A portable micro-beam X-ray fluorescence rapid detection method and system based on digital signal processing

Abstract

The application provides a portable micro-beam X-ray fluorescence rapid detection method and system based on digital signal processing. First, the micro-beam excitation parameters suitable for the field environment are constructed, then the micro-beam X-ray is used to irradiate the sample to be detected by using the micro-beam excitation parameters, and the original fluorescence signal disturbed by the environment is synchronously acquired, then the dynamic adjustment quantity is used as a calibration reference, the calibration reference and the original fluorescence signal are fused to generate a combined data stream, then the combined data stream is cooperatively processed by using the digital signal processing technology, the environmental disturbance component and the target fluorescence component are separated, and finally the sample surface element distribution map is generated based on the target fluorescence component. The technical scheme provided by the application not only solves the problems of morphology misalignment and dynamic noise interference caused by passive noise reduction design, but also improves the element detection reliability of complex morphology samples (such as soil clumps and cultural relic sections).

Description

Technical Field

[0001] This application relates to the field of intelligent material sensing and feedback control technology, and in particular to a portable microbeam X-ray fluorescence rapid detection method and system based on digital signal processing. Background Technology

[0002] In heavy metal detection scenarios such as industrial contaminated site screening, electronic waste recycling supervision, and farmland soil safety assessment, portable microbeam X-ray fluorescence (μ-XRF) technology needs to meet three core requirements: rapid on-site location of trace heavy metal pollution distribution (such as chromium and lead enrichment areas in soil particles), high-precision scanning adaptable to irregular sample surfaces (such as the edges of waste fragments and the pores of soil clumps), and signal-to-noise separation capability of characteristic fluorescence peaks such as cadmium and mercury under complex environmental interference (such as equipment vibration and light path scattering).

[0003] The currently widely adopted approach is a point-to-point scanning system combined with an electric translation stage: a laser profilometer is used to pre-define a flattened scanning grid, an X-ray beam with a fixed focal diameter (e.g., 50 μm) is used for point-by-point excitation, and a high-purity germanium detector in conjunction with a lead collimator is used to suppress environmental noise. This approach utilizes the repeatability accuracy of the mechanical platform to ensure spatial consistency of sampling points and reduces the impact of background radiation through physical hardware isolation.

[0004] However, this approach has significant bottlenecks. First, a fixed focal spot size cannot dynamically match the micro-regional distribution characteristics of heavy metal particles (such as tiny weld points or soil micro-agglomerates). Surface differences can easily lead to localized energy dissipation or blurred boundaries, resulting in missed detections of low-concentration elements (such as arsenic). Second, the passive shielding mechanism only filters low-frequency steady-state noise but cannot eliminate random high-frequency disturbances caused by equipment movement and electromagnetic crosstalk during the scanning process. This causes the characteristic peak signals of trace heavy metals (such as mercury) to overlap with environmental noise, significantly reducing the reliability of the detection results and the speed of on-site response. Summary of the Invention

[0005] This application provides a portable microbeam X-ray fluorescence rapid detection method and system based on digital signal processing, which solves the problems of inaccurate topography and dynamic noise interference caused by static focusing mechanism and passive noise reduction design in the prior art.

[0006] In a first aspect, this application provides a portable microbeam X-ray fluorescence rapid detection method based on digital signal processing, comprising:

[0007] During the mobile testing process of portable devices, the focusing diameter parameters and scanning trajectory of the micro-beam X-rays are adaptively adjusted based on the spatial morphology characteristics of the sample to be tested, thereby constructing micro-beam excitation parameters that are adapted to the on-site environment.

[0008] The microbeam excitation parameters are used to drive a microbeam X-ray to irradiate the sample under test and simultaneously acquire the original fluorescence signal affected by environmental disturbances.

[0009] The dynamic adjustment value in the microbeam excitation parameters is used as a calibration reference, and the calibration reference is fused with the original fluorescence signal to generate a combined data stream;

[0010] The combined data stream is processed collaboratively using digital signal processing technology to separate the environmental disturbance component from the target fluorescence component, wherein the environmental disturbance component does not participate in subsequent processing.

[0011] An elemental distribution map of the sample surface is generated based on the target fluorescence component.

[0012] Optionally, during mobile testing with portable devices, the focusing diameter and scanning trajectory of the micro-beam X-rays are adaptively adjusted based on the spatial morphology characteristics of the sample under test to construct micro-beam excitation parameters adapted to the on-site environment, including:

[0013] Scan the surface contour morphology of the sample to be tested to collect surface contour morphology data;

[0014] Identify regions of curvature variation and locations of abrupt elevation changes in the surface contour morphology data;

[0015] The confinement region for the micro-beam X-rays is determined based on the curvature variation region and the location of abrupt elevation changes.

[0016] The focusing diameter parameters of the micro-beam X-rays are calculated based on the dimensions of the constrained region.

[0017] Extract the spatial coordinates of the locations of abrupt elevation changes as key path points of the scanning trajectory;

[0018] By combining the focusing diameter parameter and the critical path point, the microbeam excitation parameters are constructed.

[0019] Optionally, the microbeam excitation parameters are used to drive a microbeam X-ray to irradiate the sample and simultaneously acquire the original fluorescence signal affected by environmental disturbances, including:

[0020] The aperture of the X-ray optical component is adjusted according to the focusing diameter parameter to obtain a suitable aperture value;

[0021] The micro-beam X-ray generator is guided by the adaptive aperture value and scanning trajectory to form regular geometric light spots on the surface of the sample to be tested.

[0022] When the regular geometric light spot reaches a preset stable state at the positioning point of the scanning trajectory, the fluorescence acquisition component is triggered to start.

[0023] The fluorescence acquisition component is activated in response to the activation signal, and the fluorescence acquisition component is used to capture spatial multi-directional fluorescence radiation including ambient scattering.

[0024] The spatial multi-directional fluorescence radiation is bound to the current scanning position coordinates to output the original fluorescence signal.

[0025] Optionally, the dynamic adjustment value in the microbeam excitation parameters is used as a calibration reference, and the calibration reference is fused with the original fluorescence signal to generate a combined data stream, including:

[0026] The focusing diameter parameter value was extracted from the microbeam excitation parameters and used as the basic calibration value;

[0027] Collect the scan trajectory position number from the raw fluorescence signal;

[0028] Establish an index to correspond the basic calibration values ​​to the scan trajectory position numbers;

[0029] Construct physical attribute channels based on the corresponding relationship index;

[0030] A combined data stream is generated by injecting calibrated features into the time-domain sequence of the original fluorescence signal through a physical property channel.

[0031] Optionally, the combined data stream is processed collaboratively using digital signal processing techniques to separate the environmental disturbance component from the target fluorescence component, wherein the environmental disturbance component does not participate in subsequent processing, including:

[0032] Analyze the calibration features and corresponding fluorescence time-domain signal segments in the combined data stream;

[0033] A rule-based energy change model is established based on the aforementioned calibration features.

[0034] The comparison results are obtained by comparing the fluctuation patterns of the fluorescence time-domain signal segment with those of the regular energy change model;

[0035] Extract the signal envelope structure that matches the wave pattern of the regular energy change model from the comparison results;

[0036] Identify irregular perturbation characteristics outside the signal envelope structure;

[0037] The signal envelope structure was identified as the target fluorescence component, and the irregular perturbation characteristics were identified as the environmental perturbation component.

[0038] Optionally, a regular energy change model is established based on the calibration feature quantity, including:

[0039] Decompose the focusing diameter parameter included in the calibration feature quantity;

[0040] Each focusing diameter parameter is converted into a corresponding energy distribution characterization value by a preset physical conversion relationship;

[0041] The energy distribution characterization values ​​are arranged in order of their position numbers in the calibration characteristic quantities;

[0042] The arranged energy distribution values ​​are sequentially connected to form a continuous energy distribution curve;

[0043] The continuous energy distribution curve is smoothed to form a regular energy change model.

[0044] Optionally, generating a sample surface elemental distribution map based on the target fluorescence component includes:

[0045] Extract the spatial location coordinates and associated fluorescence intensity sequences from the target fluorescence component;

[0046] Identify the peak distribution pattern in the fluorescence intensity sequence at each spatial coordinate;

[0047] Establish a correspondence between the peak distribution pattern and the preset element characteristic peak positions;

[0048] The element category at each spatial location coordinate is determined according to the correspondence, and the element categories are arranged according to the spatial location coordinates to form a distribution set;

[0049] The distribution set is transformed into a two-dimensional grid layout, and a sample surface element distribution map is generated based on the two-dimensional grid layout.

[0050] Secondly, this application provides a portable microbeam X-ray fluorescence rapid detection system based on digital signal processing, comprising:

[0051] The construction module is used to adaptively adjust the focusing diameter parameters and scanning trajectory of the microbeam X-rays based on the spatial morphology characteristics of the sample under test during mobile testing of portable devices, and to construct microbeam excitation parameters that are adapted to the on-site environment.

[0052] The acquisition module is used to drive the microbeam X-ray to irradiate the sample under test using the microbeam excitation parameters and simultaneously acquire the original fluorescence signal affected by environmental disturbances.

[0053] The fusion module is used to use the dynamic adjustment amount in the microbeam excitation parameters as a calibration reference, and fuse the calibration reference with the original fluorescence signal to generate a combined data stream;

[0054] The separation module is used to perform collaborative processing on the combined data stream using digital signal processing technology to separate the environmental disturbance component from the target fluorescence component, wherein the environmental disturbance component does not participate in subsequent processing;

[0055] The generation module is used to generate a sample surface elemental distribution map based on the target fluorescence component.

[0056] Thirdly, this application provides a computing device, including a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are invoked and executed by the processing component to implement a portable microbeam X-ray fluorescence rapid detection method based on digital signal processing as described in the first aspect above.

[0057] Fourthly, this application provides a computer storage medium storing a computer program, which, when executed by a computer, implements a portable microbeam X-ray fluorescence rapid detection method based on digital signal processing as described in the first aspect.

[0058] This application achieves a dual breakthrough in portable XRF for mobile detection through a spatial morphology-driven dynamic parameter optimization mechanism and calibration fusion signal processing technology. First, it overcomes the adaptation defects of traditional equipment to abrupt changes in surface morphology by adjusting the microbeam focusing diameter and scanning trajectory in real time based on curvature and height difference characteristics (such as avoiding steep steps or curved surface inflection points), ensuring that the excitation spot maintains a regular geometric shape on any morphological surface, thus eliminating signal distortion caused by defocusing at the source. Second, it solves the problem of interference from environmental disturbances on trace elements by converting the dynamic adjustment of excitation parameters into a calibration benchmark and fusing it with the original fluorescence signal to generate a combined data stream. Through digital signal processing technology, it collaboratively separates random high-frequency noise (such as vibrational scattering and electromagnetic crosstalk) from the target fluorescence component, significantly improving the signal-to-noise ratio of low-concentration heavy metal characteristic peaks.

[0059] Furthermore, a visualization reconstruction method for micro-area elemental distribution is provided: based on the purified target fluorescence components, the characteristic peak distribution patterns (such as the Kα peak of chromium and the Lβ peak of lead) in the fluorescence intensity sequence at each spatial coordinate are accurately extracted, and the element categories are automatically identified by matching with a preset element feature library; then, using the spatial coordinate topological relationship as a framework, the element category distribution set is transformed into a two-dimensional grid map, which intuitively presents the micro-scale enrichment areas of heavy metals on the sample surface (such as lead clusters in soil particles and cadmium leaching bands in weld joints), providing quantifiable spatial evidence for pollution source tracing or material failure analysis.

[0060] These or other aspects of this application will become more apparent in the following description of the embodiments. Attached Figure Description

[0061] To more clearly illustrate the technical solutions in this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0062] Figure 1 A flowchart of a portable microbeam X-ray fluorescence rapid detection method based on digital signal processing provided in this application is shown;

[0063] Figure 2 This paper shows a schematic diagram of the structure of a portable microbeam X-ray fluorescence rapid detection system based on digital signal processing provided in this application;

[0064] Figure 3 A schematic diagram of the structure of a computing device provided in this application is shown. Detailed Implementation

[0065] To enable those skilled in the art to better understand the present application, the technical solution of the present application will be clearly and completely described below with reference to the accompanying drawings.

[0066] In some of the processes described in the specification, claims, and accompanying drawings of this application, multiple operations appearing in a specific order are included. However, it should be clearly understood that these operations may not be executed in the order they appear herein, or may be executed in parallel. The operation numbers, such as 101, 102, etc., are merely used to distinguish different operations and do not themselves represent any execution order. Furthermore, these processes may include more or fewer operations, and these operations may be executed sequentially or in parallel. It should be noted that the descriptions such as "first," "second," etc., in this document are used to distinguish different messages, devices, modules, etc., and do not represent a chronological order, nor do they limit "first" and "second" to different types.

[0067] The technical solutions of this application will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0068] Figure 1 This application provides a flowchart of a portable microbeam X-ray fluorescence rapid detection method based on digital signal processing, as shown in the flowchart. Figure 1 As shown, the method includes:

[0069] Step 101: During the mobile detection process of the portable device, the focusing diameter parameter and scanning trajectory of the micro-beam X-ray are adaptively adjusted based on the spatial morphology characteristics of the sample to be tested, and micro-beam excitation parameters adapted to the on-site environment are constructed.

[0070] Optionally, step 101 may specifically include the following steps:

[0071] Step 1011: Scan the surface contour morphology of the sample to be tested to collect surface contour morphology data;

[0072] Step 1012: Identify the curvature variation regions and height difference abrupt change locations in the surface contour morphology data;

[0073] Step 1013: Determine the confinement area of ​​the micro-beam X-rays based on the curvature change region and the location of the abrupt change in elevation.

[0074] Step 1014: Calculate the focusing diameter parameter of the micro-beam X-ray based on the size of the constrained region;

[0075] Step 1015: Extract the spatial coordinates of the elevation change location as the critical path points of the scanning trajectory;

[0076] Step 1016: Combine the focusing diameter parameter and the critical path point to construct the microbeam excitation parameters.

[0077] In the above scheme, portable device mobile detection refers to the detection of samples by handheld or mobile devices while moving around the work site (such as a contaminated site). Spatial morphology feature adaptability is the ability to dynamically adjust the device parameters according to the geometry of the sample surface. Micro-beam X-ray is a focused X-ray beam with a diameter of less than 100 micrometers, used to excite small areas. The focusing diameter parameter is a value (unit: micrometer) that controls the size of the X-ray beam spot. The scanning trajectory is the path coordinate sequence of the X-ray probe moving on the sample surface. Micro-beam excitation parameters refer to the set of instructions including the focusing diameter and the scanning path. Surface contour morphology data is a three-dimensional point cloud or depth map describing the undulations of the sample surface. Curvature change area refers to the part of the surface where the curvature suddenly increases (such as the turning point of a steep slope). The position of abrupt change in height difference is the boundary point where the height difference between adjacent areas exceeds the threshold (such as the edge of a step). The constraint area refers to the key deformation area of ​​the surface where the X-ray beam parameters need to be adjusted. The critical path point is the coordinate of the feature point that must be accurately located in the scanning trajectory.

[0078] In this embodiment, firstly, step 1011 involves scanning the surface contour of the sample under test using a laser triangulation module, acquiring the three-dimensional coordinates of each point on the surface to generate contour morphology data. Secondly, step 1012 involves processing the contour data using a curvature gradient algorithm, calculating the surface curvature change rate, and marking areas where the curvature exceeds a set value. Simultaneously, abrupt height differences between adjacent points are detected as height change locations. Next, step 1013 involves setting the identified curvature change areas and height change points as constraint regions, defining the surface range requiring special X-ray control. Then, step 1014 involves calculating the required focusing diameter based on the minimum size of the constraint region (e.g., the narrowest groove width): a 30μm beam spot is used for regions with a width ≤ 50μm, and a 50μm beam spot is used for regions with a width > 50μm. Subsequently, step 1015 involves extracting the three-dimensional coordinates of the height change points as critical path points, providing a positioning reference for trajectory planning. Finally, step 1016 involves merging the focusing diameter parameters and the critical path point sequence to generate microbeam excitation parameters, which are then output to the control system for execution.

[0079] For example, at a heavy metal contaminated soil testing site (Site A), the operator uses a portable device (Model B) to scan soil clumps containing lead particles (Sample C). The laser ranging module scans the surface of Sample C to obtain contour morphology data, and the algorithm identifies the steep slope (curvature change area) at the edge of the clump and the central fissure (abrupt elevation change location). These areas are set as constraint areas, and the focusing diameter parameter is calculated based on the narrowest width of the fissure (40 μm) and set to 30 μm. Six coordinate points at the edge of the fissure are extracted as critical path points (step 1015), and finally, microbeam excitation parameters containing a 30 μm beam spot and six path points are generated (step 1016), guiding the device to accurately excite the lead particle enrichment area.

[0080] This method dynamically identifies surface deformation features and adjusts the beam size and positioning path in real time, ensuring that the micro-beam X-rays always match the sample geometry. During moving inspections, it avoids defocusing or positioning drift caused by surface undulations, guaranteeing the excitation accuracy of the micro-area; simultaneously, it optimizes scanning efficiency, avoiding redundant high-precision scanning in flat areas. Ultimately, this significantly improves the reliability of elemental detection for samples with complex morphologies (such as soil clumps and artifact cross-sections).

[0081] Step 102: Use the microbeam excitation parameters to drive the microbeam X-ray to irradiate the sample under test and simultaneously acquire the original fluorescence signal affected by environmental disturbances.

[0082] Optionally, step 102 may specifically include the following steps:

[0083] Step 1021: Adjust the aperture of the X-ray optical component according to the focusing diameter parameter to obtain a suitable aperture value;

[0084] Step 1022: Guide the micro-beam X-ray generator based on the adapted aperture value and scanning trajectory to form a regular geometric spot on the surface of the sample to be tested;

[0085] Step 1023: When the regular geometric light spot reaches a preset stable state at the positioning point of the scanning trajectory, the fluorescence acquisition component start signal is triggered;

[0086] Step 1024: Activate the fluorescence acquisition component in response to the activation signal, and use the fluorescence acquisition component to capture spatial multi-directional fluorescence radiation including ambient scattering;

[0087] Step 1025: Bind the spatial multi-directional fluorescence radiation to the current scanning position coordinates and output the original fluorescence signal.

[0088] In the above scheme, the raw fluorescence signal is the unprocessed signal directly acquired by the detector, which includes the fluorescence of the target element and environmental interference. The X-ray optical component refers to the combination of optical devices (such as collimators and condensers) that adjust the shape of the X-ray beam. The aperture refers to the adjustable opening in the optical component that controls the size of the beam. The adaptive aperture value is the optimal opening size (unit: micrometer) calculated based on the focusing diameter. The regular geometric spot is a circular / elliptical excitation spot formed on the sample surface. The preset stable state refers to the state in which the vibration amplitude of the device drops below the threshold and the position of the spot remains unchanged for 0.5 seconds. The fluorescence acquisition component is a signal capture device containing an X-ray detector and a filter. The start signal is the electronic command that triggers the detector to start working. Environmental scattering refers to interference signals not generated by the sample, such as dust and stray light in the air. Spatial multi-directional fluorescence radiation is the sum of fluorescence signals collected from different angles. The current scanning position coordinates refer to the real-time three-dimensional position of the probe in the scanning trajectory.

[0089] In this embodiment, firstly, in step 1021, the aperture of the optical component is adjusted by a stepper motor according to the focusing diameter parameter in the microbeam excitation parameters, and an adaptive aperture value is output (e.g., a focusing diameter of 30 μm corresponds to an aperture value of 15 μm). Secondly, in step 1022, based on the adaptive aperture value and the scanning trajectory path points, the X-ray probe is controlled to move, forming a regular geometric spot with a constant diameter on the sample surface (e.g., always maintaining a perfect circle). Subsequently, in step 1023, the accelerometer monitors the vibration state of the device. When the spot position remains stable and the vibration amplitude is below a preset threshold, a start signal is automatically sent to the fluorescence acquisition component. Then, in step 1024, after receiving the start signal, the fluorescence acquisition component activates the detector to receive spatial multi-directional fluorescence radiation containing sample fluorescence and environmental scattering at a 120-degree wide angle. Finally, in step 1025, the acquired fluorescence signal is bound to the real-time coordinates of the probe to generate a raw fluorescence signal with position markers (e.g., coordinates (2.1 mm, 3.4 mm) correspond to a set of fluorescence intensity data).

[0090] Following the specific implementation of the previous step, at the heavy metal contaminated soil testing site, the operator scans a cadmium-containing circuit board (sample C). Based on the microbeam excitation parameters (focusing diameter 40 μm) generated in step 101, the device automatically adjusts the light-transmitting aperture to 20 μm; the probe moves along a preset trajectory, forming a stable 40 μm circular light spot on the surface of the fragment; when the fragment's vibration amplitude drops to a safe threshold due to hand-held shaking, the system automatically triggers detection; the detector simultaneously captures the fluorescence radiation of cadmium-containing characteristic peaks and ambient stray light from three angles; finally, it outputs the original fluorescence signal bound to the location information (such as "coordinates (1.2, 0.8) - fluorescence intensity sequence [1200, 980, ...]").

[0091] This solution ensures high-fidelity original signals are acquired during mobile detection by dynamically controlling the spot shape and acquisition triggering mechanism. On the one hand, the spot parameters adaptively adjust with the surface morphology, avoiding signal attenuation due to defocusing. On the other hand, acquisition is triggered only when the equipment is stable, effectively reducing jitter noise pollution. Synchronously binding location information lays the data foundation for subsequent spatial analysis, improving the overall reliability and repeatability of on-site detection.

[0092] Step 103: Use the dynamic adjustment amount in the microbeam excitation parameters as a calibration reference, and fuse the calibration reference with the original fluorescence signal to generate a combined data stream.

[0093] Optionally, step 103 may specifically include the following steps:

[0094] Step 1031: Extract the focusing diameter parameter value from the microbeam excitation parameters as the basic calibration value;

[0095] Step 1032: Collect the scan trajectory position number from the original fluorescence signal;

[0096] Step 1033: Establish an index to correspond the basic calibration values ​​to the scanning trajectory position numbers;

[0097] Step 1034: Construct physical attribute channels based on the corresponding relationship index;

[0098] Step 1035: Inject calibration features into the time-domain sequence of the original fluorescence signal through the physical property channel to generate a combined data stream.

[0099] In the above scheme, the dynamic adjustment amount refers to the parameter value that changes in real time during the scanning process (such as the focusing diameter), the calibration reference is a reference standard used to identify signal characteristics, the combined data stream is a data packet that integrates the original signal and calibration information, the focusing diameter parameter value refers to the X-ray beam spot size (unit: micrometer) at the current measurement point, the basic calibration amount is the focusing diameter value used as a reference, the scanning trajectory position number refers to the sequential number that identifies the probe position (such as point number 5), the correspondence index is a mapping table that associates the calibration amount with the position number, the physical attribute channel is the data channel that transmits the device parameters, the time domain sequence refers to the fluorescence intensity data arranged in chronological order, and the calibration feature quantity is the device parameter mark of the injected original signal.

[0100] In this embodiment, firstly, step 1031 extracts the real-time changing focusing diameter parameter value from the microbeam excitation parameters and uses it as the basic calibration quantity (e.g., point 1: 30μm); secondly, step 1032 reads the scanning trajectory position number in the original fluorescence signal and records the acquisition order of each fluorescence data point; then, step 1033 establishes a correspondence index between the basic calibration quantity and the position number to form a mapping table (e.g., number 1 → 30μm, number 2 → 40μm); subsequently, step 1034 constructs a physical attribute channel based on the mapping table, which is dedicated to transmitting device parameter information; finally, step 1035 embeds the calibration feature quantity (focusing diameter value) into the time domain sequence of the original fluorescence signal through the physical attribute channel, generating a dual data stream that simultaneously contains fluorescence intensity and device parameters (e.g., time point 1.2 seconds: fluorescence value 580 + diameter 30μm marker).

[0101] Following the specific implementation of the previous step, at the heavy metal contaminated soil testing site, a chromium-containing metal part (sample C) was scanned. Step 102 acquired the raw fluorescence signal (e.g., fluorescence data at positions 1-20). The system extracted the focusing diameter value from the current microbeam excitation parameters: 35 μm for position 1 and 50 μm for position 5; simultaneously read the position number of the fluorescence signal; established a mapping relationship: position 1 corresponds to 35 μm, and position 5 corresponds to 50 μm; constructed a physical property channel, converting the diameter parameter into channel data; injected calibration feature quantities into the fluorescence time domain sequence, and finally generated a combined data stream containing dual information (e.g., "Time 3.5 seconds: Fluorescence value 1200 (diameter 50 μm)"), for subsequent noise separation.

[0102] This solution creatively transforms dynamic changes in device parameters into a calibration benchmark. Through deep fusion with the original signal, it constructs a combined data stream containing physical attribute markers. This allows the previously isolated fluorescence data to obtain a reference system for the device's operating status, laying a data foundation for accurately distinguishing real signals from environmental interference and significantly enhancing the system's signal source tracing capabilities in mobile detection scenarios.

[0103] Step 104: The combined data stream is processed collaboratively using digital signal processing technology to separate the environmental disturbance component from the target fluorescence component, wherein the environmental disturbance component does not participate in subsequent processing.

[0104] Optionally, step 104 may specifically include the following steps:

[0105] Step 1041: Analyze the calibration feature quantities and corresponding fluorescence time-domain signal segments in the combined data stream;

[0106] Step 1042: Establish a regular energy change model based on the calibrated feature quantities;

[0107] Step 1042 may include the following steps:

[0108] The focusing diameter parameter included in the calibration feature quantity is decomposed, and each focusing diameter parameter is converted into a corresponding energy distribution characterization value through a preset physical transformation relationship. The energy distribution characterization values ​​are arranged in order according to the position number in the calibration feature quantity, and the arranged energy distribution characterization values ​​are connected in sequence to form a continuous energy distribution curve. The continuous energy distribution curve is smoothed to form a regular energy change model.

[0109] Step 1043: Compare the fluctuation pattern of the fluorescence time domain signal segment with that of the regular energy change model to obtain the comparison result;

[0110] Step 1044: Extract the signal envelope structure that matches the fluctuation pattern of the regular energy change model from the comparison results;

[0111] Step 1045: Identify irregular perturbation characteristics outside the signal envelope structure;

[0112] Step 1046: The signal envelope structure is identified as the target fluorescence component, and the irregular perturbation characteristics are identified as the environmental perturbation component.

[0113] In the above scheme, digital signal processing technology is a method of processing electronic signals through algorithms. Collaborative processing refers to the simultaneous use of multiple data sources for calculation. Environmental disturbance components refer to invalid signals caused by external interference (such as electromagnetic noise). Target fluorescence components refer to the effective elemental characteristic signals generated by the sample itself. Corresponding fluorescence time domain signal segments refer to the fluorescence intensity sequence within a specific time period. Regular energy change models are theoretical signal fluctuation curves predicted based on equipment parameters. Preset physical conversion relationships are scientific formulas relating focusing diameter and X-ray energy. Energy distribution characterization values ​​refer to quantitative values ​​reflecting the strength of X-ray energy. Position sequence numbers are numbered according to the order of measurement points. Continuous energy distribution curves refer to smooth curves formed by connecting the energy values ​​of each point. Fluctuation morphology refers to the fluctuation characteristics of signal intensity changing over time. Comparison results are difference analysis data between the actual signal and the theoretical model. Signal envelope structure refers to the main fluctuation contour boundaries of the effective signal. Irregular disturbance characteristics refer to random interference signals without a fixed pattern.

[0114] In this embodiment, firstly, step 1041 involves splitting and combining the calibration feature quantity (such as the focusing diameter) and its associated fluorescence time-domain signal segment (such as data from the 5th to the 10th second) in the combined data stream; secondly, step 1042 involves extracting the focusing diameter parameter (such as 30 μm) from the calibration feature quantity, calculating the theoretical energy value using the energy distribution characterization value = k × focusing diameter² (where k is the conversion coefficient), arranging the energy values ​​of each point according to their position number (such as number 1: 1500, number 2: 1800), connecting the energy values ​​to generate an initial curve, and using a moving average algorithm to smooth the curve to form a regular energy change model; next, step 1043 involves aligning and comparing the fluctuation pattern (such as peak and valley positions) of the actual fluorescence signal segment with the curve features of the regular model; then, step 1044 involves using an envelope extraction algorithm to identify the undulating contours in the actual signal that coincide with the model curve; then, step 1045 involves analyzing the random fluctuation part without a fixed pattern in the remaining signal; finally, step 1046 involves determining the envelope structure of the matching model as the target fluorescence component and the random fluctuation as the environmental disturbance component.

[0115] Following the specific implementation of the previous step, a mercury-containing circuit board (sample C) was tested at the heavy metal contaminated soil testing site. The combined data stream (including diameter parameters and fluorescence sequences) generated in step 103 was used. The system resolved the fluorescence signal segment corresponding to point 3 (diameter 40 μm); the theoretical energy value of 1800 for this diameter was calculated using a formula, and a smooth energy curve was constructed; comparison revealed that the actual signal fluctuations within 1.5-1.8 seconds coincided with the model; the matching envelope peak shape within this time period was extracted; irregular high-frequency clutter in other time periods was identified; the envelope peak was confirmed to be mercury fluorescence (target component), and the clutter to be equipment vibration noise (environmental component).

[0116] This innovative solution transforms equipment operating parameters into theoretical signal models and achieves precise removal of environmental noise through morphological comparison. On the one hand, the regular energy change model built based on physical principles provides a scientific reference for signal recognition. On the other hand, envelope extraction technology locks out the true element features from complex signals, effectively solving the key pain point of random noise masking trace element signals in motion detection and significantly improving the reliability of element recognition.

[0117] Step 105: Generate an elemental distribution map of the sample surface based on the target fluorescence component.

[0118] Optionally, step 105 may specifically include the following steps:

[0119] Step 1051: Extract the spatial location coordinates and associated fluorescence intensity sequence from the target fluorescence component;

[0120] Step 1052: Identify the peak distribution pattern in the fluorescence intensity sequence at each spatial location coordinate;

[0121] Step 1053: Establish a correspondence between the peak distribution pattern and the preset element characteristic peak positions;

[0122] Step 1054: Determine the element category at each spatial location coordinate according to the correspondence, and arrange the element categories according to the spatial location coordinates to form a distribution set;

[0123] Step 1055: The distribution set is converted into a two-dimensional grid layout, and a sample surface element distribution map is generated based on the two-dimensional grid layout.

[0124] In the above scheme, the sample surface element distribution map is a color image showing the distribution of different elements on the sample surface. The spatial position coordinates refer to the three-dimensional position information of the detection point on the sample surface. The fluorescence intensity sequence is the fluorescence intensity data of multiple energy channels collected by a single detection point. The peak distribution pattern refers to the characteristic shape of the sudden increase in intensity in the fluorescence signal (such as a peak at a specific energy value). The preset element characteristic peak position is the standard fluorescence energy value of the known element (such as lead having a peak at 10.5 keV). The correspondence refers to the matching result between the actual signal peak and the standard element peak. The element category is the determined element type (such as iron / chromium / mercury). The distribution set refers to the coordinate set of the element classification results of all detection points. The two-dimensional grid layout is an image structure that divides the surface into a regular grid and fills in the element information.

[0125] In this embodiment, firstly, step 1051 extracts the spatial coordinates of each measurement point in the target fluorescence component and its corresponding fluorescence intensity sequence, for example, the position (2.1mm, 3.4mm) is associated with 10 sets of energy intensity data; secondly, step 1052 processes the fluorescence intensity sequence of each point using a peak detection algorithm to identify the characteristic peak positions (such as the peak at an energy value of 8.6keV); then, step 1053 compares the peak energy values ​​of each point with a preset element characteristic peak position library to establish a matching relationship (such as 8.6keV matching the characteristic peak of nickel); subsequently, step 1054 labels the element category of each point according to the matching results and arranges them in coordinate order to form a distribution set (such as [(2.1,3.4): Nickel, (2.3,3.5): Copper]); finally, step 1055 maps the distribution set to a two-dimensional grid according to coordinates, using different color blocks to represent different elements (such as red = nickel / blue = copper), generating a visualized element distribution map.

[0126] Following the specific implementation of the previous step, at the heavy metal contaminated soil testing site (continuing from the target fluorescence component output in step 104): the coordinates and fluorescence data of 20 detection points on the surface of sample C are extracted; a characteristic peak of 10.5 keV is found at coordinates (1.2, 0.8); this peak position is matched with the standard characteristic peak of lead; this point is marked as lead; all marked points are converted into a grid map, clearly showing that lead is concentrated in the grid area in the lower right corner.

[0127] This solution achieves spatial element identification by accurately matching elemental characteristic peaks, transforming abstract signals into intuitive spectra. Peak position correspondence based on physical properties ensures the accuracy of element classification. Two-dimensional grid mapping enables the visualization and localization of micro-area enrichment characteristics of trace heavy metals (such as lead clusters in contaminated soil), providing direct decision-making basis for environmental assessment or industrial quality inspection.

[0128] Figure 2 This application provides a schematic diagram of the structure of a portable microbeam X-ray fluorescence rapid detection system based on digital signal processing, as shown in the figure. Figure 2 As shown, the system includes:

[0129] Construction module 21 is used to adaptively adjust the focusing diameter parameters and scanning trajectory of the microbeam X-rays based on the spatial morphology characteristics of the sample under test during the mobile detection process of portable devices, and construct microbeam excitation parameters that are adapted to the on-site environment.

[0130] The acquisition module 22 is used to drive the microbeam X-ray to irradiate the sample under test using the microbeam excitation parameters and simultaneously acquire the original fluorescence signal affected by environmental disturbances.

[0131] The fusion module 23 is used to use the dynamic adjustment amount in the microbeam excitation parameters as a calibration reference, and fuse the calibration reference with the original fluorescence signal to generate a combined data stream;

[0132] The separation module 24 is used to perform collaborative processing on the combined data stream using digital signal processing technology to separate the environmental disturbance component from the target fluorescence component, wherein the environmental disturbance component does not participate in subsequent processing;

[0133] The generation module 25 is used to generate a sample surface elemental distribution map based on the target fluorescence component.

[0134] Figure 2 The portable microbeam X-ray fluorescence rapid detection system based on digital signal processing described above can perform... Figure 1 The implementation principle and technical effects of the portable micro-beam X-ray fluorescence rapid detection method based on digital signal processing described in the illustrated embodiment will not be repeated here. The specific operation methods of each module and unit in the portable micro-beam X-ray fluorescence rapid detection system based on digital signal processing in the above embodiments have been described in detail in the embodiments related to this method, and will not be elaborated upon here.

[0135] In one possible design, Figure 2 The portable microbeam X-ray fluorescence rapid detection system based on digital signal processing in the illustrated embodiment can be implemented as a computing device, such as... Figure 3 As shown, the computing device may include a storage component 31 and a processing component 32;

[0136] The storage component 31 stores one or more computer instructions, wherein the one or more computer instructions are invoked and executed by the processing component 32.

[0137] The processing component 32 is used for the above Figure 1 The embodiment describes a portable microbeam X-ray fluorescence rapid detection method based on digital signal processing.

[0138] The processing component 32 may include one or more processors to execute computer instructions to complete all or part of the steps in the above-described method. Alternatively, the processing component may be implemented as one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the above-described method.

[0139] Storage component 31 is configured to store various types of data to support operations at the terminal. The storage component can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0140] Of course, computing devices may also include other components, such as input / output interfaces, display components, communication components, etc.

[0141] Input / output interfaces provide interfaces between processing components and peripheral interface modules, which can be output devices, input devices, etc.

[0142] The communication components are configured to facilitate wired or wireless communication between computing devices and other devices.

[0143] The computing device can be a physical device or an elastic computing host provided by a cloud computing platform. In this case, the computing device can refer to a cloud server, and the aforementioned processing components, storage components, etc., can be basic server resources rented or purchased from the cloud computing platform.

[0144] This application also provides a computer storage medium storing a computer program, which, when executed by a computer, can perform the above-described functions. Figure 1 The illustrated embodiment presents a portable microbeam X-ray fluorescence rapid detection method based on digital signal processing.

[0145] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0146] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0147] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0148] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A portable microbeam X-ray fluorescence rapid detection method based on digital signal processing, characterized in that, include: During the mobile testing process of portable devices, the focusing diameter parameters and scanning trajectory of the micro-beam X-rays are adaptively adjusted based on the spatial morphology characteristics of the sample to be tested, thereby constructing micro-beam excitation parameters that are adapted to the on-site environment. The microbeam excitation parameters are used to drive a microbeam X-ray to irradiate the sample under test and simultaneously acquire the original fluorescence signal affected by environmental disturbances. The dynamic adjustment value in the microbeam excitation parameters is used as a calibration reference, and the calibration reference is fused with the original fluorescence signal to generate a combined data stream; The combined data stream is processed collaboratively using digital signal processing technology to separate the environmental disturbance component from the target fluorescence component, wherein the environmental disturbance component does not participate in subsequent processing. A sample surface elemental distribution map is generated based on the target fluorescence components; The process of mobile detection in portable devices, which involves adaptively adjusting the focusing diameter and scanning trajectory of the micro-beam X-rays based on the spatial morphology characteristics of the sample under test to construct micro-beam excitation parameters adapted to the on-site environment, includes: Scan the surface contour morphology of the sample to be tested to collect surface contour morphology data; Identify regions of curvature variation and locations of abrupt elevation changes in the surface contour morphology data; The confinement region for the micro-beam X-rays is determined based on the curvature variation region and the location of abrupt elevation changes. The focusing diameter parameters of the micro-beam X-rays are calculated based on the dimensions of the constrained region. Extract the spatial coordinates of the locations of abrupt elevation changes as key path points of the scanning trajectory; By combining the focusing diameter parameter and the critical path point, the microbeam excitation parameters are constructed; The step of collaboratively processing the combined data stream using digital signal processing technology to separate the environmental disturbance component from the target fluorescence component, wherein the environmental disturbance component does not participate in subsequent processing, includes: Analyze the calibration features and corresponding fluorescence time-domain signal segments in the combined data stream; A rule-based energy change model is established based on the aforementioned calibration features. The comparison results are obtained by comparing the fluctuation patterns of the fluorescence time-domain signal segment with those of the regular energy change model; Extract the signal envelope structure that matches the wave pattern of the regular energy change model from the comparison results; Identify irregular perturbation characteristics outside the signal envelope structure; The signal envelope structure was identified as the target fluorescence component, and the irregular perturbation characteristics were identified as the environmental perturbation component. The establishment of a rule-based energy change model based on the calibrated feature quantities includes: Decompose the focusing diameter parameter included in the calibration feature quantity; Each focusing diameter parameter is converted into a corresponding energy distribution characterization value by a preset physical conversion relationship; The energy distribution characterization values ​​are arranged in order of their position numbers in the calibration characteristic quantities; The arranged energy distribution values ​​are sequentially connected to form a continuous energy distribution curve; The continuous energy distribution curve is smoothed to form a regular energy change model.

2. The method according to claim 1, characterized in that, The method of using the aforementioned microbeam excitation parameters to drive a microbeam X-ray to irradiate the sample under test and simultaneously acquire the original fluorescence signal affected by environmental disturbances includes: The aperture of the X-ray optical component is adjusted according to the focusing diameter parameter to obtain a suitable aperture value; The micro-beam X-ray generator is guided by the adaptive aperture value and scanning trajectory to form regular geometric light spots on the surface of the sample to be tested. When the regular geometric light spot reaches a preset stable state at the positioning point of the scanning trajectory, the fluorescence acquisition component is triggered to start. The fluorescence acquisition component is activated in response to the activation signal, and the fluorescence acquisition component is used to capture spatial multi-directional fluorescence radiation including ambient scattering. The spatial multi-directional fluorescence radiation is bound to the current scanning position coordinates to output the original fluorescence signal.

3. The method according to claim 1, characterized in that, Using the dynamic adjustment value in the microbeam excitation parameters as a calibration reference, the calibration reference is fused with the original fluorescence signal to generate a combined data stream, including: The focusing diameter parameter value was extracted from the microbeam excitation parameters and used as the basic calibration value; Collect the scan trajectory position number from the raw fluorescence signal; Establish an index to correspond the basic calibration values ​​to the scan trajectory position numbers; Construct physical attribute channels based on the corresponding relationship index; A combined data stream is generated by injecting calibrated features into the time-domain sequence of the original fluorescence signal through a physical property channel.

4. The method according to claim 1, characterized in that, Generate a sample surface elemental distribution map based on the target fluorescence component, including: Extract the spatial location coordinates and associated fluorescence intensity sequences from the target fluorescence component; Identify the peak distribution pattern in the fluorescence intensity sequence at each spatial coordinate; Establish a correspondence between the peak distribution pattern and the preset element characteristic peak positions; The element category at each spatial location coordinate is determined according to the correspondence, and the element categories are arranged according to the spatial location coordinates to form a distribution set; The distribution set is transformed into a two-dimensional grid layout, and a sample surface element distribution map is generated based on the two-dimensional grid layout.

5. A portable microbeam X-ray fluorescence rapid detection system based on digital signal processing, applied to the portable microbeam X-ray fluorescence rapid detection method based on digital signal processing according to any one of claims 1-4, characterized in that, include: The construction module is used to adaptively adjust the focusing diameter parameters and scanning trajectory of the microbeam X-rays based on the spatial morphology characteristics of the sample under test during mobile testing of portable devices, and to construct microbeam excitation parameters that are adapted to the on-site environment. The acquisition module is used to drive the microbeam X-ray to irradiate the sample under test using the microbeam excitation parameters and simultaneously acquire the original fluorescence signal affected by environmental disturbances. The fusion module is used to use the dynamic adjustment amount in the microbeam excitation parameters as a calibration reference, and fuse the calibration reference with the original fluorescence signal to generate a combined data stream; The separation module is used to perform collaborative processing on the combined data stream using digital signal processing technology to separate the environmental disturbance component from the target fluorescence component, wherein the environmental disturbance component does not participate in subsequent processing; The generation module is used to generate a sample surface elemental distribution map based on the target fluorescence component.

6. A computing device, characterized in that, It includes a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are invoked and executed by the processing component to implement a portable microbeam X-ray fluorescence rapid detection method based on digital signal processing as described in any one of claims 1 to 4.

7. A computer storage medium, characterized in that, The device contains a computer program that, when executed by a computer, implements a portable microbeam X-ray fluorescence rapid detection method based on digital signal processing as described in any one of claims 1 to 4.

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