, and the like are used to distinguish the same items or similar items with basically the same function and effect. Those skilled in the art can understand that “first”, “second”, and the like do not limit the quantity and execution order, and “first”, “second”, and the like do not necessarily mean different. At the same time, in the embodiments of the present application, “exemplary” or “for example” means to serve as an example, illustration or description. Any embodiment or design scheme described as “exemplary” or “for example” in the embodiments of the present application should not be interpreted as more preferred or more advantageous than other embodiments or design schemes.

[0040] Exploration of carbonate clay type lithium ore resources depends on the comprehensive application of multi-disciplinary and multi-source data. Through the combination of geology, geochemistry, geophysics, remote sensing and other means, the success rate of exploration can be effectively improved, and scientific basis can be provided for the development of lithium resources. Data collection usually relies on collection personnel to go to the target area with collection equipment, and to collect hyperspectral data on the ground or near the ground. After collection is completed, the collection personnel need to bring the hyperspectral data back to the laboratory, and use professional software to process and analyze the hyperspectral data to determine whether the target area contains carbonate clay type lithium ore resources. If the analysis result shows that the target area does not contain carbonate clay type lithium ore resources, or the analysis result cannot determine whether the target area contains sufficient carbonate clay type lithium ore resources, the collection personnel need to go to the field again for supplementary collection, and then bring the data back to the laboratory for processing. This process often needs to go back and forth between the collection site and the laboratory many times, resulting in long exploration period, large consumption of manpower and material resources, and low efficiency.

[0041] Furthermore, traditional hyperspectral data processing methods typically rely on high-performance computing equipment in laboratory environments, making real-time data analysis impossible in the field. This not only increases data processing latency but may also prevent data collectors from adjusting their acquisition strategies in a timely manner, thus missing important exploration clues.

[0042] To address the problems of low efficiency in the exploration of carbonate clay-type lithium resources in existing technologies, this application provides a carbonate clay-type lithium elemental spectral data acquisition device that integrates data acquisition, data processing, and data display functions. This device can help users quickly identify and analyze the geological conditions of the acquisition area, enabling acquisition personnel to quickly adjust exploration strategies, shorten the exploration cycle, and improve exploration efficiency.

[0043] See Figure 1 The present invention provides a data acquisition device for elemental spectroscopic data of carbonate clay lithium ore (hereinafter referred to as a data display device in this application embodiment), which includes a display module, a data acquisition module and a data analysis module.

[0044] The display module is used to display the first interface. The first interface includes a preset map, which is a map corresponding to a preset region, and the preset map includes at least one sub-region.

[0045] For example, see Figure 2 The first interface 200 includes a preset map 210, which may include sub-regions 211, 212 and 213.

[0046] The data acquisition module is used to respond to an input sampling command for any sub-region within at least one sub-region, to acquire hyperspectral data of target elements corresponding to multiple sampling points within that sub-region, and to acquire environmental information for that sub-region. The target elements are associated with carbonate clay-type lithium ore. Target elements may include at least one element selected from Li, P, S, Cl, K, Ca, Ti, V, Cr, Mn, Fe, Co, Ni, Cu, Zn, As, Se, Rb, Sr, Zr, Mo, Ag, Cd, Sn, Sb, I, Ba, Hf, Ta, W, Re, Pt, Au, Hg, Pb, and Bi. The environmental information for any sub-region may include its temperature, humidity, and light intensity.

[0047] For example, see [link to example]. Figure 2 Users can click Figure 2 The location of sub-region 211 shown is used to input sampling instructions for sub-region 211 into the data acquisition module. Responding to the user-input sampling instructions for sub-region 211, the data acquisition module can acquire hyperspectral data of the target elements corresponding to multiple sampling points included in sub-region 211.

[0048] The display module is further configured to display a second interface 300 as shown in Figure 3 The second interface 300 includes a label selection area 310 and a data display area 320. The label selection area 310 is configured to display at least one label selection control (e.g., label control 311 and label control 312) in a plurality of label selection controls, and one label selection control in the plurality of label selection controls corresponds to one sampling point in a plurality of sampling points. Different label controls correspond to different sampling points.

[0049] The data analysis module is configured to determine a to-be-processed sampling point in response to an input selection operation on any label selection control in the at least one label selection control. The to-be-processed sampling point is the sampling point corresponding to the any label selection control.

[0050] The data analysis module is further configured to filter target hyperspectral data from the hyperspectral data of the target element corresponding to the to-be-processed sampling point based on environmental information of a region where the to-be-processed sampling point is located, and determine a spectral reflectance peak contrast curve corresponding to the to-be-processed sampling point. The spectral reflectance peak contrast curve is used to represent a correlation degree between the hyperspectral data of the target element corresponding to the to-be-processed sampling point and preset hyperspectral data corresponding to the target element.

[0051] Since different temperatures, humidities, and light intensities can affect the collected hyperspectral data corresponding to the sampling points, the affected degree of the hyperspectral data corresponding to the to-be-processed sampling point can be filtered based on the environmental information of the region where the to-be-processed sampling point is located, and the hyperspectral data with a smaller affected degree is determined as the target hyperspectral data. For example, if the temperature of the region where a sampling point is located is within a preset temperature range, and / or the humidity is within a preset humidity range, and / or the light intensity is within a preset light intensity range, the collected hyperspectral data corresponding to the sampling point can be determined as the target hyperspectral data. If the target hyperspectral data cannot be filtered, it indicates that the collected hyperspectral data is greatly affected by the environmental information, and prompt information prompting the user to reselect the sampling point and perform collection can be generated and displayed.

[0052] The display module is further configured to display the spectral reflectance peak contrast curve corresponding to the to-be-processed sampling point in the data display area. For example, continuing to refer to Figure 3 The display module is further configured to display the spectral reflectance peak contrast curve corresponding to the to-be-processed sampling point in the data display area 320.

[0053] Specifically, to ensure the accuracy of the sampling data, the same sampling point can be sampled repeatedly (for example, if the number of samplings is preset to 7, then the same sampling point can be sampled 7 times). Then, for each set of sampling data corresponding to the target element, the spectral reflectance coefficient peak contrast curve corresponding to that set of sampling data can be calculated, and the display module can display all the spectral reflectance coefficient peak contrast curves calculated by the data analysis module.

[0054] In some embodiments, the data analysis module can calculate the correlation between the hyperspectral data of the target element corresponding to the sampling point and the preset hyperspectral data corresponding to the target element based on a preset formula. Then, based on the correlation between the hyperspectral data of the target element corresponding to the sampling point and the preset hyperspectral data corresponding to the target element, the data analysis module can determine the peak contrast curve of the spectral reflectance coefficient corresponding to the sampling point to be processed.

[0055] In some embodiments, the preset formula may be:

[0056]

[0057] Where, y′ j This represents the preset hyperspectral data corresponding to the target element; y i This represents the hyperspectral data of the target element corresponding to the sampling point; R represents the average value of each set of hyperspectral data corresponding to the target element at the sampling point; n represents the number of sets in each set of hyperspectral data. 2 The value range of R is [0,1]. 2 The closer the value is to 1, the higher the correlation between the hyperspectral data of the target element corresponding to the sampling point and the preset hyperspectral data corresponding to the target element, and the more accurate the data results are.

[0058] It should be noted that, since the target element mentioned in this application can be multiple elements, in specific calculations, for any element included in the target element, the spectral reflectance peak comparison curve of the sampling point for that element can be determined and displayed based on the correlation between the hyperspectral data corresponding to that element and the preset hyperspectral data corresponding to that element. Furthermore, the display device provided in this application embodiment also provides a switching function, that is, the user can switch to view the spectral reflectance peak comparison curves corresponding to each element included in the target element in the sampling point. For example, the user can click... Figure 3 Control 321 in the display will switch the peak contrast curve of the spectral reflectance coefficient corresponding to the Li element in the sampling point to the peak contrast curve of the spectral reflectance coefficient corresponding to the Cu element in the sampling point.

[0059] In some embodiments, see continue to see Figure 1The data display device provided in the embodiments of the present application further comprises a map importing module. Wherein, referring to Figure 4 The display module is further configured to display a third interface 400. The third interface 400 comprises a map importing control 401. The map importing module is configured to, in response to an inputted selected operation on the map importing control 401, instruct the display module to display a map receiving page 410, and the display module is further configured to display the map receiving page 410. The map receiving page 410 is configured to receive an imported map image.

[0060] For example, continuing to refer to Figure 4 The map receiving page 410 comprises a map receiving control 411. A user can click the map receiving control 411, and the map importing module, in response to the user clicking the map receiving control 411, can call a map library and instruct the display module to display map images (for example, map image A, map image B, etc.) in the map library. The user can click a map image that needs to be uploaded to import the map image to the map importing module.

[0061] It should be noted that the map importing module can call a map library in the Internet through a wireless network to instruct the display module to display map images in the map library. Alternatively, the map importing module can also call a map library stored in other devices through communication means such as Bluetooth to instruct the display module to display map images in the map library. Alternatively, the display device provided in the embodiments of the present application can also be configured with a connection interface, and the display device can be connected with other devices or elements (for example, a U disk) to enable the map importing module of the display device to call a map library stored in other devices or elements and instruct the display module to display map images in the map library. The embodiments of the present application do not limit the source of the map library.

[0062] In some embodiments, the map importing module is further configured to, in response to an inputted importing operation on a preset map, perform a division operation on the preset map to determine the spatial distribution of at least one sub-region on the preset map.

[0063] For example, the map importing module can scan the preset map, perform a division operation on the preset map based on an edge algorithm to determine the spatial distribution of at least one sub-region on the preset map.

[0064] The map importing module is further configured to generate a region selection control corresponding to any sub-region based on the spatial distribution of the sub-region on the preset map.

[0065] The display module is further configured to, in response to an inputted operation for instructing to display the first interface, display the first interface comprising the region selection control corresponding to any sub-region.

[0066] The display module is further configured to display a fourth interface in response to a selection operation on the region selection control corresponding to any of the sub-regions in the first interface. The fourth interface includes any of the sub-regions and the region sampling control corresponding to any of the sub-regions. The area of any of the sub-regions in the fourth interface is greater than the area of any of the sub-regions in the first interface. The display module is further configured to send a sampling instruction for any of the sub-regions to the data collection module in response to a selection operation on the region sampling control corresponding to any of the sub-regions in the fourth interface.

[0067] For example, the user can click the region selection control corresponding to the sub-region 211 shown in FIG. 11A (the region selection control corresponding to the sub-region 211 is a full-transparent control, the shape of the region selection control corresponding to the sub-region 211 is exactly the same as the shape of the sub-region 211 and covers the sub-region 211; the click operation of any point in the sub-region 211 by the user is a selection operation on the region selection control corresponding to the sub-region 211). Figure 2 For example, the user can click the region selection control corresponding to the sub-region 211 shown in FIG. 11A (the region selection control corresponding to the sub-region 211 is a full-transparent control, the shape of the region selection control corresponding to the sub-region 211 is exactly the same as the shape of the sub-region 211 and covers the sub-region 211; the click operation of any point in the sub-region 211 by the user is a selection operation on the region selection control corresponding to the sub-region 211). Figure 1 For example, the user can click the region selection control corresponding to the sub-region 211 shown in FIG. 11A (the region selection control corresponding to the sub-region 211 is a full-transparent control, the shape of the region selection control corresponding to the sub-region 211 is exactly the same as the shape of the sub-region 211 and covers the sub-region 211; the click operation of any point in the sub-region 211 by the user is a selection operation on the region selection control corresponding to the sub-region 211). Figure 5 For example, the user can click the region selection control corresponding to the sub-region 211 shown in FIG. 11A (the region selection control corresponding to the sub-region 211 is a full-transparent control, the shape of the region selection control corresponding to the sub-region 211 is exactly the same as the shape of the sub-region 211 and covers the sub-region 211; the click operation of any point in the sub-region 211 by the user is a selection operation on the region selection control corresponding to the sub-region 211).

[0068] In some embodiments, the data collection module is further configured to determine a plurality of sampling points in any of the sub-regions in response to the input sampling instruction for any of the sub-regions. For any of the sampling points, the visible-near infrared spectrum data and the short-wave infrared spectrum data of the target element at the position corresponding to any of the sampling points in the preset region are obtained; the wavelength range of the visible-near infrared spectrum data is 350-1000 nanometers; the wavelength range of the short-wave infrared spectrum data is 1000-2500 nanometers. The hyperspectral data of the target element at the position corresponding to any of the sampling points in the preset region is determined based on the visible-near infrared spectrum data and the short-wave infrared spectrum data of the target element at the position corresponding to any of the sampling points in the preset region. The hyperspectral data of the target element corresponding to any of the sampling points is determined as the hyperspectral data of the target element corresponding to any of the sampling points.

[0069] It should be noted that the above description continues to refer to FIG. 11A. Figure 5The fourth interface 500 further includes a sampling point setting control 520. A user can click the sampling point setting control 520 to customize the positions of the sampling points included in the target sub-region. After the user sets the positions of the sampling points included in the target sub-region, if the user clicks the region sampling control 510, the sampling points included in the target sub-region determined by the data collection module are the sampling points set by the user. If the user does not set the positions of the sampling points included in the target sub-region and directly clicks the region sampling control 510, the data collection module can determine the sampling points included in the target sub-region by itself (the determination manner is described in the following embodiments).

[0070] In some embodiments, the data collection module is further configured to determine the number of the sampling points based on the area of the region corresponding to any sub-region in the preset area in response to the input sampling instruction for any sub-region. The data collection module is further configured to determine the sampling points in any sub-region based on the number of the sampling points, the position of the region boundary of any sub-region, and the position of the region center point of any sub-region.

[0071] In some embodiments, the data collection module is further configured to display a sampling point input interface in response to the input sampling instruction for any sub-region. The data collection module is further configured to determine the sampling points in any sub-region in response to the position information of the sampling points input in the sampling point input interface. The position information of any sampling point includes the position information of the position corresponding to any sampling point in the preset area or the position information of the position corresponding to any sampling point in any sub-region.

[0072] It should be noted that the sampling point input interface can display a map image of the sub-region, and the user can click the map image of the sub-region. The data collection module can determine the click point of the user as a sampling point. Alternatively, the sampling point input interface can display a coordinate input box, and the user can input the coordinates of the sampling point on the preset map or the preset area in the coordinate input box. The data collection module can determine the position of the coordinates input by the user as a sampling point.

[0073] In some embodiments, the data collection module is further configured to receive an input sampling point replacement instruction for indicating that a first sampling point is replaced by a second sampling point, and replace the first sampling point by the second sampling point. The plurality of sampling points include the first sampling point and do not include the second sampling point. The second sampling point is located in any sub-region.

[0074] In some embodiments, the data collection module is further configured to receive an input sampling point deletion instruction for indicating that a third sampling point in the plurality of sampling points is deleted, and delete the third sampling point from the plurality of sampling points.

[0075] In some embodiments, the display module is further configured to display a second interface in response to an input data viewing instruction. Continue to refer toFigure 3 The second interface 300 also includes a map display area 330. The display module is further configured to display a preset map in the map display area 330. Sampling regions within the preset map displayed in the map display area 330 are labeled with sampling status tags (e.g., sampling status tag 331), and each sampling region is a sub-region containing sampling points within at least one sub-region. The display module is also configured to update the content displayed in the map display area 330 in response to a selection operation on the sampling status tag corresponding to a target sampling region. For example, when a user selects a sampling status tag corresponding to a target sampling region... Figure 3 When the sampling status label 331 shown is clicked, the display module responds to the user's click on the sampling status label 331 by updating the content displayed in the map display area 330. The updated map display area can be seen in [reference needed]. Figure 6 The map display area 610 shown is shown in the figure.

[0076] The updated map display area shows the target sampling region. The area of ​​the target sampling region shown in the updated map display area is larger than the area shown in the original map display area. Sampling point labels are added to the locations of the sampling points within the target sampling region shown in the updated map display area (e.g., ...). Figure 6 The sampling point label shown is 611.

[0077] The display module is also used to display label selection controls corresponding to the sampling points included in the target sampling area in the label selection area (e.g., Figure 6 The label selection control 612 shown is also used to receive input of a selection operation on any of the label selection controls displayed in the label selection area (e.g., receiving input of a selection operation on any of the label selection controls displayed in the label selection area). Figure 6 (The label selection control 612 shown is selected). The display module is also used to display the spectral reflectance peak contrast curve corresponding to the sample point to be processed in the data display area in response to the data analysis module determining the spectral reflectance peak contrast curve corresponding to the sample point to be processed (for example, the spectral reflectance peak contrast curve corresponding to the sample point to be processed is displayed in the data display area 620, where the sample point to be processed is the sample point corresponding to the label selection control 612).

[0078] In some embodiments, see Figure 7 The second interface 700 may also include an element content display area 710. The data analysis module is further used to determine the element content of the element corresponding to the sample point to be processed based on the hyperspectral data of the target element. The display module is also used to display the element content of the element corresponding to the sample point to be processed in the element content display area 710.

[0079] The element corresponding to the sampling point to be processed is the element included in the location of the sampling point to be processed in the preset area.

[0080] In some embodiments, continuing to refer to Figure 1 The display device provided by the embodiments of the present application further includes a monitoring module. The monitoring module is configured to generate first prompt information in a case where it is determined that the element corresponding to the to-be-processed sampling point does not include the preset element.

[0081] The first prompt information is configured to prompt that the element corresponding to the to-be-processed sampling point does not include the preset element.

[0082] In some embodiments, the monitoring module is further configured to generate second prompt information in a case where it is determined that the element corresponding to the to-be-processed sampling point includes the preset element, and the element content of the preset element corresponding to the to-be-processed sampling point is less than the element content threshold corresponding to the preset element.

[0083] The second prompt information is configured to prompt that the element content of the preset element corresponding to the to-be-processed sampling point is less than the element content threshold corresponding to the preset element.

[0084] In some embodiments, the data analysis module is further configured to perform a data preprocessing operation on the hyperspectral data of the target element corresponding to the to-be-processed sampling point, and determine the spectral reflectance coefficient peak contrast curve corresponding to the to-be-processed sampling point based on the hyperspectral data of the target element corresponding to the to-be-processed sampling point after the preprocessing operation.

[0085] The data preprocessing operation includes a data cleaning operation, a data denoising operation, and a data correction operation.

[0086] It should be noted that the data analysis module in the embodiments of the present application can be built-in with a data preprocessing algorithm. The data analysis module can call the built-in data preprocessing algorithm to perform a data cleaning operation on the hyperspectral data. The data preprocessing algorithm can include an image processing algorithm, an image registration algorithm, an image enhancement algorithm, a spectral angle mapping algorithm, etc., which are not limited in the embodiments of the present application.

[0087] In some embodiments, the data analysis module is further configured to determine the element content corresponding to the to-be-processed sampling point based on the target hyperspectral data corresponding to the to-be-processed sampling point after the preprocessing operation.

[0088] In some embodiments, the display module is further configured to display an element monitoring setting page 800 as shown in FIG. 8 in response to an input element monitoring setting instruction. Figure 8 The monitoring module is further configured to set the first element as the preset element in response to a setting instruction input on the element monitoring setting page 800 and indicating that the first element is set as the preset element. The monitoring module is further configured to set the first element content value as the element content threshold corresponding to the preset element in response to a setting instruction input on the element monitoring setting page 800 and indicating that the first element content value is set as the element content threshold corresponding to the preset element.

[0089] In some embodiments, the data display device provided by the present application adopts a portable handheld design, has a shockproof function, can directly contact the sample surface, and the maximum working carrying capacity of the data acquisition module is not less than 5 hours, and the moving speed during sampling can be adjusted in the range of 0 to 10 meters / minute. See Figure 9 In use, the user can start the data acquisition module 902 to realize data acquisition by pressing the key 901, which is simple to operate and convenient to realize.

[0090] In some embodiments, the data acquisition module provided by the present application includes a spectral sensor, the spectral sensor includes a visible-near infrared spectral channel (working waveband 350-1000 nanometers) and a short-wave infrared spectral channel (working waveband 1000-2500 nanometers), adopts a thermoelectric refrigeration type CCD detector integration, and the signal-to-noise ratio is better than 1000:1. Moreover, the spectral resolution of the visible-near infrared (VNIR) waveband of the visible-near infrared spectral channel is not greater than 3 nanometers, the spectral resolution of the short-wave infrared (SWIR) waveband of the short-wave infrared spectral channel is not greater than 8 nanometers. The wavelength repeatability error of the visible-near infrared spectral channel and the short-wave infrared spectral channel is not more than ±0.1 nanometer; the integration time is adjustable in the range of 1 millisecond to 10 seconds; the optical system adopts an achromatic lens group with an f / 2.0 aperture ratio; and a standard white plate reflectivity and blackbody radiation source calibration device is built-in.

[0091] Among them, the spectral sensor is equipped with a clean moisture-proof nitrogen protection system.

[0092] In some embodiments, the data processing unit provided by the present application includes:

[0093] The X86 architecture central processing unit and the field programmable gate array (FPGA) acceleration chip integrate: a real-time data acquisition card with a sampling rate not less than 1 megahertz; a solid-state storage device with a storage capacity not less than 1 terabyte; an embedded active heat dissipation system; a positioning system (104) integrating a GPS and Beidou dual-mode satellite positioning receiver, supporting real-time kinematic (RTK) positioning technology, and having a positioning accuracy of ±2 centimeters; an environmental sensor (105) including: a high-precision temperature and humidity sensor with a measurement range covering -20℃ to 60℃ and a temperature measurement accuracy of ±0.5℃; a light intensity sensor with a measurement range of 0 to 200 kilolux; a power supply system (106) including: a lithium ion battery pack with a rated voltage of 48 volts and a capacity of 20 ampere-hours; a solar charging module with a maximum output power of 200 watts; and a control system (107) developed based on a real-time operating system (RTOS) and supporting multi-task parallel processing.

[0094] In some embodiments, the data analysis module provided by the present application includes a spectral preprocessing unit and a spectral analysis unit.

[0095] Among them, the spectral pretreatment unit is integrated with the following algorithm functions:

[0096] Savitzky-Golay smoothing filter algorithm, supporting variable window width of 9 to 15 points; threshold denoising algorithm based on db4 wavelet base; multiple scattering correction (MSC) algorithm; standard normal variable transformation (SNV) algorithm; feature extraction module (1032) is configured with: continuous removal algorithm, focusing on analyzing 2100-2350 nanometer waveband; first derivative transformation algorithm; spectral envelope analysis algorithm;

[0097] Among them, the Savitzky-Golay filter algorithm is a smoothing filter method based on local polynomial fitting, mainly used for signal denoising and smoothing processing. This algorithm performs polynomial fitting on the signal within a sliding window, retaining the high-order characteristics of the signal while reducing the influence of noise. Support variable window width of 9 to 15 points, means that when filtering, you can choose different size windows according to the actual signal characteristics, to balance the smoothing effect and the preservation of signal details.

[0098] Threshold denoising algorithm based on db4 wavelet base uses db4 (Daubechies 4) wavelet base to perform multi-scale decomposition on the signal, and removes the noise components in the wavelet coefficients through threshold processing. The specific steps include: wavelet decomposition of the signal, extraction of different frequency components, application of threshold function to high-frequency wavelet coefficients, suppression of noise, reconstruction of signal, and obtaining of denoised and smoothed signal. This method can effectively remove noise in non-stationary signals while retaining the main characteristics of the signal.

[0099] Multiple scattering correction (MSC) algorithm is mainly used to correct the scattering effect in spectral data, eliminate the baseline shift and scattering interference caused by the difference in physical properties of the sample. Its processing steps include: calculating the linear regression between each sample spectrum and the reference spectrum (usually the average spectrum), using the regression coefficient to correct the sample spectrum, adjusting its baseline and amplitude; make the corrected spectrum more comparable, reduce the influence of scattering on subsequent analysis MSC algorithm is widely used in spectral pretreatment, to improve the stability and accuracy of the model.

[0100] First derivative transformation algorithm realizes edge detection of different resolutions by adjusting the wavelet scale parameter, and enhances the ability to capture signal details.

[0101] Spectral envelope analysis algorithm uses Hilbert transform or other envelope detection algorithm to accurately extract the spectral envelope.

[0102] The spectral analysis unit is integrated with the following analysis models:

[0103] Support Vector Machine classifier, which is used to achieve efficient nonlinear classification by kernel function and parameter adjustment. Radial Basis Function (RBF) kernel is adopted with parameter γ = 0.1 and penalty coefficient C = 10; Partial Least Squares Discriminant Analysis (PLS-DA) model with 8 latent variables; Deep neural network model with a combined architecture of 3-layer Convolutional Neural Network (CNN) and 2-layer Long Short-Term Memory Network (LSTM).

[0104] Among them, "Support Vector Machine Classifier (RBF kernel function, γ = 0.1, C = 10)", which is the core implementation of nonlinear classification. It is used to distinguish different mineral subtypes of carbonate clay type lithium ore (such as hectorite, lithium chlorite, etc.). It solves the problem of nonlinear mapping of spectral features and mineral types (such as the non-simple linear correlation between Al-OH absorption depth and Li content). Kernel function selection: Radial Basis Function (RBF) can effectively handle high-dimensional nonlinear data. γ = 0.1: control the complexity of decision boundary to avoid overfitting; C = 10: balance classification error and model generalization ability.

[0105] 3-layer Convolutional Neural Network (CNN) and 2-layer Long Short-Term Memory Network (LSTM) are used to extract spatial and temporal features.

[0106] The specific processing steps are as follows:

[0107] Extracting spatial features: CNN extracts local morphological features of spectral curves (such as absorption peak shape, slope change), symmetry of Al-OH absorption peak at 2140 nm; Temporal features: LSTM captures the temporal correlation of multi-batch sampling data (such as the trend of lithium content change at different exploration points). The spatial feature extraction process is original spectrum-CNN convolution layer (extracting local features)-LSTM layer (modeling temporal dependence)-fusing feature vectors.

[0108] CNN layer design: convolution kernel width = 5 bands, capturing local spectral fluctuations; activation function: ReLU, enhancing nonlinear expression ability;

[0109] LSTM layer design: number of memory cells = 64, saving historical exploration data state; Dropout = 0.2, preventing overfitting.

[0110] Spatial feature dimension reduction, using Principal Component Analysis (PCA) to compress high-dimensional features to 8-10 dimensions.

[0111] Model fusion, concatenating CNN-LSTM features and traditional features (Al-OH depth, BDR) to input into the PLS-DA model.

[0112] Dynamic weight distribution, according to the importance of characteristics (through SHAP value analysis), adjust the weight ratio of CNN-LSTM characteristics and traditional characteristics (such as 7:3).

[0113] In some embodiments, when sample collection is performed based on the data display device provided by the application, the spectral sensor can be configured at an incident angle of 45° to perform sample data collection measurement. 3 to 5 times of spectral data are collected for each sample collection and the average value is taken; the environmental temperature, humidity and light intensity parameters are synchronously collected; the real-time positioning coordinate information is recorded.

[0114] In some embodiments, when data processing is performed based on the data display device provided by the application, the spectral scattering effect can be corrected by using a standard normal variable transformation (SNV) algorithm; the characteristic absorption peak of the 2100-2350 nanometer wave band is extracted by using a continuum removal method; the lithium element content is calculated by using a pre-trained partial least squares discriminant analysis (PLS-DA) model;

[0115] In some embodiments, based on the data display device provided by the application, an exploration report can also be generated and displayed, and the exploration report includes: a lithium element content spatial distribution map, the content accuracy reaches 0.01%; a lithium spectral reflectance map; a mineral composition quantitative analysis result; a data quality evaluation index, a determination coefficient R 2 greater than 0.95.

[0116] In some embodiments, based on the data display device provided by the present application, in the data analysis stage, a standard reflector plate can be used for radiation calibration; the abnormal spectral data line is repaired by using the adjacent pixel interpolation method. The absorption depth of Al-OH bond at 2140 nm of Al-OH bond is calculated; the characteristic absorption peak of lithium element at 2200 nm is extracted; and the band depth ratio (BDR) index is calculated. The moving window partial least squares (MWPLS) regression algorithm is used; the moving window width of 21 bands is set; and the optimal principal component number is determined by leave-one-out cross-validation. After the calculation of the absorption depth of Al-OH bond at 2140 nm, the extraction of the characteristic absorption peak of lithium element at 2200 nm and the calculation of the band depth ratio (BDR) index, the absorption depth and the characteristic peak data obtained can be calculated to remove and correct the noise, so as to ensure the accuracy and stability of the data. Methods such as spectral smoothing and baseline correction are included to reduce the influence of environmental and instrument errors. The Al-OH absorption depth at 2140 nm, the lithium element absorption peak at 2200 nm and the BDR index are fused to construct a comprehensive spectral feature vector. The multi-dimensional feature combination improves the recognition ability and classification accuracy of minerals or elements. Dimension reduction techniques such as principal component analysis (PCA) and linear discriminant analysis (LDA) are used to reduce the feature dimension and remove redundant information. The most discriminant features are selected for subsequent analysis to improve the calculation efficiency and model performance. Machine learning algorithms such as support vector machine (SVM), random forest (RF) and neural network are used for classification of the fused features. Different mineral types or element contents are identified to realize automatic mineral identification and distribution mapping. The extracted features and classification results are mapped to the spatial position to generate and display the mineral distribution map or the element content map.

[0117] It has been verified that the present application can overcome the low efficiency of the traditional method, and the actual sample collection speed is improved by 5 times. The problem of interference of complex environment in the field is solved, and the measurement error is controlled within 3% by using the environmental compensation algorithm. The data analysis delay bottleneck is broken, and real-time processing is realized on site, with a delay time of less than 1 minute. The detection limit of lithium element reaches 0.008%, which is significantly better than the 0.05% of the traditional method. The effective exploration area per day is 5 to 8 square kilometers, which is much higher than the 0.5 to 1 square kilometer of the traditional method. The measurement data has good repeatability, and the relative standard deviation (RSD) is less than 2.5%.

[0118] In some aspects, multiple embodiments of the present application can be combined, and the combined aspects can be implemented. Optionally, some operations of the embodiments are optionally combined, and / or the order of some operations is optionally changed. Also, the execution order between the embodiments is only exemplary, and does not constitute a limitation on the execution order between the embodiments, and other execution orders between the embodiments can also be used. The described execution order is not intended to indicate that these operations can only be executed in this order. A person of ordinary skill in the art will think of various ways to reorder the operations described herein. In addition, it should be pointed out that the process details of one embodiment described herein are also applicable in a similar manner to other embodiments, or different embodiments can be combined for use.

[0119] In addition, some steps in the embodiments can be replaced by other possible steps. Alternatively, some steps in the embodiments can be optional, and can be deleted in some use scenarios. Alternatively, other possible steps can be added in the embodiments. Also, the embodiments can be implemented individually or in combination.

[0120] Through the description of the above embodiments, those skilled in the art can clearly understand that, for the convenience and brevity of description, only the division of the above functional modules is taken as an example for illustration, and in actual application, the above functions can be completed by different functional modules according to needs, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above.

[0121] In several embodiments provided in the present application, it should be understood that the disclosed device can be implemented by other means. For example, the above-described device embodiments are only illustrative, for example, the division of the modules or units is only a logical function division, and actual implementation can have another division manner, for example, multiple units or components can be combined or integrated into another device, or some features can be ignored or not executed. Another point, the coupling or direct coupling or communication connection between the displayed or discussed each other can be through some interface, indirect coupling or communication connection between the devices or units, which can be electrical, mechanical or other forms.

[0122] In addition, each functional unit in each embodiment of the present application can be integrated in one processing unit, or each unit can exist physically, or two or more units can be integrated in one unit. The integrated unit can be realized in the form of hardware or in the form of a software functional unit.

[0123] The integrated unit, if implemented in the form of a software function unit and sold or used as an independent product, can be stored in a readable storage medium. Based on such understanding, the technical solutions of the embodiments of the present application can be embodied in the form of a software product in essence or in the part that contributes to the present application, or the whole or part of the technical solutions can be embodied in the form of a software product stored in a storage medium, including a plurality of instructions for causing an apparatus (which can be a single-chip microcomputer, a chip, etc.) or a processor to perform all or part of the steps of the embodiments of the present application. The aforementioned storage medium includes a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and various media that can store program codes.

[0124] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any change or replacement within the technical scope disclosed in the present application should be covered in the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A carbonate clay type lithium ore elemental spectroscopy data acquisition device, characterized by, The application relates to a method and device for displaying a map. The method comprises the following steps: displaying a first interface; the first interface comprises a preset map; the preset map is a map corresponding to a preset region; the preset map comprises at least one subregion; in response to an input sampling instruction for any one of the at least one subregion, acquiring hyperspectral data of a target element corresponding to a plurality of sampling points included in the any one subregion, and acquiring environmental information of the any one subregion; the target element is associated with a carbonate clay type lithium mine; the environmental information of the any one subregion comprises temperature, humidity and illumination intensity of the any one subregion; the display module is further used for displaying a second interface; the second interface comprises a label selection area and a data display area; the label selection area is used for displaying at least one label selection control in a plurality of label selection controls; one label selection control in the plurality of label selection controls corresponds to one sampling point in the plurality of sampling points; in response to an input selection operation on any one of the at least one label selection control, the data analysis module determines a to-be-processed sampling point; the to-be-processed sampling point is the sampling point corresponding to the any one label selection control; the data analysis module is further used for filtering target hyperspectral data from the hyperspectral data of the target element corresponding to the to-be-processed sampling point based on the environmental information of the region where the to-be-processed sampling point is located, and determining a spectral reflectance coefficient peak contrast curve corresponding to the to-be-processed sampling point based on the target hyperspectral data; the spectral reflectance coefficient peak contrast curve is used for representing the correlation degree between the hyperspectral data of the target element corresponding to the to-be-processed sampling point and preset hyperspectral data corresponding to the target element; 2. The apparatus of claim 1, wherein, the display module is further used for displaying the spectral reflectance coefficient peak contrast curve corresponding to the to-be-processed sampling point in the data display area. The application further comprises a map import module; the display module is further used for displaying a third interface; the third interface comprises a map import control; in response to an input selection operation on the map import control, the map import module instructs the display module to display a map receiving page; the map receiving page is used for receiving an imported map image; the display module is further used for displaying the map receiving page; the map import module is further used for performing a division operation on the preset map in response to an input import operation on the preset map, so as to determine the spatial distribution of the at least one subregion on the preset map; the map import module is further used for generating a region selection control corresponding to the any one subregion based on the spatial distribution of the any one subregion on the preset map; the display module is further used for displaying the first interface comprising the region selection control corresponding to the any one subregion in response to an input operation for instructing to display the first interface. The display module is further configured to display a fourth interface in response to a selection operation of a region selection control corresponding to the any sub-region input in the first interface; the fourth interface includes the any sub-region and a region sampling control corresponding to the any sub-region; an area of the any sub-region in the fourth interface is greater than an area of the any sub-region in the first interface; The display module is further configured to send a sampling instruction for the any sub-region to the data collection module in response to a selection operation of the region sampling control corresponding to the any sub-region input in the fourth interface.

3. The apparatus of claim 2, wherein, In a process in which the data collection module is configured to acquire hyperspectral data of a target element corresponding to a plurality of sampling points included in the any sub-region in response to the sampling instruction for the any sub-region input, the data collection module is specifically configured to: In response to the sampling instruction for the any sub-region input, determine a plurality of sampling points in the any sub-region; for any sampling point in the plurality of sampling points, acquire visible-near-infrared spectrum data and short-wave infrared spectrum data of the target element included in a position corresponding to the any sampling point in the preset region; a wavelength range of the visible-near-infrared spectrum data is 350-1000 nanometers; a wavelength range of the short-wave infrared spectrum data is 1000-2500 nanometers; Determine hyperspectral data of the target element included in the position corresponding to the any sampling point in the preset region based on the visible-near-infrared spectrum data and the short-wave infrared spectrum data of the target element included in the position corresponding to the any sampling point in the preset region; Determine the hyperspectral data of the target element included in the position corresponding to the any sampling point in the preset region as the hyperspectral data of the target element corresponding to the any sampling point.

4. The apparatus of claim 3, wherein, In a process in which the data collection module is configured to determine a plurality of sampling points in the any sub-region in response to the sampling instruction for the any sub-region input, the data collection module is further configured to: Receive a sampling point replacement instruction input to indicate that a first sampling point is replaced by a second sampling point, and replace the first sampling point by the second sampling point; the plurality of sampling points include the first sampling point and do not include the second sampling point; The second sampling point is located in the any sub-region; And / or, Receive a sampling point deletion instruction input to indicate that a third sampling point in the plurality of sampling points is deleted, and delete the third sampling point from the plurality of sampling points.

5. The apparatus of claim 4, wherein, In a process in which the data collection module is configured to determine a plurality of sampling points in the any sub-region in response to the sampling instruction for the any sub-region input, the data collection module is specifically configured to: In response to the sampling instruction for the any sub-region input, determine a number of the plurality of sampling points based on an area of a region corresponding to the any sub-region in the preset region; Determine the plurality of sampling points in the any sub-region based on the number of the plurality of sampling points, a region boundary position of the any sub-region, and a region center point position.

6. The apparatus of claim 4, wherein, In the data collection module is used for in response to input for any sub-region of the sampling instruction, in any sub-region of the plurality of sampling points, the data collection module is specifically used for: In response to input for any sub-region of the sampling instruction, display a sampling point input interface; In response to the position information corresponding to the plurality of sampling points input in the sampling point input interface, determine the plurality of sampling points in any sub-region; The position information corresponding to any sampling point includes the position information of the position corresponding to any sampling point in the preset area or the position information of the position corresponding to any sampling point in any sub-region.

7. The apparatus of claim 5 or 6, wherein, The display module is also used for: In response to input data viewing instruction, display the second interface; the second interface further includes a map display area; The preset map is displayed in the map display area; the sampling region in the preset map displayed in the map display area is added with a sampling state label; the sampling region is a sub-region including a sampling point in the at least one sub-region; In response to the selection operation of the sampling state label corresponding to the target sampling region, update the content displayed in the map display area; The target sampling region is displayed in the map display area after updating; the area of the target sampling region displayed in the map display area after updating is greater than that before updating; the position of the sampling point included in the target sampling region displayed in the map display area after updating is added with a sampling point label; Display the label selection control corresponding to the sampling point included in the target sampling region in the label selection area; Receive input selection operation of any label selection control displayed in the label selection area; In response to the data analysis module determining the spectral reflectance coefficient peak contrast curve corresponding to the to-be-processed sampling point, display the spectral reflectance coefficient peak contrast curve corresponding to the to-be-processed sampling point in the data display area; the spectral reflectance coefficient peak contrast curve is used to represent the correlation degree between the hyperspectral data of the target element corresponding to the to-be-processed sampling point and the preset hyperspectral data corresponding to the target element.

8. The apparatus of claim 7, wherein, The second interface further includes an element content display area; The data analysis module is also used for determining the element content corresponding to the to-be-processed sampling point based on the target hyperspectral data; the element corresponding to the to-be-processed sampling point is the element included in the position corresponding to the to-be-processed sampling point in the preset area; the display module is also used for displaying the element content corresponding to the to-be-processed sampling point in the element content display area.

9. The apparatus of claim 8, wherein, Further comprising: A monitoring module; The monitoring module is used to generate a first prompt information in the case that the element corresponding to the to-be-processed sampling point does not include the preset element in the target element; The first prompt information is used to prompt that the element corresponding to the to-be-processed sampling point does not include the preset element; And / or The monitoring module is configured to generate second prompt information when it is determined that the element corresponding to the to-be-processed sampling point includes the preset element, and the element content of the preset element corresponding to the to-be-processed sampling point is less than the element content threshold corresponding to the preset element. The second prompt information is configured to prompt that the element content of the preset element corresponding to the to-be-processed sampling point is less than the element content threshold corresponding to the preset element.

10. The apparatus of claim 9, wherein, In the data analysis module for determining the spectral reflectance coefficient peak contrast curve corresponding to the to-be-processed sampling point based on the hyperspectral data of the target element corresponding to the to-be-processed sampling point, the data analysis module is specifically configured to: perform a data preprocessing operation on the hyperspectral data of the target element corresponding to the to-be-processed sampling point; the data preprocessing operation includes a data cleaning operation, a data denoising operation and a data correction operation; determine the spectral reflectance coefficient peak contrast curve corresponding to the to-be-processed sampling point based on the hyperspectral data of the target element corresponding to the to-be-processed sampling point after the preprocessing operation; In the data analysis module for determining the element content corresponding to the to-be-processed sampling point based on the hyperspectral data of the target element corresponding to the to-be-processed sampling point, the data analysis module is specifically configured to: determine the content of the target element corresponding to the to-be-processed sampling point based on the hyperspectral data of the target element corresponding to the to-be-processed sampling point after the preprocessing operation.

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