Method and system for determining metal content of ore based on knowledge graph

Abstract

The invention discloses an ore metal content determination method and system based on a knowledge graph, and relates to the technical field of intelligent detection, and the method comprises the following steps: establishing an energy spectrum acquisition baseline, continuously acquiring signals generated by a detection device in a high energy density area in a metal content determination process, recording a trailing form and peak foot extension change, and determining the metal content of the ore according to the peak foot extension change. Generating an energy spectrum trailing data band; and extracting the starting time, the ending time and the maximum energy change position of each energy spectrum peak based on the energy spectrum trailing data band, and generating a feature point set. According to the method, signal dynamic coordination is realized by establishing an energy spectrum acquisition base line and a time control chain, peak shape expansion caused by a trailing effect is inhibited, and the real and stable energy spectrum peak shape is ensured; false peaks are recognized and eliminated through the feature point set, a real peak corresponding relation list is generated, the structural consistency of energy spectrum data and the accuracy of knowledge graph reasoning are improved, and accurate measurement of the metal content is achieved.

Description

Technical Field

[0001] This invention relates to the field of intelligent detection technology, specifically to a method and system for determining the metal content of ores based on knowledge graphs. Background Technology

[0002] Knowledge graph-based determination of ore metal content refers to the semantic association and structured modeling of multi-source ore data, including traditional chemical analysis, spectroscopic detection, micro-element analysis, and geological exploration, using knowledge graph technology. This enables intelligent reasoning and accurate identification between ore composition and metal content at the knowledge level. The method first constructs a multi-dimensional knowledge node system encompassing mineral types, elemental characteristics, mineralization environment, detection parameters, and experimental results. Then, based on the association weights between nodes, a causal relationship network is established. Semantic reasoning and feature matching algorithms are used to automatically identify and correct detection signals for unknown ore samples. Finally, under the guidance of the knowledge graph, quantitative determination results of metal content are generated. Compared to traditional single-detection-based methods, this method can utilize reasoning and tracing mechanisms between knowledge layers to achieve adaptive analysis and high-precision content assessment of complex ore samples.

[0003] Existing technologies have the following shortcomings: In the metal energy dispersive spectroscopy (EDS) analysis process, when the detection equipment acquires signals in the high-energy-density region, a tailing effect easily occurs, caused by detector response lag, signal accumulation imbalance, or charge transfer residue. This results in the energy dispersive spectral peaks that originally correspond to a single metal element being stretched and expanded in the high-energy region, forming a pseudo-continuous peak sequence. Since the knowledge graph node matching logic of existing technologies typically relies on fixed thresholds and static feature templates for peak identification, when encountering the aforementioned tailing signal, the system struggles to distinguish the energy difference between the true peak and the tailing peak. This can easily lead to misclassification of tailing peaks of the same element as independent metal feature nodes, resulting in incorrect elemental attribution relationships in the spectral structure. This problem not only causes an artificial increase in the number of metal species but also disrupts the semantic consistency of the EDS data, causing a shift in the knowledge reasoning chain and severely affecting the accuracy and reliability of metal content determination.

[0004] The information disclosed in the background section is only intended to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0005] The purpose of this invention is to provide a method and system for determining the metal content of ores based on knowledge graphs, so as to solve the problems in the background art mentioned above.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a method for determining the metal content of ores based on knowledge graphs, comprising the following steps: Establish an energy spectrum acquisition baseline, continuously acquire signals generated by the detection equipment in the high energy density region during the metal content determination process, record the tail shape and peak extension changes, and generate energy spectrum tail data bands for feature identification. Based on the energy spectrum tail data band, the start time, end time and the position of the largest energy change of each energy spectrum peak are extracted to generate a set of feature points for determining the tail range. Based on the set of feature points, the rising and falling segments of each energy spectrum peak are compared point by point to separate and eliminate false peaks caused by the tailing effect, and generate a list of correspondences of real metal energy spectrum peaks. A time control chain for signal acquisition and data matching is established based on a list of real metal energy spectrum peak correspondences. The signal exposure rhythm and readout interval are determined according to the time control chain, and a time control command to suppress trailing interference is generated. According to the time control command, short-term quiescence, delay compensation and reverse energy release operations are performed during the signal attenuation stage to dynamically adjust the energy spectrum signal, so that the tailing effect is weakened and kept under control during the energy transfer process, thereby obtaining accurate metal content determination results.

[0007] Preferably, the steps for generating the energy spectrum tail data band are as follows: Based on the energy response characteristics of the detection equipment in the process of metal content determination, the energy coverage range and sampling time resolution of signal acquisition are determined, and complete signal response curves are continuously acquired in the high energy density region to form an energy spectrum response sequence. The tailing pattern of the energy spectrum response sequence in the high-energy region is dynamically tracked and continuously recorded. The energy change interval is divided into multiple time windows and the energy value, change rate and decay duration are recorded. The energy change information within the time window is rearranged in a unified manner according to the energy decay direction to generate a tailing extension mapping sequence corresponding to the energy response curve of the detection device. Based on the temporal sequence and energy distribution pattern of signal acquisition, the continuously recorded data are integrated to form an energy spectrum tail data band for feature identification.

[0008] Preferably, the steps for generating the feature point set are as follows: After the energy spectrum tail data band is formed, the continuous energy response information contained in the energy spectrum tail data band is analyzed in a hierarchical manner according to the time order to generate an energy distribution sequence with equal time intervals to establish a peak start and end time boundary framework. In the time series structure of energy distribution, the energy inflection point of the peak rising segment is identified based on the energy change trend over time, and the moment when the energy response begins to increase significantly is determined as the starting time of the energy spectrum peak. The energy decay process is tracked and the end time of the energy spectrum peak is identified. The temporal correspondence structure of the peak shape is determined by comparing the continuity of the rising and falling segments of the peak shape on the time axis. After determining the start and end times, the location with the greatest energy change is identified based on the energy change trajectory, and the start time, end time, and location with the greatest energy change are integrated to generate a set of feature points.

[0009] Preferably, during the generation of the feature point set, the location with the greatest energy change is limited to the energy peak range of the energy spectrum peak, and the energy change trajectory between the start time and the end time is synchronously correlated with the energy decay process of the peak foot extension region, so that the feature point set simultaneously reflects the entire process of peak energy rise, peak and decay, ensuring that the time and energy boundaries of the tail range correspond consistently.

[0010] The preferred steps for generating the list of corresponding peaks in the energy spectrum of real metals are as follows: Based on the set of feature points, the time boundary information of each energy spectrum peak is segmented and organized. The energy spectrum peak is divided into energy sequences according to key positions within the range from the start time to the end time, so that the rising segment and the falling segment form a continuous correspondence in the energy change trend. The rising and falling segments are compared point by point. The tailing characteristics of the energy spectrum peak energy extension are identified based on the energy change trend. The continuity and discontinuity of energy change are determined to distinguish between the true peak and the tailing peak. The energy range exhibiting a tailing effect is separated and spurious peaks are removed. The tailing portion is removed from the set of independent peaks by the difference between the direction of energy change and the energy amplitude. The start time, end time, and position of the greatest energy change of the retained true peaks are correlated to form a list of corresponding true metal energy spectrum peaks.

[0011] Preferably, when comparing the rising and falling segments point by point, the direction of energy change and the continuity of time are used as the basis for comparison. The energy residual range of the tailing region is determined by identifying the phenomenon of local energy re-rise within the energy decay interval. When removing false peaks, the extended signal within the tailing interval is separated from the energy spectrum data based on the overlap of the start and end times of adjacent peaks.

[0012] Preferably, the steps for generating time control instructions to suppress trailing interference are as follows: Based on the list of correspondences of real metal energy spectrum peaks, the time information of each real peak is centrally organized, and the start time, end time and the position of the largest energy change are used as time reference points to form a time sequence framework. Based on the time sequence framework, a time control chain is constructed for the signal acquisition stage. The start and end intervals of each real peak are arranged according to the strength and duration of the energy response, and a buffer time interval is set between adjacent peaks. The signal exposure rhythm and readout interval are determined based on the time distribution relationship and energy response law of each node in the time control chain, so that the signal acquisition process is synchronized with the energy change trend of the energy spectrum peak. Based on the overall structure of the time control chain, time control commands are generated, integrating the acquisition action, energy response status, and readout timing into a continuous time command sequence.

[0013] Preferably, the generation of time control instructions includes arranging the signal acquisition start instruction, exposure duration instruction, signal reading instruction, and acquisition end instruction sequentially according to the time nodes in the list of correspondences of real metal energy spectrum peaks, so that each instruction corresponds to the start time, end time, and position of the greatest energy change of the real peak, thereby realizing time synchronization control of the signal acquisition and energy decay process.

[0014] Preferably, according to the time control command, short-time stillness, delay compensation, and reverse energy release operations are sequentially executed during the signal attenuation phase. The dynamic energy adjustment steps for the energy spectrum signal are as follows: Under the guidance of time control commands, the energy response process during the signal attenuation phase is initially statically controlled, so that the signal receiving state is switched to a short-term static state to release the internal accumulated charge. After the short-term static operation is completed, a delay compensation operation is performed, and the signal acquisition interval is adjusted according to the time control command to keep the energy release rhythm and decay rate synchronized. After the delay compensation operation, a reverse energy release operation is performed to offset the residual energy through a reverse energy release pulse, thereby accelerating the energy release process. Based on the time control command, short-term quiescence, delay compensation and reverse energy release operations are continuously linked to dynamically adjust the energy spectrum signal to achieve controlled balance of the energy transfer process.

[0015] The knowledge graph-based ore metal content determination system includes an energy spectrum acquisition baseline construction module, a feature point extraction module, a false peak identification and removal module, a time control chain generation module, and a dynamic energy adjustment module. The energy spectrum acquisition baseline construction module establishes an energy spectrum acquisition baseline, continuously acquires signals generated by the detection equipment in the high energy density region during the metal content determination process, records the tail shape and peak extension changes, and generates energy spectrum tail data bands for feature identification. The feature point extraction module extracts the start time, end time, and the location of the greatest energy change for each energy spectrum peak based on the energy spectrum tail data band, and generates a set of feature points to determine the tail range. The false peak identification and elimination module compares the rising and falling segments of each energy spectrum peak point by point based on the feature point set, separates and eliminates false peaks caused by the tailing effect, and generates a list of correspondences of real metal energy spectrum peaks. The time control chain generation module establishes a time control chain for signal acquisition and data matching based on a list of real metal energy spectrum peak correspondences. It determines the signal exposure rhythm and readout interval according to the time control chain and generates time control commands to suppress trailing interference. The dynamic energy adjustment module performs short-term quiescence, delay compensation, and reverse energy release operations during the signal attenuation phase according to time control commands. This dynamically adjusts the energy spectrum signal, weakening and controlling the tailing effect during energy transfer, thereby obtaining accurate metal content determination results.

[0016] The technical effects and advantages provided by the present invention in the above technical solution are as follows: This invention establishes an energy spectrum acquisition baseline during metal content determination and combines it with a time control chain for signal modulation. This ensures dynamic coordination of time and energy in the energy spectrum signal during the acquisition and attenuation phases, effectively suppressing peak expansion caused by tailing effects. Through the linkage of continuous acquisition and dynamic energy adjustment, the detection equipment's response is more stable in high energy density regions, and the energy release process is more balanced. This guarantees that the energy spectrum peak shape remains true and complete, reduces energy misjudgment caused by peak tailing, and makes the energy distribution of metal elements closer to their true physical response state.

[0017] This invention identifies and eliminates spurious peaks caused by tailing effects based on a set of feature points, generating a list of true metal energy spectrum peak correspondences. This results in a highly consistent peak structure in the energy spectrum data across both time and energy dimensions. This structured peak relationship ensures the correct attribution of element nodes in knowledge graph association modeling, avoiding node redundancy and energy confusion caused by misjudgment of tailing peaks. This improves the semantic consistency and inference reliability of metal content determination, enabling accurate identification and quantitative determination of metal components in complex ore samples. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this invention. For those skilled in the art, other drawings can be obtained based on these drawings.

[0019] Figure 1 This is a flowchart of the method for determining the metal content of ores based on knowledge graphs according to the present invention.

[0020] Figure 2 This is a schematic diagram of the modules of the knowledge graph-based ore metal content determination system of the present invention. Detailed Implementation

[0021] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, they are provided so that the description of this disclosure will be more complete and fully convey the concept of the exemplary embodiments to those skilled in the art.

[0022] This invention provides, for example Figure 1 The knowledge graph-based method for determining the metal content of ores includes the following steps: Establish an energy spectrum acquisition baseline, continuously acquire signals generated by the detection equipment in the high energy density region during the metal content determination process, record the tail shape and peak extension changes, and generate energy spectrum tail data bands for feature identification. To effectively acquire energy spectrum signals with stable tailing characteristics within the high-energy-density region of the detection equipment, it is necessary to establish an energy spectrum acquisition baseline and form an energy spectrum tailing data band for feature identification. Addressing the energy accumulation and peak distortion issues present in the signal response of the detection equipment in the high-energy region, a baseline construction process combining continuous acquisition and response modeling is proposed. This ensures that the tailing shape and peak extension information are completely captured and recorded during the signal acquisition phase, providing accurate data for subsequent feature identification and signal separation. The specific steps are as follows: Before establishing the energy spectrum acquisition baseline, the energy coverage range and sampling time resolution of the signal acquisition were determined based on the energy response characteristics of the detection equipment during metal content determination. In this stage, by controlling the signal input intensity and sampling frequency, the detection equipment was able to continuously acquire complete signal response curves in the high energy density region. During signal acquisition, the process of the raw energy response received by the detector changing over time was closely monitored. All response data in the energy rise interval, peak interval, and energy decay interval were recorded as a continuous time series, forming a preliminary energy spectrum response sequence. This sequence not only includes the energy distribution of the main peak but also retains the peak extension and gradual attenuation characteristics of the energy tail, providing a basis for the identification and modeling of the tailing pattern. This continuous acquisition process ensures that the energy response of the detection equipment is not truncated or missed in the high-energy region, enabling subsequent analysis to accurately reflect the energy expansion and morphological evolution of the signal.

[0023] After obtaining the complete energy spectrum response sequence, the tailing morphology of the signal in the high-energy region is dynamically tracked and continuously recorded. To ensure the complete fidelity of the tailing morphology and peak extension changes, the energy spectrum response sequence formed in the first sub-step is divided into time-divided segments in this stage. Following the natural attenuation law of signal energy from the peak to the tail, the energy change interval is divided into multiple continuous time windows. Within each time window, the energy value, rate of change, and attenuation duration of the output signal from the detection device are recorded. By observing the energy differences and trends between windows, the morphological change process of the energy spectrum peak transitioning from the main peak to the energy tail is continuously depicted. This continuous recording method allows the weak energy signal within the tailing interval to be accumulated and recorded over time, ensuring that the energy drift, tail length, and tail energy distribution characteristics during the peak extension process are completely preserved. This provides directly accessible basic data on the energy spectrum tailing morphology for subsequent feature point extraction.

[0024] After continuously recording the tail pattern, the energy change information of each time window is rearranged according to the energy decay direction to generate a tail extension mapping sequence corresponding to the energy response curve of the detection device. This mapping sequence consists of the tail recording data obtained in the previous step, reflecting the gradual decay trajectory of the signal from the peak to the tail, and demonstrating the extension law of the energy at the peak foot. In this stage, the focus is on synchronizing the energy accumulation value and signal duration of each time window to ensure that the time distribution of the energy spectrum response in the energy decay direction is consistent with the actual tail response. In this way, an energy decay path record corresponding to the original energy spectrum signal is formed, making the energy change in the tail region exhibit a traceable dynamic transition. At the same time, in the process of generating the tail extension mapping sequence, the zero-energy region reference signal of the acquisition baseline is incorporated into the overall sequence to form a continuous response baseline from the energy start to the energy end, thereby constructing a complete energy spectrum acquisition benchmark framework covering the high-energy region to the low-energy region. This benchmark framework not only reflects the response stability of the detection device in different energy regions, but also provides a standardized energy coordinate reference for the subsequent formation of energy spectrum tail data bands.

[0025] Based on the generated tail extension mapping sequence, and according to the temporal sequence of signal acquisition and energy distribution patterns, continuously recorded data are integrated to form an energy spectrum tail data band for feature identification. This energy spectrum tail data band primarily uses the original response curve of the detection device in the high energy density region, fusing the signal distribution, energy extension, peak attenuation, and background noise characteristics of the tail section into a continuous data structure. This continuous integration method establishes a one-to-one continuous correlation between the main peak and the tail portion of the energy spectrum signal in both time and energy dimensions. This allows for precise positioning of the tail range in subsequent metal peak feature identification and peak value determination based on this data band. The formation of this data band not only includes continuously recorded information on peak shape changes but also retains the energy reference of the acquisition baseline in different energy regions, ensuring the traceability and correctability of the tail effect in subsequent processing stages. Through the multi-stage process of continuous acquisition, dynamic recording, energy mapping and data integration described above, a complete energy spectrum acquisition baseline can be constructed under the high energy density detection conditions for metal content determination, and a high-precision energy spectrum tail data band can be formed. This provides a stable, continuous and hierarchical energy characteristic data foundation for the entire metal content determination process, thereby providing accurate data support for subsequent feature point extraction, tail identification and energy correction.

[0026] Based on the energy spectrum tail data band, the start time, end time and the position of the largest energy change of each energy spectrum peak are extracted to generate a set of feature points for determining the tail range. After obtaining the energy spectrum tail data band for feature identification, in order to accurately determine the energy variation range of each energy spectrum peak within the tail region, it is necessary to extract the start time, end time, and location of the largest energy change for each energy spectrum peak based on the energy spectrum tail data band, in order to form a set of feature points that can characterize the tail range. By performing layer-by-layer analysis of the time series and energy response of the energy spectrum tail data band, the temporal identifiers of the peak start and end boundaries and the location relationship of energy change feature points are established to ensure that the tail boundary and peak shape contour of each energy spectrum peak can be completely identified in the high energy density segment. The specific steps are as follows: Based on the formation of the energy spectrum tail data band, the continuous energy response information contained in this data band is analyzed hierarchically according to time sequence. In this stage, by using the time axis of the energy spectrum tail data band as the dominant reference, the change of energy response values ​​over time is unfolded into an energy distribution sequence with equal time intervals. The energy response value within each time interval reflects the instantaneous signal strength of the detection device at that moment, while the continuous time intervals together constitute the complete rising segment, peak segment, and decay segment of the energy spectrum peak. By structurally recording the changing trend of the energy response within continuous time periods, the transition position of the signal between the rapid energy rise interval and the slow energy decay interval can be clearly identified. The core of this hierarchical analysis lies in segmenting the continuous energy change of the energy spectrum tail data band based on time, enabling the determination of the peak's start and end times within a clear time range, and establishing a time boundary framework for feature point extraction. Simultaneously, this stage also requires preserving the original energy density and energy change direction of each time interval to ensure that the signal response of each time interval forms a continuous correspondence with the energy spectrum tail data band obtained in the previous stage.

[0027] After obtaining the time-series structure of the energy distribution, the start time of each energy peak in the energy spectrum tail data band is identified. In this stage, the energy change trend over time is used as the primary criterion. By analyzing the change in the energy growth rate in the early rising phase of the peak, the position where the energy spectrum peak begins to form is determined. Specifically, in the continuous time series, when the energy response gradually transitions from a stable background signal to a significant rising state, an energy inflection point appears at the beginning of the peak. The time corresponding to this inflection point is the start time of the energy spectrum peak. To ensure the accuracy of the start time, the energy change direction and energy density information retained from the previous sub-step's layered analysis are combined during the identification process. The starting point of the energy rising trend is correlated with the moment when the energy response significantly increases, ensuring that the start time accurately reflects the transition process of the signal from the background region to the rising phase of the peak. In this process, the time-series information and energy response data in the energy spectrum tail data band are used synchronously, ensuring that the peak start time identification not only depends on the energy change amplitude but also maintains consistency with temporal continuity, providing a continuous time interval reference for subsequent peak end time identification.

[0028] After determining the start time of each energy spectrum peak, the attenuation process of the peak shape is tracked to identify the end time of the energy spectrum peak. Since the energy spectrum tail data band contains a complete energy attenuation segment, the end boundary of the peak shape can be determined by continuously monitoring the gradual decrease in energy response over time during this stage. When the energy response gradually declines and approaches the background energy level, it indicates that the energy release process of the peak shape is nearing its end, and the time corresponding to this moment is the end time of the energy spectrum peak. To ensure the correspondence between the end time and the start time, the rising and falling segments of the peak need to be continuously compared on the time axis during the identification process, so that the start and end intervals of the peak shape can form a one-to-one time sequence correspondence structure. At the same time, the energy attenuation trend within the tail interval also needs to be included in the judgment range to ensure that the end time not only reflects the end point of the energy attenuation of the main peak, but also includes the entire process of peak extension and tail attenuation to a stable background state. In this way, a complete peak life cycle can be formed in the time dimension of the energy spectrum tail data band. The determination of the start and end times makes the temporal boundary of the energy spectrum peak clear, providing an accurate time frame for the identification of subsequent energy change positions.

[0029] After determining the start and end times of each energy spectrum peak, the location of the most significant energy change in the tail band of the energy spectrum is identified to determine the position of the maximum energy change of the energy spectrum peak. This stage involves analyzing the energy change trajectory within the start and end time intervals to identify key locations where the energy response changes abruptly, and marking these locations as the positions of maximum energy change. During the identification process, the energy change rate must be combined with the time interval so that the position of the most significant energy change reflects both the turning point from peak rise to decay and the main concentrated area of ​​energy transfer within the peak. This position is usually located at the energy peak apex or the energy abrupt change in the tail region, serving to identify the characteristics of the peak's energy distribution. To ensure the completeness of the feature points, when locating the position of maximum energy change, the start and end times are comprehensively matched with the energy change curve, ensuring that the determined position is consistent with the peak's time boundary and reflects the magnitude of energy decay in the tail region. Finally, by summarizing and integrating the start and end times of each energy spectrum peak and the position of maximum energy change, a set of feature points is formed to determine the tail range. This set of feature points comprehensively records the formation, development, and decay processes of energy spectrum peaks in high-energy-density regions, providing a complete temporal and energy localization basis for subsequent tailing signal separation and true peak identification. Through the above sequential steps, precise extraction of peak shape time and energy boundaries can be achieved based on the energy spectrum tailing data band, thereby establishing a high-precision feature identification basis for the entire metal content determination process.

[0030] Based on the set of feature points, the rising and falling segments of each energy spectrum peak are compared point by point to separate and eliminate false peaks caused by the tailing effect, and generate a list of correspondences of real metal energy spectrum peaks. After extracting the feature point set, to further ensure that false peaks caused by the tailing effect in the energy spectrum signal do not interfere with the true identification of metal elements, it is necessary to compare the rising and falling segments of each energy spectrum peak point by point according to the feature point set. This distinguishes the energy change characteristics of true peaks from tailing peaks. False peaks are then eliminated through a continuous signal separation and screening process, ultimately generating a list of corresponding true metal energy spectrum peaks. This step establishes a continuous energy comparison chain during the rise and decay of energy spectrum peaks, using time series as clues and energy changes as features to achieve peak shape differentiation and relationship summarization. The specific implementation method is as follows: After obtaining the feature point set, the time boundary information of each energy spectrum peak is segmented and organized. The energy sequence of the energy spectrum peak is divided according to the key positions defined in the feature point set within the range from the start time to the end time. In this stage, the rising and falling segments of the energy spectrum peak are treated as two independent but continuously connected energy change processes. The energy response sequence is reorganized with time as the main line, so that the rising and falling segments have a one-to-one correspondence in terms of energy change trends. The rising segment reflects the accumulation process of energy from low to high, and the falling segment reflects the release process of energy from high to low. Together, they constitute the complete energy life cycle of the peak shape. Through this segmentation method, the energy growth and energy decay intervals of each energy spectrum peak can be clearly divided on the time axis, providing a stable time reference for subsequent point-by-point comparison. At the same time, the continuity of energy response values ​​and energy change directions is maintained within each time segment, ensuring that the end point of the rising segment and the start point of the falling segment correspond completely in the time and energy dimensions, providing an accurate peak shape continuity basis for false peak identification.

[0031] After segmenting and organizing the energy sequence, the rising and falling segments of the energy spectrum peaks are compared point-by-point to identify energy matching characteristics between peaks. In this stage, the energy growth curve of the rising segment and the energy decay curve of the falling segment are compared one-to-one in chronological order. By analyzing the energy change trends at adjacent time points, it can be determined whether the energy extension of the peak shape exhibits a tailing characteristic. If there is a continuous energy decay in the falling segment without forming a new energy growth trend, it indicates that this interval belongs to the tailing region of the peak foot extension, rather than the formation process of a new independent peak. Conversely, if there is a local energy resurgence in the falling segment, and this upward trend overlaps in time or is close in energy to the energy peak interval of the rising segment, it can be determined that this part of the signal originates from the energy residue of the tailing effect, rather than a true metallic peak. Through this point-by-point comparison method, the continuity and discontinuity of energy changes in the time-continuous energy spectrum curve can be identified, thereby determining which energy responses belong to the natural tailing of the same peak and which energy responses belong to independent peak signals. This comparison process relies on information such as peak start time, end time, and the location of maximum energy change contained in the feature point set, so that each comparison is carried out under the dual constraints of time and energy, thereby ensuring the accuracy and continuity of peak shape differentiation.

[0032] After point-by-point comparison of the rising and falling segments, energy regions exhibiting tailing effects are separated, and spurious peaks are eliminated. The key to this stage is utilizing the energy difference information generated during the comparison process to distinguish the energy tail portion belonging to the same spectral peak from the energy rising portion of truly independent peaks. By comparing the direction of energy change and the range of energy amplitude changes in the peak shape, signal regions that only extend continuously in the falling segment but do not form a stable energy peak can be identified and eliminated from the overall energy spectrum. Simultaneously, the energy transition region between adjacent peaks needs to be analyzed. If the start and end times of two peaks overlap and the energy change amplitudes are continuously connected, the latter peak can be determined to be a tailing extension of the former peak, rather than an independent peak. In this case, the tailing portion is removed from the set of independent peaks through energy region elimination, restoring the peak structure of the energy spectrum signal to an independent energy response corresponding to the real metal element. Through the above separation and elimination process, spurious peaks are effectively removed, and signal redundancy caused by the tailing effect is eliminated, thus providing a clean energy spectrum data foundation for establishing the correspondence between real metal peaks.

[0033] After removing spurious peaks, the energy spectrum signals, after comparison and separation, are integrated to form a list of corresponding peaks for true metals. This stage establishes a time-energy correspondence by associating the start time, end time, and position of maximum energy change for each retained true peak. Each relationship represents the energy response characteristics of a true metal peak, including complete time-series information on peak formation, energy decay, and tail suppression. During list formation, the time intervals, energy distribution differences, and energy transfer directions between adjacent true peaks are also recorded, ensuring clear correspondences between different metal peaks in both time and energy space. Through this list-based approach, the energy spectrum signal transforms from a multi-peak overlap state into an independent set of peaks, completely eliminating spurious peaks caused by tailing effects. The list of corresponding peaks for true metals, as the final result, not only reflects the actual response of the detection equipment in the high energy density region but also provides complete peak shape data for subsequent signal matching, time control, and metal content determination.

[0034] A time control chain for signal acquisition and data matching is established based on a list of real metal energy spectrum peak correspondences. The signal exposure rhythm and readout interval are determined according to the time control chain, and a time control command to suppress trailing interference is generated. After obtaining the list of true metal energy spectrum peak correspondences, in order to further control the signal acquisition rhythm and reduce the interference of tailing effects during metal content determination, it is necessary to establish a time control chain for signal acquisition and data matching based on this list. This ensures that the exposure duration, readout interval, and energy release sequence during acquisition correspond to the true response patterns of the energy spectrum peaks, thereby achieving precise control and dynamic coordination of the energy spectrum signal in the time dimension. By systematically organizing and dynamically matching the time parameters and energy distribution characteristics in the list of true metal energy spectrum peak correspondences, a time correlation chain is constructed during the signal acquisition process, enabling each acquisition stage to adaptively adjust the acquisition sequence according to the peak shape change pattern. The specific steps are as follows: After establishing a list of corresponding peaks in the true metal energy spectrum, the temporal information of each true peak in the list is centrally organized to determine the basic time nodes for establishing the time control chain. This stage uses the start time, end time, and position of maximum energy change for each true peak as three types of core time reference points, and generates a preliminary time sequence framework based on the distribution of these time points throughout the entire energy spectrum acquisition cycle. In this process, the time interval between adjacent true peaks is used as the basic unit of acquisition rhythm, allowing the signal acquisition process to be segmented based on the energy response differences between peaks. In this way, a continuous acquisition chain composed of multiple true peaks can be formed at the temporal level, providing stable time coordinates for subsequent exposure and readout rhythm adjustments. Simultaneously, the time delay information of the true peaks during the energy decay phase must also be included in the organization, ensuring that the end time of each peak is reasonably connected to the start time of subsequent peaks in the control chain, guaranteeing that the temporal continuity of the energy spectrum signal will not result in acquisition overlap or signal conflict due to tailing effects.

[0035] After obtaining the temporal sequence framework, a time control chain for the signal acquisition stage needs to be constructed based on the time node characteristics in the list of corresponding peaks in the real metal energy spectrum. This stage uses the organized time nodes as the order, arranging the start and end intervals of each real peak in an orderly manner according to the strength and duration of the energy response. This ensures that the time control chain reflects the complete change in energy from peak value to decay during the acquisition process. The key aspect of this process is allocating the position of maximum energy change of the peak to its start and end times in a proportional time ratio, ensuring that the exposure process aligns with the peak segment of the energy response, thereby guaranteeing the acquisition accuracy of the high-energy signal. Simultaneously, the delay characteristics of the tailing segment must be considered in the construction of the time control chain. By introducing buffer time intervals between adjacent peaks, the tailing signal is prevented from overlapping with the exposure stage of subsequent peaks in time. This creates an acquisition time chain with dynamic response characteristics, allowing the detection equipment to automatically switch the acquisition state according to the peak shape characteristics within each acquisition cycle, thus achieving synchronous control of time and energy response. Through the above construction process, the time control chain not only describes the temporal sequence of signal acquisition, but also determines the energy state corresponding to each acquisition stage, providing a timing reference for subsequent exposure and readout rhythm settings.

[0036] After the time control chain is established, the signal exposure rhythm and readout interval are determined based on the time distribution relationship and energy response law of each node in the time chain. In this stage, by using the start time, end time, and the position of maximum energy change in the time control chain as time anchors for exposure control and readout control, the signal acquisition process can be synchronized with the energy change trend of the energy spectrum peaks. Specifically, during the rapid energy rise phase, the exposure time should be appropriately shortened to prevent signal oversaturation; while during the energy decay phase, the exposure time can be extended to obtain detailed information at the peak tail. Simultaneously, the readout interval length is reasonably set according to the time interval between adjacent peaks, ensuring that signal readout is coordinated with the energy decay cycle and avoiding the reading of incompletely decayed residual signals during the tailing phase. To maintain a dynamic balance between the exposure rhythm and readout interval within the acquisition cycle, the time transition zone between different peaks in the time control chain should be used as a buffer for exposure rhythm adjustment, providing the detection equipment with a stable transition period during acquisition state transitions and preventing the tailing effect from generating new signal extensions within the time overlap interval. By using this exposure and readout coordination method based on the true peak timing characteristics, the acquisition rhythm and energy response can form a time closed loop, thereby reducing the cumulative effect of trailing interference at the signal level.

[0037] After determining the signal exposure rhythm and readout interval, time control commands to suppress tail interference are generated based on the overall structure of the time control chain. This stage uses the time control chain as the dominant framework, integrating the acquisition actions, energy response states, and readout sequences at each time point into a continuous sequence of time commands. The time control commands include acquisition start command, exposure duration command, signal readout command, and acquisition end command, each corresponding to a time point of the true peak. By sequentially executing these time control commands during signal acquisition, dynamic coordination between signal acquisition and energy decay processes can be achieved at the physical level, isolating the tail signal in time from the acquisition stage of the next peak, thereby suppressing the cumulative propagation of tail interference. Simultaneously, when generating time control commands, the temporal distribution differences between peaks in the list of true metal energy spectrum peak correspondences must be considered, allowing the execution cycle of the commands to be flexibly adjusted according to the response characteristics of different peaks. In this way, the time control commands can accurately match the temporal distribution patterns of the true peaks and form dynamic rhythm control during the acquisition stage, maintaining a stable rhythm and energy consistency in the signal acquisition process over time. Ultimately, the generation and execution of time control commands achieved synchronous coordination between signal acquisition and energy attenuation, which fully suppressed the tailing effect in the time dimension, providing a reliable timing basis for the accurate determination of metal content.

[0038] According to the time control command, short-term quiescence, delay compensation and reverse energy release operations are performed during the signal attenuation stage to dynamically adjust the energy spectrum signal, so that the tailing effect is weakened and kept under control during the energy transfer process, thereby obtaining accurate metal content determination results. After generating time control commands based on the time control chain, in order to effectively suppress the tailing effect and maintain the dynamic balance of signal energy during the energy transfer phase of the energy spectrum signal, short-time stillness, delay compensation, and reverse energy release operations need to be executed sequentially during the signal attenuation phase. This achieves dynamic energy regulation of the energy spectrum signal, weakening and controlling the tailing effect during energy transfer, thereby ensuring the accuracy and stability of metal content determination. Through a time- and energy-coupled control strategy, the energy release process is regulated in a phased response manner during the signal attenuation phase, enabling the detection equipment to achieve gradual energy balance and dynamic attenuation in different energy ranges. The specific steps are as follows: Guided by time control commands, initial static control is applied to the energy response process during the signal attenuation phase to achieve short-term static operation. During this phase, when the energy spectrum signal enters the attenuation region from the peak region, the signal receiving state of the detection device switches from continuous acquisition to short-term static state, ensuring that the signal input channel maintains an interruption in energy input within a preset time window. The purpose of this short-term static operation is to allow the high-energy charge accumulated inside the detector to be partially released during natural attenuation through intermittent pauses in time, thereby avoiding charge accumulation caused by continuous signal superposition under high energy density. This short-term static operation corresponds to the end time node in the time control chain, ensuring that the static time matches the natural attenuation rate of the energy spectrum peak, guaranteeing that the static process does not affect the overall energy attenuation trend. This short-term static operation effectively suppresses the excessive extension of the tail signal in the high-energy region, restoring the energy response of the peak tail to a controlled attenuation trajectory, and creating a stable energy release environment for subsequent delay compensation operations.

[0039] After the short-term pause operation is completed, a delay compensation operation is performed during the signal attenuation phase to correct for energy distribution differences caused by excessively rapid energy decay or uneven energy release. During this phase, the signal acquisition interval of the detection device is fine-tuned according to the time allocation parameters in the time control command, ensuring that the rhythm of energy release is synchronized with the peak attenuation rate. The core of delay compensation lies in slowing down the rapid loss of local energy during energy attenuation by adjusting the signal release interval, allowing the energy distribution of the energy spectrum signal to form a smooth attenuation curve within the peak tail region. In this process, the delay compensation operation needs to consider the energy state after the previous short-term pause and gradually restore the response sensitivity of the acquisition channel according to the actual energy attenuation trend, enabling the detection device to re-receive the signal at the delayed sampling time point. This not only makes the energy release during attenuation more uniform but also prevents sudden energy drops within the tail region, avoiding tail fluctuations caused by excessive energy differences. Through the delay compensation operation, the energy output during the signal attenuation phase maintains temporal stability and energy continuity, providing a balanced energy input state for the subsequent reverse energy release operation.

[0040] After completing the delay compensation operation, to further reduce the tailing effect caused by residual charge during energy transfer, a reverse energy release operation needs to be performed during the signal attenuation stage. The key to this stage is to switch the energy release path of the detection device from unidirectional attenuation to a short-term reverse release state by reversing the energy transfer direction under the guidance of time control commands. During the reverse energy release process, the detection device applies a transient energy release pulse opposite to the peak energy direction within the energy attenuation range to counteract the residual energy remaining in the detector's charge transfer channel, thereby accelerating the completion of the energy release process. This operation can force the delayed charge to be released in a short time without changing the overall energy attenuation curve, thus reducing the time-dependent tailing delay of energy. The execution time of the reverse energy release is determined jointly by the time control commands and the energy state after delay compensation, ensuring that the reverse release only occurs when energy attenuation enters a low-speed phase, thus maintaining dynamic balance in the energy transfer process. Through the reverse energy release operation, the tailing effect at the end of signal attenuation can be effectively weakened, enabling the energy spectrum signal to converge simultaneously in both time and energy dimensions, laying the foundation for the final stabilization of the signal attenuation stage.

[0041] After completing the reverse energy release operation, to ensure the dynamic stability of the energy transfer process, the energy spectrum signal undergoes overall dynamic energy regulation, keeping the tailing effect under control during energy transfer. In this stage, the three operations—short-time quiescent, delay compensation, and reverse energy release—are continuously linked along the time axis using the full-cycle parameters in the time control command, creating a periodic adjustment mechanism for the attenuation phase of the energy spectrum signal. The core of dynamic energy regulation lies in maintaining a stable transition of energy distribution during signal attenuation, ensuring a smooth rather than abrupt transition from peak to baseline. This continuous energy regulation allows the detection device to achieve a dynamic balance between energy input and output during energy release, preventing the tailing effect from expanding due to energy accumulation or residue. During the dynamic energy regulation stage, the time control command continuously monitors the signal attenuation process and sequentially triggers short-time quiescent, delay compensation, and reverse energy release operations within each time interval, maintaining a synchronously controlled state of the signal in both time and energy dimensions. Through this process, the attenuation of the energy spectrum signal is subdivided into controllable energy release stages. The energy transfer in each stage is consistent with the time control chain, effectively weakening the tailing effect. Ultimately, the dynamically energy-regulated signal maintains continuous, stable, and controllable transmission characteristics during energy attenuation. The tailing range of the energy spectrum signal is compressed to a minimum, and the energy response closely matches the true peak shape, thus obtaining accurate metal content determination results. By employing the above continuous steps of short-time stillness, delay compensation, reverse energy release, and dynamic energy regulation, a complete energy control chain can be established during the signal attenuation stage, achieving dynamic balance and tailing suppression in the energy transfer process of the energy spectrum signal. This ensures high stability and reliability of the metal content determination results at both the energy and time levels.

[0042] This invention establishes an energy spectrum acquisition baseline during metal content determination and combines it with a time control chain for signal modulation. This ensures dynamic coordination of time and energy in the energy spectrum signal during the acquisition and attenuation phases, effectively suppressing peak expansion caused by tailing effects. Through the linkage of continuous acquisition and dynamic energy adjustment, the detection equipment's response is more stable in high energy density regions, and the energy release process is more balanced. This guarantees that the energy spectrum peak shape remains true and complete, reduces energy misjudgment caused by peak tailing, and makes the energy distribution of metal elements closer to their true physical response state.

[0043] This invention identifies and eliminates spurious peaks caused by tailing effects based on a set of feature points, generating a list of true metal energy spectrum peak correspondences. This results in a highly consistent peak structure in the energy spectrum data across both time and energy dimensions. This structured peak relationship ensures the correct attribution of element nodes in knowledge graph association modeling, avoiding node redundancy and energy confusion caused by misjudgment of tailing peaks. This improves the semantic consistency and inference reliability of metal content determination, enabling accurate identification and quantitative determination of metal components in complex ore samples.

[0044] This invention provides, for example Figure 2 The knowledge graph-based ore metal content determination system shown includes an energy spectrum acquisition baseline construction module, a feature point extraction module, a false peak identification and removal module, a time control chain generation module, and a dynamic energy adjustment module. The energy spectrum acquisition baseline construction module establishes an energy spectrum acquisition baseline, continuously acquires signals generated by the detection equipment in the high energy density region during the metal content determination process, records the tail shape and peak extension changes, and generates energy spectrum tail data bands for feature identification. The feature point extraction module extracts the start time, end time, and the location of the greatest energy change for each energy spectrum peak based on the energy spectrum tail data band, and generates a set of feature points to determine the tail range. The false peak identification and elimination module compares the rising and falling segments of each energy spectrum peak point by point based on the feature point set, separates and eliminates false peaks caused by the tailing effect, and generates a list of correspondences of real metal energy spectrum peaks. The time control chain generation module establishes a time control chain for signal acquisition and data matching based on a list of real metal energy spectrum peak correspondences. It determines the signal exposure rhythm and readout interval according to the time control chain and generates time control commands to suppress trailing interference. The dynamic energy adjustment module performs short-term quiescence, delay compensation, and reverse energy release operations during the signal attenuation phase according to time control commands. This dynamically adjusts the energy spectrum signal, weakening and controlling the tailing effect during energy transfer, thereby obtaining accurate metal content determination results.

[0045] The knowledge graph-based method for determining the metal content of ores provided in this invention is implemented through the aforementioned knowledge graph-based system for determining the metal content of ores. For details on the specific methods and procedures of the knowledge graph-based system for determining the metal content of ores, please refer to the embodiments of the knowledge graph-based method for determining the metal content of ores, which will not be repeated here.

[0046] The foregoing has only described certain exemplary embodiments of the present invention by way of illustration. Undoubtedly, those skilled in the art can modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the foregoing drawings and descriptions are illustrative in nature and should not be construed as limiting the scope of protection of the claims of the present invention.

Claims

1. A method for determining the metal content of ores based on knowledge graphs, characterized in that, Includes the following steps: Establish an energy spectrum acquisition baseline, continuously acquire signals generated by the detection equipment in the high energy density region during the metal content determination process, record the tail shape and peak extension changes, and generate energy spectrum tail data bands. Based on the energy spectrum tail data band, the start time, end time, and position of the largest energy change of each energy spectrum peak are extracted to generate a set of feature points; Based on the set of feature points, the rising and falling segments of each energy spectrum peak are compared point by point to separate and eliminate false peaks caused by the tailing effect, and generate a list of correspondences of real metal energy spectrum peaks. A time control chain for signal acquisition and data matching is established based on a list of real metal energy spectrum peak correspondences. The signal exposure rhythm and readout interval are determined according to the time control chain, and a time control command to suppress trailing interference is generated. According to the time control command, short-term quiescence, delay compensation and reverse energy release operations are performed during the signal attenuation stage to dynamically adjust the energy spectrum signal, so that the tailing effect is weakened and kept under control during the energy transfer process.

2. The method for determining the metal content of ores based on knowledge graphs according to claim 1, characterized in that, The steps for generating the energy spectrum tail data band are as follows: Based on the energy response characteristics of the detection equipment in the process of metal content determination, the energy coverage range and sampling time resolution of signal acquisition are determined, and complete signal response curves are continuously acquired in the high energy density region to form an energy spectrum response sequence. The tailing pattern of the energy spectrum response sequence in the high-energy region is dynamically tracked and continuously recorded. The energy change interval is divided into multiple time windows and the energy value, change rate and decay duration are recorded. The energy change information within the time window is rearranged in a unified manner according to the energy decay direction to generate a tailing extension mapping sequence corresponding to the energy response curve of the detection device. Based on the temporal sequence of signal acquisition and the energy distribution pattern, the continuously recorded data are integrated to form an energy spectrum tail data band.

3. The method for determining the metal content of ores based on knowledge graphs according to claim 2, characterized in that, The steps for generating the feature point set are as follows: After the energy spectrum tail data band is formed, the continuous energy response information contained in the energy spectrum tail data band is analyzed in a hierarchical manner according to the time order to generate an energy distribution sequence with equal time intervals to establish a peak start and end time boundary framework. In the time series structure of energy distribution, the energy inflection point of the peak rising segment is identified based on the energy change trend over time, and the moment when the energy response begins to increase significantly is determined as the starting time of the energy spectrum peak. The energy decay process is tracked and the end time of the energy spectrum peak is identified. The temporal correspondence structure of the peak shape is determined by comparing the continuity of the rising and falling segments of the peak shape on the time axis. After determining the start and end times, the location with the greatest energy change is identified based on the energy change trajectory, and the start time, end time, and location with the greatest energy change are integrated to generate a set of feature points.

4. The method for determining the metal content of ores based on knowledge graphs according to claim 3, characterized in that, In the process of generating the feature point set, the location with the greatest energy change is limited to the energy peak range of the energy spectrum peak, and the energy change trajectory between the start time and the end time is synchronously correlated with the energy decay process of the peak foot extension region, so that the feature point set simultaneously reflects the entire process of peak energy rise, peak and decay.

5. The method for determining the metal content of ores based on knowledge graphs according to claim 3, characterized in that, The steps to generate a list of peak correspondences in the energy spectrum of real metals are as follows: Based on the set of feature points, the time boundary information of each energy spectrum peak is segmented and organized. The energy spectrum peak is divided into energy sequences according to key positions within the range from the start time to the end time, so that the rising segment and the falling segment form a continuous correspondence in the energy change trend. The rising and falling segments are compared point by point. The tailing characteristics of the energy spectrum peak energy extension are identified based on the energy change trend. The continuity and discontinuity of energy change are determined to distinguish between the true peak and the tailing peak. The energy range exhibiting a tailing effect is separated and spurious peaks are removed. The tailing portion is removed from the set of independent peaks by the difference between the direction of energy change and the energy amplitude. The start time, end time, and position of the greatest energy change of the retained true peaks are correlated to form a list of corresponding true metal energy spectrum peaks.

6. The method for determining the metal content of ores based on knowledge graphs according to claim 5, characterized in that, When comparing the rising and falling segments point by point, the direction of energy change and the continuity of time are used as the basis for comparison. The energy residual range of the tailing region is determined by identifying the phenomenon of local energy re-rise in the energy decay interval. When removing false peaks, the extended signal in the tailing interval is separated from the energy spectrum data based on the overlap of the start and end times of adjacent peaks.

7. The method for determining the metal content of ores based on knowledge graphs according to claim 5, characterized in that, The steps for generating time control commands to suppress trailing interference are as follows: Based on the list of correspondences of real metal energy spectrum peaks, the time information of each real peak is centrally organized, and the start time, end time and the position of the largest energy change are used as time reference points to form a time sequence framework. Based on the time sequence framework, a time control chain is constructed for the signal acquisition stage. The start and end intervals of each real peak are arranged according to the strength and duration of the energy response, and a buffer time interval is set between adjacent peaks. The signal exposure rhythm and readout interval are determined based on the time distribution relationship and energy response law of each node in the time control chain, so that the signal acquisition process is synchronized with the energy change trend of the energy spectrum peak. Based on the overall structure of the time control chain, time control commands are generated, integrating the acquisition action, energy response status, and readout timing into a continuous time command sequence.

8. The method for determining the metal content of ores based on knowledge graphs according to claim 7, characterized in that, The generation of time control instructions involves arranging the signal acquisition start instruction, exposure duration instruction, signal reading instruction, and acquisition end instruction sequentially according to the time nodes in the list of correspondences of real metal energy spectrum peaks, so that each instruction corresponds to the start time, end time, and position of the greatest energy change of the real peak.

9. The method for determining the metal content of ores based on knowledge graphs according to claim 7, characterized in that, According to the time control command, short-time stillness, delay compensation, and reverse energy release operations are sequentially executed during the signal attenuation phase. The dynamic energy adjustment steps for the energy spectrum signal are as follows: Under the guidance of time control commands, the energy response process during the signal attenuation phase is initially statically controlled, so that the signal receiving state is switched to a short-term static state to release the internal accumulated charge. After the short-term static operation is completed, a delay compensation operation is performed, and the signal acquisition interval is adjusted according to the time control command to keep the energy release rhythm and decay rate synchronized. After the delay compensation operation, a reverse energy release operation is performed to offset the residual energy through a reverse energy release pulse, thereby accelerating the energy release process. Based on the time control command, short-term quiescence, delay compensation and reverse energy release operations are continuously linked to dynamically adjust the energy spectrum signal to achieve controlled balance of the energy transfer process.

10. A knowledge graph-based system for determining the metal content of ores, used to implement the knowledge graph-based method for determining the metal content of ores according to any one of claims 1-9, characterized in that, It includes a baseline construction module for energy spectrum acquisition, a feature point extraction module, a spurious peak identification and removal module, a time control chain generation module, and a dynamic energy adjustment module. The energy spectrum acquisition baseline construction module establishes an energy spectrum acquisition baseline, continuously acquires signals generated by the detection equipment in the high energy density region during the metal content determination process, records the tail shape and peak extension changes, and generates energy spectrum tail data bands. The feature point extraction module extracts the start time, end time, and position of the greatest energy change for each energy spectrum peak based on the energy spectrum tail data band, and generates a set of feature points. The false peak identification and elimination module compares the rising and falling segments of each energy spectrum peak point by point based on the feature point set, separates and eliminates false peaks caused by the tailing effect, and generates a list of correspondences of real metal energy spectrum peaks. The time control chain generation module establishes a time control chain for signal acquisition and data matching based on a list of real metal energy spectrum peak correspondences. It determines the signal exposure rhythm and readout interval according to the time control chain and generates time control commands to suppress trailing interference. The dynamic energy regulation module performs short-term quiescence, delay compensation, and reverse energy release operations during the signal attenuation phase according to time control commands, thereby dynamically regulating the energy spectrum signal and weakening and keeping the tailing effect under control during energy transfer.

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