A lithium ore exploration method based on spectral domain folding reconstruction

Abstract

The present application relates to the field of mineral spectral detection, and particularly relates to a lithium ore exploration method based on spectral domain folding reconstruction. The method receives a mixed spectrum digital spectrum vector and a collection state parameter, maps to a high-dimensional frequency space after preprocessing, constructs a spectral domain folding conflict graph and generates a reversible folding index table, performs nonlinear frequency domain compression on a non-anchor band, performs lithium anchor maintenance on a lithium ore characteristic anchor point band, combines a folding domain compressed spectrum, lithium anchor bypass data and a reversible folding index table to perform inverse folding reconstruction, and writes back feedback parameters according to inverse folding residuals. The scheme can reduce the risk of covering the lithium ore characteristic band by the background band, improve the separation degree, observability and stability of the lithium ore signal under strong mixing.

Description

Technical Field

[0001] This invention relates to the field of mineral spectral detection, specifically a lithium ore exploration method based on spectral domain folding reconstruction. Background Technology

[0002] Lithium ore spectroscopic detection typically relies on hyperspectral, shortwave infrared, thermal infrared, Raman, laser-induced breakdown spectroscopy, or other electrical acquisition spectrometers to obtain the spectral response of ore, core, mineral powder, field outcrops, or mineral samples. The spectroscopic acquisition equipment converts the sample's response to incident light, excitation light, or plasma radiation into an analog electrical signal, which is then converted into a digital spectral vector through analog-to-digital conversion. The digital spectral vector typically includes the response influenced by the target lithium mineral, surrounding rock, altered minerals, weathering layers, instrument noise, and variations in acquisition conditions.

[0003] In lithium ore detection scenarios, the characteristic responses of lithium ore are often not isolated strong peaks. Local absorption features in lepidolite, lithium-bearing muscovite, or lithium-rich pegmatite systems may overlap with the responses of hydrous minerals, clay minerals, or other altered minerals. Spodumene may lack stable direct features in some visible to short-wave infrared bands and is easily masked by background responses from kaolinite, illite, montmorillonite, etc. For field remote sensing, online sorting, or core sample scanning scenarios, mixed pixels, particle contact, surface weathering, acquisition noise, and changes in light source can further cause the weak response of lithium ore to be covered by the strong background response.

[0004] Existing spectral processing methods typically employ spectral filtering, baseline correction, feature band selection, principal component analysis, nonnegative matrix factorization, linear or nonlinear unmixing, machine learning classification, and deep autoencoder reconstruction to process mixed spectra. While these methods can reduce noise or extract mineral features to some extent, they still suffer from the following problems.

[0005] First, conventional dimensionality reduction or compression methods primarily aim at data compression or pre-classification processing, failing to control the risk of coverage between weak lithium ore feature bands and strongly correlated background bands within folded units. Second, while conventional feature selection methods can preserve local anchor points, they easily lose the full spectral context, making it difficult to recover the compressed or rearranged lithium ore response. Third, although deep autoencoder reconstruction can generate an approximate reconstructed spectrum, its reconstruction process easily degenerates into a black-box approximate recovery, lacking traceable paths for preserving lithium ore anchor points and residual write-back. Fourth, simple classification output cannot determine whether the enhanced lithium ore response originates from measurable technical signals in the original electrically acquired spectrum. Summary of the Invention

[0006] The purpose of this invention is to provide a technical solution to address the aforementioned problems existing in the prior art. Specifically, this invention is achieved through the following technical solution: A lithium ore exploration method based on spectral domain folding reconstruction includes the following steps: Step 1: Receive the aliased spectrum digital spectral vector and acquisition status parameters output by the spectral acquisition module, and obtain the calibration spectral vector after preprocessing. Step 2: Map the calibration spectral vector to a high-dimensional frequency space to form an enhanced spectral representation; Step 3: Based on the lithium ore characteristic anchor point frequency band, interfering mineral candidate frequency band, the acquisition state parameters, and the frequency band energy, peak shape relationship, and noise state in the boosted spectrum representation, construct a spectral domain folding conflict map that includes frequency band coupling degree, co-folding unit coverage risk, and folding unit occupancy state. Step 4: Generate a reversible folding index table including frequency band number, folding unit number, lithium anchor holding position and compression coefficient based on the spectral domain folding collision map, and determine the folding unit allocation relationship according to lithium anchor collision penalty, folding unit capacity constraint and lithium anchor bypass constraint; Step 5: Perform nonlinear frequency domain compression with amplitude sign retention on the non-anchor frequency band according to the reversible folding index table, and perform lithium anchor retention processing on the lithium ore characteristic anchor point frequency band configured with the lithium anchor retention bit to form folded domain compressed spectrum and lithium anchor bypass data; Step 6: Perform inverse folding reconstruction based on the folded domain compressed spectrum, the lithium anchor bypass data, and the reversible folding index table to obtain the lithium ore characteristic response enhancement spectrum; Step 7: Calculate the inverse folding residual, and determine the source of the residual as compression loss, background aliasing, or acquisition drift based on the distribution of the inverse folding residual in the folding unit, the lithium ore characteristic anchor point frequency band, and the acquisition state parameters. Step 8: When the inverse folding residual, the area deviation of the lithium ore feature anchor frequency band, or the folding unit conflict rate exceeds their respective preset upper limits, write back data related to the source of the residual into the feedback parameter table to adjust the folding parameters, compression gain, or acquisition control parameters in the next spectral processing.

[0007] Furthermore, the lithium ore characteristic anchor point frequency band includes: A candidate anchor window set is generated based on the prior knowledge of the mineral type corresponding to the target lithium ore type. Local absorption parameters are calculated for the candidate frequency bands in the candidate anchor window set, and the importance score of the candidate frequency band in the training samples is obtained. Stability scores are calculated in training samples with different signal-to-noise ratios and different mixing ratios. Candidate frequency bands with importance scores not lower than the importance threshold and stability scores not lower than the stability threshold are written into the lithium anchor retention table, and the lithium anchor retention position is generated according to the lithium anchor retention table. The local absorption parameters include absorption area, absorption depth, peak position shift, full width at half maximum (FWHM), and absorption asymmetry.

[0008] Furthermore, the frequency band coupling degree is determined by weighting the response similarity, peak overlap, acquisition noise coupling state, and spectral peak adjacency state of the lithium ore characteristic anchor frequency band and the interfering mineral candidate frequency band in the enhanced spectrum representation; the co-folding unit coverage risk is determined by the frequency band coupling degree, the lithium anchor candidate member identifier corresponding to the lithium ore characteristic anchor frequency band, and the remaining capacity of the folding unit.

[0009] Furthermore, the reversible folding index table is generated during the training phase based on the inter-class divergence between the lithium ore feature anchor frequency band set and the interfering mineral candidate frequency band set, the intra-class divergence within each of the lithium ore feature anchor frequency band set and the interfering mineral candidate frequency band set, the lithium anchor collision penalty, the folding unit capacity constraint, and the lithium ore feature anchor frequency band reservation constraint, and is fixed in the folding index cache in the form of a lookup table; during the online processing phase, based on the residual source determined by the previous frame or historical spectral processing, only the folding units related to the residual source are locally updated.

[0010] Furthermore, the nonlinear frequency domain compression employs a monotonic compression function that preserves the amplitude sign. The compression gain of the monotonic compression function is adjusted based on the folding unit occupancy status, the acquisition noise status, and the historical inverse folding residual. The lithium ore characteristic anchor point frequency band configured with the lithium anchor holding position does not participate in the monotonic compression or the co-folding unit aggregation of non-anchor frequency bands.

[0011] Furthermore, the inverse fold reconstruction includes a strictly reversible mode and a near-lossless enhancement mode. During the inverse fold reconstruction process, an inverse fold prediction spectrum is generated based on the fold domain compressed spectrum, the lithium anchor bypass data, and the reversible fold index table. In the strictly reversible mode, the inverse fold residual is losslessly written into the residual cache. In the near-lossless enhancement mode, the inverse fold residual is quantized and written into the residual cache, and the residual norm, lithium ore feature anchor band area deviation, and spectral angle deviation between the inverse fold prediction spectrum and the reference enhancement spectrum meet their respective preset upper limits.

[0012] Furthermore, the candidate anchor window set is determined in layers according to the target lithium ore type; when the target lithium ore type is lepidolite or lithium-rich pegmatite system, the candidate anchor window set includes candidate absorption bands obtained by screening the local absorption parameters, importance scores and stability scores in the range of 1400nm to 2500nm; when the target lithium ore type is spodumene, which is a direct identification target, the candidate anchor window set includes thermal infrared candidate diagnostic response bands obtained by screening calibrated samples in the range of 9μm to 12μm.

[0013] Furthermore, when the source of the residual is determined to be compression loss, if the folding unit associated with the compression loss has not reached its physical capacity limit, the allocable capacity of the folding unit is increased, or the compression gain of the folding unit is decreased; if the folding unit has reached its physical capacity limit, the compression gain of the folding unit is decreased, or some of the low-collision non-anchor frequency bands in the folding unit are reallocated to other folding units that have not reached their physical capacity limit; when the source of the residual is determined to be background aliasing, the spectral domain folding collision map is updated and the allocation relationship of folding units in high-collision frequency bands is adjusted; when the source of the residual is determined to be acquisition drift, the acquisition control parameters or acquisition state correction amount are adjusted.

[0014] Furthermore, the aliased spectral digital spectral vector is written to the spectral vector cache via the direct memory access controller, the reversible folding index table is stored in the folding index cache, the lithium anchor holding bit is stored in the lithium anchor holding bit register, and the inverse folding residual and its source identifier are stored in the residual cache; the direct memory access controller writes the non-anchor frequency band data into the folding domain cache according to the folding unit number in the reversible folding index table, and writes the lithium ore characteristic anchor point frequency band data into the lithium anchor bypass cache according to the lithium anchor holding bit.

[0015] Furthermore, the spectral domain folding conflict graph consists of a node table, an edge table, and an occupancy status table. The node table records the frequency grid number, wavelength or frequency position, whether it belongs to the lithium ore characteristic anchor band, whether it belongs to the interfering mineral candidate band, and the current acquisition noise status. The edge table records the response similarity, peak overlap, spectral peak adjacency, frequency band coupling, and co-folding unit coverage risk between two frequency grids. The occupancy status table records the number of frequency grids currently allocated to each folding unit, the lithium anchor occupancy status, the non-anchor background occupancy status, and the remaining capacity.

[0016] Compared with the prior art, the present invention has the following advantages and beneficial effects: This invention uses a spectral domain folding conflict diagram to represent the risk of co-folding unit coverage between the lithium ore feature anchor band and the interfering mineral candidate band. This makes the allocation of folding units no longer a simple dimensionality reduction or fixed compression, but a frequency domain rearrangement constrained by lithium ore anchor protection, frequency band collision state, and folding unit occupancy state.

[0017] This invention excludes the characteristic anchor band of lithium ore from the aggregation of co-folding units in non-anchor bands by maintaining the lithium anchor position, and forms lithium anchor bypass data. This prevents the weak characteristic band of lithium ore from being swallowed by the strong background response during nonlinear compression, which is beneficial to maintaining the peak shape, area and local response relationship of lithium ore anchor points.

[0018] This invention performs inverse folding reconstruction by combining folded domain compressed spectrum, lithium anchor bypass data, and reversible folding index table, so that the formation process of lithium ore enhancement spectrum has a traceable index path and anchor point path, avoiding the degradation of lithium ore enhancement into ordinary classification confidence increase or display brightness enhancement.

[0019] This invention determines the source of residuals by identifying the distribution of inverse folding residuals in folding units, lithium ore characteristic anchor bands, and acquisition state parameters. The source of residuals is identified as compression loss, background aliasing, or acquisition drift. Based on the source of residuals, folding parameters, compression gain, or acquisition control parameters are written back, enabling subsequent spectral frames to be differentiated according to the source of residuals, thereby improving the stability of lithium ore signal enhancement under strong aliasing backgrounds.

[0020] This invention enables hardware deployment in edge or online detection devices through spectral vector caching, folded index caching, lithium anchor holding bit register, lithium anchor bypass caching, folded domain caching, residual caching, and direct memory access controller, avoiding the limitation of only offline algorithm expression. Attached Figure Description

[0021] The accompanying drawings, which are included to provide a further understanding of embodiments of the invention and form part of this application, do not constitute a limitation thereof. In the drawings: Figure 1 This is a flowchart illustrating a lithium ore exploration method based on spectral domain folding reconstruction. Detailed Implementation

[0022] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to embodiments and accompanying drawings. The illustrative embodiments and descriptions of this invention are for illustrative purposes only and are not intended to limit the invention. It should be noted that this invention is already in the actual research and development stage.

[0023] Example 1 like Figure 1 As shown, a lithium ore exploration method based on spectral domain folding reconstruction includes the following steps: Step 1: Receive the aliased spectrum digital spectral vector and acquisition status parameters output by the spectral acquisition module, and obtain the calibration spectral vector after preprocessing. Step 2: Map the calibration spectral vector to a high-dimensional frequency space to form an enhanced spectral representation; Step 3: Based on the lithium ore characteristic anchor point frequency band, interfering mineral candidate frequency band, the acquisition state parameters, and the frequency band energy, peak shape relationship, and noise state in the boosted spectrum representation, construct a spectral domain folding conflict map that includes frequency band coupling degree, co-folding unit coverage risk, and folding unit occupancy state. Step 4: Generate a reversible folding index table including frequency band number, folding unit number, lithium anchor holding position and compression coefficient based on the spectral domain folding collision map, and determine the folding unit allocation relationship according to lithium anchor collision penalty, folding unit capacity constraint and lithium anchor bypass constraint; Step 5: Perform nonlinear frequency domain compression with amplitude sign retention on the non-anchor frequency band according to the reversible folding index table, and perform lithium anchor retention processing on the lithium ore characteristic anchor point frequency band configured with the lithium anchor retention bit to form folded domain compressed spectrum and lithium anchor bypass data; Step 6: Perform inverse folding reconstruction based on the folded domain compressed spectrum, the lithium anchor bypass data, and the reversible folding index table to obtain the lithium ore characteristic response enhancement spectrum; Step 7: Calculate the inverse folding residual, and determine the source of the residual as compression loss, background aliasing, or acquisition drift based on the distribution of the inverse folding residual in the folding unit, the lithium ore characteristic anchor point frequency band, and the acquisition state parameters. Step 8: When the inverse folding residual, the area deviation of the lithium ore feature anchor frequency band, or the folding unit conflict rate exceeds their respective preset upper limits, write back data related to the source of the residual into the feedback parameter table to adjust the folding parameters, compression gain, or acquisition control parameters in the next spectral processing.

[0024] Specifically, in one embodiment, the spectral acquisition circuit includes a spectral sensor, an analog front-end, a gain control circuit, an integration control circuit, and an analog-to-digital converter (ADC). The spectral sensor receives the reflection, emission, scattering, or plasma spectral responses generated by the mineral sample and converts the spectral responses into analog electrical signals. The ADC converts the analog electrical signals into aliased spectral digital vectors, and the acquisition control unit synchronously records the integration time, exposure time, gain, dark current estimation, noise estimation, and temperature drift state to form acquisition state parameters.

[0025] Acquisition control parameters refer to control quantities that can be adjusted by system feedback and affect the subsequent spectral acquisition state. These include at least one of the following: integration time control, exposure time control, analog gain control, light source intensity control, noise correction, and acquisition state correction. Acquisition state parameters describe the actual state corresponding to the current spectral acquisition result, while acquisition control parameters are used to adjust the next spectral acquisition process under the control of the feedback parameter table; both can correspond to the measured and control values ​​of the same physical channel.

[0026] The digital spectral vector of the aliased spectrum is preprocessed. Preprocessing includes at least one of the following: dark current subtraction, bad channel correction, radiometric normalization, continuum removal, band alignment, noise estimation, and anomalous channel masking. The preprocessed calibration spectral vector is denoted as: ; in, Represents the calibration spectral vector. Represents the digital spectral vector of the aliased spectrum. Indicates the collected status parameters. This represents the preprocessing function. The preprocessing function can be determined based on the dark current meter, gain meter, channel noise meter, and light source intensity meter of the acquisition device.

[0027] When there are saturated channels, null channels, or bad channels in the digital spectral vector of the aliased spectrum, the preprocessing function marks the corresponding channels as channels that cannot be used for lithium anchor determination and folded conflict map construction; when the channel where the lithium ore feature anchor band is located is marked as an abnormal channel, the system invalidates the lithium anchor holding position of the lithium ore feature anchor band and enables the reference anchor corresponding to the adjacent candidate anchor window or historical stable frame.

[0028] Map the calibration spectral vector to a high-dimensional frequency space. Let the calibration spectral vector be... The lifting spectrum is then expressed as: ; in, Indicates the boost spectrum representation, This indicates the number of spectral channels in the calibration spectral vector. This indicates an increase in the dimension of the spectral representation. This represents a high-dimensional frequency mapping function. When strict invertibility is required... A reversible boosting structure can be used, allowing the calibration spectral vector to be recovered from the boosted spectral representation; when using a near-lossless enhancement mode, It can be formed by splicing together the original spectral vector, local difference spectrum, frequency domain transform component, and peak shape enhancement component; among which, Indicates the boost spectrum representation, The first element in the enhancement spectrum representation represents the... The response value corresponding to each frequency grid point , This indicates the dimension of the boosted spectral representation. This represents a high-dimensional frequency mapping function. When dealing with the ... During frame spectral processing, Indicates the first In the frame lift spectrum representation, the first The response value corresponding to each frequency grid point.

[0029] In one specific implementation, the boosted spectral representation includes the original spectral response component, the first-order difference component, the local average component, and the frequency domain basis component. The original spectral response component preserves the amplitude of each sampling channel; the first-order difference component characterizes local peak shape variations; the local average component suppresses high-frequency noise; and the frequency domain basis component characterizes the frequency grid energy distribution over a relatively wide spectral band.

[0030] The boost spectrum can be represented as: ; in, This represents the first-order difference vector of the calibration spectral vector; This represents the second-order difference vector of the calibration spectral vector; The frequency domain basis matrix can be a discrete cosine transform basis, a wavelet basis, or an invertible boosting basis determined by calibration samples. , , , These represent the normalized weights of the original spectral response, first-order difference response, second-order difference response, and frequency domain basis response, respectively. This indicates transpose. The normalized weights are determined by calibration samples, training samples, or device calibration parameters, and satisfy the condition that each component in the boost spectrum representation is within a comparable amplitude range.

[0031] The first-order difference vector and the second-order difference vector can be represented as follows: ; in, Indicates the first First-order difference response at each position Represents the first spectral vector in the calibration spectrum. The response value of each spectral channel. Indicates the first The response value of each spectral channel.

[0032] ; in, Indicates the first The second-order difference response at each position, , , These represent the response values ​​of adjacent spectral channels. For boundary spectral channels, padding can be achieved by copying adjacent channels, zero-padding, or boundary mirroring.

[0033] The aforementioned enhanced spectrum representation allows the local peak shape changes of the lithium ore characteristic anchor band, the broadband response of the background mineral candidate band, and the acquisition noise state to be expressed in the same high-dimensional frequency space, providing frequency band energy, peak shape relationship, and noise state for subsequent construction of spectral domain folding conflict map.

[0034] The system generates a set of candidate anchor windows based on the prior knowledge of the target lithium ore type. For each candidate frequency band, it calculates the local absorption area, absorption depth, peak position shift, full width at half maximum (FWHM), and absorption asymmetry. Then, it combines the importance score and stability score of the training samples to determine whether a candidate frequency band should be included in the lithium anchor retention table. The lithium anchor retention table records the final set of lithium ore feature anchor frequency bands. The lithium anchor retention position table is generated by mapping from the lithium anchor retention table and is used to identify the frequency bands that need to be bypassed and retained in the reversible foldable index table.

[0035] Let the candidate anchor window be The candidate anchor window contains several spectral channels, and the continuum baseline corresponding to the candidate anchor window is... Then the absorption area of ​​the candidate frequency band can be expressed as: ; in, Indicates the first The absorption area of ​​each candidate anchor window; Indicates the first Each spectral channel corresponds to a wavelength or frequency position; Indicates candidate anchor window exist The continuous baseline value at the location; Indicates the calibration spectral vector at The response value at the location; The absorption depth of the candidate frequency band can be expressed as: ; in, Indicates the first The absorption depth of each candidate anchor window; This indicates the wavelength or frequency corresponding to the position of strongest absorption in the candidate anchor window; This represents the baseline value of the continuum at that location; This indicates the calibration spectral response value at that location; This indicates that positive numbers with a denominator of zero should be avoided.

[0036] Candidate Anchor Window The absorption peak positions in the image can be represented as: ; If the first The prior center location of the mineral type corresponding to each candidate anchor window is: Then the peak position shift can be expressed as: ; in, Indicates the first Peak position shift of each candidate anchor window; This indicates the a priori center location of the mineral type corresponding to the candidate anchor window.

[0037] The half-height and width of the candidate anchor window can be determined based on its half-depth position. Let the first... The half-depth position of each candidate anchor window satisfies: ; in, Indicates the position of the wavelength or frequency to be determined within the candidate anchor window; This represents the baseline value of the continuum at that location; This indicates the calibration spectral response value at that location; Suppose that the above conditions are met and it is located at The wavelength or frequency positions on the left and right sides are respectively and Then the half-height and width can be expressed as: ; in, Indicates the first The half-height width of each candidate anchor window; and These represent the left and right positions of the half-depth, respectively. If the sampling points cannot directly satisfy the half-depth condition, it can be obtained through linear interpolation between adjacent spectral channels. and .

[0038] Absorption asymmetry can be expressed as: ; in, Indicates the first Absorption asymmetry of candidate anchor windows; Indicates the candidate anchor window located in The absorption area on the left side; Indicates the candidate anchor window located in The absorption area on the right side; In one implementation, the training sample importance scores can be obtained by normalization from one or more feature importance sources. Let the... The source of class importance for the first The original importance of the output of each frequency grid point is Then the normalized importance can be expressed as: ; in, Indicates the first The source of class importance for the first Normalized importance of each frequency grid point; Indicates the original importance; and These represent taking the maximum and minimum values ​​over all frequency grid points, respectively; The importance score of the training samples can be expressed as: ; in, Indicates the first The importance score of the training samples at each frequency grid point; Indicates the number of sources of importance; Indicates the first The weights from which class importance is derived, and When only one source of importance is used, .

[0039] The stability score of the candidate anchor window can be expressed as: ; in, Indicates the first The stability score of each candidate anchor window; The coefficient of variation represents the absorption area of ​​the candidate anchor window in training samples with different signal-to-noise ratios and different mixing ratios. The coefficient of variation of the candidate anchor window absorption depth in training samples with different signal-to-noise ratios and different mixing ratios; The coefficient of variation represents the absorption peak position of the candidate anchor window in training samples with different signal-to-noise ratios and different mixing ratios. The coefficient of variation of the candidate anchor window's half-width at half-maximum in training samples with different signal-to-noise ratios and different mixing ratios; The coefficient of variation represents the absorption asymmetry of candidate anchor windows in training samples with different signal-to-noise ratios and different mixing ratios. This represents an exponential function. When not all local absorption parameters are used in a particular implementation, the stability score can be obtained from the combination of the coefficients of variation corresponding to the actual local absorption parameters used.

[0040] In one implementation, the candidate anchor window level stability score is mapped to a frequency grid point level stability score through the association between frequency grid points and candidate anchor windows. Let... Indicates the first The candidate anchor window number to which the frequency grid point belongs, when the... When a frequency grid point does not belong to any candidate anchor window Empty. The frequency grid-level stability score can be expressed as: ; in, Indicates the first Stability score per frequency grid point; Indicates the first The stability score of the candidate anchor window to which each frequency grid point belongs; Indicates the first The candidate anchor window number to which each frequency grid point belongs; This indicates that no candidate anchor window exists. Through this mapping, the stability screening results of the candidate anchor windows can be written into the frequency grid-level lithium anchor holding position table.

[0041] The lithium anchor holding position can be generated in the following way: ; in, Indicates the first Identifiers of lithium anchor candidate members at each frequency grid point; Indicates the first Each frequency grid point corresponds to a wavelength or frequency position; Represents the set of candidate lithium anchors; This represents the location of an anchor point in the candidate lithium anchor set; Indicates half the width of the anchor window; This is an indicator function that takes the value 1 when the condition is true and 0 when the condition is false.

[0042] The final lithium anchor holding position can be represented as: ; in, Indicates the first Lithium anchor holding position at each frequency grid point; Indicates the importance score threshold; Indicates the stability score threshold; The importance score and stability score of the training samples are only used to determine the lithium anchor holding position and are not directly output as the final classification label of lithium ore.

[0043] The nodes of a spectral domain folding conflict graph are frequency grid points or frequency bands in the lifted spectral representation. The edges or state variables of the spectral domain folding conflict graph characterize the coverage risk when two frequency bands are assigned to the same folding unit.

[0044] In one implementation, the set of interfering mineral candidate frequency bands can be determined by frequency bands in the non-anchored frequency bands that exhibit response coupling with lithium ore candidate anchor points. Let... Let the set of candidate frequency bands for interfering minerals be represented, then: ; in, This represents the set of candidate frequency bands for interfering minerals; Indicates the candidate non-anchor frequency band number; Indicates frequency band Lithium anchor candidate member identifier; Indicates the frequency band of lithium ore candidate anchor points With candidate non-anchor frequency band Frequency band coupling between them; Indicates the frequency band coupling threshold; Indicates frequency band Local energy in the boosted spectral representation; This represents the local energy threshold.

[0045] Local energy can be expressed as: ; in, Indicates frequency band The corresponding local window. Through this screening rule, the interfering mineral candidate frequency bands originate from non-anchor frequency bands that have high coupling or high-energy background responses with the lithium anchor candidate frequency bands.

[0046] For the characteristic anchor point frequency band of lithium ore and interfering mineral candidate frequency band Response similarity can be expressed as: ; in, Indicates frequency band With frequency band Response similarity; The boost spectrum represents the mid-frequency band. The corresponding local response vector; The boost spectrum represents the mid-frequency band. The corresponding local response vector; Represents the dot product of vectors; Represents the vector norm; Peak overlap is used to characterize the degree of coincidence between two frequency bands in local peak shape variations. In one implementation, for frequency bands... and frequency band Extract the normalized local peak shape vectors respectively: ; in, Indicates frequency band The normalized local peak shape vector; Indicates frequency band Local response vector in the boosted spectral representation; This represents the mean of the corresponding local response vector; Represents a vector consisting entirely of 1s; ; in, Indicates frequency band The normalized local peak shape vector; Indicates frequency band Local response vector in the boosted spectral representation; This represents the mean of the corresponding local response vector; Peak overlap can be expressed as: ; in, Indicates frequency band With frequency band Peak overlap; The adjacency state of spectral peaks can be represented as: ; in, Indicates frequency band With frequency band The spectral peak adjacency state; Indicates frequency band The corresponding wavelength or frequency position; Indicates frequency band The corresponding wavelength or frequency position; Indicates the adjacency scale parameter; The noise coupling state of the acquisition can be represented as: ; in, Indicates frequency band With frequency band The noise coupling state of the acquisition; Indicates frequency band The noise estimate for the corresponding channel; Indicates frequency band The noise estimate for the corresponding channel; This represents the function that takes the maximum value. Frequency band coupling can be expressed as: ; in, , , , These represent the weights of the four categories of factors mentioned above. The weights satisfy the following: ; in, This indicates the fourth category of factors. The weights corresponding to class factors. Based on the above constraints, when… , , and When all are normalized to the range of 0 to 1, the frequency band coupling degree It also falls within the range of 0 to 1.

[0047] In one implementation, the risk of coverage by the same folding unit can be calculated per candidate folding unit. This applies to the lithium ore candidate anchor point frequency band. Non-anchor frequency band and candidate folding units The risk score for folded unit coverage can be expressed as: ; in, Indicates frequency band With frequency band In candidate folding units Unnormalized risk scores that occur within the coverage area; and They represent frequency bands respectively. and frequency band Lithium anchor candidate member identifier; Indicates candidate folding unit The number of frequency bands currently allocated or temporarily allocated; Indicates candidate folding unit Maximum capacity; To facilitate subsequent background aliasing scoring and coverage risk write-back, the coverage risk of the same folded cell can also be normalized as follows: ; in, This indicates the coverage risk of the same folded cell after normalization; Represents the amplitude limiting function; Candidate folding unit The remaining capacity can be expressed as: ; in, Indicates candidate folding unit The remaining capacity; Indicates candidate folding unit The number of frequency bands currently allocated or temporarily allocated. Because... Follow The occupancy ratio and remaining capacity of the folded cells can jointly characterize the capacity status of the folded cells, as the folded cell occupancy ratio and remaining capacity increase or decrease.

[0048] When the final reversible foldable index table has not yet been generated The allocation can be determined by the temporary allocation results during the training phase, the occupancy status of the folded units in the previous frame, or the initial capacity status. For the same high-collision frequency band pair... You can take: ; in, Indicates frequency band With frequency band Maximum normalization of the risk of folded cell coverage; The reversible folding index table includes at least the frequency band number, folding cell number, folding cell allocation relationship, folding cell occupancy status, lithium anchor holding bit, compression coefficient, and reverse reconstruction index. The reversible folding index table can be generated and fixed as a lookup table through offline training, or it can be locally updated online based on the current acquisition status and historical residuals.

[0049] In one implementation, inter-class divergence and intra-class divergence are calculated in units of folded cells. For the first... A folding unit, set Indicates being assigned or being a candidate for assignment to the first The set of frequency bands of lithium ore characteristic anchor points of each folded unit Indicates being assigned or being a candidate for assignment to the first The set of interfering mineral candidate frequency bands for each folded unit. The mean vectors of the lithium ore feature anchor point frequency band set and the interfering mineral candidate frequency band set are respectively expressed as: ; in, Indicates the first The mean vector of the set of lithium ore feature anchor point frequency bands in each folded unit; Represents a set The number of elements; Indicates the first The local response vector of each frequency grid point in the boosted spectral representation; ; in, Indicates the first The mean vector of the set of interfering mineral candidate frequency bands in each folded cell; Represents a set The number of elements; No. The inter-class divergence of a folded unit can be expressed as: ; in, Indicates the first The inter-class scatter matrix of each folded unit; No. The intra-class divergence of a folded unit can be expressed as: ; in, Indicates the first The intra-class scatter matrix of each folded unit; When the lithium ore characteristic anchor point frequency band set in each folded unit Empty, or a set of candidate frequency bands for interfering minerals. When empty, the folded cell does not participate in the calculation of the inter-class divergence to intra-class divergence ratio; or the inter-class divergence contribution corresponding to the folded cell is set to 0, while retaining the capacity constraint and lithium anchor collision penalty term. Specifically, a valid identifier can be introduced: ; in, Indicates the first Does each folded unit simultaneously contain a set of lithium ore characteristic anchor frequency bands and a set of interfering mineral candidate frequency bands? The goal of generating the folded index table is to improve the separability of lithium ore feature anchor bands and interfering mineral candidate bands in the folded domain, while reducing the collision risk between lithium ore feature anchor bands and strongly correlated background bands. The folded index table can be determined according to the following objective function: ; in, This represents the optimized reversible foldable index table; Indicates the candidate fold index table; Indicates the number of folded units; Represents the trace of a matrix; Indicates the collision penalty weight for lithium anchors; Indicates the first Number of lithium anchor collisions within each folded unit; This indicates the reserved penalty weight for the frequency band of lithium ore characteristic anchor points; This represents the set of frequency grid points corresponding to the characteristic anchor point frequency band of lithium ore; Indicates the first Each frequency grid point is assigned to a folding cell; Indicates the first Reserved folding units or bypass channels corresponding to the frequency bands of lithium mine characteristic anchor points.

[0050] No. The number of collisions between the lithium ore feature anchor frequency band and the interfering mineral candidate frequency band within a folded unit can be expressed as: ; in, This represents the set of frequency band pairs marked as high-collision in the spectral domain folding collision map; Indicates frequency band The corresponding folding unit number; Indicates frequency band The corresponding folding unit number; When the above optimization function is not used, a deterministic allocation method can also be used to generate a reversible folding index table. The deterministic allocation method includes: first, configuring lithium anchor holding bits and reserving bypass channels for the lithium ore feature anchor frequency band; then, allocating the interfering mineral candidate frequency bands that form high-conflict frequency band pairs with the lithium ore feature anchor frequency bands to different folding units; subsequently, allocating the low-conflict background frequency bands to folding units according to the folding unit capacity and compression factor; finally, generating a reversible folding index table that records the frequency band number, folding unit number, folding unit occupancy status, lithium anchor holding bits, and compression factor.

[0051] The reversible folding index table can also be generated according to the following process: First, read the lithium anchor reservation table, configure lithium anchor reservation bits for lithium ore feature anchor frequency bands, and mark the lithium ore feature anchor frequency bands configured with the lithium anchor reservation bits as bypass frequency bands; Second, calculate the maximum co-folding unit coverage risk between each non-anchor frequency band and the lithium ore feature anchor frequency band according to the spectral domain folding conflict map, and sort the non-anchor frequency bands from high to low according to the maximum co-folding unit coverage risk; Third, for each sorted non-anchor frequency band, calculate the allocation cost when the non-anchor frequency band is allocated to each candidate folding unit; Fourth, allocate the non-anchor frequency band to the folding unit with the minimum allocation cost and without exceeding the capacity limit, and update the folding unit occupancy status; Fifth, output a reversible folding index table containing the frequency band number, folding unit number, lithium anchor reservation bit, compression coefficient, folding unit occupancy status, and reverse folding index.

[0052] Non-anchor frequency band Assigned to folding unit The allocation cost at that time can be expressed as: ; in, This indicates the non-anchor frequency band. Distributed to folding unit The cost of allocation; Indicates non-anchor frequency band With folding unit The conflict cost between frequency bands of the already allocated lithium ore characteristic anchor points; This indicates the non-anchor frequency band. Distributed to folding unit The resulting gain in inter-class separability; This represents the update cost relative to the folding allocation of the previous frame during online partial updates; , , , These represent the weights of conflict cost, capacity cost, separability gain, and update cost, respectively. Cost of Conflict It can be represented as: ; in, Indicating the characteristic anchor frequency band of lithium ore and non-anchor frequency band In candidate folding units The normalized folded cell coverage risk. If a conservative allocation method is adopted, it can also be... Replace with .

[0053] Inter-class separability gain It can be represented as: ; in, This indicates the non-anchor frequency band. Add folding unit The inter-class scatter matrix after that; This indicates the non-anchor frequency band. Add folding unit The subsequent intra-class scatter matrix; In calculation and When, if the frequency band to be allocated If it is a non-anchor frequency band, then the non-anchor frequency band will be... Join the Set of interfering mineral candidate frequency bands of folded units , obtain the updated set Then recalculate the inter-class scatter matrix and the intra-class scatter matrix; if the frequency band to be assigned... If a frequency band is subsequently configured as a lithium ore characteristic anchor point, then that frequency band will not enter the non-anchor folding allocation process, but will instead enter the lithium anchor holding processing process.

[0054] Cost of online partial updates It can be represented as:

[0055] in, Indicates the non-anchor frequency band in the previous frame The corresponding folding unit number; Indicates non-anchor frequency band The updated penalty weights. For non-anchor bands independent of the source of the inverse folding residual, Take a higher value to suppress unnecessary updates; for non-anchor bands related to the source of inverse folding residuals, Choose a lower value to allow for local adjustments.

[0056] When processing the digital spectral vector of the first frame's aliased spectrum, or when no available historical residual source exists in the feedback parameter table, the system uses the reversible folding index table fixed during the training phase as the initial index table. If the index table fixed during the training phase is not configured, the system generates an initial reversible folding index table based on the spectral domain folding conflict map, lithium anchor candidate member identifiers, and folding cell capacity constraints. The initialization of the reversible folding index table does not depend on the inverse folding residuals that have not yet been generated in the current frame.

[0057] Local updates during the online processing phase can be performed based on residual sources determined by the previous frame or historical spectral processing. If the current frame has not yet completed its inverse folding reconstruction, the system does not use residual sources not yet generated for the current frame to update the reversible folding index table of the current frame. Instead, it reads the residual sources from the previous or historical frames recorded in the feedback parameter table and performs local updates on the folding units associated with those residual sources.

[0058] Let the lifting spectrum be represented as , No. The response of each frequency grid point is Then the first folded domain compression spectrum A folded unit can be represented as: ; in, Indicates the first The first frame in the folded domain compression spectrum The value of each folded unit; Indicates the first The first frame The folding unit number corresponding to each frequency grid point; Indicates the first The symbol coefficients at each frequency grid point; Representation of frequency grid points The folding unit and the The nonlinear compression function corresponding to the frame; The nonlinear compression function can be expressed as: ; in, Indicates the first The frequency grid point at the th frequency is The compressed value in the frame; express The symbol; Indicates the first Frame frequency grid The dynamic compression gain corresponding to the folding unit; express The absolute value; This represents the natural logarithm function. This function is used to illustrate monotonic compression that preserves the sign of the magnitude; however, it does not preclude the use of other compression functions that satisfy both the sign-preserving and monotonicity constraints.

[0059] Sign coefficient This is used to reduce the offset caused by the superposition of the same sign in non-anchor frequency bands within the same folding unit. In one implementation, the sign coefficients can be pre-stored by a reversible folding index table, or determined based on the frequency band number and the folding unit number. ; in, Represents a deterministic hash function; This represents the modulo operation; Folding unit The compression gain can be dynamically determined based on the occupancy status of the folding unit, the acquisition noise status, and the historical inverse folding residuals: ; in, Indicates the first Folding units in frame spectral processing Compression gain; Indicates the base compression gain; Indicates the first In-frame folding unit The average noise estimate for the corresponding frequency band; Indicates the first In-frame folding unit The number of allocated frequency bands; Indicates the folded unit in the previous frame Normalized inverse folding residuals; , , These represent the adjustment weights for noise status, occupancy status, and historical residuals, respectively. Indicates the lower limit of compression gain; Indicates the upper limit of compression gain; The average noise estimate of the folded cell can be expressed as: ; in, Indicates the first Assigned to folding units in the frame The frequency grid set; Indicates the first The first frame The noise estimate of the channel corresponding to each frequency grid point; When the folding cell noise is high, the cell is fully occupied, or the historical reverse folding residual is large, the compression gain is reduced to avoid nonlinear compression amplifying noise or causing pseudo-enhancement of the weak lithium ore response.

[0060] Lithium anchor bypass data can be represented as: ; in, Indicates the first Lithium anchor bypass data corresponding to each frequency grid point; when At that time, the first Each frequency grid point does not form lithium anchor bypass data.

[0061] The inverse folding reconstruction module reads the folded domain compressed spectrum, lithium anchor bypass data, and reversible folding index table, and generates an inverse folding prediction spectrum. For frequency grid points configured with lithium anchor hold bits, the inverse folding reconstruction module directly reads the lithium anchor bypass data; for non-anchor frequency grid points without lithium anchor hold bits, the inverse folding reconstruction module generates predicted values ​​based on the folding cell number, compression factor, local context, and lithium anchor bypass constraints.

[0062] Inverse folding prediction can be expressed as: ; in, Indicates the first The first frame Inverse folding prediction value for each frequency grid point; This represents the inverse folding prediction function; The parameters represent the inverse folding prediction function; Indicates the first The first frame The fold domain compression value of the fold cell where each frequency grid point is located; Indicates the first Lithium anchor bypass data within the neighborhood of each frequency grid point; Indicates the first The local neighborhood corresponding to each frequency grid point; This represents local context information provided by the reversible folding index table, which includes at least one of the following: folding unit occupancy status, compression coefficient, frequency band adjacency relationship, and acquisition status correction amount.

[0063] The inverse folding prediction function can be a lookup table function, a linear prediction function, a sparse regression function, a lightweight neural network function, or a combination of the above. When the inverse folding prediction function is implemented using a neural network, the input to the neural network is the fold domain compressed spectrum, lithium anchor bypass data, fold index state, and acquisition state parameters, and the output is the inverse folding prediction spectrum. During the training phase, the boosted spectrum representation of the calibration samples or training samples is used as the supervision target, and during the inference phase, it is only used to generate the inverse folding prediction spectrum and does not directly output the lithium ore classification label.

[0064] The inverse folding residual can be expressed as: ; in, Indicates the first The first frame Inverse folding residuals at frequency grid points; Indicates the first The frame lift spectrum representation or reference lift spectrum of the first frame Reference values ​​for each frequency grid point; The reconstructed lift spectrum can be represented as: ; in, Indicates the first The first frame Reconstructed values ​​of each frequency grid point; Indicates the first The residual terms used for reconstruction in the frame. In strictly reversible mode, The reverse-folded residuals are stored or written back without loss; in near-lossless enhancement mode, To The quantized residual term, and the quantization error is constrained by a preset upper limit.

[0065] The global inverse folding residual and the folded cell normalized inverse folding residual can be expressed as follows: ; in, Indicates the first Global inverse folding residuals of frames; ; in, Indicates the first Globally normalized inverse folding residuals of frames; ; in, Indicates the first In-frame folding unit Normalized inverse folding residuals; The sources of inverse folding residuals can be categorized into compression loss, background aliasing, and acquisition drift. The compression loss score can be expressed as: ; in, Indicates the first In-frame folding unit Compression loss score; Background aliasing score can be expressed as: ; in, Indicates the first Frame background aliasing score; Indicates the first Frame Intermediate Frequency Band With frequency band Maximum normalization of the risk of folded cell coverage; and They represent the first Frame Intermediate Frequency Band and frequency band The inverse folding residual; In residual source determination and coverage risk write-back... The default represents the first Frame Intermediate Frequency Band With frequency band The maximum normalized coverage risk of folded cells, namely: ; in, Indicates the first Frame Intermediate Frequency Band With frequency band In candidate folding units The risk of normalization and folding unit coverage.

[0066] The acquisition drift score can be expressed as: ; in, Indicates the first Frame acquisition drift score; This represents the function for calculating the correlation coefficient. Indicates from the first Frame to the The global inverse folding residual sequence of frames; Indicates from the first Frame to the The first frame Collect the sequence of changes in state parameters; It can be used for integration time, exposure time, analog gain, dark current estimation, temperature drift state, or channel noise estimation. This indicates the length of the historical frame window used to calculate the correlation; This indicates that the maximum correlation is taken for various types of data acquisition status parameters.

[0067] When the variance of the historical frame global inverse folding residual sequence or the acquisition state parameter change sequence is zero, the correlation coefficient calculation function... The output is set to 0 to avoid the inability to calculate drift scores under zero variance conditions.

[0068] The global score of compression loss can be expressed as: ; in, Indicates the first The maximum value of the compression loss score for each folded unit in the frame; The three-category normalized score can be expressed as: ; in, Indicates the first Normalized compression loss score in frames; Indicates the upper limit for determining compression loss; ; in, Indicates the first Normalized background aliasing score in the frame; Indicates the upper limit for determining background aliasing; ; in, Indicates the first Normalized acquisition drift score in frames; This indicates the upper limit for determining data acquisition drift; When the maximum value among the three types of normalized scores is greater than 1, the source of the residual is determined as the corresponding score type; when multiple normalized scores are all greater than 1 and the difference is less than the preset difference upper limit, a mixed source identifier is generated and the mixed source identifier is written into the feedback parameter table.

[0069] When generating a hybrid source identifier, the system can allocate write-back weights according to the proportions of each type of normalized score. Let the three types of normalized scores be... , and Then the write-back weights corresponding to compression loss, background aliasing, and acquisition drift can be expressed as: ; in, Indicates the first The write-back weights corresponding to the compression loss in the frame; ; in, Indicates the first Write-back weights corresponding to background aliasing in the frame; ; in, Indicates the first The write-back weight corresponding to frame acquisition drift; when hardware resources are limited, write-back actions can also be performed sequentially from high to low according to the normalized score.

[0070] The system writes the write-back data corresponding to compression loss, background aliasing, or acquisition drift into the feedback parameter table. The feedback parameter table includes a folding parameter sub-table, a compression gain sub-table, and an acquisition control parameter sub-table. The folding parameter sub-table records the folding unit numbers and frequency band allocation relationships that need to be adjusted; the compression gain sub-table records the compression coefficients that need to be reduced or increased; and the acquisition control parameter sub-table records the integration time, exposure time, analog gain, or noise correction parameters that need to be adjusted.

[0071] The preset upper limit can be determined in the following ways: ; in, Indicates the first The preset upper limit of the evaluation indicators; This refers to the first calibration sample, training sample, or historical stable frame. The mean of the evaluation indicators; This refers to the first calibration sample, training sample, or historical stable frame. Standard deviation of the evaluation index; This represents the upper limit coefficient determined by equipment calibration or application requirements; When the source of the residual is determined to be compression loss, the compression gain of the corresponding folded unit can be updated as follows: ; in, Indicates the folded unit in the next frame Compression gain; Indicates the compression gain update step size; When the source of the residual is determined to be background aliasing, the coverage risk of high-collision frequency band pairs can be updated as follows: ; in, Indicates the intermediate frequency band of the next frame. With frequency band Maximum normalization of the risk of folded cell coverage; Indicates the step size for risk coverage updates; This represents the maximum absolute value of the inverse folding residuals for all frequency grid points in the current frame; When the system only updates the risk of specific candidate folding units, it can update directly. And by the updated Recalculate .

[0072] When the source of the residual is determined to be acquisition drift, the acquisition state correction can be updated as follows: ; in, Indicates the number of frames in the next frame Correction amount for the type of acquired status parameters; Indicates the first in the current frame Correction amount for the type of acquired status parameters; Indicates the step size for correcting the acquisition status; The reconstructed enhanced spectrum represents the lithium ore characteristic response enhancement spectrum generated through inverse mapping or reconstruction mapping. The lithium ore characteristic response enhancement spectrum is used for subsequent identification, display, storage, or alarm. However, the lithium ore characteristic response enhancement spectrum itself is a digital signal processing result formed by the original electrically acquired spectral signal through folding indexing, lithium anchor holding, lithium anchor bypassing, inverse folding residual source determination, and feedback parameter write-back. It is not simply a classification label or display brightness result.

[0073] Example 2 In this embodiment, the characteristic anchor frequency band of lithium ore is determined according to the target mineral type. When the target mineral type is lepidolite, lithium-bearing muscovite, or lithium-rich pegmatite system, the candidate anchor window may include candidate absorption bands in the visible to short-wave infrared range. In one specific embodiment, the candidate anchor window may include multiple candidate bands in the range of 1400 nm to 2500 nm. The above-mentioned candidate anchor window is used to characterize the local absorption area, absorption depth, peak position shift, full width at half maximum (FWHM), and absorption asymmetry, and does not imply that any single band is the only necessary feature for all lithium ores.

[0074] When directly identifying the target mineral as spodumene, candidate anchor windows can include thermal infrared candidate diagnostic response bands. In a specific scenario, multiple local anchor windows can be selected within the 9μm to 12μm thermal infrared band, and anchor windows with high stability and low background aliasing can be screened using calibration samples.

[0075] In determining the frequency band of lithium ore characteristic anchor points, the system first generates a set of candidate anchor windows based on prior knowledge of the ore type, and then calculates local absorption parameters for each candidate anchor window. These local absorption parameters include absorption area, absorption depth, peak offset, full width at half maximum (FWHM), and absorption asymmetry. Subsequently, the system eliminates unstable candidate anchor windows based on the importance and stability scores of the training samples, forming a lithium anchor retention table. Finally, the lithium anchor retention table is written to the lithium anchor holding bit register to participate in the generation of the reversible folding index table.

[0076] In one embodiment, the stability score is determined based on the degree to which a candidate frequency band retains its response under multiple noise levels and multiple mineral mixing ratios. For the same candidate frequency band, if the local absorption area of ​​the candidate frequency band changes little under different noise levels and can still maintain a response trend related to the target lithium ore type under different mixing ratios, then the candidate frequency band has a high stability score.

[0077] Example 3 In this embodiment, the spectral domain folding conflict graph consists of a node table, an edge table, and an occupancy status table. The node table records the frequency grid number, wavelength or frequency position, whether it belongs to the lithium ore characteristic anchor band, whether it belongs to the interfering mineral candidate band, and the current acquisition noise status. The edge table records the response similarity, peak overlap, spectral peak adjacency, frequency band coupling, and co-folding unit coverage risk between two frequency grids. The occupancy status table records the number of frequency grids currently allocated to each folding unit, the lithium anchor occupancy status, the non-anchor background occupancy status, and the remaining capacity.

[0078] The reversible folding index table is generated from the spectral domain folding conflict map. The generation process includes: prioritizing the allocation of lithium anchor holding positions to lithium ore feature anchor frequency bands; identifying high-conflict frequency band pairs based on the spectral domain folding conflict map; assigning high-conflict interference frequency bands to folding cells different from the corresponding lithium ore feature anchor frequency bands; assigning low-conflict background frequency bands to folding cells according to folding cell capacity and compression factor; and recording the frequency band number, folding cell number, compression factor, and local context state required for inverse folding reconstruction.

[0079] In one implementation, the data structure of the reversible collapsible index table is as follows:

[0080] The reversible folded index table can be generated offline during the training phase and stored in the folded index cache, or it can be locally updated during the online processing phase based on the residual sources determined by the previous frame or historical spectral processing. During online updates, the update scope is limited to the folded units related to the residual sources, avoiding parameter oscillations caused by frequent rewriting of the entire folded path. Example 4 In this embodiment, the reverse folding residual write-back includes a strictly reversible mode and a near-lossless enhancement mode. The reverse folding prediction spectrum is an intermediate spectral representation in the reverse folding reconstruction process, generated from the folded domain compressed spectrum, lithium anchor bypass data, and a reversible folding index table in both the strictly reversible and near-lossless enhancement modes. The difference between the strictly reversible and near-lossless enhancement modes lies in the storage method and error constraint method of the reverse folding residual, not in whether a reverse folding prediction spectrum is generated.

[0081] In strictly reversible mode, the difference between the inverse folded predicted spectrum and the boosted spectral representation is fully recorded as the inverse folded residual. During inverse folding reconstruction, the system reads the inverse folded predicted spectrum and the complete inverse folded residual to recover the boosted spectral representation or a reconstructed spectrum that meets the device's accuracy requirements. Strictly reversible mode is suitable for offline analysis, laboratory validation, and scenarios where the original spectral information needs to be preserved.

[0082] In near-lossless enhancement mode, the system quantizes or limits the inverse folding residuals and controls the reconstruction error according to a preset upper limit. This near-lossless enhancement mode is suitable for edge detection, online sorting, or high-throughput scenarios. In this mode, the system uses the inverse folding residual norm, the area deviation and spectral angle deviation of the lithium ore feature anchor band, and the folding unit conflict rate as parameters for adjustment. When the inverse folding residual exceeds the preset upper limit, the system performs differentiated write-back based on the source of the residual.

[0083] In one implementation, the data structure of the feedback parameter table is as follows:

[0084] When the source of the residual is determined to be compression loss, the system writes the data back to the compression gain sub-table or the capacity allocation sub-table. Folded units associated with compression loss refer to the folded units with the highest compression loss score exceeding the compression loss determination upper limit when the source of the residual is determined to be compression loss, or the set of folded units whose compression loss score exceeds the compression loss determination upper limit. When multiple folded units are simultaneously associated with compression loss, the system processes them sequentially from highest to lowest according to the normalized inverse folded residuals of each folded unit, or according to the priority of the folded units recorded in the feedback parameter table.

[0085] When a folding cell associated with compression loss has not reached its physical capacity limit, the system increases the allocatable logic capacity of that folding cell or decreases its compression gain. When a folding cell has reached its physical capacity limit, the system decreases its compression gain or reallocates some of the low-collision non-anchor frequency bands within that folding cell to other folding cells that have not reached their physical capacity limits. When the source of residual error is determined to be background aliasing, the system writes back data to the folding parameter sub-table, updates the spectral domain folding collision map, and adjusts the allocation relationship of folding cells in high-collision frequency bands. When the source of residual error is determined to be acquisition drift, the system writes back data to the acquisition control parameter sub-table and adjusts the acquisition control parameters or acquisition state correction.

[0086] Example 5 In one implementation, the system includes a spectral acquisition module, a spectral vector cache, a preprocessing module, a high-dimensional frequency domain mapping module, a spectral domain folding conflict graph construction module, a reversible folding index generation module, a nonlinear frequency domain compression module, a lithium anchor holding module, a reverse folding reconstruction module, a residual source determination and write-back module, an output module, a direct memory access controller, a folding index cache, a lithium anchor holding bit register, a lithium anchor bypass cache, a folding domain cache, a residual cache, and a feedback parameter table.

[0087] The spectral acquisition module includes a spectral sensor, an analog front-end, and an analog-to-digital converter. The module converts the spectral response of the mineral sample into a digital spectral vector of aliased spectra and outputs acquisition status parameters.

[0088] The spectral vector buffer receives aliased digital spectral vectors written by the direct memory access controller. The spectral vector buffer provides spectral vector data to the preprocessing module and the current frame identifier and buffer address to the residual source determination and write-back module.

[0089] The preprocessing module is connected to the spectral vector cache and the acquisition status parameter interface of the spectral acquisition module. It is used to read the digital spectral vector of the aliased spectrum and the acquisition status parameters, and perform at least one of the following processes: dark current subtraction, bad channel correction, radiation normalization, continuum removal, spectral band alignment, noise estimation, and abnormal channel shielding, to form a calibration spectral vector. The preprocessing module outputs the calibration spectral vector to the high-dimensional frequency domain mapping module.

[0090] The high-dimensional frequency domain mapping module is connected to the preprocessing module and is used to perform high-dimensional frequency mapping on the calibration spectral vector to form an enhanced spectral representation. The high-dimensional frequency domain mapping module can be implemented by a digital signal processor, graphics processor, field-programmable gate array, embedded processor, or dedicated computing circuit.

[0091] The spectral domain folding conflict map construction module reads the enhanced spectral representation, lithium ore feature anchor frequency bands, interfering mineral candidate frequency bands, and acquisition status parameters to construct a spectral domain folding conflict map. This module then outputs high-conflict frequency band pairs, folding cell occupancy status, and conflict level to the reversible folding index generation module.

[0092] The reversible folding index generation module generates a reversible folding index table based on the spectral domain folding conflict graph and writes the reversible folding index table into the folding index cache. The reversible folding index table includes the frequency band number, folding cell number, folding cell allocation relationship, folding cell occupancy status, lithium anchor holding bit, compression factor, and local context state required for defolding reconstruction.

[0093] The nonlinear frequency domain compression module reads the reversible folded index table from the folded index buffer and performs nonlinear frequency domain compression that preserves the amplitude sign on the non-anchor frequency band. The nonlinear frequency domain compression module writes the compressed frequency band response into the folded domain buffer.

[0094] The lithium anchor holding module reads the lithium anchor holding bit from the lithium anchor holding bit register and writes the frequency band configured with the lithium anchor holding bit into the lithium anchor bypass buffer. The lithium anchor holding module is also used to prevent the lithium ore characteristic anchor point frequency band from entering the co-folding cell aggregation of non-anchor frequency bands.

[0095] The inverse folding reconstruction module reads the folded domain compressed spectrum, lithium anchor bypass data, and reversible folding index table to generate an inverse folding prediction spectrum, and combines it with the inverse folding residual to form an enhanced spectrum of lithium ore characteristic response.

[0096] The residual source determination and write-back module calculates the inverse folding residual and, based on the distribution of the inverse folding residual in the folding unit, the lithium ore characteristic anchor frequency band, and the acquisition state parameters, determines the residual source as compression loss, background aliasing, or acquisition drift, and writes the write-back data related to the residual source into the feedback parameter table. The feedback parameter table stores the residual source, the object to be adjusted, and the write-back data, and includes at least one of a folding parameter sub-table, a compression gain sub-table, and an acquisition control parameter sub-table. The feedback parameter table provides feedback parameters to the acquisition control interface of the reversible folding index generation module, the nonlinear frequency domain compression module, or the spectral acquisition module.

[0097] The output module outputs an enhanced spectrum of lithium ore characteristic response. This spectrum can be fed into subsequent mineral identification modules, display modules, storage modules, or online sorting control modules. The classification results from the subsequent mineral identification modules do not replace the generation process of the enhanced lithium ore characteristic response spectrum.

[0098] In the hardware implementation, the direct memory access controller (DMC) can be used to transfer aliased spectral digital spectral vectors, reversible folding index tables, lithium anchor hold bit tables, lithium anchor bypass data, folded domain compressed spectra, inverse folding residuals, and feedback parameter tables between external memory, on-chip cache, and computing modules. The folding index cache can be in the form of a lookup table. The lithium anchor hold bit register can be in the form of a bitmap. The lithium anchor bypass cache is used to store the frequency band data of lithium ore characteristic anchor points identified by the lithium anchor hold bits. The residual cache can be in the form of a ring buffer. The system can use a streaming interface to transmit spectral vectors, folded domain compressed spectra, and enhanced spectra, and use control registers to configure the spectral length, folded length, operating mode, number of lithium anchors, and residual upper limit.

[0099] Example 6 In one implementation, the system handles the following exceptions.

[0100] When a saturated channel exists in the digital spectral vector of the aliased spectrum, the system marks the saturated channel as a channel that cannot participate in lithium anchor determination and non-anchor compression, and determines an alternative reference based on adjacent valid channels or historical stable frames.

[0101] When the noise level of the channel corresponding to the lithium ore feature anchor band exceeds the upper limit of the anchor noise, the system temporarily reduces the importance score or stability score of the lithium ore feature anchor band, or removes the lithium ore feature anchor band from the lithium anchor reservation table and activates the backup anchor window.

[0102] When no valid lithium anchor candidates satisfying the importance and stability thresholds are found in the candidate anchor window set, the system performs anchorless anomaly handling. This anchorless anomaly handling includes at least one of the following: enabling backup candidate anchor windows, reducing the importance or stability threshold based on calibration samples, delaying the update of the lithium anchor holding position table, or switching to a non-anchor conservative compression mode. In the non-anchor conservative compression mode, the system reduces the overall compression gain and increases the weight of the inverse folding residual write-back to avoid over-compressing weak lithium mine responses when no lithium anchor is confirmed.

[0103] When the occupancy status of a folded cell exceeds the allocatable logical capacity of the folded cell or approaches the physical capacity limit, the system reduces the number of non-anchor frequency bands aggregated in that folded cell and transfers some low-conflict frequency bands to idle folded cells that have not reached the physical capacity limit.

[0104] When the inverse folding residual exceeds the preset upper limit for multiple consecutive frames, the system determines the source of the anomaly. If the source of the anomaly is acquisition drift, the system adjusts the acquisition control parameters or triggers recalibration; if the source of the anomaly is background aliasing, the system updates the spectral domain folding conflict map and regenerates the local reversible folding index table; if the source of the anomaly is compression loss, the system identifies the folding unit related to the compression loss, and increases the allocatable logical capacity of the folding unit if it has not reached the physical capacity limit, and reduces the compression coefficient, migrates low-conflict non-anchor frequency bands, or enables strictly reversible mode if the folding unit reaches the physical capacity limit.

[0105] When it is necessary to increase the capacity of a folding unit and the folding unit has not reached its physical capacity limit, the system increases the number of allocable frequency bands for the folding unit; when the folding unit has reached its physical capacity limit, the system triggers a capacity overrun processing branch, which includes at least one of reducing the compression gain of the folding unit, migrating low-conflict non-anchor frequency bands to an idle folding unit, enabling a strictly reversible mode, or delaying the processing of the digital spectral vector of the next frame of aliased spectrum.

[0106] When continuous updates of feedback parameters cause oscillations in the folding path, the system limits the number of folding units that can be updated per unit time, or sets the feedback parameter update period to multiple frame periods that are longer than the single frame processing period, thereby avoiding frequent write-backs that could lead to instability in the enhancement spectrum.

[0107] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A lithium ore exploration method based on spectral domain folding reconstruction, characterized in that, Includes the following steps: Step 1: Receive the aliased spectrum digital spectral vector and acquisition status parameters output by the spectral acquisition module, and obtain the calibration spectral vector after preprocessing. Step 2: Map the calibration spectral vector to a high-dimensional frequency space to form an enhanced spectral representation; Step 3: Based on the lithium ore characteristic anchor point frequency band, interfering mineral candidate frequency band, the acquisition state parameters, and the frequency band energy, peak shape relationship, and noise state in the boosted spectrum representation, construct a spectral domain folding conflict map that includes frequency band coupling degree, co-folding unit coverage risk, and folding unit occupancy state. Step 4: Generate a reversible folding index table including frequency band number, folding unit number, lithium anchor holding position and compression coefficient based on the spectral domain folding collision map, and determine the folding unit allocation relationship according to lithium anchor collision penalty, folding unit capacity constraint and lithium anchor bypass constraint; Step 5: Perform nonlinear frequency domain compression with amplitude sign retention on the non-anchor frequency band according to the reversible folding index table, and perform lithium anchor retention processing on the lithium ore characteristic anchor point frequency band configured with the lithium anchor retention bit to form folded domain compressed spectrum and lithium anchor bypass data; Step 6: Perform inverse folding reconstruction based on the folded domain compressed spectrum, the lithium anchor bypass data, and the reversible folding index table to obtain the lithium ore characteristic response enhancement spectrum; Step 7: Calculate the inverse folding residual, and determine the source of the residual as compression loss, background aliasing, or acquisition drift based on the distribution of the inverse folding residual in the folding unit, the lithium ore characteristic anchor point frequency band, and the acquisition state parameters. Step 8: When the inverse folding residual, the area deviation of the lithium ore feature anchor frequency band, or the folding unit conflict rate exceeds their respective preset upper limits, write back data related to the source of the residual into the feedback parameter table to adjust the folding parameters, compression gain, or acquisition control parameters in the next spectral processing.

2. The lithium ore exploration method based on spectral domain folding reconstruction according to claim 1, characterized in that, The lithium ore characteristic anchor point frequency band includes: A candidate anchor window set is generated based on the prior knowledge of the mineral type corresponding to the target lithium ore type. Local absorption parameters are calculated for the candidate frequency bands in the candidate anchor window set, and the importance score of the candidate frequency band in the training samples is obtained. Stability scores are calculated in training samples with different signal-to-noise ratios and different mixing ratios. Candidate frequency bands with importance scores not lower than the importance threshold and stability scores not lower than the stability threshold are written into the lithium anchor retention table, and the lithium anchor retention position is generated according to the lithium anchor retention table. The local absorption parameters include absorption area, absorption depth, peak position shift, full width at half maximum (FWHM), and absorption asymmetry.

3. The lithium ore exploration method based on spectral domain folding reconstruction according to claim 1, characterized in that, The frequency band coupling degree is determined by weighting the response similarity, peak overlap, acquisition noise coupling state, and spectral peak adjacency state of the lithium ore characteristic anchor frequency band and the interfering mineral candidate frequency band in the boosted spectrum representation; the co-folding unit coverage risk is determined by the frequency band coupling degree, the lithium anchor candidate member identifier corresponding to the lithium ore characteristic anchor frequency band, and the remaining capacity of the folding unit.

4. The lithium ore exploration method based on spectral domain folding reconstruction according to claim 1, characterized in that, The reversible foldable index table is generated during the training phase based on the inter-class divergence between the lithium ore feature anchor frequency band set and the interfering mineral candidate frequency band set, the intra-class divergence within each of the lithium ore feature anchor frequency band set and the interfering mineral candidate frequency band set, the lithium anchor collision penalty, the folding unit capacity constraint, and the lithium ore feature anchor frequency band reserved constraint, and is fixed in the foldable index cache in the form of a lookup table. During the online processing phase, based on the residual source determined by the previous frame or historical spectral processing, only the folding units related to the residual source are locally updated.

5. The lithium ore exploration method based on spectral domain folding reconstruction according to claim 1, characterized in that, The nonlinear frequency domain compression employs a monotonic compression function that preserves the amplitude sign. The compression gain of the monotonic compression function is adjusted based on the folding unit occupancy status, the acquisition noise status, and the historical inverse folding residual. The lithium ore characteristic anchor point frequency band configured with the lithium anchor holding position does not participate in the monotonic compression or the co-folding unit aggregation of non-anchor frequency bands.

6. The lithium ore exploration method based on spectral domain folding reconstruction according to claim 1, characterized in that, The inverse fold reconstruction includes a strictly reversible mode and a near-lossless enhancement mode, and in the process of inverse fold reconstruction, an inverse fold prediction spectrum is generated based on the fold domain compression spectrum, the lithium anchor bypass data and the reversible fold index table. In the strictly reversible mode, the reverse-folded residual is written losslessly to the residual cache; In the near-lossless enhancement mode, the inverse folding residual is quantized and written into the residual cache, and the residual norm, lithium ore feature anchor band area deviation, and spectral angle deviation between the inverse folding predicted spectrum and the reference enhanced spectrum are made to meet their respective preset upper limits.

7. The lithium ore exploration method based on spectral domain folding reconstruction according to claim 2, characterized in that, The candidate anchor window set is determined in layers according to the target lithium ore type; when the target lithium ore type is lepidolite or lithium-rich pegmatite system, the candidate anchor window set includes candidate absorption bands in the range of 1400nm to 2500nm obtained by screening with the local absorption parameters, the importance score and the stability score; when the target lithium ore type is spodumene, which is a direct identification target, the candidate anchor window set includes thermal infrared candidate diagnostic response bands in the range of 9μm to 12μm obtained by screening with calibrated samples.

8. The lithium ore exploration method based on spectral domain folding reconstruction according to claim 1, characterized in that, When the source of the residual is determined to be compression loss, if the folding unit associated with the compression loss has not reached the physical capacity limit, the allocable capacity of the folding unit is increased, or the compression gain of the folding unit is decreased. If the folding unit reaches its physical capacity limit, reduce the compression gain of the folding unit, or reallocate some of the low-conflict non-anchor frequency bands in the folding unit to other folding units that have not reached their physical capacity limit. When the residual source is determined to be background aliasing, the spectral domain folding conflict map is updated and the folding unit allocation relationship of the high conflict frequency band is adjusted; When the source of the residual is determined to be acquisition drift, the acquisition control parameters or acquisition status correction amount are adjusted.

9. The lithium ore exploration method based on spectral domain folding reconstruction according to claim 1, characterized in that, The aliased spectral digital spectral vector is written to the spectral vector cache via the direct memory access controller. The reversible folding index table is stored in the folding index cache, the lithium anchor holding bit is stored in the lithium anchor holding bit register, and the inverse folding residual and its source identifier are stored in the residual cache. The direct memory access controller writes the non-anchor frequency band data into the folding domain cache according to the folding unit number in the reversible folding index table, and writes the lithium ore characteristic anchor point frequency band data into the lithium anchor bypass cache according to the lithium anchor holding bit.

10. A lithium ore exploration method based on spectral domain folding reconstruction according to claim 1, characterized in that, The spectral domain folding conflict graph consists of a node table, an edge table, and an occupancy status table. The node table records the frequency grid number, wavelength or frequency position, whether it belongs to the lithium ore characteristic anchor frequency band, whether it belongs to the interfering mineral candidate frequency band, and the current acquisition noise status. The edge table records the response similarity, peak overlap, spectral peak adjacency relationship, frequency band coupling degree, and co-folding unit coverage risk between two frequency grids. The occupancy status table records the number of frequency grids currently allocated to each folded cell, the occupancy status of lithium anchors, the occupancy status of non-anchor backgrounds, and the remaining capacity.

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